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Understanding the impact of precipitation bias-correction and statistical downscaling methods on projected changes in flood extremes
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| Type: | Resource | |
| Storage: | The size of this resource is 5.0 GB | |
| Created: | Oct 09, 2023 at 5:12 p.m. | |
| Last updated: | Mar 04, 2024 at 9:31 p.m. (Metadata update) | |
| Published date: | Mar 04, 2024 at 9:31 p.m. | |
| DOI: | 10.4211/hs.45930399530b42c391e642f3c5202a8d | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 374 |
| Downloads: | 56 |
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Abstract
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.
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Content
readme.txt
# Michalek et al. (2024) Hydroshare containing code and data for: Michalek, A. T., Villarini, G., & Kim, T. (2024). Understanding the impact of precipitation bias‐correction and statistical downscaling methods on projected changes in flood extremes. Earth's Future, 12, e2023EF004179. https://doi.org/10.1029/2023EF004179. The contents below contain information on the code and data. The codes are labeled based in the order to run (i.e. step1, step2, etc....) ## Data -HLM Folder This folder contains the flood peak data for all simulations in the csv file for the locations of interest. The setup folder contains the files used to run the HLM model with the source code available at: https://github.com/ssmall41/asynch -USGS Folder This folder contains a CSV file containing all the annual flood peaks for USGS locations in Iowa. The second csv contains the metadata for the gages. ## Analysis This folder contains all of the analysis steps for the paper. -distfit In this folder the codes to fit the distributions for the observed and simulated data (step 1) and get the confidence intervals (step 2) are contained. Next the steps to plot the AEP and quantile change boxplots are provided (steps 5 and 6). -precip Codes to process and plot the SDII by basin (Steps 7 to 9). Note the raw data for precipiation is note provided as it was a few terabytes. Step 7 was used to process the netcdf files with downscaled precipitation. The results of that step are provided in zip files in this directory for each climate model. -validation Codes for validation of distribution fitting analysis based on USGS data (steps 3 and 4). ## Figures Figures from the manuscript.
Related Resources
| This resource is referenced by | Michalek, A. T., Villarini, G., & Kim, T. (2024). Understanding the impact of precipitation bias‐correction and statistical downscaling methods on projected changes in flood extremes. Earth's Future, 12, e2023EF004179. https://doi.org/10.1029/2023EF004179 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| Iowa Department of Transportation | ||
| U.S. Department of Defense |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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