Reservoir water quality simulation with data mining models.

Water pollution is a concern in the management of water resources. This paper presents a statistical approach for data mining of patterns of water pollution in reservoirs. Genetic programming (GP), artificial neural network (ANN), and support vector machine (SVM) are applied to reservoir quality modeling. Input data for GP, ANN, and SVM were derived with the CE-QUAL-W2 numerical water quality simulation model. A case study was carried out using measured reservoir inflow and outflow, temperature, and nitrate concentration to the Amirkabir reservoir, Iran. Data mining models were evaluated with the MAE, NSE, RMSE, and R2 goodness-of-fit criteria. The results indicated that using the SVM model for determining nitrate pollution is time saving and more accurate in comparison with GP, ANN, and particularly CE-QUAL-W2. The SVM model reduces the runtime of nitrate concentration simulation by 581, 276, and 146 s compared with CE-QUAL-W2, GP, and ANN, respectively. The goodness-of-fit results showed that the highest values (R2 = 0.97, NSE = 0.92) and the lowest values (MAE = 0.034 and RMSE = 0.007) corresponded to SVM predictions, indicating higher model accuracy. This study demonstrates the potential for application of data mining tools to solute concentration simulation in reservoirs.

Inter-basin hydropolitics for optimal water resources allocation.

Efficient, just, and sustainable water resources' allocation is difficult to achieve in multi-stakeholder basins. This study presents a multi-objective optimization model for water resources allocation and reports its application to the Sefidrud basin in Iran. Available water resources are predicted until 2041with the artificial neural network algorithm (ANN). This is followed by multi-objective optimization of water resource allocation. The first objective function of the optimization model is maximization of revenue, and the second objective function is the achievement of equity in water resources allocation in the basin. This study considers two scenarios in the optimization scheme. The first scenario concerns the water allocation with existing dams and dams under construction. The second scenario tackles water allocation adding dams currently in the study stage to those considered in Scenario 1. The Gini coefficient is about 0.1 under the first scenario, indicating the preponderance of economic justice in the basin. The Gini coefficient is about 0.4 under the second scenario, which signals an increase of injustice in water allocation when considering the future operation of dams currently under study.

Analysis of the effect of inputs uncertainty on riverine water temperature predictions with a Markov chain Monte Carlo (MCMC) algorithm.

Water temperature is a key characteristic defining chemical, physical, and biologic conditions in riverine systems. Models of riverine water quality require many inputs, which are commonly beset by uncertainty. This study presents an uncertainty analysis of inputs to the stream-temperature simulation model HFLUX. This paper's assessment relies on a Markov chain Monte Carlo (MCMC) analysis with the DREAM algorithm, which has fast convergence rate and good accuracy. The inputs herein considered are the river width and depth, percent shade, view to sky, streamflow, and the minimum and maximum values of inputs required for uncertainty analysis. The results are presented as histograms for each input specifying the input's uncertainty. A comparison of the observational data with the DREAM algorithm estimates yielded a maximum error equal to 7.5%, which indicates excellent performance of the DREAM algorithm in ascertaining the effect of uncertainty in riverine water quality assessment.

State-of-art of genetic programming applications in water-resources systems analysis.

Evolutionary algorithms (EAs) have become competitive solvers of a wide variety of water-resources optimization problems. Genetic programming (GP) has become a leading EA since its inception in 1985. This paper reviews the state-of-the-art of GP and its applications in water-resources systems analysis. A comprehensive knowledge about GP's theory and modeling approach is essential for its successful application in water-resources systems analysis. This review presents variants of GP that have been proven useful in various applications to water resources problems. Several examples of applications of GP in water-resources systems analysis are herein presented. This review reveals GP's capability and superiority compared to other conventional methods, which makes it suitable for solving a wide variety of water-related problems including rainfall-runoff modeling, streamflow sediment prediction, flood prediction and routing, evaporation and evapotranspiration forecasting, reservoir operation, groundwater modeling, water quality modeling, water demand forecasting, and water distribution systems.

Groundwater safe yield powered by clean wind energy.

