Analysis of Statistical Downscaling Model (SDSM) projected future rainfall in Northwestern, Western and Southern provinces of Sri Lanka

Mohamed Riflan, Miyuru B. Gunathilake, Jayanga T. Samarasinghe, Isuru M. Bandara, Imiya M. Chathuranika, Upaka Rathnayake


Even though an extensive amount of climate change studies have been carried out in different parts of the world, Sri Lanka is one of the least focused countries in this regard. Climate projections are important and encouraged to manage the futuristic adverse impacts. Identifying this research gap, future rainfall projections were carried out in three provinces in Sri Lanka, i.e. Northwestern (Puttalam and Kurunegala), Western (Katunayake and Colombo), and Southern province (Galle and Hambantota). The Canadian Earth System Model (CanESM2) under the Representation Concentration Pathways (RCP8.5) was downscaled using the Statistical DownScaling Model (SDSM). Non-parametric tests, including Mann-Kendall (MK) test and the Sen’s Slope estimator, were used to determine the significance of trends and magnitude of the slope of the historical trends (1990-2019) and future projected trends (2020-2100). The trends were analyzed for four major seasons in Sri Lanka, including First Inter-monsoon (FIM), Southwest monsoon (SWM), Second Inter-monsoon (SIM), and the Northeast monsoon (NEM). The standard error and model bias at rainfall stations were 0.014-0.034 mm and 1-1.1 respectively, which are acceptable when compared to previous studies. Several significant rainfall trends were identified, including positive trends in the mid-future (2041-2070), and negative trends in the far-future (2071-2100). In addition, rainfall indices, including Rx5day, R20mm, Consecutive dry days (CDD), and Consecutive wet days (CWD) were tested in future projected and historical rainfalls. The results of the present study will be useful for policymakers for decision-making processes in water resources management and agriculture.
Keywords: Non-parametric tests, projected rainfall, rainfall indices, rainfall trends, Statistical Down Scaling Model (SDSM).

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