2.4.2. Water Equilibrium Simulation Model

The models HEC-3, HEC-5, and HEC-RAS were used in a simulation study of the reservoir system in each watershed [43]. Water balance principles were used. In this study, a simulation model of the reservoir system was created by using the same principles as in the above model, to facilitate connection with the Honey Bee Mating Optimization and begin calculating the water balance of each reservoir. To begin calculating the water balance of each reservoir from the rule curves, the initial storage volume of the reservoir was set at full capacity or the maximum storage level; the discharge volume could be calculated following the Standard Operating Rule as shown in Figure 3 and Equation (11). Then, the available water cost of the reservoir could be calculated for the next month with the principles of the water balance equation as shown in Equation (12).

$$R\_{\nu,\mathsf{T}} = \begin{cases} D\_{\mathsf{T}} + \mathcal{W}\_{\mathsf{V},\mathsf{T}} - y\_{\mathsf{T}\mathsf{\prime}} \text{ for } \mathcal{W}\_{\mathsf{V},\mathsf{T}} \ge y\_{\mathsf{\prime}} + D\_{\mathsf{\prime}}\\ D\_{\mathsf{\prime}\prime} \text{ for } \mathsf{x}\_{\mathsf{\tau}} \le \mathcal{W}\_{\mathsf{\prime},\mathsf{\tau}} < y\_{\mathsf{\tau}} + D\_{\mathsf{\prime}}\\ D\_{\mathsf{\tau}} + \mathcal{W}\_{\mathsf{\prime},\mathsf{\tau}} - \mathsf{x}\_{\mathsf{\tau}\prime} \text{ for } \mathsf{x}\_{\mathsf{\tau}} - D\_{\mathsf{\tau}} \le \mathcal{W}\_{\mathsf{\prime},\mathsf{\tau}} < \mathsf{x}\_{\mathsf{\tau}}\\ 0, \text{ otherwise} \end{cases} \tag{11}$$

where *Rυ*,*<sup>τ</sup>* is the amount of water discharged from the reservoir during the year υ in the month *τ* (*τ* is 1 to 12 referring to January to December); *Dτ* is the demand for water at the bottom of the basin during month *τ*; *x<sup>τ</sup>* is the lower boundary of the rule curves of the month *τ*; *y<sup>τ</sup>* the upper boundary of the rule curves of the month *τ*; and *Wυ*,*<sup>τ</sup>* is the amount of original water level available in the basin of the month *τ*.

$$\mathcal{W}\_{\nu,\tau+1} = \mathcal{S}\_{\nu,\tau} + Q\_{\nu,\tau} - R\_{\nu,\tau} - E\_{\tau} - DS \tag{12}$$

where *Sυ*,*<sup>τ</sup>* is the amount of water stored in the reservoir at the end of the month *τ*; *Qυ*,*<sup>τ</sup>* is the average streamflow in the month *τ*; *Eτ* is the evaporation loss in the month *τ*; and *DS* (dead storage) is unused storage volume.

**Figure 3.** Standard water discharge criteria.

The reservoir rule curves were generated using the HBMO Algorithm Optimal Solution in this study. In the instance of shortage frequency, the target function for determining the solution was the least average shortage, as illustrated in Equation (13).

$$\operatorname{Min}(Aver\_{Sh}) = \frac{1}{n} \sum\_{v=1}^{n} Sh\_v \tag{13}$$

where *n* is the length of the original water quantity data set; *Shv* is the amount of water shortage in the year *v* (The amount of water released is less than the water demand target).

#### 2.4.3. Reservoir Rule Curves Efficiency Evaluation

By analyzing the frequency of occurrence of an incident, the rule curves assessment was set to evaluate two parts: water scarcity and excess release water with mean and maximum values of Magnitude and Duration through the performances of the test rule curves with future monthly streamflow scenarios from 2020 to 2049. Changes in greenhouse gas emissions are RCP4.5 and RCP8.5, which are two different types of RCP.

