*3.2. Optimal Reservoir Rule Curves with HBMO Algorithm Technique*

#### 3.2.1. Optimal Reservoir Rule Curves by HBMO Algorithm

The findings of the Ubolratana Reservoir rule curves generated with the HBMO Algorithm approach based on the CIMP5 climate change impacts of 5 models under RCP4.5 and RCP8.5 projection cases were compared to the present Ubolratana Reservoir rule curves. The rule curves in both predicted situations were discovered to be identical to the existing rule curves. However, from July to September, the newly developed upper rule curves were higher than the current rule curves. This effected an increase in the amount of water stored in the reservoir, resulting in a sufficient water supply for the next dry season. In the upper rule curves of the two forecast cases, the shape corresponded to the current rule curves, but the lower rule curves developed lower than the current ones during the dry season from December to April. This means that the reservoir can release more water than with the current rule curves. It can reduce water scarcity, making it possible to respond to water users in irrigated areas (Figures 9 and 10). According to recent study, applying the Harris Hawks Optimization (HHO) technique for searching in the Ubolratana reservoir, Thailand, the optimal rule curves with the HHO technique was similar to the current rule curves. The upper rule curves developed were higher than the current rule curves throughout the rainy season, allowing for additional water storage at the end of the rainy season [44].

**Figure 9.** Rule curves of Ubolratana reservoir developed using HBMO algorithm technique based on climate change impacts under the RCP4.5 projection case.

**Figure 10.** Rule curves of Ubolratana reservoir developed using HBMO algorithm technique based on climate change impacts under the RCP8.5 projection case.

#### 3.2.2. Reservoir Rule Curves Efficiency Evaluation

The purpose of evaluating the efficiency of reservoir rule curves is to test the functions of the rule curves in order to know the results that could support the changing water situations due to various uncertainties, whether in past periods or for scenarios that may occur in the future. The assessment of rule curves had two parts, namely, water shortage and excess release water by assessing the frequency of occurrence of an incident through mean and maximum values of Magnitude and Duration.

We evaluated the efficiency of the current reservoir rule curves and the reservoir rule curves obtained from future streamflow during 2020–2049, which yielded five CIMP5 models of climate change under the RCP4.5 scenario. In all models except the MIROC5 model, the reservoir rule curves were able to lower the mean water deficit and mean overflow when compared to the present rule curves. Under the RCP4.5 scenario, the reservoir rule curves from the MIROC\_ESM model were the most efficient ones in reducing mean water deficit and mean overflow when compared to the reservoir rule curves in other models (Table 6). Under the RCP8.5 scenario, the results showed that the reservoir rule curves in all models were able to reduce the average water shortage compared to the current rule curves. Moreover, the reservoir rule curves from the MIROC5 model could also help reduce the over-average water flow. The efficiency evaluation indicated that the reservoir rule curves from the MIROC5 model were able to reduce the average water shortage and average overflow the best when compared to the reservoir rule curves of all models (Table 7).


**Table 6.** Estimated results of water shortage and overflow events of the Ubolratana reservoir rule curves from the MIROC\_ESM model under the RCP4.5 projection case.

**Table 7.** Estimated water shortage and overflow events of the Ubolratana reservoir rule curves from the MIROC5 model under the RCP8.5 projection case.



#### **Table 7.** *Cont.*

#### **4. Conclusions**

There were two primary objectives of this research. The first was to investigate how global climate change has affected the quantity of streamflow that flows into the Ubolratana Reservoir in the years 2020–2592. Second, these modifications will be utilized as data for improving the suitable reservoir rule curves using the HBMO algorithm approach, as well as evaluating the effectiveness of the newly designed reservoir rule curves.

The results of this study showed that future streamflow data are based on the SWAT model. The forecast years 2020–2049 were projected to be influenced by climate change from the CIMP5 model, according to the findings of this study. Both RCP4.5 and RCP8.5 were expected to rise under the anticipated conditions. Under RCP4.5 and RCP8.5, the future overall average annual streamflow will rise by 32% and 65%, respectively. The MIROC\_ESM model had the highest average annual streamflow compared to other models. However, there is a different study (FGOALS\_g2 model, under the RCP4.5 forecast case), which indicates that the future annual mean streamflow tends to decline. When we considered the average monthly streamflow volume in the future according to seasons, it was found that the trend of change in streamflow volume was consistent with both under the forecasting cases. The average monthly streamflow volume was expected to increase markedly during the wet season (August to November) and at the beginning of the dry season (December).

Then, the Ubolratana Reservoir rule curves developed by HBMO Algorithm was created. There were five CIMP5 climate models under the RCP4.5 and 8.5 forecast cases, for which the developed rule curves were shaped in accordance with the current rule curves. Moreover, the developed rule curves could also allow the reservoir to hold more water during the rainy season. This should ensure that there will be enough water in the next dry season. In addition, during the dry season, reservoirs will be able to release more water, thereby reducing water scarcity. Finally, the future rule curves in the reservoir as a result of the climate change examined in this study would be able to answer the objective functions, which is to acquire the least average water scarcity amount. The rule curves will also be rated for their efficiency in reducing water scarcity and overflow compared to the current rule curves.

**Author Contributions:** Conceptualization, S.S. and A.K.; methodology, S.S. and A.K.; validation, S.S. and A.K.; formal analysis, S.S. and A.K.; investigation, S.S. and A.K.; writing—original draft preparation, S.S. and A.K.; writing—review and editing, S.S. and A.K.; supervision, S.S. and A.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research project was financially supported by Mahasarakham University.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** This study did not report any data.

**Acknowledgments:** The authors would like to acknowledge the Hydro–Informatics Institute, the Land Development Department, the Thai Meteorological Department, the Royal Irrigation Department and the Electricity Generating Authority, Thailand for supporting data in this study. The authors would like to thank the editor and the anonymous reviewers for their comments that helped in improving the quality of the paper.

**Conflicts of Interest:** The authors declare no conflict of interest.
