Assessment of the uncertainties of global climate models in the evaluation of standardized precipitation and runoff indices: a case study

ABSTRACT

Uncertainties in climate change projection can originate from various sources and cause challenges. Thus, two specific approaches were developed in this study, for use in the selection of global climate models and in the assessment of drought occurrence. Considering the bias-corrected data, the performance of global climate models was evaluated using statistical methods, and the 14 best-ranked models were selected. These climate scenarios were used in the Long Ashton Research Station (LARS) downscaling model to obtain the precipitation and temperature time series. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) was used to model the runoff time series. Standardized precipitation and runoff indices were considered to assess the probability of meteorological and hydrological droughts. Finally, the Bayesian method was used to analyse the uncertainty assessment of drought occurrence. This methodology was applied in the Karkheh River basin and presented the moderate drought condition as the most probable state.

KEYWORDS:

• climate change
• uncertainty
• global climate models
• standardized precipitation index
• standardized runoff index
• Bayesian method

Editor A. Fiori Associate Editor E. M. Mendiondo

Editor A. Fiori Associate Editor E. M. Mendiondo

Acknowledgements

The authors thank the two anonymous reviewers for their very insightful comments and suggestions that were helpful in improving this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.