THE DEVELOPMENT OF THE NILE HYDROMETEOROLOGICAL FORECAST SYSTEM1

ABSTRACT: The National Oceanic and Atmospheric Administration is developing a river forecast system for the Nile River in Egypt. The river forecast system operates on scientific work stations using hydrometeorological models and software to predict inflows into the high Aswan Dam and forecast flow hydrographs at selected gaging locations above the dam The Nile Forecasting System (NFS) utilizes satellite imagery from the METEOSAT satellite as the input to the forecast system. Satellite imagery is used to estimate precipitation over the Blue Nile Basin using five different techniques. Observed precipitation data and climatic statistics are used to improve precipitation estimation. Precipitation data for grid locations are input to a distributed water balance model, a hill slope routing model, and a channel routing model. A customized Geographic Information System (GIS) was developed to show political boundaries, rivers, terrain elevation, and gaging network. The GIS was used to develop hydrologic parameters for the basin and is used for multiple display features.     

COMPUTING THE RISKS ASSOCIATED WITH WASTELOAD) ALLOCATION MODELING1

ABSTRACT: The risks associated with a traditional wasteload allocation (WLA) analysis were quantified with data from a recent study of the Upper Trinity River (Texas). Risk is define here as the probability of failing to meet an established in-stream water quality standard. The QUAL-TX dissolved oxygen (DO) water quality model was modified to a Monte Carlo framework. Flow augmentation coding was also modified to allow an exact match to be computed between the predicted and an established DO concentration standard, thereby providing an avenue for linking input parameter uncertainty to the assignment of a wasteload permit (allowable mass loading rate). Monte Carlo simulation techniques were employed to propagate input parameter uncertainties, typically encountered during WLA analysis, to the computed effluent five-day carbonaceous biochemical oxygen demand requirements for a single major wastewater treatment plant (WWTP). The risk of failing to meet an established in-stream DO criterion may be as high as 96 percent. The uncertainty associated with estimation of the future total Kjeldahl nitrogen concentration for a single tributary was found to have the greatest impact on the determination of allowable WWTP loadings.     

Nutrient Uptake in a Large Urban River1

Abstract: Small streams have been shown to be efficient in retaining nutrients and regulating downstream nutrient fluxes, but less is known about nutrient retention in larger rivers. We quantified nutrient uptake length and uptake velocity in a regulated urban river to determine the river’s ability to retain nutrients associated with wastewater treatment plant (WWTP) effluent. We measured net uptake of soluble reactive phosphorus (SRP), dissolved organic phosphorus, ammonium (NH₄), nitrate, and dissolved organic nitrogen in the Chattahoochee River, Atlanta, GA by following the downstream decline of nutrients and fluoride from WWTP effluent on 10 dates under low flow conditions. Uptake of all nutrients was sporadic. On many dates, there was no evidence of measurable nutrient uptake lengths within the reach; indeed, on several dates release of inorganic N and P within the sample reach led to increased nutrient export downstream. When uptake occurred, SRP uptake length was negatively correlated with total suspended solids and temperature. Uptake velocities of SRP and NH₄ in the Chattahoochee River were lower than velocities in less‐modified systems, but they were similar to those measured in other WWTP impacted systems. Lower uptake velocities indicate a diminished capacity for nutrient uptake.     

A Preliminary Index of Biotic Integrity for Monitoring the Condition of the Rio Paraiba do Sul,Southeast Brazil

The biodiversity of many Brazilian rivers is seriously threatened by industrial and municipal pollution, and Rio Paraiba do Sul, located between two major industrial centers is one example of this situation. A survey of the fish assemblage was conducted from October 1998 to September 1999 and the data were used to develop an index of biotic integrity (IBI). We sampled three zones in bracketing a large urban–industrial complex to evaluate water quality changes and the usefulness of the IBI as a monitoring tool. Water quality was classified as poor upstream of the effluent discharges, very poor near the discharges, and poor–fair downstream of the discharges, with this latter situation revealing the current biological capacity of the river. Physical and chemical habitat characteristics were also measured at each site to construct an independent environmental index to validate the IBI. The habitat and IBI indices were highly correlated, suggesting this IBI would be applicable to other large rivers in southeast Brazil.     

Towards an Integrated Approach to Natural Hazards Risk Assessment Using GIS: With Reference to Bushfires

This paper develops a GIS-based integrated approach to risk assessment in natural hazards, with reference to bushfires. The challenges for undertaking this approach have three components: data integration, risk assessment tasks, and risk decision-making. First, data integration in GIS is a fundamental step for subsequent risk assessment tasks and risk decision-making. A series of spatial data integration issues within GIS such as geographical scales and data models are addressed. Particularly, the integration of both physical environmental data and socioeconomic data is examined with an example linking remotely sensed data and areal census data in GIS. Second, specific risk assessment tasks, such as hazard behavior simulation and vulnerability assessment, should be undertaken in order to understand complex hazard risks and provide support for risk decision-making. For risk assessment tasks involving heterogeneous data sources, the selection of spatial analysis units is important. Third, risk decision-making concerns spatial preferences and/or patterns, and a multicriteria evaluation (MCE)-GIS typology for risk decision-making is presented that incorporates three perspectives: spatial data types, data models, and methods development. Both conventional MCE methods and artificial intelligence-based methods with GIS are identified to facilitate spatial risk decision-making in a rational and interpretable way. Finally, the paper concludes that the integrated approach can be used to assist risk management of natural hazards, in theory and in practice.     

