A remote sensing-based tool for assessing rainfall-driven hazards. (April 2017) - Wright, Daniel B.; Mantilla, Ricardo; Peters-Lidard, Christa D.
Abstract: 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. Highlights: We introduce RainyDay, a software tool for assessment of rainfall-driven hazards. RainyDay couples rainfall remote sensing with stochastic storm transposition (SST). SST explicitly considers multi-scale hazard impacts of rainfall space-time structure. SST offers advantages potential advantages for nonstationary flood hazard modeling. Limitations exist in complex terrain and due to biases in remote sensing data.