https://www.marinespecies.org/i/index.php?title=Special:NewPages&feed=atom&hideredirs=1&limit=50&offset=&namespace=0&username=&tagfilter=&size-mode=max&size=0MarineSpecies Introduced Traits Wiki - New pages [en]2024-03-28T12:26:22ZFrom MarineSpecies Introduced Traits WikiMediaWiki 1.31.7https://www.marinespecies.org/introduced/wiki/Economic_valuation_of_goods_and_services_of_the_UK_coastal_and_marine_ecosystemEconomic valuation of goods and services of the UK coastal and marine ecosystem2024-03-03T16:23:28Z<p>Dronkers J: </p>
<hr />
<div><br />
<br />
This article illustrates an attempt to estimate the value of [[Ecosystem services|ecosystem goods and services]] for a concrete case: the British coastal zone. Therefore, the links between marine biodiversity and the provision of services are analyzed in order to attach an indicative monetary value to each service where possible. Estimating the [[Total Economic Value|total economic value]] of the coastal ecosystem in monetary terms can prevent overexploitation and environmental degradation due to overlooking less obvious ecosystem services, such as nutrient cycling.<br />
<br />
<br />
<br />
==The valuation of biodiversity==<br />
Biodiversity is a broad concept that encompasses many different levels, from genetic variation among individuals and populations to diversity of species, assemblages, habitats, landscapes, and biogeographical provinces. Several indices have been developed to enable [[measurements of biodiversity]]. However, these indices do not tell what value should be attached to biodiversity. One way to value biodiversity is to consider the goods and services provided to society by ecosystems. This requires identification of the different ecosystem processes and components that provide goods and services to satisfy human needs, directly or indirectly. Enhancing or impairing one of the ecosystem processes or components generally has consequences for several goods and services. Some single services can provide additional value when considered in the context of other services with which they coexist, on a broader scale (spatial or temporal) than the scale of investigation. An important notion is therefore that the exploitation of one of the ecosystem services can influence other services, usually in a negative and sometimes positive way. A serious caveat is that existing knowledge is generally insufficient to fully understand [[ecosystem functioning]] and the way in which different ecosystem processes and components are affected by human interventions.<br />
<br />
==Value of goods and services==<br />
A [[Ecosystem services|goods and services]] approach was used to determine the economic value of marine biodiversity in the UK. This study was initiated to collect evidence about the possible renewal of UK marine legislation<ref name=B6>Beaumont, N., Townsend, M., Mangi, S. and Austen, M.C. 2006. Marine Biodiversity, An economic valuation. DEFRA, UK</ref>. Table 1 provides a description of the methods used for the valuation of goods and services provided by the UK coastal and marine ecosystem. <br />
<br />
Most of the monetary values in the last column of Table 1 are underestimates because only one or a few components of the total good or service have been valued due to missing data. There are several limitations associated with the monetary data. Market values generally do not fully represent the true value of a resource because not all added value has been taken into account. Contingent valuation and techniques for estimating monetary values of avoidance and substitution are discussed in the article [[Socio-economic evaluation]]. This article provides links to more detailed underlying articles that discuss other assumptions and limitations of methods for determining use and non-use values. This applies in particular to the questionable assumption that biodiversity goods and services can be perfect substitutes for man-made alternatives.<br />
<br />
<br />
''Table 1. An overview of goods and services provided by UK marine biodiversity (adapted from Beaumont et al., 2006<ref name=B6/>)''<br />
{| class="wikitable" style="font-size:80%"<br />
|-<br />
!| || Good/service || Definition || Goods and services considered for the UK || Annual value in million UK pounds<br />
|-<br />
! Production services<br />
|Food provision || Plants and animals taken from the marine environment for human consumption || Market value of fish landings + unreported catches (no data) + value of fish processing industry (no data) || > 513<br />
|-<br />
!<br />
|Raw materials ||The extraction of marine organisms for all purposes, except human consumption || Commercial value of harvested seaweed + industrial conversion (no data) + other raw biogenic materials (no data) || > 81.5<br />
|-<br />
!Regulation services<br />
|Gas and climate regulation || The balance and maintenance of the chemical composition of the atmosphere and oceans by marine living organisms || Damage avoided from sequestered carbon through primary production + damage avoided by regulation of the chemical composition of the atmosphere (no data), see [[Greenhouse gas regulation]] || > (420 -8,470)<br />
|-<br />
!<br />
| Disturbance prevention and alleviation || The dampening of environmental disturbances by biogenic structures || Reduced investment and maintenance costs of sea defence structures thanks to the presence of salt marshes || > 300<br />
|-<br />
!<br />
|Bioremediation of waste || Removal of pollutants through storage, dilution, transformation and burial || Potential savings on conventional waste water treatment due to the bioremediation function of marine benthic organisms ||<br />
|-<br />
!Cultural services<br />
|Cultural heritage and identity ||The cultural value associated with the marine environment, e.g. for religion, folklore, painting, cultural and spiritual traditions || Lack of information ||<br />
|-<br />
!<br />
|Cognitive values || Cognitive development, including education and research, resulting from marine organisms || Added market value of marine research and development + education and training || < 317<br />
|-<br />
!<br />
|Leisure and recreation || The refreshment and stimulation of the human body and mind through the perusal and engagement with, living marine organisms in their natural environment || Economic value of marine leisure + recreation + holiday tourism + cruising + leisure craft services || < 11,770<br />
|-<br />
!<br />
|Non-use values – bequest and existence ||Value which we derive from marine organisms without using them || Willingness to pay to maintain all sea mammal species || > (500-1,100)<br />
|-<br />
!Option use value <br />
| Option use value || Currently unknown potential future uses of the marine environment || Insufficient information on the value of genetic diversity for future medicines ||<br />
|-<br />
!Supporting services<br />
|Nutrient cycling || The storage, cycling and maintenance of availability of nutrients mediated by living marine organism || Value of continuous treatment UK waters, however this biodiversity function is also needed to maintain the marine ecosystem|| 800-2,320<br />
|-<br />
!<br />
|Resilience and resistance || The extent to which ecosystems can absorb recurrent natural and human perturbations and continue to regenerate without slowly degrading or unexpectedly flipping to alternate states ||Insufficient quantitative knowledge of the relationship between biodiversity and resilience ||<br />
|-<br />
!<br />
|Biologically mediated habitat || Habitat which is provided by living marine organisms || Insufficient information on the probable high value of marine biologically mediated habitats ||<br />
|}<br />
<br />
<br />
The aim of this valuation process was not to determine a single value for UK marine biodiversity, but to detail current knowledge, focus future research and clarify the role of valuation in conservation of marine biodiversity.<br />
The strength of the UK goods and services valuation data lies in its capacity to raise awareness of the importance of marine biodiversity. This valuation data, however, should only be used alongside the qualitative information and with a clear understanding of the associated limitations. Descriptive text for each of the goods and services is as important as the monetary data, and clarifies the linkages between biodiversity and the provision of these functions in UK coastal and shelf waters.<br />
<br />
A decline in UK marine biodiversity could result in a varying and, at present, unpredictable change in the provision of goods and services. This could result in severe impacts on society and the economy, including reduced [[resilience and resistance]] to change, declining marine environmental health and water quality, reduced fisheries potential, loss of [[Leisure|recreational opportunities]], decreased employment and reduced carbon uptake. Effective management of marine biodiversity is critical to ensure the continued supply of goods and services<ref name="ma">[https://www.researchgate.net/publication/306030378_Marine_Biodiversity_and_Ecosystem_Functioning Heip, C., Hummel, H., van Avesaath, P., Appeltans, W., Arvanitidis, C., Aspden, R., Austen, M., Boero, F., Bouma, TJ., Boxshall, G., Buchholz, F., Crowe, T., Delaney, A., Deprez, T., Emblow, C., Feral, JP., Gasol, JM., Gooday, A., Harder, J., Ianora, A., Kraberg, A., Mackenzie, B., Ojaveer, H., Paterson, D., Rumohr, H., Schiedek, D., Sokolowski, A., Somerfield, P., Sousa Pinto, I., Vincx, M., Węsławski, JM., Nash, R. (2009). Marine Biodiversity and Ecosystem Functioning. Printbase, Dublin, Ireland ISSN 2009-2539]</ref>.<br />
<br />
<br />
<br />
[[Image:25.JPG|thumb|centre|700px| <div style="text-align: center;"><br />
All of these marine plants and animals contribute to the production of the food we have on our tables.</div>]]<br />
<br />
<br />
<br />
==Related articles==<br />
:[[Socio-economic evaluation]]<br />
:[[Total Economic Value]]<br />
:[[Valuation and assessment of biodiversity]]<br />
:[[Ecosystem services]]<br />
<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
[[Category:Practice, projects and case studies in coastal management]]<br />
[[Category:Evaluation and assessment in coastal management]]<br />
[[Category:Coastal and marine ecosystems]]<br />
[[Category:Integrated coastal zone management]]<br />
[[Category:MarBEF Wiki]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Plunging_wave_in_slow_motionPlunging wave in slow motion2024-02-29T19:54:03Z<p>Dronkers J: Created page with " Plunging wave, still no. 143 from the video. This article points to the videos of a '''[https://www.youtube.com/playlist?lis..."</p>
<hr />
<div><br />
[[File:WavePlungeLipari.jpg|thumb|350px|right|Plunging wave, still no. 143 from the video.]] <br />
<br />
This article points to the videos of a '''[https://www.youtube.com/playlist?list=PLV5KQfKi7EFge-1gDEMyPGOlNCEQ9QDNM plunging wave event in slow motion]''', exhibiting all the stages of the [[wave breaking]] onto a sandy beach on a windless day. <br />
<br />
The onset and development of breakers in the shoreface impresses because of their rapidity, dynamic range, and dramatic mixture of chaotic and cyclic behaviours. This recording shows remarkably clearly a [[shoaling]] wave that steepens suddenly, grows into a sequence of narrowly spaced plunging breakers, and dissolves explosively into spray and foam on a natural beach. Upon close inspection, also clearly visible are the sand entrained within the curling wave and that stirred and suspended in the [[foreshore]]. <br />
<br />
The individual frames of the videos are commented and distributed under a licence CC BY-SA (attribution and share-alike) at Zenodo<ref>Lipari, 2023. The sequence of motions of water and sediment in a single plunging breaker on a sandy beach. Zenodo. https://doi.org/10.5281/zenodo.7601924</ref>. The videos are derivative work of an amateur recording published in the Wikipedia Commons<ref>Vincentz, 2013. Pájara - Morro Jable - Playa del Matorral https://commons.wikimedia.org/wiki/File:P%C3%A1jara_-_Morro_Jable_-_Playa_del_Matorral_(0)_09.ogv </ref>. <br />
<br />
<br />
<br />
==Location description==<br />
The video was taken on 11 February 2013 at the sandy beach Playa del Matorral, in the locality Morro Jable, municipality of Pájara, in the island of Fuerteventura, Canary Islands, Spain. The Playa del Matorral faces SSW and SE and thus, roughly speaking, the African coast. The region is tidal: the water-level excursion at the nearby Puerto del Rosario is reported to be 2.3 m at springs and 1.6 m at neaps. The seabed declines fairly steeply, approximately down to -500 m within 2 km or less from the mean sea level line. <br />
<br />
The development of a plunging breaker is clear evidence that the beach is behaving as a reflective one. If present at all, [[nearshore sandbars]] do not appear to affect the breaking process. Indeed, the closeness to the shore of the breaking line, the narrow shoreface, and the collision of backwash and incoming waves are indicators of a steep [[shoreface profile]]. In a steady beach configuration, these plunging waves must bring ashore sufficient sediment to compensate for the offshore transport down the steep slope.<br />
<br />
==Breaker description==<br />
The videos, slowed down up to eightfold the physical speed, unveil the scales of motion interleaved within the mixture of water, sand and air in such a sudden and rapid event. Despite some processes are occluded out of the view, the recordings uncover the following stages of development and features:<br />
<br />
*The waves shoal with long arrival times. A single episode of breaking seems to occur as in an isolated solitary wave, except for the backrush of the previous event reinforcing the steepening of the following. Extrapolating the evidence from the recording, a small-amplitude swell or edge wave with a period of 13-16 seconds could cause the event on record;<br />
*The recording is taken on a windless day and no extensive surf zone influences the nearshore zone seaward of the plunge point;<br />
* The sand moving and floating in the water's edge is visible both in the waves and in the water that rises as the breaking wave steepens and curls (see picture above);<br />
*The backrush from the previously broken wave has largely returned seawards when a fresh plunger engulfs the foreshore; this illustrates the cycling behaviour expected in a wave also as regards the sand motion;<br />
*The breaking does not unfold as a single wave form that seamlessly steepens, curls, and collapses after a single point of instability has been touched. Instead, the first wave face spawns new crests of water while advancing over the small plunge distance. Each crest surges ahead and leaves behind the previous one, which nonetheless impinges on the back of the leading wave face and breaks behind it. The breaking of this seeming single breaker is, in fact, remarkably composite and nonlinear;<br />
* Because of the considerable steepening, the plunging wave engulfs a considerable volume of air that produces the impressive explosion of spray in turn.<br />
<br />
This evidence on a natural beach can usefully be contrasted with the laboratory studies of Sumer et al (2013<ref>Sumer, B.M., H.A.A. Guner, N.M. Hansen, D.R. Furham, and J. Fredsøe. 2013. Laboratory observations of flow and sediment transport induced by plunging regular waves. Journal of Geophysical Research: Oceans, 118:6161-6182, DOI:10.1002/2013JC009324</ref>), who compare the breaking of a train of plunging waves in a flume with and without a sand bed; and of Hafsteinsson et al (2017<ref>Hafsteinsson, H.J., Evers, F.M., and Hager, W.H. 2017. Solitary wave run-up: wave breaking and bore propagation. J. Hydr. Res. 55: 787-798 DOI: 10.1080/00221686.2017.1356756</ref>), who report systematic observations of spilling, plunging and surging breakers in a narrow channel with a uniform bed slope.<br />
<br />
==Related articles==<br />
:[[Breaker index]]<br />
:[[Wave transformation]]<br />
:[[Shallow-water wave theory]]<br />
:[[Surf similarity parameter]]<br />
:[[Waves]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
<br />
{{author<br />
|AuthorID=45026<br />
|AuthorFullName=Giordano Lipari<br />
|AuthorName=Lipari G}}<br />
<br />
<br />
[[Category:Physical coastal and marine processes]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Total_economic_value_in_coastal_management_practiceTotal economic value in coastal management practice2024-02-28T14:33:28Z<p>Dronkers J: </p>
<hr />
<div><br />
<br />
This article is about the question of what monetary value can be attributed to coastal zones? This question is important because of the increasingly intensive use of coastal zones, which has consequences for the 'services' they can provide to society in the short and long term. The term 'services' should be understood in the broad sense of providing welfare and wellbeing. It includes not only direct economic profit (e.g. as a port/waterway facility, tourist destination), but also creating conditions for indirect profit elsewhere (e.g. natural protection of the hinterland, nursery for marine fish stocks, water/soil purification), for later use (by future generations) or for landscape beauty and spiritual experiences (e.g. source of inspiration for painters and poets). A key notion for valuation is that coastal zones are a so-called 'scarce resource', with limited availability and competing uses.<br />
<br />
==Common property== <br />
The coastal zone is generally not someone's private property. Users of the coastal zone do not have to pay to a private owner. It is a common property managed by organizations that are part of or affiliated with local, regional or national authorities. These organizations can grant use permits and charge money for this through land and use taxes. How much a user has to pay (in money or returned services) depends on the extent to which the use positively or negatively affects the value of the coastal zone. But what is that value and how can it be determined?<br />
<br />
Before going into this further, it should be noted that the above-described approach assumes an ideal situation in which coastal management is actually based on trade-offs between, on the one hand, the social benefits (services provided, employment, money) of granting permits for use of the coastal zone and, on the other hand, the costs of the possibly reduced capacity to provide other services for society. This ideal situation rarely exists in practice, but should be strived for.<br />
<br />
==Correcting market failure==<br />
What is the value of the coastal zone? An unequivocal answer to this question is not possible. There are major differences in views on what services are provided by coastal zones and how these services should be valued. Understanding the different views is important to reach consensus on regulations for coastal management. The issue of valuing coastal areas is not unique. Any management of publicly-owned natural systems and resources raises similar questions. Much research has been done and knowledge acquired in the field of environmental economics that addresses precisely these questions<ref>Anderson, D. 2019. Environmental Economics and Natural Resource Management. Routledge, New York</ref>.<br />
<br />
A core concept in environmental economics is the so-called market failure. Market failures occur when markets do not reflect the full social costs or benefits of a good. When it comes to the use of natural systems and resources, free market mechanisms do not result in allocation to users in a way that produces the greatest social welfare. The reason for this is the so-called externalities, the social costs associated with the possible degradation of the capacity of natural systems and resources to provide other services for society. Examples are degraded water quality, depletion of fish stocks, coastal erosion, loss of biodiversity, etc. Society is not a market party that can negotiate social costs with private users. Directly affected local stakeholders are not the only party. Interests in coastal services extend far and wide, both geographically and over time. This is where the concept of sustainability comes into play, stating that the needs of future generations should also be met.<br />
<br />
Not all services provided by coastal areas are subject to externalities. Services not subject to external effects are called 'public goods'. An unobstructed sea view is a public good. Enjoying the view does not prevent others from enjoying it too. However, if a sea panorama attracts a large crowd due to the presence of other recreational facilities, the enjoyment will be less. In fact, open access to public property can lead to overuse or overexploitation (the so-called '[[The Tragedy of the Commons - The Tuna Example|tragedy of the commons]]') and ultimately to destruction of the property. <br />
<br />
Because of market failure and overexploitation, uses of the coastal zone must be regulated by coastal management authorities. Their decisions should repair market failures such that uses of the coastal zone are allocated in a way that produces the greatest social welfare. Coastal management authorities should therefore be informed of the total economic value of the services potentially provided by coastal zones, including use values and non-use values. <br />
<br />
==Values to be considered==<br />
When estimating the total economic value of the coastal zone, not only values related to opportunities for economic development should be considered. The goods and services provided by the coastal ecosystem are equally important. Therefore different ecosystem functions can be distinguished:<ref>De Groot, R.S., Wilson, M.A. and Boumans, R.M.J. 2002. A typology for the classification, description, and valuation of ecosystem functions, goods, and services. Ecological Economics 41: 393-408</ref><br />
*Regulation functions: the capacity of natural and seminatural ecosystems to support and regulate natural processes, providing e.g. healthy water and soil, natural defense against flooding<br />
*Habitat functions: contribution to coastal zone biodiversity and resilience, nursery function<br />
*Production functions: provision of ecosystem goods for human consumption and use (e.g. seafood, biomass, pharmaceuticals)<br />
*Information functions: contributions to the maintenance of human health by providing opportunities for reflection, spiritual enrichment, cognitive development, re-creation and aesthetic experience. <br />
Externalities, the 'hidden' social costs of economic development, often relate particularly to the possible impairment of these ecosystem goods and services.<br />
<br />
==Estimation of use values==<br />
The economic use value of the coastal zone is associated with the potential of different types of uses to generate net financial benefits. It includes current uses and possible future uses (optional uses), but excludes mutually exclusive competing uses. It is the price for a use permit that would be paid on the market if there were no externalities. It can be estimated with market theoretical models, although the uncertainty margins are often considerable. This value is commonly called the use value. <br />
<br />
==Estimation of non-use values==<br />
Estimating the value of non-economic uses (non-use value) and externalities is more difficult than estimating the use value. Not only must the question be answered what are the potential non-use values, but also to what extent these are affected by different economic uses. <br />
<br />
Methods for measuring the non-use economy are generally based on the choices people make and what they are willing to pay for, rather than on the preferences of governments. People express their preferences through the choices and trade-offs they make, given their budgetary options. Several methods have been developed to identify and estimate these non-use values, which are described in several Coastal Wiki articles.<br />
<br />
*Travel cost valuation measures the value based on the costs people pay (or the price they are willing to pay) to visit a coastal destination as an expression of its recreational value. See [[Travel cost method]].<br />
<br />
*Contingent valuation measures how much money people would be willing to pay (or willing to accept) to maintain the existence of (or be compensated for the loss of) a coastal feature, such as natural coastal landscape or coastal biodiversity. See [[Contingent Valuation Method]].<br />
<br />
*Hedonic value is an estimate of the economic value of ecosystem or environmental services that directly affect market prices, for example, the added value of sea view to the price of real estate. See [[Hedonic Evaluation Approach]] and [[Values of amenities in coastal zones]].<br />
<br />
*Value (or benefit) transfer method consists of estimating economic values by transferring existing benefit estimates from studies already completed for another location or issue. See [[Value Transfer]]. <br />
<br />
*Bequest value measures the willingness of individuals to pay for maintaining or preserving an asset or resource that has no use now, so that it is available for future. See [[Non-use value: bequest value and existence value]]<br />
<br />
*Existence value measure the willingness for individuals to pay for the sense of well being, of simply knowing that coastal zones are preserved.<br />
<br />
==The case that not all values can be monetarized==<br />
Decisions in coastal management often involve choosing between different options. For example, choosing between different options for granting user or development rights. If a monetary valuation of all aspects/criteria of the different options is available, the optimal choice can be determined by means of a cost-benefit analysis. However, it can be challenging to express all aspects in economic use values and non-use values. In many cases, the valuations will have considerable margins of uncertainty.<br />
<br />
Other decision support methods can therefore be considered, alongside or instead of a cost-benefit analysis. Multi-criteria methods can be an attractive additional alternative. With this method, valuation criteria are not expressed exclusively in money. An assessment grade can be given to criteria for which a good monetary estimate is not possible. In a first step, all scores, both monetary and non-monetary rating grades, are normalized so that the best scoring option on a given criterion receives a 1, while other options receive a proportionately lower score. In a second step, weights are assigned to all criteria, by which the normalized scores are multiplied. By adding the results together, the final best-scoring option is found. The crucial step is determining the weights of the criteria. Weights can be determined for example through expert judgment and/or stakeholder consultation. <br />
<br />
The use of both monetary and non-monetary decision support methods is generally preferable to the use of a single method.<br />
<br />
<br />
==Related articles==<br />
:[[Travel cost method]]<br />
:[[Contingent Valuation Method]]<br />
:[[Hedonic Evaluation Approach]]<br />
:[[Value Transfer]]<br />
:[[Economic Value]]<br />
:[[Socio-economic evaluation]]<br />
:[[Non-use value: bequest value and existence value]]<br />
:[[Values of amenities in coastal zones]]<br />
:[[Economic valuation of goods and services of the UK coastal and marine ecosystem]]<br />
:[[Multifunctionality and Valuation in coastal zones: concepts, approaches, tools and case studies]]<br />
:[[Multifunctionality and Valuation in coastal zones: introduction]]<br />
<br />
<br />
== References ==<br />
<references/><br />
<br />
<br />
{{author<br />
|AuthorID=120<br />
|AuthorFullName=Job Dronkers<br />
|AuthorName=Dronkers J}}<br />
<br />
[[Category:Evaluation and assessment in coastal management]]<br />
[[Category:Integrated coastal zone management]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Invading_species_in_the_Baltic_SeaInvading species in the Baltic Sea2024-02-21T10:56:32Z<p>Dronkers J: Created page with " == Previous changes== ===Invading species=== Over 100 new species established themselves in the Baltic Sea during the 20th century. This is a significant addition t..."</p>
<hr />
<div><br />
<br />
== Previous changes==<br />
<br />
===Invading species===<br />
Over 100 new [[species]] established themselves in the [[Baltic Sea]] during the 20th century. This is a significant addition to the biodiversity of this species-poor, brackish sea, which only supports approximately 900 species altogether. Since the Baltic is young in evolutionary terms (<3,000 years old), its colonisation is probably not complete, and newcomers are easily filling available space and [[ecosystem function|ecosystem functions]]<ref name="ma">[https://www.researchgate.net/publication/306030378_Marine_Biodiversity_and_Ecosystem_Functioning Heip, C., Hummel, H., van Avesaath, P., Appeltans, W., Arvanitidis, C., Aspden, R., Austen, M., Boero, F., Bouma, TJ., Boxshall, G., Buchholz, F., Crowe, T., Delaney, A., Deprez, T., Emblow, C., Feral, JP., Gasol, JM., Gooday, A., Harder, J., Ianora, A., Kraberg, A., Mackenzie, B., Ojaveer, H., Paterson, D., Rumohr, H., Schiedek, D., Sokolowski, A., Somerfield, P., Sousa Pinto, I., Vincx, M., Węsławski, JM., Nash, R. (2009). Marine Biodiversity and Ecosystem Functioning. Printbase, Dublin, Ireland ISSN 2009-2539]</ref>. <br />
<br />
===Examples of invading species===<br />
For example, the roundhead goby ([http://www.marinespecies.org/aphia.php?p=taxdetails&id=126916 ''Neogobius melanostomus'']) from the [[Black Sea|Black]] and Caspian Seas is now playing the previously-vacant role of small, coastal predator on bivalves. At present, there are no documented examples of any negative ecological impact of these new species on the Baltic ecosystem.<br />
<br />
A number of new [http://www.marinespecies.org/aphia.php?p=taxdetails&id=1207 gammarid]<br />
amphipods frequenting the southern Baltic coast are well mixed with resident species and all show irregular ups and downs in abundance. However, small-scale problems for humans<br />
have been recorded, such as local problems with pipelines clogged with the zebra mussel [http://www.marinespecies.org/aphia.php?p=taxdetails&id=181565 ''Dreisena''], or fishing nets covered by the copepod [http://www.marinespecies.org/aphia.php?p=taxdetails&id=234024 ''Cercopagis''].<br />
<br />
A number of former invaders are now playing an important role in the [[benthic]] system. The bivalve [http://www.marinespecies.org/aphia.php?p=taxdetails&id=140430 ''Mya arenaria''], which arrived from North America during the medieval period, is now one<br />
of the [[keystone species|key species]] and sediment bioturbators, and it is also a source of food for fish and birds. The sessile barnacle, [http://www.marinespecies.org/aphia.php?p=taxdetails&id=106218 ''Balanus improvisus''], a 19<sup>th</sup>-century invader, is now one of the few species that builds stable biogenic structures (it is a bioconstructor and habitat builder) in this system<ref name="ma"/>. <br />
<br />
===Changes in native species===<br />
So far, the only documented extinction from the Baltic Sea is the sturgeon, [http://www.marinespecies.org/aphia.php?p=taxdetails&id=126279 ''Acipenser sturio''], a species that is now also believed to be a medieval invader from North America.<br />
<br />
Shifts and changes have occurred before. In the early 20<sup>th</sup> century, the dominant top predators in the Baltic were marine mammals ([http://www.marinespecies.org/aphia.php?p=taxdetails&id=137080 gray], [http://www.marinespecies.org/aphia.php?p=taxdetails&id=159021 ringed] and [http://www.marinespecies.org/aphia.php?p=taxdetails&id=137084 common seal] and [http://www.marinespecies.org/aphia.php?p=taxdetails&id=137117 harbour porpoise]). The seal populations declined by about 95% during the last century as a result, initially, of hunting (1900–1940) and later of [[Portal:Ecotox|toxic pollutants]] (1965–1975)<ref name="ma"/>.<br />
<br />
== Predictions==<br />
<br />
[[Climate change]]-related increases in temperature will provide the opportunity for more new species to settle in the Baltic Sea. However, all of the new species will have to cope with the effect of [[Effect of Climate Change in the Baltic Sea Area|decreasing salinity]], which forms the key environmental factor structuring this ecosystem, and it will probably continue to remain a key factor in the future<ref name="ma"/>.<br />
<br />
==Related articles==<br />
:[[Baltic Sea]]<br />
:[[Effect of Climate Change in the Baltic Sea Area]]<br />
:[[Non-native species invasions]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
[[Category:Baltic]]<br />
[[Category: MarBEF Wiki]]<br />
[[Category:Climate change, impacts and adaptation]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Effect_of_Climate_Change_in_the_Baltic_Sea_AreaEffect of Climate Change in the Baltic Sea Area2024-02-21T10:17:44Z<p>Dronkers J: Created page with " ==Baltic Sea – background information== [[image:Balticsea_Fig1.jpg|thumb|200px|left|'''Fig. 1.''' The Baltic Sea.<ref name="helcom">[http://www.helcom.fi HELCOM]</ref> ]]..."</p>
<hr />
<div><br />
==Baltic Sea – background information==<br />
<br />
[[image:Balticsea_Fig1.jpg|thumb|200px|left|'''Fig. 1.''' The Baltic Sea.<ref name="helcom">[http://www.helcom.fi HELCOM]</ref> ]]<br />
<br />
The [[Baltic Sea]] is a relatively shallow sea in north-eastern Europe, surrounded by the Scandinavian Peninsula, the mainland of central and east Europe and the Danish islands. It is connected with [[North Sea]] through the Kattegatt, Øresund, the Great Belt and the Little Belt. It covers area of about 415 000 km<sup>2</sup> and its average depth is 55 m. The central part of the Baltic Sea is known as Baltic Proper, other large parts which might be distinguished are the Bothnian Bay, the Bothnian Sea, the Gulf of Finland, the Gulf of Riga, and the Gulf of Gdansk, the Bornholm and Arkona basins, followed by the Sound, the Belt Sea and the Kattegat. <br />
<br />
==Baltic Sea Area==<br />
<br />
[[image:Balticsea_Fig2.jpg|thumb|200px|right|'''Fig. 2.''' The Baltic Sea Area<ref name="helcom"/>]]<br />
<br />
The term Baltic Sea Area is usually used to denote the Baltic Sea drainage basin. The Baltic catchment includes territories from 14 states (nine countries bordering to the sea and five other countries: Belarus, Czech Republic, Slovak Republic, Norway and Ukraine) and has total land area of approximately 1.7 million km<sup>2</sup>. About 16 million people live on the coast, and around 85 million in the entire catchment area of the Baltic Sea. More than 200 large rivers flow into the Baltic bringing around 480 km3 of freshwater annually. That makes the Baltic Sea the largest brackish water body in the world. Among many others the biggest rivers entering Baltic are Neva, Vistula, Oder, Neman, Daugava. See also [[Baltic Sea]].<br />
<br />
<br />
==Past climate in the Baltic Sea Area==<br />
<br />
Climate models predict a 2-4ºC rise in water temperature along with a rise in sea levels in<br />
the current century. This will have major implications for [[species]], [[ecosystems]] and food webs.<br />
<br />
The MarBEF project MarFISH examined archaeological evidence from the waters around Denmark (the Kattegat, Skagerrak, the Belt Sea and Bornholm) during a warm period from 7000-3900BC and showed that, during this period, there were several warm-water fish species present.<br />
<br />
These species were: smoothhound ([http://www.marinespecies.org/aphia.php?p=taxdetails&id=105732 ''Mustelus sp.'']), common stingray ([http://www.marinespecies.org/aphia.php?p=taxdetails&id=105851 ''Dasyatis pastinaca'']), anchovy ([http://www.marinespecies.org/aphia.php?p=taxdetails&id=126426 ''Engraulis encrasicolus'']), European sea bass ([http://www.marinespecies.org/aphia.php?p=taxdetails&id=126975 ''Dicentrarchus labrax'']), black sea bream ([http://www.marinespecies.org/aphia.php?p=taxdetails&id=127066 ''Spondyliosoma cantharus'']) and swordfish ([http://www.marinespecies.org/aphia.php?p=taxdetails&id=127094 ''Xiphias gladius'']). <br />
<br />
