05 Jun 2024
 | 05 Jun 2024
Status: this preprint is open for discussion.

Assessing the Hydrological Impact Sensitivity to Climate Model Weighting Strategies

Mehrad Rahimpour Asenjan, Francois Brissette, Richard Arsenault, and Jean-Luc Martel

Abstract. Climate change impact studies rely on ensembles of General Circulation Model (GCM) simulations. Combining ensemble members is challenging due to uncertainties in how well each model performs. The concept of model democracy where equal weight is given to each model, is common but criticized for ignoring regional variations and dependencies between models. Various weighting schemes address these concerns, but their effectiveness in impact studies, which integrate GCM outputs with separate impact models, remains unclear.

This study evaluated the impact of six weighting strategies on future streamflow projections using a pseudo-reality approach, where each GCM is treated as “the true” climate. The analysis involved an ensemble of 22 CMIP6 climate simulations and used a hydrological model across 3,107 North American catchments. Since climate model outputs often undergo bias correction before being used in hydrological models, this study implemented two approaches: one with bias correction applied to precipitation and temperature inputs, and one without. Weighting schemes were evaluated based on biases relative to the pseudo-reality GCM for annual mean temperature, precipitation and streamflow.

Results show that unequal weighting schemes produce significantly better precipitation and temperature projections than equal weighting. For streamflow projections, unequal weighting offered minor improvement only when bias correction was not applied. However, with bias correction, both equal and unequal weighting delivered similar results. While bias correction has limitations, it remains essential for realistic streamflow projections in impact studies. A pragmatic strategy may be to combine model democracy with selective model exclusion based on robust performance metrics.

This study provides insights on how weighting affects hydrological assessments. It emphasizes the need for careful approaches and further research to manage uncertainties in climate change impact studies. These findings will help improve the accuracy of climate projections and improve the reliability of hydrological impact assessments in a changing climate.

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Mehrad Rahimpour Asenjan, Francois Brissette, Richard Arsenault, and Jean-Luc Martel

Status: open (until 31 Jul 2024)

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Mehrad Rahimpour Asenjan, Francois Brissette, Richard Arsenault, and Jean-Luc Martel
Mehrad Rahimpour Asenjan, Francois Brissette, Richard Arsenault, and Jean-Luc Martel


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Short summary
Our study examines how combining climate models impacts future streamflow predictions, crucial for understanding climate change. Comparing six methods across 3,107 North American catchments, we found unequal weighting significantly improves rainfall and temperature projections. However, for streamflow, both equal and unequal weighting perform similarly with bias correction. Our findings underscore the need to carefully select weighting methods and correct biases for accurate climate projections.