Journal article
A remote sensing-based tool for assessing rainfall-driven hazards
Environmental Modelling and Software, Vol.90, pp.34-54
2017
DOI: 10.1016/j.envsoft.2016.12.006
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.
Details
- Title: Subtitle
- A remote sensing-based tool for assessing rainfall-driven hazards
- Creators
- Daniel B Wright - University of Wisconsin–MadisonRicardo I Mantilla - University of Iowa, Civil and Environmental EngineeringChrista D Peters-Lidard - Goddard Space Flight Center
- Resource Type
- Journal article
- Publication Details
- Environmental Modelling and Software, Vol.90, pp.34-54
- DOI
- 10.1016/j.envsoft.2016.12.006
- ISSN
- 1364-8152
- eISSN
- 1873-6726
- Publisher
- Elsevier Ltd
- Grant note
- DOI: 10.13039/100006225, name: Oak Ridge Associated Universities; DOI: 10.13039/100007015, name: University of Wisconsin-Madison; DOI: 10.13039/100001395, name: Wisconsin Alumni Research Foundation; name: Iowa Flood Center; DOI: 10.13039/100008893, name: University of Iowa
- Language
- English
- Date published
- 2017
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9983706375902771
Metrics
36 Record Views