These species currently have a more southerly distribution and their presence near Denmark in the past was presumably caused by the warmer temperatures at that time. However, fishermen in this area are now capturing commercially important quantities (tens and thousands of tonnes) of some of these species. This is mainly the case for small-sized southern species while large, northern species have shifted their distributions to more northern and deeper waters. These changes have also been seen in scientific fisheries surveys which annually monitor the species composition of the [[North Sea]] fish community<ref name="ma">[https://www.researchgate.net/publication/306030378_Marine_Biodiversity_and_Ecosystem_Functioning Heip, C., Hummel, H., van Avesaath, P., Appeltans, W., Arvanitidis, C., Aspden, R., Austen, M., Boero, F., Bouma, TJ., Boxshall, G., Buchholz, F., Crowe, T., Delaney, A., Deprez, T., Emblow, C., Feral, JP., Gasol, JM., Gooday, A., Harder, J., Ianora, A., Kraberg, A., Mackenzie, B., Ojaveer, H., Paterson, D., Rumohr, H., Schiedek, D., Sokolowski, A., Somerfield, P., Sousa Pinto, I., Vincx, M., Węsławski, JM., Nash, R. (2009). Marine Biodiversity and Ecosystem Functioning. Printbase, Dublin, Ireland ISSN 2009-2539]</ref>.<br />
<br />
During the substantially colder climate in the 17th century, the herring [http://www.marinespecies.org/aphia.php?p=taxdetails&id=234068 ''Clupea harengus membras''] fishery in the North East Baltic Sea (Gulf of Riga) mostly took place during the summer months (June-July). This was probably because the fish migrated later to the spawning areas close to the coast where they were caught. In contrast, nowadays, in much warmer climate conditions, the [[coastal area|coastal]] trapnet herring fishery in spawning grounds takes place a few months earlier<ref name="ma"/>.<br />
<br />
==Recent climate change in the Baltic Sea Area==<br />
Two major climate types dominate in much of the Baltic Sea Area:<br />
# Most of middle and northern areas are determined by the temperate coniferous-mixed forest zone with long, cold, wet winters, where mean temperatures of warmest month is no lower than 10°C and that of the coldest month is no higher than −3°C, and where the rainfall is, on average, moderate in all seasons;<br />
# Much of the southwestern and southern areas belong to the marine west-coast climate, where prevailing winds constantly bring in moisture from the oceans and the presence of a warm ocean current provides for moist and mild winters, with frequent thawing periods even in mid-winter.<ref name="helcom2">Climate Change in the Baltic Sea Area – HELCOM Thematic Assessment in 2007 Baltic Sea Environment Proceedings No. 111, HELCOM, 2007.</ref><br />
<br />
Examples of other [[Effects_of_global_climate_change_on_European_marine_biodiversity#Effects_on_the_phenological_relationships_and_community_structure|effects on phenolgy]]<br />
<br />
===Temperature trends over the Baltic Sea Region===<br />
<br />
[[image:Balticsea_Fig3.jpg|thumb|350px|right|'''Fig. 3.''' Annual mean 2-m air temperature anomalies for the Baltic Sea basin 1871–2004, based solely on land stations. The blue color relates to the area to the north of 60 °N, and the red color to the area south of that latitude. The dots represent individual years, and the smoothed curves highlight variability on time scales longer than ten years.<ref name="helcom2">Climate Change in the Baltic Sea Area – HELCOM Thematic Assessment in 2007 Baltic Sea Environment Proceedings No. 111, HELCOM, 2007.</ref>]]<br />
<br />
The annual warming trend for the Baltic Sea basin is about 0.08°C/decade. This is higher than the worldwide trend which is about 0.05°C/decade. This warming trend can be observed as a decrease in the number of very cold days during winter as well as a decrease in the duration of the ice cover and its thickness in many rivers and lakes, particularly in the eastern and southeastern Baltic Sea basin. Furthermore, an increasing length of the growing season in the Baltic Sea basin has been observed during this period.<br />
<br />
In the 20th century, temperatures in the Baltic Sea basin increased during the early part of the century (termed the early 20th century warming) until the 1930s; then there was a slightly cooler period till 1960s, followed by another warming period continuing today (Figure 3).<br><br />
<br />
Warming is characterized by a pattern where mean daily minimum temperatures have increased more than mean daily maximum temperatures. The strongest warming trend is observed in spring whilst wintertime temperature increase is irregular but larger than in summer and autumn.<br />
The climate warming is reflected also in time series data on the maximum annual extent of sea ice and the length of the ice season in the Baltic Sea. Regarding the ice extent, the shift towards a warmer climate took place in the second half of the 19th century. The length of the ice season showed a decreasing trend by 14–44 days during the 20th century, the exact number depending on the location around the Baltic Sea.<ref name="baltex">BALTEX Assessment of Climate Change for the Baltic Sea Basin, Göteborg 2006.</ref><br />
<br />
===Precipitation trends===<br />
The external water budget of the Baltic Sea is dominated by water import from river discharge, exchange with North Sea water, and net precipitation (precipitation minus evaporation). Water inflow from North Sea is very limited and only Kattegat deep water contributes to the Baltic Sea water renewal. Taking into account freshwater supply the estimated residence time of water in Baltic Sea is about 33 years.<br />
Recent observations have provided an estimate (for the past 30 years) of mean annual precipitation of 750 mm/year for the entire Baltic Sea basin, including both land and sea. <br />
The highest precipitation occurs in the mountain regions in Scandinavia and southern Poland, while the lowest amounts of precipitation occur in the northern and northeastern part of the basin as well as over the central Baltic Sea. Precipitation has increased on average but there is no uniform spatial distribution of this increase. Within the Baltic Sea area the largest increase has occurred in Sweden and the eastern coast of the Baltic Sea. Seasonally largest increases have occurred in winter and spring. Changes in summer are characterized with increases in the northern and decreases in the southern parts of the Baltic Sea basin.<br />
Winters are projected to become wetter in most of the Baltic Sea basin and summers to become drier in southern areas for many scenarios. Northern areas could generally expect winter precipitation increases of about 25% to 75%, while the projected summer changes lie between 5% and 35%. Southern areas could expect increases ranging from about 20% to 70% during winter, while summer changes would be negative, showing decreases of as much as 45%. Taken together, these changes lead to a projected increase in annual fresh water inflow for the entire basin.<ref name="baltex">BALTEX Assessment of Climate Change for the Baltic Sea Basin, Göteborg 2006.</ref><br />
<br />
==Projected climate change effects==<br />
<br />
===Salinity and temperature===<br />
A lowering of salinity (due to generally higher precipitation and river discharge) is thought to have a major influence on the distribution, growth and reproduction of the Baltic Sea fauna. The salinity is already so low today that some fish species have adapted their physiology to be able survive. Freshwater species are expected to enlarge their significance, and invaders from warmer seas (such as the zebra mussel [http://en.wikipedia.org/wiki/Zebra_mussel ''Dreissena polymorpha''] or the North American jelly comb [http://en.wikipedia.org/wiki/Mnemiopsis_leidyi ''Mnemiopsis leidyi'']) are expected to enlarge their distribution area.<ref name="baltex">BALTEX Assessment of Climate Change for the Baltic Sea Basin, Göteborg 2006.</ref> <br />
If climate change leads to a further decrease in Baltic Sea salinity, this will reduce the number<br />
of marine fish species, even though one might otherwise predict that the increasing<br />
temperature should allow warm water-adapted species to immigrate. The Baltic Sea example<br />
shows that it will be important to consider multiple aspects of climate change, especially in coastal areas, if we are to estimate how [[Marine Biodiversity|marine biodiversity]] will change in future<ref name="ma"/>. <br />
<br />
[[Climate change]] will also have non-thermal impacts on fish populations. For example, changes in the strength, direction and location of [[Ocean circulation|ocean currents]] can affect the probability that fish eggs and larvae survive and grow. <br />
<br />
===Eutrophication===<br />
Climate-induced changes in marine ecosystems would include changes in nutrient cycling and contaminant distribution and changes at all trophic levels from bacteria to seabirds and marine mammals. As temperature rises, the ability of the ocean to retain [[oxygen]] will decrease. In many coastal areas in Europe (e.g., bays, straits, [[estuaries]]) the combination of rising temperature and decreasing oxygen will lead to [[Eutrophication in coastal environments|eutrophication]], especially in areas which already also receive high levels of [[nutrients]]. This will reduce the [[habitat]] size of bottom-living fish species such as cod and flatfishes. These species will become less abundant and widespread as coastal areas experience longer and more frequent [[anoxic]] periods<ref name="ma"/>.<br />
<br />
===Sea level rise===<br />
Another impact of climate change will be [[sea level rise|the rise in sea level]] due to melting of land-based glaciers and the expansion of seawater as it warms up. Both factors will cause flooding<br />
of coastal lowlands. Newly flooded coastal areas can provide more fish habitat, especially for [[benthic]] juveniles stages which are common in coastal areas<ref name="ma"/>.<br />
<br />
==Adaptation to Climate Change in the Baltic Sea Region==<br />
<br />
[[image:Balticsea_Fig4.jpg|thumb|350px|right|'''Fig. 4.''' Adaptation and mitigation concepts in climate change management.<ref name="hilpert">Hilpert, K., Mannke, F., Schmidt-Thomé, P. (2007): Towards Climate Change Adaptation in the Baltic Sea Region, Geological Survey of Finland, Espoo.</ref>]]<br />
<br />
The Baltic Sea Region faces a variety of challenges due to recently observed and projected climate changes, in particular the general trend of increasing temperature and changes in precipitation pattern. Different approaches are needed to deal with problems on regional and local scales. Some of the most important issues which require management strategies are:<br />
:*coastal protection;<br />
:*flooding events prediction and appropriate mitigation aftermath;<br />
:*water shortage.<br />
The concepts of adaptation and mitigation related to climate change are presented in figure 4. Adaptation activities help to diminished the negative climate change impacts or exploit beneficial opportunities of climate change. Mitigation activities are concerned with strategies and measures for greenhouse gas emission reduction.<ref name="hilpert">Hilpert, K., Mannke, F., Schmidt-Thomé, P. (2007): Towards Climate Change Adaptation in the Baltic Sea Region, Geological Survey of Finland, Espoo.</ref><br />
<br />
Adaptation strategies to climate change impacts are hardly implemented in policies of the Baltic Sea Region countries. More detailed analyses of current and future risks are needed for development of adequate adaptation solutions. However, adaptation and mitigation approaches should not be regarded as a separate topic. They are be to integrated in different fields of policies on regional, local, national and international level.<br />
<br />
<br />
==Related articles==<br />
:[[Baltic Sea]]<br />
:[[Baltic Monitoring Programme]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
<br />
{{author<br />
|AuthorID=19244<br />
|AuthorFullName=Frackiewicz, Anna<br />
|AuthorName=Frackiewicz}}<br />
<br />
[[Category:Climate change, impacts and adaptation]]<br />
[[Category: MarBEF Wiki]]<br />
[[Category:Baltic]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Microbial_loopMicrobial loop2024-02-20T20:14:22Z<p>Dronkers J: Created page with " {{ Definition| title = Microbial loop | definition = The trophic pathway through which dissolved organic carbon (DOC) released by marine organisms is returned to higher troph..."</p>
<hr />
<div><br />
{{ Definition| title = Microbial loop<br />
| definition = The trophic pathway through which dissolved organic carbon (DOC) released by marine organisms is returned to higher trophic levels via its incorporation into bacterial biomass grazed by phytoplankton.}}<br />
<br />
<br />
[[Image:MicrobialLoop.png|thumb|400px|left|Classical marine food chain including the microbial loop (Stawiarski and Buitenhuis, 2013<ref>Stawiarski, B. and Buitenhuis, E. (2013) Picophytoplankton physiology and the microbial loop. eposter European Geosciences Union General Assembly, Vienna 2013</ref>)]] <br />
<br />
Phytoplankton and other marine organisms release organic molecules called Dissolved Organic Matter (DOM) or Dissolved Organic Carbon (DOC). DOM includes liquid wastes of zooplankton and cytoplasm that leaks out of phytoplankton cells. In the microbial loop, bacteria consume DOM that cannot be directly ingested by larger organisms. Each millilitre of seawater contains approximately 1 million bacterial cells, many of which utilise DOM as a source of energy and nutrition. Bacteria are eaten by microflagellates. The abundance of the bacteria is, to a large extent, regulated by the grazing effects of heterotrophic nano-flagellates (2 – 20 μm in diameter). Ciliates, which are small enough to eat microflagellates, are eaten by zooplankton. Micro-flagellates and ciliates help to recycle organic matter back into the marine food web. Bacteria also help to facilitate phytoplankton growth by releasing nutrients when they absorb DOM. Viruses are the smallest and most abundant organisms in the sea, viral activity produces DOM, thus helping to drive energy cycles for ocean life.<br />
The main difference of the microbial loop between estuarine and coastal waters is that coastal waters tend to have lower population densities of bacteria and of the organism that prey on them.<br />
<br />
====Related articles====<br />
:[[Marine Plankton]]<br />
:[[Marine microorganisms]]<br />
:[[Algal bloom dynamics]]<br />
:[[Nutrient conversion in the marine environment]]<br />
<br />
<br />
====References====<br />
<references/><br />
<br />
[[Category:Eutrophication]]<br />
[[Category:Coastal and marine ecosystems]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Pollution_prevention,_detection_and_mitigation_in_European_coastal_watersPollution prevention, detection and mitigation in European coastal waters2024-02-20T14:24:29Z<p>Dronkers J: Created page with " Coastal Wiki articles related to '''Pollution prevention, detection and mitigation''' are listed in the :Category:Coastal and marine pollution. Pollution is defined as..."</p>
<hr />
<div><br />
<br />
Coastal Wiki articles related to '''Pollution prevention, detection and mitigation''' are listed in the [[:Category:Coastal and marine pollution]].<br />
<br />
Pollution is defined as the state of water when it contains a large amount of foreign materials so that it is no longer fit for its intended use, whether it is drinkwater, cold water for the cooling of engines, or clean water for tourism. This topic, which is of utmost importance for the European commission and national governments, covers a broad scope of knowledge including chemical processes of pollutants synthesis, hydraulic transport of compounds, biological impacts on human health or ecosystems, socio-economic consequences of a pollution event and engineering techniques for the mitigation of pollutions. The issue of observing the state of pollution and improving water quality near coastal zones has been addressed in different ways in most coastal countries, through regulations and the organisation of alert and protection systems.<br />
<br />
However, in spite of the great efforts which have been made in the past years to reduce the number of accidental pollutions and the impacts of chronic pollutions in coastal zones, one cannot but notice a large step still has to be taken in order to improve the understanding of sources and consequences of pollution and to create better warning procedures and reliable tools to mitigate the impacts of pollution.<br />
<br />
<br />
=The current state (2008) of maritime pollution in Europe and worldwide=<br />
<br />
==Accidental pollution==<br />
During the second half of the twentieth century, when economies started to widen across national borders, the amount of trade grew a lot in the world, dragging along the volume of maritime trafic between occidental countries. Governments were neither prepared nor used to face the consequences of such an economic revolution and found themselves unable to deal with the ever growing figures of oil spills and accidental pollutions near their coastlines. Therefore, it was no sooner than the 1970s that the first measures were taken to reduce the number and severity of accidental pollutions, with for instance the United Nations conventions [[North Sea pollution from shipping: legal framework| Marpol]].<br />
<br />
The main source of accidental pollution is oil spills. An overview of their trends and relevance is given in the Coastal Wiki article [[Overview of oil spills events from 1970 to 2000]].<br />
<br />
Oil is not the only source of accidental pollution. The sinking of the [https://en.wikipedia.org/wiki/Ievoli_Sun Ievoli Sun] in the Channel Sea in 2000 confirmed the existence of a chemical risk, when the ship went into the waters with its 6000 tonnes load of chemical products, including 4000 tonnes of styrene, 1000 tonnes of trichlorosilane and 1000 tonnes of isopropyl alcohol. It made decision makers notice the importance of preparing adapted responses, using innovative technologies and taking advantage of the experience of other countries.<br />
<br />
<br />
==Chronic pollution==<br />
Even if disasters have a huge impact on public opinion due to their depicting in medias, accidental pollutions only stand for a small part of the whole water pollution which is mostly caused by chronic pollutions and waste discharge in coastal waters by chemical industries.<br />
<br />
===Chemical pollution===<br />
====Nitrates and phosphates====<br />
In many cases, coastal water pollution is caused by the discharge in the nearshore or estuaries of nutrients such as nitrates and phosphates which are used by agriculture and human activities. There are many sources of nutrient runoffs : unsewered urban areas, construction sites, combustion of fossil fuels by traffic, industries and households, see [[What causes eutrophication?]]. Other natural processes may be responsible for an increased concentration of nitrates and phosphates in coastal waters : atmospheric deposition of nitrogen on seas, soil erosion. The rate of water renewal plays a critical role in this field, since stagnant water can collect more nutrients than other zones with replenished water supplies.<br />
<br />
Nutrient pollution often results in eutrophication. Eutrophication promotes excessive plant growth and favors specific species above others which leads to an excessive amount of aquatic vegetation or phytoplankton in the water and may cause other problems such as a lack of oxygen (hypoxia or anoxia) needed for fish to survive and a limitation of sunlight available to bottom-dwelling organisms. The process may also cause competitive release by making available a normally-limited nutrient which may cause a shift in species composition of ecosystems, see [[Possible consequences of eutrophication]].<br />
<br />
Human population are also concerned through economic problems caused by the decrease of resource value of coastal seas with hindered fishing activities and beaches less attractive for tourists.<br />
<br />
In Europe, the whole [[Baltic Sea]] is affected by eutrophication and nutrient related problems are widespread in esturaries and fjords, see [[Eutrophication in coastal environments]]. In the Celtic seas, the phenomenon takes place only in the Irish sea, estuaries and coastal lagoons. In the [[Mediterranean Sea]], eutrophication is more restricted and limited to specific coastal and adjacent offshore areas, especially in the Adriatic, Gulf of Lion and northern Aegean Sea. The main increase in nutrient loads took place during the middle of the 20<sup>th</sup> century, with a doubling of nitrogen loads from the 1950s to the 1980s and a fourfold increase of phosphorus loads from the 1940s to the 1970s in the Baltic and North Sea regions, and probably similar values in the Mediterranean Sea. Trend analyses in the Baltic and North Sea land-based regions however show a recent decrease in phosphorus loads in the 1990s due to improved sewage treatment and use of phosphate free detergents, but there is no discernible reduction in the nitrogen loads. Generally, nutrient loads are decreasing in all parts of Europe. However, nutrient concentrations do not show a similar trend.<br />
<br />
====Heavy metals====<br />
[[Heavy metals]] is a general collective term which applies to the group of metals and metalloids with an atomic density greater than 4 g/cm³. The category includes cadmium, chromium, copper, mercury, lead, zinc, arsenic, boron and the platinum group metals.<br />
<br />
Heavy metals in trace amounts are normal constituents of marine organisms and some of them, such as zinc, copper and cobalt, are absolutely essential for normal growth and development. In coastal regions, these metals are normally supplied to the sea in river water. However, at present time, additional quantities of metals are being added to estuaries and coastal regions from industrial effluents, from sewage and atmospheric pollution. At sufficiently high concentrations, those heave metals become toxic to living organisms.<br />
<br />
Since heavy metals are basic elements of the periodic classification, they can not be broken down and will persist in the environment. In some specific areas where water flows are concentrated in confined zones like estuaries or lakes, they will even tend to accumulate with time. Many of the heavy metals are toxic to organisms at low concentrations. Effects on the organisms are manifest when the natural regulation mechanism of the body concentration of metals breaks down as a result of either an insufficiency or an excess of metal.<br />
<br />
The level of exposition to heavy metals pollution can be estimated through indicators linked to the speed of refreshment of water in a delimited surface, such as residence times, see the Coastal Wiki article [[Dispersion processes in estuaries]].<br />
<br />
====[[Oil spill|Oil]]====<br />
The largest source of oil pollution in coastal waters is not the numerous disasters happening along the European coasts which only represent 5 % of the total amount of oil spilled in water (6.11 million tonnes in 1973, 2.35 million tonnes in 1990), but the continuous pipelines leaks and runoff, with 61 % of oil coming from river oil pollution and urban runoff and 30 % due to intentional discharges from tankers. Figure 1 shows that chronic sources of oil pollution are relevant compared to accidental disasters and have to be taken into account when elaborating action plans for the mitigation of oil pollution in coastal waters.<br />
<br />
[[Image:input_oil_pollution.png|500px|thumbnail|<center>Figure 1: Inputs of oil pollution in the marine environment</center>]]<br />
<br />
In this field, a general decrease of total oil pollution is usually observed in the last decades, although this global situation may differ at regional level. This is mainly due to the entry into force of stricter requirements to activities accompanied by oil discharges. In 1981, oil transportation and shipping in general were responsible for discharging about 1.4 million tons of oil products. This amount was reduced to 0.56 million tons in 1990. The reduction mainly occurred as a result of adopting stricter international regulations concerning transportation operations in the sea (International Convention for Prevention of Pollution from Ships and others). The total oil pollution input into the sea during the same period dropped from 3.20 to 2.35 million tons. See also[[ Overview of oil spills events from 1970 to 2000]].<br />
<br />
====Persistent organic pollutants====<br />
Persistent organic pollutants (POPs) include a wide range of substances: industrial chemicals (such as polychlorinated biphenyls – PCB) and by-products of industrial processes (e.g., hexachlorobenzene – HCB, and dioxins) whose toxic characteristics are unintentional, and others, such as pesticides (e.g., DDT) and herbicides (e.g., lindane – HCH), that are designed to have toxic properties. POPs containing chlorine are referred to as organochlorines.<br />
<br />
POPs are of special concern because they persist in the environment for long periods of time, which allows them to be transported large distances from their sources, are often toxic, and have a tendency to bioaccumulate; many POPs biomagnify in food chains.<br />
<br />
===Biological pollution===<br />
====Endocrine disruptive compounds====<br />
Endocrine disruptive compounds (EDCs) are compounds that may be hormonally active at low concentrations because they are exogenous agents that interfere with the synthesis, secretion, transport, binding, action or elimination of natural hormones in the body that are responsible for the maintenance of homeostasis, reproduction, development and behaviour. EDCs mimick endogenous hormones necessary for the regulation of vital functions in living organisms.<br />
<br />
Substances which are potential EDCs are phytoestrogens, synthetically-produced hormones, natural hormones and synthetic industrial compounds such as phtalates, alkylphenols, organochlorine pesticides. <br />
<br />
It is quite difficult to characterize the evolution of concentrations of EDCs in coastal waters since they are usually present in very small quantities and may be active at low concentrations. Nevertheless, research should be carried out to improve assessment of different kinds of EDCs, identify the most sensitive areas and characterize the effects of those compounds on human health as well as the interactions between various chemicals. Further details can be found in the [[Portal:Ecotox|Portal on Ecotoxicology]] and the article [[Endocrine disrupting compounds in the coastal environment]].<br />
<br />
===Physical pollution===<br />
====Intrusion of saltwater====<br />
Saltwater intrusion is a natural process that occurs in virtually all coastal aquifers, where they are in hydraulic continuity with seawater. It consists in salt water flowing inland in freshwater aquifers. This behaviour is caused by the fact that sea water has a higher density than freshwater. This higher density has the effect that the pressure beneath a column of saltwater is larger than that beneath a column of the same height of freshwater. If these columns were connected at the bottom, then the pressure difference would trigger a flow from the saltwater column to the freshwater column. Pumping of freshwater reduces the water level and water pressure and intensifies the effect, making groundwater unfit for its usual uses : drinking and irrigation. See also [[Groundwater management in low-lying coastal zones]]. <br />
<br />
As shown on figure 2, the issue of saltwater intrusion is most important in Southern Europe regions, where coastal aquifers are overexploited for the needs of agriculture and the human population living near the sea. Some specific problems are also observed along the Eastern coastlines of Denmark towards the Baltic Sea and, to a smaller extent, in Germany.<br />
<br />
[[Image:saltwater_intrusion.png|600px|thumbnail|center|<center>Figure 2: Places of saltwater intrusion in Europe (Source : EEA 2000)''</center>]]<br />
<br />
<br />
====Thermal pollution====<br />
Thermal pollution results primarily from electric power plants that use large quantities of cooling water and discharge it at temperatures as much as 10 Celsius degrees above that of the surrounding water. The increase in water temperature disrupts the life cycle of many marine organisms and encourages invasion by creatures usually living in warmer waters.<br />
<br />
Hot water generally decreases the concentration of dissolved oxygen, thus harming aquatic animals. Thermal pollution may also increase the metabolic rate of poikilotherms organisms, acting as an enzyme and resulting in these organisms consuming more food than usually. Thus an increased temperature may lead to food shortage in coastal ecosystems. It is known that temperature changes of even one or two degrees can cause significant changes in organism metabolism, with an alteration of enzyme metabolism, coagulation of proteins, affecting mortality and reproduction. Furthermore, changes in the environment may also make fishes and aquatic animals migrate to more suitable environment.<br />
<br />
<br />
=Pollution assessment=<br />
<br />
==Observation tools==<br />
<br />
The detection and measurement of chemical or biological compounds at sea is a quite difficult task, as it is usually expensive to take samples of water to analyse it, and some compounds are present at so low concentrations that there are nearly impossible to observe, even with a sample of water. Other types of chemicals transported at sea mix with water and are rapidly dispersed, or sink to the seafloor because of their higher density and low solubility.<br />
<br />
Many tools have been developed and are currently implemented to detect pollution and evaluate water quality in coastal zones. The Coastal Wiki gives examples of local initiatives based on academic research and yielding interesting results in the detection and assessment of maritime pollutions, see the articles [[Oil spill monitoring]], [[Oil spill pollution impact and recovery]], [[Oil sensitivity mapping]] and [[Common biomarkers for the assessment of marine pollution]].<br />
<br />
<br />
==Transport and dispersion models==<br />
Fast and precise pollution dispersion models can help to predict the fate of contaminated compounds in the nearshore seas and thus, with the help of predictive climate and oceanologic models, to protect coastal populations and ecosystems from the impacts of chemical or biological pollution. A lot of efforts have to be done to improve those models and our understanding of the physical processes behind transport and mixing of pollutants at different temporal and spatial scales. For further details on transport and dispersion models can be found in the Coastal Wiki articles [[Seawater intrusion and mixing in estuaries]] and [[Transport and dispersion of pollutants, nutrients, tracers in mixed nearshore water]].<br />
<br />
<br />
==Risk analysis==<br />
To protect coastal population from the impacts of marine pollutions, three methods can be applied, simultaneously or successively :<br />
<br />
* prevent the pollution from happening by applying strict regulations on the potentially-dangerous human activities and monitoring the concentration and load of pollutants in high-risk zones such as ship ways and industrial zones ;<br />
* compensate for the impacts of pollution by limiting the dissemination of pollutants in coastal areas and applying mitigation techniques (cleansing of beaches and nearshore waters, transformation of the pollutant in a less toxic body by addition of a chemical or biological reactive,...) ;<br />
* flee from the danger by evaluating the level of risk of coastal regions and make human and animal populations retreat in safer areas which have first been prepared to shelter them.<br />
<br />
Most of the time, those three aspects are implemented together, although the last one may be not be well accepted by inhabitants of coastal zones who have to leave their home. Nevertheless, after a major pollution event arises which has strong consequences on human population, people become aware of the danger and are willing to settle in safe areas.<br />
<br />
Whether it is for the prevention of pollution or for the identification of dangerous areas, a risk analysis is necessary, combining an observation of the actual state of pollutants loads, an evaluation of the neighboring vulnerabilities and an estimation of the socio-economic consequences if an event occurs. This analysis can lead to the drawing of a risks map along the European coastlines which will give decision makers a better understanding of the dangers they have to face in their own regions, helping them identifying priorities in the prevention and mitigation actions. <br />
<br />
[[Environmental risk assessment for pollution of marine activities]] : this article gives a comprehensive definition of risk assessment in the case of pollution due to marine activities and establishes a framework for the risk evaluation<br />
<br />
[[Case study risk analysis of marine activities in the Belgian part of the North Sea]] : this case study shows how a full risk analysis can be carried on with the example of the Belgian part of the North sea<br />
<br />
[[Index of vulnerability of littorals to oil pollution]] : this article gives an easy framework to evaluate the vulnerability of different kind of littorals to oil pollution, with respect to their exposition to swell and tidal waves and their soil composition.<br />
<br />
<br />
=Legal framework and regulations=<br />
<br />
==European level==<br />
Since the first issues regarding coastal waters pollution were raised by scientists and local decision makers, the European commission has been dealing with the problem by elaborating recommendations for the member states and publishing directives for the prevention of pollution and the improvement of coastal environment. Two major documents about coastal zones have to be quoted in this framework :<br />
<br />
* the [[EU ICZM Recommendation|Commission communication on the European strategy on integrated coastal zone management (ICZM)]] ;<br />
* the European parliament and the Council recommendation concerning the implementation of ICZM in Europe (2002/413/EC).<br />
<br />
Coastal zones are also indirectly addressed in other European directives : <br />
<br />
* the [http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2012:026:0001:0021:EN:PDF European Directive on Environmental Impact Assessment (EIA, 2001)]<br />
* the [[Water Framework Directive]] (2000) <br />
* the [https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32006L0007 quality of bathing water directive] (1976, amended in 2006) <br />
* the [https://eur-lex.europa.eu/EN/legal-content/summary/quality-of-shellfish-waters.html directive on quality required of shellfish waters] (1979) <br />
* the [[European Marine Strategy Framework Directive]] (2005) <br />
<br />
Regarding dangerous substances discharged into the aquatic environment by industrial plants, [https://environment.ec.europa.eu/topics/chemicals/reach-regulation_en EU REACH legislation] has introduced a system of stricter limit values, while at the same time leaving member states free to choose the system of quality objectives, with the corresponding obligation to show that these objectives are being complied with. The basic directive 76/464 adopted in 1976, contains a blacklist of 129 substances declared dangerous by virtue of their toxicity and bio-accumulation; it was supplemented in December 1979 by Directive 80/68 on the protection of groundwater against pollution caused by certain dangerous substances. Pursuant to these directives, specific directives were introduced prescribing limit values and quality objectives for the discharge of cadmium, hexachlorocyclohexane (HCH) and mercury. <br />
<br />
==International level==<br />
Most of the European countries have also signed the '''[[UN Convention on the Law of the Sea|United Nations convention on the law of the sea]]''' (UNCLOS) proposed by the International maritime organisation (IMO) and entered into force in 1994. Its 145<sup>th</sup> article require member states to cooperate in the field of prevention, reduction and control of pollution to the marine environment, including the coastline. Particular attention must be paid to the need for protection from harmful effects of such activities as drilling, dredging, excavation, disposal of waste, construction and operation or maintenance of pipelines. This includes pollution from land-based sources, seabed activities, dumping, shipping, and atmospheric exchanges.<br />
<br />
Various international agreements were also signed at the end of the 20<sup>th</sup> century to reduce pollution in the worst affected areas. The '''Bonn agreement''' deals with the protection of the North Sea against hydrocarbons and other harmful substances. Its main topics are the protection of ecosystems and the prevention of pollution from ships and drilling platforms. The '''Warsaw convention''' tries to establish a framework for the conservation of living resources of the Baltic Sea. The Mediterranean sea is also the subject of a number of agreements, dealing with the prevention of pollution caused by dumping from ships.<br />
<br />
