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Stochastic bias-correction of daily rainfall scenarios for hydrological applications

机译：水文应用中每日降雨情景的随机偏差校正

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摘要

The accuracy of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes. In fact, the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows and groundwater recharge. In this study, a comparison between statistical properties of rainfall observations and model control simulations from a Regional Climate Model (RCM) was performed through a robust and meaningful representation of the precipitation process. The output of the adopted RCM was analysed and re-scaled exploiting the structure of a stochastic model of the point rainfall process. In particular, the stochastic model is able to adequately reproduce the rainfall intermittency at the synoptic scale, which is one of the crucial aspects for the Mediterranean environments. Possible alteration in the local rainfall regime was investigated by means of the historical daily time-series from a dense rain-gauge network, which were also used for the analysis of the RCM bias in terms of dry and wet periods and storm intensity. The result is a stochastic scheme for bias-correction at the RCM-cell scale, which produces a realistic representation of the daily rainfall intermittency and precipitation depths, though a residual bias in the storm intensity of longer storm events persists.
机译：气候模型提供的降雨预测的准确性对于评估气候变化对水文过程的影响至关重要。实际上，在降尺度的降水中存在偏差可能会在评估土壤湿度动态，河流流量和地下水补给方面产生较大的偏差。在这项研究中，通过强有力且有意义的降水过程表示，对降雨观测的统计特性与区域气候模型（RCM）的模型控制模拟之间进行了比较。利用点降雨过程随机模型的结构，对所采用的RCM的输出进行了分析和重新定标。特别是，随机模型能够在天气尺度上充分再现降雨间歇性，这是地中海环境的关键方面之一。利用密集雨量计网络的历史每日时间序列调查了当地降雨状况的可能变化，这些时间序列还用于分析干旱和湿润时期以及风暴强度方面的RCM偏差。结果是在RCM信元规模上进行偏差校正的随机方案，该方案可真实表示每日的降雨间歇性和降水深度，尽管长期风暴事件的风暴强度仍然存在残留偏差。

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