A two-phase grey fuzzy optimization approach for water quality management of a river system

An extension of the Grey Fuzzy Waste Load Allocation Model (GFWLAM) developed in an earlier work is presented here to address the problem of multiple solutions. Formulation of GFWLAM is based on the approach for solving fuzzy multiple objective optimization problems with max–min as the operator, which usually may not result in a unique solution. The multiple solutions of fuzzy multiobjective optimization model should be obtained as parametric equations or equations that represent a subspace. A two-phase optimization technique, two-phase GFWLAM, is developed to capture all alternative or multiple solutions of GFWLAM. The optimization model in Phase 1 is exactly same as the optimization model described in GFWLAM. The optimization model in Phase 2 maximizes the upper bounds of fractional removal levels of pollutants and minimizes the lower bounds of fractional removal levels of pollutants keeping the value of goal fulfillment level same as obtained from Phase 1. The widths of the interval-valued fractional removal levels play an important role in decision-making as these can be adjusted within their intervals by the decision-maker considering technical and economic feasibility in the final decision scheme. Two-phase GFWLAM widens the widths of interval-valued removal levels of pollutants, thus enhancing the flexibility in decision-making. The methodology is demonstrated with a case study of the Tunga-Bhadra river system in India.     

Uncertainty assessment for watershed water quality modeling: A Probabilistic Collocation Method based approach

Watershed water quality models are increasingly used in management. However, simulations by such complex models often involve significant uncertainty, especially those for non-conventional pollutants which are often poorly monitored. This study first proposed an integrated framework for watershed water quality modeling. Within this framework, Probabilistic Collocation Method (PCM) was then applied to a WARMF model of diazinon pollution to assess the modeling uncertainty. Based on PCM, a global sensitivity analysis method named PCM-VD (VD stands for variance decomposition) was also developed, which quantifies variance contribution of all uncertain parameters. The study results validated the applicability of PCM and PCM-VD to the WARMF model. The PCM-based approach is much more efficient, regarding computational time, than conventional Monte Carlo methods. It has also been demonstrated that analysis using the PCM-based approach could provide insights into data collection, model structure improvement and management practices. It was concluded that the PCM-based approach could play an important role in watershed water quality modeling, as an alternative to conventional Monte Carlo methods to account for parametric uncertainty and uncertainty propagation.     

A stochastic conflict resolution model for water quality management in reservoir–river systems

In this paper, optimal operating rules for water quality management in reservoir–river systems are developed using a methodology combining a water quality simulation model and a stochastic GA-based conflict resolution technique. As different decision-makers and stakeholders are involved in the water quality management in reservoir–river systems, a new stochastic form of the Nash bargaining theory is used to resolve the existing conflict of interests related to water supply to different demands, allocated water quality and waste load allocation in downstream river. The expected value of the Nash product is considered as the objective function of the model which can incorporate the inherent uncertainty of reservoir inflow. A water quality simulation model is also developed to simulate the thermal stratification cycle in the reservoir, the quality of releases from different outlets as well as the temporal and spatial variation of the pollutants in the downstream river. In this study, a Varying Chromosome Length Genetic Algorithm (VLGA), which has computational advantages comparing to other alternative models, is used. VLGA provides a good initial solution for Simple Genetic Algorithms and comparing to Stochastic Dynamic Programming (SDP) reduces the number of state transitions checked in each stage. The proposed model, which is called Stochastic Varying Chromosome Length Genetic Algorithm with water Quality constraints (SVLGAQ), is applied to the Ghomrud Reservoir–River system in the central part of Iran. The results show, the proposed model for reservoir operation and waste load allocation can reduce the salinity of the allocated water demands as well as the salinity build-up in the reservoir.     

Risk minimization in water quality control problems of a river system

Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte–Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga–Bhadra river system in southern India, with a steady state BOD–DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality.     

