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首页> 外文期刊>Water Research >COMPARISON OF STATISTICAL METHODS FOR ESTIMATION OF NUTRIENT LOAD TO SURFACE RESERVOIRS FOR SPARSE DATA SET: APPLICATION WITH A MODIFIED MODEL FOR PHOSPHORUS AVAILABILITY
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COMPARISON OF STATISTICAL METHODS FOR ESTIMATION OF NUTRIENT LOAD TO SURFACE RESERVOIRS FOR SPARSE DATA SET: APPLICATION WITH A MODIFIED MODEL FOR PHOSPHORUS AVAILABILITY

机译：稀疏数据集估算地表水库营养负荷的统计方法的比较：磷有效性模型的改进

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摘要

Nutrient budget models for lakes and reservoirs critically respond to the input pollutant loading. yet little consensus exists on how to estimate the load, particularly for the common but challenging case of sparse nutrient concentration measurements and abundant input flos data. A statistical load calculation using cluster (in this case. annual) mean concentration and stratified (monthly) flow was compared to estimates by sample mean and ratio estimator methods for phosphorus loading to Whitney Reservoir in North Central Texas. The results varied considerately for the various estimator methods during the six-year study period with the cluster and stratified mean approach estimating extreme high loading periods not captured by the other methods. The variable loading patterns were then tested in phosphorus budget model simulations for Whitney Reservoir that considered vertical stratication of the water column, water-sediment phosphorus interaction. and seasonal variations in water quality. For independently determined settling. interlayer dispersion. recycling rates, and sediment burial rates estimated for the respective loading calculation. the cluster and stratified mean loading pattern provided a better statistical fit of phosphorus concentration measurements in the epilimnion than when ratio estimator load calculations were used. The two loading functions described hypolimnion concentration data equally well. The lesson of this exercise is that various methods of load estimation should be examined in order to develop as reliable a management model as possible when only a sparse data set is available for calibration.
机译：湖泊和水库的营养预算模型对输入的污染物负荷至关重要。然而，在如何估算负荷方面尚无共识，特别是在营养素浓度测量稀疏且输入浮游物数据丰富的常见但极具挑战性的情况下。使用群集均值（在这种情况下为年均值）和分层（每月）流量的统计负载计算与通过样本均值和比率估算器方法对德克萨斯州中北部惠特尼水库的磷负载的估算值进行了比较。在六年的研究期间，对于各种估算器方法，结果均发生了显着变化，采用聚类和分层均值方法估算了其他方法无法捕获的极高负荷时期。然后在惠特尼水库的磷预算模型模拟中测试了变量加载模式，该模拟考虑了水柱的垂直分层，水-沉积物磷的相互作用。和水质的季节性变化。用于独立确定的沉降。层间分散。回收率和沉积物埋藏率，分别用于计算负荷量。与使用比率估计器负载计算时相比，群集和分层的平均负载模式提供了上生中磷浓度测量值的更好统计拟合。这两个加载函数同样很好地描述了次品浓度数据。本练习的经验教训是，当只有稀疏数据集可用于校准时，应研究各种负荷估算方法，以便开发出尽可能可靠的管理模型。

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