Finer resolutions and targeted process representations in earth systems models improve hydrologic projections and hydroclimate impacts

TL;DR

This study demonstrates that higher resolution and targeted process representations in earth system models significantly improve hydrologic projections and impact assessments, especially for major rivers and populations.

Contribution

It provides empirical evidence that incorporating finer resolutions and advanced process models enhances the accuracy of runoff projections in climate models.

Findings

Finer resolutions and process models improve runoff projections.

CMIP6 models show 40% of rivers with decreased runoff by 2100.

Impacts affect 260 million people.

Abstract

Earth system models inform water policy and interventions, but knowledge gaps in hydrologic representations limit the credibility of projections and impacts assessments. The literature does not provide conclusive evidence that incorporating higher resolutions, comprehensive process models, and latest parameterization schemes, will result in improvements. We compare hydroclimate representations and runoff projections across two generations of Coupled Modeling Intercomparison Project (CMIP) models, specifically, CMIP5 and CMIP6, with gridded runoff from Global Runoff Reconstruction (GRUN) and ECMWF Reanalysis V5 (ERA5) as benchmarks. Our results show that systematic embedding of the best available process models and parameterizations, together with finer resolutions, improve runoff projections with uncertainty characterizations in 30 of the largest rivers worldwide in a mechanistically explainable manner. The more skillful CMIP6 models suggest that, following the mid-range SSP370 emissions scenario, 40% of the rivers will exhibit decreased runoff by 2100, impacting 260 million people.