World continental modeling for water resources using system dynamics

This research assesses the severity of future water scarcity and its impact on the growth of human civilization through system dynamics modeling of the world at regional level. Six sectors of activities are modeled in each continent to represent the human society. Continental interactions such as migration and trade are also modeled to express the synergy of activities among the various continents. Results of the model simulations from 1960 to 2100 show that water scarcity, unlike other limitations such as nonrenewable resources and persistent pollution, gives severe, detrimental problems within short delays after its occurrence.     

Protein‐like fluorescence intensity as a possible tool for determining river water quality

The results of a comparison between chemical water quality determinants and river water fluorescence on the River Tyne, NE England, demonstrate that tryptophan‐like fluorescence intensity shows statistically significant relationships between nitrate, phosphate, ammonia, biochemical oxygen demand (BOD) and dissolved oxygen. Tryptophan‐like fluorescence intensity at the 280 nm excitation/350 nm emission wavelength fluorescence centre correlates with both phosphate (r = 0·80) and nitrate (r = 0·87), whereas tryptophan‐like fluorescence intensity at the 220 nm excitation/350 nm emission wavelength centre correlates with BOD (r = 0·85), ammonia (r = 0·70) and dissolved oxygen (r = ?0·65). The strongest correlations are between tryptophan‐like fluorescence intensity and nitrate and phosphate, which in the Tyne catchment derive predominantly from point and diffuse source sewage inputs. The correlation between BOD and the tryptophan‐like fluorescence intensity suggests that this fluorescence centre is related to the bioavailable or labile dissolved organic matter pool. The weakest correlations are observed between tryptophan‐like fluorescence intensity and ammonia concentration and dissolved oxygen. The weaker correlation with ammonia is due to removal of the ammonia signal by wastewater treatment, and that with dissolved oxygen due to the natural aeration of the river such that this is not a good indicator of water quality. The observed correlations only hold true when treated sewage, sewerage overflows or cross connections, or agricultural organic pollutants dominate the water quality—this is not true for two sites where airport deicer (propylene glycol, which is non‐fluorescent) or landfill leachate (which contains high concentrations of humic and fulvic‐like fluorescent DOM) dominate the dissolved organic matter in the river. Mean annual tryptophan‐like fluorescence intensity agrees well with the General Water Quality Assessment as determined by the England and Wales environmental regulators, the Environment Agency. Copyright © 2004 John Wiley & Sons, Ltd.     

A stochastic differential equation approach to soil moisture

The dynamics of water within the unsaturated root zone of the soil are represented by a pair of stochastic differential equations (SDE's), one representing the so-called surplus state of the moisture and the other the deficit condition. The inputs to the model are the climatically controlled random infiltration events and evapotranspiration which are modeled as a compound Poisson process and a Wiener (Brownian motion) process, respectively.The solutions to these SDE's are not in close-form but sample functions are obtained by numerical integration. The moment properties of the soil moisture evolution process have also been derived analytically including the mean, variance, covariance and autocorrelation functions.To illustrate the model, climatic parameters representing the surplus and deficit cases and properties of clay loam soil have been used to numerically derived the corresponding sample functions. With proper selection of all the parameters, physically realistic sample trajectories can be obtained for the model.     

River system sediment flow modeling using artificial neural networks

Sediment leads to problems with navigation,agricultural productivity,and water pollution.The study of sediment flow in river reaches,which is a non-linear and complex process,is,thus,essential to addressing these issues.The application of artificial neural networks(ANN)to such problems needs to be investigated.For unsteady flow in a river system,river reach storage is an important variable that needs to be considered in data-driven models.However,previous research on sediment modeling did not involve the explicit use of storage variables in such models as is investigated in the current study.In the current study,storage variables have been explicitly(Model 2)used to predict the output state of the system at time't+1'from the input state at time't'using ANNs.Sediment discharge at six gaging stations on the Mississippi River system,USA,has been considered as the state variable.The model has been compared with a model considering implicit variation of the storage parameter in the river system(Model 1).Dynamic ANNs are used for time-series datasets,which are more suitable for incorporating the sequential information within the dataset.Focussed gamma memory neural networks have been used in the current study.The numbers of hidden layers and hidden nodes,activation function,and learning rate have been varied step by step to obtain the optimal ANN configurations.The best selected input-output variables are those used in Model 2 as it performed slightly better than the other model in terms of Nash-Sutcliffe efficiency coefficient(CE)values.Model performance evaluated using normal-ized root mean square error(NRMSE)and CE shows satisfactory results.NRMSE was＜10％for all the outputs except for the Venedy and Murphysboro locations and CE values for sediment loads were＞0.45 for all locations except Murphysboro indicating acceptable performance by both the models.The models proved highly efficient(CE＞0.80,i.e.,very good predictions)for predicting sediment discharge at lo-cations along the main river channel with acceptable accuracy(CE＞0.45)for other locations and the storage change for the river system.These models can be used for real-time forecasting and management of sediment-related problems.     

