Abstract
This study focusses on identifying a set of representative climate model projections for the Upper Indus Basin (UIB). Although a large number of General Circulation Models (GCM) predictor sets are available nowadays in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for the step-wise shortlisting and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on: (a) range of projected mean changes, (b) range of projected extreme changes, and (c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, from of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to the year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range from a slight annual increase of 2.2% under the drier scenarios to as high as 15.9% in the wet scenarios. Moreover, for all scenarios, future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the “Dry-Warm” and the “Median” scenarios over the late 21st century.
Highlights
Future climate projections provided by general circulation models (GCMs) can serve as the basic input for climate change impact studies on water resources
The main aim of the study was to select a set of GCM simulations that can represent the full spectrum of the future climate, as projected by the entire pool of climate models, in term of both means and extremes, and which can be subsequently used as climate forcing for hydrological modelling to assess a wider range of possible climate change hydrological impacts, especially for the expected changes in water yield, annual cycle, high and low flows, and floods
We found that Coordinated Regional Downscaling Experiment (CORDEX)-regional climate model (RCM) model projections were available for four of the selected GCMs at the 1st rank and one GCM at the 2nd rank
Summary
Future climate projections provided by general circulation models (GCMs) can serve as the basic input for climate change impact studies on water resources. The inherent uncertainties, along with other factors such as time limitations, human resource availability, or computational constraints, make it imperative to sort out the most appropriate individual GCM or small ensemble of GCMs suitable for downscaling and subsequent use in the assessment of climate change impacts. This aforementioned selection of GCMs is not simple or straightforward, as there can be nearly an unlimited number of criteria and approaches through which climate models can be evaluated for their skill and suitability for specific purposes and regions. The first approach, which considers all the possible projected futures (stretching from warm and wet to cold and dry, or opting for the middle path of all possible futures) is becoming more relevant, especially in regions such as the Hindu Kush Himalayas (HKH) and UIB, where
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