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首页> 外文会议>Eighth International Conference on Tailings and Mine Waste '01, Jan 16-19, 2001, Fort Collins, Colorado, USA >Stochastic modeling of stormwater and receiving stream concentrations
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Stochastic modeling of stormwater and receiving stream concentrations

机译：雨水和接收流浓度的随机模拟

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摘要

A stochastic approach was developed and applied to the Butte, Montana hillside abandoned mining site for modeling stormwater runoff and subsequent receiving stream loadings. This approach enabled capture and quantification of the uncertainty associated with stormwater quality data and allowed the prediction of copper and zinc concentrations caused by runoff from the Butte hillside during storm events. Runoff flows were generated in a spreadsheet model using the rational method and stormwater concentrations were input as probability distribution functions (PDFs). Correlations between sampling sites were also incorporated into the model. The PDFs were combined with runoff hydrographs and stochastically modeled using Monte Carlo simulation. Stream loadings predicted by the model PDFs were combined with ambient stream flow and quality in a mass balance to generate expected stream concentrations in the form of cumulative distributions functions (CDFs). The final stream concentration CDFs were used to evaluate the probabilities of exceeding instream standards at various locations during a specific storm event.
机译：开发了一种随机方法，并将其应用于蒙大拿州比尤特山坡的废弃采矿场，以对雨水径流进行建模并随后接收水流负荷。这种方法可以捕获和量化与雨水质量数据相关的不确定性，并可以预测暴风雨期间因比尤特山坡径流引起的铜和锌浓度。使用合理的方法在电子表格模型中生成径流，并输入雨水浓度作为概率分布函数（PDF）。采样点之间的相关性也被纳入模型。将PDF与径流水位图结合，并使用Monte Carlo模拟随机建模。由模型PDF预测的水流负荷与质量流平衡中的环境水流和质量相结合，以累积分布函数（CDF）的形式生成预期的水流浓度。最终河流浓度CDF用于评估特定风暴事件期间各个位置超出河流标准的可能性。

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