Abstract

Many studies indicate that climate change impacts on water availability and extreme events will be on both global and local scales. The humid tropical basin of Upper Bernam River, Malaysia is taken as case study to evaluate the impact of climate change on water availability on local scale by using Downscaled Global Climate Change Model (DGCM) data with a distributed hydrologic model. DGCM data was downscaled by using a Regional Climate Model called “Providing Regional Climates for Impacts Studies” (PRECIS). The study area is important since it is the main source of irrigation water using run-of-the-river irrigation system serving 20,000 ha of rice cultivation. The study answers the question of whether any trend in the annual and monthly series of temperature and rainfall can be detected at the local scale. DGCM temperature and rainfall data were compared with the actual observed data. The results of the comparison indicate that DGCM temperature data for specific grids were close to the actual temperature data unlike the rainfall data. The actual data and DGCM data were then used with SWAT model to simulate stream flow. The results show that stream flows were more accurately simulated by using actual observed data than using DGCM data. For average monthly flows using weather station data, Nash and Sutcliffe Efficiency (ENS) and coefficient of determination (R²) were found to be 0.79 and 0.80 respectively while ENS and R² for average monthly flows using DGCM data were found to be -3.273, 0.085 respectively. The average monthly flows during the study period with data from weather station and DGCM were 45.5 and 73.3 m³/s respectively. The Mann–Kendall test was then used to examine the presence of trend for temperature, rainfall and flow time series of actual data and DGCM. Mann–Kendall test results show that there is an increasing trend for temperature in all cases. However, there is no trend detected for rainfall in all cases. The flow trend analysis by SWAT using weather station inputs resulted in increasing trend with more than 90% probability level of significance, whereas no flow trend was detected by SWAT using DGCM data.

Key Words: Climate change, Water Availability, GCM, RCM, Hydrological Model