Supplementary information files for Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places

Supplementary information files for article Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places

Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls within Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions.

针对数据稀疏地区，本文提供了关于空间和时间尺度化的子日极端降雨信息文件的补充资料。全球范围内提升水资源、排水和卫生服务的努力受到水文气象数据匮乏以及气候变化不确定性的阻碍。通常使用强度-历时-频率（IDF）表格来设计水基础设施，从而为适应工程标准提供切入点。本文首先提出了一种新颖的程序，用于指导使用媒体报道的洪涝灾害来模拟重降雨的预测变量选择。随后，我们展示了三个步骤的工作流程：(1) 将日降雨数据从网格到点分辨率进行空间尺度化；(2) 将日序列时间尺度化至子日极端降雨；(3) 在区域气候模型（RCM）模拟的气候变化条件下测试极端降雨的时间尺度化方法。关键在于，我们比较了将日降雨年最大值序列时间尺度化为子日极端降雨的方法，同时考虑了降雨的间歇性。这些方法应用于乌干达坎帕拉和肯尼亚基苏木，使用统计降尺度模型（SDSM），两个涵盖东非的RCM模拟（CP4和P25），以及混合形式（RCM-SDSM）。我们证明了Gumbel参数（以及IDF表格）可以可靠地尺度化为3小时的历时。与直接RCM输出相比，我们的混合RCM-SDSM尺度化减少了当前气候条件下IDF估计的错误。在气候变化条件下的RCM模拟中，还发现了可信的参数尺度化关系。然后，我们讨论了将此类工作流程应用于其他城市区域的实际方面。