**1. Introduction**

Uncertainty has a direct influence on the understanding of hydrology and water resource cycles caused by global climate change, as well as the growing frequency and intensity of droughts and floods throughout the world; these events are jeopardizing the management and development of water resources to meet global demands in all industries, making management more complex and difficult. For the past two decades, climate change has had a global impact on water resource management. Several study groups have sought to create ways for controlling water at its sources in order to deal with the fluctuation of supply sides and demand sides. The majority of such studies have evaluated the consequences of future climate change based on prediction findings from climate models combined with hydrological models to analyze impacts on water allocation efficiency for consumption [1], irrigation [2,3] hydroelectric power generation [4], and procurement of new reservoirs in the future [5].

In Thailand after the Great Flood of 2011, numerous watershed areas experienced drought between 2012 and 2019. The primary reason for this is that rainfall was below normal [6]. Many rivers' average discharge was lower than usual [7]. Government agencies must implement campaign initiatives to encourage consumers and farmers to consume

**Citation:** Songsaengrit, S.; Kangrang, A. Dynamic Rule Curves and Streamflow under Climate Change for Multipurpose Reservoir Operation Using Honey-Bee Mating Optimization. *Sustainability* **2022**, *14*, 8599. https://doi.org/10.3390/ su14148599

Academic Editors: Alban Kuriqi and Luis Garrote

Received: 25 June 2022 Accepted: 11 July 2022 Published: 14 July 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

water most efficiently and cost-effectively as possible. The northeastern area of Thailand comprises more than 60% agricultural land and is mostly dependent on seasonal rainfall in off-season cultivation, especially for rice cultivation, as it requires water from irrigation systems which rely on the cost of water from reservoirs. Meanwhile, the demand for water downstream in various sectors tends to increase. Many large and medium-sized reservoirs are unable to allocate water to meet the needs of all sectors effectively. In addition, the development of water resource management through efficient tools and methodology, alongside the consideration of the conditions of complex and nonlinear problems in all dimensions, is required especially for the management of reservoir water resources in situations of global climate change volatility [5]. It is, therefore, necessary to make an urgent adjustment.

Over the past decade, climate and streamflow were included in future hydrological models. These two factors have been used in combination with reservoir management. It is an approach that has been widely used in studies across the world. In Thailand, the Hydro-Informatics Institute created and released the Coupled Model Intercomparison Project Phase 5 (CMIP5) family of global climate models. This model has undergone bias correction using a Gamma-Gamma (GG) transformation optimization approach [8] to make future computation results more dependable. The products from CMIP5 have been used to analyze the effects of climate change in Thailand's watershed areas [9], hydrological systems in Southeast Asia [10], and many other places across the world [11,12].

A hydrological model is used to forecast future streamflow. In this study, a semi-diffuse hydrological model was investigated. The SWAT [13] is the world's most popularly used climate model, because of its integration of geographic information (GIS) data and regional climatic data in watershed areas of every size. As a result, the analysis is trustworthy. SWAT has been used in Thailand to examine and analyze the quantity of streamflow in various scenarios [14,15], and for the future management [16,17] of water resources in watersheds and reservoirs [18]. The precision of SWAT calculation results could improve when compared to the real measurements and this was accomplished by employing the SWAT-CUP model and the SUFI-2 approach [19] to choose the most appropriate sensitivity variables for analyzing the studied watershed regions. Therefore, based on the strengths of the CMIP5-derived products, once they were imported into SWAT, the results were expected to be future streamflow that differ from the new projection of greenhouse gas emissions. The Representative Concentration Pathway (RCP) as defined in the fifth Assessment Report (AR5) by the IPCC [20] provides cost information for appropriate reservoir management to situations of future hydrological variation.

