Optimal Management Of Water Resources Research Articles

Benefits of Embungs in the Rawa Pening Catchment Area for Reducing Tuntang River Flood Discharge

Rawa Pening is a natural lake as a source of water for Tuntang River which is used for hydropower, raw water source, main irrigation water source in Glapan weir located in Grobogan District, Central Java provinces. Rawa Pening will be developed as location of national and international ecotourism. An Optimal Water Resources Management is required with several studies. This study is the beginning of a series of studies planned to determine the potential of embungs and its utilization as flood control, sediment control of Rawa Pening and for reducing weeds growth.

Transboundary sediment transfer from source to sink using a mineralogical analysis. Case study: Roseires Reservoir, Blue Nile, Sudan

ABSTRACTSediment accumulation hampers optimal water resources management of reservoirs. In the Roseires Reservoir across the Blue Nile River, in Sudan, about 30% of the storage capacity has been lost by sedimentation before dam heightening (2012), despite regular sediment sluicing and flushing. At the same time, increasing soil erosion in the upper river basin in Ethiopia is significantly reducing land productivity.

Long-term streamflow forecasting using SWAT through the integration of the random forests precipitation generator: case study of Danjiangkou Reservoir

AbstractLong-term streamflow forecasting is of great significance to the optimal management of water resources. However, the forecast lead time of long-term streamflow forecasting is relatively long and the forecasted precipitation within the forecast lead time has inherent uncertainty, so long-term streamflow forecasting has major challenges. In this paper, a hybrid forecasting model is developed to improve accuracy of long-term streamflow forecasting by combining random forests (RF) and the Soil and Water Assessment Tool (SWAT). The RF model is used to forecast monthly precipitation which is further downscaled to a daily dataset according to the hydrological similarity principle for use in the SWAT model of the Danjiangkou Reservoir basin, China. Performance of this hybrid model is compared to that of seasonal autoregressive (SAR (P)) model. Results show the RF precipitation generator yields accurate predictions at the monthly scale and the hybrid model produces acceptable streamflow series in long-term forecasting cases. In addition, the comparison shows that in the Danjiangkou Reservoir basin, the hybrid model performs better than the SAR (P) model, with average Nash–Sutcliffe efficiency (NSE) values of 0.94 and 0.51, which is better when it is closer to 1. This study provides a method of improving accuracy of long-term streamflow forecasting.

New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R2). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.

Hydrological Studies and Water Quality Analysis Around The Limestone Mining in the Hial Area of Bolangir District ,Odisha for The Optimal Use And Sustainable Management of Available Water Resources

Water is one of the most widely distributed resources of earth, which influence economic, industrial and agricultural growth of mankind. The different forms of water are surface water and subsurface water or groundwater. Most part of fresh water (80%) is used for agriculture and industrial purposes. In India safe drinking water is not yet available to all human beings as most of the surface water has been contaminated by anthropogenic intervention. Hence, it is absolutely necessary to assess and monitor the quality ground water in places of mining and industrial area and check its suitability for drinking and agriculture so that the people, livestock, or crops of the area are not affected by contamination of the water.Water parameters such as pH, EC, TDS, Total Hardness, Alkalinity, cations and anions in water were determined in the laboratory. Trace metals in water were determined by Atomic Absorption Spectrophotometer. Several quality parameters such as SAR, %Na, KR, MR, PS, PI, RSC, CR were derived from analytical data.In general, groundwater is good for irrigation and industrial use as observed from analytical data. Proper management strategies should be adopted in the area for development of groundwater potential and its utilization.

Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration

Evapotranspiration is one of the most important components of hydrologic cycle for optimal management of water resources, especially in arid and semi-arid regions such as Iran. The main objective of the present research is to investigate the performance of empirical equations and soft computing approaches including gene expression programming (GEP), two types of support vector machine (SVM) namely SVM-polynomial (SVM-Poly) and SVM-radial basis function (SVM-RBF), as well as multivariate adaptive regression splines (MARS) in estimating monthly mean reference evapotranspiration (ETo) in Iran. In the present study, 16 empirical equations from temperature-based, mass transfer-based, radiation-based and meteorological parameters-based categories were utilized. Monthly mean data of 44 stations in the study region was used to estimate the monthly mean ETo. 50% of the data (22 stations) for the calibration/training step and the remaining 50% of the data (22 stations) were applied for the validation/testing stage of the empirical equations/soft computing methods. At first, 16 empirical equations were locally calibrated on the basis of FAO-56 Penman-Monteith method (as standard method). The results revealed that the calibration process improved the performance of equations in comparison with the original form of them. Then, the capability of the GEP, SVM-Poly, SVM-RBF and MARS models was evaluated for estimation of the monthly mean ETo. The selection of models’ inputs was conducted based on the used parameters in the empirical equations. It was found that the MARS and SVM-RBF methods generally performed better than GEP and SVM-Poly. At the end part of study, the accuracy of empirical equations and soft computing methods was compared. Overall, the performance of the MARS and SVM-RBF was better than used empirical equations.

Hybrid Optimization Method for Strategic Control of Water Withdrawal from Water Reservoir with Using Support Vector Machines

The aim of strategic control is the effort to achieve optimal water resource management (water reservoir). Classic strategic control of water withdrawal from water reservoir are based mainly on the rules and rules curves, which are created by generalization of historical data of water inflows to the reservoirs and water demand. Discharge series are changing in the time due to expected climate change. It is necessary to looking for intelligent water withdrawal control, which will allow to react on these hydrological changes and contribute to the efficient use of accumulated water for ensure water demand. The paper will describe the algorithm based on adaptive control. The normal adaptive control required knowledge of the water flow medium-term prediction into the reservoir. The created algorithm of intelligent water withdrawal control does not require knowledge of hydrological predictions. This control method is based on a suitable combination optimization method with the Support vector machines method. The control algorithm is one of the possible measures to mitigate the negative impacts of droughts and water scarcity. The algorithm of adaptive control is applied to the control of water withdrawal from selected single-reservoir. The results are compared with usual rules for water withdrawal control.

An analytical method of regional water resources carrying capacity in karst area – a case study in Guizhou province, China

The uneven temporal and spatial distributions and the unreasonable development and utilization of water resources have caused increasingly severe water resources shortage around the world. Studying on regional water carrying capacity is beneficial for the optimal management of water resources, and can provide assistance for the sustainable utilization of water resources. The typical karst area in China – Guizhou province was applied as the research area in the paper. Meanwhile, twelve indexes were selected from three perspectives including water supply, water demand and social economy to establish the evaluation index system of regional water carrying capacity. Afterwards, the analysis hierarchy process was utilized to construct the evaluation model of regional water resources carrying capacity, so as to evaluate, compare and analyze the regional water carrying capacity in each city of Guizhou province, China. This model verified the feasibility of the evaluation method. Relevant studies can provide certain theory evidence and method support for the investigation on regional water carrying capacity.

Hydrological stream flow modeling in the Talar catchment (central section of the Alborz Mountains, north of Iran): Parameterization and uncertainty analysis using SWAT-CUP

AbstractThere are several methods and techniques for measuring the parameters and forecasting the errors in the hydrological models. In this study, semi distributed Soil and Water Asseeement Tool (SWAT) model and SWAT-CUP (CUP – Calibration and Uncertainty Programs) have been applied using SUFI2 program. After collection of data, the whole Talar watershed located in the central section of the Alborz Mountains, north of Iran was separated into 219 hydrological response units (HRU) in 23 sub-watersheds.In order to improve the simulation parameters and obtain better correlation of observed and simulated values, the sensitive parameters were validated to obtain finally the acceptable value of both R 2 and Nash–Sutcliffe (NS) coefficients equal to 0.93. Final P-value and t-state were also estimated for sensitive parameters. As a result, the CN2 parameter, which was critical in the initial stage of this research was replaced by the SOL-K parameter (electrical conductivity saturated soil layers) as a critical parameter in the later stage. Results of this study show that the SWAT model can be an effective and useful tool for the assessment and optimal management of water and soil resources.

Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India

AbstractEvapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR) and moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), Thomas Feiring, etc. Out of these models ARIMA model has been found to be more suitable for analysis and forecasting of hydrological events. Therefore, in this study ARIMA models have been used for forecasting of mean monthly reference crop evapotranspiration by stochastic analysis. The data series of 102 years i.e. 1224 months of Bokaro District were used for analysis and forecasting. Different order of ARIMA model was selected on the basis of autocorrelation function (ACF) and partial autocorrelation (PACF) of data series. Maximum likelihood method was used for determining the parameters of the models. To see the statistical parameter of model, best fitted model is ARIMA (0, 1, 4) (0, 1, 1)12.

Optimal water resources management and system benefit for the Marcellus shale-gas reservoir in Pennsylvania and West Virginia

A major concern associated with current shale-gas extraction is high consumption of water resources. However, decision-making problems regarding water consumption and shale-gas extraction have not yet been solved through systematic approaches. This study develops a new bilevel optimization problem based on goals at two different levels: minimization of water demands at the lower level and maximization of system benefit at the upper level. The model is used to solve a real-world case across Pennsylvania and West Virginia. Results show that surface water would be the largest contributor to gas production (with over 80.00% from 2015 to 2030) and groundwater occupies for the least proportion (with less than 2.00% from 2015 to 2030) in both districts over the planning span. Comparative analysis between the proposed model and conventional single-level models indicates that the bilevel model could provide coordinated schemes to comprehensively attain the goals from both water resources authorities and energy sectors. Sensitivity analysis shows that the change of water use of per unit gas production (WU) has significant effects upon system benefit, gas production and pollutants (i.e., barium, chloride and bromide) discharge, but not significantly changes water demands.

A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method

The mathematical form to estimate the evaporation plays an important role in sustainable development and optimal water resources management of the countries facing water crisis. In this paper, a general regression analysis is proposed to estimate the daily pan evaporation with respect to input variables including the air temperature (T), relative humidity (R), wind speed (W) and sunshine hours (H) from two weather stations (i.e. Chahnimeh Zabol reservoirs and Iranshar station) in Iran. A conjugate gradient optimization method is used to calibrate the nonlinear mathematical function for accurate prediction of daily pan evaporation. Three mathematical functions with nonlinear forms are calibrated on data points of daily pan evaporation using proposed general regression analysis by proposed conjugate line search method. Then, the accuracies of three nonlinear models are compared with adaptive neuro-fuzzy inference system (ANFIS) and M5 Model Tree (M5Tree) models for two stations. Results indicated that that the conjugate gradient method was successfully applied to estimate the nonlinear models for predictions of daily pan evaporation with explicit mathematical function. The proposed mathematical models (RMSE of 4.14mm for Chahnimeh and 1.94mm for Iranshar) performed better than the ANFIS predictions for Iranshar (RMSE of 2.41mm) and M5tree (RMSE of 4.31mm for Chahnimeh and 2.19mm for Iranshar).

Application of the Firefly Algorithm to Optimal Operation of Reservoirs with the Purpose of Irrigation Supply and Hydropower Production

AbstractPopulation growth and socioeconomic changes in developing countries over the past few decades have created severe stresses on the available water resources across the world, particularly in arid and semiarid regions, which are predominant in Iran. Hence, the optimal management of water resources is imperative. Reservoir operation is a challenging problem that involves complexities in terms of nonlinear functions, larger numbers of decision variables, and multiple constraints. Evolutionary or metaheuristic algorithms have become an attractive alternative to the classical methods for solving complex reservoir problems. This paper applies a metaheuristic algorithm named the firefly algorithm (FA) to reservoir operation and demonstrates the superiority of this algorithm against the genetic algorithm (GA), a commonly used optimization algorithm, using (1) five mathematical test functions, (2) the operation of a reservoir system with the purpose of irrigation supply, and (3) the operation of a reservoir...

Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand

Streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. Despite operational application of Ensemble Streamflow Prediction (ESP) for long-range streamflow forecasts in United States of America by the National Weather Service River Forecast System, no such approach has been explored in New Zealand. The objective of the present paper is to explore ESP-based forecasts in New Zealand catchments, highlighting its capability for seasonal flow forecasting. In this paper, a probabilistic forecast framework based on ESP technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. In the present study employs the ESP-based approach using the TopNet hydrological model with a range of past forcing data and current initial conditions. An ensemble stream flow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the Rangitata, Ahuriri, and Hooker and Jollie catchments in South Island, New Zealand. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23 to 69 % (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The ESP based forecast exhibited higher skill for a greater percentage of the forecasting period than climatology. As a result, the ESP forecast can provide better over all information for integrated water resources management purpose. ESP-based forecasts using the TopNet hydrological model have potential as tools for water resource management in New Zealand catchments.

EVALUATION OF WILLINGNESS TO PAY FOR IRRIGATION WATER: HARRAN PLAIN SAMPLING IN GAP REGION - TURKEY

The aim of the paper is to evaluate the willingness to pay (WTP) for irrigation and explore the potential factors that contribute to it. The data used in this study come from a sample of farmers in The Southeastern Anatolian Project (GAP)-Turkey, who were chosen via simple random sampling method and interviewed face to face by given questionnaire. Based on the collected survey data, econometric model for the contingent valuation and double bond maximum likelihood methods were used for analysis by limited dependent package program. Results indicate that farmers are willing to pay 71.69% more than the existing price under certain conditions, but are paying 98 Turkish Liras/ha less. Accordingly, the yearly annual income loss of the water user associations is $9,748,011. Explanatory factors, such as price of irrigation water, education, location, irrigation type (gravity or pumping), and attitudes toward associations, significantly explained WTP. This study is one of the first of its type in Turkey. The study determines the primary factors influencing farmers' WTP for the irrigation water for optimal management of associations and water resources. The results are important for policymakers and contain useful information for Turkey and other countries with similar technical and socio-cultural characteristics.

Multi-Objective Optimization Model for the Allocation of Water Resources in Arid Regions Based on the Maximization of Socioeconomic Efficiency

The escalating world population has led to a drastic increase in water demand in the municipal and drinking water, agriculture and industry sectors. This situation necessitates application of effective measures for the optimal and efficient management of water resources. With this respect, a two-objective socioeconomic model (aimed at job creation) has been presented in this study for the optimum allocation of water resources to industry, agriculture and municipal water sectors. In the agriculture sector, the production function of each product has been determined and then, based on the production functions, areas under cultivation, product yield and the income obtained from each product, the combined objective function has been specified. In the industry sector, since water demand is a function of the amount of produced products, price of supplied water and the price of other supplies, the demand function of this sector was determined regionally. Also, considering the existing necessity in meeting the municipal water requirement, the total amount of water needed by this sector was fully allocated. Then by using two meta-heuristic algorithms, i.e. genetic algorithm (GA) and particle swarm optimization (PSO), the objective functions were maximized and the water resources were optimally allocated between agriculture and industry sectors and the results were compared. Ultimately, comparing the results gained by PSO and GA algorithms, PSO with an economic and profit growth of 54 % and a 13 % rise in employment relative to the base condition, turned out to be more efficient in this application.

Rainfall-runoff modeling for predictive infiltration and recharge areas for water management in semi-arid basin: a case study from Saadine watershed, Tunisia

Due to the water supply increase and drought-associated problems, optimal water resources management is of a prior need in arid and semi-arid area. The groundwater recharge as an optimal utilization and storage of floodwater constitutes an ideal strategy in water resources management. The purpose of this study is to identify and characterize the spatial distribution of the infiltration and suitable sites for groundwater artificial recharge in a semi-arid basin in Tunisia. The Kineros2 rainfall-runoff model and Geographic Information Systems were used. The datasets required for the model parameters were extracted from maps of soil properties and land cover or extracted from the Digital Elevation Model of the area. The results of simulations reveal that the infiltration values depend on the initial soil moisture. The infiltration rate was closely related to the precipitation quantity and rainstorm duration. The strong rain often leads to overland-flow, overwhelming the infiltration; in contrast, medium and weak but continuous rainfalls favor infiltration process.

