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Automatic calibration and improvements on an instream chlorophyll a simulation in the HSPF model

机译:自动校准和改进HSPF模型中的录像录像

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Accurate prediction of chlorophyll a (Chl a) concentration in surface water bodies such as lakes or rivers is crucial for water quality management. This study improved the predictive simulation of instream Chl a with the Hydrological Simulation Program-FORTRAN (HSPF) by adding automatic calibration and modifying the growth-temperature formulation of phytoplankton in the original HSPF model. A total of 62 model parameters, selected from a series of sensitivity analyses, were automatically calibrated in a stepwise manner for different variables in the order of flow, sediment, water temperature, ammonia/nitrate couple, and phosphate/Chl a couple. With finer temporal resolution (5-8 days) data than those of majority of the existing HSPF studies, the automatic calibration procedure provided the model with performance ratings of 'satisfactory' or better for all the variables including nutrients and Chl a: The percent bias values ranged from -18% - 54% and -20% - 62% for nutrients and Chl a, respectively. The original linear equation on the growth-temperature relationship of phytoplankton in simulating instream Chl a was modified using a quadratic equation and an exponential equation. The exponential equation outperformed the original linear and quadratic equations, particularly in simulating the excess concentrations of Chl a observed during summer seasons. For the validation data set, the exponential equation predicted 78% of the eutrophic cases while the linear and quadratic equation only predicted 53% and 13% of the eutrophic cases, respectively. The modified HSPF model offers an improved prediction of instream Chl a. This approach will be useful for providing early warning of algal blooms, facilitating the implementation of effective management of stream water quality.
机译:精确预测湖泊或河流等地面水体中的叶绿素A(CHL A)浓度对于水质管理至关重要。本研究通过添加自动校准和改变原始HSPF模型中Phytoplankton的生长温度制剂,改善了仪器CHL A与水文模拟程序 - 福斯特兰(HSPF)的预测模拟。从一系列灵敏度分析中选择的62种型号参数,以流量,沉积物,水温,氨/硝酸盐夫妇和磷酸盐/ CHL一对夫妇自动校准不同的变量。具有比现有的HSPF研究的大多数的时间分辨率(5-8天)数据,自动校准程序提供了具有“令人满意”的性能评级的模型,或者对于包括营养素和CHL A的所有变量,偏差百分比值分别为-18%-54%和-20%-62%,分别为营养素和CHL A.用二次方程和指数方程修改了模拟仪器CHL A的浮游植物生长温度关系的原始线性方程。指数方程优于原始的线性和二次方程,特别是在模拟夏季期间观察到的CHL A的过量浓度。对于验证数据集,指数方程预测了富营养的病例的78%,而线性和二次方程仅预测了富营养病例的53%和13%。改进的HSPF模型提供了对仪器中央CHL A的改进预测。这种方法对于提供藻类盛开的预警,促进了流水质量有效管理的预警。

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