Wind energy has been used by humans for thousands of years. Yet, the relatively low economic cost and availability of fossil fuels upstaged the use of wind power. Fossil fuel resources are not renewable and will decline until exhaustion in the future. At the same time, humans have become aware of the adverse effects on the environment caused by reliance on fossil fuel energy. Wind, on the other hand, is a renewable energy source with minimal adverse environmental impacts that does not involve greenhouse gas emissions. Agricultural irrigation systems use fossil fuel energy resources in various forms. Groundwater withdrawal is central to supplying agricultural water demand in arid and semi-arid regions. Such withdrawal is mostly based on water extraction with pumps powered by diesel, gasoline, or electricity (which is commonly produced by fossil fuels). This paper coupled the non-sorted genetic algorithm (NSGA-II) as the optimization tool to the mathematical formulation of the wind-powered groundwater production problem to determine the potential of wind energy for groundwater withdrawal in an arid area. The optimal safe yield and the optimal size of regulation reservoir are determined considering two objectives: (1) maximizing total extraction of groundwater and (2) minimizing the cost of reservoir construction. The safe yield and the two objectives are optimized for periods lasting 1, 2, 3, 4, and 6 months over a 1-year planning horizon. This paper's methodology is evaluated with groundwater and wind-power data pertinent to Eghlid, Iran. The optimal safe yield increases by increasing the period length. Specifically, increasing the period length from 1 to 6 months increases the safe yield from 12 to 29 m3. Application of the proposed NSGA-II-based optimization of groundwater production identifies the best design and operational variables with computational efficiency and accuracy.

Application of particle swarm optimization to water management: an introduction and overview.

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.

Logical genetic programming (LGP) application to water resources management.

Genetic programming (GP) is a variant of evolutionary algorithms (EA). EAs are general-purpose search algorithms. Yet, GP does not solve multi-conditional problems satisfactorily. This study improves the GP's predictive skill by development and integration of mathematical logical operators and functions to it. The proposed improvement is herein named logical genetic programming (LGP) whose performance is compared with that of GP using examples from the fields of mathematics and water resources. The results of the examples show the LGP's superior performance in both examples, with LGP producing improvements of 74 and 42% in the objective functions of the mathematical and water resources examples, respectively, when compared with the GP's results. The objective functions minimize the mean absolute error (MAE). The comparison of the LGP and GP results with alternative performance criteria demonstrate a better capability of the former algorithm in solving multi-conditional problems.

Climate-environment-water: integrated and non-integrated approaches to reservoir operation.

Integrated water planning and management face multiple challenges, among which are the competing interests of several water-using sectors and changing climatic trends. This paper presents integrated and non-integrated climate-environment-water approaches for reservoir operation, illustrated with Karkhe reservoir, Iran. Reservoir operation objectives are meeting municipal, environmental, and agricultural water demands. Results show the integrated approach, which relies on multi-objective optimization of municipal, environmental, and agricultural water supply, improves the municipal, environmental, and agricultural objectives by 70, 32, and 65% compared with the objectives' values achieved with the non-integrated approach, which implements a standard operating policy.

Optimizing stormwater low-impact development strategies in an urban watershed considering sensitivity and uncertainty.

Stormwater management in an urban environment is beset by uncertainties about future development. Dynamic strategies must be devised to cope with such uncertain environment. This work proposes a simulation-optimization model that minimizes the costs of low-impact development (LID) measures for mitigating impacts of future urban development on runoff. This paper's methodology is tested in an urban watershed in Tehran, Iran, relying on the stormwater management model (SWMM) coupled with the genetic algorithm (GA) to function as a simulation-optimization method for urban-runoff control by means of LID stormwater control measures. A sensitivity analysis of the calculated optimal solution revealed the impacts the most sensitive LIDs would have on runoff considering a set of plausible future development scenarios in the urban catchment. A comparison of the results from two different scenarios of future development with the existing stormwater system's performance shows the cost increase in redesigning the existing system to make it LID sensitive would equal 20% of the existing system's cost. The additional cost of redesigning the existing system without LID features would be 45% of the existing system's cost. These results demonstrate the importance of assessing the sensitivity of designed units in a stormwater management system and studying the trade-offs between possible decisions and future uncertainties concerning development in the watershed.

Application of non-animal-inspired evolutionary algorithms to reservoir operation: an overview.

Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.

Assessment of potential of intraregional conflicts by developing a transferability index for inter-basin water transfers, and their impacts on the water resources.