Improved simulation of river water and groundwater exchange in an alluvial plain using the SWAT model

Hydrological interaction between surface and subsurface water systems has a significant impact on water quality, ecosystems and biogeochemistry cycling of both systems. Distributed models have been developed to simulate this function, but they require detailed spatial inputs and extensive computation time. The soil and water assessment tool (SWAT) model is a semi‐distributed model that has been successfully applied around the world. However, it has not been able to simulate the two‐way exchanges between surface water and groundwater. In this study, the SWAT‐landscape unit (LU) model – based on a catena method that routes flow across three LUs (the divide, the hillslope and the valley) – was modified and applied in the floodplain of the Garonne River. The modified model was called SWAT‐LUD. Darcy's equation was applied to simulate groundwater flow. The algorithm for surface water‐level simulation during flooding periods was modified, and the influence of flooding on groundwater levels was added to the model. Chloride was chosen as a conservative tracer to test simulated water exchanges. The simulated water exchange quantity from SWAT‐LUD was compared with the output of a two‐dimensional distributed model, surface–subsurface water exchange model. The results showed that simulated groundwater levels in the LU adjoining the river matched the observed data very well. Additionally, SWAT‐LUD model was able to reflect the actual water exchange between the river and the aquifer. It showed that river water discharge has a significant influence on the surface–groundwater exchanges. The main water flow direction in the river/groundwater interface was from groundwater to river; water that flowed in this direction accounted for 65% of the total exchanged water volume. The water mixing occurs mainly during high hydraulic periods. Flooded water was important for the surface–subsurface water exchange process; it accounted for 69% of total water that flowed from the river to the aquifer. The new module also provides the option of simulating pollution transfer occurring at the river/groundwater interface at the catchment scale. Copyright © 2015 John Wiley & Sons, Ltd.     

西太湖湖滨典型河网区与太湖水量的交换

以西太湖湖滨典型河网区——宜兴大浦河网区为研究区域,对于该典型河网区与太湖之间的水量交换情况进行了系统的计算研究．考虑降雨因素的影响,采用考虑下垫面分类的产汇流模型,对各计算年内(2002年(基准年)．1998年(丰水年),1995年(平水年),1994年(枯水年))典型河网区及其西部山地区域的降雨径流过程进行了产汇流模拟．建立起适合该河网区域的非稳态河网水量模型,结合径流计算结果,对各年内典型河网区内水流的动态变化进行了计算模拟,对典型河网区与太湖水体的水量交换情况进行系统研究,分别对顺流与逆流情况下交换水量的年内分布与年际变化规律进行了分析．     

Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks

By taking advantage of the close relationship between quality and quantity of water, we investigated the potential improvements of the in-reservoir water quality through the optimization of reservoir operational strategies. However, the few available techniques for optimization of reservoir operational strategies present some limitations, such as restrictions on the number of state/decision variables, the impossibility considering stochastic characteristics and difficulties for considering simulation/prediction models. One technique which presents great potential for overcoming some of these limitations is applied here and investigated for the first time in such complex system. The method, named stochastic fuzzy neural network (SFNN), can be defined as a fuzzy neural network (FNN) model stochastically trained by a genetic algorithm (GA) based model to yield a quasi optimal solution. The term “stochastically trained” refers to the introduction of a new loop within the training process which accounts for the stochastic variable of the system and its probabilities of occurrence. The SFNN was successfully applied to the optimization of the monthly operational strategies considering maximum water utilization and improvements on water quality simultaneous. Results showed the potential improvements on the water quality through means of hydraulic control.     

