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首页> 外文期刊>Environmental Modelling & Software >Development of catchment water quality models within a realtime status and forecast system for the Great Barrier Reef
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Development of catchment water quality models within a realtime status and forecast system for the Great Barrier Reef

机译:大屏障礁的实时状态和预测系统内的集水质水质模型的发展

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摘要

Realtime information on sediment and nutrients generated and transported from catchments is essential to inform management decisions aimed to improve the ecosystem health of the Great Barrier Reef (GBR). A water quality modelling methodology is developed to provide accurate and reliable estimates of sediments, dissolved and particulate nutrients, i.e., Nitrogen and Phosphorus for historical simulations, realtime status and forecasts. A water quality model is built for seven key water quality constituents at ten locations in eight GBR catchments using a non-linear multivariate regression technique. Covariates used in the multivariate regression models were derived from either streamflow, baseflow, or time-based cyclical processes. The performance of models varied by site location and constituent and out of 67 models developed here, 27 models have NSE values 0.5 and 57 models have NSE values 0.3. These 67 hourly models have been used to generate historical simulations and forecasts of concentration and load.
机译:有关集水区产生和运输的沉积物和营养素的实时信息对于提供信息,旨在改善大堡礁(GBR)的生态系统健康的管理决策至关重要。开发出水质建模方法,以提供沉积物,溶解和颗粒营养素,即氮气和磷,即历史仿真,实时状态和预测的准确且可靠的估计。使用非线性多元回归技术为八个GBR集水区的七个地点建造了水质模型。多元回归模型中使用的协变量来自流流,基础流或基于时间的周期性过程。使用现场位置和组成的模型的性能和在此开发的67个型号中,27型号具有NSE值> 0.5和57型号具有NSE值> 0.3。这67小时的模型已被用于产生历史模拟和浓度和负荷预测。

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