Irrigation is the major user of total water use in most of the tropical countries located in Southeast Asia. Therefore, knowledge on future changes in irrigation demand in the context of climate change is very important for long-term planning and management of water resources of the region. This paper investigated the changes in irrigation water demand in an intensive irrigated area of Malaysia under climate change scenario. Global circulation models (GCMs) outputs are downscaled using statistical downscaling model (SDSM) to project the future changes in rainfall and temperature at local scale. The irrigation water demand estimation model known as CROPWAT is calibrated and validated with historical data and then used to project the future changes in irrigation demand under SDSM projected climatic condition. The results show that temperature and rainfall in the area will increase by 0.2° C and 4% per decade, respectively over the time period 2020–2099. The changes in climate will reduce irrigation water demand at a rate of 0.9% per decade in the area. However, the irrigation demand will be much more variable in future, and therefore, the major challenge in water resources management will be to handle the uncertainty. It is expected that the seasonal and annual variations in irrigation demand projected by the study will help in operating reservoirs and sustainable management of water resources in the region in the context of climate change.

The management planning of Pedu–Muda reservoir, Kedah, was investigated in the context of the climate change evolution. The aim of this study was to evaluate the impact of the climate change to the reservoir operating management system and its sustainability. The study was divided into two sections; Analysis 1 refers to the reservoir optimization adapted with the climate assessment. The statistical downscaling model reacted as the climate model to generate the long-term pattern of the local climates affected by the greenhouse gases. Analysis 2 refers to the reservoir optimization but excluded the climate changes assessment in the analyses. The non-dominated sorting genetic algorithm version II (NSGA-II) was applied in both analyses to optimize the water use due to the multi-objectives demand, maximizing water release, minimizing water shortage and maximizing reservoir storage. The formation of Pareto optimal solutions from both analyses was measured and compared. The results showed the Analysis 1 potential to produce consistence monthly flow with lesser error and higher correlation values. It also produced better Pareto optimal solution set and considered all the objectives demands. The NSGA-II also successfully improves and re-manages the reservoir storage efficiently and reduce the dependancy of these reservoirs.

The impact of global warming is resulting in flood, land slide, soil erosion and drought that are anticipated to become more intense and frequent. The history record shows that the flood has been occur repeatedly in this study area. The study of future climate needs to be done to a greater extent as a planning for the infrastructure and as mitigation actions on flood based on the future climate. The aims of this study, to ascertain the climate projection by CMIP6 are compatible to be subjected for the future climate and to identify the trend of future climate projection in Kemaman, Terengganu. The comprehensive study of future climate that using CMIP6 that consist of SSPs help in providing a clear explanation of future society’s socio-economic appraisal in assessment modelling. The Sen’s Slope Test were used to analyze the trend of future climate projection in this study area. In this study, we found out that the trend of future rainfall is increasing with the positive values of Sen’s Slope Test. Study shows that the Sen’s Slope Test values for eight (8) stations in Kemaman, which is Ban Ho, Hulu Jabor, Rumah Pam Paya Kempian, JPS Kemaman, Klinik Bidan Kg Ibok, SK Kemasek, Jambatan Tebak, and Jambatan Air Putih are 2.381, 1.158, 1.333, 1.252, 2.293, 1.06, 3.113, and 1.961 respectively. The frequency and intensity of forecasting rainfall also increasing compared to the historical data. In conclusion, the proper planning and mitigation action need to be done to minimize the losses that happen due to climate changes impacts.

The statistical downscaling model (SDSM) been used to analyse the potential changes of local climate trend in the long term. The difficulty of the SDSM model in selecting the best predictors group which having good association to the local climate. Even the SDSM provides screening process to analyse the predictor-rainfall relationship, however it has limited ability in analysing multiple variables from 26 predictors with 10 rainfall stations around Kedah state, Malaysia. In this regard, the Canonical Correlation Analysis (CCA) been used to analyse the multi predictor-rainfall relationships. The concept of canonical coefficient is sufficient to show the capability and reliability of the predictors based on the percentages of variance that can explained in the dependent variable using the independent variable. There were 10 predictors’ group have been developed and one predictor’s group was built based on the CCA result. The performances of these predictors groups were tested using statistical analyses. Results revealed that the predictors group selected by the CCA method has produced smaller values of MAE and MSE for all stations except at station of Ladang Tanjung Pauh. The box plot’s results, which generated from one hundred simulated samples, indicated that the performance of CCA method was remarkable. The presence of discrepancies in the HadCM3-A2 and HadCM3-B2 scenario simulations were relatively small and considered acceptable.