Performance evaluation of statistical downscaling models for future climate change scenario projection.

Abstract

Abstract Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many SD 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 the past period 1961–2001 and then for 2001–2099 under future climate change scenario A2. 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. Future projection results indicated that an increase in precipitation, Tmax, and Tmin patterns at all stations would occur between 2001–2099. Although statistical measures (R2, RMSE, SSE, NSE and MAE) showed a close agreement between observed and predicted climate variables, the overall accuracy of the LS-SVM model was better than the SDSM model. Thus, the present study revealed that LS-SVM could be used as a superior statistical downscaling model over the SDSM in future studies.