Sustainable water resources management involves social, economic, environmental, water use, and resources factors. This study proposes a new framework of strategic planning with multi-criteria ...decision-making to develop sustainable water management alternatives for large scale water resources systems. A fuzzy multi-criteria decision-making model is developed to rank regional management alternatives for agricultural water management considering water-resources sustainability criteria. The decision-making model combines hierarchical analysis and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The management alternatives were presented spatially in the form of zoning maps at the level of irrigation zones of the study area. The results show that the irrigation management zone No.3 (alternative A3) was ranked first based on agricultural water demand and supply management in five among seven available scenarios, in which the scenarios represents a possible combination of weights assigned to the weighing criteria. Specifically, the results show that irrigation management zone No.3 (alternative A3) achieved the best ranking values of 0.151, 0.169, 0.152, 0.174 and 0.164 with respect to scenarios 1, 4, 5, 6 and 7, respectively. However, irrigation management zone No.2 (alternative A2) achieved the best values of 0.152 and 0.150 with respect to the second and third scenarios, respectively. The model results identify the best management alternatives for agricultural water management in large-scale irrigation and drainage networks.
•Long short-term memory neural network is proposed for groundwater prediction.•The proposed method relies only on in-situ piezometric observations.•The model is used for daily and monthly prediction ...for the Edward aquifer.•The results highlight the importance of long-term, groundwater level data.
The application of neural networks (NN) in groundwater (GW) level prediction has been shown promising by previous works. Yet, previous works have relied on a variety of inputs, such as air temperature, pumping rates, precipitation, service population, and others. This work presents a long short-term memory neural network (LSTM-NN) for GW level forecasting using only previously observed GW level data as the input without resorting to any other type of data and information about a groundwater basin. This work applies the LSTM-NN for short-term and long-term GW level forecasting in the Edwards aquifer in Texas. The Adam optimizer is employed for training the LSTM-NN. The performance of the LSTM-NN was compared with that of a simple NN under 36 different scenarios with prediction horizons ranging from one day to three months, and covering several conditions of data availability. This paper’s results demonstrate the superiority of the LSTM-NN over the simple-NN in all scenarios and the success of the LSTM-NN in accurate GW level prediction. The LSTM-NN predicts one lag, up to four lags, and up to 26 lags ahead GW level with an accuracy (R2) of at least 99.89%, 99.00%, and 90.00%, respectively, over a testing period longer than 17 years of the most recent records. The quality of this work’s results demonstrates the capacity of machine learning (ML) in groundwater prediction, and affirms the importance of gathering high-quality, long-term, GW level data for predicting key groundwater characteristics useful in sustainable groundwater management.
The Western Jianghan Plain (WJHP) lies in the middle reaches of the Yangtze River. It has been impacted by anthropogenic activities during the past decades. The long-term variations of the WJHP's ...regional aquifer's hydrochemistry and groundwater quality have not been previously assessed. Sixteen physiochemical parameters at 29 monitoring wells within the Western Jianghan Plain were monitored during 1992–2010 and analyzed with multiple approaches. The confined groundwater is predominantly of the HCO3-Ca-Mg type with Cl−, SO42−, NH4-N, and NO3-N showing remarkable spatial variations. Correlation analysis was used to identify the origins and contamination sources of groundwater. The seasonal Mann-Kendall test revealed that pH, NO3-N, and Cl− concentrations at 27, 26 and 15 wells, respectively, exhibited significant increasing trends during 1992–2010. The increase of pH may be attributed to CO2 degassing caused by extensive groundwater extraction. Regional average NO3-N concentrations of groundwater increased coincidently with the increased use of fertilizer, which suggests that nitrate pollution is caused by agricultural activities. Abnormally high values of Cl− and SO42− at some wells were induced by industrial chemicals. In addition, the similarity of the temporal variations of the regional average of pH, NH4-N, and NO3-N concentrations in groundwater with those in the Yangtze River at the outlet of the Three Gorges Reservoir (TGR) suggests that the variations of these parameters in the WJHP is partly due to water storage by the TGR. This study presents an analysis of temporal variations of groundwater quality in the WJHP that reveals a relation between the creation of the TGR and downstream groundwater quality. This paper's findings provide clues for measures that could be taken to protect the groundwater quality of the WJHP's aquifer.
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•Spatial-temporal variations of groundwater quality were characterized.•CO2 degassing caused by groundwater extraction increased the groundwater pH.•NO3-N increased coincidently with the increased use of fertilizer.•The Three Gorges Dam contributes partly to the variations of pH, NH4-N and NO3-N.
Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. ...However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.