There have already been some studies on applying optimization techniques to reservoir management, particularly in the development of suitable reservoir rule curves. Mathematicians have created evolutionary optimization approaches throughout the last decade. Appropriate reservoir rule curves were created using metaheuristic optimization techniques. Several approaches are popular in Thailand and across the world, such as Genetic Algorithm (GA) [18,21–23] Ant Colony Optimization (ACO) [24], Firefly Algorithm (FA) [25], Grey Wolf Optimization (GWO) [26], Tabu Search Algorithm (TSA) [27,28], and Particle Swarm Optimization (PSO) [21,22]. However, a new kind of evolutionary technique has been created, which is a natural-inspired approach to solving problems and finding answers in engineering. It is the Honey-Bee Mating Optimization (HBMO) algorithm [29], a process for optimization by imitating bee swarm behavior.

However, the solution to reservoir water allocation challenges caused by climate change affects future streamflow volumes. It was discovered that there were not many studies in the northeastern part of Thailand, along with forecasts of the variance in water demand from diverse activities in the downstream areas, especially for reservoirs in remote places where functionality is essential. Ubolratana Reservoir is the first significant multi-purpose reservoir in Thailand's northeast that provides hydroelectric electricity by combining irrigation and rainwater harvesting to reduce floods during the wet season. However, in the last ten years, dry-season water resource management has encountered a

water insufficiency problem. Water intake to reservoirs has been lower than the average amount. In contracts, in certain years, the volume of water flowing into the reservoir surpasses the storage capacity during the rainy season. The water must be drained onto the downstream side, causing floods in residential and agricultural regions. As a result, when Ubolratana Reservoir has to develop suitable and efficient water distribution criteria, taking into account the diversity of hydrological circumstances in the past, present, and future together with the application of evolutionary optimization techniques to create more efficient rule curves. This would be expected to be of great benefit for water resource management.

In the past, the consideration of improving the reservoir rule curves of Ubolratana Reservoir, and the other reservoirs in Northeastern Thailand was a case study based on climate change forecasting from the AR4 models [18,30]. This research draws on climate forecasting data from the CMIP5 model based on the RCP4.5 and RCP8.5 scenarios that use bias correction to be more accurate, including there are various types and different model resolutions. The integration of SWAT hydrological models into the analysis of streamflow conditions has not been previously studied, and the same applies to experiments that link these models to the development of the optimal reservoir rule curves with the HBMO technique. Consequently, the expected outcome of the study is the optimal rule curves, appropriate outcome for the climate change situation and the variation on streamflow in many cases.

The purpose of this research was to use the CMIP5 and SWAT models to examine how global climate change affects the quantity of streamflow input into the Ubolratana Reservoir, as well as to improve the reservoir rule curves by employing the approach of the HBMO and considering the objective function, which is to minimize the quantity of water that is scarce and the amount of water that overflows the reservoir, respectively. The results of this study were predicted to be useful in predicting water scarcity and extreme water circumstances for flexible water management, provided as decision-support information for stakeholders to use as information for climate change policy planning and evaluation of water allocation guidelines to assist future activities.

#### **2. Materials and Methods**

## *2.1. Research Area*

The research site was Ubolratana Reservoir in Ubolratana District, Khon Kaen Province. The study focused on five watershed areas; Lam Pha Niang, Lam Nam Phue, Upper Lam Nam Phong, Lam Nam Choen, and Lam Nam Phrom, all of which are tributaries of the Chi River Basin in northeast Thailand (Figure 1), with a total water intake area of around 12,000 square kilometers. The reservoir is a rock-fill dam with a clay core with a height of 2 m. The dam crest is 185.00 m above sea level. The basin receives an average of 2470 MCM of water each year. The normal water storage capacity is 2431.3 MCM, with a reservoir area of 370 square kilometers. The main functions of the reservoir are for generating electricity with an annual power generation capacity of approximately 56.1 million kilowatt-hours, irrigation covering an area of approximately 480 square kilometers, flood relief, fisheries, and intercity transportation travel.

**Figure 1.** Study areas of tributaries in the Chi River Basin.
