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The Use of Inverse Distance Weighted Interpolation to Calibrate the Vertical Hydraulic Conductivity below an Estuary

机译:使用反距离加权插值法校准河口以下的垂直水力传导率

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The finite-difference model SEAWAT was used to model the flow of submarine groundwater discharge (SGD) into the Indian River Lagoon, an estuary in central Florida. The anisotropy ratio was spatially calibrated in the entire domain using four methods: (1) estimation of the root mean square error (RMSE), (2) estimation of the Nash-Sutcliffe efficiency (NSE) index, (3) two-sided t-test, and (4) visual comparisons of measured and model-predicted freshwater equipotential lines below the entire transect. The measured freshwater equipotential lines were obtained by the inverse distance weighted interpolation (IDW) of measured heads at 17 points. Calibration by the RMSE calculation was found to be meaningless since model predictions under or over predicted the annual SGD by 43 to 211% even when RMSE values were small (0.048 m to 0.085 m). The NSE statistic was not reliable when comparisons were made at the 17 measured points. However, adding 18 more points based on the IDW greatly improved the NSE calibration. The results provided by the two-sided t-test agreed well with the visual comparison. It is recommended that the IDW be conducted to obtain a complete distribution of the freshwater equipotential lines prior to conducting statistical analyses. The IDW not only improves the statistical analyses but also provides another calibration tool through visual comparison. Both the two-sided t-test and the visual comparison indicated that good statistical results were obtained when the predominant anisotropy ratio was 100 and values of 10,000 or higher were used close to the lagoon bed.
机译:有限差分模型SEAWAT用于模拟流入佛罗里达州中部河口印度河泻湖的海底地下水排放量(SGD)。使用四种方法在整个域中对各向异性比率进行了空间校准:(1)估计均方根误差(RMSE),(2)估计Nash-Sutcliffe效率(NSE)指数,(3)双向t测试,以及(4)对整个样条线以下的实测和模型预测的淡水等电势线进行视觉比较。通过在17个点对测得的水头进行反距离加权插值(IDW)获得测得的淡水等势线。由于即使RMSE值很小(0.048 m至0.085 m),模型预测值仍将年均SGD预测为43%至211%,因此通过RMSE计算进行的校准被认为是没有意义的。当在17个测量点进行比较时,NSE统计数据并不可靠。但是,基于IDW再增加18个点可以大大改善NSE校准。双面t检验提供的结果与视觉比较非常吻合。建议在进行统计分析之前进行IDW以获取淡水等势线的完整分布。 IDW不仅可以改善统计分析,还可以通过视觉比较提供另一种校准工具。双向t检验和视觉比较均表明,当主要各向异性比为100且在泻湖床附近使用10,000或更高的值时,可获得良好的统计结果。

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