## Materials and Methods
### Downscaling of GCMs Outputs  
Adoption of multi-models is essential and recommended for impact studies and adaptation strategies [30; 31], because single GCM does not actually provide useful information in assessing the climate change impacts. Therefore, ten global climate projections were acquired from 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 (RCP 4.5, RCP 6.0, and RCP 8.5). The spatial resolution from the output of GCMs cannot provide a good climate change scenario for a specific watershed, since GCMs run on a broad spatial scale. Hence, downscaling is needed to represent the impact of climate change on a catchment area. A developed CSDSS for downscaling hydro-meteorological variables [32], was adopted to downscale the extracted GCMs outputs. The CSDSS was built in Matrix Laboratory (MATLAB) environment using \"Delta change factor\" statistical downscaling method. The method has been widely applied in hydrological modeling because it allows speed of application, direct scaling of the scenarios with respect to changes suggested by the  
Figure 2: Framework for the adaptive water allocation under climate change.  
GCMs and also multi-models could be used with result from various GCM scenarios [33; 34]. In the \"Delta change factor\" method, the mean values of GCM simulated baseline and future climates were estimated as [16]:  
\[\overline{GCM_{b}}=\sum_{i=1}^{Nb}GCM_{bi}/N_{b}, \tag{1}\]  
\[\overline{GCM_{f}}=\sum_{i=1}^{Nf}GCM_{fi}/N_{f}, \tag{2}\]  
where, \(\overline{GCM_{b}}\) and \(\overline{GCM_{f}}\), \(GCM_{b}\), and \(GCM_{f}\) are the mean values and values from \(GCM\) baseline and \(GCM\) future climate scenario, respectively. \(N_{b}\) and \(N_{f}\) are the total number of values in the downscaling for baseline and future periods, respectively.  
The monthly additive (CF\({}_{\text{add}}\)) and multiplicative Change Factor (CF\({}_{\text{mul}}\)) changes between the baseline period and future period 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 GCMs averages representing baseline from the future.  
\[CF_{Rain}=\left(\frac{\overline{P}_{GCM,fut,m}}{\overline{P}_{GCM,base,m}}\right), \tag{3}\]  
\[CF_{Temp}=\left(\overline{T}_{GCM,fut,m}-\overline{T}_{GCM,base,m}\right), \tag{4}\]  
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 give perturbed climate series.  
\[P_{adj,fu,d}=P_{obs,d}\times CF_{Rain}, \tag{5}\]  
\[T_{fut,d}=T_{obs,d}+CF_{Temp}, \tag{6}\]  
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; \(GCM\),\(fut\),\(m\) and \(GCM\),\(base\),\(m\) are the average monthly values of GCM output and baseline periods, respectively.  
A simulation dialog window appears after clicking on a specific command button from the CSDSS main dialog window, to generate daily hydro-meteorological variable(s) as shown in Figure 3. The program was calibrated and validated using the observed and simulated monthly average values for the baseline period. The outputs can be viewed from the \"Analysis and Statistics\" button as tables and graphs.