Optimal rule curves are necessary guidelines in the reservoir operation that have been used to assess performance of any reservoir to satisfy water supply, irrigation, industrial, hydropower, and environmental conservation requirements. This study applied the conditional genetic algorithm (CGA) and the conditional tabu search algorithm (CTSA) technique to connect with the reservoir simulation model in order to search optimal reservoir rule curves. The Ubolrat Reservoir located in the northeast region of Thailand was an illustrative application including historic monthly inflow, future inflow generated by the SWAT hydrological model using 50‐year future climate data from the PRECIS regional climate model in case of B2 emission scenario by IPCC SRES, water demand, hydrologic data, and physical reservoir data. The future and synthetic inflow data of reservoirs were used to simulate reservoir system for evaluating water situation. The situations of water shortage and excess water were shown in terms of frequency magnitude and duration. The results have shown that the optimal rule curves from CGA and CTSA connected with the simulation model can mitigate drought and flood situations than the existing rule curves. The optimal future rule curves were more suitable for future situations than the other rule curves.

This paper reviews applications of optimization techniques connected with reservoir simulation models to search for optimal rule curves. The literature reporting the search for suitable reservoir rule curves is discussed and examined. The development of optimization techniques for searching processes are investigated by focusing on fitness function and constraints. There are five groups of optimization algorithms that have been applied to find the optimal reservoir rule curves: the trial and error technique with the reservoir simulation model, dynamic programing, heuristic algorithm, swarm algorithm, and evolutionary algorithm. The application of an optimization algorithm with the considered reservoirs is presented by focusing on its efficiency to alleviate downstream flood reduction and drought mitigation, which can be explored by researchers in wider studies. Finally, the appropriate future rule curves that are useful for future conditions are presented by focusing on climate and land use changes as well as the participation of stakeholders. In conclusion, this paper presents the suitable conditions for applying optimization techniques to search for optimal reservoir rule curves to be effectively applied in future reservoir operations.

Reservoir rule curves are basic guides for the long-term operation of reservoir systems. This paper presents a simulated annealing (SA) algorithm connected with a simulation model to determine optimal reservoir rule curves. The proposed model was applied to the Bhumibol and Sirikit reservoirs in Thailand. The pattern of the obtained rule curves was similar to existing rule curves and the rule curves obtained using genetic algorithms (GAs). The obtained rule curves were used to simulate the Bhumibol and Sirikit reservoir systems with synthetic inflows and these results were compared with the GA-obtained rule curves. The results from the two techniques, in considering both water shortage and excess release of water, were analogous. This indicates that the proposed SA algorithm is effective in determining optimal rule curves for reservoirs.

Climate and land use changes are major factors affecting runoff in regional basins. Understanding this variation through considering the interactions among hydrological components is an important process for water resource management. This study aimed to assess the variation of future runoff in the Upper Chi Basin, Northeastern Thailand. The QSWAT hydrological model was integrated into three CMIP6 GCMs—ACCESS-CM2, MIROC6, and MPI-ESM1-2-LR—under SSP245 and SSP585 scenarios for the period 2023–2100. The Land Change Modeler (LCM) was also used for future land use simulation. The results revealed that the future average long-term precipitation and temperature tended to increase while forest land tended to decrease and be replaced by sugarcane plantations. The accuracy assessment of the baseline year runoff calculation using QSWAT for the period 1997–2022 showed an acceptable result, as can be seen from the R², NSE, RSR, and PBIAS indices. This result could lead to the temporal and spatial simulation of future runoff. Likewise, the runoff of the two SSP scenarios tended to increase consecutively, especially in the SSP585 scenario. In addition, in cases of long-term spatial changes in the subbasins scale, over 90% of the area—from upstream to the outlet point—tended to be higher due to two major factors; namely, future increased precipitation and changes in cultivation, which would be influential to groundwater and interflow components, respectively. The methodology and result of this study can be useful to stakeholders in understanding changes in hydrological systems so that they can apply it to developing a strategy for water resource management and to handling factors affecting different dimensions properly and sustainably.

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior to harvest at the province scale for fourteen provinces in Thailand’s Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and the variance inflation factor (VIF). The temperature condition index (TCI), the normalized difference vegetation index (NDVI), land surface temperature (LST_nighttime), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root-mean-square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region will be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation.