Evaluation of statistical downscaling model's performance in projecting future climate change scenarios

Author:

Shukla Rituraj1ORCID,Khare Deepak2,Kumar Dwivedi Anuj3,Rudra Ramesh Pal1,Palmate Santosh S.4,Ojha C. S. P.2,Singh Vijay P.5

Affiliation:

1. a School of Engineering, University of Guelph, Guelph, Ontario, Canada

2. b Indian Institute of Technology Roorkee, Roorkee Uttarakhand, India

3. c National Institute of Hydrology Roorkee, Uttarakhand, India

4. d Texas A&M AgriLife Research, El Paso Centre, Texas A&M University, El Paso, Texas, USA

5. e Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A & M University, College Station, Texas, USA

Abstract

Abstract Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or multi-linear regression and the Least Square Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from general circulation model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for 1961–2001 and then for 2001–2099. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models.

Publisher

IWA Publishing

Subject

Management, Monitoring, Policy and Law,Atmospheric Science,Water Science and Technology,Global and Planetary Change

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