Ensembling Downscaling Techniques and Multiple GCMs to Improve Climate Change Predictions in Cryosphere Scarcely-Gauged Catchment

Muhammad Azmat (), Muhammad Uzair Qamar (), Shakil Ahmed (), Muhammad Adnan Shahid (), Ejaz Hussain (), Sajjad Ahmad () and Rao Arsalan Khushnood ()
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Muhammad Azmat: National University of Sciences and Technology (NUST)
Muhammad Uzair Qamar: University of Agriculture
Shakil Ahmed: National University of Sciences and Technology (NUST)
Muhammad Adnan Shahid: University of Agriculture Faisalabad
Ejaz Hussain: National University of Sciences and Technology (NUST)
Sajjad Ahmad: Mirpur University of Science and Technology
Rao Arsalan Khushnood: National University of Sciences and Technology (NUST)

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 9, No 15, 3155-3174

Abstract: Abstract Future projections of climate variables are the key for the development of mitigation and adaptation strategy to changing climate. However, such projections are often subjected to large uncertainties which make implementation of climate change strategies on water resources system a challenging job. Major uncertainty sources are General Circulation models (GCMs), post-processing and climate heterogeneity based on catchment characteristics (e.g. scares data and high-altitude). Here we presents the comparisons between different GCMs, statistical downscaling and bias correction approaches and finally climate projections, with the integration of gridded and converted (monthly to daily) data for a high-altitude, scarcely-gauged Jhelum River basin, Pakistan. Current study relies on climate projections obtained from factorial combination of 5-GCMs, 2 statistical downscaling and 2 bias correction methods. In addition, we applied bias corrected APHRODITE, converted daily data using MODAWEC model and observed data. Further, five GCMs (CGCM3, HadCM3, CCSM3, ECHAM5 and CSIRO-MK3.5) were tested to scrutinize two suitable GCMs integrated with Statistical Downscaling Model (SDSM) and Smooth Support Vector Machine (SSVM). Results illustrate that the CGCM3 and HadCM3 were suitable GCMs for selected study basin. Both downscaling techniques are able to simulate precipitation, however, SSVM performed slightly better than SDSM. We found that the integration of CGCM3 with SSVM (SSVM-CGCM3) generates precipitation and temperature better than the CGCM3 (SDSM-CGCM3) and HadCM3 (SDSM-HadCM3) with SDSM. Furthermore, the low elevation stations were influenced by monsoon, significantly prone to rise in precipitation and temperature, while high-altitude stations were influenced by westerlies circulations, less prone to climate change. The projections indicated rise in basin-wide annual precipitation by 25.51, 36.76 and 45.52 mm and temperature by 0.64, 1.47 and 2.79 °C, during 2030s, 2060s and 2090s, respectively. The methods and results of this study can be adopted to evaluate climate change implications in the catchments of characteristics similar to Jhelum River basin.

Keywords: GCMs; Climate change; Downscaling; Gridded datasets; Bias corrections (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-018-1982-9

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