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Forecast of Natural Aquifer Discharge Using a Data-Driven, Statistical Approach

机译：使用数据驱动的统计方法预测天然含水层流量

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摘要

In the Western United States, demand for water is often out of balance with limited water supplies. This has led to extensive water rights conflict and litigation. A tool that can reliably forecast natural aquifer discharge months ahead of peak water demand could help water practitioners and managers by providing advanced knowledge of potential water-right mitigation requirements. The timing and magnitude of natural aquifer discharge from the Eastern Snake Plain Aquifer (ESPA) in southern Idaho is accurately forecast 4 months ahead of the peak water demand, which occurs annually in July. An ARIMA time-series model with exogenous predictors (ARIMAX model) was used to develop the forecast. The ARIMAX model fit to a set of training data was assessed using Akaike's information criterion to select the optimal model that forecasts aquifer discharge, given the previous year's discharge and values of the predictor variables. Model performance was assessed by application of the model to a validation subset of data. The Nash-Sutcliffe efficiency for model predictions made on the validation set was 0.57. The predictor variables used in our forecast represent the major recharge and discharge components of the ESPA water budget, including variables that reflect overall water supply and important aspects of water administration and management. Coefficients of variation on the regression coefficients for streamflow and irrigation diversions were all much less than 0.5, indicating that these variables are strong predictors. The model with the highest AIC weight included streamflow, two irrigation diversion variables, and storage.
机译：在美国西部，由于水的供应有限，对水的需求往往不平衡。这导致了广泛的水权冲突和诉讼。能够可靠地预测高峰用水之前数月的自然含水层流量的工具可以通过提供有关潜在的水权缓解要求的高级知识来帮助水务从业者和管理人员。在爱达荷州南部的东部蛇平原含水层（ESPA）排放天然含水层的时间和数量，比每年7月每年出现的需水高峰提前了4个月。使用带有外生预测变量的ARIMA时间序列模型（ARIMAX模型）来进行预测。使用Akaike的信息标准对适合于一组训练数据的ARIMAX模型进行了评估，从而根据前一年的流量和预测变量的值，选择了预测含水层流量的最佳模型。通过将模型应用于数据的验证子集来评估模型性能。在验证集上进行模型预测的Nash-Sutcliffe效率为0.57。我们的预测中使用的预测变量代表ESPA水预算的主要补给和排放部分，包括反映总体供水量以及水行政管理重要方面的变量。径流和灌溉引水的回归系数的变异系数都远小于0.5，表明这些变量是有力的预测指标。 AIC权重最高的模型包括流量，两个灌溉分流变量和存储量。

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  • 来源
    《Ground water》 |2014年第6期|853-863|共11页
  • 作者

    Kevin G. Boggs; Rob Van Kirk; Gary S. Johnson; Jerry P. Fairley;

  • 作者单位

    University of Idaho, Department of Geological Sciences, Boise, ID 83702;

    Henry's Fork Foundation, P.O. Box 550, Ashton, ID 83420 and Humboldt State University, Department of Mathematics, Arcata, CA 95521;

    University of Idaho, Department of Geological Sciences, Moscow, ID 83844;

    University of Idaho, Department of Geological Sciences, Moscow, ID 83844;

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