Evaluation of CMIP6 HighResMIP for hydrologic modeling of annual maximum discharge in Iowa

This repository contains codes for a study titled \"Evaluation of CMIP6 HighResMIP for hydrologic modeling of annual maximum discharge in Iowa\" submitted to Water Resources Research (Article DOI: 10.1029/2022WR034166) by Alexander T. Michalek, Gabriele Villarini, Taereem Kim, Felipe Quintero, Witold F. Krajewski, and Enrico Scoccimarro.

The resources include R codes for data analysis, figures, and precipitation bias-correction and downscaling. Additionally, codes are provided related to the setup of the hydrologic model (HLM) utilized in the study and found at https://asynch.readthedocs.io/en/latest/index.html. Finally, a subset of data from the simulations is provided for which the analysis is conducted. Full simulation datasets and CMIP6 forcings are not provided as they are too large to store and can be provided upon reasonable request.

Abstract:
The High-Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 (CMIP6) represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25- and 50-km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope-Link Model (HLM) at ~1000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff-based simulations can capture the empirical distribution of flood peaks in five of the ten models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community-level assessment, we use nine statistical approaches to bias-correct and downscale HighResMIP precipitation to a 4-km resolution. The bias-correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state.