极端降水范围的空间模拟和未来预测
Spatial modeling and future projection of extreme precipitation extents
摘要：
具有大空间范围的极端降水事件可能比局部事件产生更严重的影响，因为它们可能导致大范围的洪水。人们对气候变化如何影响极端降水的空间范围存在争议，对极端降水的调查往往直接依赖于气候模型的模拟。在这里，我们使用不同的策略来研究未来的变化，在两个流域（多瑙河和密西西比河）的气候带和季节的降水极端的空间范围不同。我们依靠观测到的极端降水，同时利用基于物理的平均温度协变量，这使我们能够预测未来的降水范围。我们将协变量纳入新开发的时变$r$ -Pareto过程中，使用适当选择的空间聚集功能$r$。该模型捕捉降水极端的空间依赖结构的时间非平稳性，将其与温度协变量，我们从模型校准观测和去偏气候模拟（CMIP 6）预测。对于这两个流域，我们的研究结果表明，空间范围和温度协变量之间的负相关性为大部分的雨季和增加的趋势，在边缘，这表明在气候变暖的雨季降水强度增加的空间降水范围减少局部。
英文摘要：
Extreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change may affect the spatial extent of precipitation extremes, whose investigation often directly relies on simulations from climate models. Here, we use a different strategy to investigate how future changes in spatial extents of precipitation extremes differ across climate zones and seasons in two river basins (Danube and Mississippi). We rely on observed precipitation extremes while exploiting a physics-based mean temperature covariate, which enables us to project future precipitation extents. We include the covariate into newly developed time-varying $r$-Pareto processes using a suitably chosen spatial aggregation functional $r$. This model captures temporal non-stationarity in the spatial dependence structure of precipitation extremes by linking it to the temperature covariate, which we derive from observations for model calibration and from debiased climate simulations (CMIP6) for projections. For both river basins, our results show negative correlation between the spatial extent and the temperature covariate for most of the rain season and an increasing trend in the margins, indicating a decrease in spatial precipitation extent in a warming climate during rain seasons as precipitation intensity increases locally.
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