REGIONAL WATER QUALITY MANAGEMENT BY THE GENERALIZED REDUCED GRADIENT METHOD1

ABSTRACT: A river basin-wide water quality management system is considered. The river receives thermal as well as organic wastes. At-source treatment of these pollutants is imposed to control the basin-wide water quality. The related water quality standards are: the minimum DO concentration, the maximum allowable BOD concentration, the maximum allowable stream temperature, and the allowable rise in stream temperature. The general dynamic mathematical model representing water quality in streams and the thermal effects on BOD and DO concentrations is presented. The model is highly nonlinear in nature. The optimal management problem involving the model is solved by a recently developed nonlinear propgramming technique - the generalized reduced gradient (GRG) method. Comparison of results obtained by the GRG method vs. dynamic programming, and of results using a more realistic mathematical model vs. a simple model are presented. The analysis procedure can be applied to designing new and examining existing water quality programs, and to study the influence of alternate policies and constraints.     

Modeling Flow and Sediment Transport in a River System Using an Artificial Neural Network

A river system is a network of intertwining channels and tributaries, where interacting flow and sediment transport processes are complex and floods may frequently occur. In water resources management of a complex system of rivers, it is important that instream discharges and sediments being carried by streamflow are correctly predicted. In this study, a model for predicting flow and sediment transport in a river system is developed by incorporating flow and sediment mass conservation equations into an artificial neural network (ANN), using actual river network to design the ANN architecture, and expanding hydrological applications of the ANN modeling technique to sediment yield predictions. The ANN river system model is applied to modeling daily discharges and annual sediment discharges in the Jingjiang reach of the Yangtze River and Dongting Lake, China. By the comparison of calculated and observed data, it is demonstrated that the ANN technique is a powerful tool for real-time prediction of flow and sediment transport in a complex network of rivers. A significant advantage of applying the ANN technique to model flow and sediment phenomena is the minimum data requirements for topographical and morphometric information without significant loss of model accuracy. The methodology and results presented show that it is possible to integrate fundamental physical principles into a data-driven modeling technique and to use a natural system for ANN construction. This approach may increase model performance and interpretability while at the same time making the model more understandable to the engineering community.     

Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data1

Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313‐1327. Abstract: Large‐scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (?) 8% to (+) 20% in the Ganges basin, by (?) 15 to (+) 12% in the Brahmaputra basin, and by (?) 15 to (+) 19% in the Meghna basin. Our large‐scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs.     

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.     

The integrated project AquaTerra of the EU sixth framework lays foundations for better understanding of river-sediment-soil-groundwater systems

The integrated project "AquaTerra" with the full title "integrated modeling of the river-sediment-soil-groundwater system; advanced tools for the management of catchment areas and river basins in the context of global change" is among the first environmental projects within the sixth Framework Program of the European Union. Commencing in June 2004, it brought together a multidisciplinary team of 45 partner organizations from 12 EU countries, Romania, Switzerland, Serbia and Montenegro. AquaTerra is an ambitious project with the primary objective of laying the foundations for a better understanding of the behavior of environmental pollutants and their fluxes in the soil-sediment-water system with respect to climate and land use changes. The project performs research as well as modeling on river-sediment-soil-groundwater systems through quantification of deposition, sorption and turnover rates and the development of numerical models to reveal fluxes and trends in soil and sediment functioning. Scales ranging from the laboratory to river basins are addressed with the potential to provide improved river basin management, enhanced soil and groundwater monitoring as well as the early identification and forecasting of impacts on water quantity and quality. Study areas are the catchments of the Ebro, Meuse, Elbe and Danube Rivers and the Brévilles Spring. Here we outline the general structure of the project and the activities conducted within eleven existing sub-projects of AquaTerra.     

NONLINEAR MODELING AND PREDICTION OF A RIVER FLOW SYSTEM1

ABSTRACT: Model estimation and prediction of a river flow system are investigated using nonlinear system identification techniques. We demonstrate how the dynamics of the system, rainfall, and river flow can be modeled using NARMAX (Nonlinear Autoregressive Moving Average with eXogenuous input) models. The parameters of the model are estimated using an orthogonal least squares algorithm with intelligent structure detection. The identification of the nonlinear model is described to represent the relationship between local rainfall and river flow at Enoree station (inputs) and river flow at Whitmire (output) for a river flow system in South Carolina.     