Rapid population growth, rising water demands, inefficient management, and various distributions of water are the major causes of increased pressure on water resources and the consequent increased water-based conflicts especially in arid and semi-arid regions in Iran being a case in point. Iran is the second largest country in the Middle East. The country-wide average annual precipitation is about 250 mm, which is about one third of the world's average. Therefore, Iran is one of the driest countries in the world. The water supply for human activities in Iran's provinces has become an increasingly complex task. One of the conventional methods to supply water to these regions is through inter-basin water transfers, from water-endowed regions to water-scarce regions. For such projects, it is necessary but also difficult and expensive to estimate the total water storage of every province with traditional methods. This study employs the GRACE satellite data for 2002-2016 are used and develops a method to assess the linkage between water scarcity and conflicts in Iran's provinces. In addition, a transferability index is formulated based on population and conveyable water parameters demonstrating the conditions of the provinces in inter-basin water transfer for reaching equitable compromises. This index leads to an evaluation of the possibility of conflicts arising from inter-basin water transfer projects in Iran. This work's results show that the Bushehr region has a significant amount of conveyable water and low population and hence is suitable to be one of the water-exporting provinces in the inter-basin water projects. The results of this work also demonstrate that the western provinces are likely to experience serious depletion of water resources, and conflicts may arise in the western and central basins due to the changes in water quantity exacerbated by the inter-basin transfer projects.

Upstream flood pattern recognition based on downstream events.

Reverse stream flood routing determines the upstream hydrograph in a stream reach given the downstream hydrograph. The Muskingum model of flood routing involves parameters that govern the routed hydrograph. These parameters are herein estimated using simulation methods coupled with optimization tools to achieve optimized parameters. Different simulation methods are shown to perform unequally in the estimation of nonlinear Muskingum parameters. This paper presents two simulation methods for nonlinear Muskingum reverse flood routing: (1) Euler equations and (2) Runge-Kutta 4th order equations. Moreover, the generalized reduced gradient (GRG) is used as the optimization tool that minimized the sum of the squared deviations (SSQ) between observed and routed inflows in a benchmark flood routing problem. Results show the Runge-Kutta 4th order equations yield better routed hydrographs with smaller SSQ than obtained in previous research and with the first simulation method (Euler equations).

Real-time reservoir operation using data mining techniques.

The optimal operation of hydropower reservoirs is essential for the planning and efficient management of water resources and the production of hydroelectric energy. Various techniques such as the genetic algorithm (GA), artificial neural networks (ANN), support vector machine (SVM), and dynamic programming (DP) have been employed to calculate reservoir operation rules. This paper implements the data mining techniques SVM and ANN to calculate the optimal release rule of hydropower reservoirs under "forecasting" and "non-forecasting" scenarios. The employment of data mining techniques accounting for data uncertainty to calculate optimal hydropower reservoir operation is novel in the field of water resource systems analysis. The optimal operation of the Karoon 3 reservoir, Iran, serves as a test of the proposed methodology. The upstream streamflow, storage records, and several lagged variables are model inputs. Data obtained from solving the reservoir optimization problem with nonlinear programming (NLP) are applied to train (calibrate) the SVM, and ANN, SVM, and ANN are executed in the "non-forecasting" scenario based on all inputs along with their time-lagged variables. In contrast, current parameters are removed from the set of inputs in the "forecasting" scenario. The results of the SVM model are compared with those of the ANN model with the correlation coefficient (R), the mean error (ME), and the root mean square error (RMSE). This paper's results indicate performance of the SVM is better than that of the ANN model by 1.5%, 400%, and 10% with respect to the R, ME, and RMSE diagnostic statistics, respectively. In addition, SVM and ANN overcome data uncertainty ("forecasting" scenario) to produce optimal reservoir operation.

Environmental water demand assessment under climate change conditions.

Measures taken to cope with the possible effects of climate change on water resources management are key for the successful adaptation to such change. This work assesses the environmental water demand of the Karkheh river in the reach comprising Karkheh dam to the Hoor-al-Azim wetland, Iran, under climate change during the period 2010-2059. The assessment of the environmental demand applies (1) representative concentration pathways (RCPs) and (2) downscaling methods. The first phase of this work projects temperature and rainfall in the period 2010-2059 under three RCPs and with two downscaling methods. Thus, six climatic scenarios are generated. The results showed that temperature and rainfall average would increase in the range of 1.7-5.2 and 1.9-9.2%, respectively. Subsequently, flows corresponding to the six different climatic scenarios are simulated with the unit hydrographs and component flows from rainfall, evaporation, and stream flow data (IHACRES) rainfall-runoff model and are input to the Karkheh reservoir. The simulation results indicated increases of 0.9-7.7% in the average flow under the six simulation scenarios during the period of analysis. The second phase of this paper's methodology determines the monthly minimum environmental water demands of the Karkheh river associated with the six simulation scenarios using a hydrological method. The determined environmental demands are compared with historical ones. The results show that the temporal variation of monthly environmental demand would change under climate change conditions. Furthermore, some climatic scenarios project environmental water demand larger than and some of them project less than the baseline one.

The adsorption/desorption of phosphorus in freshwater sediments from buffer zones: the effects of sediment concentration and pH.

