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A method to quantify water quality change in data-limited estuaries

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dc.contributor.author Taljaard, Susan
dc.contributor.author Lemley, DA
dc.contributor.author Van Niekerk, Lara
dc.date.accessioned 2022-06-24T08:01:22Z
dc.date.available 2022-06-24T08:01:22Z
dc.date.issued 2022-08
dc.identifier.citation Taljaard, S., Lemley, D. & Van Niekerk, L. 2022. A method to quantify water quality change in data-limited estuaries. <i>Estuarine, Coastal and Shelf Science, 272.</i> http://hdl.handle.net/10204/12440 en_ZA
dc.identifier.issn 0272-7714
dc.identifier.issn 1096-0015
dc.identifier.uri https://doi.org/10.1016/j.ecss.2022.107888
dc.identifier.uri http://hdl.handle.net/10204/12440
dc.description.abstract Situated at the land-sea interface, estuaries are susceptible to land- and sea-based sources of pollution. Despite growing scientific evidence of deterioration in water quality, quantitative methods to translate such information in support of effective environmental management and planning practice is lacking, particularly in data-limited environments such as South Africa. Here we expand and improve on existing box model-type methods suitable for data-limiting environments, by providing greater spatial (zoning) and temporal (seasonal and exceedance patterns) resolution. In doing so, usefulness and repeatability for valuation of ecological or socio-economic responses are enhanced. Framed by three key design principles, the method comprises six main steps. First, hydrological simulations representative of long-term freshwater inflow patterns is generated, for example for natural, present, or potential future development or climate change scenarios. Then the estuary is divided into representative homogenous zones, to reduced spatial complexities while still allowing for some longitudinal resolution. Step three involves the identification of characteristic physical states linked to typical freshwater inflow patterns, followed by the development of water quality matrices depicting zonal distribution of biogeochemical properties representative of each physical state. Using the simulated monthly hydrological time series as input, the water quality matrices are then extrapolated over the longer period to derive seasonal- and exceedance distribution patterns for selected scenarios. A similarity index is applied to demonstrate how outputs could be aggregated into an overall water quality condition. Based on a real-world application, the method is considered useful as a systematic and transparent process to capture change in water quality in a digestible, quantitative manner in support of effective evidence-based management interventions. The method can be applied at any data resolution, but the confidence of outputs will depend on the amount and accuracy of available data to define hydrological simulations and to construct the water quality matrices. The method aligns well with existing methods applied in estuarine management in South Africa, and we pose this as a useful approach for application in estuaries with similar linear-like, shallow characteristics for example, along the North and South American temperate coasts, the Mediterranean and the Australian south-west and south-eastern coasts. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S0272771422001470 en_US
dc.source Estuarine, Coastal and Shelf Science, 272 en_US
dc.subject Estuarine management en_US
dc.subject Hydrology en_US
dc.subject Biogeochemistry en_US
dc.subject Spreadsheet model en_US
dc.subject Swartkops estuary en_US
dc.title A method to quantify water quality change in data-limited estuaries en_US
dc.type Article en_US
dc.description.pages 11pp en_US
dc.description.note © 2022 Elsevier Ltd. All rights reserved. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://www.sciencedirect.com/science/article/pii/S0272771422001470 en_US
dc.description.cluster Smart Places en_US
dc.description.impactarea Coastal Systems en_US
dc.identifier.apacitation Taljaard, S., Lemley, D., & Van Niekerk, L. (2022). A method to quantify water quality change in data-limited estuaries. <i>Estuarine, Coastal and Shelf Science, 272</i>, http://hdl.handle.net/10204/12440 en_ZA
dc.identifier.chicagocitation Taljaard, Susan, DA Lemley, and Lara Van Niekerk "A method to quantify water quality change in data-limited estuaries." <i>Estuarine, Coastal and Shelf Science, 272</i> (2022) http://hdl.handle.net/10204/12440 en_ZA
dc.identifier.vancouvercitation Taljaard S, Lemley D, Van Niekerk L. A method to quantify water quality change in data-limited estuaries. Estuarine, Coastal and Shelf Science, 272. 2022; http://hdl.handle.net/10204/12440. en_ZA
dc.identifier.ris TY - Article AU - Taljaard, Susan AU - Lemley, DA AU - Van Niekerk, Lara AB - Situated at the land-sea interface, estuaries are susceptible to land- and sea-based sources of pollution. Despite growing scientific evidence of deterioration in water quality, quantitative methods to translate such information in support of effective environmental management and planning practice is lacking, particularly in data-limited environments such as South Africa. Here we expand and improve on existing box model-type methods suitable for data-limiting environments, by providing greater spatial (zoning) and temporal (seasonal and exceedance patterns) resolution. In doing so, usefulness and repeatability for valuation of ecological or socio-economic responses are enhanced. Framed by three key design principles, the method comprises six main steps. First, hydrological simulations representative of long-term freshwater inflow patterns is generated, for example for natural, present, or potential future development or climate change scenarios. Then the estuary is divided into representative homogenous zones, to reduced spatial complexities while still allowing for some longitudinal resolution. Step three involves the identification of characteristic physical states linked to typical freshwater inflow patterns, followed by the development of water quality matrices depicting zonal distribution of biogeochemical properties representative of each physical state. Using the simulated monthly hydrological time series as input, the water quality matrices are then extrapolated over the longer period to derive seasonal- and exceedance distribution patterns for selected scenarios. A similarity index is applied to demonstrate how outputs could be aggregated into an overall water quality condition. Based on a real-world application, the method is considered useful as a systematic and transparent process to capture change in water quality in a digestible, quantitative manner in support of effective evidence-based management interventions. The method can be applied at any data resolution, but the confidence of outputs will depend on the amount and accuracy of available data to define hydrological simulations and to construct the water quality matrices. The method aligns well with existing methods applied in estuarine management in South Africa, and we pose this as a useful approach for application in estuaries with similar linear-like, shallow characteristics for example, along the North and South American temperate coasts, the Mediterranean and the Australian south-west and south-eastern coasts. DA - 2022-08 DB - ResearchSpace DP - CSIR J1 - Estuarine, Coastal and Shelf Science, 272 KW - Estuarine management KW - Hydrology KW - Biogeochemistry KW - Spreadsheet model KW - Swartkops estuary LK - https://researchspace.csir.co.za PY - 2022 SM - 0272-7714 SM - 1096-0015 T1 - A method to quantify water quality change in data-limited estuaries TI - A method to quantify water quality change in data-limited estuaries UR - http://hdl.handle.net/10204/12440 ER - en_ZA
dc.identifier.worklist 25772 en_US


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