Specific agreements were signed to solve major issues related to marine pollutions, and are adopted by an increasing number of states in the world. The '''Convention on the prevention of marine pollution by dumping of wastes and other matter''', known as the '''London Convention''' of 1972, is the primary international agreement controlling the deliberate dumping of non-ship generated wastes at sea. Since entering into force in 1975, the London Convention has become more restrictive over the years. The '''Global program of action for the protection of the marine environment from land-based activities''' was decided in 1995 by the member states and constitutes a practical source of guidance for action which must take place at the national and regional level, aiming to reduce land-based pollution in the nearshore seas. The '''Internal convention on civil liability for oil pollution damage''', which was adopted in 1969 just prior to the first "Fund convention" for compensation of oil pollution damage in 1971, established the IFOP and the first procedure for assisting member states in the compensation of impacts of oil pollution. The '''Marpol convention''' was probably one of the most effective in the 1980s and resulted in a dramatic decrease of the number of maritime pollution disasters. Finally, the '''OPCR convention''' (International convention on oil pollution preparedness, response, and cooperation) which entered into force in 1995, provides an international framework for a better collaboration between states in the field of prevention, detection and alert of oil pollution incidents and the mitigation and compensation measures. Similar conventions were adopted a few years later and generalised this framework to other pollutants : the International convention on liability and compensation for damage in connection with the carriage of hazardous and noxious substances by sea (1996), the International convention on supplemental compensation for nuclear damage (1997).<br />
<br />
A comprehensive list of international agreements as well as national regulations can be found in the Coastal Wiki article [[North Sea pollution from shipping: legal framework]].<br />
<br />
<br />
<br />
=Priority issues=<br />
<br />
Particular care should be taken to improve knowledge and practice in the following unresolved issues :<br />
* Observation and monitoring of pollution <br />
** Observation system of priority substances settled by EU<br />
** Characterisation of pollutants and contaminants <br />
** Determination of chronic pollution sources <br />
** Technology for tracking and monitoring of pollutants <br />
* Management of risk with respect to the ecosystem<br />
** Integrated assessment of ecological and socio-economic impacts<br />
** Decision making process to assess sanitary impact <br />
** Vulnerability indices and sensitivity maps<br />
* The fate and impacts of pollutants<br />
** Chemical and physical processes from source to the living organisms<br />
** Impacts of pollutants on living organisms and ecosystems<br />
** Pressure on human activities in coastal zones<br />
<br />
<br />
=Conclusion=<br />
<br />
This section of the Coastal Wiki provides an overview of current and future issues related to maritime pollutions as well as related scientific or policies topics like socio-economic impacts of pollutants in coastal zones, assessment of pollution and methods for risk management and mitigation, and national and European policies dealing with those issues.<br />
<br />
Nevertheless, maritime pollution and water quality are highly shifting topics and techniques and policies related to them are in constant evolution, gradually integrating new aspects when major events occur through the world sometimes resulting in human or ecological losses but always involving a progressive awareness of the threats and vulnerabilities and feeding the improvement of mitigation techniques and prevention policies. Therefore this state of the art is only a snapshot at a given time and cannot be regarded as a final document : it has to keep living and being fed with international events and technological news.<br />
<br />
Whereas many initiatives were taken during the previous years in the form of international agreements and proved successful by succeeding in decreasing the number and severity of accidental pollutions and reducing the discharge of pollutants in coastal zones, some major issues remain unexplored until today. The chemical and geographical fate of pollutants needs some scientific development as many operational forecasting tools proved inefficient to predict the consequences of a local pollution, even on a short time basis. Moreover, the impacts of pollutants on living organisms need to be better assessed on a long-time scale. As far as the current state is concerned, local research initiatives carried out to assess pollutants loads and impacts in bounded regions have to be merged on a continental scale in a European observatory to benefit from the results of each other. Finally, risk management practices could be improved and their use generalised as a whole component of integrated coastal zone management.<br />
<br />
[[Category:Coastal and marine pollution]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Preparation_of_offshore_wind_farm_development_in_GermanyPreparation of offshore wind farm development in Germany2024-02-20T10:21:55Z<p>Dronkers J: Created page with "==Introduction== The initiation or expansion of offshore wind farms represents a good development opportunity of many nations located at coasts. Especially in Denmark, Ireland..."</p>
<hr />
<div>==Introduction==<br />
The initiation or expansion of offshore wind farms represents a good development opportunity of many nations located at coasts. Especially in Denmark, Ireland, Sweden, the Netherlands and Great Britain wind energy plants offshore contribute to energy supply. Also other countries like France, Sweden, United States, China, Spain and Germany have announced their offshore wind power development plans. The increase of wind energy contributes to climate protection, sustainable development, economic growth, technical development and will boost significantly the export potential. Another driving factor for wind farm development in the sea is the aim of creating jobs, especially in structurally weak regions such as Lower Saxony, Schleswig-Holstein and Mecklenburg-Western Pomerania. Germany expects 20,000 additional jobs (Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit, 2007).<br />
<br />
The political date of birth of the offshore wind energy was in March 2000 with the implementation of the Renewable Energy Sources Act (German: Erneuerbare Energiengesetz - EEG)[http://www.bmu.de/english/renewable_energy/doc/6465.php]. The act is a support instrument for the expansion of renewable energies and aims a sustainable energy system. It has regulated for the first time feed-in-tariffs for offshore wind farms in the German coastal zone and the [[exclusive economic zone]] (EEZ) in the North and the Baltic Sea.<br />
<br />
==Frame conditions==<br />
According to the [[Legislation for the sea|United Nations Convention on the Law of the Sea]], UNCLOS (German: Seerechtsübereinkommen) nations are allowed to use areas up to 200 nautical miles in their exclusive economic zones for the construction of wind energy plants. By offshore wind turbine is meant any turbine that is located at least three nautical miles from the coastline. In the sea wind speed is much higher than on land and open space is available to a large extent. The North Sea exhibits adequate conditions as it belongs to the wind richest regions in the world. The wind blows in more than 90 % of the time with a speed of 4 meter per second (German Energy Agency, dena). <br />
<br />
==Expansion targets in Germany==<br />
The Federal States of Germany intend, according to the Renewable Energy Sources Act, to promote the share of renewable energies with the goal of generating at least 12.5 % of Germany's electricity needs from renewables by 2010, and at least 20 % by 2020. To ensure a sustainable energy extraction in the future, the further development of offshore wind farms in the North and Baltic Sea is one important part of the strategy. <br />
<br />
To begin with the commercial construction of wind energy farms in the German waters in the period between 2008 and 2010 has high priority. By 2011 the installation of an output of about 1,500 MW shall be started which seems to be a realistic estimation provided that needed resources such as installation vehicles are available. By the end of the year 2015 an output of at least 3,000 MW shall be reached and by the year 2020 at least 10,000 MW. In this way a growing market will be initiated. The contribution made by offshore wind power is expected to rise by 2030 to an output of 20,000 to 25,000 MW. The potential annual electricity yield from offshore wind farms is estimated at 85 to 100 TWh, accounting for approximately 15 % of Germany’s electricity consumption in the reference year 1998 (Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit, 2007).<br />
<br />
[[image:expansion of offshore wind power use.jpg|thumb|left|Forecasted expansion of offshore wind power use. Cautiously optimistic expansion (according to Deutsche WindGuard).]]<br />
<br />
==Economic aspects==<br />
The installation of an offshore power plant and its connection to the grid is a complicated process that demands high investment cost in the beginning. The investors of wind farms need labour force, wind turbines, steel, concrete, cables, transport and logistics. The investment costs amount about more than hundred million Euro for a single wind farm. For that reason only wind farms with an output of more than 100 MW are profitable. <br />
<br />
One concern is the cost-effective use of offshore wind power which is linked to the Renewable Energy Sources Act (EEG). It defines the so called feed-in-tariffs. The basic idea is to grant wind farm operators a fixed payment for 20 years to ensure an economic operation of the plants. The fee that is paid for the electricity depends on the date of commissioning. For newly installed plants the refund decreases annually. At time electricity from offshore wind farms is guaranteed a feed-in tariff of 9.1 cents/kWh, provided the plants commenced operation prior to the end of 2010. In other countries such as the Netherlands or Great Britain 15 cents/kWh are paid or national investment grants are given.<br />
<br />
==Implementation of the Government’s offshore strategy==<br />
To achieve the targets formulated by the government, Germany follows a strategy consisting of mainly tree parts. First there is a need for optimizing the legal framework conditions. Then it is waited for the first results of the test field (Fino 1) [http://www.fino-offshore.de/] next to the island of Borkum in the North Sea. Moreover the German Environment Ministry is promoting research projects from its renewable energy research programme to accompany the development of offshore wind power use (Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit, 2007). <br />
<br />
The “Strategy of the German Government on the use of Offshore wind energy” [http://www.bmu.de/files/pdfs/allgemein/application/pdf/offshore.pdf]published in 2006 aims to follow a step-by-step process during the next three decades. <br />
<br />
1. Preparation phase 2001-2006 - MW<br />
2. First expansion phase 2007-2010 2,000 – 3,000 MW<br />
3. Additional expansion phases 2011-2030 20,000 – 25,000 MW<br />
<br />
For improving the acceptance, gathering information and gaining experiences, the offshore wind energy foundation (“Stiftung der deutschen Wirtschaft zur Nutzung und Erforschung der Windenergie auf See") [http://www.offshore-stiftung.de/]has been founded. Members are manufacturers of wind energy plants, planners, energy supply companies, banks, insurance companies, several associations, engineer firms and the federal states in northern Germany. They initiated a test and research platform in the area Borkum West in the North Sea. The data gained at the platform Fino 1 are the basis for the development of scientific and technological standards for the approval process. In addition these data are needed for the investigation of technical, ecological and marine issues. Since September 2003 from this platform data on meteorology (wind speed, temperature, humidity, air pressure), oceanography (oxygen content, waves, water level and stream profiles) and biology in the German bight are collected systematically. Since 2006 a second platform, named Fino 2 works in a similar way. It is located in the Baltic Sea north of the island of Rügen.<br />
<br />
[[image:Fino 1.jpg|thumb|right|Research platform Fino 1. Source: Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit, 2007.]]<br />
<br />
In terms of the selection of locations for wind farms and cables the federal regional planning act (Raumordnungsgesetz, ROG)[http://www.iuscomp.org/gla/statutes/ROG.htm] provides regulation tools in the German EEZ. The law is used to bring different demands in coast utilisation (e.g. shipping, military use, mari culture) and ocean exploitation (e.g. fishing, drilling for oil and gas) in order. It helps to identify suitable areas which can be determined as priority areas for energy exploitation. That excludes other activities in the same area which would hinder wind farm projects. <br />
<br />
==Actual status==<br />
According to the Annual Report 2006 of the Hydrographic Agency (BSH) (pdf-file in German) [http://www.bsh.de/de/Produkte/Infomaterial/Jahresbericht/Jahresbericht2006/Jahresbericht2006.pdf]it is planned to set up 34 offshore wind energy farms in the North Sea and 6 in the Baltic Sea. Up to now, 15 wind energy farms with 1097 wind energy plants have been licensed. So far only two isolated offshore plants with an output of 4.5 MW and 2.5 MW have been built in the vicinity of the coast near Emden and Rostock. The first wind farms are due in 2008 and 2009. The Infrastructure Planning Acceleration Act guarantees the grid connection for all offshore plants for which construction has commenced prior to 31st December 2011.<br />
<br />
[[image:Approved projects.jpg|thumb|left|Licensed offshore wind farms in the North and Baltic Seas as per August 2006 (BSH.]]<br />
<br />
In autumn 2006 the deployment of the test field Alpha ventus [http://www.alpha-ventus.de/] has been launched, financed by EWE [http://www.ewe.de/], E.ON Energie [http://www.eon-energie.com/pages/eea_de/index.htm] and Vattenfall Europe [http://www.vattenfall.de/] as well as the manufacturers of the wind energy plants Multibrid and REpower Systems. The test field embraces six 5 MW wind energy plants of the tripod–type that will be installed by end of September 2008. <br />
<br />
[[image:Wind farm North Sea.jpg|thumb|left|German offshore projects in the North Sea in 2007<br />
Source: Windenergie-Agentur Bremerhaven/Bremen e.V. (2007).]]<br />
<br />
[[image:Wind farm Baltic.jpg|thumb|right|German offshore projects in the Baltic Sea in 2007<br />
Source: Windenergie-Agentur Bremerhaven/Bremen e.V. (2007).]]<br />
<br />
==Problems in setting up wind farms in the German EEZ==<br />
In contrast to other countries in Europe Germany is not allowed to construct the wind energy farms in the coastal waters due to protected areas in the Wadden Sea. The German Government’s strategy aims to exclude wind energy facilities from national parks [http://en.wikipedia.org/wiki/National_park] and NATURA 2000 areas [http://www.bfn.de/0316_natura2000.html]. For that reason long distances to the coast (30-100 km) and sea depths of up to 40 m must be considered. That means a major challenge for the foundations, the encapsulation of the gondola and the materials. Since offshore wind energy plants can be compared only partly with wind turbines on land, a lack of experience in technical issues hinders the progress. Many ideas for construction types such as tripod, monopole, jacket etc. have been developed but at time there is no information in view of applicability and in shipping safety of the plants.<br />
<br />
Additionally, long distances to the shore pose major challenges. The transportation of large quantities of electricity to the mainland is also very labour-intensive, as is the associated maintenance and servicing work. <br />
<br />
Another problem in Germany refers to different responsibilities when licensing a wind plant and the attached cable. Whereas the wind power plant itself in the EEZ is licensed by the BSH and the national law, the underground cables lie within the coastal waters (12 nautical mile zone) where the Federal States with their federal laws are in charge. Up to twelve administrative facilities have to submit their license. Due to this complicated process it can take 10 years from planning stage to electricity production. <br />
<br />
The licensing process of the cables is still too complicated because of lacks in the federal regional planning act (Raumordnungsgesetz, ROG) that gives no instructions for an adequate planning procedure. Additionally there are no legal and economic basics for a preferable bundling of cables for different wind farms. <br />
<br />
Another reason that offshore wind farms are not operating yet is the dramatic increase of the prices for steel and cupper since 2002. That results in exorbitant costs of the prospected wind farm. This explains the cautious behaviour of investors who prefer to wait for lower prices.<br />
<br />
The instructions for licensing wind energy plants in the EEZ specified by the Marine Facilities Ordinance (German: Seeanlagenverordnung)[http://www.umwelt-online.de/recht/wasser/schifffahrt/seean_ges.htm] ascribes Environment and Nature protection high significance. But definite rules are missing by now to protect the environment in a better way and to ensure legal security for investors. <br />
<br />
Significant restraints will occur, when wind farms are connected to the grid and electricity is fed in. The recent grid is not able to deal with the future electricity amount. <br />
<br />
==Solutions for goal achievement==<br />
That offshore wind farms have not yet been installed, has its reason lastly in uncertainties due to missing knowledge and experience. In general various surveys should be carried out in future to gain experiences with offshore wind farms in the location of the German EEZ. Aims are to lower cost of electricity production, increase yields, ensure reliable operation of the wind farms, advance the level of grid integration, and organise the expansion of offshore wind power in an eco-friendly way that does not impinge on nature (Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit, 2007). All this gives reason for research in the field of offshore wind power. The investigations will help to eliminate present barriers.<br />
<br />
Considerable uncertainties exist how intensive the wind energy plants will effect on wildlife. Studies of the marine species and their behaviour in the vicinity of offshore wind energy plants must be pursued. Furthermore the influences of each possible construction type must be regarded and assessed. The same refers to shipping safety in combination with wind energy plants. Though the wind farms should not be constructed in water ways with high shipping frequencies, the risk of collision can not be excluded. <br />
<br />
The enhancement and stabilization of the national grid is an absolutely necessary measure because it is not able to deal with high energy amounts and to store energy. The grid study (in German) [http://www.offshore-wind.de/page/index.php?id=9582&L=1] of the German Energy Agency (dena) [http://www.dena.de/] from 2005 calls for a rapid expansion of transfer capacities. Part 2 of the grid study will examine the integration of renewable energies into Germany's extra-high voltage grid and will provide technically innovative solutions. The new Renewable Energy Sources Act (EEG) forces grid operators to supply sufficient transfer capacities. <br />
<br />
==Conclusion and outlook==<br />
Due to these obstructions the development goal up to 2010 is not achievable. But the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) [http://www.bmu.de/english/aktuell/4152.php] states there is no doubt that long-term goals can be reached and that challenges occurring nowadays are solvable.<br />
<br />
As the development of wind energy on land the achievement of the goals of the offshore wind energy market is only possible when having the right economic frame conditions for investors. How fast the further development will be depends on the speed of the creation of these conditions and the solution of relevant problems. Highly important is also the law amendment of the Renewable Energy Sources Act in the year 2008 that will fix new feed-in-tariffs.<br />
<br />
A significant delay of the offshore development will cause a significant risk that offshore wind farms will be realised with minor participation of the German economy but with high participation of foreign companies.<br />
<br />
==References==<br />
<br />
Bundesamt für Seeschifffahrt und Hydrographie (BSH) (2006): Jahresbericht. Hamburg und Rostock<br />
http://www.bsh.de/de/Produkte/Infomaterial/Jahresbericht/Jahresbericht2006/Jahresbericht2006.pdf<br />
<br />
Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (2007): Offshore wind power deployment in Germany.<br />
http://www.bmu.de/files/pdfs/allgemein/application/pdf/offshore_wind_deployment_de_en.pdf<br />
<br />
Umweltbundesamt (2007): Entwicklung einer Umweltstrategie für die Windenergienutzung an Land und auf See Kurzfassung: Ergebnisse und Handlungsempfehlungen. Dessau.<br />
http://www.umweltdaten.de/publikationen/fpdf-k/k3242.pdf<br />
<br />
Windenergie-Agentur Bremerhaven/Bremen e.V. (2007): Offshore Windenergie. Der Wind, das Meer und die Zukunft der Energieversorgung. Das Magazin.<br />
http://www.windenergie-agentur.de/deutsch/PDFs/OffshoreMagazin_reduced.pdf<br />
<br />
==Further useful Links==<br />
<br />
Federal Ministry of Ecucation and Research – Coastal Futures information portal http://www.coastal-futures.de/<br />
Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) http://www.erneuerbare-energien.de<br />
Federal Maritime and Hydrographic Agency (BSH) http://www.bsh.de<br />
Federal Environmental Agency (UBA) http://www.umweltbundesamt.de<br />
Federal Agency for Nature Conservation (BfN) http://www.bfn.de<br />
German Energy Agency (dena) http://www.offshore-wind.de<br />
Offshore Wind Energy Foundation http://www.offshore-stiftung.de<br />
German Wind Energy Association (BWE) http://www.wind-energie.de/de/themen/offshore/<br />
Bundesverband der Windindustrie http://www.deutsche-windindustrie.de/fakten/offshore/<br />
German Engineering Federation (VDMA) http://www.vdma.org/windenergie<br />
Competence Network Rostock http://www.offshore-energies.de<br />
wab - Windenergie Agentur Bremerhaven/Bremen e.V http://www.windenergie-agentur.de<br />
CEwind - Center of Excellence http://www.cewind.de<br />
windcomm - Netzwerkagentur Schleswig-Holstein http://www.windcomm.de<br />
Forwind - Center for Wind Energy Research http://www.forwind.de<br />
Pushing Offshore Wind Energy Regions http://www.offshore-power.net<br />
<br />
<br />
<br />
<br />
{{author<br />
|AuthorID=16487<br />
|AuthorFullName=Anne Steinbrenner<br />
|AuthorName=AnneS}}<br />
<br />
[[Category:Coastal and marine human activities and uses]]<br />
[[Category:Marine energy]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/PEGASO_case_study_North_Adriatic_SeaPEGASO case study North Adriatic Sea2024-02-16T11:07:04Z<p>Dronkers J: Created page with "left <u>'''CASE description'''</u> The North Adriatic is a transboundary case comprising coastal zones of three different natio..."</p>
<hr />
<div>[[Image: North_adriatic_map.jpg|380px|thumb|left]]<br />
<u>'''CASE description'''</u> <br />
<br />
The North Adriatic is a transboundary case comprising coastal zones of three different nations bordering the Upper Adriatic sea; the North Adriatic sea southern limit is given by the ideal line linking the city of Ancona in Italy and the City of Zadar in Croatia.The CASE includes, from south west, the coastal zone of 4 Italian Regions: Marche, Emilia Romagna, Veneto and Friuli-Venezia Giulia; the Slovenian coastal zone and the coast of 4 Croatian Counties: Istria, Rijeka, Lika and Senj, Zadar, for a total coastline length of about 2,227 km of coastline of mainland and 1,502 km of islands.<br />
The western Adriatic Italian coast is generally low, merging into the marshes and lagoons (e.g. Venice, Marano and Grado lagoons) on both sides of the protruding Po river delta, the most important river flowing into the Adriatic sea. Further north the landscape gets rockier and steeper. From the south of the Istrian peninsula, which is divided between Italy, Slovenia and Croatia, a fringe of over thousand islands extends as far south as Dubrovnik.<br />
<br />
[[Image:North_adriatic_map2.jpg|300px|thumb|right|Left:Marine Protected areas in the North Adriatic sea (Author: Francois Morisseau). Right above: River Plume (Archivio Magistrato alle Acque di Venezia – Consorzio Venezia Nuova). Right below: Venice flooded (Consorzio Venezia Nuova)]]<br />
<br />
<br />
<br />
<u>'''ICZM phase'''</u><br />
<br />
[[ICZM_Process_diagram/Establishment|Establishment]]<br />
<br />
<br />
<u>'''Main coastal issues'''</u><br />
<br />
- Climate change impacts and risks assessment in the CASE coastal areas<br />
<br />
- Water quality assessment (particularly concerning bathing and tourism) <br />
<br />
- Lack of common vision for the implementation of the ICZM protocol in the Adriatic<br />
<br />
<br />
<u>'''Relation between the Coastal issue and the ICZM protocol principles and articles.'''</u><br />
<br />
In our view, the identified coastal issues relate at least to seven specific ICZM protocol articles: art. 7 (Coordination), art. 14 (Participation), art. 15 (Awareness-raising, training, education and research), art. 18 (National Coastal strategies, plans and programs), art. 22 (Natural hazards), art. 25 (Training and research) and art. 28 (Transboundary cooperation). <br />
Climate change impacts and water quality assessment imply an interdisciplinary scientific research which aims to define appropriate indicators, to formulate ICZM strategies, to identify priorities and ecosystem management measures (art. 15, 18, 22, 25). The lack of cooperation between and within the different national authorities and management bodies highlights the need to avoid sectoral approaches and foster stakeholders involvement (see art. 7, 14 and 28).<br />
<br />
<br />
<u>'''Relevance of the coastal issue'''</u><br />
<br />
The North Adriatic zone is a traditional European destination for seaside tourism (almost 20 million of international arrivals in 2008 if considering also Slovenia and Croatia). The North Adriatic has many factors of attraction, such as nature, culture and gastronomy. Sea quality is extremely important not only for the economic weight directly related to bathing tourism, but also for its crucial role in the whole North Adriatic image and tourism system (e.g. cruise sector as well as nautical tourism). Italian national agencies estimated a daily consumption of almost 80 euro per tourist for sea destination. The same parameter for mountain, lake, cultural and gastronomic tourism ranges from 90 to 110 euro. However, the greater number of presences makes Italian seaside destination able to compete with the cultural ones (which are the most important in term of provided income). Data referring to seaside tourism performances, in the context of the North Adriatic, are available only for Emilia Romagna and Veneto. These two regions represent respectively 21,6% and 16.2% of the total Italian seaside tourism presences.<br />
<br />
{| border="1" cellspacing="0" width="600px" style="margin: 1em auto 1em auto;"<br />
|+''Emilia Romagna and Veneto seaside tourism performances in 2011 salt-marshes.''<br />
<br />
|- <br />
|<br />
|Presences<br />
|Arrivals<br />
<br />
|-<br />
|Emilia Romagna<br />
|27,9 million<br />
|5,5 million<br />
<br />
|-<br />
|Veneto<br />
|26,5 million<br />
|3,9 million <br />
|}<br />
<br />
An important indicator for the tourism sector service is the number of bed provided by hotel and other accommodations. The below table illustrates the situation of Veneto tourism districts.<br />
<br />
{| border="1" cellspacing="0" width="600px" style="margin: 1em auto 1em auto;"<br />
|+''Veneto main seaside tourism districts supply (beds and regional quotas of bed).''<br />
<br />
|-<br />
|Bibione-Caorle<br />
|129.889 (18,7%)<br />
<br />
|-<br />
|Jesolo- Eraclea<br />
|108.459 (15,6%)<br />
<br />
|-<br />
|Cavallino<br />
|55.603 (8,0%)<br />
<br />
|-<br />
|Chioggia<br />
|24.926 ( 3,6%)<br />
|}<br />
<br />
Chioggia tourism district, the case study for the implementation of the Bathing Water Advisory Model (BHAM), experienced in 2011 more than 261.500 arrivals and more than 2 million presences (7.5% of the seaside holidays regional quota).<br />
<br />
<br />
'''Pollution issue: safe bathing conditions'''</br><br />
<br />
According to the parameters stated in the Decree 116/2008 and in the Ministerial Decree 30/03/2010, no bathing coastal waters of Veneto region are nowadays banned. The situation has improved in the last two years (no banned zones in 2010 and only two zones banned in 2011) however, very large and persistent bathing prohibitions were declared right up to 2009.<br />
The graph below, produced by the Veneto Environmental Regional Agency (ARPAV), shows the monitoring activity of ten bathing tourist districts since 1997. The values represent the percentage of the total monitored zones (several for each district) which did not passed the hygienic and health tests. Chioggia district is the most affected by the issue of bathing water quality, as a matter of fact, with the exceptions of 2007, 2010 and 2011, every year at least one of its zone failed the water quality assessment. For instance in 2009, 100% of the district was banned to bathing activities. Caorle and Rosolina are the second and the third most problematic districts with notable and repeated percentages of banned zones (33% of Rosolina district was declared banned zones in 2009). Also the other districts showed notable percentages of banned zones. Jesolo, Porto Viro, and Porto Tolle tourist districts, failed the tests at least once during the 15 years of monitoring. In 2000, Porto Viro experienced instead the peak of 50% banned zones, but it seems an isolated event.<br />
<br />
[[Image: Bathing_suitablility_test.jpg|400px|thumb|center| ''Veneto negative bathing suitability tests percentage from 1997 to 2011.'' ]]<br />
<br />
{| border="1" cellspacing="0" width="600px" style="margin: 1em auto 1em auto;"<br />
|+''Veneto number of negative bathing suitability tests zones from 1997 to 2011.''<br />
<br />
|-<br />
|'''SITES'''<br />
|1997<br />
|1998<br />
|1999<br />
|2000<br />
|2001<br />
|2002<br />
|2003<br />
|2004<br />
|2005<br />
|2006<br />
|2007<br />
|2008<br />
|2009<br />
|2010<br />
|2011<br />
<br />
|-<br />
|'''Caorle'''<br />
|2<br />
|2<br />
|4<br />
| -<br />
| -<br />
|4<br />
| -<br />
|1<br />
|2<br />
| -<br />
| -<br />
|1<br />
|1<br />
| -<br />
|2<br />
<br />
|-<br />
|'''Jesolo'''<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
|1<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
<br />
|-<br />
|bgcolor="pink" |'''Chioggia'''<br />
|bgcolor="pink" | 9<br />
|bgcolor="pink" | 6<br />
|bgcolor="pink" | 1<br />
|bgcolor="pink" | 4<br />
|bgcolor="pink" | 5<br />
|bgcolor="pink" | 9<br />
|bgcolor="pink" | 4<br />
|bgcolor="pink" | 6<br />
|bgcolor="pink" | 4<br />
|bgcolor="pink" | 3<br />
|bgcolor="pink" | -<br />
|bgcolor="pink" | 7<br />
|bgcolor="pink" |11<br />
|bgcolor="pink" | -<br />
|bgcolor="pink" | -<br />
<br />
|-<br />
|'''Rosolina'''<br />
| -<br />
|1<br />
|2<br />
| -<br />
|2<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
|3<br />
| -<br />
| -<br />
<br />
|-<br />
|'''Porto Viro'''<br />
| -<br />
| -<br />
| -<br />
|1<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
<br />
|-<br />
|'''Porte Tolle'''<br />
|1<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
| -<br />
|}<br />
<br />
The table illustrates the number of negative bathing suitability tests zones from 1997 to 2011. During 15 years of monitoring Chioggia sampling zones were affected by bathing restriction 69 times (an annual average of almost 44% of total zones not suitable), recording the worst water quality situation of the whole Veneto region.<br />
<br />
<br />
<br />
<u>'''Objectives'''</u><br />
<br />
- To support the development of coastal adaptation strategies<br />
<br />
- To improve monitoring activities of coastal water qualities<br />
<br />
- To foster cooperation among countries at institutional level for common vision of Marine protected areas<br />
<br />
<br />
[[image: North_adriatic.jpg|300px|thumb|left| Sottomarina beach (Author: archivio fotografico Regione Veneto)]]<br />
<br />
<u>'''End Products'''</u><br />
<br />
DSS Climate change- Water Quality Model - North Adriatic transboundary strategy, with particular reference to Marine Protected Areas<br />
<br />
<br />
<u>'''PEGASO tools developed and used'''</u><br />
<br />
indicators and participation ([[Participation in the North-Adriatic (DSS-DESYCO)|DSS-Desyco (DEcision support SYstem for COastal climate change impact assessment)]] and [[Participation in the North Adriatic (BHAM)|BHAM (Beach Health Advisory Model)]]). <br />
<br />
<br />
<u>'''CASE Responsible'''</u> <br />
<br />
Stefano Soriani - Ca’ Foscari University of Venice - email: soriani@unive.it<br />
<br />
<br />
<span style="color: Blue"><small>Elaboration: Stefano Soriani, Fabrizia Buono, Monica Camuffo, Marco Tonino, University Ca’ Foscari of Venice.</small></span><br />
<br />
[[Category:PEGASO study sites]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Interpolation_of_remote_sensing_imagesInterpolation of remote sensing images2024-02-12T16:34:39Z<p>Dronkers J: Created page with " Image:Mueller_1.jpg|thumb|left|'''Figure 1''': To apply the ordinary Kriging technique to satellite data, the following steps have to be taken: the data and the missing da..."</p>
<hr />
<div><br />
<br />
[[Image:Mueller_1.jpg|thumb|left|'''Figure 1''': To apply the ordinary Kriging technique to satellite data, the following steps have to be taken: the data and the missing data positions are onto the same grid, the spatial trend - estimated by a weighted monthly mean - is removed from the data. To save computation time the variogram is calculated from a subset of data and the Kriging technique takes only a fixed number of nearest-neighbours to the estimation location into account.]]<br />
<br />
===Introduction===<br />
Satellite images can reveal spatial patterns of e.g. the distribution of phytoplankton. Its abundance is represented by the chlorophyll concentration, which is one of the standard products of [[MERIS]] (Medium Resolution Imaging Spectrometer). The images have a temporal resolution of approximately one per day, the spatial resolution is 1.2 km, which corresponds to unprojected "Reduced Resolution" scenes (compared to 300 m Full Resolution mode).<br />
The concentrations of three colour-defining substances in the water are derived from the monitored bands in the visual spectral range. The pigments of the phytoplankton -for the most abundant species that is chlorophyll - change the spectral characteristics of the backscattered light mainly by absorption; the total amount of anorganic particles (total suspended matter) accounts mainly for scattering and organic matter for absorbtion (yellow substance). Because of the dependence on the visual spectral range clouds are responsible for missing data.<br />
To study the spatial and temporal variability of phytoplankton in the North Sea by satellite images, geo- statistical methods are applied to cope with the missing data, e.g. the Kriging technique (for an overview consult Wackenagel, 2003<ref name="W03">Wackernagel, H. (2003). Multivariate Geostatistics: an introduction with Applications. Springer-Verlag, Berlin, 3rd ed., pp.387, ISBN 978-3-540-44142-7.</ref> or Olea, 1999<ref name="O99">Olea, Ricardo A. (1999). Geostatistics for Engineers and Earth Scientists, Springer-Verlag, 324 p., Hardcover, ISBN 978-0-7923-8523-3.</ref>). For further investigations the ESA standard product algal_2 is used which represents algal concentrations in case2 waters.<br />
<br />
{| style="border:1px solid darkgray" cellpadding="1" cellspacing="0" align="center"<br />
|-<br />