Evaluating precipitation products for hydrologic modeling over a large river basin in the Midwestern USA

ABSTRACT

We evaluated precipitation estimates, TRMM (Tropical Rainfall Measuring Mission 3B42V7), CFSR (Climate Forecast System Reanalysis), GHCN-D (Global Historical Climatology Network-Daily Version 3.24), and Daymet, using the Soil and Water Assessment Tool (SWAT). The suitability and quality of TRMM, CFSR and Daymet in forcing the SWAT-based hydrological model was examined by means of model calibration. A calibrated TRMM-driven model slightly overestimated streamflow, while a calibrated CFSR-driven model performed worst. The Daymet-driven model performance was as good as the GHCN-D-driven model in reproducing observations. In addition, the temperature was far less sensitive compared with precipitation in driving SWAT. TRMM 3B42V7 showed great potential in streamflow simulation. The results and findings from this study provide new insights into the suitability of precipitation products for hydrological and climate impact studies in large basins, particularly those in typical climates and physiographic settings similar to the Midwestern USA.     

Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates

Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of all the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results.     

Modelling of water withdrawal for pollutant flushing in the tidal river network,Pearl River Delta,China

Abstract

A one-dimensional water quantity and quality mathematical model was developed to evaluate the effects of joint gate–pump operation in terms of water withdrawal for pollutant flushing. The study was carried out in dry seasons in the Foshan River channel, China. The results indicate that the input of freshwater into the upper and middle reaches of the Foshan River can improve the water quality of the lower reaches. However, the backwater effect due to water diversion in the middle reaches of the river can greatly offset the cleaning processes in the upper reaches of the Foshan River. The results indicate that water quality in the upper Foshan River (Jiebian) may degrade with an increase in the rate of water withdrawal from the middle river when the discharge pumped from the upper Foshan River is less than 10m³/s; optimal water quality improvement is obtained with discharge values of 30 and 20 m³/s, respectively, at the upper and middle reaches of the Foshan River.

Editor D. Koutsoyiannis

Citation Liu, C.-L., Jiang, T., Zhang, Q., Zhu, S. and Li, K., 2012. Modelling of water withdrawal for pollutant flushing in the tidal river network, Pearl River Delta, China. Hydrological Sciences Journal, 57 (3), 576–590.     

A coupled physical-biochemical lake model for forecasting water quality

A new one-dimensional numerical model that includes physical and biochemical processes has been developed. The biochemical processes, influenced by the lake dynamics, are required for forecasting water quality. The model is used to investigate the effects of different internal restoration measures, such as artificial mixing, input of oxygen and drainage of deep water.The model is applied to the Northern Basin of Lake Lugano, a Swiss-Italian border lake. The lake is highly eutrophic and chemically stratified throughout the year. The model was calibrated over one year and validated over a period of several years. The results agree well with the measured data. The coupled model reproduces the observed depth dependency of conductivity even during long simulation times. Due to the predominant mixing, decoupled physical models cannot maintain such gradients. The forecasting capabilities of the model are demonstrated for different case studies. The impact of restoration measures on water quality is rather small. Best results are achieved by reducing the external nutrient loading. Caution is recommended for internal measures as these have to be studied in greater detail.     