This study assesses the feedbacks between water, food, and energy nexus at the national level with a dynamic-system model, taking into account the qualitative and quantitative environmental water ...needs. Surface and groundwater resources are considered jointly in the water resources subsystem of this dynamic system. The developed model considers the effects of reducing the per capita use water and energy on its system's components. Results indicate that due to feedbacks the changes in per capita uses of water and energy have indirect and direct effects. About 40% of the total water savings achieved by the per capita change policy was related to energy savings, in other words, it is an indirect saving. Implementation of per capita use reductions compensates for 9% of the decline of Iran's groundwater reservoirs (non-renewable resources in the short term) that occur during the five-year study period. The Manageable and Exploitable Renewable Water Stress Index (MRWI) corresponding to water and energy savings equals 214.5%, which is better than its value under the current situation (which is equal to 235.1%).
Efficient water allocation in a transboundary river basin is a complex issue in water resources management. This work develops a framework for the allocation of transboundary river water between the ...countries located in the river basin to evaluate the characteristics of allocation approaches. The allocation of river water is obtained based on initial-water conditions, cooperative, and non-cooperative game-theoretic approaches. The initial-conditions water allocation approach assigns 34, 40, and 26% of the Harirud River flow to Afghanistan, Iran, and Turkmenistan, respectively. The game-theoretic cooperative approach assigns 36, 42, and 22% of the river flow to Afghanistan, Iran, and Turkmenistan, respectively. The non-cooperative game-theoretic approach establishes that the most stable water allocation was 42, 38, and 20% of the Harirud River flow for Afghanistan, Iran, and Turkmenistan, respectively. Human and agricultural water-stress criteria are used to evaluate the water allocations in the Harirud River basin. The criterion of human water stress has the largest influence in Iran, and the criterion of agricultural water stress has the smallest influence in Afghanistan. This work's results indicate the initial-conditions water allocation approach favors Turkmenistan, whereas the cooperative and the non-cooperative game-theoretic approaches favors Iran and Afghanistan, respectively. The results show that the priorities of each country governs water allocation, and cooperation is shown to be necessary to achieve sustainable development.
• Development of gravity search algorithm (GSA).• Verification of GSA in mathematical functions.• Application of GSA to multi-reservoir operation optimization.
Complexities in river discharge, ...variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems.
Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different ...fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO’s applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall–runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.
The Muskingum model is a popular hydrologic flood routing technique; however, the accurate estimation of model parameters challenges the effective, precise, and rapid-response operation of flood ...routing. Evolutionary and metaheuristic optimization algorithms (EMOAs) are well suited for parameter estimation task associated with a wide range of complex models including the nonlinear Muskingum model. However, more proficient frameworks requiring less computational effort are substantially advantageous. Among the EMOAs teaching-learning-based optimization (TLBO) is a relatively new, parameter-free, and efficient metaheuristic optimization algorithm, inspired by the teacher-student interactions in a classroom to upgrade the overall knowledge of a topic through a teaching-learning procedure. The novelty of this study originates from (1) coupling TLBO and the nonlinear Muskingum routing model to estimate the Muskingum parameters by outflow predictability enhancement, and (2) evaluating a parameter-free algorithm's functionality and accuracy involving complex Muskingum model's parameter determination. TLBO, unlike previous EMOAs linked to the Muskingum model, is free of algorithmic parameters which makes it ideal for prediction without optimizing EMOAs parameters. The hypothesis herein entertained is that TLBO is effective in estimating the nonlinear Muskingum parameters efficiently and accurately. This hypothesis is evaluated with two popular benchmark examples, the Wilson and Wye River case studies. The results show the excellent performance of the "TLBO-Muskingum" for estimating accurately the Muskingum parameters based on the Nash-Sutcliffe Efficiency (NSE) to evaluate the TLBO's predictive skill using benchmark problems. The NSE index is calculated 0.99 and 0.94 for the Wilson and Wye River benchmarks, respectively.
Confronting climate change is a daunting challenge that requires policies for climate adaptation in the field of water resources management. This paper proposes a method for reservoir operation ...associated with climate-change projections aimed at ensuring the sustainability of agricultural water supply. The method is applied to the Aidoghmoush reservoir in East Azerbaijan province (Iran) employing climate-change projections for 2040–2069, and compares the future-period results with those calculated for the baseline period (1971–2000). The water-supply system depending on the Aidoghmoush reservoir is simulated using the climate-change projections. The water-supply system simulations are ranked with two multi-criteria decision-making (MCDM) methods according to their suitability for satisfying agricultural water demands and sustain cropping patterns. These are the multi-criteria optimization and compromise resolution (VIKOR) and the Fuzzy Order Weighted Average (FOWA) MCDM methods. The MCDM methods identify the best water-supply management alternatives for climate-change adaptation.
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