| align="center" width="150px" | [[Image:Mueller_2.jpg|thumb|center|'''Figure 2''': MERIS chlorophyll product ‘algal_2’ for May 10th 2006. High chlorophyll concentrations (in µg/l) and strong patterns are found in the Danish North Sea. The data has been smoothed by a 3-by-3-pixel median filter.<br/><br/>]]<br />
| align="center" width="150px" | [[Image:Mueller_3.jpg|thumb|center|'''Figure 3''': Weighted monthly mean of chlorophyll concentration. 13 images of data two weeks before and after May 10th 2006.<br/><br/><br/><br/><br/>]]<br />
| align="center" width="150px" | [[Image:Mueller_4.jpg|thumb|center|'''Figure 4''': Chlorophyll concentration (May 10th 2006) and cloud mask (February 2nd 2008). For 80230 data points estimations can be compared to original data.<br/><br/><br/><br/>]]<br />
|-<br />
| align="center" width="150px" | [[Image:Mueller_5.jpg|thumb|center|'''Figure 5''': Estimates and original data combined. Remaining gaps persist at locations, where no trend data is available.<br/><br/><br/><br/><br/>]]<br />
| align="center" width="150px" | [[Image:Mueller_6.jpg|thumb|center|'''Figure 6''': Difference between original data and estimates.<br/><br/><br/><br/><br/><br/><br/>]]<br />
| align="center" width="150px" | [[Image:Mueller_7.jpg|thumb|center|'''Figure 7''': To measure the reliability of estimates under a cloud cover, a cloud cover was simulated. The distribution shows the correlation of the original values versus the estimates under the simulated cloud cover of the same satellite image.]]<br />
|}<br />
<br />
===Estimation of missing data values===<br />
The most commonly used method to avoid the correction for missing data and to gathering information about the whole area of interest is the calculation of the monthly mean (or median). By sacrificing temporal resolution, gaps can be filled by accumulating successive satellite images.<br />
To illustrate the [[Data interpolation with Kriging|kriging technique]], an exceptionally cloud-free satellite image of the North Sea (Fig. 2) is chosen (May 10th 2006) and overlaid with a cloud mask (February 2nd 2008) (Fig. 4). To estimate the missing data values several steps in data processing are arranged (Fig. 1). <br />
For the applicability of [[Data interpolation with Kriging|Kriging technique]] the trend - i.e. the pattern of the expected value over the area - has to be negligible. The North Sea cannot be regarded as an area without spatial trend, because it exhibits almost all-the-year high chlorophyll concentrations in the coastal areas and shows recurring patterns of algal blooms in the spring season. <br />
To correct for the time-dependent, dominant spatial trend a weighted monthly mean (Fig. 3) is calculated from any data which has been collected two weeks before and after the day of interest. Data close to the day of interest receive the higher weights. After removal of the trend the residuals, which are less spatially patterned than the original chlorophyll data, are used for geostatistical prediction. The estimates can be considered as corrections to the weighted monthly mean. These results will cover the area where the trend has been calculated, and therefore this process might not be sufficient to fill all missing data points. In a second step, the remaining missing values can be calculated relying on the original data and the newly estimated values. <br />
The variogram, which measures the variance of pairs of points within certain distance classes, is calculated from the residuals and used in the ordinary [[Data interpolation with Kriging|Kriging procedure]]. This estimates the residuals at locations that are covered by the cloud mask and where a data point is available for comparison. In combination with the weighted monthly mean the (estimated) residuals function as corrections to the spatial trend. Remaining gaps persist at locations where no trend data is available (Fig. 4).<br />
The difference between estimates and original data is small (Fig. 6 and 7). Major differences occur at locations where the trend has been calculated from a single sample which is distant in time to the original image and lies in a region of high temporal variability.<br />
<br />
<br />
==Related articles==<br />
:[[Data interpolation with Kriging]]<br />
:[[Remote sensing]]<br />
:[[Plankton remote sensing]]<br />
:[[Plankton remote sensing North Sea]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br/><br />
<br/><br />
<br />
{{author <br />
|AuthorID=19536<br />
|AuthorFullName= Müller, Dagmar<br />
|AuthorName=Username}}<br />
<br />
[[Category:Coastal and marine observation and monitoring]]<br />
[[Category:Observation of biological parameters]]<br />
[[Category:North Sea]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Plankton_remote_sensing_North_SeaPlankton remote sensing North Sea2024-02-12T15:48:30Z<p>Dronkers J: </p>
<hr />
<div><br />
===Introduction===<br />
<br />
[[Image:Phytoplankton_2.jpg|thumb|right|400px|<small>'''Figure 1''' MERIS scene of the North Sea: the radiance at the top of the atmosphere (RGB image), (a) is split into two major components by using the atmospheric correction procedure: the path radiance, which consists of the radiance scattered in the atmosphere and specularly reflected at the sea surface (b), and the radiance leaving the water (c), which is used in the next processing step to determine the water constituents.</small>]]<br />
<br />
Coastal seas are highly dynamical areas. In particular, the water of the shallow soft-bottom coast of the North Sea, with its strong tidal currents, continuously changes its *properties. Monitoring these properties, such as suspended particulate matter with all its components and phyto- plankton, is a very demanding challenge. Ocean colour remote sensing is one possibility to determine some of the key variables and to provide weekly or monthly maps of their distribution with a high spatial resolution. <br />
In contrast to the open ocean, the optically complex coastal waters, with different classes of substances, pose a number of problems for optical remote sensing. With the launch of the Medium Resolution Imaging Spectrometer MERIS on the European earth observing satellite ENVISAT in 2002, a new generation of instruments became available, which allows also remote sensing of turbid coastal water. <br />
<br />
The sensor has a spatial resolution of 300 m, a revisit period of 1-2 days at mid latitudes and 15 spectral bands. For using this instrument for coastal water remote sensing, special procedures are necessary to derive the in-water optical properties and concentrations from the reflectance spectra. At the Institute for Coastal Research of GKSS, the required algorithms have been developed, which are based on artificial neural networks, and which are implemented in the ground segment for the routine processing of MERIS data at the European Space Agency ESA (Schiller & Doerffer, 2005<ref name="SD">Schiller, H. & Doerffer, R. (2005). Improved Determination of Coastal Water Constitutent Concentrations from MERIS data. IEEE Transactions of Geoscience and Remote Sensing, 43, 1585-1591.</ref>, Doerffer & Schiller, 2007<ref name="DS">Doerffer, R. & Schiller, H. (2007). The MERIS Case 2 water algorithm. Int. Journal of Remote Sensing, 28 (3-4), 517-535.</ref>).<br />
In this chapter we provide an overview of the steps which are necessary to compute a concentration map from the level 1 data of MERIS, i.e. the spectral radiances at the top of the atmosphere (TOA) .<br />
<br />
===Methods and techniques===<br />
<br />
[[Image:Phytoplankton_3.jpg|thumb|right|400px|<small>'''Figure 2''' All constituents of a water sample are grouped into 4 optical components: the absorption coefficient of the water, which passes a filter with a pore size of 0.47 µm is defined as yellow substance (Gelbstoff). The absorption coefficient of the particles which remain on the filter is split by a bleaching process into 2 components, i.e. the absorption of phytoplankton pigments (difference before and after bleaching) and the absorption of the material after bleaching. A further component is the scattering coefficient of all particles in water, including phytoplankton. The absorption coefficient of phytoplankton pigment is converted into chlorophyll concentration and the scattering of all particles into suspended matter dry weight per litre. </small>]]<br />
<br />
Ocean colour remote sensing requires two major steps (Fig. 1). First, the influence of the atmosphere and the reflectance at the water surface have to be computed to get the radiance reflectance leaving the water, i.e. the sunlight, which is backscattered by the water molecules and all water constituents, including phytoplankton (Fig. 2). This atmospheric correction step is extremely critical, because the atmosphere, in most cases, causes more than 90 percent of the radiance at the top of the atmosphere. As the second step, the inherent optical properties, which are the absorption and scattering coefficients, as well as the concentrations of the substances in water have to be derived from the radiance reflectance spectrum leaving the water. For this purpose, different kinds of procedures have been developed, which are partly empirical, but for coastal waters they are based on radiative transfer models in most cases.<br />
<br />
For the correction of the atmosphere, an aerosol optical model has to be defined, because scattering by different aerosols, including thin cirrus clouds, is the most variable optical component in the atmosphere within the visible spectrum. The properties are selected according to measurements of the sun photometer network AERONET. GKSS operates one of these instruments on the island of Helgoland. A Monte Carlo photon tracing model is then used to simulate TOA radiance reflectances, which are the basis to train a neural network. Finally, this neural network is included into the processor to determine the radiances leaving the water from the TOA radiances as measured by MERIS.<br />
<br />
[[Image:Phytoplankton_4.jpg|thumb|left|500px|<small>'''Figure 3''' The three products which are derived from the radiance leaving the water: (a) the absorption coefficient at 443 nm of Gelbstoff and bleached particles, (b) the chlorophyll concentration and (c) the SPM dry weight concentration. </small>]]<br />
<br />
For the second step, a bio-optical model of the water constituents is necessary. This model is derived from various optical measurements in coastal waters, which have been performed by GKSS and partner institutes during the past years. <br />
Based on this model, radiance spectra leaving the water are simulated which cover the range of concentrations found in most coastal waters. This set of spectra again is used to train a neural network, which determines the optical properties and the concentrations from the radiance spectra leaving the water. The result are maps of absorption and scattering coefficients, of the concentrations of suspended matter and phytoplankton and of the water transparency (Fig. 3).<br />
<br clear=all><br />
<br />
===Seasonal patterns of phytoplankton in the North Sea===<br />
<br />
[[Image:Phytoplankton_5.jpg|thumb|right|<small>'''Figure 4''' The penetration depth of light [m] is important fpr primary production of phytoplankton, MERIS on April 22nd 2008. </small>]]<br />
<br />
One application of ocean colour remote sensing is investigation of the horizontal distribution patterns of phytoplankton in the North Sea at different seasons. It might change in the future due to climate change, with possibly significant consequences for the North Sea ecosystem including its fish stocks.<br />
In springtime, when the water is rich in nutrients, light is the most important factor which controls the development of phytoplankton growth (Fig. 4). During winter conditions, the days are too short combined with a low sun elevation and the water column is well mixed in most parts of the North Sea. Since a phytoplankton cell can be everywhere in the water column, including the dark depths, it will not get sufficient light during 24 hours to grow. When the days become longer, phytoplankton growth starts in the shallow water, where the light per day is sufficient even when the water column is well mixed. In the surface layers of deep waters growth can start only when calm and sunny weather with sufficient heating produces a stratification of the water column. Under these conditions the phytoplankton can stay in the upper mixed layer without sinking into the dark deeper layers. It gets enough light and starts to grow rapidly. In the deep northern North Sea water the growing season normally begins around mid April. The growing phase is interrupted or stops when strong winds destroy the stratification or when the nutrients are depleted. Also, grazing by zooplankton, which follows the phytoplankton development, reduces the phytoplankton biomass. <br />
A special case exists in the Skagerrak and the Norwegian Trench. Here, stratification can be caused by less saline light water from the Balitc Sea, which flows into the North Sea as an upper layer and follows the Norwegian coast due to the normally anticlockwise circulation in the North Sea. Thus, the phytoplankton growing season starts much earlier due to the better light conditions in this upper layer (Fig. 5).<br />
<br />
[[Image:Phytoplankton_6.jpg|thumb|left|<small>'''Figure 5''' MERIS view of a bloom of ''Emilia huxleyii'' (Coccolithophore) in the Skagerrak in June 2003Later in the year, a bloom of ''Coccolithophores'' is observed quite frequently in the Skagerrak and along the southern Norwegian coast. </small>]]<br />
<br />
These algae make the water extremely bright because of the high scattering coefficient of their coccoliths. These are plates which form quasi a shield of calcium carbonate around the cell.<br />
During the summer period, other phytoplankton blooms occur in the southern North Sea, which are summarised under "red tides", because - due to their pigments - they discolour the water from green to red (Fig. 6). Some of these blooms are harmful to other marine organisms, e.g. to mussels or fish, and can severely damage maricultures. <br />
<br />
[[Image:Phytoplankton_7.jpg|thumb|right|300px|<small>'''Figure 6''' MERIS view of a red tide of ''Myrionecta rubra'' in the German Bight on August 3rd 2008. </small>]]<br />
[[Image:Phytoplankton_8.jpg|thumb|right|400px|<small>'''Figure 7''' The maximum chlorophyll concentration in the North Sea per month for spring/summer 2007 derived from MERIS. </small>]]<br />
<br />
<br />
Along the coast of the Netherlands and in the German Bight, high phytoplankton concentrations can be observed throughout the summer, due to the high nutrient concentrations, which are collected by the coastal current from all the rivers which discharge into the North Sea and which are transported to the north. In contrast in the central and northern North Sea, the chlorophyll concentration decreases during the summer period due to nutrient depletion and grazing by zooplankton. In autumn stronger winds and less insolation causes a breakdown of the stratification so that fresh nutrients are mixed up from deeper layers to the surface. A second phytoplankton bloom can briefly develop until the decreasing light ends the growing season.<br />
Another factor which limits the growth of phytoplankton and thus controls the distribution pattern is turbidity, i.e. the attenuation of light by suspended matter. This factor can also be estimated from the spectral reflectance data of MERIS.<br />
<br />
<br />
====Maximum chlorophyll concentration in the North Sea====<br />
In March maximum values are found along the coasts of the southern North Sea. Also, the shallow Dogger Bank shows higher values due to the better light conditions. In the deeper northern part of the North Sea values are low. Obvious are the high concentrations in the Skagerrak and around the southern part of Norway. <br />
In April we see the whole North Sea with chlorophyll concentration around 5 mg/m3 except for the Dogger Bank where the nutrients ar now depleted. <br />
In May we see maximum concentrations also in the northern North Sea, while they have dropped in the southern central part. <br />
In July the chlorophyll concentrations are low in the central part, but high along the coasts including the Skagerrak (Fig. 7).<br />
<br clear=all><br />
<br />
<br />
==Related articles==<br />
:[[Plankton remote sensing]]<br />
:[[Interpolation of remote sensing images]]<br />
:[[Light fields and optics in coastal waters]]<br />
:[[Remote sensing]]<br />
:[[Optical remote sensing]]<br />
:[[The Baltic Algae Watch System - a remote sensing application for monitoring cyanobacterial blooms in the Baltic Sea]]<br />
<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
{{author <br />
|AuthorID=15443<br />
|AuthorFullName= Doerffer,Roland<br />
|AuthorName=Username}}<br />
<br />
{{author <br />
|AuthorID=16888<br />
|AuthorFullName= Schiller, Helmut<br />
|AuthorName=Username}}<br />
<br />
{{author <br />
|AuthorID=16906<br />
|AuthorFullName= Heymann, Kerstin<br />
|AuthorName=Username}}<br />
<br />
{{author <br />
|AuthorID=16889<br />
|AuthorFullName= Röttgers, Rüdiger<br />
|AuthorName=Username}}<br />
<br />
{{author <br />
|AuthorID=16887<br />
|AuthorFullName= Schönfeld, Wolfgang<br />
|AuthorName=Username}}<br />
<br />
{{author <br />
|AuthorID=16895<br />
|AuthorFullName= Krasemann, Hansjörg<br />
|AuthorName=Username}}<br />
<br />
[[Category:Coastal and marine observation and monitoring]]<br />
[[Category:Observation of biological parameters]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Plankton_remote_sensingPlankton remote sensing2024-02-11T14:15:26Z<p>Dronkers J: </p>
<hr />
<div><br />
==Ocean colour==<br />
<br />
[[Image:Envisatsummerbloom.png|300px|thumb|right|<small>Earth-observing satellite Envisat MERIS image of a phytoplankton bloom in the South Atlantic Ocean: different types and quantities of phytoplankton exhibit different colours, such as the blues and greens in this image(Photo Credit: ESA) </small>]]<br />
<br />
An important reason to measure ocean colour is to study [[phytoplankton]], the microscopic algae which are at the base of the oceanic food web. Remote sensing is an invaluable tool for the detection, monitoring and prediction of [[Algal_bloom|algal blooms]] in the marine environment as these algae are considered a potential threat when they form so-called [[Harmful_algal_bloom|harmful algal blooms]] and so appropriate measures can be taken. In situ measurements are useful when more information is required on the type of algae present but when there is a sudden shift in time and location these methods become too expensive. <br />
Satellite sensors detect the reflected light by the sea surface in different wavelengths. The "colour" of the ocean is determined by the interaction of light with the water and any colored particles or dissolved chemicals in the water. Colour is the light reflected by the water and the substances present in it. When light hits a water molecule or a coloured substrate in it, the different colours (wavelengths) can be absorbed or scattered in differing intensities. The colour we see results from the colours that are reflected. The substances in seawater which most affect the water colour are: phytoplankton, total suspended matter (TSM), coloured dissolved organic matter (CDOM, also called 'gelbstoff' due to the brown-yellow reflected colour), and the water molecules themselves. <br />
<br />
==Eutrophication and algal blooms==<br />
Phytoplankton contains green-coloured chlorophyll-a (Chl-a, necessary to produce organic carbon using light and carbon dioxide during [[photosynthesis]]) which absorbs red and blue light and reflects green light. The ocean colour is therefore an indicator of the health of oceans. The chlorophyll concentrations can be inferred from satellite data by calculating the blue/green ratio of the ocean. When blue is more absorbed, green is more reflected which indicates a higher concentration of phytoplankton in the water and vice versa. It is in some cases also possible to distinguish between different phytoplankton species due to their specific algal pigment absorption, such as phycobiliproteins for cyanobacteria, fucoxanthin for diatoms and peridinin for dinoflagellates<ref name=BP>Blondeau-Patissier, D., Gower, J.F.R., Dekker, A.G., Phinn, S.R. and Brando, V.E. 2014. A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans. Progress in Oceanography 123: 123–144</ref>. Coccolithophore blooms (in particular ''Emiliania huxleyi'') in their senescent stage are often identifiable through ocean color remote sensing by the milky blue–green color, accompanied by the typical high scattering across all spectral bands (400–800 nm) that results from the detached coccoliths<ref>Shutler, J.D., Grant, M.G., Miller, P.I., Rushton, E. and Anderson, K. 2010. Coccolithophore bloom detection in the north east Atlantic using SeaWiFS: algorithm description, application and sensitivity analysis. Remote Sensing of Environment 114: 1008–1016</ref>. The nitrogen-fixing cyanobacterium ''Trichodesmium'' can be detected in satellite reflectance spectra due to the strong absorption by its pigment phycoerythrin in the blue green channels and reflectance in the red-near-infrared channels<ref>Dupouy, C., Whiteside, A., Tan, J., Wattelez, G., Murakami, H., Andréoli, R., Lefèvre, J., Röttgers, R., Singh, A. and Frouin, R. A 2023. Review of Ocean Color Algorithms to Detect Trichodesmium Oceanic Blooms and Quantify Chlorophyll Concentration in Shallow Coral Lagoons of South Pacific Archipelagos. Remote Sens. 15, 5194</ref>. Harmful algae also have distinct spectral characteristics (significant absorption bands in around 500 nm, 675 nm, and reflectance peaks in 550 nm and 700 nm) that allow detection by remote sensing and make it even possible to distinguish between different species<ref>Shen, L., Xu, H. and Guo, X. 2012. Satellite Remote Sensing of Harmful Algal Blooms (HABs) and a Potential Synthesized Framework. Sensors 12: 7778-7803</ref>. Once a bloom begins, an ocean colour sensor can make an initial identification of its chlorophyll pigment, and therefore its species and toxicity. <br />
<br />
Remote sensing can therefore provide insight into algae dynamics and eutrophication processes. However, determining Chl-a concentrations within a range of ±35% is not always possible using the same band ratio algorithms<ref>Hu, C., Carder, K.C. and Muller-Karger, F.E. 2000. How precise are SeaWiFS ocean color estimates? Implications of digitization-noise errors. Remote Sensing of Environment 76: 239–249</ref>. Blue/green band ratio algorithms are suitable for open ocean waters, but their use in coastal waters is limited, due to the influence of colored dissolved organic matter (CDOM) and total suspended solids (TSM) at these wavelengths. Alternatively, many studies have shown the potential of red to NIR band ratios to detect Chl-a and algal blooms in coastal waters, as this spectral region is less affected by CDOM and TSM<ref>Lins, R.C., Martinez, J-M., da Motta Marques, D., Cirilo, J.A. and Fragoso Jr, C.R. 2017. Assessment of Chlorophyll-a Remote Sensing Algorithms in a Productive Tropical Estuarine-Lagoon System. Remote Sens. 9, 516</ref>. To better understand the mechanisms and dynamics of algal blooms, it is important to combine satellite data with datasets from other sources (in situ data, ecosystem models) and analyze them with statistical methods.<ref name=BP/><br />
<br />
Examples of colour satellites sensors are [[SeaWiFS]] (Sea-viewing Wide Field of view Sensor), [[MODIS]] (Moderate Resolution Imaging Spectroradiometer) and [[MERIS]] (Medium Resolution Imaging Spectrometer). An overview of satellites and sensors used in Earth Observation is found [https://ioccg.org/resources/missions-instruments/current-ocean-colour-sensors/ here].</P><br />
<br />
<br />
<br />
==Related articles==<br />
:[[Plankton remote sensing North Sea]]<br />
:[[Light fields and optics in coastal waters]]<br />
:[[Remote sensing]]<br />
:[[Optical remote sensing]]<br />
:[[The Baltic Algae Watch System - a remote sensing application for monitoring cyanobacterial blooms in the Baltic Sea]]<br />
:[[Remote sensing of zooplankton]]<br />
:[[Detecting the unknown - novelty detection of exceptional water reflectance spectra]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
==Other sources==<br />
:[https://www.nrcan.gc.ca/sites/www.nrcan.gc.ca/files/earthsciences/pdf/resource/tutor/fundam/pdf/fundamentals_e.pdf Canada Centre for Remote Sensing Tutorial: Fundamentals of Remote Sensing]<br />
:[http://www.esa.int/SPECIALS/Eduspace_EN/ ESA Eduspace]<br />
:[https://earthobservatory.nasa.gov/ NASA earth observation]<br />
:[http://en.wikipedia.org/wiki/History_of_photography History of Photography]<br />
<br />
<br />
{{2Authors <br />
|AuthorID1=26102<br />
|AuthorFullName1= Knockaert, Carolien<br />
|AuthorName1=Carolienk<br />
|AuthorID2=120<br />
|AuthorFullName2=Job Dronkers<br />
|AuthorName2=Dronkers J<br />
}}<br />
<br />
[[Category:Coastal and marine observation and monitoring]]<br />
[[Category:Observation of biological parameters]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Random_Forest_RegressionRandom Forest Regression2024-02-09T19:55:49Z<p>Dronkers J: </p>
<hr />
<div>{{ Definition| title = Regression tree<br />
| definition = A regression tree (a decision tree with a numerical target variable) is a machine learning algorithm where clusters of target output variables with corresponding input variables are determined following optimal classification criteria derived from the training data. <br />
}}<br />
<br />
<br />
==Short introduction==<br />
A simple regression tree algorithm starts with defining an optimal splitting value separating input and corresponding output training data into two clusters (subdomains of input training data and corresponding output data). In the following step each cluster is split again, till at the end of each tree branch a cluster is left that contains only a small more or less homogeneous subdomain of the output data (a so-called leaf). The training of the regression tree on known output data is based on finding the most efficient split for each input variable at each level of the regression tree. This consists of determining for each split the average value of the target variables in the two clusters and searching the optimal split that minimizes the total sum of the squared residuals of the target training variables with respect to the average value in each of the two clusters. The simple regression tree tends to overfit the training data; the noise (observation inaccuracies, random fluctuations) in the training data is reflected in the regression tree. Noise can be minimized by considering a multitude of regression trees, each trained on a subset of the training data and a subset of the input variables. The subsets are chosen at random and some of the training data may appear more than once in the subsets. This multitude of regression trees is called a random forest. The final output is the mean of the outputs of all the regression trees. By constructing all possible different random forests, and comparing the final outputs with test data, the random forest with the best prediction will be selected as the final choice. Some regression trees make use of a boosting algorithm (Adaboost) to enhance the performance of a weak classifier by attributing different weights to the outcomes of the forest of random regression trees.<br />
<br />
<br />
{| class="wikitable" style=" font-size:90%"<br />
|- <br />
! Analysis technique !! Strengths !! Limitations !! Application example<br />
|-<br />
| Prediction tool based on machine learning from training data ||* Handles nonlinear relationships <br> * Does classification and regression <br> * Resilient to data noise and data gaps <br> * Computationally efficient <br> * Low overfitting risk ||* Black box, no easy interpretation of results, no probability estimates <br> * Less efficient if many trees <br> * Not reliable outside the range of trained situations transformation || Time series forecasting<br> Pattern recognition from images, e.g. interpretation remote sensing images<br />
|}<br />
<br />
<br />
For more detailed explanations see:<br />
:[https://www.youtube.com/watch?v=g9c66TUylZ4 StatQuest: Regression Trees, Clearly Explained by Josh Starmer]<br />
:[https://www.youtube.com/watch?v=J4Wdy0Wc_xQ StatQuest: Random Forests Part 1 - Building, Using and Evaluating by Josh Starmer]<br />
:[https://en.wikipedia.org/wiki/Decision_tree_learning Wikipedia Machine learning decision tree]<br />
:[https://en.wikipedia.org/wiki/Random_forest Wikipedia Random forest]<br />
<br />
<br />
==Related articles==<br />
:[[Data analysis techniques for the coastal zone]]<br />
<br />
<br />
<br />
{{author<br />
|AuthorID=120<br />
|AuthorFullName=Job Dronkers<br />
|AuthorName=Dronkers J}}<br />
<br />
<br />
[[Category:Data analysis methods]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Support_Vector_RegressionSupport Vector Regression2024-02-09T19:10:56Z<p>Dronkers J: </p>
<hr />
<div>{{ Definition| title = Support Vector Regression<br />
| definition = Support vector regression (SVR) is a machine learning algorithm that can be trained to learn the nonlinear relationship between input data and a target output variable without prior description of the underlying processes. SVR is based on support vector machine theory that classifies data by representing them in a multidimensional space.<br />
}}<br />
<br />
==Short introduction==<br />
A support vector machine is a classifier that maps a subdomain of the input variables onto a subdomain of the output target variable(s). If the dependence of the output variable(s) on the input variables is nonlinear, it is not possible to define a linear classifier that separates input variables and corresponding output variables in distinct clusters. By performing a so-called nonlinear kernel transformation, a low dimensional data space is converted into a high dimensional space where a linear hyper-plane can classify the data points (i.e. define distinct clusters of input data and corresponding output data). Given the kernel function, the support vector machine does a systematic search to determine the hyperplane that most efficiently separates the training data into different input-output clusters. Support vectors correspond to the data points that are near to the hyperplane and help in orienting it. Once the hyperplane is known, the position of a new input data point (e.g. from the test data) relative to the hyperplane determines the cluster to which it belongs. Support vector regression assumes that clusters correspond to restricted value ranges of the target variable. A number of kernel functions exist such as Polynomial Functions (mapping data onto a finite-dimensional space) or Radial Basis Functions (mapping data onto an infinite-dimensional space) that enable non-linear classification. Support Vector Regression further assumes that the training and test data are independent and preprocessed in order to follow identical distributions (e.g., subtraction of the mean value and division by the square root of the variance). Being a highly sophisticated and mathematically sound algorithm, Support Vector Regression is one of the most accurate machine learning algorithms.<br />
<br />
<br />
{| class="wikitable" style=" font-size:90%"<br />
|-<br />
! Analysis technique !! Strengths !! Limitations !! Application example<br />
|-<br />
| Prediction tool based on machine learning from training data || * Handles unstructured data and nonlinear relationships in high dimensional spaces <br> * Does classification and regression <br> * Robust method based on sound mathematical principles <br> * Efficient for small datasets <br> * Overfitting can easily be avoided ||* Black box, no easy interpretation of results, no probability estimates <br> * Sensitivity to noise and outliers <br> * Less efficient for large datasets <br> * Not reliable outside the range of trained situations <br> * Results influenced by the choice of the kernel transformation || Pattern recognition from images, e.g. interpretation remote sensing images<br />
|}<br />
<br />
<br />
For more detailed explanations see:<br />
:[https://www.youtube.com/watch?v=efR1C6CvhmE StatQuest: Support Vector Machines Part 1: Main Ideas by Josh Starmer]<br />
:[https://en.wikipedia.org/wiki/Support_vector_machine Wikipedia Support Vector Machine]<br />
<br />
<br />
==Related articles==<br />
:[[Data analysis techniques for the coastal zone]]<br />
<br />
<br />
{{author<br />
|AuthorID=120<br />
|AuthorFullName=Job Dronkers<br />
|AuthorName=Dronkers J}}<br />
<br />
<br />
[[Category:Data analysis methods]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Oil_slickOil slick2024-02-08T19:35:04Z<p>Dronkers J: Created page with "{{Definition|title= Oil slick |definition= a layer of oil that is floating over a large area of the surface of the sea.}} ==Related articles== :Oil spill :Oil spill p..."</p>
<hr />
<div>{{Definition|title= Oil slick<br />
|definition= a layer of oil that is floating over a large area of the surface of the sea.}} <br />
<br />
<br />
==Related articles==<br />
:[[Oil spill]]<br />
:[[Oil spill pollution impact and recovery]]<br />
:[[Overview of oil spills events from 1970 to 2000]]<br />
:[[Oil sensitivity mapping]]<br />
:[[Oil spill monitoring]]<br />
:[[Index of vulnerability of littorals to oil pollution]]<br />
:[[Coastal pollution and impacts]]<br />
<br />
<br />
<br />
<br />
[[Category:Definitions]]<br />
[[Category:Oil spills]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Oil_spillOil spill2024-02-08T19:29:04Z<p>Dronkers J: Created page with "{{Definition|title= Oil spill |definition= An oil spill is the release of liquid petroleum hydrocarbon into the environment as a result of human activity.}} ==Notes== The te..."</p>
<hr />
<div>{{Definition|title= Oil spill<br />
|definition= An oil spill is the release of liquid petroleum hydrocarbon into the environment as a result of human activity.}} <br />
<br />
==Notes==<br />
The term often refers to marine oil spills, where oil is released into the ocean or coastal waters. Oil can refer to many different materials, including crude oil, refined petroleum products (such as gasoline or diesel fuel) or by-products, ships' bunkers, oily refuse or oil mixed in waste. Oil is also released into the environment from natural geologic seeps on the seafloor, for example along the California coastline. Most man-made oil pollution comes from land-based activity, but public attention and subsequent regulation has tended to focus most sharply on seagoing oil tankers.<br />
<br />
==Related articles==<br />
:[[Oil spill pollution impact and recovery]]<br />
:[[Overview of oil spills events from 1970 to 2000]]<br />
:[[Oil sensitivity mapping]]<br />
:[[Oil spill monitoring]]<br />
:[[Index of vulnerability of littorals to oil pollution]]<br />
:[[Coastal pollution and impacts]]<br />
<br />
See also https://en.wikipedia.org/wiki/Oil_spill<br />
<br />
<br />
[[Category:Definitions]]<br />
[[Category:Oil spills]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Testpage1Testpage12024-02-06T16:48:57Z<p>Dronkers J: Blanked the page</p>
<hr />
<div></div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Oil_sensitivity_mappingOil sensitivity mapping2024-02-06T14:42:09Z<p>Dronkers J: </p>
<hr />
<div><br />
<br />
===Introduction===<br />
The Wadden Sea, a region of tidal flats and salt marshes, is of enormous value as a cleansing site for North Sea water, as a nursery for young fish, as a feeding ground for many bird species and as a recreation area for thousands of tourists. It covers an area of nearly 10,000 km<sup>2</sup> along the North Sea coast of the Netherlands, Germany and Denmark. One major concern is that this nature reserve (a national park in Germany) could be damaged for many years by oil pollution in case of a ship accident. Although it is impossible to protect the whole coast in such a case, a contingency plan for oil spill response can help to minimise the effects for the most sensitive areas. For this purpose oil sensitivity maps have been developed, which consist of an automated expert model and digital maps (GIS) for different seasons.<br />
The study comprises a large scale habitat-survey covering the entire intertidal of the German Wadden Sea, including a classification of their sensitivity with regard to distinct disturbances, especially oil pollution. <br />
The project was financed by the Havariekommando (Central Command for Maritime Emergencies, Germany), which needs the results as a basis for strategic concepts.<br />
<br />
