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首页> 外文期刊>Journal of the American Water Resources Association >Calibrating a Basin-Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration
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Calibrating a Basin-Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration

机译：校准流域规模的地下水模型以估算地下水的蒸散量

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摘要

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ETg) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ETg. An iterative approach is provided that calibrates GWM to ETg derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11-year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ETg to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100m GWM-grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first-order second moment analysis shows ETg todtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ETg despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ETg appears to bias ETg during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness.
机译：遥感植被指数对应于冠层活力和覆盖度，并已成功地用于估算大时空尺度上的地下水蒸散量（ETg）。但是，这些数据未提供地下水模型（GWM）计算ETg所需的地下水深度（dtgw）信息。提供了一种迭代方法，可将Landsat增强植被指数（EVI）的估算值推导出来，将GWM校正为ETg。该方法适用于内华达州梅森谷地区长达11年的不同植被类型。进行不确定性分析以估计ETg与dtgw之间关系的平均值和90％的置信区间，以量化与植物生理复杂性，物种变异性和参数平滑化至100m GWM网格，土壤水分的时间变异性和土壤非均匀性相关的误差。解决方案。此外，一阶二阶矩分析表明，尽管消光深度和水力传导率存在较大不确定性，但ETg todtgw关系几乎只对估计的陆地表面或最大ETg敏感。在异常潮湿的春季/夏季条件下的几年中，估计ETg的EVI方法似乎会使ETg产生偏差。排除这些年份可以显着改善模型的性能，但强调需要开发一种方法，该方法不仅要考虑数量，还要考虑年降水量对植物绿度的影响。

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