Development of Web-based Stochastic Storm Transposition Toolkit for Physically-based Rainfall and Flood Hazard Analysis

Project ID: 1735
Principal Investigator: Kathleen Holman
Research Topic: Managing Hydrologic Events
Funded Fiscal Years: 2017, 2018 and 2019
Keywords: None

Research Question

Can more realistic representations of extreme rainfall using Stochastic Storm Transposition (SST) improve estimates of flood hazard and uncertainty?
Spatial and temporal variability of precipitation can lead to a wide range of flood outcomes, only some of which have actually been experienced. The limits of historical rainfall data translate to uncertainty in flood hazard estimates. The RainyDay© software uses the SST technique to create both rainfall intensity-duration-frequency (IDF) curves and large numbers of realistic extreme rainfall scenarios from short records. SST is able to produce accurate IDF and flood estimates with return periods of at least 1000 years using only 10 years of input data, while a new 33-year, 100-member ensemble rainfall dataset will enable simulation of much rarer storms and better estimation of uncertainties. Rainfall scenarios from RainyDay can combine with hydrologic models to provide robust estimates of flood hazard and uncertainty.

Need and Benefit

The proposed research addresses Priority Areas (PA) 4.01 and 4.04 in Brekke et al., "Addressing Climate Change in Long-Term Water Resources Planning and Management" (2011). PA 4.01 is focused on determining strengths and weaknesses of watershed hydrologic methods to support scoping decisions. The proposed work speaks directly to this area by comparing results from advanced stochastic methods and rainfall-runoff models with results from previous, conventional analyses. The proposed work also addresses PA 4.04, which is focused on identifying strengths and weaknesses of spatially distributed weather data that may be used for hydrologic model development. The proposed research addresses this gap by exploring the benefits of ensemble datasets and RainyDay. The ensemble dataset should improve estimates of uncertainty surrounding precipitation observations, while the RainDay will streamline generation of precipitation inputs. Both tools may help identify future distributed data needs.

Contributing Partners

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Research Products

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Last Updated: 4/4/17