[[Image:Kleeberg_1.jpg|thumb|left|Fig. 1: Reference map of the benthos and changes of tidal flat topography with a temporal distance of about 15 years.]]<br />
[[Image:Kleeberg_2.jpg|thumb|left|Fig. 2: Census areas for birds defined by GKSS.]]<br />
<br />
===Methods and Techniques===<br />
Four classes have been defined to scale the oil sensitivity from low (1) to high (4). The sensitivity of a particular area depends largely upon the physical characteristics of the habitat, the susceptibilities of individual benthic species and their roles within the community. <br />
A central and intensive part of the study was the fieldwork for the habitat mapping, which was carried out during three years (2003 - 2006). For this part, the experience of the previous project “Sensitivity Mapping of Intertidal Flats” of GKSS (1987 until 1992) served as a very important basis.<br />
The data is devided into geographic and thematic data.<br />
<br />
===Geographic data===<br />
To get a digital map of the oil sensitivity, the thematic data must be linked to a reference map. To navigate the data of the habitat survey a recent map of the tidal flat topography and the borders of the analysis areas (areas with consistent habitat characteristics) are needed. This reference map is the base layer for the so called benthos sensitivity (Fig. 1). A second reference map is needed for the bird data. The map comprises the borders of bird censuses. Fig. 2 shows such “bird areas” in the East Frisian region. These areas are much larger compared to the benthos analysis areas. This map provides a base layer for the bird sensitivity.<br />
<br />
[[Image:Kleeberg_3.jpg|thumb|300px|right|Fig. 3: Workflow of data, processing steps and results.]]<br />
===Thematic data - Habitat mapping===<br />
During the years 2003 and 2006 a set of 70 different parameters were collected at nearly 1000 predefined locations (1 km grid). The parameter set consists of a combination of qualitative and quantitative values. The qualitative values were recorded on a standardised protocol (“record sheet”). They comprise for example information about the presence of different micro- and macroalgae, surface structure (i.e. ripple) and sediment properties. The quantitative values were restricted to sediment cores, like: grain size, water content of sediments and macrofauna species. <br />
<br />
===Thematic data - Bird census=== <br />
The seasonal aspects of the sensitivity were calculated using monitoring data of breeding and migratory birds, which are compiled yearly by the national park authorities of Schleswig-Holstein, Hamburg and Lower Saxony. These tabular data have been entered into a database and pre-processed. The result is the sensitivity index for each bird counting area. An overview about all data and processing steps is given in Fig. 2.<br />
<br />
[[Image:Kleeberg_4.jpg|thumb|left|Fig. 4: Implementation of the digital sensitivity map into the VPS system of the Havariekommando.]]<br />
===Analysis===<br />
The complex and heterogeneous data from GKSS and National Park authorities were organised in form of a GIS and a database. All data were integrated into the reference maps for benthos areas and bird areas. For updating and formatting the geographic data the GIS editing tools were used also for the update and analysis of the thematic maps. This includes the information about the seagrass and mussel bed distribution and also the link between the bird statistics and the bird areas.<br />
An intermediate step for the calculation of the final sensitivity map is the separate computation of the benthos and bird sensitivity maps.<br />
<br />
[[Image:Kleeberg_T1.jpg|thumb|right|Table 1: Seasonal aspects of bird data.]]<br />
===Benthos – sensitivity (Index)===<br />
The sensitivity of each station was calculated on the basis of the collected and pre-processed data using an automated expert system developed by GKSS. This system is based on the artificial neural network technique and on advanced classification methods. The expert model makes it possible that other authorities, like the Havariekommando, can calculate the sensitivity of the benthos in most cases without an expert.<br />
<br />
[[File:Kleeberg_5.jpg|thumb|300px|left|Fig. 5: Part of the sensitivity map for benthos and birds - summer scenario.]]<br />
===Bird – sensitivity (Index)===<br />
The bird censuses per area are split into two groups: breeding and migratory birds. The maximum number of a single bird species was calculated for a period of 5 years. In the case of breeding birds the number of breeding pairs was counted. In a next step the resulting maximum number was weighted with a predefined value for each species. This weighting was defined by an expert with respect to the behaviour, ecological importance and rareness of the species (Red list of endangerd species). The resulting values were transformed to the four sensitivity classes. With GIS tools the highest possible sensitivity for a bird counting area was analysed and the sensitivity value was linked to the bird reference map.<br />
<br />
===Oil sensitivity of the Wadden Sea=== <br />
The final value of the oil sensitivity was calculated by combining the sensitivities of benthos and bird areas based on their spatial and seasonal extension. For the benthos only one index value is determined while for the birds, the index value depends on the breeding and/or migration period. Tab. 1 shows the definition of the bird seasons. The final sensitivity map is shown in Fig. 5. The digital map in Fig. 4 is the input for the GIS system developed for the Havariekommando. More information on the sensitivity raster of the German North Sea is available in Van Bernem et al. (2007<ref name="V07">Van Bernem, K.-H., Doerffer, R., Grohnert, A., Heymann, K., Kleeberg, U., Krasemann, H., Reichert, J., Reichert, M. and Schiller, H. 2007. Sensitivitätsraster Deutsche Nordseeküste II - Aktualisierung und Erstellung eines operationellen Modells zur Vorsorgeplanung bei der Ölbekämpfung - Projektbericht im Auftrag des Havariekommandos. Geesthacht: GKSS Forschungs- zentrum Geesthacht GmbH.</ref>). <br />
<br />
[[File:WaddenOilSpill.jpg|thumb|right|300px|Fig. 6. Numerical simulation of oil spill locations 5 days after hypothetical oil releases on 15th of March 2008 at 04:00 UTC from a black oil tanker and a blue oil tanker. Dashed spills are mixed over the water column by dispersant application. Full black and blue spills are non-dispersed surface slicks driven by winds. Redrawn from Schwichtenberg et al. (2017<ref>Schwichtenberg, F., Callies, U., Groll, N. and Maßmann, S. 2017. Effects of chemical dispersants on oil spill drift paths in the German Bight—probabilistic assessment based on numerical ensemble simulations. Geo-Mar. Lett. 37: 163–170</ref>).]]<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
==Use of dispersants to protect the Wadden Sea from offshore oil slicks==<br />
<br />
Dispersants are mixtures of surfactants in one or more solvents designed for application to oil slicks with the aim of reducing the interfacial tension between the oil and the water phase. Dispersants promote the natural breakup of floating oil into small droplets in the water column. For being effective, the dispersant must be able to physically mix with the polluting oil. If the oil is too viscous, chemical dispersion will generally not be possible. Dispersion is most efficient with light, low viscosity oils. A minimum wave height and resulting turbulence are required for effective dispersion. On the other hand, too high waves make dispersant application infeasible and also less necessary because of effective natural dispersion<ref>Zeinstra-Helfrich, M., Koops, W. and Murk, A.J. 2015. The NET effect of dispersants - a critical review of testing and modelling of surface oil dispersion. Mar. Pollut. Bull. 100: 102–111</ref>.<br />
<br />
Enlarging the overall contact surface of the oil will in most cases promote bio-degradation by naturally occurring marine microorganisms. Breaking up oil slicks not only reduces the oiling of sea birds and mammals, but also the wind drift of oil slicks towards sensitive coastal areas, such as tidal flats and marshes, as illustrated in Fig. 6. However, the increased concentration of oil components within the water column resulting from the oil dispersion can potentially increase toxic effects on pelagic, demersal and benthic living organisms. Hence, there is a trade-off among different habitats and species with different ecological, social, and economic values<ref name=G18>Grote, M., van Bernem, C., Böhme, B., Callies, U., Calvez, I., Christie, B., Colcomb, K., Damian, H-P., Farke, H., Gräbsch, C., Hunt, A., Höfer, T., Knaack, J., Kraus, U., Le Floch, S., Le Lann, G., Leuchs, H., Nagel, A., Nies, H., Nordhausen, W., Rauterberg, J., Reichenbach, D., Scheiffarth, G., Schwichtenberg, F., Theobald, N., Voss, J. and Wahrendorf, D-S. 2018. The potential for dispersant use as a maritime oil spill response measure in German waters. Marine Pollution Bulletin 129: 623–632</ref>. Any decision in the trade-off between harmful effects can be criticized. The priorities for protection may be different between different stakeholders, such as fishermen, tourism managers or environmentalists. Transparency of the decision-making process is therefore essential<ref name=G18/>. <br />
<br clear=all><br />
<br />
<br />
==Related articles==<br />
:[[Oil spill pollution impact and recovery]]<br />
:[[Oil spill monitoring]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br/><br />
<br/><br />
<br />
{{author <br />
|AuthorID=16899<br />
|AuthorFullName= Kleeberg, Ulrike<br />
|AuthorName=Username}}<br />
<br />
{{author <br />
|AuthorID=16883<br />
|AuthorFullName= van Bernem, Karl-Heinz<br />
|AuthorName=Username}}<br />
<br />
{{author <br />
|AuthorID=16895<br />
|AuthorFullName= Krasemann, Hansjoerg<br />
|AuthorName=Username}}<br />
<br />
<br />
{{Review<br />
|name=Job Dronkers|AuthorID=120|<br />
}}<br />
<br />
<br />
[[Category:Coastal and marine ecosystems]]<br />
[[Category:Coastal and marine pollution]]<br />
[[Category:Oil spills]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Oil_spill_pollution_impact_and_recoveryOil spill pollution impact and recovery2024-02-05T11:15:22Z<p>Dronkers J: </p>
<hr />
<div><br />
<br />
==Oil spill impacts on the coastal ecosystem==<br />
In general, three categories of effects caused by an oil spill can be distinguished: direct lethal effects, direct sublethal effects and indirect effects (Penela-Arenaz et al., 2009<ref name=P9>Penela-Arenaz, M., Bellas, J. and Vázquez, E. 2009. Chapter Five: Effects of the Prestige Oil Spill on the Biota of NW Spain: 5 Years of Learning. Advances in Marine Biology 56: 365-396</ref>): <br />
*Direct lethal effects are due to physical and chemical responses to direct oil contact, even without ingestion of pollutants by organisms. Mortality is due to smothering, hypothermia (very common in oiled seabirds), coating (which interferes with an individual's movement, hindering food capture, and escape from predators), or acute toxicity of fuel.<br />
*Sublethal effects, are caused by the permanence of different fuel components in the environment. They do not lead to the death of organisms, but reduce the fitness of the affected species owing to the impact on the physiology, behaviour or reproductive capability of the organisms. These alterations may also alter the distribution, abundance, composition and diversity of impacted communities. <br />
*Indirect effects include changes in habitat, predator–prey dynamics, interactions among competitors, productivity levels and food webs, due to the loss of key species. Species with small populations are more strongly affected. Important losses of reproductive and breeding habitats may occur in low-energy environments such as rías, bays, estuaries or coastal marshes, which tend to trap oil and to accumulate hydrocarbon pollutants in the sediments.<br />
The effects of hydrocarbon pollution also depend on the species impacted. Gastropods and polychaetes are usually the least sensitive species, while corals, bivalves, decapod crustacea and echinoderms are the most sensitive<br />
<br />
<div style="border:1px solid #000000;float: right; background-color:#CEECF2;width: 350px;text-align: justify; padding:1em 1em 1em 1em; font-size:80%; margin-left: 1em"><br />
<br />
'''Options for oil spill cleanup''' from Zhu et al. (2001<ref>Zhu, X., Venosa, A., Suidam, M., Lee, K. 2001. Guidelines for the bioremediation of marine shorelines and freshwater wetlands. U.S. Environmental Protection Agency. Office of Research and Development National Risk Management Research Laboratory. Land Remediation and Pollution Control Division, Cincinnati, USA</ref> and 2004<ref>Zhu, X., Venosa, A.D. and Suidan, M.T. 2004. Literature review on the use of commercial bioremediation agents for cleanup of oil-contaminated estuarine environments. National Risk Management Research Laboratory Office of Research and Development U.S. Environmental Protection Agency Cincinnati, USA</ref>)<br />
<br />
''Natural methods''<br> <br />
Weathering and recovery by natural processes are basically a no-action option, allowing oil to be removed and broken down. For some spills, it is likely to be more cost-effective and environmentally responsible to leave an oil-contaminated site to recover naturally than to attempt to intervene. Important natural processes that result in the removal of oils include:<br />
*Evaporation: Evaporation is the primary natural cleansing process during the early stages of an oil spill and results in the removal of lighter components in oil. Depending on the composition of the spilled oil, up to 50 percent of an oil's more toxic, lighter components can evaporate within the first 12 hours after a spill.<br />
*Photo-oxidation: Photo-oxidation occurs when oxygen reacts with oil components under sunlight. Photooxidation leads to the breakdown of more complex compounds into simpler compounds that are lighter in weight and more soluble in water, allowing them to be further removed by other processes.<br />
*Biological degradation: Several types of microorganisms capable of oxidizing petroleum hydrocarbons are widespread in nature. Biodegradation is an important mechanism to remove the non-volatile components of oil from the environment. A prerequisite is sufficient availability of nutrients and oxygen. A nutrient shortage can be compensated by supplying fertilizer. This so-called biostimulation is less effective in anoxic environments as anaerobic biodegradation is slow. Even under optimal conditions, biodegradation typically takes months to years for microorganisms to decompose a significant portion of an oil stranded in the sediments of marine and/or freshwater environments.<br />
Natural dispersion and emulsification also contribute to the weathering processes that occur after oil release. <br />
<br />
''Physical methods''<br><br />
Physical containment and recovery of bulk or free oil is the first response option of choice for the cleanup of oil spills in marine and freshwater shoreline environments. Commonly used physical methods include: <br />
*Booming and skimming: Use of booms to contain and control the movement of floating oil and use of skimmers to recover it. Minimal environmental impact, efficient for small spills in quiet water, but low oil recovery rate on the high seas. <br />
*Wiping with absorbent materials: Use of hydrophobic materials to wipe up oil from the contaminated surface. Disposing of contaminated waste requires the necessary attention.<br />
*Mechanical removal: Collection and removal of oiled surface sediments by using mechanical equipment. This method should be used only when limited amounts of oiled materials have to be removed. It should not be considered for cleanup of sensitive habitats or where beach erosion may result. <br />
*Washing: washing of the oil adhering along the shorelines to the water’s edge for collection. Washing strategies range from low-pressure cold water flushing to high-pressure hot water flushing. This method, especially using high-pressure or hot water, should be avoided for wetlands or other sensitive habitats. <br />
*Sediment relocation and tilling: Movement of oiled sediment from one section of the beach to another or tilling and mixing the contaminated sediment to enhance natural cleansing processes by facilitating the dispersion of oil into the water column and promoting the interaction between oil and mineral fines. Oil penetration deep into coastal sediments and release of oil and oiled sediment into adjacent water bodies are issues of concern.<br />
*In-situ burning: Oil on the shoreline is burned usually when it is on a combustible substrate such as vegetation, logs, and other debris. This method may cause significant air pollution and destruction of plants and animals. <br />
<br />
''Chemical methods''<br> <br />
Chemical methods, especially dispersants, are routinely used as a response option in many countries. There are contrasting opinions about the effectiveness of these methods and concerns about their toxicity and long-term environmental effects. Major existing chemical agents include: <br />
*Dispersants: dispersing agents, which contain surfactants, are used to remove floating oil from the water surface to disperse it into the water column before the oil reaches and contaminates the shoreline. This is done to reduce toxicity effects by dilution to benign concentrations and accelerate oil biodegradation rates by increasing its effective surface area. <br />
*Demulsifiers: Used to break oil-in-water emulsions and to enhance natural dispersion. <br />
*Solidifiers: Chemicals that enhance the polymerization of oil can be used to stabilize the oil, to minimize spreading, and to increase the effectiveness of physical recovery operations. <br />
*Surface film chemicals: Film-forming agents can be used to prevent oil from adhering to shoreline substrates <br />
</div><br />
<br />
<br />
==Recovery from three major oil spill accidents==<br />
The ecological impacts of three major oil spills have each been monitored over a period of more than ten years. There are similarities, but also some differences between the three cases. For each case, this article summarizes some important conclusions regarding the recovery of the impacted ecosystems.<br />
<br />
===Exxon Valdez===<br />
<br />
[[File:ExxonValdezShigenaka.jpg|thumb|left|530px|Fig. 1. Exxon Valdez at Outside Bay, May 1989. Photo credit Gary Shigenaka, NOAA.]]<br />
<br />
The supertanker Exxon Valdez (Fig. 1) ran aground on a reef in Prince William Sound on the Gulf of Alaska just after midnight March 24, 1989, after being loaded with crude oil the previous day. A leak in the tanker caused 37,000 tons of oil to flow into the sea, much of which ended up on the coast a few days later driven by storm waves and currents. The oiling would eventually extend about over kilometers through Prince William Sound and down the Alaska Peninsula. The spill cleanup operation would peak at an estimated 10,000 workers, 1,000 vessels, 100 aircraft and helicopters, and extend into four years. Exxon estimated its cleanup costs to be $2.1 billion<ref>Shigenaka, G. 2014. Twenty-Five Years After the Exxon Valdez Oil Spill: NOAA’s Scientific Support, Monitoring, and Research. Seattle: NOAA Office of Response and Restoration. 78 pp</ref>. <br />
<br />
The oil, with a lower viscosity than commercial asphalt, caused a mass slaughter of marine animals, including more than 100,000 seabirds, thousands of sea otters and hundreds of harbor seals. In the first few years the amount of oil that had washed ashore sharply decreased as a result of evaporation, cleaning, weathering, dispersal and degradation. Microbial biodegradation stimulated by oleophilic nitrogen-containing liquid fertilizers was most effective<ref>Bragg, J.R., Prince, R.C., Harner, E.J. and Atlas, R.M. 1994. Nature 368: 413-418</ref>. Hopane, a saturated multicyclic hydrocarbon, was selected as an indicator of bioremediation effectiveness, because of its great resistance to biodegradation. In 1992 an estimated 2% of the initial oil spill, from which all volatile and most toxic components had been removed, was still present<ref>Short, J.W., Lindeberg, M.R., Harris, P.M., Maselko, J.M., Pella, J.J., Rice, S.D. 2004. Estimate of oil persisting on the beaches of Prince William Sound 12 years after the Exxon Valdez oil spill. Environ. Sci. Technol. 38: 19–25</ref><ref>Boehm, P.D., Page, D.S., Brown, J.S., Neff, J.M. and Gundlach, E. 2015. Long-Term Fate and Persistence of Oil from the Exxon Valdez Oil Spill: Lessons Learned or History Repeated? International Oil Spill Conference Proceedings 2014(1): 63-79</ref>. By 1997, monitoring provided strong inferential evidence that intertidal populations within Prince William Sound experienced a substantial amount of recovery from the effects of the 1989 oil spill. <br />
<br />
A survey 26 years after the disaster revealed that approximately 0.6% of the oil is remaining sequestered in the subsoil below 10–20 cm of clean sediments. These oil residues are protected from hydrological washing and contain a high fraction of polar compounds recalcitrant to biodegradation. These observations suggest that sequestration limits the bioavailability of the oil despite the fact that it still retains toxic compounds<ref>Lindeberg, M.R., Maselko, J., Heintz, R.A., Fugate, C.J. and Holland, L. 2018. Conditions of persistent oil on beaches in Prince William Sound 26 years after the Exxon Valdez spill. Deep-Sea Research Part II 147: 9–19</ref>.<br />
<br />
<br />
===Prestige===<br />
<br />
[[File:PrestigeSinkingWikimedia.jpg|thumb|530px|left|Fig. 2. Sinking of the Prestige. Photo Wikimedia.]]<br />
<br />
On November 13, 2002, the hull of the 26-year-old oil tanker Prestige burst during a storm off the coast of Galicia, Spain. The oil-leaking ship was not allowed to go to a sheltered port for repairs, but had to sail away from the coast by order of the Spanish, French and Portuguese authorities. On November 19, the ship broke in two on the high seas (Fig. 2), about 200 kilometers off the coast. Almost the entire cargo, 60,000 tons of heavy fuel oil, ended up in the sea. Part of the fuel sank to the seafloor and part of it drifted to the Spanish, French and Portuguese coasts. More than 2,000 km of coastline and more than 1,000 beaches were polluted with oil.<br />
<br />
Manual cleaning and washing using hot pressurized water had limited effectiveness on sandy beaches and even less along shorelines where the average grain size was pebble or cobble size<ref name=G6>Gallego, J.R., González-Rojas, E., Peláezm A.I., Sánchez, J., García-Martínez, M.J., Ortiz, J.E., Torres, T. and Llamas, J.F. 2006. Natural attenuation and bioremediation of Prestige fuel oil along the Atlantic coast of Galicia (Spain). Organic Geochemistry 37: 1869-1884</ref>. Hydro-cleaning machines were the preferential method to remove oil from exposed rocky shores. Areas inaccessible to mechanical cleaning methods (over 60,000 m2 of rocky surface area) were treated by bioremediation. The Prestige fuel oil that reached the Spanish coasts was characterized by low solubility and low capacity for dispersion, slow degradation, and high viscosity, adherence and density that hindered rapid weathering, specifically biodegradation, suggesting that the bioavailability of heavy fractions was very low at most of the sites. It consisted of approximately 25% aliphatics, 20% resins, 20% asphalthenes and 35% aromatics - the most toxic oil component for marine biota<ref name=P9/> (see [[#Annex Crude oil constituents and biodegradation]]). Due to the very high viscosity of the oil, application of dispersants was judged to be ineffective (see [[#Annex Use of dispersants]]). <br />
<br />
More than two years after the spill, the sites where no remediation treatment was performed still maintained over 50% of the initial amount of aromatic compounds; however, light and medium n-alkanes were almost totally degraded in the first months following the spill. <br />
Application of the oleophilic fertiliser S200 (a microemulsion of a saturated solution of urea in oleic acid containing phosphate esters) was compared at various sites with natural attenuation. Depending on the fuel compounds, an additional hydrocarbon depletion ranging from 10% up to 30% was achieved. However, at the sites studied, and despite initial successful results, effect did not persist over the following winter and spring. Microbial fuel degradation was enhanced where humidity, dissolved oxygen and nutrient availability were optimal and fuel adhesion was physically weakened, suggesting the increased effectiveness of bioremediation when irrigated with fresh water<ref name=G6>Gallego, J.R., González-Rojas, E., Peláezm A.I., Sánchez, J., García-Martínez, M.J., Ortiz, J.E., Torres, T. and Llamas, J.F. 2006. Natural attenuation and bioremediation of Prestige fuel oil along the Atlantic coast of Galicia (Spain). Organic Geochemistry 37: 1869-1884</ref>. <br />
<br />
[[File:EuropeanShagChristophMoning.jpg|thumb|right|250px|Fig. 3. European shag (''Gulosus aristotelis''). Photo credit Christoph Monin [https://ebird.org/science/status-and-trends ebird.org] ]]<br />
<br />
One of the rare documented long-term effects of oil spill pollution regards the European shag (Fig. 3), of which the reproductive success was reduced by 45% in oiled colonies compared with unoiled ones, while reproductive success did not differ before the Prestige accident. This impairment lasted for at least the first 10 years<ref>Barros, A., Alvarez, D. and Velando, A. 2014. Long-term reproductive impairment in a seabird after the Prestige oil spill. Biol. Lett. 10: 20131041</ref>. It was suggested that seabird populations may have suffered from the sub-lethal effects of oil exposure and reduced food availability after the Prestige oil spill. However, this effect was not triggered at the base of the trophic chain because long-term monitoring surveys showed that the effect of the Prestige oil spill on phytoplankton activity and net primary production was ephemeral, if at all present<ref>Varela, M., Bode, A., Lorenzo, J., Teresa Alvarez-Ossorio, M., Miranda, A., Patrocinio, T., Anadon, R., Viesca, L., Rodriguez, N., Valdes, L., Cabal, J., Lopez-Urrutia, A., Garcia-Soto, C., Rodriguez, M., Alvarez-Salgado, X.A. and Groom, S. 2006. The effect of the 'Prestige' oil spill on the plankton in the N-NW Spanish coast. Marine Pollution Bulletin 53: 272-286</ref>. <br />
<br />
Five years after the sinking of the Prestige some oil was still leaking from the wreck. There is also some evidence that part of the oil initially accumulated along the continental shelf (300 kg/m2 in January 2003 and 0.5 kg/m2 in October 2004) is gradually transported onshore<ref name=B13>Bernabeu, A.M., Fernandez-Fernandez, S., Bouchette, F., Rey, D., Arcos, A., Bayona, J.M. and Albaiges, J. 2013. Recurrent arrival of oil to Galician coast: the final step of the Prestige deep oil spill. J. Hazard. Mater. 251: 82–90</ref>. Even nine years after the accident, oil was detected in the intertidal area of both beaches in all campaigns. Tar balls were highly biodegraded suggesting that the oil was accumulated on the seafloor for a long time before being transported to the coast by the action of waves<ref name=B13/>.<br />
<br />
===Deepwater Horizon===<br />
<br />
[[File:DeepwaterHorizonWikimedia.jpg|thumb|530px|left|Fig. 4. The Deepwater Horizon on fire. Photo Wikimedia.]]<br />
<br />
The Deepwater Horizon floating oil platform in the Gulf of Mexico exploded on April 20, 2010, due to a blowout while drilling an oil well (the so-called Macondo well) at a depth of 1500 m (Fig. 4). The ultimate cause was a deficient valve in the blowout preventer, which caused the high gas pressure in the well to go out of control. Prior to the blowout, several incidents had occurred that had been ignored to avoid delaying the drilling program. The explosion killed nine crew members on the platform and two engineers. Close to half a million tons of oil (about 4,000,000 m3) was spilled into the sea. The total cost of the disaster was close to US$150 billion<ref>Lee, Y.G., Garza-Gomez, X. and Lee, R.M. 2018. Ultimate Costs of the Disaster: Seven Years After the Deepwater Horizon Oil Spill. Journal of Corporate Accounting & Finance 29: 69–79</ref>.<br />
<br />
The leaking liquid oil consisted by weight of approximately 38% natural gas and 62% liquid oil. The Macondo oil is a light, sweet oil, with a relatively high content of low molecular weight hydrocarbons and a relatively low sulfur and asphaltene content. Methane (20-30 mass percent) completely dissolved during ascent. Approximately 25% of the spilled oil was recovered or burned, 5–15% evaporated, and the remaining 60–70% spread and weathered within the Gulf of Mexico. It was concentrated in two locations: on the sea surface, where large droplets of liquid oil formed a slick of mostly insoluble, hydrocarbon-type compounds and in a deep intrusion layer that formed at depths between 900 and 1,300 meters<ref>Ryerson, T.B., Camilli, R., Kessler, J.D., Kujawinski, E.B., Reddy, C.M., Valentine, D.L., Atlas, E., Blake, D.R., de Gouw, J., Meinardi, S., Parrish, D.D., Peischl, J., Seewald, J.S. and Warneke, C. 2012. Chemical data quantify Deepwater Horizon hydrocarbon flow rate and environmental distribution. PNAS 109: 20246–53</ref>. Shortly after the accident the dispersants [https://en.wikipedia.org/wiki/Corexit Corexit] 9500A and 9527 were applied onto the surface slick, and approximately 3000 m3 of Corexit 9500A were released at depths directly into the plume of the escaping oil<ref>Gros, J., Socolofsky, S.A., Dissanayake, A.L., Jun, I., Zhao, L., Boufadel, M.C., Reddy, C.M. and Arey, J.S. 2017. Petroleum dynamics in the sea and influence of subsea dispersant injection during Deepwater Horizon. PNAS 114:10065–70</ref>. <br />
<br />
A variety of physical, chemical, and biological mechanisms helped to transform, remove, and redisperse the oil and gas that was released. Mechanical skimming and burning removed 3-4% and 6-8% of the total spill, respectively<ref>Etkin, D.S and Nedwed, T.J. 2021. Effectiveness of mechanical recovery for large offshore oil spills. Marine Pollution Bulletin 163: 111848</ref>. Biodegradation removed up to 60% of the oil in the intrusion layer but was less efficient in the surface slick, due to nutrient limitation. Photochemical processes altered up to 50% (by mass) of the floating oil<ref name=PO>Passow, U. and Overton, E.B. 2021. The Complexity of Spills: The Fate of the Deepwater Horizon Oil. Annu. Rev. Mar. Sci. 13: 109–36</ref>.<br />
<br />
<div style="border:1px solid #000000;float: right; background-color:#CEECF2;width: 350px;text-align: justify; padding:1em 1em 1em 1em; font-size:80%; margin-left: 1em"><br />
MOSSFA stands for Marine Oil Snow Sedimentation and Flocculent Accumulation and describes the gravitational settling of oil in association with ballasting particles and its deposition onto the seafloor. Different types of oil–particle associations can produce MOSSFA events, including (a) the aggregation and sedimentation of large phytoplankton blooms that forms MOS; (b) the formation of bacteria–oil aggregations, which are biofilm-like structures initiated by microbes in response to oil exposure; and (c) the formation of oil-particle aggregates, where fine sediment particles, such as drilling mud, coat and penetrate oil droplets.<br />
</div><br />
<br />
The oil spill from the well resulted in a deep-sea plume of petroleum hydrocarbons and marine oiled snow sedimentation and flocculent accumulation (MOSSFA). About 20% of the unrecovered oil was deposited in this way on the seabed over an area of more than 100,000 km2. The flocculent layer remained in place for years until benthic life had recovered sufficiently for soil bioturbation and subsequent biodegradation. Seabed contaminated with oil from the well was found more than 500 km from the accident site<ref name=PO>Passow, U. and Overton, E.B. 2021. The Complexity of Spills: The Fate of the Deepwater Horizon Oil. Annu. Rev. Mar. Sci. 13: 109–36</ref>.<br />
<br />
An estimated 10-30% of the surface oil came ashore a few months after the accident, mainly along the Louisiana shoreline, but also on the shorelines of Mississippi, Alabama, and Florida. In total, over 2,000 km of coast were oiled, half of which were beaches and half were wetlands. When oil reached the salt marshes, it was absorbed into sediments or remained on the sediment and grass surfaces. Some stranded oil supplies showed biodegradation within weeks. Oil filtered into the sand of warm, well-aerated, and physically dynamic beaches led to half-lives of less than a month. Alkanes and PAHs buried in sandy beaches were largely biodegraded within 3 years, while slower biodegradation of sediment-oil agglomerates overlying the sand took place through mechanical and photooxidative processes<ref>Bociu, I., Shin, B., Wells, W.B., Kostka, J.E., Konstantinidis, K.T. and Huettel, M. 2019. Decomposition of sediment-oil agglomerates in a Gulf of Mexico sandy beach. Sci. Rep. 9: 10071</ref>. In contrast, biodegradation of PAHs and alkanes hardly occurred in oil mats buried in anaerobic layers of marsh sediments. Oil concentrations that were 100-1000 times above pre-spill values then dropped to 10 times higher after 8 years, demonstrating long-term contamination by oil or oil residues that persists for decades<ref>Turner, R.E., Rabalais, N.N., Overton, E.B., Meyer, B.M., McClenachan, G., Swenson, E.M., Besonen, M., Parsons, M.L. and Zingre, J. 2019. Oiling of the continental shelf and coastal marshes over eight years after the 2010 Deepwater Horizon oil spill. Environ. Pollut. 252: 1367-1376</ref>. Even 10 years after the spill, oil from the accident continued to occasionally wash up on beaches.<br />
<br />
More than 80 deep sea octocoral communities at distances up to 20-30 km from the Macondo well contained traces of oil, as well as surfactant used in the dispersant Corexit. Branch loss was observed on some colonies, and hydroids colonized damaged portions of the colonies, impeding tissue regeneration and weakening the coral’s skeleton due to the added epibiont mass. The initial level of total impact in 2011 had a significant positive effect on the proportion of new growth after 2014. However, growth was not sufficient to compensate for branch loss at one of the impacted sites where corals are expected to take an average of 50 years to grow back to their original size<ref>Girard. F., Cruz. R., Glickman. O., Harpster, T. and Fisher, C.R. 2019. In situ growth of deep-sea octocorals after the Deepwater Horizon oil spill. Elem. Sci Anthr. 7: 12</ref>.<br />
<br />
Sediment profile and plan view imaging data collected in 2011 and 2014 showed a rapid benthic functional response to the Deepwater Horizon oil spill. Adverse effects related to organic enrichment decreased along a spatial gradient away from the wellhead. Although the spatial signal of these effects was still significant and detectable in a few variables 4 years after the spill, the data indicated that significant and meaningful functional benthic recovery had occurred<ref>Guarinello, M.L., Sturdivant, S.K., Murphy, A.E., Brown, L., Godbold, J.A., Solan, M., Carey, D.A. and Germano, J.D. Evidence of Rapid Functional Benthic Recovery Following the Deepwater Horizon Oil Spill. ACS ES&T Water.2c00272</ref>.<br />
<br />
According to sensitivity analyses<ref>Ainsworth, C.H., Paris, C.B., Perlin, N., Dornberger, L.N., Patterson, W.F.III, Chancellor, E., Murawski, S., Hollander, D., Daly, K., Romero, I.C., Coleman, F. and Perryman, H. 2018. Impacts of the Deepwater Horizon oil spill evaluated using an end-to-end ecosystem model. PLoS ONE 13(1): e0190840</ref>, the biomass of large reef fish may have decreased by 25% to 50% in the areas most affected by the spill, and the biomass of large demersal fish by as much as 40% to 70%. The oil pollution impacts on reef and demersal forages may have caused starvation deaths of predators and increased reliance on pelagic forages. The consequences for the food web indicate possible consequences of the spill far away from the oil area. Effects on age structure indicate possible delayed effects on fishing yields. Generally, recovery of high-turnover populations is predicted to occur within ten years, but some slower-growing populations may take more than thirty years to fully recover.<br />
<br />
<br />
==Annex Crude oil constituents and biodegradation==<br />
<div style=" float: center; background-color:#fff;width: 900px;text-align: justify; padding:1em 1em 1em 1em; font-size:95%; margin-left: 1em"><br />
Crude oil contains four broad fractions:<ref name=V17>Varjani, S.J. 2017. Microbial degradation of petroleum hydrocarbons. Bioresource Technology 223: 277–286</ref> Aliphatics, Aromatics, Resins and Asphaltenes. <br><br />