Riparian buffer zones adjacent to reservoirs and lakes protect aquatic ecosystems from polluted surface runoff. Sediments, collected from the buffer zones of Danjiangkou Reservoir (SR) and Honghu Lake (SL) in an ecologically fragile region in central China, were evaluated to reveal their phosphorus-adsorbing/desorbing properties and storage capacities. A nonlinear regression method was used to fit the pseudo-second-order kinetic and the modified crossover-type Langmuir isotherm models to the experimental data. It is shown that the adsorption of phosphorus onto the studied sediments followed the pseudo-second-order kinetic expression. The modified crossover-type Langmuir isotherm model was found to be a suitable method for describing adsorption/desorption processes in the experimental sediments. The maximum adsorption capacities (Q m), partitioning coefficients (K p), native adsorbed exchangeable phosphorus (NAP), and equilibrium phosphorus concentration (EPC0) were subsequently obtained for the experimental sediments. The effects of sediment concentration and pH were also investigated by batch experiments and Fourier transformation infrared and scanning electron microscopy analyses. The adsorption/desorption characteristics of different phosphate species on the sediments from reservoir and lake buffer zones were identified.

Groundwater for People and the Environment: A Globally Threatened Resource.

The intensity of global groundwater use rose from 124 m3 per capita in 1950 to 152 m3 in 2021, for a 22.6% rise in the annual per capita use. This rise in global per capita water use reflects rising consumption patterns. The global use of groundwater, which provides between 21% and 30% of the total freshwater annual consumption, will continue to expand due to the sustained population growth projected through most of the 21st century and the important role that groundwater plays in the water-food-energy nexus. The rise in groundwater use, on the other hand, has inflicted adverse impacts in many aquifers, such as land subsidence, sea water intrusion, stream depletion, and deterioration of groundwater-dependent ecosystems, groundwater-quality degradation, and aridification. This paper projects global groundwater use between 2025 and 2050. The projected global annual groundwater withdrawal in 2050 is 1535 km3 (1 km3 = 109 m3 = 810,713 acre-feet). The projected global groundwater depletion, that is, the excess of withdrawal over recharge, in 2050 equals 887 km3 , which is about 61% larger than in 2021. This projection signals probable exacerbation of adverse groundwater-withdrawal impacts, which are worsened by climatic trends and the environmental requirement of groundwater flow unless concerted national and international efforts achieve groundwater sustainability.

Integrated strategic planning and multi-criteria decision-making framework with its application to agricultural water management.

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.

Evaluation of cooperative and non-cooperative game theoretic approaches for water allocation of transboundary rivers.

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.

The effect of reducing per capita water and energy uses on renewable water resources in the water, food and energy nexus.

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%).

Sensitivity of non-conditional climatic variables to climate-change deep uncertainty using Markov Chain Monte Carlo simulation.

There is substantial evidence suggesting climate change is having an adverse impact on the world's water resources. One must remember, however, that climate change is beset by uncertainty. It is therefore meaningful for climate change impact assessments to be conducted with stochastic-based frameworks. The degree of uncertainty about the nature of a stochastic phenomenon may differ from one another. Deep uncertainty refers to a situation in which the parameters governing intervening probability distributions of the stochastic phenomenon are themselves subjected to some degree of uncertainty. In most climatic studies, however, the assessment of the role of deep-uncertain nature of climate change has been limited. This work contributes to fill this knowledge gap by developing a Markov Chain Monte Carlo (MCMC) analysis involving Bayes' theorem that merges the stochastic patterns of historical data (i.e., the prior distribution) and the regional climate models' (RCMs') generated climate scenarios (i.e., the likelihood function) to redefine the stochastic behavior of a non-conditional climatic variable under climate change conditions (i.e., the posterior distribution). This study accounts for the deep-uncertainty effect by evaluating the stochastic pattern of the central tendency measure of the posterior distributions through regenerating the MCMCs. The Karkheh River Basin, Iran, is chosen to evaluate the proposed method. The reason for selecting this case study was twofold. First, this basin has a central role in ensuring the region's water, food, and energy security. The other reason is the diverse topographic profile of the basin, which imposes predictive challenges for most RCMs. Our results indicate that, while in most seasons, with the notable exception of summer, one can expect a slight drop in the temperature in the near future, the average temperature would continue to rise until eventually surpassing the historically recorded values. The results also revealed that the 95% confidence interval of the central tendency measure of computed posterior probability distributions varies between 0.1 and 0.3 °C. The results suggest exercising caution when employing the RCMs' raw projections, especially in topographically diverse terrain.