Spatial Calibration and Temporal Validation of Flow for Regional Scale Hydrologic Modeling1

Abstract: Physically based regional scale hydrologic modeling is gaining importance for planning and management of water resources. Calibration and validation of such regional scale model is necessary before applying it for scenario assessment. However, in most regional scale hydrologic modeling, flow validation is performed at the river basin outlet without accounting for spatial variations in hydrological parameters within the subunits. In this study, we calibrated the model to capture the spatial variations in runoff at subwatershed level to assure local water balance, and validated the streamflow at key gaging stations along the river to assure temporal variability. Ohio and Arkansas‐White‐Red River Basins of the United States were modeled using Soil and Water Assessment Tool (SWAT) for the period from 1961 to 1990. R² values of average annual runoff at subwatersheds were 0.78 and 0.99 for the Ohio and Arkansas Basins. Observed and simulated annual and monthly streamflow from 1961 to 1990 is used for temporal validation at the gages. R² values estimated were greater than 0.6. In summary, spatially distributed calibration at subwatersheds and temporal validation at the stream gages accounted for the spatial and temporal hydrological patterns reasonably well in the two river basins. This study highlights the importance of spatially distributed calibration and validation in large river basins.     

A COMPARATIVE STUDY OF LINEAR AND NONLINEAR TIME SERIES MODELS FOR WATER QUALITY1

ABSTRACT: Surface water quality data are routinely collected in river basins by state or federal agencies. The observed quality of river water generally reflects the overall quality of the ecosystem of the river basin. Advanced statistical methods are often needed to extract valuable information from the vast amount of data for developing management strategies. Among the measured water quality constituents, total phosphorus is most often the limiting nutrient in freshwater aquatic systems. Relatively low concentrations of phosphorus in surface waters may create eutrophication problems. Phosphorus is a non-conservative constituent. Its time series generally exhibits nonlinear behavior. Linear models are shown to be inadequate. This paper presents a nonlinear state-dependent model for the phosphorous data collected at DeSoto, Kansas. The nonlinear model gives significant reductions in error variance and forecasting error as compared to the best linear autoregressive model identified.     

Lake eutrophication management modeling using dynamic programming

Lake eutrophication problems have received considerable attention in Taiwan, especially because they relate to the quality of drinking water. In this study, steady-state river water quality and lake eutrophication models are solved using dynamic programming algorithms to find the nutrient removal rates for eutrophication control during dry season. The kinetic cycle of chlorophyll-a, phosphorus and nitrogen for a complete-mixed lake is considered in the optimization framework. The Newton-iterative technique is adopted to solve the nonlinear equations for the steady-state lake eutrophication model. The optimization framework is applied to Cheng-Ching Lake in southern Taiwan. Several nutrient loading scenarios for eutrophication control are studied. Optimization results for nutrient removal rates and corresponding wastewater treatment capacities of each reach of the Kao-Ping River define the least cost approach to lake eutrophication control. A natural purification method, structural free water surface wetland, is also suggested to save more investment and improve river water quality at the same time.     

Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event

This paper develops a framework for regional scale flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-HMS/RAS). The San Antonio River Basin (about 4000 square miles, 10,000 km2) in Central Texas, USA, is the domain of the study because it is a region subject to frequent occurrences of severe flash flooding. A major flood in the summer of 2002 is chosen as a case to examine the modeling framework. The model consists of a rainfall-runoff model (HEC-HMS) that converts precipitation excess to overland flow and channel runoff, as well as a hydraulic model (HEC-RAS) that models unsteady state flow through the river channel network based on the HEC-HMS-derived hydrographs. HEC-HMS is run on a 4 x 4 km grid in the domain, a resolution consistent with the resolution of NEXRAD rainfall taken from the local river authority. Watershed parameters are calibrated manually to produce a good simulation of discharge at 12 subbasins. With the calibrated discharge, HEC-RAS is capable of producing floodplain polygons that are comparable to the satellite imagery. The modeling framework presented in this study incorporates a portion of the recently developed GIS tool named Map to Map that has been created on a local scale and extends it to a regional scale. The results of this research will benefit future modeling efforts by providing a tool for hydrological forecasts of flooding on a regional scale. While designed for the San Antonio River Basin, this regional scale model may be used as a prototype for model applications in other areas of the country.     

