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Predicting stream N and P concentrations from loads and catchment characteristics at regional scale: A concentration ratio method

机译:从区域规模的负荷和集水特征预测河流中氮和磷的浓度:一种浓度比方法

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We used a concentration ratio method to predict yearly and summer averages of stream total nitrogen, nitrate and total phosphorus concentrations at a regional scale. The ratio of the median daily concentration on the flow weighted annual concentration was used. This ratio characterizes the concentration dynamics of a catchment. We took advantage of the commonly used budget type models applied at a regional scale to relate concentrations to loads instead of directly to land uses, as has previously been done. The relationship was modeled with Boosted Regression Trees using catchment and stream characteristics along with loads and flows obtained from the SPARROW budget model. The ratio modeling approach was compared to a direct approach for concentration prediction, and also to a simple method where the mean ratio was used. The modeling performances of the ratio models were overall satisfying (r~2 of 49% to 78%), and a better choice than the two other methods tested. This ratio modeling approach is based on a steady state assumption and largely ignores temporal dynamics. As such, this modeling technique does not replace the more physically-based techniques, but allows for hybrid approaches for improved spatial interpolations. This method could be used to predict effectively the impact (at equilibrium) of land use change and management scenarios on water quality at a regional scale.
机译:我们使用浓度比方法来预测区域范围内河流总氮,硝酸盐和总磷浓度的年平均值和夏季平均值。使用中位数日浓度与流量加权的年浓度之比。该比率表征了集水区的浓度动态。我们利用了在区域规模上应用的常用预算类型模型来将集中度与负荷相关联,而不是像以前所做的那样直接与土地利用相关联。使用集水和河流特征以及从SPARROW预算模型获得的负荷和流量,使用Boosted回归树对这种关系进行建模。将比率建模方法与浓度预测的直接方法进行了比较,还与使用平均比率的简单方法进行了比较。比率模型的建模性能总体令人满意(r〜2为49%到78%),并且比其他两种测试方法更好。这种比率建模方法基于稳态假设,并且很大程度上忽略了时间动态。这样,该建模技术不会替代基于物理的技术,而是允许使用混合方法来改善空间插值。该方法可用于有效预测区域范围内土地利用变化和管理情景对水质的影响(处于平衡状态)。

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