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首页> 外文期刊>Hydrology and Earth System Sciences >Evaluating the relative importance of precipitation, temperature and land-cover change in the hydrologic response to extreme meteorological drought conditions over the North American High Plains
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Evaluating the relative importance of precipitation, temperature and land-cover change in the hydrologic response to extreme meteorological drought conditions over the North American High Plains

机译:评估北美高平原地区降水,温度和土地覆盖变化对极端气象干旱条件的水文响应的相对重要性

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摘要

Drought is a natural disaster that may become more common in the future under climate change. It involves changes to temperature, precipitation and/or land cover, but the relative contributions of each of these factors to overall drought severity is not clear. Here we apply a high-resolution integrated hydrologic model of the High Plains to explore the individual importance of each of these factors and the feedbacks between them. The model was constructed using ParFlow-CLM, which represents surface and subsurface processes in detail with physically based equations. Numerical experiments were run to perturb vegetation, precipitation and temperature separately and in combination. Results show that decreased precipitation caused larger anomalies in evapotranspiration, soil moisture, stream flow and water table levels than increased temperature or disturbed land cover did. However, these factors are not linearly additive when applied in combination; some effects of multifactor runs came from interactions between temperature, precipitation and land cover. Spatial scale was important in characterizing impacts, as unpredictable and nonlinear impacts at small scales aggregate to predictable, linear large-scale behavior.
机译:干旱是一种自然灾害,在气候变化下,将来可能会变得更加普遍。它涉及温度,降水和/或土地覆盖的变化,但是尚不清楚这些因素中的每一个对总体干旱严重程度的相对影响。在这里,我们应用高平原地区的高分辨率综合水文模型来探讨这些因素各自的重要性以及它们之间的反馈。该模型是使用ParFlow-CLM构建的,该模型使用基于物理的方程式详细表示了表面和地下过程。进行了数值实验,分别或组合干扰植被,降水和温度。结果表明,降水减少引起的蒸散量,土壤水分,溪流和地下水位的异常大于温度升高或土地覆盖受到干扰的现象。但是,这些因素在组合使用时不是线性累加的。多因素运行的一些影响来自温度,降水和土地覆盖之间的相互作用。空间尺度在表征影响方面非常重要,因为小尺度上不可预测的非线性影响会聚合为可预测的线性大尺度行为。

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