A Probabilistic Multi Stage Approach for Statistical Downscaling of Temperature Data

Abstract

Near Surface Air Temperature is an important climatic variable that affects the hydrological response of a river basin, and forms an input to most of the hydrological models. General Circulation Models (GCMs) simulate the response of temperature and other climate variables to the variations in emission concentrations, but their outputs are too coarse to be used in most hydrological models. A multi stage statistical downscaling approach is proposed for downscaling GCM predicted temperatures. In the first stage the Relevance Vector Machine (RVM) is used to develop a statistical model between the GCM simulated historical climate variables and the observed historical temperature for spatially downscaling monthly GCM simulations. A weather generator is then used to generate daily data from the spatially downscaled temperature data. On fine scales, lack of correlation between precipitation and temperature data used for hydrological modelling can lead to large uncertainties in the generated hydrological series. Thus, a distribution free post processing is performed for reproducing the observed regional correlation between temperature and precipitation, in the generated temperature data. The methodology is then applied to the Bharathapuzha catchment in Kerala, India, to downscale temperature from the climate models BNU-ESM, CESM1-BGC, CMCC-ESM2, FGOALS-G2, FIO-ESM-2.0 and MIROC4h. The statistical models set up using RVM show consistent performance during the calibration (1969-1980) and validation (1981-2005) phases, with Nash-Sutcliffe efficiency (NSE) between 0.64 to 0.83. The weather generator is then run to generate daily temperature data from the monthly downscaled series. Across the different climate models, daily maximum temperature is generated with RMSE between 2.5°C to 3.3°C, while the minimum temperature has RMSE ranging from 1.7°C to 2.0°C. The probabilistic nature of the procedure enables the generation of multiple series from the same set of predictors. The simulation band from the multiple GCMs is studied for the period 2016 to 2021 to understand the deviation in predicted temperature for the future scenario. The prediction band for maximum temperature has an average band width of 6.7°C and for minimum temperature, the average band width is 4.9°C.