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Assimilation of Vegetation Conditions Improves the Representation of Drought over Agricultural Areas

机译：植被条件的同化提高了农业区域干旱的代表性

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This study presents an evaluation of the impact of vegetation conditions on a land surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental United States from 1979 to 2017. Leaf area index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP's dynamic vegetation scheme improves the model's ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation toward improved simulation of agricultural drought.
机译：本研究评估了植被条件对农业干旱地面模型（LSM）模拟的影响。Noah MP LSM用于模拟1979年至2017年美国大陆的水和能量通量和状态，并使用百分位数将其转化为干旱类别。叶面积指数（LAI）观测值被纳入Noah MP的动态植被方案。评估使用每周运行的干旱监测系统（美国干旱监测系统）。结果表明，将LAI同化到Noah MP的动态植被方案中，可以提高模型代表干旱的能力，尤其是在农田地区。LAI同化改进了干旱类别的模拟、干旱条件的检测，并减少了干旱假警报的发生。这些地区LAI的同化不仅纠正了植被模拟中的模型错误，还可以帮助表示未建模的物理过程，如灌溉，以改进农业干旱模拟。

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