Water quality modelling

Water quality modeling involves water quality based data using mathematical simulation techniques. Water quality modeling helps people understand the eminence of water quality issues and models provide evidence for policy makers to make decisions in order to properly mitigate water.[1] Water quality modeling also helps determine correlations to constituent sources and water quality along with identifying information gaps.[2] Due to the increase in freshwater usage among people, water quality modeling is especially relevant[3] both in a local level and global level. In order to understand and predict the changes over time in water scarcity, climate change, and the economic factor of water resources,[1] water quality models would need sufficient data by including water bodies from both local and global levels.

A typical water quality model consists of a collection of formulations representing physical mechanisms that determine position and momentum of pollutants in a water body.[4] Models are available for individual components of the hydrological system such as surface runoff;[5] there also exist basin wide models addressing hydrologic transport and for ocean and estuarine applications. Often finite difference methods are used to analyze these phenomena, and, almost always, large complex computer models are required.[6]

Building A Model

Water quality models have different information, but generally have the same purpose, which is to provide evidentiary support of water issues. Models can be either deterministic or statistical depending on the scale with the base model,[2] which is dependent on if the area is on a local, regional, or a global scale. Another aspect to consider for a model is what needs to be understood or predicted about that research area along with setting up any parameters to define the research. Another aspect of building a water quality model is knowing the audience and the exact purpose for presenting data like to enhance water quality management[7] for water quality law makers for the best possible outcomes.  

Formulations and associated Constants

Water quality is modeled by one or more of the following formulations
  • Advective Transport formulation
  • Dispersive Transport formulation
  • Surface Heat Budget formulation
  • Dissolved Oxygen Saturation formulation
  • Reaeration formulation
  • Carbonaceous Deoxygenation formulation
  • Nitrogenous Biochemical Oxygen Demand formulation
  • Sediment oxygen demand formulation (SOD)
  • Photosynthesis and Respiration formulation
  • pH and Alkalinity formulation
  • Nutrients formulation (fertilizers)
  • Algae formulation
  • Zooplankton formulation
  • Coliform bacteria formulation (e.g. Escherichia coli )

SPARROW Models

A SPARROW model is a SPAtially-Referenced Regression on Watershed attributes, which helps integrate water quality data with landscape information.[2] More specifically the USGS used this model to display long-term changes within watersheds to further explain in-stream water measurement in relation to upstream sources, water quality, and watershed properties. These models predict data for various spatial scales and integrate streamflow data with water quality at numerous locations across the US.[2] A SPARROW model used by the USGS focused on the nutrients in the Nation's major rivers and estuaries; this model helped create a better understanding of where nutrients come from, where they are transported to while in the water bodies, and where they end up (reservoirs, other estuaries, etc.).[2]

See also

References
  1. Tang, Ting; Strokal, Maryna; van Vliet, Michelle T.H.; Seuntjens, Piet; Burek, Peter; Kroeze, Carolien; Langan, Simon; Wada, Yoshihide (February 2019). "Bridging global, basin and local-scale water quality modeling towards enhancing water quality management worldwide". Current Opinion in Environmental Sustainability. 36: 39–48. Bibcode:2019COES...36...39T. doi:10.1016/j.cosust.2018.10.004.
  2. Preston, S.D. "SPARROW MODELING—Enhancing Understanding of the Nation's Water Quality". USGS – via US Dep of Interior.
  3. Bozorg-Haddad, Omid; Soleimani, Shima; Loáiciga, Hugo A. (July 2017). "Modeling Water-Quality Parameters Using Genetic Algorithm–Least Squares Support Vector Regression and Genetic Programming". Journal of Environmental Engineering. 143 (7): 04017021. doi:10.1061/(ASCE)EE.1943-7870.0001217. ISSN 0733-9372.
  4. Zhang, Wanshun; Wang, Yan; Peng, Hong; Li, Yiting; Tang, Jushan; Wu, K. Benjamin (February 2010). "A Coupled Water Quantity–Quality Model for Water Allocation Analysis". Water Resources Management. 24 (3): 485–511. doi:10.1007/s11269-009-9456-8. ISSN 0920-4741. S2CID 153922326.
  5. Vallet, B.; Muschalla, D.; Lessard, P.; Vanrolleghem, P.A. (2014-04-03). "A new dynamic water quality model for stormwater basins as a tool for urban runoff management: Concept and validation". Urban Water Journal. 11 (3): 211–220. doi:10.1080/1573062X.2013.775313. ISSN 1573-062X. S2CID 111045671.
  6. Liu, Yaoze; Li, Sisi; Wallace, Carlington W.; Chaubey, Indrajeet; Flanagan, Dennis C.; Theller, Lawrence O.; Engel, Bernard A. (September 2017). "Comparison of Computer Models for Estimating Hydrology and Water Quality in an Agricultural Watershed". Water Resources Management. 31 (11): 3641–3665. doi:10.1007/s11269-017-1691-9. ISSN 0920-4741. S2CID 158035959.
  7. Tang, Ting; Strokal, Maryna; Van Vliet, Michelle T.H.; Seuntjens, Piet; Burek, Peter; Kroeze, Carolien; Langan, Simon; Wada, Yoshihide (2019-02-01). "Bridging global, basin and local-scale water quality modeling towards enhancing water quality management worldwide". Current Opinion in Environmental Sustainability. 36: 39–48. Bibcode:2019COES...36...39T. doi:10.1016/j.cosust.2018.10.004. ISSN 1877-3435.
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