Hydrological Projections under CMIP5 and CMIP6: Sources and Magnitudes of Uncertainty

Abstract

Abstract Projections of future hydrological conditions rely largely on global climate models, but model performance varies greatly. In this study, we investigated projected changes in runoff ( R ), precipitation ( P ), evapotranspiration (ET), and soil moisture (SM) based on the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) and quantified the uncertainties of their projected changes on annual and seasonal scales. The results indicate that all four hydrological variables show an increase over most of the global land: annual projections of R , P , ET, and SM from CMIP6 increase in 72%, 81%, 82%, and 66% of the global land area, respectively, under a high emissions scenario during the period 2080–99 relative to 1970–99. We estimated the uncertainties in CMIP6 from different sources on an annual scale and found that model uncertainty dominates the total projected uncertainties during the twenty-first century [76% ( R ), 73% ( P ), 89% (ET), and 95% (SM) in the 2090s], and the contribution of internal variability decreases with time. The low-latitude regions have the greatest uncertainty in hydrological projections. In CMIP6, the uncertainty of projected changes in P contributes the most to the uncertainty of projected changes in R , with a contribution of 93% on annual scale, followed by ET and SM. Overall, the performances of the CMIP5 and CMIP6 models are similar in terms of hydrological changes and the composition of their uncertainties. This study provides a theoretical reference for the further improvement and development of hydrological components in global climate models. Significance Statement Previous studies concerning future hydrological changes have predominantly focused on trends in future drying or wetter conditions but often ignored or discounted obvious disparities in model performance across distinct regions. The purpose of this study is to investigate future hydrological changes, quantify the agreement among CMIP6 models regarding these changes, and then decompose the sources that contribute to the discrepancies in hydrological projections. This study has the potential to strengthen the reliability of hydrological components in global climate models, thereby contributing to more accurate future projections of global water conditions.

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