Multimodel Ensemble Projection of Hydro-climatic Extremes for Climate Change Impact Assessment on Water Resources

Abstract

Projected changes in climatic extremes, compared to the mean climate, exhibit a greater negative impact on the natural environment. Several studies reported that multi-model ensemble approach can improve the reliability of hydro-climatic extreme projection by extracting important information from a large number of general circulation models (GCMs). However, most of the available multi-model assembling methods do not consider both the spatial and temporal variabilities. Thus, this study reflects both the spatial and temporal climate characteristics during multi-model averaging through the Taylor diagram skill metrics. The capability of the proposed multi-model assembling approach was evaluated for reproducing the multitude of climate extreme indices. Moreover, the reliability of a multi-model assembling approach was assessed for preserving the maximum variability of the GCMs output. In general, the results showed that multi-model assembling approach outperformed the individual climate models for reproducing the hydro-climatic extremes; however, it artificially corrupted and narrowed the projected climate extremes variability of the GCMs output. Thus, it is worthwhile to consider both the individual climate models and multi-model ensemble projections toward an improved projection of hydro-climatic extremes. In general, the study proved that the impacts of climate change on the hydro-climatic extremes are more amplified compared to the changes in mean climate. Hence, this study suggests that meaningful efforts should be put in the future to proactively manage the risks of climate extremes.

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Correspondence to Getachew Tegegne.

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Tegegne, G., Melesse, A.M. Multimodel Ensemble Projection of Hydro-climatic Extremes for Climate Change Impact Assessment on Water Resources. Water Resour Manage (2020). https://doi.org/10.1007/s11269-020-02601-9

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Keywords

  • Climate change
  • Hydro-climatic extremes
  • Multi-model ensemble