#### **3. Results and Discussion**

#### *3.1. Streamflow Analysis Using the SWAT Model*

#### 3.1.1. Model Performance Assessment

Evaluation of model performance assessed the accuracy between the calculation of streamflow from the SWAT model calculated from the average monthly streamflow volume during 2011–2019 and the streamflow data from 4 measurement stations in the study areas, namely E68A Station (Lam Pha Niang River Basin), E29 Station (Upper Phong River Basin), Ubolratana Dam Station, and E85 Station (Lam Nam Choen River Basin) in the same period. The model's performance was evaluated using an index of R2 ranging from 0.62–0.88 and NSE between 0.50–0.81, which were both within the acceptable accuracy range as shown in Table 4.

**Table 4.** Index values for evaluating the accuracy of SWAT calculation results comparing streamflow volumes from measurement stations.


Comparative results of streamflow volumes from the SWAT model and streamflow data from Ubolratana Dam Station are shown in Figure 4. The average annual streamflow from the SWAT model is 5147.34 MCM and that of the measurement station is 2385.56 MCM.

**Figure 4.** Comparison of streamflow between the data from Ubolratana Dam Station and the calculated results from the SWAT model during 2011–2019.

#### 3.1.2. Forecasting of Future Streamflow Volumes

Forecasted future streamflow from 2020 to 2049 were expected to be impacted by climate change based on the CIMP5 model under the RCP4.5 projection case. In total, there was a 32% increase in the average annual streamflow in the future. With the MIROC\_ESM model, the streamflow volume was likely to increase to a maximum of 4734.97 MCM (98.49%), and with the MIROC5 model, it was expected to rise by 3889.10 MCM (63.03%). In the BNU model, it increased to 2905.53 MCM (21.80%), and in the CanESM model, it increased to 2758.80 MCM (15.65%). However, the FGOALS\_g2 model indicated that the average annual streamflow in the future was expected to decrease by 1528.95 MCM (−35.91%) (Figure 5). It was found that, overall, the average monthly streamflow volume increased during the rainy season, accounting for 2930.95 MCM (29.82%), and in the dry season, it accounted for 232.53 MCM (81.82%). When considering each model, there were 4 models, MIROC\_ESM, BNU, CanESM, and MIROC5. There was an increase in the average monthly streamflow during the rainy season between 2516.67–4479.10 MCM (11.47–98.40%), and the monthly average streamflow volume would increase significantly during the dry

season, especially in October showing a significantly higher proportion (Figure 6). The highest increase in the MIROC5 model was 356.00 MCM (178.36%). However, the study from the FGOALS\_g2 model expressed a trend of lower average monthly streamflow in both rainy and dry seasons which were 1467.22 MCM (−35.01%) and 61.73 MCM (−51.73%) respectively. The results were in line with the average annual streamflow (Table 5).

Climate change was projected to influence future streamflow levels between 2020 and 2049, according to the CIMP5 model under the RCP8.5 forecast. The results showed that the average annual streamflow across all models tended to increase. The MIROC5 model rose by 5828.46 MCM (144.32%), the BNU model climbed by 3704.05 MCM (55.27%), and the CanESM model increased by 3704.05 MCM (55.27%) (55.27%). Model FGOALS\_g2 grew to 2854.40 MCM (19.65%) and 3419.62 MCM (43.35%) (Figure 7). Looking at the seasonal average monthly streamflow volumes, the trend of change in average monthly water volume was similar under the RCP4.5 projection case but had a greater proportion of increase. Overall, the average monthly streamflow volume increased during the rainy season by 3551.80 MCM (57.32%) and by 401.32 MCM (213.81%) in the dry season. The increase was significant in both the rainy and dry seasons compared to the other models (Table 5), with a significant increase in percentage in October (Figure 8).

**Table 5.** Average monthly base year streamflow and seasonal forecasts.


**Figure 6.** Monthly streamflow from the base year SWAT model 2011–2019 and under the forecast of RCP 4.5 between 2020–2049.

**Figure 7.** Annual streamflow from the base year SWAT model 2011–2019 and under the forecast of RCP 8.5 between 2020–2049.

**Figure 8.** Monthly streamflow from the base year SWAT model 2011–2019 and under the forecast of RCP 8.5 between 2020–2049.
