The uncertainties of climate and land use changes have directly impacted the inflows and water resource management in reservoirs. The optimal reservoir rule curve is a tool for the mitigation of droughts and floods, which are situations that occur often. This study applied the Genetic Algorithm (GA) to connect with a reservoir simulation model to search optimal reservoir rule curves during the period 2014-2064 for Lampao Reservoir located in the northeast of Thailand. It considered the impact of climate change with the PRECIS model under two emission scenarios: A2 and B2, and created future land use maps using the CA Markov model, including an assessment of the future inflow into the reservoir using the hydrologic model SWAT in the Upper-Lampao Basin, which is the headwater area of the reservoir. The results showed that the new rule curves were improved by the GA connected simulation model and can mitigate the frequency of water shortage situations and the releases of excess water during inflow changes in the future, including a situation where the water demand increased due to the expansion of irrigation areas.

A digital elevation model (DEM) is an important spatial input for automatic extraction of topographic parameters for the soil and water assessment tool (SWAT). The objective of this study was to investigate the impact of DEM resolution (from 5 to 90 m) on the delineation process of a SWAT model with two types of watershed characteristics (flat area and mountain area) and three sizes of watershed area (about 20,000, 200,000, and 1,500,000 hectares). The results showed that the total lengths of the streamline, main channel slope, watershed area, and area slope were significantly different when using the DEM datasets to delineate. Delineation using the SRTM DEM (90 m), ASTER DEM (30 m), and LDD DEM (5 m) for all watershed characteristics showed that the watershed sizes and shapes obtained were only slightly different, whereas the area slopes obtained were significantly different. The total lengths of the generated streams increased when the resolution of the DEM used was higher. The stream slopes obtained using the small area sizes were insignificant, whereas the slopes obtained using the large area sizes were significantly different. This suggests that water resource model users should use the ASTER DEM as opposed to a finer resolution DEM for model input to save time for the model calibration and validation.

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.

Multi-objective future rule curves are imperative recommendations for operating multipurpose reservoirs throughout long term periods. This research utilized the conditional tabu search algorithm (CTSA) and conditional genetic algorithm (CGA) combining to the reservoir simulation model through contemplating the multiple-purpose functionals when exploring processes for finding adaptable rule curves of a single reservoir. The historic inflow data during 1966-2016 (51 years) including the future inflow during 2017-2041 (25 years) in case of the B2 scenario of IPCC for the Ubolrat Reservoir in Thailand were applying to create the searching conditions. The 500 synthetic events of historical inflow and 25 years of future inflow were used to calculate the reservoir operation process for assessing the obtained rule curves. As a result, the predicament of water scarcity and spill water were illustrated in terms of frequency scale and duration along with the maintained water at the edge of the rainy period. The operation outcomes suggest that the multi-objective rule curves developed by the CGA can alleviate the frequency of flooding and drought situations appropriately than the CTSA during the future period. However, the rule curves obtained from both optimization techniques indicate better performance correlated to the actual rule curves along with having more maintained water at the end of the rainy period (November), which has continued benefits betwixt the dry period because the reservoir can discharge water in sufficient quantities to the downstream area.

This research aims to apply the Harris hawks optimization (HHO) technique connected with a reservoir simulation model to search optimal rule curves of the network reservoir system in Thailand. The downstream water demand from the network reservoir that required shared water discharge, hydrological data, and physical data were considered in the reservoir simulation model. A comparison of the situation of water shortage using optimal rule curves from HHO technique, genetic algorithm (GA), and wind-driven optimization (WDO) is presented. The results showed that the new rule curves derived from the HHO technique with network reservoir searching were able to alleviate the water shortage and over-flow situations better than the current rule curves. The efficiency of using rule curves from HHO technique compared to GA and WDO techniques showed that the HHO technique can provide a better solution that reduced water scarcity and average over-flow compared with the current rule curves by up to 4.80%, 4.70%, and 4.50%, respectively. In addition, HHO was efficient in converging rule curve solutions faster than GA and WDO techniques by 15.00% and 54.00%, respectively. In conclusion, the HHO technique can be used to search for optimal network reservoir rule curves solutions effectively.