'''Aliphatics''' are saturated or unsaturated hydrocarbons consisting of linear or branched open-chain structures. They include alkanes (e.g. methane, etane, propane), iso-alkanes (e.g. isobutane), naphthenes, terpenes and steranes. <br />
'''Aromatics''' are ringed hydrocarbon molecules. They include monocyclic aromatic hydrocarbons (e.g. benzene, toluene, ethylbenzene, xylenes) and polycyclic aromatic hydrocarbons (PAHs) such as naphthalene (two-ringed), phenanthrene and anthracene (three-ringed), pyrene and chrysenes (four-ringed), fluoranthene and benzo[a]pyrene (five-ringed). <br />
'''Resins''' are amorphous solids dissolved in oil. They contain numerous polar functional groups formed with N, S, O and trace metals (Ni, V, Fe) and are structurally similar to surface-active molecules in crude oil and act as peptizing agents. <br />
'''Asphaltenes''' are viscous and high molecular weight compounds composed of polycyclic clusters, variably substituted with alkyl groups, which contributes to their resistance to biodegradation. They are soluble in light aromatic hydrocarbons such as benzene and toluene. <br />
<br><br />
<br><br />
Biodegradation is a process in which microorganisms (bacteria, fungi, algae) mitigate, degrade or reduce hazardous organic pollutants (alkanes, aromatics) to innocuous compounds such as CO<sub>2</sub>, CH<sub>4</sub>, H<sub>2</sub>O and microbial biomass without adversely affecting environment<ref name=V17/>. Bacteria are the primary degraders and most active agents in petroleum pollutant degradation. Microorganisms in polluted areas adapt to the environment through genetic mutations induced in subsequent generations, priming them to become hydrocarbon decomposers. Hydrocarbon-degrading microorganisms in unpolluted ecosystems constitute less than 0.1% of the microbial community, whilst this fraction may increase to 1-10% of the total population in an oil-polluted environment<ref>Atlas, R.M. 1991. Microbial hydrocarbon degradation-bioremediation of oil spills. J. Chem. Technol. Biotechnol. 52: 149–156</ref>. Pathways of microbial degradation of hydrocarbon pollutants involve various reactions viz. oxidation, reduction, hydroxylation and dehydrogenation. Saturated hydrocarbons are more easily biodegradable than the aromatic hydrocarbons, which pose more deteriorating effects in environment and life forms. Biodegradability of hydrocarbons can be ranked as: linear alkanes > branched alkanes > low-molecular-weight alkyl aromatics > monoaromatics > cyclic alkanes > polyaromatics > asphaltenes<ref>Atlas, R.M. 1981. Microbial degradation of petroleum hydrocarbons: an environmental perspective. Microbiol. Rev. 45: 180–209</ref>. Complete degradation of complex hydrocarbon mixture requires synergistic action of different microbial species. <br />
<br />
</div><br />
<br />
==Annex Use of dispersants==<br />
<div style=" float: center; background-color:#fff;width: 900px;text-align: justify; padding:1em 1em 1em 1em; font-size:95%; margin-left: 1em"><br />
Dispersion of oil, i.e. the breaking up of large oil slicks into small droplets, is a natural process that depends on the characteristics of the oil, its weathering stage and environmental parameters such as wave energy, salinity, temperature, etc. This process can be enhanced by application of specific chemicals, so-called dispersants. Dispersants are mixtures of surfactants in one or more solvents designed for application to oil spills with the aim of reducing the interfacial tension between the oil and the water phase. Dispersants promote the natural breakup of floating oil into small droplets in the water column.<br />
<br />
For being effective, the dispersant must be able to physically mix with the polluting oil. If the oil is too viscous, chemical dispersion will generally not be possible. Dispersion is most efficient with light, low viscosity oils. A minimum wave height and resulting turbulence are required for effective dispersion. On the other hand, too high waves make dispersant application infeasible and also less necessary because of effective natural dispersion<ref>Zeinstra-Helfrich, M., Koops, W. and Murk, A.J. 2015. The NET effect of dispersants - a critical review of testing and modelling of surface oil dispersion. Mar. Pollut. Bull. 100: 102–111</ref>.<br />
<br />
Enlarging the overall contact surface of the oil will in most cases promote bio-degradation by naturally occurring marine microorganisms. Breaking up oil slicks not only reduces the oiling of sea birds and mammals, but also the wind drift of oil slicks towards sensitive coastal areas, such as tidal flats and marshes (see [[Oil sensitivity mapping]]). However, the increased concentration of oil components within the water column resulting from the oil dispersion can potentially increase toxic effects on pelagic, demersal and benthic living organisms. Hence, there is a trade-off among different habitats and species with different ecological, social, and economic values<ref name=G18>Grote, M., van Bernem, C., Böhme, B., Callies, U., Calvez, I., Christie, B., Colcomb, K., Damian, H-P., Farke, H., Gräbsch, C., Hunt, A., Höfer, T., Knaack, J., Kraus, U., Le Floch, S., Le Lann, G., Leuchs, H., Nagel, A., Nies, H., Nordhausen, W., Rauterberg, J., Reichenbach, D., Scheiffarth, G., Schwichtenberg, F., Theobald, N., Voss, J. and Wahrendorf, D-S. 2018. The potential for dispersant use as a maritime oil spill response measure in German waters. Marine Pollution Bulletin 129: 623–632</ref>. Dispersants developed over the past decades are commonly less toxic than dispersed oil. With these dispersants it is the toxicity of the oil that drives the toxicological effects, not the toxicity of the dispersant<ref>National Research Council, 2005. Oil Spill Dispersants: Efficacy and Effects, Washington, DC.</ref>. Typically 2 to 5% of modern dispersants are added to the volume of the treated oil spill. Therefore, oil-dispersant mixtures are largely dominated by mineral oil components. However, any decision in the trade-off between harmful effects can be criticized. The priorities for protection may be different between different stakeholders, such as fishermen, tourism managers or environmentalists. Transparency of the decision-making process is therefore essential<ref name=G18/>.<br />
</div><br />
<br />
==Related articles==<br />
:[[Oil spill monitoring]]<br />
:[[Index of vulnerability of littorals to oil pollution]]<br />
:[[Oil sensitivity mapping]]<br />
:[[Coastal pollution and impacts]]<br />
:[[Bioremediation of marine ecosystems]]<br />
<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
<br />
{{author<br />
|AuthorID=120<br />
|AuthorFullName=Job Dronkers<br />
|AuthorName=Dronkers J}}<br />
<br />
<br />
[[Category:Coastal and marine pollution]]<br />
[[Category:Oil spills]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Bathymetry_from_remote_sensing_wave_propagationBathymetry from remote sensing wave propagation2024-01-28T14:19:55Z<p>Dronkers J: </p>
<hr />
<div><br />
==Surface topographic patterns==<br />
Water motion produces undulation patterns at the water surface that contain information about the seafloor bathymetry. This offers the possibility to determine the bathymetry through observation of specific features of the water surface topography. Over the past fifty years, various techniques have been developed that provide information about patterns at the water surface. These techniques use remote sensing and require much less measurement effort than traditional measurements carried out with ships. On the other hand, unlike direct ship-based measurements of water depth, remote sensing data provides indirect information that can only be interpreted using advanced analysis techniques. This article discusses some of the processes that determine the relationship between bathymetry and surface undulation patterns. The focus is on the nearshore zone and surface patterns produced by propagating waves.<br />
<br />
Several remote sensing techniques can be used to determine wave patterns at the water surface:<br />
* Video camera mounted on a drone<ref>Bergsma, E.W.J., Almar, R., de Almeida, L. P.M. and Sall, M. 2019. On the operational use of UAVs for video-derived bathymetry. Coastal Engineering 152, 103527</ref><ref>Lange A.M.Z., Fiedler, J.W., Merrifield, M.A. and Guza, R.T. 2023. UAV video-based estimates of nearshore bathymetry. Coastal Engineering 185, 104375</ref> or on a fixed tower<ref>Stockdon, H.F. and Holman, R.A. 2000. Estimation of wave phase speed and nearshore bathymetry from video imagery. Journal of Geophysical Research 105(C9): 22015–22033</ref><ref>Aarninkhof, S.G.J., Ruessink, B.G. and Roelvink, J.A. 2005. Nearshore subtidal bathymetry from time-exposure video images. J. Geophys. Res. 110, C06011</ref>, see also [[Argus applications]] <br />
* LiDAR (Light Detection And Ranging) mounted on an UAV<ref>Fiedler, J.W., Kim, L., Grenzeback, R. L., Young, A.P. and Merrifield, M.A. 2021. Enhanced surf zone and wave runup observations with hovering drone-mounted lidar. Journal of Atmospheric and Oceanic Technology 38(11): 1967–1978</ref><br />
* Radar microwave X-band radar<ref>Bell, P. S. 1999. Shallow water bathymetry derived from an analysis of X-band marine radar images. Coastal Engineering 37: 513-527</ref><ref>Greidanus, H. 1997. The use of radar for bathymetry in shallow seas. The Hydrographic Journal 83: 13–18</ref><ref>Gawehn, M., van Dongeren, A., de Vries, S., Swinkels, C., Hoekstra, R., Aarninkhof, S. and Friedman, L. 2020. The application of a radar-based depth inversion method to monitor near-shore nourishments on an open sandy coast and an ebb-tidal delta. Coastal Engineering 159, 103716</ref>, see [[Use of X-band and HF radar in marine hydrography]]<br />
* Satellite optical imagery<ref> Almar, R., Bergsma, E.W., Thoumyre, G., Baba, M.W., Cesbron, G., Daly, C., Garlan, T. and Lifermann, A. 2021. Global satellite-based coastal bathymetry from waves. Remote Sensing 13, 4628</ref>, see [[Satellite-derived nearshore bathymetry]]<br />
<br />
In this article we will not discuss the retrieval of surface wave patterns from remote sensing images; information can be found in the articles cited above. Without giving details of how this is done, we will assume that wavelength, wave period and wave height can be determined from the remote sensing images in every point of the considered nearshore area.<br />
<br />
<br />
==Depth inversion algorithms==<br />
The wave dispersion relation is the key to determining bathymetry from the wavelength <math>\lambda</math>, the wave period <math>T = 2 \pi / \omega</math> and the wave height <math>H</math>. According to linear wave theory, the wave dispersion relation can be written as <br />
<br />
<math>c = \Large\frac{gT}{2 \pi }\normalsize \, \tanh kh , \qquad (1)</math><br />
<br />
where <math>c = \lambda / T = \omega / k</math> is the wave celerity, <math>k = 2 \pi / \lambda</math> is the wave number (the length of the wave number vector <math>\vec{k}</math>), <math>\omega</math> is the radial wave frequency and <math>h</math> is the still water depth. The local water depth <math>h</math> can be found by inversion of this formula:<br />
<br />
<math>h = \Large\frac{1}{k }\normalsize \, \tanh^{-1} \Big( \Large\frac{2 \pi c}{g T}\normalsize \Big), \qquad (2)</math><br />
<br />
In shallow water, <math> kh << 1</math>, the relationship (1) between water depth and wave celerity becomes <math>c^2 \approx gh</math>. Knowledge of the wave celerity is sufficient to determine the water depth. In deep water, <math>k h > 1</math>, and <math>\tanh kh \approx 1</math>. According to Eq. (1), the wave celerity in deep water does not depend on the depth, meaning that the depth cannot be determined from inversion of the dispersion relation.<br />
<br />
Another restriction on the use of Eq. (1) are the assumptions underlying linear wave theory. These assumptions are: (i) irrotational wave flow, (ii) <math>H/h <<1</math> and (iii) <math>H / \lambda <<1</math>. However, these assumptions are not satisfied in the nearshore zone where waves become skewed and asymmetric. <br />
<br />
If weak nonlinearity is assumed in the shoaling zone (prior to wave breaking) nonlinear Stokes theory can be applied if the Ursell number <math>U_r = kH / (kh)^3</math> is small. In this case the dispersion relation can be approximated by<ref>Whitham, G.B. 1974. Linear ands nonlinear waves. Wiley-Interscience</ref><br />
<br />
<math>c = \Large\frac{gT}{2 \pi }\normalsize \, \sigma \Big( 1 + \Large\frac{9 - 10 \sigma^2+9 \sigma^4}{32 \sigma^4}\normalsize (kH)^2 \Big) + O[(kH)^4] , \qquad <br />
\sigma = \tanh kh .\qquad (3)</math><br />
<br />
From this formula <math>\sigma</math> and <math>h</math> can be determined by a numerical inversion procedure.<br />
<br />
Field observations of wave height and wave celerity show that the shallow water linear dispersion relation underestimates the wave speed at wave breaking and inside the surf zone. Measured celerity values can be 20% higher than predicted by the linear dispersion relation<ref name=TG/> or even more<ref>Suhayda, I.N. and Pettigrew, N.R. 1977. Observations of wave height and wave celerity in the surf zone. J. Geophys. Res. 82: 1419-1424</ref>. Weak nonlinearity cannot be assumed in the zone where waves are breaking. If the wave after breaking is surfing onshore like a bore, the bore formula for the celerity can be applied (see [[Tidal bore dynamics]], Eq. (1) ),<br />
<br />
<math>c = \sqrt{gh} \, \sqrt{(1+\large\frac{H}{h}\normalsize)(1+\large\frac{H}{2h}\normalsize)} . \qquad (4)</math> <br />
<br />
An alternative approach is applying cnoidal wave theory. This gives<ref name=TG>Thornton, E.B. and Guza, R.T. 1982. Energy saturation and phase speeds measured on a natural beach. J. Geophys. Res. 87, 9499</ref><br />
<br />
<math>c \approx \sqrt{g h} \, \sqrt{1 + \alpha \large\frac{H}{h}\normalsize } , \qquad (5)</math><br />
<br />
where <math>\alpha</math> is a function of the Ursell number with value close to 1. Empirical evidence<ref>Holland, T.K. 2001. Application of the linear dispersion relation with respect to depth inversion and remotely sensed imagery. IEEE Trans. on Geos. and Rem. Sens. 39: 2060-2071</ref> suggests <math>c \approx \sqrt{g h} \, \sqrt{1 + 0.45 \large\frac{H_s}{h}\normalsize } </math>, where <math>H_s</math> is the significant wave height. <br />
<br />
A physics-based approach uses a modified dispersion relation according to the Boussinesq theory that describes the propagation of weakly nonlinear and weakly dispersive waves for Ursell numbers of order unity (<math>O[H/h] \sim O[(kh)^2] <<1</math>). In this theory, nonlinear interactions between resonant triads of frequencies (<math>\omega, \pm \omega', \omega \mp \omega'</math>) lead to the growth of forced high-frequency components that modify the wave shape in shallow water. The resulting dispersion relation to order <math>(kh)^2</math> is<ref>Herbers, T.H.C., Elgar, S., Sarap, N.A. and Guza, R.T. 2002. Nonlinear dispersion of surface gravity waves in shallow water. Journal of Physical Oceanography 32: 1181–1193</ref><ref name=M22>Martins, K., Bonneton, P., de Viron, O., Turner, I.L., Harley, M.D. and Splinter, K. 2022. New Perspectives for Nonlinear Depth-Inversion of the Nearshore Using Boussinesq Theory. Geophysical Research Letters 50, e2022GL100498</ref><br />
<br />
<math>c(\omega) = \Large\frac{\omega}{k(\omega)}\normalsize = \sqrt{gh} \Big[ 1 + \Large\frac{h \omega^2}{3g} + \frac{h^2 \omega^4}{36g^2} - \frac{1}{h}\normalsize \gamma_{am} \Big]^{-1/2} , \qquad \gamma_{am} = \Large\frac{3}{2 | \hat{\eta}(\omega)|^2}\normalsize \, \int_{-\infty}^{\infty} \Re \big( \hat{\eta}(\omega') \hat{\eta}(\omega - \omega') \hat{\eta}^*(\omega) \big) d \omega' , \qquad (6)</math><br />
<br />
where <math>\hat{\eta}(\omega) = \hat{\eta}(k, \omega)</math> is the Fourier transform of the surface elevation <math>\eta (x, t) = \int \int \hat{\eta}(k, \omega) \exp(i(kx-\omega t)) dk d\omega</math>. Application of this dispersion relation requires datasets of the free surface elevation with high space and time resolution from which <math>\hat{\eta}(\omega)</math> can be determined. Lidars currently offer the most robust and practical solution for collecting such highly-resolved surface elevation data in the field. The depth <math>h</math> can be determined from Eq. (6) by a least-squares fit to values of <math>c(\omega)</math> around the peak wave frequency<ref name=M22/>. Using this theory, reasonable agreement was found (within 10%) in laboratory experiments between the real depth profiles in the shoaling and surf zones and depth profiles derived from the interpretation of water surface patterns using the dispersion relation Eq. (6) <ref name=M22/>. <br />
<br />
<br />
<br />
==Symbols==<br />
{| class="wikitable"<br />
|-<br />
! Variable !! Description !! Variable !! Description !! Variable !! Description <br />
|-<br />
| <math>c = \omega / k</math> || wave celerity || <math>H</math> || wave height ||<math>x</math>|| cross-shore coordinate <br />
|-<br />
| <math>g</math> || gravitational acceleration || <math>t</math>|| time ||<math>\eta(x,t)</math>|| wave surface elevation <br />
|-<br />
| <math>k = 2 \pi / \lambda</math>|| wave number || <math>T</math>|| wave period ||<math>\lambda</math>|| wavelength<br />
|-<br />
| <math>h = h(x)</math>|| local still water depth || <math>U_r = kH / (kh)^3</math>|| Ursell number ||<math>\omega</math>|| radial wave frequency<br />
|}<br />
<br />
==Related articles==<br />
:[[Use of X-band and HF radar in marine hydrography]]<br />
:[[Satellite-derived nearshore bathymetry]]<br />
:[[Bathymetry German Bight from X-band radar]]<br />
:[[Waves and currents by X-band radar]]<br />
:[[Statistical description of wave parameters]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
<br />
{{author<br />
|AuthorID=120<br />
|AuthorFullName=Job Dronkers<br />
|AuthorName=Dronkers J}}<br />
<br />
[[Category:Coastal and marine observation and monitoring]]<br />
[[Category:Observation of physical parameters]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Bathymetry_German_Bight_from_X-band_radarBathymetry German Bight from X-band radar2024-01-26T19:11:39Z<p>Dronkers J: </p>
<hr />
<div><br />
<br />
<br />
{{Review<br />
|name= Andrea Taramelli<br />
|AuthorID=20734<br />
}}<br />
<br />
<br />
It is possible to determine the bathymetry of a shallow coastal areas using radar data. This article discusses measurements carried out during storm conditions off the Island of Sylt in the German Bight. Radar data are analyzed through inverse application of the non-linear and linear wave theory; observed wave characteristics are used to derive information on water depths and currents. Radar techniques for the detection of wave characteristics are explained in the article [[Use of X-band and HF radar in marine hydrography]]. <br />
<br />
== Introduction ==<br />
The determination of the [[bathymetry]] in coastal environments by utilizing the ocean wave-shoaling photographic imagery and the observed reduction of ocean wave phase speed with decreasing water depth, is used since the WW-II (Williams 1946)<ref>Williams, W.W. 1946, The determination of gradients of enemy-held beaches. Geographical Journal 107, 76–93.</ref>. The development of various ground-based observation instruments (mainly radar and video imagery) and the exponential increase of computational power have promoted new methodologies for bathymetry survey based on observation of the sea surface, e.g. Bell 1999<ref> P. Bell 1999, Shallow water bathymetry derived from an analysis of X-band radar images of waves, Coastal Engineering 3-4, pp. 513-527.</ref>, Seemann et al. 1999<ref>Seemann J., C. Senet, H. Dankert, Hatten, H., Ziemer, F. 1999, Radar image sequence analysis of inhomogeneous water surfaces, in proc. of the SPIE'99 Conference - Applications of Digital Image Processing XXII. vol. 3808, pp. 536-546.</ref>, Stockdon and Holman 2000<ref>Stockdon, H.F., Holman, R.A. 2000, Estimation of wave phase speed and nearshore bathymetry from video imagery. Journal of Geophysical Research 105 (C9), pp. 22015–22033.</ref>, Dankert 2003<ref>Dankert, H. 2003, Retrieval of Surface-Current Fields and Bathymetries using Radar-Image Se-quences, International Geoscience and Remote Sensing Symposium, Toulouse, France.</ref>, Bell et al. 2004<ref name="bell">Bell, P., J. Williams, S. Clark, B. Morris and A. Vila-Concejo 2004, Nested Radar Systems for Remote Coastal Observations, Journal of Coastal Research SI39, pp. 483-487.</ref>, Catalan and Haller 2008<ref>Catalan, P.A. and Haller, M.C., 2008, Remote sensing of breaking wave phase speeds with ap-plication to non-linear depth inversions. Coastal Engineering, 55(1), pp. 93-111.</ref>, Senet et al. 2008<ref name="sen">Senet, C. M., J. Seemann, S. Flampouris, F. Ziemer 2008, Determination of Bathymetric and Current Maps by the Method DiSC Based on the Analysis of Nautical X–Band Radar-Image Sequences of the Sea Surface, IEEE Transaction on Geoscience and Remote Sensing 46(7), pp.1-9.</ref>. The core of these methods is the inverse use of linear or non-linear models for the propagation of the wavefield over an uneven seabed, in order to derive information on water depths and currents. <br />
<br />
<br />
As an illustration we discuss here twelve hourly radar datasets acquired during storm conditions, which are analyzed with two methods: (1) the non-linear method of Bell et al. 2004<ref name="bell"/> (henceforth BW04), which is based on inversed application of the non-linear [[Dispersion (waves)|wave dispersion]] equation of Hedges (1976)<ref>Hedges, T.S. 1976, An empirical modification to linear wave theory, Proc. Inst. Civ. Eng., 61, pp. 575-579.</ref> and (2) the Dispersive Surface Classificator (henceforth DiSC08, Senet et al. 2008<ref name="sen"/>), which is based on inversion of the linear wave theory. The performance of the two methods is assessed by comparing the resulting bathymetries to an echo sounder survey.<br />
<br />
<br />
== Area of investigation and data acquisition ==<br />
<br />
The radar was mounted close to the lighthouse List West on the Island of Sylt in the German Bight. The acquired radar images cover the tidal inlet at List West, the Lister Landtief (maximum depth 11m), and parts of the Lister Tief (maximum depth 40m). The coastal waters around List West were observed because of the extremely high morphodynamic activity. A sand bar between the Lister Landtief and the Lister Tief is in the process of breaking, which leads to a profound change of the local tidal-stream situation; the tidal range of the area is 2m. <br />
<br />
[[Image:BathyListWest.jpg|thumb|350px|left|Figure 1. Bathymetry of area of investigation acquired by multibeam echosounder.]]<br />
<br />
The instrument used for the acquisition of sea-surface image sequences is a ground-based nautical X-band radar with horizontal polarization. The device utilized during the experiments is a software–hardware combination consisting of a commercial, navigational Furuno X-band radar antenna and radar device, a WaMoS II analog–digital converter and acquisition of radar image sequences software. The radar was mounted from 1996 to 2007; the image sequences of the present investigation were recorded hourly from 23:00 UTC on 26.08.2003 to 10:00 UTC on 27.08.2003. During the data acquisition the minimum and maximum wind speed were 12 m/s and 17 m/s, respectively and the minimum and maximum significant wave height, 1.2 m and 1.6 m respectively, as measured by deployed instruments in the area of investigation. For the determination of datum, tidal gauge measurements were also used. A dataset of the [[bathymetry]] of part of the radar-covered area was acquired by multibeam echo sounder (by KOK/GKSS) on 25th of August 2003 (Fig. 1); the spatial resolution of the bathymetric data set is 2m x 2m and has been averaged over the grid of the radar results.<br />
<br clear=all/><br />
<br />
== Data analysis ==<br />
<br />
The BW04 and DiSC methods have common steps of analysis which are presented shortly in the current paragraph.<br />
The analysis of both methods begins with the conversion from the polar coordinates in which the raw radar data are recorded to a georeferenced Cartesian grid. The location of the transmitter and hence the origins of the polar conversions were determined by DGPS. <br />
A Fourier transform was carried out on each pixel through time in the image sequences to isolate individual wave frequencies. Each frequency layer within the transform was then analyzed to determine the variations in wavelength across the area viewed by the radars, using a discrete 2-D Fourier transform technique that isolates the strongest wave signal in the sub-images. Small subsections of the Fourier layer were used for this analysis. The wavelengths calculated from this analysis were then used in a least squares fit to find the water depth at each pixel, calculated both from linear wave theory (DiSC) and from a non-linear [[Dispersion (waves)|wave dispersion]] equation (BW04). For an explanation of these theories, see the article [[Bathymetry from remote sensing wave propagation]]. <br />
<br />
== Results ==<br />
<br />
[[File:WaterLevels.jpg |thumb|right|300px| Figure 2. Time series of water level calculated with BW04, DiSC in the deepest part of the tidal channel together with the measured water level.]]<br />
<br />
<br />
The results of the analysis are 12 hourly bathymetries from both methods. The 12-hour average is shown in Fig. 3 for the BW04 method. The spatial resolution for BW04 is 60m and for the DiSC is 40m. The time series of instant water depth in the deepest point from which the mean of each time series was subtracted, are plotted together with the tide gauge data in Fig. 2. The comparison of the hourly water levels with the tidal gauge data demonstrates that although none of the two methods is precise for the determination of the water level, the results of BW04 have higher correlation with the measured water level, due to the used model. The Hedge’s dispersion function includes the significant wave height; therefore the “noise” of the actual wave conditions is reduced in comparison of the linear dispersion used in DiSC. <br />
<br />
Comparison of the BW04 and DiSC bathymetries to the echo sounder data reveals the relative errors of the BW04 (Fig. 4) and DiSC (Fig. 5) methods. <br />
<br />
<br />
{| border="0"<br />
|-<br />
| valign="top"|<br />
[[File:BathyWB04.jpg |thumb|left|280px| Figure 3. Averaged bathymetry over 12h obtained from the BW04 method.]]<br />
| valign="top"|<br />
[[File:ErrorWB04.jpg |thumb|left|280px| Figure 4. Map of the relative error of the BW04 method.]]<br />
| valign="top"|<br />
[[File:ErrorDiSC.jpg |thumb|left|280px| Figure 5. Map of the relative error of the DiSC method.]]<br />
|}<br />
<br />
<br />
== Discussion ==<br />
<br />
[[File:05_scatter_wb04.jpg |thumb|left|250px| Figure 6. Scatter plot of the BW04 depth compared to the surveyed depth.]]<br />
[[File:07 disc scatter.jpg |thumb|right|250px| Figure 7. Scatter plot of the DiSC depth compared to the surveyed depth.]]<br />
<br />
The depths obtained from the two inversion methods were corrected with the measured tidal level. The results are compared to the echo sounder data in Figs. 6 and 7.<br />
<br />
The scatter plots for both methods demonstrate clustering of the results. The first cluster lays between 4.5m (the lower limit depends on the available echosoundings bathymetry) and 10m for BW04 and 12m for DiSC. At this cluster both methods present significant correlation (1 by 1) with the surveyed bathymetry. The second cluster is formed from the depths greater than 12m, where the depth is clearly underestimated. The underestimation of the deeper areas is the main reason for the inclination of the trend lines from the y=x line.<br />
<br />
The spatial distribution of the error illustrates the main sources of the error. In both cases, the bathymetry for deeper areas than 12m is underestimated. This is due to the lack of sufficiently long waves; the wave propagation celerity does not strongly depend on depth for wavelengths smaller than <math>2 \pi \times</math> depth. The bathymetric gradient is the second source of the error. Both methods fail to determine the bathymetry over the steep sides of the ship channel and at the areas with sand dunes. This is obvious by comparing the map of the high resolution survey (Fig. 1) with the error maps. The BW04 approach has higher accuracy at the shoals due to the adapted wave model, but the mean accuracy of the DiSC is higher (approximately 90%) than the BW04 (more than 80%).<br />
<br />
The above example shows that in principle, the inversion of the wave field propagation for the determination of the depth is a reasonable method with significant results for shallow coastal areas and it could be used in operational base at hot spots of crucial sediment motion, such as Sylt.<br />
<br />
==See also==<br />
*[[Satellite-derived nearshore bathymetry]]<br />
*[[Use of X-band and HF radar in marine hydrography]]<br />
*[[Bathymetry from remote sensing wave propagation]]<br />
*[[Waves and currents by X-band radar]]<br />
*[[HyMap: Hyperspectral seafloor mapping and direct bathymetry calculation in littoral zones]]<br />
*[[Instruments for bed level detection]]<br />
<br />
<br />
== References ==<br />
<br />
<references/><br />
<br />
<br />
{{author <br />
|AuthorID=16910<br />
|AuthorFullName= Flampouris, Stylianos<br />
|AuthorName=Username}}<br />
<br />
[[Category:Coastal and marine observation and monitoring]]<br />
[[Category:Observation of physical parameters]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Tidal_asymmetry_and_tidal_inlet_morphodynamicsTidal asymmetry and tidal inlet morphodynamics2024-01-19T16:48:45Z<p>Dronkers J: </p>
<hr />
<div><br />
This article describes the physical processes responsible for tidal wave deformation in shallow coastal inlet systems – tidal lagoons and estuaries. A qualitative discussion is given of the mutual interaction between tidal asymmetry generation and morphological development of these systems, which is generally referred to by the term ''self-organizing morphodynamics''. This article is largely based on the book ''Dynamics of Coastal Systems'' <ref name=Dr>Dronkers, J. 2017. Dynamics of Coastal Systems. World Scientific Publ. Co, Singapore, 740 pp.</ref>. <br />
<br />
==Tidal wave skewness==<br />
<br />
Tides result from the response of ocean water bodies to the attractive gravitational forces of sun and moon. Tidal motion in the oceans can be described by a number of sinusoidal components, because earth's rotation and the relative movements of earth, sun and moon have a cyclical character, see the article [[Ocean and shelf tides]]. The semidiurnal lunar tide (M2) is usually the dominant component, in which case the ocean tide can be described fairly accurately with a single sine function.<br />
<br />
Ocean tidal waves are distorted when they propagate into shallow coastal waters. Here the term tidal wave distortion is to be understood as a systematic (long-term averaged) difference between the periods of rising and falling tide, also called tidal asymmetry. We will use the term positive duration asymmetry for tides with a longer fall and shorter rise and negative duration asymmetry for tides with a shorter fall and longer rise. Nonlinear hydrodynamic processes, which are insignificant in the deep ocean, become important when the tidal range exceeds a small fraction of the mean water depth. In other words, non-linear terms become important in the equations describing the propagation of tidal waves. For example, quadratic terms introduce tidal components at twice the frequency of the semidiurnal lunar M2 tide. This so-called M4 tidal component can strengthen the flood current and weaken the ebb current, or vice versa. A quantitative measure of tidal asymmetry is the so-called 'skewness' parameter <math>\gamma</math>,<br />
<br />
<math>\gamma = \sqrt{nT} \, \Big(\int_0^{nT} u^2(t) dt \Big)^{-3/2} \int_0^{nT} u^3(t) dt , \qquad (1)</math><br />
<br />
where <math>u(t)</math> is the tidal velocity, <math>T</math> the period of the semidiurnal tide and <math>n</math> a large integer. If <math>u(t) > 0</math> for flood flow, then a positive value of <math>\gamma</math> implies flood dominance and a negative value ebb dominance. <br />
<br />
A non zero value of <math>\gamma</math> has consequences for the long-term average transport of sediment, because the dependence of the sediment transport <math>q_s</math> on the flow strength follows the approximate formula<br />
<br />
<math>q_s \propto u |u|^{\alpha} , \qquad (2)</math><br />
<br />
where the exponent <math>\alpha</math> can take values between 2 and 4 (see e.g., [[Sand transport]]). A positive (negative) skewness implies net upstream (downstream) tide-induced sediment transport.<br />
<br />
Because in practice the tidal elevation <math>\zeta (t)</math> is measured more easily than the flow velocity <math>u(t)</math>, tidal asymmetry is usually related to the skewness coefficient <math>\gamma_1</math>, which is the same as Eq. (1) with <math>u(t)</math> replaced by <math>\partial \zeta / \partial t</math>. The skewness coefficient <math>\gamma_1</math> is a measure of the average difference between periods of falling tide and rising tide. The interpretation of <math>\gamma_1</math> is similar to the interpretation of <math>\gamma</math> in situations where tidal flow is mainly driven by the water level slope <math>\partial \zeta / \partial x</math> (Nidzieko, 2010<ref>Nidzieko, J. 2010. Tidal asymmetry in estuaries with mixed semidiurnal/diurnal tides. J. Geophysical Research 115, C08006, doi:10.1029/2009JC005864</ref>).<br />
<br />
Linear ocean tides naturally have nonzero skewness. The skewness would be zero if the frequencies of the tidal components were random numbers. However, this is not the case; they are all multiples of a limited number of fundamental frequencies. For example, the sum of the frequencies of the two main diurnal tidal components K1 and O1 is equal to the frequency of the M2 tide. Any linear superposition of the M2, K1, and O1 tides therefore produces nonzero skewness according to Eq. (1). Most linear superpositions of tidal frequencies thus breach ebb-flood asymmetry and produce net tide-induced sediment transport. The strongest net transport is produced by the triplet M2, K1, O1. Although this was known for a long time (Doodson, 1921)<ref> Doodson, A.T. 1921. The harmonic development of the tide-generating potential. Proc.R.Soc.London, Ser.A 100: 305-329</ref>, it has received renewed attention more recently due to the observations and analyses of Hoitink et al. (2003)<ref> Hoitink, A.F.J., Hoekstra, P. and van Mare, D.S. 2003. Flow asymmetry associated with astronomical tides: Implications for residual transport of sediment. J.Geophys.Res. 108: 13-1 - 13-8</ref> and Zhang et al. (2018 <ref>Zhang, W., Cao, Y., Zhu, Y., Zheng, J., Ji, X., Xu, Y., Wu, Y. and Hoitink, A.F.J. 2018. Unravelling the causes of tidal asymmetry in deltas. Journal of Hydrology 564: 588–604</ref>). A world map showing ocean regions with different tidal asymmetry was published by Song et al. (2011<ref> Song, D., X. H. Wang, A. E. Kiss, and Bao, X. 2011. The contribution to tidal asymmetry by different combinations of tidal constituents. J. Geophys. Res., 116, C12007</ref>), based on tidal constants derived from the TPXO7-ATLAS<ref>[https://g.hyyb.org/archive/Tide/TPXO/TPXO_WEB/atlas.html TPXO ATLAS Global and Regional Solutions]</ref>. This map is shown in the article [[Ocean and shelf tides#Asymmetric ocean tides]]. Important asymmetric tides (with either positive or negative skewness <math>\gamma_1</math>) occur mainly in regions where tides have a mixed character, with comparable magnitudes of diurnal and semidiurnal tidal components. <br />
<br />
Although the asymmetry of ocean tides can be significant, tidal asymmetry can become much stronger due to the generation of shallow-water overtides in shallow coastal areas with a large semidiurnal tidal range (dominated by M2). The distortion of the tide can be so strong that the durations of rising tide and falling tide become very different and that a large difference arises between the peak flow velocities of flood and ebb. Often the duration of rising tide is much shorter than the duration of falling tide. The asymmetry of the tidal wave increases when propagating up-estuary. This is illustrated in Fig. 1 for the [[Morphology of estuaries#Hooghly estuary|Hooghly estuary]]. In the most extreme case, the duration of tidal rise becomes so short that a hydraulic jump develops at the front of the tidal wave. The front of the tidal wave appears as a propagating wall of water, a so-called tidal bore, as explained in the article [[Tidal bore dynamics]]. <br />
