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首页> 外文期刊>Journal of the American Water Resources Association >ASSESSING CALIBRATION UNCERTAINTY AND AUTOMATION FOR ESTIMATING EVAPOTRANSPIRATION FROM AGRICULTURAL AREAS USING METRIC
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ASSESSING CALIBRATION UNCERTAINTY AND AUTOMATION FOR ESTIMATING EVAPOTRANSPIRATION FROM AGRICULTURAL AREAS USING METRIC

机译：使用METRIC评估估计不确定度和自动化以估算农业区蒸发蒸腾量。

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Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin-wide water budgets. Each METRIC evapotranspiration (ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first-order ET estimates that are similar to time intensive manual efforts with less time investment.
机译：农业灌溉在美国西部占总用水量的很大一部分。使用内标化（METRIC）遥感能量平衡模型进行高分辨率的蒸散量估算，已用于估算内华达州西部的历史农业用水量，以评估全流域的水预算。每个METRIC蒸散量（ET）估计值都必须由训练有素的用户校准，这需要花费一些迭代时间，并导致用户之间的ET估计值有所不同。针对METRIC模型的自动校准算法被设计为通过模仿手动校准过程来生成与经过培训的用户的ET估计值可比的ET估计值。自动校准可在最少人工干预的情况下快速生成METRIC ET估算值，并对模型进行不确定性和灵敏度分析。发现由自动校准算法生成的ET估算值的变化与手动ET估算值的变化相似。结果表明，低ET水平的田间不确定性最高，而ET高水平的田间不确定性最低，所有田间的季节平均不确定度约为5％。此外，在盲目比较中，每日和季节性自动ET估算值与多个站点的流量塔测量ET数据进行了比较。自动化方法可以生成一阶ET估计值，类似于耗时的手动工作，而所花费的时间却更少。

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