A methodology for 3D modeling and visualization of geological objects

Geological body structure is the product of the geological evolution in the time dimension, which is presented in 3D configuration in the natural world. However, many geologists still record and process their geological data using the 2D or 1D pattern, which results in the loss of a large quantity of spatial data. One of the reasons is that the current methods have limitations on how to express underground geological objects. To analyze and interpret geological models, we present a layer data model to organize different kinds of geological datasets. The data model implemented the unification expression and storage of geological data and geometric models. In addition, it is a method for visualizing large-scaled geological datasets through building multi-resolution geological models rapidly, which can meet the demand of the operation, analysis, and interpretation of 3D geological objects. It proves that our methodology is competent for 3D modeling and self-adaptive visualization of large geological objects and it is a good way to solve the problem of integration and share of geological spatial data. Supported by National High Technology Research and Development Program of China (Grant Nos. 2006AA12Z220, 2006AA12Z114, 2007AA12Z226), and Open Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (Grant No. WKL(06)0304)     

A discrete space continuous time modeling approach to nonsteady flow in a leaky aquifer system of finite configuration

Existing analytical procedures for nonsteady flow in a leaky confined aquifer assume that the aquifer system is areally infinite. A technique is presented that treats a leaky confined aquifer system of finite configuration. By means of a discrete space continuous time (DSCT) modeling approach, the partial differential equation governing the flow system is transformed into a set of ordinary differential equations that can be easily integrated numerically on a high speed digital computer using available scientific subroutines. The finite difference formulation is in effect an explicit scheme. A criterion is developed for which the scheme is computationally stable. A numerical example is presented.     

Infiltration of river water to a shallow aquifer investigated with H/He, noble gases and CFCs

Noble gas isotopes (³He, ⁴He, Ne, Ar, Kr, Xe), tritium (³H), chlorofluorocarbons (CFCs) and dissolved oxygen (O₂) were seasonally measured in a small groundwater system recharged by infiltration of river water at Linsental, northeastern Switzerland. All Groundwater samples contained an excess of atmospheric noble gases (‘excess air’) usually with an elemental composition equal to air. The concentrations of atmospheric noble gases in the groundwater were used to calculate the excess air component and the water temperature at recharge. The noble gas temperatures (NGTs) in the boreholes close to the river vary seasonally, however, the average NGT of all samples lies close to the mean annual temperature of the river water. Groundwater ages were calculated using the tritium/helium-3 (³H/³He) dating method. The water ages of the samples obtained near the river depend on the amount of recently infiltrated river water and are young during times of active river discharge. In contrast, the mean water age of about 3 years of the deep aquifer remained nearly constant over the sampling period. The observed CFC-11 (CFCl₃) and CFC-12 (CF₂Cl₂) concentrations are significantly higher than the atmospheric equilibrium concentrations and therefore CFCs do not provide any direct information on the residence time of the groundwater. Nevertheless, the CFC excess in the groundwater shows a linear increase with the ³H/³He age. Additionally, both accumulation of radiogenic He (⁴He_rad) and O₂ consumption are strongly correlated with residence time. All these correlations can be interpreted either in terms of mixing of recently infiltrated river water with older groundwater or in terms of accumulation/consumption rates.     

Floodplain storage of mine tailings in the belle fourche river system: A sediment budget approach

Arsenic-contaminated mine tailings that were discharged into Whitewood Creek at Lead, South Dakota, from 1876 to 1978, were deposited along the floodplains of Whitewood Creek and the Belle Fourche River. The resulting arsenic-contaminated floodplain deposit consists mostly of overbank sediments and filled abandoned meanders along White-wood Creek, and overbank and point-bar sediments along the Belle Fourche River. Arsenic concentrations of the contaminated sediments indicate the degree of dilution of mine tailings by uncontaminated alluvium. About 13 per cent of the 110 × 10⁶ Mg of mine tailings that were discharged at Lead were deposited along the Whitewood Creek floodplain. Deposition of mine tailings near the mouth of Whitewood Creek was augmented by an engineered structure. About 29 per cent of the mine tailings delivered by Whitewood Creek were deposited along the Belle Fourche River floodplain. About 60 per cent of that sediment is contained in overbank deposits. Deposition along a segment of the Belle Fourche River was augmented by rapid channel migration. The proportions of contaminated sediment stored along Whitewood Creek and the Belle Fourche River are consistent with sediment storage along the floodplains of perennial streams in other, similar sized watersheds.