Decision support system for water allocation in a rice irrigation scheme under climate change scenarios

• Main Author: Dlamini, Nkululeko Simeon
• Format: Thesis
• Language: English
• Published: 2017
• Online Access: http://psasir.upm.edu.my/id/eprint/71187/1/FK%202017%2057%20-%20IR.pdf; http://psasir.upm.edu.my/id/eprint/71187/

Irrigation is the major user of total water use in Malaysia for production of rice, its staple food, and therefore knowledge on future changes in irrigation demands due to climate change is critical for managing water allocations. General Circulation Models (GCMs) suggest that increase in emission of greenhouse gases will have significant implications on a number of hydrological processes at local scale including future streamflow fluctuations and evapotranspiration. These issues need to be quantified and accounted in future irrigation allocation and planning. The present thesis describes the development of a decision support system (DSS) tool for modelling water allocations in a local rice irrigation scheme under climate scenarios. The DSS is developed with climate outputs from GCMs, hydrological data, irrigation canal data and crop data. Four basic modules; Stochastic Rainfall Generator, Reference Evapotranspiration, Water Demand and Water Allocation Modules were developed and integrated in the MATALAB graphical user interface. Future climate scenarios for the study area were extracted from ten global climate models (GCMs) under three Representative Concentration Pathways (RCPs) scenarios (RCP4.5, RCP6.0 and RCP8.5) obtained from the Program for Climate Model Diagnosis and Inter-comparison (PCMDI). Future projections of multi-GCMs in Upper Bernam River basin have shown that temperature will increase under scenarios, with the largest changes during the dry season months (February–June). Projected increase in maximum temperature ranges from 0.7–1.6 °C, 0.5–1.9 °C and 0.8–3.3 °C, under RCP4.5, RCP6.0 and RCP8.5, respectively. Rainfall projections showed variation between the two cropping seasons. The RCP4.5, 6.0 and 8.5 respectively projected average changes of –2.4%, –3.2% and –3.7% for the dry season months, and 1.0%, 0.8% and 2.4% for the wet season months. The results showed the watershed will likely experience warmer periods accompanied by dry climate during dry season months. The impact of climate change on the flows of the Upper Bernam River Basin was studied using the SWAT hydrological model. The model was evaluated using 25 years of records (1981-2005). Results of the coefficient of determination, (R2), Nasch- Sutcliff, (NSE) and Percent Bias, (PBIAS) were 0.67, 0.62 and -9.4 during calibration period, and 0.62, 0.61 and -4.2 during validation period. Future streamflow projections with the validated SWAT model showed that during the dry season months, annual streamflow is likely to decrease by up to (−6.6%) by the late century (2080s). Streamflow is predicted to increase by up to 11.4% in the same future period during the wet season. On the basis of these results, it can be inferred that the water resource of the Bernam River Basin could be sufficient up to the end of the century. However, these results also highlight some potential risk that climate change could impose on rice production during future dry season months. This requires integrated water management solutions to ensure sustainable rice production. A user-friendly climate-smart decision support system (CSDSS) model was developed for modeling irrigation water allocation in Tanjung Karang Rice Irrigation Scheme (TAKRIS) under climate change scenarios. First, climate scenarios are downscaled within the system by perturbing observed series using change factors derived from GCMs outputs. The FAO-56 Penman-Monteith model was used for estimating reference evapotranspiration under future forcing. A stochastic rainfall model was adopted to simulate future rainfall series using the first-order two states Markov Chain Approach based on future emission scenario forcing. Then water demand model was developed from reference evapotranspiration and crop coefficient. Generated irrigation water requirements are converted into irrigation deliveries based on canal command area. The model is capable of generating several realizations of irrigation deliveries using individual GCMs and/or multi-models (ensemble) projections. The model was evaluated for irrigation deliveries at the study area using one year water supply data. The analyses showed that the average weekly irrigation supplies for measured supplies (without climate change) and simulated supplies (with climate change) under RCP4.5, RCP6.0 and RCP8.5 scenarios were respectively, 2.69 m3/s, 2.00 m3/s, 2.19 m3/s and 1.94 m3/s during off-season, and 2.55 m3/s, 1.47 m3/s, 1.76 m3/s and 1.49 m3/s during main-season. The results revealed that actual supply (without climate climate) was higher than the model simulated supplies (with climate change) for all the three climate scenarios in both cropping seasons. This could be suggestive of poor scheduling in the scheme, leading to undue excess water supply. The application of the model in assessing long-term changes in irrigation water demands in response to the projected changes in reference evapotranspiration and effective rainfall is demonstrated using three future time slices (2020s, 2050s and 2080s) with respect to baseline period (1976-2005). The results generated from the DSS model suggest that monthly reference evapotranspiration is likely to increase in all scenarios up to 14.2% under RCP8.5 during February to July. Similarly, annual effective rainfall is predicted to slightly increase in future although with monthly variations. The irrigation water needs are projected to increase in the off season months and decrease during the main season months. At the present, the scheme requires a supply of 610 mm and 404 mm depth of water, for the respective off and main seasons, while future requirements will reach up to 675 mm and 376 mm under the highest scenario (RCP8.5). The results will be helpful for water managers in planning adaptation measures in those months where rainfall is predicted to be not sufficient to fulfill the crop water demands. Based on the results, it can be inferred that the DSS can serve as a practical tool for simulating climate scenarios based on the outputs from global climate models (GCMs) to carry out standard calculations for reference evapotranspiration, rice water requirements and irrigation demands, for daily and/or periodic water allocations under climate scenarios. This will allow Water Management Authorities to assess climate change signals and thus promote adoption of appropriate adaptation strategies that could potentially lead to more sustainable water management at farm level. Additional beauty of the model is its flexibility updating future climate scenarios as new climate models become available, and also, with suitable locally derived data the tool can be extended to other geographical locations. Finally, the DSS has some application limitations which are highlighted in the thesis, and this form basis for future improvements.