一种基于遥感的降雨驱动灾害评估工具。
A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards.
作者信息
Wright Daniel B, Mantilla Ricardo, Peters-Lidard Christa D
机构信息
University of Wisconsin-Madison, Madison, Wisconsin.
University of Iowa, Iowa City, Iowa.
出版信息
Environ Model Softw. 2017 Apr;90:34-54. doi: 10.1016/j.envsoft.2016.12.006. Epub 2017 Jan 13.
RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.
“雨天”（RainyDay）是一个基于Python的平台，它将降雨遥感数据与随机风暴转移（SST）相结合，用于模拟由降雨引发的灾害，如洪水和山体滑坡。SST通过对观测到的风暴进行时间重采样和空间转移，有效地延长了极端降雨记录，从周边地区创建了许多极端降雨情景。强度-持续时间-频率（IDF）曲线常用于灾害建模，但需要长期记录来描述降雨深度和持续时间的分布，且不提供降雨时空结构的信息，限制了它们在小尺度上的实用性。相比之下，“雨天”（RainyDay）利用1至2十年的数据可用于许多灾害应用，并且输出的降雨情景包含来自遥感的详细时空结构。由于全球卫星覆盖，“雨天”（RainyDay）可用于难以到达的地区以及缺乏地面测量的发展中国家，不过结果会受到遥感误差的影响。“雨天”（RainyDay）对于非平稳条件下的灾害建模可能有用。
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