# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## 3 Results and Discussion
### Water Demand Projection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Irrigation Season**} & \multicolumn{2}{c}{**Period**} & \multicolumn{2}{c}{**Annual Changes (\%) in Water Demand under RCPs**} \\ \cline{2-5}  & & **RCP4.5** & **RCP6.0** & **RCP8.5** \\ \hline \multirow{5}{*}{Dry Season} & 2020s & 4.36 & 5.54 & 10.03 \\  & 2050s & 6.21 & \(-\)11.40 & 11.02 \\  & 2080s & 6.21 & \(-\)11.40 & 11.09 \\  & Average & 5.59 & \(-\)5.75 & 10.71 \\ \hline \multirow{5}{*}{Wet Season} & 2020s & \(-\)2.3 & \(-\)4.5 & \(-\)5.4 \\  & 2050s & \(-\)2.4 & 17.5 & \(-\)5.9 \\ \cline{1-1}  & 2080s & \(-\)3.3 & 14.0 & \(-\)7.3 \\ \cline{1-1}  & Average & \(-\)2.6 & 9.0 & \(-\)6.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Projected annual changes in water demand under multi-model projections based on RCP scenarios. Figure 9: Projected mean monthly water demand from multiple models for three RCP scenarios (RCPs 4.5, 6.0, and 8.5) for three future periods of 2010–2039, 2040–2069, and 2070–2099 relative to the baseline period of 1976–2005. during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. The main challenge, however, will be to properly manage the monthly variation of irrigation water demand in the command area. Failure to correctly manage the irrigation system will lead to much greater uncertainty as the project is a run-of-the-river irrigation scheme with limited storage facility. The anticipated irrigation demands of the rice crop and the expected available water for supply to the scheme were examined for the future emission scenarios. Figure 10, based on the model outputs using multiple models, demonstrates that the water supply to the scheme is not regulated based on the water demand. Irrigation supply is thus observed to fluctuate during the seasons with over-supply in some periods and under-supply in others. An over-supply occurs when the ratio of available water for supply to the water demand is greater than 1, and under-supply is considered if the ratio is less than unity. A balance between water supply and demand is attained when the ratio is 1. The model outputs reveal that, in future, the scheme would be over-supplied in January-February and May-June during the dry season period, and November-December during the wet season, with notable excess supplies in June and December (Table 6). However, during March-April in the dry season and July-October (except October in the 2050s and 2080s for RCP 6.0) in the wet season, a supply shortage is projected based on the irrigation demand for RCPs 4.5, 6.0, and 8.5. Since the project is a run-of-the-river project, a reservoir to store more water for future use based on the modeled discharge at the supply intake would be useful. For effective irrigation water management, a good balance between water supply and demand needs to be achieved. This could be attained through operational actions, many of which require predictive models and instruments to be applied [47]. Therefore, the developed model could be integrated with adaptive cropping patterns to enhance the water allocation through the proper supply of the needed water in the scheme, thereby avoiding over-supply in the period of low demand and under-supply in the period of high demand. Figure 10: Projected ratio of available water for supply (WS) to water demand (WD) from multiple models for three RCP scenarios (RCPs 4.5, 6.0, and 8.5) for three future periods of 2010–2039, 2040–2069, and 2070–2099 relative to the baseline period of 1976–2005.