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Incorporating Uncertainty Into the Ranking of SPARROW Model Nutrient Yields From Mississippi/Atchafalaya River Basin Watersheds1

机译:将不确定性纳入密西西比河/阿查法拉雅河流域流域的SPARROW模型养分产量的排名1

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摘要

Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient-reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight-digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.
机译:支流河运来的过多养分与墨西哥湾缺氧有关。将减少养分的目标对准流域,将营养物产量最高的流向敏感的下游水域,将有助于减少墨西哥湾缺氧区并改善河流和溪流水质的管理工作。挑战之一是,在这些管理决策中使用的大多数传统分水岭建模方法(例如,机械模型)在营养物产量及其下游输送的预测中没有考虑不确定性。然而,越来越多地使用参数估计程序来统计地估计模型系数,使得可以可靠地估计这些预测中的不确定性。在这里,我们将稳健的引导程序应用于混合统计/机械分水岭模型SPARROW(分水岭属性的空间参考回归)先前应用的结果,以开发一种统计可靠的方法来识别“高优先级”管理区域,基于整个流域分水岭输送的养分产量的概率排名。该方法旨在供管理人员用来对流域划分优先级,在这些流域中可以进行额外的水流监测和营养减少策略的评估。我们的排序程序结合了模型预测的置信区间信息和所输送养分产量的相应分水岭等级。从这种量化的不确定性中,我们估计单个流域处于营养物产量最高的流域集合中的概率。通过确定150个流域向墨西哥湾输送的养分产量最高的流域,我们说明了该程序在密西西比河/阿恰法拉亚河流域的818个八位数字水文单位流域中的应用。最高的产量来自俄亥俄州密西西比州中部和密西西比河下游流域的流域。有了90%的置信度,只有少数流域可以可靠地置于最高150个类别中;但是,由于不属于最高150个类别,因此可以排除更多分水岭。该分级程序的结果提供了有关流域养分产量的可靠信息,可以使管理工作受益,以减少流向下游沿海水域(如墨西哥湾)或当地接收河流和水库的养分。

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