Assessment of Climate Change Impact on Precipitation Using Machine Learning Based Statistical Downscaling Method

Abstract

Future predictions of precipitation are highly important for effective water resources management. The Global circulation models (GCMs) are commonly used to make such predictions. In this paper, the effect of climate change on precipitation was investigated for Damaturu station located in Yobe state, Nigeria from 2050-2080. For this purpose, the BNU-ESM GCMs under the emission scenario RCP 4.5 was used to downscale observed precipitation data via Artificial Neural Network (ANN). Various climatic predictors were considered and ranked according to their impact on precipitation using the mutual information (MI) method. A total of 5 ANN models were subsequently developed using different combinations of predictors as inputs to downscale the precipitation data. The Determination Coefficient (DC) and Root Mean Square Error (RMSE) performance indicators were then employed. M1 which used a combination of top 8 ranked predictors was found to have the best performance in both downscaling and projection phases. The final results from M1 showed that, over the specified period, the Damaturu region will generally experience a decrease in precipitation, which will be more prevalent in months that experience the most precipitation with the most decrease of 20% in monthly precipitation sum occurring during the wettest month of August, towards the end of the 21st century.