垫江县次级河流环境现状分析

垫江县次级河流部分水质指标不容乐观,以种植污染、养殖污染和生活污染为代表的农村面源污染日渐成为垫江河流污染的主要因素。改变垫江河流水质现状,必须尽量减少化肥农药施用,改变传统养殖模式,完善农村垃圾收运系统,加快建设农村废水治理设施。     

A two-dimensional water-quality model for a winding and topographically complicated river

In this paper, a two-dimensional numerical calculation algorithm for water-quality modeling is presented. The algorithm is designed specifically for river systems with complicated geometric conditions. When velocity field data of the river are not available, the numerical calculation algorithm for the water-quality modeling can be used to project river-water quality by using a topographic map of the river course and a finite element method. The calculation results of the water-quality model can show the concentration fields of various pollutants. The water-quality model was applied to a case-study in the Hengyang City section of Xiangjiang River in Hunan Province, China. The river under consideration is winding and has an isle between two branches. In 1995, Chinese government secured a World Bank loan to conduct a Waterways Project in the study region. It was expected that construction works in the river section might affect water quality. Given that the project would change the hydrological regime of the river system and discharges, and so would affect water quality, there would be a need for model results that would predict the water-quality impacts of the Waterways Project. In particular, the study intended to apply the model to identify changes in river-water quality associated with the construction of Dayuandu navigation key project. It is hoped that the numerical calculation algorithm for the water-quality modeling presented in this paper can also be applied to other shallow rivers with similar topographical conditions.     

Integrating Large Wood Jams into Hydraulic Models: Evaluating a Porous Plate Modeling Method

Large wood (LW) jams are key riverine habitat features that affect hydraulic processes and aquatic habitat. The hydraulic influence of LW jams is poorly understood due to the complexity of fluid dynamics around irregular, porous structures. Here we validated a method for two‐dimensional hydraulic modeling of porous LW jams using the open‐source modeling software Delft3D‐FLOW. We sampled 19 LW jams at three reaches across the Columbia River Basin in the United States. We used computer‐generated porous plates to represent LW jams in the modeling software and calibrated our modeling method by comparing model outputs to measured depths and velocities at validation points. We found that modeling outputs are error‐prone when LW jams are not represented. By representing LW jams as porous plates we reduced average velocity root mean square error (RMSE) values (i.e., improved model accuracy) by 42.8% and reduced average depth RMSE values by 5.2%. These differences impacted habitat suitability index modeling. We found a 15.1% increase in weighted useable area for juvenile steelhead at one test site when LW jams were simulated vs. when they were ignored. We investigated patterns in average RMSE improvements with varying jam size, bankfull obstruction, porosity, and structure type, and river complexity. We also identified research gaps related to field estimation of LW jam porosity and porous structure modeling methods.     

RIVERWARE: A GENERALIZED TOOL FOR COMPLEX RESERVOIR SYSTEM MODELING1

ABSTRACT: Water management agencies seek the next generation of modeling tools for planning and operating river basins. Previous site‐specific models such as U.S. Bureau of Reclamation's (USBR) Colorado River Simulation System and Tennessee Valley Authority's (TVA) Daily Scheduling Model have become obsolete; however, new models are difficult and expensive to develop and maintain. Previous generalized river basin modeling tools are limited in their ability to represent diverse physical system and operating policy details for a wide range of applications. RiverWare(tm), a new generalized river basin modeling tool, provides a construction kit for developing and running detailed, site‐specific models without the need to develop or maintain the supporting software within the water management agency. It includes an extensible library of modeling algorithms, several solvers, and a rich “language” for the expression of operating policy. Its point‐and‐click graphical interface facilitates model construction and execution, and communication of policies, assumptions and results to others. Applications developed and used by the TVA and the USBR demonstrate that a wide range of operational and planning problems on widely varying basins can be solved using this tool.     

DYNAMIC MODELING OF STREAM QUALITY BY INVARIANT IMBEDDING1

ABSTRACT. The estimator equations obtained using invariant imbedding is used to estimate the parameters in river or stream pollution. By using these equations, the parameters can be estimated directly from differential equations representing the pollution model and from measured noisy data such as BOD and DO. Another advantage of this approach is that a sequential estimation scheme is obtained. By using this sequential scheme, only current data are needed to estimate current or future values of the unknown parameters. Consequently, a large amount of computer time and computer memory can be saved. Furthermore, not only the parameters but also the concentrations of pollutants can be estimated. Thus, it also forms an effective forecasting technique. The classical least squares criterion is used in the estimation. Several examples are solved to illustrate the technique. (KEY WORDS: dynamic modeling; water pollution; invariant imbedding; forecasting; least squares criterion; estimation)     

Morphodynamic Effects on the Habitat of Juvenile Cyprinids (<Emphasis Type="Italic">Chondrostoma nasus</Emphasis>) in a Restored Austrian Lowland River

