# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## Materials and Methods
### Downscaling of GCMs Outputs  
Adoption of multiple models is essential and recommended for impact studies and adaptation strategies [27; 28], because a single GCM usually provides uncertain information in assessing the climate change impacts. Therefore, 10 global climate projections were acquired from the Program for Climate Model Diagnosis and Inter-comparison (PCMDI). A baseline period (1976-2005) was adopted and three future periods of 30-year time segments were defined as the 2020s (2010-2039), 2050s (2040-2069), and 2080s (2070-2099) under three RCP scenarios (RCP4.5, RCP6.0, and RCP8.5). The spatial resolution from the output of GCMs cannot provide a suitable climate change scenario for a target watershed because GCMs operate at a larger spatial scale. Therefore, downscaling is required to represent the impact of climate change on a catchment area. A developed CSDSS for downscaling hydro-meteorological variables [15] was adopted to downscale the extracted GCM outputs. The CSDSS was built in the MATLAB environment using the \"delta change factor\" statistical downscaling method. This method has been widely applied in hydrological modeling because it allows speed of application and direct scaling of the scenarios with respect to changes suggested by the GCMs; moreover, multiple models could be used with the results from various GCM scenarios [29; 30]. In the delta change factor method, high resolution future variables at the station scale were created by an ordinary bias correction. Three stations with reliable climate data across the study area were used. The algorithm modifies the observed daily time series of the climate variables by adding and/or  
Figure 2: Framework for the development of the climate-smart agro-hydrological model. multiplying monthly mean changes of GCM outputs using adjustment formulas. The mean values of GCM simulated baseline and future climates were estimated as:  
\[\overline{GCM_{b}}=\sum\
olimits_{i=1}^{Nb}GCM_{bi}/N_{b}, \tag{1}\]  
\[\overline{GCM_{f}}=\sum\
olimits_{i=1}^{Nf}GCM_{fi}/N_{f}, \tag{2}\]  
where, \(\overline{GCM_{b}}\) and \(\overline{GCM_{f}}\), \(GCM_{b}\), and \(GCM_{f}\) are the mean values and values from the GCM baseline and future climate scenario, respectively. \(N_{b}\) and \(N_{f}\) are the total number of values in the downscaling for baseline and future periods, respectively. Subsequently, monthly additive (\(CF_{\mathit{add}}\)) and multiplicative (\(CF_{\mathit{mul}}\)) change factors for the baseline and future periods in the equivalent climate variable of interest were calculated for the GCM grid box using Equations (3) and (4). Relative change factors were used in the case of rainfall (\(\Delta P\)), derived from the ratio of projected-to-baseline averages, while absolute change factors were used for temperature, relative humidity, solar radiation, and wind speed (\(\Delta T\)), by subtracting the GCM averages representing the baseline from the future. \[CF_{\mathit{Rain}}=\left(\frac{\overline{P}_{\mathit{GCM},fut,m}}{\overline{P} _{\mathit{GCM},\mathit{base},m}}\right), \tag{3}\]  
\[CF_{\mathit{Temp}}=\left(\overline{T}_{\mathit{GCM},fut,m}-\overline{T}_{ \mathit{GCM},\mathit{base},m}\right), \tag{4}\]  
Finally, local-scaled future climate values were obtained by applying the change factors using Equations (5) and (6). This involves superimposing the change factors suggested by the GCM-scenario combinations to the daily baseline time series to yield perturbed climate series. \[P_{\mathit{adj},fut,d}=P_{\mathit{obs},d}\times CF_{\mathit{Rain}}, \tag{5}\]  
\[T_{\mathit{fut},d}=T_{\mathit{obs},d}+CF_{\mathit{Temp}}, \tag{6}\]  
where \(P\) and \(T\) are the rainfall and climatic variables, respectively; the subscript _adj,fut,d_ denotes the downscaled future daily variable; _obs,d_ denotes daily observations; CF denotes calculated additive and multiplicative change factors for rainfall and climatic variables; and _GCM,fut,m_ and _GCM,base,m_ are the average monthly values of GCM output and baseline periods, respectively.