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Water quality modelling in the San Antonio River Basin driven by radar rainfall data

机译:雷达降雨数据驱动的圣安东尼奥河流域水质模型

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Continuous monitoring of stream water quality is needed as it has significant impacts on human and ecological health and well-being. Estimating water quality between sampling dates requires model simulation based on the available geospatial and water quality data for a given watershed. Models such as the Soil and Water Assessment Tool (SWAT) can be used to estimate the missing water quality data. In this study, SWAT was used to estimate water quality at a monitoring station near the outlet of the San Antonio River. Precipitation data from both rain gauges and weather radar were used to force the SWAT simulations. Virtual rain gauges which were based on weather radar data were created in the approximate centres of the 163 sub-watersheds of the San Antonio River Basin for SWAT simulations. This method was first tested in a smaller watershed in the middle of the Guadalupe River Basin resulting in increased model efficiency in simulating surface run-off. The method was then applied to the San Antonio River watershed and yielded good simulations for surface run-off ( R 2 = 0.7), nitrate ( R 2 = 0.6) and phosphate ( R 2 = 0.5) at the watershed outlet (Goliad, TX ?¢???? USGS (United States Geological Survey) gauge) as compared to observed data. The study showed that the proper use of weather radar precipitation in SWAT model simulations improves the estimation of missing water quality data.
机译:需要对溪流水质进行连续监测,因为它会对人类和生态健康及福祉产生重大影响。估计采样日期之间的水质需要基于给定流域的可用地理空间和水质数据进行模型模拟。诸如土壤和水评估工具(SWAT)之类的模型可用于估计缺失的水质数据。在这项研究中,SWAT用于估算圣安东尼奥河出口附近监测站的水质。来自雨量计和天气雷达的降水数据被用于强制进行SWAT模拟。在圣安东尼奥河流域163个小流域的近似中心创建了基于天气雷达数据的虚拟雨量计,以进行SWAT模拟。该方法首先在瓜达卢佩河流域中部的一个较小流域进行了测试,从而提高了模拟地表径流的模型效率。然后将该方法应用于圣安东尼奥河流域,并在流域出口(德克萨斯州戈利阿德)的地表径流(R 2 = 0.7),硝酸盐(R 2 = 0.6)和磷酸盐(R 2 = 0.5)方面得到了很好的模拟。与观测数据相比,USGS(美国地质调查局)规。研究表明,在SWAT模型模拟中正确使用天气雷达降水可以改善对缺失水质数据的估计。

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