<br />
<br />
[[Image:HooghlyTidalWaveDeformation.jpg|center|700px|thumb|Figure 1: Tide curves in the Hooghly estuary observed during a high springtide (19/9/2009) at different tide gauges along the estuary. Data from Banerjee et al. (2015)<ref>Banerjee, A.P., Dutta, S. and Majumdar, A. 2015. Quest for the determination of environmental flow assessment for hilsa fish of the Hooghly estuary by hydraulic rating method. ARPN Journal of Engineering and Applied Sciences 10: 7885-7899</ref>]]<br />
<br />
<br />
In the following we review the major nonlinear processes involved in initiating shallow-water tidal distortion. We focus on asymmetry generated by the interaction of tides with topographic characteristics. Like most analytical studies of overtide generation, we consider estuaries and tidal rivers with simple one-dimensional geometries. The influence of channel curvature, secondary channels or local channel constrictions on tidal asymmetry are ignored. Asymmetries related to density gradients or wind-driven currents are also left out of consideration. A discussion of these processes can be found in the articles [[Morphology of estuaries]], [[Seawater intrusion and mixing in estuaries]], [[Estuarine circulation]] and [[Estuarine turbidity maximum]].<br />
<br />
This article contains many formulas. At the end of the article a list of symbols used is presented.<br />
<br />
<br />
==Tidal wave deformation in the absence of friction==<br />
<br />
The distortion of the tide described above is related to a difference in propagation speed of the high-water crest of the tidal wave and the low-water trough. This difference already occurs when the water depth below the wave crest is slightly greater than the water depth below the wave trough. This can be easily demonstrated for a tidal wave that propagates in <math>x</math>-direction with time <math>t</math> in deep water with little loss of friction. The most simple geometry is an infinite prismatic channel. The mean (tide-averaged) channel depth is called <math>h</math> (constant), the tidal elevation is called <math>\zeta(x,t)</math>, the total instantaneous water depth <math>D(x,t)=h+\zeta(x,t)</math> and the cross-sectionally averaged velocity <math>u(x,t)</math>. <br />
<br />
In the absence of friction, the tidal equations for mass and momentum read:<br />
<br />
<math>\Large\frac{\partial \zeta}{\partial t}\normalsize +\Large\frac{\partial((h+\zeta)u)}{\partial x}\normalsize=0 ; \qquad \Large\frac{\partial u}{\partial t}\normalsize + u \Large\frac{\partial u}{\partial x} \normalsize + g \Large\frac{\partial \zeta}{\partial x}\normalsize = 0 , \qquad (3)</math><br />
<br />
where <math>g</math> is the gravitational acceleration. Overtides are due to the nonlinear terms <math>\partial(\zeta u) / \partial x </math> and <math> u \partial u / \partial x</math>. The quadratic nature of these terms implies that the first and most important overtide (M4) has twice the frequency of the M2 tide. <br />
<br />
The tidal equations (3) can be cast in the form of two characteristic equations<br />
<br />
<math>\Large\frac{d}{dt}\normalsize[u(x(t),t) +2c(x(t),t)] = 0, \; \Large\frac{d x(t)}{dt}\normalsize = u(x(t),t) + c(x(t),t) ; \qquad \Large\frac{d}{dt}\normalsize[u(x(t),t) - 2c(x(t),t)] = 0, \; \Large\frac{d x(t)}{dt}\normalsize = u(x(t),t) - c(x(t),t) , \qquad (4)</math><br />
<br />
where <math>c = \sqrt{gD}</math>. The first equation represents a wave propagating in positive <math>x</math>-direction and the second a wave propagating in negative <math>x</math>-direction. From these equations it follows that the speed at which the wave crest propagates in positive <math>x</math>-direction is given by<br />
<br />
<math>\Large\frac{dx^+}{dt}\normalsize = c^+ = u_{crest} + \sqrt{g(h+a)} , \qquad (5) </math><br />
<br />
where <math>a</math> is the wave crest height. If we assume <math>a <<h</math>, then the velocity at the wave crest <math>u_{crest}</math> can be derived from the linearized equations (3) (i.e., replacing <math>\partial(Du)/\partial x</math> by <math>\partial(hu)/\partial x</math> and neglecting the term <math>u \partial u / \partial x</math>), with the result <math>u_{crest} = (a/h) \sqrt{gh}</math>. Substitution in Eq. (5) yields<br />
<br />
<math>c^+ \approx \Large\frac{a}{h}\normalsize \sqrt{gh} + \sqrt{g(h+a)} \approx \sqrt{gh} (1 + \Large\frac{3a}{2h}\normalsize) . \quad \quad (6)</math><br />
<br />
[[Image:PropagatingWaveDeformation.jpg|right|400px|thumb|Figure 2: Deformation of a frictionless propagating wave. The blue curve is the sinusoidal tidal wave with amplitude <math>a</math> of 2 m, at <math>\small x=0</math>, <math>\small\zeta(x=0,t) = a \cos \omega t</math>. The red curve is the distorted tidal wave after travelling a distance of <math>\small x=</math> 100 km without friction in a channel of 10 m mean depth, according to the second-order solution of Eqs. (3) for <math>a/h<<1</math>, which is given by <math>\small \zeta(x,t) = \zeta^{(1)} + \zeta^{(2)} </math>, with <math>\small \zeta^{(1)} = a \cos(\omega t - kx)</math>, <math>\small\zeta^{(2)} = \large\frac{3 a^2}{4h} \small kx \sin(2 \omega t – 2kx) </math> (see appendix). The dotted red line is the M4 overtide <math>\small \zeta^{(2)} </math>. The red curve displays a shorter tidal rise and a longer tidal fall: the tidal wave crest has propagated faster than the tidal wave trough.]]<br />
<br />
In the same way one finds for the propagation velocity of the low-water (LW) location <math>c^- \approx \sqrt{gh} (1 – \frac{3a}{2h})</math>. Another method to find this result is presented in the appendix. As HW propagates faster than LW, the tidal wave front will steepen progressively; the duration of rising tide shortens while the duration of falling tide is lengthened. This positive tidal asymmetry increases with the relative tidal amplitude <math>a/h</math> as a consequence of the nonlinear terms <math>\partial(\zeta u) / \partial x </math> and <math> u \partial u / \partial x</math> in the tidal equations. The resulting tidal distortion is illustrated in Fig. 2. Equation (6) suggests that after some time the high-water wave crest will overtake the low-water wave trough. However, this can only happen when the amplitude of the M4 overtide is of the same order of magnitude as the M2 tide, which violates the approximations used in Eq. (6).<br />
<br />
The tidal equations for a prismatic channel do not well represent tidal propagation in estuaries because of neglect of the friction term. The equations (3) are more representative for the [[Coriolis and tidal motion in shelf seas|along-shore propagation of a tidal Kelvin wave]], far away from hard boundaries where the tidal wave can reflect or from hydrodynamic boundaries where the Kelvin wave meets other tidal wave systems. An increasing tidal asymmetry develops in situations where the coastal zone is shallow and the tidal amplitude is large. The increasing tidal asymmetry along the coast of Normandy (France) can be understood in this way (Fig. 3), as well as the increasing tidal asymmetry along the North Sea coast of Holland (see: [[Coriolis and tidal motion in shelf seas]]). The tidal wave that enters estuaries situated along such coasts (the Seine at Le Havre, for instance) exhibits already significant positive-duration tidal asymmetry.<br />
<br />
<br />
[[Image:ChannelFrance.png|center|600px|thumb|Figure 4. Increasing positive asymmetry (<math>\Delta_{FR}</math> is the difference of the durations of tidal fall and tidal rise) of the tidal wave propagating along the coast of Normandy (France). Tide gauge data of 29 September 2015.]]<br />
<br />
<br />
==Tidal wave deformation in the presence of friction and intertidal areas==<br />
<br />
Tidal asymmetry develops during up-channel propagation into a shallow tidal basin. Pronounced tidal asymmetry (possibly leading to tidal bore formation) only occurs if during propagation a sufficiently large ratio <math>a/h</math> is maintained. In many cases this condition is not met, because the tidal amplitude decreases during propagation. The two main reasons for decrease of the tidal amplitude are: (1) tidal wave damping by friction and (2) lateral spreading of the flood tidal wave. These two nonlinear processes also influence the propagation of the high-water wave crest and the low-water wave trough. <br />
<br />
We consider a tidal basin where the longitudinal tidal flow <math>u(x,t)</math> is confined in a single tidal channel with depth <math>D(x,t)</math> and width <math>B_C(x)</math>. Flood water can spread over intertidal areas; the width of the intertidal area <math>B_I(x,t)</math> is a function of the water level <math>\zeta(x,t)</math>. The tidal propagation in this basin can be described by the mass and momentum balance equations<br />
<br />
<math>B \Large\frac{\partial \zeta}{\partial t}+\frac{\partial}{\partial x}\normalsize (B_C Du) = 0 , \qquad (7)</math><br />
<br />
<math>\Large\frac{\partial u}{\partial t}\normalsize + u \Large\frac{\partial u}{\partial x} \normalsize + g \Large\frac{\partial \zeta}{\partial x}\normalsize + F = 0 , \qquad (8)</math><br />
<br />
where <math>B= B_C+B_I </math> is the total surface width. The symbol <math>F</math> in Eq. (8) stands for the frictional momentum dissipation which is usually represented by a quadratic expression of the form <math>F= c_D \large\frac{|u|u}{D} </math>. Although it appears from detailed measurements that this expression is a rough approximation – the friction coefficient <math>c_D</math> is found to be variable both temporally and spatially (Lewis and Lewis, 1987<ref> Lewis, R. E., and Lewis, J. O. 1987. Shear-stress variations in an estuary. Estuarine Coastal Shelf Sci. 25: 621–635</ref>; Stacey and Ralston, 2005<ref name=SR> Stacey, M.T. and Ralston, D.K. 2005. The Scaling and Structure of the Estuarine Bottom Boundary Layer. J. Physical Oceanography 35: 55-71</ref>; Lefebvre et al., 2012<ref> Lefebvre, A., Ernstsen, V.B and Winter, C. 2012. Estimation of roughness lengths and flow separation over compound bedforms in a natural-tidal inlet. Continental Shelf Research 61–62: 98-111 </ref>) – we consider here a further approximation assuming that the quadratic dependence on <math>u</math> can be ignored: <math>F = r \large\frac{u}{D}</math>. The linear friction coefficient <math>r</math> is constant with dimension [m/s]; it relates momentum dissipation at the channel bed linearly to the depth-averaged current velocity. Its value typically ranges between 0.001 - 0.004 m/s <ref name=Dr></ref>. The precise value of the friction coefficient for estuarine flow is generally not well known as it is influenced by many factors such as density stratification (lower friction), small bed forms (higher friction) and fluid mud layers (lower friction). The friction coefficient may even be different for ebb and flood because of differences in salinity stratification<ref name=SR></ref>. In most estuaries, the tidal discharge amplitude is much larger than the river discharge, which therefore does not strongly influence frictional dissipation in the absence of salinity stratification. In this case, the linearization of the friction term is a minor approximation compared to the uncertainty in the value of the friction coefficient. The situation where tidal discharge and river discharge are comparable is discussed in the article [[River tides]].<br />
<br />
[[Image:PrismaticTidalFlatChannelNEW.jpg|right|300px|thumb|Figure 4: Prismatic estuarine channel with tidal flats.]]<br />
<br />
The tidal equations (7) and (8) are too complicated for an analytic treatment in which tidal asymmetry is explicitly related to the nonlinear terms. An analytical solution of the tidal equations requires further approximations, by assuming that the contribution of nonlinear terms is relatively small and can be linearized (Lanzoni and Seminara, 1998<ref> Lanzoni, S. and Seminara, G. 1998. On tide propagation in convergent estuaries, J. Geophys. Res. 103: 30793–30812</ref>).<br />
<br />
Therefore we consider a prismatic tidal channel with a uniform mean depth <math>h</math> much larger than the tidal amplitude <math>a</math>, such that the friction term can be approximated by<br />
<br />
<math>F = r \Large\frac{u}{D}\normalsize =r \Large\frac{u(x,t)}{h+\zeta(x,t)}\normalsize \approx r\Large\frac{u(x,t)}{h}\normalsize (1 - \Large\frac{\zeta(x,t)}{h}\normalsize) . \qquad (9)</math> <br />
<br />
We further assume that the intertidal storage width <math>B_I</math> is much smaller than the channel width <math>B_C</math> and that it increases linearly with the water level <math>\zeta(x,t)</math> (see Fig. 4):<br />
<br />
<math>B_I = \Delta b (1 + \Large\frac{\zeta(x,t)}{h}\normalsize), \quad <B>=B_C+\Delta b . \qquad (10)</math> <br />
<br />
In shallow estuaries (<math>h \le 5 m</math>) with strong tides, the nondimensional friction coefficient <math>r / (h \omega)</math> is substantially larger than 1 (the symbol <math>\omega \equiv 2 \pi / T</math> is the M2 tidal radial frequency). In this case the inertial terms <math>\partial u / \partial x </math> and <math> u \partial u / \partial x</math> are much smaller than the friction term <math>F</math>.<br />
<br />
In prismatic well-mixed estuaries, where salinity stratification can be ignored and where frictional effects are much stronger than inertial effects, the one-dimensional cross-section-averaged tidal equations (7) and (8) for small values of <math>a/h</math> can be simplified to <br />
<br />
<math><B> \Large \frac{\partial \zeta}{\partial t} \normalsize + h B_C \Large \frac{\partial u}{\partial x}\normalsize + \Delta b \Large \frac{\zeta}{a} \frac{\partial \zeta}{\partial t} \normalsize + B_C \Large \frac{\partial (\zeta u)}{\partial x}\normalsize =0 , \quad \quad (11)</math><br />
<br />
<math>g\Large \frac{\partial \zeta}{\partial x}\normalsize + r \Large \frac{u}{h}\normalsize – r \Large \frac{u \zeta}{h^2}\normalsize =0 . \quad \quad (12)</math><br />
<br />
<br />
In such friction-dominated estuaries the tide does not travel as a propagating wave, but rather advances into the estuary through a diffusion-type process, as shown by Eq. (B5) and described by Blondeaux (1978) for the Saint Lawrence Estuary <ref name=LB> LeBlond, P. 1978. On tidal propagation in shallow rivers, J. Geophys. Res., 83: 4717–4721</ref>. The tidal wave crest does not coincide with the time of high water, but lags behind. The same applies to low water.<br />
<br />
The nonlinear terms in the tidal equations are much smaller than the linear terms if <math>\Delta b << B_C</math> and <math>a/h << 1</math>. In this case a first order solution <math>\zeta^{(1)}</math> can be found by substituting the solution of the linear equations in the nonlinear terms. The nonlinear terms then generate a small M4 tidal component <math>\zeta^{(2)}</math>, which affects the duration of tidal rise and tidal fall. This is because the propagation speed <math>c^+</math> of the high-water wave crest differs from the propagation speed <math>c^-</math> of the low-water wave trough (see appendix):<br />
<br />
<math>c^{\pm} \approx [1 \pm (2 - \sqrt{2}) \Large\frac{a}{h}\normalsize \mp \Large\frac{\Delta b}{2B_C}\normalsize ] \; \sqrt{gh} \; \sqrt{ \Large\frac{2 \omega h}{r} \frac{B_C}{<B>}\normalsize} . \quad \quad (13) </math> <br />
<br />
This expression exhibits the effect of other nonlinearities in tidal propagation than those considered in the example of the prismatic channel: the effect of intertidal areas in Eq. (11) and the effect of depth dependence of the friction term in Eq. (12). The nonlinearity in the friction term Eq. (9) implies less friction in the period around HW compared to the period around LW and a corresponding increase of the HW propagation speed compared to the LW propagation, yielding positive tidal asymmetry. The nonlinearity related to the width increase with rising water level represented by the third term in Eq. (11) implies a decrease of the HW propagation velocity compared to the LW propagation, yielding negative tidal asymmetry (Speer and Aubrey, 1985<ref> Speer, P.E. and Aubrey, D.G. 1985. A study of non-linear tidal propagation in shallow inlet/estuarine systems. Part II: theory. Estuarine, Coastal Shelf Sci. 21: 207-224</ref>). This is because the HW crest of the tidal wave is delayed when propagating into the estuary by diversion of flood water over the intertidal area, while the LW wave trough remains confined within the narrower channel when propagating into the estuary. Hence, shallowness of the channel (large <math>a/h</math>) and large intertidal area (large <math>\Delta b/B_C</math>) have counteracting effects on tidal wave distortion.<br />
<br />
==Morphology of shallow tidal basins with small river inflow==<br />
<br />
In the foregoing it was shown that the tidal wave that enters a shallow prismatic channel is distorted due to the opposite effects of friction and intertidal areas on the up-channel propagation of HW on the one hand and LW on the other. In short tidal basins, these effects are partially offset by the reflected tidal wave at the landward basin boundary<ref name=Dr></ref>. However, in the case of strong friction, the reflected tidal wave is much smaller than the incoming tidal wave in a large part of the basin. In this part of the tidal basin the tidal velocity <math>u</math> is mainly determined by the water surface slope <math>\partial \zeta /\partial x</math>, according to Eq. 12. A short period of tidal rise compared to the period of tidal fall implies steeper water surface slopes during flood than during ebb. Hence, maximum flood velocities are higher than maximum ebb velocities in the absence of significant river inflow. <br />
<br />
[[Image:EquilibriumTidalLagoonsNEW.jpg|right|500px|thumb|Figure 5: The relative tidal amplitude <math>a/h</math> versus the relative intertidal area <math>\Delta b/<B></math> for a large number of tidal basins with small (or without) river inflow. Many of these basins (but not all) are back-barrier tidal basins or tidal lagoons: basins that are semi-closed by a sand barrier at the entrance. The figure shows a positive correlation between <math>a/h</math> and <math>\Delta b/<B></math>. The correlation between these basin characteristics also depends on other parameters, in particular the tidal asymmetry already existing at the basin entrance, which is different for each basin. Therefore, one should not expect that all the point lie on a single line. Adapted from<ref name=Dr></ref>.]]<br />
<br />
Because the transported sediment load increases more than linearly with the current velocity (according to Eq. 2), sediment fluxes during flood tide are higher than sediment fluxes during ebb tide. Flood-dominant tidal asymmetry thus produces a net import of sediment into the basin. Sediment infill could possibly go on until no tidal basin is left. This has happened in the past to some tidal basins, but many tidal basins without river inflow still survive. The reason is that flood dominance is neutralized by several processes. One of these processes is wave action, which can suspend large amounts of sediment in the HW period that are subsequently transported out of the basin by ebb currents<ref>Friedrichs, C.T. 2011. Tidal Flat Morphodynamics: A Synthesis. In: Treatise on Estuarine and Coastal Science, vol. 3, Estuarine and Coastal Geology and Geomorphology. Ed.:J. D. Hansom and B. W. Fleming, Elsevier, Amsterdam: 137-170</ref><ref> Desguée, R., Robin, N., Gluard, L., Monfort, O., Anthony, E.J., Levoy, F. 2011. Contribution of hydrodynamic conditions during shallow water stages to the sediment balance on a tidal flat: Mont-Saint-Michel bay, Normandy, France. Estuarine<br />
Coastal Shelf Sci. 94: 343–354</ref>. However, tidal basins do not depend only on wave action for their survival. One reason is the so-called Stokes transport, the water outflow compensating for the net influx due to greater mean water depth during flood than during ebb. Another reason is the reduction of tidal asymmetry due to the presence of intertidal areas as discussed in the previous section. During the development of intertidal areas by flood-dominant sediment transport, tidal asymmetry is weakened until the average sediment transport by flood currents has become comparable to the average transport by ebb currents. In order to neutralize flood dominance with increasing relative tidal amplitude <math>a/h</math>, the counteracting effect of intertidal areas should also increase. Fig. 5 shows that this is indeed the case for natural tidal basins with small (or without) river inflow: tidal basins with larger relative tidal amplitude have larger intertidal areas. One may thus conclude that self-organizing processes can produce a natural equilibrium morphology for tidal basins in a sedimentary environment without geological constraints (see also the article [[Morphology of estuaries]]). A mathematical derivation of the relationship between <math>a/h</math> and <math>\Delta b/<B></math> is given in Dronkers (1998<ref>Dronkers, J. 1998. Morphodynamics of the Dutch Delta. In: Physics of estuaries and coastal seas. Ed.: J.Dronkers and M.B.A.M. Scheffers, Balkema, Rotterdam: 297-304</ref> and 2017<ref name=Dr/>).<br />
<br />
<br />
==Tidal wave deformation in a converging channel==<br />
<br />
The influence of friction on tidal propagation increases with decreasing depth. The LW propagation is slowed down more strongly than the HW propagation, which results in a larger tidal asymmetry. However, the tidal amplitude is decreased by frictional damping. During the past century many estuaries have been deepened for navigational purposes and intertidal areas have been reclaimed. The effect of these interventions on tidal propagation is illustrated in Fig. 6 for the Seine estuary and tidal river system. The tide propagates now much faster into the estuary and the tidal amplitude is much larger, especially in the upstream river. The tide propagation speed has increased more for the low waters than for the high waters, although the propagation of the high waters also benefits of the reduction of the intertidal areas. In the past a high tidal bore developed each spring tide in the downstream Seine river. After the interventions (especially the dredging of the mouth bar) it takes a much larger distance before the HW wave crest overtakes the LW wave trough. A small tidal bore now develops far upstream and only for very high tidal coefficients<ref> Bonneton, N., Bonneton, P., Parisot, J-P., Sottolichio, A. and Detandt G. 2012. Tidal bore and Mascaret - example of Garonne and Seine Rivers. Comptes Rendus Geosciences, 344, 508-515</ref>. <br />
<br />
<br />
[[Image: SeineTidalDeformation.jpg|center|800px|thumb|Figure 6: Tidal wave distortion in the Seine estuary. The figures at the left show simultaneously recorded tide curves during springtide, for locations at various distances from the estuarine mouth. The pictures on the right show the morphology of the Seine estuary. The upper figures relate to the situation in the 19th century, when the morphology of the Seine estuary was almost in a natural state, with large shoals at the mouth (mouth bars) and an inner system with multiple channels and extensive intertidal areas. The corresponding 1876 tide curves (Comoy, 1881 <ref> Comoy, M. 1881. Etude pratique sur les marées fluviales. Gauthiers-Villars, Paris</ref>) display strong damping and delay in tidal propagation, especially for the low-waters. Tidal propagation over this complex shallow geometry resulted in a tidal bore that reached its largest amplitude at about 50 km from the mouth. The lower panels relate to the current situation. In the course of the 20th century, and especially in the period 1970-1980, the morphology of the estuary was greatly changed by artificial interventions. The estuarine main channel was deepened, especially in the mouth zone, and fixed by submerged dikes. Large parts of the intertidal areas were diked and filled with dredged materials. Tidal damping and tidal distortion were greatly reduced. At present a small tidal bore occurs only under extreme tides and further inland than in the past.]]<br />
<br />
[[Image:ConvergingEstuarySchematization.jpg|right|450px|thumb|Figure 7. Schematization of a strongly converging estuary. (a) Plan view; (b) 3D view.]]<br />
<br />
As discussed before, the expansion of the tidal flood wave over large intertidal areas decreases its height and propagation speed. The opposite occurs when the tide propagates into a tidal channel that becomes progressively narrower in up-channel direction, see Fig. 7. Instead of expanding laterally, the tidal wave is contracted when propagating. In the hypothetical case of no friction, conservation of the tidal energy flux along the channel requires up-channel amplification of the tidal amplitude (according to Green's law<ref name=J>Jay, D.A. 1991. Green's law revisited: tidal long-wave propagation in channels with strong topography. J.Geophys.Res. 96: 20,585-20,598</ref>). Many estuaries with significant river inflow have an upstream converging channel. Intertidal areas are rather small, partly as a result of natural sedimentation but often also as a result of human reclamation. The channel depth along the thalweg is fairly uniform<ref name=Sa>Savenije, H.H.G. 2012. Salinity and Tides in Alluvial Estuaries, second ed., Salinity and Tides in Alluvial Estuaries, second ed., www.salinityandtides.com </ref>, but shoals may be present in the mouth zone. The uniformity of the depth can also be due to dredging works for navigation purposes. <br />
<br />
Insight in the role of the most important nonlinear terms can be gained when simplifications are made. In the following we consider an idealized estuary with exponentially converging width. The corresponding estuarine geometry is shown in Fig. 7. The mean water depth <math>h</math> is uniform throughout the estuary; the channel width <math>B</math> converges exponentially and the intertidal width <math>B_I</math> increases linearly from the LW level up to the HW level, <br />
<br />
<math>B_C = b_C e^{-x/L_b} , \; B_I = e^{-x/L_b} \Delta b (1 + \zeta / a), \; <B>=b e^{-x/L_b} ; \; b = b_C+\Delta b. \quad \quad (14)</math><br />
<br />
It should be borne in mind that although many estuaries have an upstream converging width, the assumption of exponential width convergence and uniform depth is for most estuaries a very rough approximation. Often only a limited part of the estuary can be represented in this way. <br />
<br />
Tidal propagation in this part of the estuary can be described by the mass and momentum balance equations (7) and (8). The instantaneous local depth is <math>D(x,t)=h+\zeta(x,t)</math> and the cross-sectional averaged tidal velocity is <math>u(x,t)</math>. Density gradients are left out of consideration. When substituting the expressions (14) for the width and omitting all nonlinear terms we obtain<br />
<br />
<math> \Large \frac{b}{b_C} \frac{\partial \zeta}{\partial t}\normalsize + h \Large \frac{\partial u}{\partial x}\normalsize – u \Large \frac{h}{L_b} \normalsize = 0 . \quad \quad (15)</math><br />
<br />
<math>\Large\frac{\partial u}{\partial t}\normalsize + g \Large \frac{\partial \zeta}{\partial x}\normalsize + r \Large \frac{u}{h}\normalsize = 0 . \quad \quad (16)</math><br />
<br />
Solving these linear equations (only tide, no river discharge) yields <br />
<br />
<math>\zeta = a e^{-\mu x} \; cos(kx-\omega t) \quad </math> with <math>\quad 2 L_b \; \mu = \normalsize -1 + \Large[\normalsize 1 – (\Large \frac{1}{2} \normalsize + 2 K_0^2) +\large[\normalsize (\Large \frac{1}{2}\normalsize + 2 K_0^2 )^2+ 4 (K_c^2 - K_0^2) \large]^{\large 1/2} \Large]^{\large 1/2} \normalsize, \quad \quad (17)</math><br />
<br />
where <math>K_0 = \Large \frac{\omega L_b}{\sqrt{ghb_C/b}}\normalsize , \; K_c = \Large \frac{r \omega L_b^2b}{g h^2b_C} \normalsize </math>. The damping factor <math>\mu</math> is positive for large friction and large convergence length (<math>K_c >K_0</math>). However, for small friction and small convergence length (<math>K_c <K_0</math>) the damping factor is negative: the tide is amplified when propagating up-channel. Even in the case of strong friction, the tide is only slightly damped or even amplified if the convergence length <math>L_b</math> is sufficiently small. In cases where tidal damping dominates over the effect of channel convergence (large <math>L_b</math>), the relative tidal amplitude decreases along the estuary; tidal asymmetry then becomes less relevant for upstream sediment transport. As noted before, the friction factor <math>r</math> can vary greatly between estuaries because of salinity stratification and the type of bed sediments (coarse or muddy) and bedforms. <br />
<br />
The solution of the Eqs. (15) and (16) also yields an expression for the wave propagation velocity <math>c</math>:<br />
<br />
<math>c = \Large \frac{\omega}{k}\normalsize = 2 \omega L_b \Large[\normalsize - 1 + (\Large \frac{1}{2} \normalsize + 2 K_0^2) +\large[\normalsize (\Large \frac{1}{2}\normalsize + 2 K_0^2 )^2+ 4 [K_c^2 - K_0^2] \large]^{\large 1/2} \Large]^{\large -1/2}\normalsize . \quad \quad (18)</math><br />
<br />
<br />
The expressions (17) and (18) show that tidal wave propagation depends on only two parameters, <math> K_0</math> and <math> K_c </math>. It should be noted that the linear equations (15) and (16) do not describe tidal wave deformation; for this, nonlinear terms have to be included (<math>u \partial u /\partial x</math> and time varying water depth <math>h+\zeta</math> in Eq. (15) and <math>b \zeta \partial u / \partial x</math> in Eq. (16)). <br />
<br />
In strongly converging friction-dominated estuaries the nondimensional wave numbers <math> K_0</math> and <math> K_c </math> have similar order of magnitude (consider, for example, <math>r \approx</math> 0.003 m/s, and the typical geometries <math>\Delta b <<b_C</math>, convergence length <math>L_b \approx</math> 25 km and depth <math>h \approx</math> 8 m, or convergence length <math>L_b \approx</math> 10 km and depth <math>h \approx</math> 5 m). In this case <math>\mu \approx 0</math>: the longitudinal variation of the tidal amplitude is small. Although friction causes damping of the tidal amplitude along the estuary, observations show that the tidal amplitude in many estuaries is fairly uniform along the estuary (Friedrichs and Aubrey, 1994<ref name=Fr> Friedrichs C.T. and Aubrey, D.G. 1994. Tidal propagation in strongly convergent channels. J.Geophys.Res. 99: 3321-3336</ref>; Prandle, 2004<ref> Prandle, D. 2004. How tides and river flows determine estuarine bathymetries. Progress in Oceanography 61: 1–26</ref>; Savenije, 2012<ref name=Sa></ref> ); see also the article [[Physical processes and morphology of synchronous estuaries]]. The reason is that tidal amplification by the funneling effect approximately cancels tidal damping due to friction. In a strongly, exponentially converging estuary the tide is propagating upstream with a phase difference of approximately 90° between tidal elevation and tidal velocity (Jay, 1991<ref name=J></ref>). In some estuaries the funneling effect even produces an increase of the tidal amplitude in the strongly converging part of the estuary; examples are the [[Morphology of estuaries#Hooghly estuary|Hooghly]] (Fig. 1), [[Morphology of estuaries#Western Scheldt and Scheldt estuary|Scheldt]], Humber, Gironde-Garonne. <br />
<br />
In the case of strong friction and strong exponential width convergence, the tidal equations (7) and (8) can be simplified by neglecting the term <math>B_C \partial (Du) / \partial x</math> compared to <math>Du \partial B_C / \partial x</math> in Eq. (7) and the terms <math>\partial u / \partial t</math>, <math> u \partial u / \partial x</math> compared to <math>ru/h</math> in Eq. (8). The two simplified equations can be combined by eliminating <math>u</math>. This yields the simple characteristic equation<br />
<br />
<math>\Large\frac{d}{dt}\normalsize \zeta(x(t),t) = 0 , \quad \Large\frac{d x(t)}{dt}\normalsize = c = \Large\frac{g B_C D^2}{r B L_b}\normalsize . \quad \quad (19)</math><br />
<br />
We assume that the relative tidal amplitude <math>a/h</math> and the relative intertidal area <math>\Delta b / b_C</math> are small, then according to Eq. (14), <br />
<br />
<math>D^2 \approx h^2 (1 + 2 \Large\frac{\zeta}{h}\normalsize), \quad \Large\frac{B}{B_C}\normalsize \approx \Large\frac{b}{b_C}\normalsize (1+\Large\frac{\zeta}{a}\frac{\Delta b}{b}\normalsize ) .</math><br />
<br />
The HW propagation velocity <math>c^+</math>, and LW propagation velocity <math>c^-</math> then follow directly from Eq. (19): <br />
<br />
<math> c^{\pm} \approx \Large\frac{g b_C h^2}{r L_b b}\normalsize \; (1 \pm \Large\frac{2a}{h}\normalsize \mp \Large\frac{\Delta b}{b}\normalsize ) . \qquad (20)</math><br />
<br />
A similar expression is given by Friedrichs and Aubrey (1994) <ref name=Fr></ref> from an analytical model of converging estuaries with small relative tidal amplitudes and small intertidal areas. The role of the nonlinear terms for tidal distortion is similar as for the prismatic channel. The nonlinearity in the friction term Eq. (9) implies less friction in the period around HW compared to the period around LW and a corresponding increase of the HW propagation speed compared to the LW propagation, yielding positive tidal asymmetry. The nonlinearity related to the width increase with rising water level Eq. (10) implies a decrease of the HW propagation velocity compared to the LW propagation, yielding negative tidal asymmetry.<br />
<br />
[[Image:CharenteSpringNeapTide.jpg|left|350px|thumb|Figure 8: Tidal elevation (solid) and current velocity (dotted) curves in the Charente estuary for springtide (red) and neap tide (blue). The spring tidal curves exhibit a steep tidal rise and flood currents that are stronger than ebb currents. Hardly any tidal asymmetry occurs during neap tide and ebb currents are stronger than flood currents. Data from Toublanc et al. (2015) <ref>Toublanc, F., Brenon, I., Coulombier, T. and LeMoine, O. 2015. Fortnightly tidal asymmetry inversions and perspectives on sediment dynamics in a macrotidal estuary (Charente, France). Continental Shelf Res. 94: 42–54</ref>. Characteristic parameters for the Charente estuary are: channel depth <math>h \approx</math> 6.5 m, convergence length <math>L_b \approx</math> 10 km.]]<br />
<br />
The examples of tidal propagation in a prismatic channel and tidal propagation in a strongly converging estuary show that strong positive tidal asymmetry will develop only in estuaries with a large relative tidal amplitude <math>a/h</math> and small relative intertidal area <math>\Delta b / b_C</math>. The analytic models can only be evaluated for small values of <math>a/h</math>. However, the physical mechanisms for the development of strong tidal asymmetry are basically the same when <math>a/h</math> is no longer a small quantity, as confirmed by fully nonlinear mathematical models (Peregrine, 1966<ref> Peregrine, D.H. 1966. Calculations of the development of an undular bore J. Fluid Mech. 25: 321–30</ref>; Filippini, 2019<ref> Filippini, A.G., Arpaia, L., Bonneton, P. and Ricchiuto, M. 2019. Modeling analysis of tidal bore formation in convergent estuaries. European Journal of Mechanics - B/Fluids 73: 55-68</ref>). The importance of the parameter <math>a/h</math> for the development of positive tidal asymmetry is illustrated by observations that show a positive tidal asymmetry at spring tide and a negative tidal asymmetry at neap tide in the Pungue estuary (Mozambique; Nzualo et al., 2018<ref> Nzualo, T.N.M., Gallo, M.N. and Vinzon, S.B. 2018. Short-term tidal asymmetry inversion in a macrotidal estuary (Beira, Mozambique). Geomorphology 308: 107–117</ref>) and the [[Morphology of estuaries#Charente estuary|Charente estuary]] (France; Toublanc et al., 2015<ref> Toublanc, F., Brenon, I., Coulombier, T. and LeMoine, O. 2015. Fortnightly tidal asymmetry inversions and perspectives on sediment dynamics in a macrotidal estuary (Charente,France). Continental Shelf Res. 94: 42–54</ref>). This is illustrated in Fig. 8 for the [[Morphology of estuaries#Charente estuary|Charente estuary]], by comparing the curves for tidal elevation and current velocity for springtide (large <math>a/h</math>) and neap tide (small <math>a/h</math>). During springtide the tidal rise is much steeper than for neap tide. The maximum flood current velocity is larger than the maximum ebb tidal velocity for springtide, while the opposite holds for neap tide. <br />
<br />
