Understanding the impact of precipitation bias-correction and statistical downscaling methods on projected changes in flood extremes

This contains the data and codes for the study: \"Understanding the impact of precipitation bias-correction and statistical downscaling methods on projected changes in flood extremes\" by Michalek et. al. (2023). The code for the analysis is provided below. The file name provided the order of the steps taken for the analysis. Note any precipitation related files are not included as they are too large for Hydroshare. Abstract: This study evaluates five bias correction and statistical downscaling (BCSD) techniques for daily precipitation and examines their impacts on the projected changes in flood extremes (i.e., 1%, 0.5%, and 0.2% floods). We use climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to conduct hydrologic simulations across watersheds in Iowa and determine historical and future flood extreme estimates based on generalized extreme value distribution fitting. Projected changes in these extremes are examined with respect to four Shared Socioeconomic Pathways (SSPs) alongside five BCSD techniques. We find the magnitude of future annual exceedance probability (AEPs) estimates are expected to increase for the future under all SSPs, especially for the emission scenarios with higher greenhouse gases concentrations (i.e., SSP370 and SSP585). Our results also suggest the choice of BCSD impacts the magnitude of the projected changes, with the SSPs that exert limited sensitivity compared to the choice of downscaling method. The variability in projected flood changes across Iowa is similar across the downscaling technique but increases as the AEP increases. Our findings provide insights into the impact of downscaling techniques on flood extremes’ projections and useful information for climate planning across the state.