# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## 3 Results and Discussion
### Climate-Smart Agro-Hydroological Model Simulation Outputs
#### 3.3.2 Temperature Projection  
Temperature (maximum and minimum) has the greatest effect on the estimation of irrigation water demand [42]. The output of the model using multiple models indicates an increase in the future temperature (Table 3). The mean maximum temperature would increase by 1.18 \({}^{\circ}\)C, 1.14 \({}^{\circ}\)C, and 1.97 \({}^{\circ}\)C for RCPs 4.5, 6.0, and 8.5, respectively, compared to the baseline period (1976-2005). The model also reveals a noticeable increment (3.25 \({}^{\circ}\)C) from the most severe scenario (RCP8.5) in the latter part of the century (2080s). A similar change pattern is observed for the minimum temperature, however, with a higher rate. The projected temperature will assist in anticipating the condition of the basin for the purpose of future irrigation water demand planning, especially during the dry season months.  
Although climate change impacts will vary due to differences in crop variety and local variations in growing seasons, crop management, etc., rising temperature and precipitation may escalate the deteriorating condition of the soil, which could have negative effects on rice production. Most crop yields will be negatively impacted by temperature rise and erratic rainfall, floods, and droughs [43]. According to [44], an increase in minimum temperature would be advantageous for rice production, however, higher increases in rainfall and mean temperature will negatively affect its production in the future. It is also noted that not all rice varieties could tolerate temperature rise while sustaining yields.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**GCMs** & \multicolumn{3}{c}{**RCP4.5**} & \multicolumn{3}{c}{**RCP6.0**} & \multicolumn{3}{c}{**RCP8.5**} \\ \hline  & **2020s** & **2050s** & **2080s** & **2020s** & **2050s** & **2080s** & **2020s** & **2050s** & **2080s** \\ \hline CANESM2 & – & – & – & – & – & – & 16.38 & 27.72 & 37.85 \\ CCSM4 & \(-\)0.98 & 3.86 & 3.95 & \(-\)6.46 & \(-\)2.97 & 0.59 & \(-\)2.28 & 5.46 & \(-\)3.65 \\ CNRM & \(-\)6.58 & \(-\)0.95 & \(-\)4.47 & – & – & – & \(-\)1.88 & \(-\)7.05 & 0.00 \\ CSIRO & \(-\)1.87 & 1.33 & 8.07 & \(-\)4.65 & \(-\)9.70 & \(-\)7.14 & 0.29 & \(-\)33.5 & \(-\)72.20 \\ GFDL-ESM2G & 3.35 & 6.14 & 3.55 & 4.68 & 6.62 & 8.09 & 1.24 & 14.60 & 0.00 \\ GFDL-ESM2M & 5.56 & 2.92 & 6.47 & 7.23 & 14.46 & 14.16 & 11.72 & 5.15 & 9.28 \\ HadGEM2-CC & 13.75 & \(-\)5.13 & 5.46 & – & – & – & 4.09 & \(-\)1.47 & 0.14 \\ HadGEM2-ES & 1.97 & \(-\)9.42 & \(-\)0.65 & \(-\)55.31 & \(-\)55.46 & \(-\)62.29 & \(-\)0.78 & \(-\)8.44 & \(-\)8.23 \\ MPI-ESM-LR & 34.1 & 28.7 & 43.6 & – & – & – & 44.9 & 34.7 & 27.3 \\ MRI-CGCM3 & 2.72 & \(-\)2.88 & 10.14 & \(-\)5.43 & \(-\)0.16 & \(-\)6.08 & \(-\)3.17 & 3.85 & \(-\)5.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Projected annual changes in effective rainfall (%) by global climate models (GCMs) under Representative Concentration Pathways (RCPs) 4.5, 6.0, and 8.5 for the periods 2010–2039, 2040–2069, and 2070–2099 relative to the 1976–2005 baseline period.  
Figure 3: Projected average monthly effective rainfall using multiple models under RCPs 4.5, 6.0, and 8.5 for the periods 2010–2039, 2040–2069, and 2070–2099 with respect to the 1976–2005 baseline.  
In addition, the standing crop failure due to prolonged submergence, flooding, and salinity should be addressed through the development of anticipatory adaptation strategies.  
Our investigation of how climate change impacts irrigation water planning and management represents an attempt towards quantifying future changes in rice production. Consequently, a proper study of future changes in rice production for the scheme is necessary, as this was not covered in the present study because other yield-related data were not yet available.