[[Image:TidalAsymmetryEstuaries.jpg|right|350px|thumb|Figure 9: The relative tidal amplitude <math>a/h</math> and corresponding relative difference between HW and LW propagation speed <math>\Delta c / c</math> for different estuaries, derived from tide gauge stations in the converging part of the estuary. Adapted from<ref name=Dr></ref>.]]<br />
<br />
In Fig. 9 the relative difference between HW and LW propagation speeds <math>\Delta c / c = 2(c^+ -c^-)/(c^++c^-)</math> are compared for estuaries with different relative tidal amplitude <math>a/h</math>. The figure shows a positive correlation between <math>\Delta c / c </math> and <math>a/h</math>. Although <math>a/h</math> is the most important parameter, other factors also influence the relation between <math>a/h</math> and <math>\Delta c / c</math>, such as <math>K_0</math> and <math>K_c</math> (representing depth <math>h</math>, convergence length <math>L_b</math> and friction parameter <math>r</math>), the relative intertidal area <math>\Delta b/b_C</math> and the mean river discharge <math>Q_R</math>. The dependence of <math>\Delta c / c </math> on <math>a/h</math> is therefore different for each estuary. <br />
<br />
Equation (20) yields an estimate for the location <math>x</math> where the high-water wave crest overtakes the low-water wave trough, assuming that the tide at the estuarine mouth (<math>x=0</math>) is approximately symmetric and assuming that Eq. (20) remains approximately valid in the strong nonlinear case. For an estuary with small intertidal areas the distance <math>x</math> is given by<br />
<br />
<math>x = \Large\frac{\pi}{4}\frac{gh^2}{\omega r L_b}\frac{h}{a}\normalsize . \quad \quad (21)</math><br />
<br />
For example, in the case of the Gironde-Garonne estuary and tidal river system (<math>a \approx 2.5 m, h \approx 8 m, L_b \approx 35 km, r \approx 0.0025 m/s</math>) we find <math>x \approx 130 </math> km from the estuarine mouth, which is close to the location where a tidal bore is often observed. This example shows that a tidal bore will form if (1) frictional damping of the tidal wave is compensated by the funneling effect of width convergence and (2) the distance over which the tide can propagate into the estuary is sufficiently long (see also the article [[Tidal bore dynamics]]). <br />
<br />
In estuaries where frictional damping is not compensated by the funneling effect of width convergence, tidal asymmetry is generated in a similar way by the nonlinear processes described above (Friedrichs and Madsen, 1992<ref> Friedrichs, C.T. and Madsen, O.S. 1992. Non-linear diffusion of the tidal signal in frictionally dominated embayments. J.Geophys.Res. 97: 5637-5650</ref>). However, reduction of the relative tidal amplitude <math>a/h</math> by damping of the tidal wave may prevent tidal bore development. <br />
<br />
<br />
==Morphology of estuaries with tidal rivers==<br />
<br />
In a converging (funnel-shaped) estuary with strong friction, the tidal velocity <math>u</math> is mainly determined by the water surface slope <math>\partial \zeta /\partial x</math>, according to Eq. 12. A short period of tidal rise compared to the period of tidal fall implies steeper water surface slopes during flood than during ebb, with maximum flow rates that are higher during flood than during ebb, as illustrated in Fig. 8. Therefore, sediment fluxes during flood tide are higher than sediment fluxes during ebb tide, resulting in a net import of sediment into the estuary. As shown before, flood dominance increases with increasing relative tidal amplitude <math>a/h</math>. Tidal asymmetry develops as the tidal wave propagates in the estuary. The time it takes for the tidal wave to propagate through the convergent part of the estuary, <math>L_b/c</math>, is therefore another factor that determines the degree of tidal asymmetry. A corresponding nondimensional quantity that is indicative of the time duration for tidal asymmetry development is given by <math>L_b \omega/ c</math>. It can thus be expected that the strength of flood dominance is positively correlated with the nondimensional parameter <math>a L_b \omega / (h c)</math>. <br />
<br />
[[Image:TidalAsymmetryRiverFlow.jpg|right|400px|thumb|Figure 10: Tidal asymmetry indicator versus river flow indicator for different estuaries. A positive correlation is an indication that river flow makes an important contribution to compensating for tide-induced sediment import. The spread in the data is related to many other factors that influence sediment import/export. Adapted from <ref name=D17>Dronkers, J. 2017. Convergence of estuarine channels. Continental Shelf Res. 144: 120–133</ref>.]]<br />
<br />
Infill of estuaries is limited by sediment export through river flow, although dredging may also play a role. The influence of river flow on sediment export can be represented by the nondimensional parameter <math>Q_R/Q_{tide}</math>, where <math>Q_R</math> is the mean river discharge and <math>Q_{tide}</math> the maximum tidal discharge in the mid-estuarine zone (<math>x \approx L_b/2</math>). For estuaries in morphological equilibrium, sediment import due to tidal asymmetry (flood dominance) should be approximately balanced by export due to river flow. Comparing different estuaries one may thus expect a positive correlation between the parameters <math>a L_b \omega / (h c)</math> and <math>Q_R/Q_{tide}</math>. As shown in Fig. 10, such a positive correlation exists, although the spread in the data is large. This spread can be due to many other factors, which influence sediment import and export in different ways. Possible important factors are<ref name=Dr/>:<br />
* nonequilibrium morphology, due to dredging and other engineering interventions, <br />
* import/export by [[Morphology of estuaries#Wave-dominated systems|wave activity]],<br />
* import by [[estuarine circulation]],<br />
* sediment recirculation in ebb/flood-channel cells,<br />
* sediment import/export related to settling and erosion time lags,<br />
* fluvial sediment supply,<br />
* influence of intertidal areas on tidal asymmetry,<br />
* type of [[Coastal and marine sediments|sediment]].<br />
A mathematical derivation of the relation between <math>L_b \omega/ c</math> and <math>Q_R/Q_{tide}</math> is given in Dronkers (2017<ref name=D17/>).<br />
<br />
<br />
==Symbols==<br />
{| class="wikitable"<br />
|-<br />
! Variable !! Description !! Variable !! Description !! Variable !! Description <br />
|-<br />
| <math>a</math> || tidal amplitude || <math>D</math> || total depth ||<math>q_s</math>|| sediment transport<br />
|-<br />
| <math>b</math> || tide-mean width at the inlet (<math>x=0</math>) || <math>F</math>|| friction term ||<math>r</math>|| coefficient linear friction <br />
|-<br />
| <math>\Delta b</math>|| tide-mean with intertidal zone at the inlet|| <math>g</math>|| gravitational acceleration ||<math>t</math>|| time<br />
|-<br />
| <math>b_C</math>|| tidal channel width at the inlet (<math>x=0</math>) || <math>h</math>|| tide-mean depth ||<math>T</math>|| semidiurnal tidal period<br />
|-<br />
| <math>B</math> ||total estuary width || <math>HW</math>|| high water ||<math>x</math>|| longitudinal coordinate<br />
|-<br />
| <math>B_C</math> || tidal channel width || <math>K_0</math>|| dimensionless wavenumber ||<math>\gamma</math>|| skewness coefficient<br />
|-<br />
| <math>B_I</math> || width intertidal zone || <math>K_c</math>|| dimensionless damping number||<math>\zeta</math>|| tide elevation<br />
|-<br />
| <math>c</math> || tidal wave celerity || <math>L</math>|| tidal wavelength ||<math>\mu</math>|| damping coefficient<br />
|-<br />
| <math>\Delta c</math> || difference HW - LW propagation speeds || <math>L_b</math>|| estuary convergence length ||<math>\omega</math>|| semidiurnal radial frequency<br />
|-<br />
| <math>c_D</math> || friction coefficient || <math>LW</math>|| low water ||<math> < …> </math>|| tide averaged value<br />
|}<br />
<br />
<br />
==Related articles==<br />
:[[Morphology of estuaries]]<br />
:[[Ocean and shelf tides]]<br />
:[[Tidal bore dynamics]]<br />
:[[Tidal motion in shelf seas]]<br />
:[[Estuarine circulation]]<br />
:[[Estuarine turbidity maximum]]<br />
:[[River tides]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
==Appendix==<br />
<br />
The solution of the first order linear equations (3) is <br />
<br />
<math>\zeta^{(1)}=a \cos \theta , \; u^{(1)} = \Large\frac{c_0}{h}\normalsize \zeta^{(1)} , \quad \quad (A1)</math> <br />
<br />
where <math>\theta= k_0 x-\omega t</math>, <math>\omega</math> is the M2 radial frequency, the wave number <math>k_0 = \Large \frac{\omega}{gh} \normalsize</math> and <math>c_0=\omega / k</math>. <br />
<br />
After substitution in the nonlinear terms we have a linear equation for the second order approximation. The solution is<br />
<br />
<math>\zeta = \zeta^{(1)} + \zeta^{(2)} , \quad \zeta^{(2)} = \Large \frac{3 a^2}{4h}\normalsize kx \sin 2 \theta . \quad \quad (A2)</math><br />
<br />
The location of the wave crest at time <math>t</math> is called <math>x^+(t) </math>. At this location the surface slope is zero:<br />
<br />
<math> \Large \frac{\partial \zeta}{\partial x}\normalsize (x^+(t),t) = -a k_0 \sin \theta^+ + \Large \frac{3 a^2}{4h}\normalsize k_0 [ \sin 2 \theta^+ + 2kx \cos 2 \theta^+ ] = 0 \quad </math> , with <math>\theta^+=k_0 x^+(t) - \omega t . \quad \quad (A3)</math><br />
<br />
Because <math>a/h</math> is small, the wave crest is at a location where <math>\theta^+</math> is small (<math>|\theta^+ |<<1</math>). An approximate expression for the location of the wave crest is then given by <br />
<br />
<math>x^+(t) \approx (1 - \Large \frac{3a}{2h}\normalsize)^{-1} c_0 t . \quad \quad (A4)</math>. <br />
<br />
The propagation speed of the HW wave crest (Eq. A4) follows from <math>c^+(t)=dx^+/dt</math>. <br />
<br />
The first order linear equations (9) and (10) can be solved by eliminating <math>u(x,t)</math>, yielding a diffusion equation for the tidal elevation <math>\zeta(x,t)</math>:<br />
<br />
<math>\Large\frac{\partial \zeta^{(1)} }{\partial t}\normalsize = \Large\frac{h^2}{r}\frac{B_C}{<B>}\frac{\partial^2 \zeta^{(1)} }{\partial x^2}\normalsize .\quad \quad (A5)</math><br />
<br />
The first order solution is<br />
<br />
<math>\zeta^{(1)} = \Large\frac{1}{2}\normalsize a e^{i (\kappa x - \omega t)} + c.c. </math>, <br />
where <math>c.c. </math> is the complex conjugate, <math>\kappa = k+i \mu , \; k = \mu = \sqrt { \Large \frac{\omega r}{2 g h^2}\normalsize }. \quad \quad (A6) </math><br />
<br />
After substitution in the nonlinear terms the second order linear equations can be solved, yielding<br />
<br />
<math>\zeta = \zeta^{(1)} + \zeta^{(2)} , \quad \zeta^{(2)} \approx a (\Large \frac{\Delta b}{8 B_C} - \frac{a}{2h}\normalsize) e^{-2i \omega t} (e^{2i \kappa x} -e^{\sqrt{2} i \kappa x} ) + \Large \frac{a^2}{4h}\normalsize (1 – e^{-2 \mu x}) + c.c. \quad \quad (A7)</math><br />
<br />
In the same way as before the location of the wave crest is derived from the condition <br />
<br />
<math> \Large \frac{\partial \zeta}{\partial x} \normalsize (x^+(t),t) =0</math> for <math>| k x^+ - \omega t|<< 1</math>. <br />
<br />
The propagation speed of the HW wave crest (Eq. 13) follows from <math>c^+(t)=dx^+/dt</math>; this expression holds for the lower portion of the estuary where <math>kx<<1</math>.<br />
<br />
<br />
<br />
<br />
{{2Authors<br />
|AuthorID1=120<br />
|AuthorFullName1=Job Dronkers<br />
|AuthorName1=Dronkers J<br />
|AuthorID2=15152<br />
|AuthorFullName2= Philippe Bonneton <br />
|AuthorName2= Bonneton P<br />
}}<br />
<br />
<br />
[[Category: Physical coastal and marine processes]]<br />
[[Category: Estuaries and tidal rivers]]<br />
[[Category: Morphodynamics]]<br />
[[Category:Hydrodynamics]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Strand_plainStrand plain2024-01-13T10:25:36Z<p>Dronkers J: </p>
<hr />
<div><br />
<br />
<br />
{{ Definition| title = Strand plain<br />
| definition = Strand plains are shoreline deposits of coastal plains that form progradational terraces or step-like constructional terraces<ref>Price, W.A. 1955. Environment and formation of the chenier plain. Quaternaria 2: 75-86</ref>.<br />
}}<br />
<br />
==Notes==<br />
Strand plains are large accretions that form on wave-dominated coasts which are abundantly supplied with sediment from nearby rivers. The accretions typically take the form of several shore-parallel belts. Examples are the beach-ridge or dune-ridge plain (Figure 1) and the [[chenier]] plain (Figure 2). A beach-ridge plain forms if the supplied sediment is sandy; dune ridges are formed by aeolian sand transport. A [[chenier]] plain forms if the supplied sediment consists of mud.<br />
<br />
{| border="0" align="center"<br />
|-<br />
| valign="top"|<br />
[[File:DuneBeltsGoeree2005.jpg|thumb|left|500px|Fig. 1. Strand plain of dune ridges that has developed after construction of the Rhine discharge sluices ('Haringvliet sluices') in 1970. Photo credit Rens Jacobs, Beeldbank Rijkswaterstaat.]]<br />
| valign="top"|<br />
[[File:ChenierPlainGuinee.jpg|thumb|left|400px|Fig. 2. Chenier plain in Guinée (West Africa). Image Google Earth.]]<br />
|}<br />
<br />
<br />
==References==<br />
<references/></div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/Seabed_armoringSeabed armoring2023-12-30T14:08:07Z<p>Dronkers J: </p>
<hr />
<div><br />
{{Definition|title=Seabed armoring<br />
|definition= The formation of a top layer of coarse sediment hiding and protecting a sublayer of finer sediment from erosion.}}<br />
<br />
<br />
==Seabed armoring processes==<br />
Seabed armoring is a ubiquitous process in coastal environments where the sediment bed contains a mixture of fine- and coarse-grained sediment particles (so-called ''''graded sediment'''').<br />
<br />
<br />
An erosion-resistant top layer of coarse sediment is formed when fine sediment is removed from the seabed surface by the scouring action of energetic waves. This coarse sediment top layer hides the underlying layer of finer sediments, which is therefore protected from scouring.<br />
<br />
<br />
Wave action can produce seabed armoring also by 'inverse grading', a process called 'kinematic vertical sorting' <ref>Legros, F. 2002. Can dispersive pressure cause inverse grading in grain flows? Journal of Sedimentary Research 72: 166– 170</ref>. This phenomenon has been observed, for example, in the case of wave-induced [[sheet flow]]<ref>Hassan, W.N. and Ribberink, J.S. 2005. Transport processes of uniform and mixed sands in oscillatory sheet flow. Coastal Engineering 52: 745– 770</ref>. When wave-induced interparticle collisions lift a coarse sediment particle from its position in the soil matrix, the vacated pore space is readily filled with finer particles, preventing the coarse particle from bouncing back. The coarsest sediments are therefore lifted gradually to the seabed surface, leading to the formation of an armor top layer covering a sublayer of finer sediment. This armor layer inhibits suspension of the fine sediments, whilst the coarser particles are more exposed and set in motion more easily. The term 'inverse grading' points to the intuition that one would expect a layer of fine sediment at the surface rather than beneath it.<br />
<br />
<br />
If the seabed is loosely packed and poorly drained, wave action can induce soil liquefaction. Il this case coarser particles will sink, rather than being uplifted - see [[wave-induced soil liquefaction]]. <br />
<br />
<br />
<br />
==Related articles==<br />
:[[Coastal and marine sediments]]<br />
:[[Shoreface profile]]<br />
:[[Sediment deposition and erosion processes]]<br />
:[[Sand transport]]<br />
<br />
<br />
==References==<br />
<references/><br />
<br />
<br />
{{author<br />
|AuthorID=120<br />
|AuthorFullName=Job Dronkers<br />
|AuthorName=Dronkers J}}<br />
<br />
<br />
[[Category:Physical coastal and marine processes]]<br />
[[Category:Sediment]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/HyMap:_Hyperspectral_seafloor_mapping_and_direct_bathymetry_calculation_in_littoral_zonesHyMap: Hyperspectral seafloor mapping and direct bathymetry calculation in littoral zones2023-12-07T11:10:08Z<p>Dronkers J: Created page with " {{Review |name=Job Dronkers|AuthorID=120| }} This article describes how the technique "hyperspectral sensing", an example of a remote sensing technique can be used to g..."</p>
<hr />
<div><br />
{{Review<br />
|name=Job Dronkers|AuthorID=120|<br />
}}<br />
<br />
<br />
This article describes how the technique "hyperspectral sensing", an example of a [[remote sensing]] technique can be used to generate maps of the seafloor and the [[bathymetry]] in littoral zones (up to 20 m.). This technique is also called Hyperspectral Mapping (HyMap). This article describes experiences with HyMap in Western Australia and explains how HyMap can also be applied in other areas as well. <br />
<br />
==Introduction==<br />
===What is Hyperspectral mapping?===<br />
Hyperspectral sensing allows us to view the earth not only in a few, but quasi-continuous in up to hundreds of different spectral channels over a wide wavelength range and to map the surface composition based on the spectral signatures observed. <br />
Hyperspectral methods facilitate to discriminate more independent environmental variables than multi-spectral methods. This is demonstrated in this article, where sea floor properties and [[bathymetry]] for littoral zones up to 20m are mapped simultaneously from HyMap (Hyperspectral Mapping) airborne spectrometer [[data]]. Atmospheric properties, surface and [[sun glitter]] effects are corrected as well as the optical properties of water constituents in the water bulk. The physics based modular inversion and processing system MIP enables to derive these properties in very different environments such as coastal zones or inland waters. <br />
<br />
===Experiences with Hyperspectral Mapping===<br />
HyMap data was collected for several aquatic R&D projects in Western Australia and Germany: over the Ningaloo Reef, near Yardie Creek, in N-WA, another over Rottnest Island near Perth and over Lake Constance in Germany. The standardized products allow seamless mosaicking of multiple flight lines and demonstrate a high level of accuracy compared to conventional mapping methods. Furthermore they provide 100 % coverage and results on a pixel by pixel base compared to interpolated results derived from line profiling methods. <br />
However, so far only hyperspectral airborne sensors such as AISA, ARES, CASI, HYMAP or ROSIS accomplish data rates that facilitates a spatial resolution in the range of few meters, that are needed for most coastal applications. Moreover, for operational physics based and ground truth independent methods we especially rely on well calibrated and radiometric stable sensor data.<br />
<br />
===Application===<br />
The Australian built HyMap [[sensors|sensor]] <br />
<ref> Cocks, T.D., R. Jenssen, A. Stewart, I. Wilson and T. Shields, 1998. The HyMap Airborne Hyperspectral Sensor: The System, Calibration and Performance, Proceedings of 1st EARSEL Workshop on Imaging Spectroscopy, Zurich </ref><br />
with its 126 spectral channels covering the VIS-SWIR (0.45 to 2.5 um) spectral regions was initially designed for the mineral exploration market, but has since conquered many other areas, water remote sensing being one of them. It allows airborne RS data to be collected with a spatial resolution of down to 3m pixel size and collects the spectral information in one optical path. <br />
The high signal to noise ratio allows for good spectral discrimination in the visible wavelength region and the added SWIR spectral modules allow for improved sun-glint removal techniques to be applied. By having the SWIR spectral region available, we also can extend the applications area to the bordering land component of the survey area and provide seamless analysis for the catchment and drainage areas affecting the water body being investigated. <br />
<br />
Aquatic HS applications such as the mapping of sea floor substrate, submerged vegetation, [[bathymetry]] and water constituents are the dominating examples in the marine and limnological environments. However general environmental monitoring and pollution mapping are also important aspects in the aquatic areas of interest. With the ongoing concerns about the quality and availability of drinking water and the status of marine life and habitats, HS mapping offers a unique opportunity to provide ‘state of the art’ baseline mapping and best practise for regular monitoring of environmentally high sensitive areas. <br />
<br />
==Generation of output==<br />
<br />
===Modular Inversion and processing system MIP===<br />
The generation of thematic products for aquatic systems from calibrated HyMap radiances is performed using the Modular Inversion and Processing System MIP (Heege et al, 2003<br />
<ref> Heege, T. , Häse, C. , Bogner, A., Pinnel, N. , 2003. Airborne Multi-spectral Sensing in Shallow and Deep Waters. Backscatter 1/2003: 17-19 </ref>, Heege & Fischer, 2004<ref> Heege, T., Fischer, J., 2004. Mapping of water constituents in Lake Constance using multi-spectral airborne scanner data and a physically based processing scheme. Can. J. Remote Sensing, Vol. 30(1): 77–86 </ref>, that is developed by the German Aerospace Center, the Technical University of Munich and EOMAP GmbH & Co.KG.<br />
MIP is designed for the physically based recovery of hydro-biological parameters from multi- and hyperspectral remote sensing data and used for the environmental mapping of aquatic shallow and deep waters of inland waters, coastal zones and wetlands. The architecture of the program binds a set of general and transferable computational schemes in a chain, connecting bio-physical parameters with the measured sensor radiances. <br />
The physical background of the hyperspectral and full transferable system incorporates the Finite Element Method for forward calculations of the radiative transfer in a multilayer atmosphere-ocean system (Kisselev & Bulgarelli, 2004)<ref> Kisselev, V. and Bulgarelli, B., 2004. Reflection of light from a rough water surface in numerical methods for solving the radiative transfer equation. J. Quant. Spectrosc. Ra., 85:419–435.</ref> . It is used for the atmospheric-, [[sun glitter]]-, water surface- and Q-factor -correction of the underwater light field as explained in Heege & Fischer (2004<ref> Heege, T., Fischer, J., 2004. Mapping of water constituents in Lake Constance using multi-spectral airborne scanner data and a physically based processing scheme. Can. J. Remote Sensing, Vol. 30(1): 77–86</ref>).<br />
The different program modules support transferable algorithms. The adjustment of algorithms to sensor specifications and recording conditions is supported automatically in MIP. The inversion itself is based on a spectral matching technique.<br />
With data from the hyperspectral sensor HyMap, all essential information needed for the data processing can be extracted from the hyperspectral signal itself. Additional ground truth measurements are not needed due to the high calibration accuracy and signal sensitivity of the sensor on one side and the completely physically based structure of MIP on the other side. However, the final quantitative values of the data product bathymetry can be improved by adjusting the scaling factors by use of few ground control points. <br />
Program modules of MIP used here provide the retrieval of aerosols, pixel by pixel [[sun glitter]] correction, atmosphere and water surface corrections, retrieval of water constituents in optically deep waters, water column correction and the classification of substrates such as coral reef, seagrass vegetation and bottom sediments (Heege et al, 2004<ref> Heege, T., Bogner, A., Pinnel, N., 2004. Mapping of submerged aquatic vegetation with a physically based process chain. Remote Sensing of the Ocean and Sea Ice 2003. Editors: Charles R. Bostater, Jr. & Rosalia Santoleri. Proc. of SPIE Vol. 5233 pp. 43-50</ref>, Heege et al, 2007<ref> Heege, T., Hausknecht, P, Kobryn, H. (2007): Hyperspectral seafloor mapping and direct bathymetry calculation using HyMap data from the Ningaloo reef and Rottnest Island areas in Western Australia. Proceedings 5th EARSeL Workshop on Imaging Spectroscopy. Bruges, Belgium, April 23-25 2007, p. 1-8</ref>).<br />
The processing system has been tested and validated in many surveys over German inland waters and Australian coastal zones, that were performed by airborne and satellite sensors.<br />
<br />
===Atmospheric and sun glitter correction===<br />
Aerosol concentrations are retrieved using a coupled inversion procedure of atmospheric properties and water constituents according to Miksa et al (2006<ref>Miksa, S., Haese, C. & Heege, T. (2006): Time series of water constituents and primary production in Lake Constance using satellite data and a physically based modular inversion and processing system. Proc. Ocean Optics Conf. XVIII Oct.9-13, pp.10</ref>).<br />
The [[sun glitter]] correction algorithm corrects the sun glint radiance individually for each pixel and also accounts for coupled bidirectional atmospheric effects. The resulting sun glint free radiances at sensor altitude are converted into subsurface reflectances. A bi-directional correction for the underwater light field is applied by use of the so called Q- database of MIP.<br />
[[Image:atmospheric_sunglitter_corr_Ningaloo800.jpg |thumb|700px|left| Atmospheric & sunglitter corrected HyMap data of Ningaloo Reef, Yardie Creek Australia, 2005]]<br />
<br />
===Bathymetry and sea floor mapping===<br />
The transformation of subsurface reflectance to the bottom albedo is done here based on the equations published by Albert and Mobley (2003<ref> Albert, A.and Mobley, C.D., 2003. An analytical model for subsurface irradiance and [[remote sensing]] reflectance in deep and shallow case-2 waters. Optics Express, vol. 11, pp. 2873-2890.</ref>).<br />
The unknown input value of depth is calculated iteratively in combination with the spectral unmixing of the respective bottom reflectance. The unmixing procedure produces the sea floor coverage of three main bottom components and the residual error between the model bottom reflectance and the calculated reflectance. The final depth, bottom reflectance and bottom coverage is achieved at the minimum value of the residual error. The final step of the thematic processing classifies the bottom reflectance due to the spectral signature of different bottom types and species using a Fuzzy Logic method and assignment of individual probability functions for each defined sea floor component. <br />
Having ‘input’ spectra into the algorithm of the ‘to be expected’ or ‘actually present’ sea floor cover components will improve the end results as demonstrated by Pinnel (2007<ref> Pinnel, N., 2007. A method for mapping submerged macrophytes in lakes using hyperspectral remote sensing. PhD thesis Technical University Munich. pp. 191</ref>)<br />
for macrophyte species in inland waters.<br />
[[Image:Bathymetry_Ningaloo800.jpg |thumb|700px|left| Bathymetric map of Ningaloo Reef as processed from HyMap data]]<br />
<br />
==Application examples==<br />
Examples of application of HyMap are given for two area's in Western Australia (WA). Experiences have also been with HyMap in Lake Constance (Germany), Adriatic Sea nearby Brindiy (Italy) <br />
<br />
=== Rottnest Island, WA ===<br />
==== Location description ====<br />
Rottnest Island is a marine reserve which lies 20km offshore of Perth. It has a subtropical climate and due to the south flowing warm Leeuwin Current many tropical as well as temperate marine species are found here. Many marine organisms are considered as isolated, at their southernmost extent (xiii). The marine reserve is mostly in shallow (less than 20m depth) and is made up by the following main categories of habitats: sand, seagrass mixed seagrass and reef, reef, intertidal platform and reef wash. The largest area is made up by the reef habitat (~45%), followed by sand (20%) and seagrass (21%) (xiv). The island also has important but not extensive cover of coral communities. [[Bathymetry]] of the waters surrounding Rottnest Island is quite varied, owing to the presence of many submerged limestone formations, favourite spots for divers and snorkellers. Waters along the west coast of WA are generally nutrient poor and low in turbidity which makes them ideal for optical remote sensing methods. The ability of the environmental management agencies to sustainably manage marine parks is closely linked to the availability of basic data sets such as high resolution marine habitat maps and bathymetry.<br />
<br />
==== HYMAP data ====<br />
A HyMap survey was flown in 2004 as part of a joint R&D effort over Rottnest Island and 4 data lines collected with a spatial resolution of 3.5 m pixel size. Three of these four HyMap lines can be seen in Figure 2 both uncorrected and corrected and mosaiced seamlessly. As part of the HyVista standard processing water and land surfaces can be separated and processed independently. Bathymetry was calculated for all three lines and a comparison with echo sounding showed quite good results (see detail in figure 3). The average retrieved RMS is 20% and lower in waters 0 to 15m depth. Depth retrieved from deeper areas (>15m) has a fairly constant but larger error. For other data sets (e.g. the Ningaloo data), even deeper areas up to 20m are generating stable, consistent results. However, the final quantitative values of the bathymetric data product had to be improved by simple adjusting the scaling by use of few ground control points. Further results and a detailed analysis of the Rottnest Island data will be published in an upcoming PhD thesis by Harvey (xv). <br />
<br />
=== Ningaloo reef, WA ===<br />
==== Location description ====<br />
The Ningaloo Reef is over 300km long and is the longest fringing reef in Australia. It is located some 1200km north of Perth and spans two bioregions: Ningaloo Bioregion and the Pilbara Bioregion (xvi) Most of the area is now protected within the recently declared marine park. Many varied substrate types and oceanographic conditions support diverse and unique habitats and high species richness. Barrier enclosing the lagoon shelters the waters and allowed for development of extremely varied coral colonies. This lagoon is mostly shallow (< 20m) and varies in width between 200m to less than 7km. <br />
The climate of the area is arid with less than 300mm of rain, mostly in summer during cyclone season. Biota of Ningaloo area are high in species richness and many of the species are endemic. Some 200 species of coral, 600 species of molluscs and 500 species of fish occur in the area. The area is also very important for turtles, dugongs, whale sharks, and manta rays. <br />
Because of its high biodiversity and relatively easy access to the reef, this region has recently seen an expansion in tourism as well as recreational fishing. These pressures are combined with a number of oil and gas production facilities in the region and have added urgency for the management agencies to devise plans to protect and conserve the environment (xvii). While broad marine habitat maps exist; there is an urgent need for high-resolution bathymetry and improved mapping of shallow water habitats. Hyperspectral [[remote sensing]] offers unique opportunity to provide these basic data sets and to help manage this fragile environment. <br />
<br />
==== HYMAP data ====<br />
A HyMap survey was flown in 2005 as part of a HyVista sponsored R&D effort over the Ningaloo reef area near Yardie Creek (visible in the central area in Figure 4) and 3 data lines collected with a spatial resolution of 3.2 m pixel size. A colour composite of the survey lines can be seen in Figure 4 both uncorrected (left) and corrected and mosaiced seamlessly (right).<br />
Figure 5 shows some of the processing results derived with the MIP software. On the left hand image a three channel colour composite of the resulting bottom reflectance is displayed. It is normalized and the influence of both water depth and water body properties are corrected for. One can think of it as if we would look at the seafloor and all the water above is removed, looking directly at it. The right hand image shows a group classification result of the seafloor bottom coverage. The colour composite displays sediments in red, vegetation components in green and remaining benthic substrates in blue colours, with mixtures coloured according to the chart in Figure 5. This product is already a very powerful demonstration of what we can achieve with hyperspectral sensing over coral reef areas, since even such a simple classification in high detail and accuracy can not be obtained easily with other methods. <br />
Even more impressive is the determination of detailed bathymetry over the whole survey area calculated independently for each flight line and mosaicked seamlessly over the entire survey area. Note the spatial resolution of the HyMap data is here 3.2 m pixel size. The bathymetry values range from 0.1 up to 25m. Depth differences of 10cm can clearly be identified in the shallow water regions at the reef. Obvious errors are sparsely distributed and visible in the region of breaking waves and white caps. Hence doing such a survey in calm sea state conditions is desirable. <br />
<br />
An enlarged and north orientated section of the right hand display of Figure 6 can be seen in Figure 7, emphasising again the high spatial resolution by showing the fast drop off at the outer reef area and distribution of large coral ‘bombies’ in shallower sections of the Ningaloo reef. <br />
<br />
==Conclusions about the application of HyMap==<br />
HyMap works well for coral reef applications, even if no additional field data are available. [[Bathymetry]] was determined successfully with a relative error of 20% up to a depth of 15 in comparison with echo sounding data (where the error bar was unknown). Further data products such as the sea floor coverage of the main components give reasonable results, but could not be compared with ground truth measurements up to now. The same does apply for the final fuzzy logic classification: Knowledge about the specific spectral characteristics of different vegetation species, coral reef habitats and sediments can be directly transformed to an extensive classification result of the whole reef area using this procedure.<br />
<br />
The MP data processing is stable, applicable for extensive mappings and worldwide transferable. Comparable results with exactly the same standardized processing procedure were retrieved for several inland waters such as Lake Starnberg or Lake Constance in Germany. The validation here confirms the good results for bathymetry (RMS of 0.15m in Lake Constance), main bottom classes (accuracy of 73%) and vegetation species (Pinnel, 2007). <br />
<br />
Based on our HyMap results, the Australian Institute for Marine Science commissioned a large scale HyMap survey in April 2006 covering the whole Ningaloo Marine Park with 65 flight lines at 3.5 m spatial resolution covering about 3500 sqkm in total (xviii), the largest such survey ever undertaken. This data will provide basic data sets such as bathymetry and high resolution habitat information in a collaborative research project “Reef use, biodiversity and socio-economics for integrated management strategy evaluation of Ningaloo”.<br />
<br />
==See also==<br />
===Internal links===<br />
* [[Argus video monitoring system]]<br />
* [[Use of aerial photographs for shoreline position and mapping applications]]<br />
* [[Satellite-derived nearshore bathymetry]]<br />
* [[Light fields and optics in coastal waters]]<br />
* [[Optical remote sensing]]<br />
* [[Use of X-band and HF radar in marine hydrography]]<br />
<br />
==References==<br />
<references/><br />
<br />
----<br />
<br />
{{author<br />
|AuthorID=12962<br />
|AuthorFullName=Thomas Heege<br />
|AuthorName=Thomas Heege}}<br />
<br />
[[Category:Coastal and marine observation and monitoring]]<br />
[[Category:Observation of physical parameters]]</div>Dronkers Jhttps://www.marinespecies.org/introduced/wiki/SunglintSunglint2023-12-07T11:00:54Z<p>Dronkers J: </p>
<hr />
<div>{{Definition|title=Sunglint<br />
|definition= Sunglint (also called sun glitter) is defined as (spatial usually very variable) the contribution of direct sunlight that is reflected at the water surface and substantially increases the intensity of radiance measured at the sensor. It appears at specific geometric recording conditions between sun, the water surface, and the sensor, and affects approximately 30-70% of all earth observation images. See also [[remote sensing]].<br />
}}<br />
<br />
==See also==<br />
* [[Optical remote sensing]]<br />
* [[HyMap: Hyperspectral seafloor mapping and direct bathymetry calculation in littoral zones]]</div>Dronkers J