Sustainable Water Allocation in Umarkhed Taluka through Optimization of Reservoir Operation in the Wardha Sub-Basin, India

Climate change is causing shifts in seasonal weather patterns and variation in seasonal time scales in India. Factors including uneven distribution of water, faulty agricultural practices and water policies, low prices of farm products, and debt are leading farmers to commit suicide in Umarkhed Taluka of the Yavatmal District. This study aimed to develop a sustainable solution to water scarcity in the surrounding watershed by introducing optimization modeling in reservoir operation. Past studies have conducted different hydrologic analyses to address the water scarcity issue in this region. However, none of the studies incorporated optimization in their models. This study developed an integrated hydrologic and optimization model that can predict the daily reservoir releases for climate change scenarios from 2020 to 2069 based upon Representative Concentration Pathway (RCP-4.5 and RCP-8.5) climate change scenarios from 2020 to 2069. The integrated simulations were able to deliver around 19% more water than the historical discharge at the most downstream station of the Wardha Watershed. The simulated approaches store less water than the actual unoptimized scenario and deliver water when there is a need at the downstream locations. Finally, because the downstream locations of the Wardha Watershed receive more water, a localized storage system can be developed and a transfer method can be utilized to deliver sufficient water to the Umarkhed Taluka.

Reducing impacts of rice fields nitrate contamination on the river ecosystem by a coupled SWAT reservoir operation optimization model

The present study proposes a multipurpose reservoir operation optimization for mitigating impact of rice fields’ contamination on the downstream river ecosystem. The developed model was applied in the Tajan River basin in Mazandaran Province, Iran, in which the rice is the main crop. We used soil and water assessment tool (SWAT) to simulate inflow of the reservoir and nitrate load at downstream river reach. Nash–Sutcliffe model efficiency coefficient was used to measure the robustness of SWAT. NSE indicated that SWAT is acceptable to simulate nitrate load of the rice fields. The results of SWAT was applied in the structure of a multipurpose reservoir operation optimization in which three metaheuristic algorithms including differential evolution algorithm, particle swarm optimization and biogeography-based algorithm were utilized in the optimization process. Reliability index, mean absolute error and failure index were used to measure the robustness of the optimization algorithms. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution was utilized to select the best algorithm. Based on results, particle swarm optimization is the best method to optimize reservoir operation in the case study. The reliability index and mean absolute error for water supply are 0.6 and 5 million cubic meters, respectively. Furthermore, the failure index of contamination is 0.027. Hence, it could be concluded that the proposed optimization system is reliable and robust to mitigate losses and nitrate contamination simultaneously. However, its performance is not perfect for minimizing impact of contamination in all the simulated months.

Construction and Application of Reservoir Flood Control Operation Rules Using the Decision Tree Algorithm

Current conventional and optimal reservoir flood control operation methods insufficiently utilize historical reservoir operation data, which include rainfall, runoff generation, and inflow from the watershed, as well as the operational experience of decision makers over many years. Therefore, this study proposed and evaluated a new method for extracting reservoir flood control operation rules from historical operation data using the C4.5 algorithm. Thus, in this paper, the C4.5 algorithm is first introduced; then, the generation of the flood control operation dataset, the construction of decision tree-based (DT-based) rules, and the subsequent design of a real-time operating scheme are detailed. A case study of the Rizhao Reservoir is then employed to demonstrate the feasibility and even superiority of the operating scheme formulated using DT-based rules. Compared with previously proposed conventional and optimal reservoir operation methods, the DT-based method has the advantages of strong and convenient adaptability, enabling decision makers to effectively guide real-time reservoir operation.

Deriving reservoir operating rules considering ecological demands of multiple stations

Reservoirs play important roles in hydropower generation, flood control, water supply, and navigation. However, the regulation of reservoirs is challenged due to their adverse influences on river ecosystems. This study uses ecoflow as an ecological indicator for reservoir operation to indicate the extent of natural flow alteration. Three reservoir optimization models are established to derive ecological operating rule curves. Model 1 only considers the maximization of average annual hydropower generation and the assurance rate of hydropower generation. Model 2 incorporates ecological objectives and constraints. Model 3 not only considers the hydropower objectives but also simulates the runoff and calculates the ecological indicator values of multiple downstream stations. The three models are optimized by a simulation-optimization framework. The reservoir ecological operating rule curves are derived for the case study of China's Three Gorges Reservoir. The results represent feasible schemes for reservoir operation by considering both hydropower and ecological demands. The average annual power generation and assurance rate of a preferred optimized scheme for Model 3 are increased by 1.06% and 2.50%, respectively. Furthermore, ecological benefits of the three hydrologic stations are also improved. In summary, the ecological indicator ecoflow and optimization models could be helpful for reservoir ecological operations.

A Bi-level Neuro-Fuzzy System Soft Computing for Reservoir Operation

Reservoir operation studies purely based on the storage level, inflow, and release decisions during dry periods only fail to serve the optimal reservoir operation policy design because of the fact that the release decision during this period is highly dependent on wet season water conservation and flood risk management operations. Imperatively, the operation logic in the two seasons are quite different. If the two operations are not sufficiently coordinated, they may produce poor responses to the system dynamics. There are high levels of uncertainties on the model parameters, values and how they are logically operated by human or automated systems. Soft computing methods represent the system as an artificial neural network (ANN) in which the input- output relations take the form of fuzzy numbers, fuzzy arithmetic and fuzzy logic (FL). Neuro-Fuzzy System (NFS) soft computing combine the approaches of FL and ANN for single purpose reservoir operation. Thus, this study proposes a Bi-Level Neuro-Fuzzy System (BL-NFS) soft computing methodology for short and long term operation policies for a newly inaugurated irrigation project in Gidabo Watershed of Main Ethiopian Rift Valley Basin. Keywords: Bankruptcy rule, BL-NFS, Reservoir operation, Sensitivity analysis, Soft computing, Water conservation.