At the Sulm River, an Austrian lowland river, an ecologically orientated flood protection project was carried out from 1998-2000. Habitat modeling over a subsequent 3-year monitoring program (2001-2003) helped assess the effects of river bed embankment and of initiating a new meander by constructing a side channel and allowing self-developing side erosion. Hydrodynamic and physical habitat models were combined with fish-ecological methods. The results show a strong influence of riverbed dynamics on the habitat quality and quantity for the juvenile age classes (0+, 1+, 2+) of nase (Chondrostoma nasus), a key fish species of the Sulm River. The morphological conditions modified by floods changed significantly and decreased the amount of weighted usable areas. The primary factor was river bed aggradation, especially along the inner bend of the meander. This was a consequence of the reduced sediment transport capacity due to channel widening in the modeling area. The higher flow velocities and shallower depths, combined with the steeper bank angle, reduced the Weighted Useable Areas (WUAs) of habitats for juvenile nase. The modeling results were evaluated by combining results of mesohabitat-fishing surveys and habitat quality assessments. Both, the modeling and the fishing results demonstrated a reduced suitability of the habitats after the morphological modifications, but the situation was still improved compared to the pre-restoration conditions at the Sulm River.     

Restoring Ecological Integrity in Highly Regulated Rivers: The Role of Baseline Data and Analytical References

The goal of restoring ecological integrity in rivers is frequently accompanied by an assumption that a comparative reference reach can be identified to represent minimally impaired conditions. However, in many regulated rivers, no credible historical, morphological or process-based reference reach exists. Resilient restoration designs should instead be framed around naturalization, using multiple analytical references derived from empirically-calibrated field- and model-based techniques to develop an integrated ecological reference condition. This requires baseline data which are rarely collected despite increasing evidence for systematic deficiencies in restoration practice. We illustrate the utility of baseline data collection in restoration planning for the highly fragmented and regulated lower Merced River, California, USA. The restoration design was developed using various baseline data surveys, monitoring, and modeling within an adaptive management framework. Baseline data assisted in transforming conceptual models of ecosystem function into specific restoration challenges, defining analytical references of the expected relationships among ecological parameters required for restoration, and specifying performance criteria for post-project monitoring and evaluation. In this way the study is an example of process-based morphological restoration designed to prompt recovery of ecosystem processes and resilience. For the Merced River, we illustrate that project-specific baseline data collection is a necessary precursor in developing performance-based restoration designs and addressing scale-related uncertainties, such as whether periodic gravel augmentation will sustain bed recovery and whether piecemeal efforts will improve ecological integrity. Given the numerous impediments to full, historical, restoration in many river systems, it seems apparent that projects of naturalization are a critical step in reducing the deleterious impacts of fragmented rivers worldwide.     

Backwater Confluences of the Ohio River: Organic and Inorganic Fingerprints Explain Sediment Dynamics in Wetlands and Marinas

In the Ohio River (OR), backwater confluence sedimentation dynamics are understudied, however, these river features are expected to be influential on the system’s ecological and economic function when integrated along the river’s length. In the following paper, we test the efficacy of organic and inorganic tracers for sediment fingerprinting in backwater confluences; we use fingerprinting results to evidence sediment dynamics controlling deposition patterns in confluences used for wetland and marina functions; and we quantify the spatial extent of tributary drainages with wetland and marina features in OR confluences. Both organic and inorganic tracers statistically differentiate sediment from stream and river end‐members. Carbon and nitrogen stable isotopes produce greater uncertainty in fingerprinting results than inorganic elemental tracers. Uncertainty analysis of the nonconservative tracer term in the organic matter fingerprinting application estimates an apparent enrichment of the carbon stable isotopes during instream residence, and the nonconservativeness is quantified with a statistical approach unique to the fingerprinting literature. Wetland and marina features in OR confluences impact 42% and 11% of tributary drainage areas, respectively. Sediment dynamics show wetland and marina confluences experience deposition from river backwaters with longitudinally linear and nonlinear patterns, respectively, from sediment sources.     

Model‐Based Nitrogen and Phosphorus (Nutrient) Criteria for Large Temperate Rivers: 1. Model Development and Application

An initial inquiry into model‐based numeric nitrogen and phosphorus (nutrient) criteria for large rivers is presented. Field data collection and associated modeling were conducted on a segment of the lower Yellowstone River in the northwestern United States to assess the feasibility of deriving numeric nutrient criteria using mechanistic water‐quality models. The steady‐state one‐dimensional model QUAL2K and a transect‐based companion model AT2K were calibrated and confirmed against low‐flow conditions at a time when river loadings, water column chemistry, and diurnal indicators were approximately steady state. Predictive simulation was then implemented via nutrient perturbation to evaluate the steady‐state and diurnal response of the river to incremental nutrient additions. In this first part of a two‐part series, we detail our modeling approach, model selection, calibration and confirmation, sensitivity analysis, model outcomes, and associated uncertainty. In the second part (Suplee et al., 2015) we describe the criteria development process using the tools described herein. Both articles provide a fundamental understanding of the process required to develop site‐specific numeric nutrient criteria using models in applied regulatory settings.