Exploring Generational Differences Towards Water Resources and Policy Preferences of Water Re-Allocation in Alberta, Canada

It is a challenging task for policy makers to design optimal water resource management policies that accommodate increasing demand while minimizing social and environmental impacts of water extraction. We used four surveys of the general community and irrigators in Alberta’s South-Saskatchewan River Basin to explore the values people assign to water and their preferences for water re-allocation policies, focusing particularly on generational differences. The findings suggest that significant generational differences exist: with the younger generation more environmentally concerned, although it favoured less government intervention in water re-allocation. Generational differences also exist regarding residential and irrigation water use values, and in policy preferences for how to protect the environment and the rights of existing water right holders (irrigators). It was also found that urban–rural context and economic dependence (farmers versus non-farmers) on water mediate generational differences in values and preferences.

Optimum Operation of Reservoir Using Two Evolutionary Algorithms: Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA)

Water scarcity is one of the most serious problems in many parts of the world that affects negatively on the environment, society, and economy. In order to mitigate the negative effects of this issue, optimal water resource management is pivotal. In current paper, imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA) which they are two new evolutionary methods, were used in optimal operation of reservoir. Firstly, these algorithms were used in solving several benchmark problems. Afterwards, optimal operation policy of Karun4 reservoir was extracted. Karun4 is located in Chaharmahal Va Bakhtiari province in western of Iran. Finally, the results which obtained from these methods were compared with genetic algorithm (GA) and nonlinear programing (NLP). In benchmark problems, COA converges to optimal point appropriately well and shows best performance. In these problems, ICA represent suitable ability to achieve global optimum. Both COA and ICA algorithms showed high performance in extraction of optimal operation policies from Karun4, which was conducted over a period of 360 months, with the aim of maximizing productivity. COA indicated the best performance with average value of 5.454 for objective function, and ICA with 6.461 value was at the second rank. In addition, GA objective function value was 6.869. Also NLP solver of Lingo11 was used in order to optimal operation of Karun4 reservoir for evaluating the ability of these algorithms to achieve global solution. Objective function value which was gained by NLP method was 5.243. The results reflect the strength of COA in approaching global optimum.

Analyses of freshwater stress with a couple ground and surface water model in the Pra Basin, Ghana

The optimal management of water resources requires that the collected hydrogeological, meteorological, and spatial data be simulated and analyzed with appropriate models. In this study, a catchment-scale distributed hydrological modeling approach is applied to simulate water stress for the years 2000 and 2050 in a data scarce Pra Basin, Ghana. The model is divided into three parts: The first computes surface and groundwater availability as well as shallow and deep groundwater residence times by using POLFLOW model; the second extends the POLFLOW model with water demand (Domestic, Industrial and Agricultural) model; and the third part involves modeling water stress indices—from the ratio of water demand to water availability—for every part of the basin. On water availability, the model estimated long-term annual Pra river discharge at the outflow point of the basin, Deboase, to be 198 m3/s as against long-term average measurement of 197 m3/s. Moreover, the relationship between simulated discharge and measured discharge at 9 substations in the basin scored Nash–Sutcliffe model efficiency coefficient of 0.98, which indicates that the model estimation is in agreement with the long-term measured discharge. The estimated total water demand significantly increases from 959,049,096 m3/year in 2000 to 3,749,559,019 m3/year in 2050 (p < 0.05). The number of districts experiencing water stress significantly increases (p = 0.00044) from 8 in 2000 to 21 out of 35 by the year 2050. This study will among other things help the stakeholders in water resources management to identify and manage water stress areas in the basin.