Hydroclimatic projection: statistical learning and downscaling model for rainfall and runoff forecasting

Abstract

The study is carried out to investigate the surface runoff depth with changing precipitation due to climate change in the study area where sandy loam and loamy soil are dominant. In this study, future rainfall is projected by a statistical downscaling model (SDSM) using a set of predictors derived from a Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) [the Norwegian Earth System Model (NorESM)] with updated scenarios SSP 4.5 and SSP 8.5. Daily rainfall values for the observed period (1981 to 2014) are validated using statistical learning and evaluated with matrices, namely, root mean square error (RMSE), coefficient of correlation, and Nash–Sutcliffe efficiency (NSE), which are found to be valid for further predictions. Rainfall projections show a decrease in rainfall trend of 50% from 2030 to 2040 for scenario SSP 4.5 and an increase of 7% from 2040 to 2050. Predicted rainfall for scenario SSP 8.5 shows a similar trend of decreasing rainfall of 24% for the period 2030–2040 and an increase of 19% in the period 2040–2050. Furthermore, these rainfall values are spatially modelled in a geographic information system (GIS) and rainfall maps are obtained. The obtained rainfall map, land-use map, and soil map are overlaid to compute curve numbers and runoff depths. A similar trend of decrease in runoff is observed for the period 2030–2050. The overall trend of climate change shows a water-stressed scenario. HIGHLIGHTS The study downscaled a CMIP6 GCM to evaluate the effect of climate change on rainfall.; The study conducts hydrological assessments to predict surface runoff for future scenarios.; The SDSM model, when subjected to statistical learning analysis, shows good performance in simulating rainfall values.; The study predicts a decrease in rainfall and runoff in the years 2030, 2040, and 2050.; Machine learning-boosted SDSM is a strong tool.;

Keywords

• climate change
• future surface runoff
• hydrological assessment
• noresm
• sdsm
• statistical learning