Valuing river characteristics using combined site choice and participation travel cost models

This paper presents new welfare measures for marginal changes in river quality in selected English rivers. The river quality indicators used include chemical, biological and habitat-level attributes. Economic values for recreational use of three types of river-upland, lowland and chalk-are presented. A survey of anglers was carried out and using these data, two travel cost models were estimated, one to predict the numbers of trips and the other to predict angling site choice. These models were then linked to estimate the welfare associated with marginal changes in river quality using the participation levels as estimated in the trip prediction model. The model results showed that higher flow rates, biological quality and nutrient pollution levels affect site choice and influence the likelihood of a fishing trip. Consumer surplus values per trip for a 10% change in river attributes range from pound 0.04 to pound 3.93 ( pound 2001) depending on the attribute.     

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, an artificial neural network (ANN) black-box modeling approach was used to acquire the knowledge base of a real wastewater plant and then used as a process model. The study signifies that the ANNs are capable of capturing the plant operation characteristics with a good degree of accuracy. A computer model is developed that incorporates the trained ANN plant model. The developed program is implemented and validated using plant-scale data obtained from a local wastewater treatment plant, namely the Doha West wastewater treatment plant (WWTP). It is used as a valuable performance assessment tool for plant operators and decision makers. The ANN model provided accurate predictions of the effluent stream, in terms of biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solids (TSS) when using COD as an input in the crude supply stream. It can be said that the ANN predictions based on three crude supply inputs together, namely BOD, COD and TSS, resulted in better ANN predictions when using only one crude supply input. Graphical user interface representation of the ANN for the Doha West WWTP data is performed and presented.     

AN INTERACTWE MODELING ENVIRONMENT FOR NON-POINT SOURCE POLLUTION CONTROL1

ABSTRACT: Non-point source pollution cuntinues to be an important environmental and water quality management problem. For the moat part, analysis of non-point source pollution in watersheds has depended on the use of distributed models to identify potential problem areas and to assess the effectiveness of alternative management practices. To effectively use these models for watershed water quality management, users depend on integrated geographic information systems (GIS)-based interfaces for input/output data management. However, existing interfaces are ad-hoc and the utility of GIS is limited to organization of input data and display of output data. A highly interactive water quality modeling interface that utilizes the functional components and analytical capability of GIS is highly desirable. This paper describes the tight coupling of the Agricultural Non-point Source (AGNPS) water quality model and ARC/INFO GIS software to provide an interactive hybrid modeling environment for evaluation of non-point source pollution in a watershed. The modeling environment is designed to generate AGNPS input parameters from user-specified GIS coverages, create AGNPS input data files, control AGNPS model simulations, and extract and organize AGNPS model output data for display. An example application involving the estimation of pesticide loading in a southern Iowa agricultural watershed demonstrates the capability of the modeling environment. Compared with traditional methods of watershed water quality modeling using the AGNPS model or other ad-hoc interfaces between a distributed model and GIS, the interactive modeling environment system is efficient and significantly reduces the task of watershed analysis using tightly coupled GIS databases and distributed models.     

River pollution from non-point sources: a new simplified method of assessment

Assessment of the pollution of water bodies from non-point sources is a complex data- and time-consuming task. The potential non-point pollution index (PNPI), is a new tool designed to assess the global pressure exerted on rivers and other surface water bodies by different land uses. The main feature of PNPI is the wide availability of its input data. Very detailed input maps, often lacking over many areas, are not needed for PNPI calculation. As a consequence of the input data used, the modelling of physical reality and of processes is heavily simplified. The authors counterbalanced such a simplification using an 'expert system' approach. The system bypasses the accurate representation of the physical reality to assess globally the pollution potential of different land uses according to the judgement of scientists. The scientific community proposes many models for depicting the dynamics of pollutants coming from diffuse sources. Most of them can be grouped into two broad categories: statistical models and physically based models. PNPI belongs to neither of the above-mentioned groups. PNPI is a GIS-based, watershed-scale tool designed to inform decision makers and public opinion about the potential environmental impacts of different land management scenarios. PNPI applies the multicriteria technique to pollutant dynamics and water quality. The pressure exerted on water bodies by diffuse pollution coming from land units is expressed as a function of three indicators: land use, run-off and distance from the river network. They are calculated from land use data, geological maps and a digital elevation model (DEM). The weights given to different land uses and to the three indicators were set according to experts' evaluations and allow calculation of the value of the PNPI for each node of a grid representing the watershed; the higher the PNPI of the cell, the greater the potential impact on the river network. The output of the calculation is presented in the form of maps that highlight areas that are more likely to produce pollution. Last, possibilities, strategies and results of the validation of the PNPI are described. In the authors' view, the explicit link between land use and potential pollution on which PNPI is based, together with its high communication potential, make it particularly interesting for a participatory and integrated approach to land management and environmental protection.     

Wastewater Effluent,Combined Sewer Overflows,and Other Sources of Organic Compounds to Lake Champlain1

Abstract: Some sources of organic wastewater compounds (OWCs) to streams, lakes, and estuaries, including wastewater‐treatment‐plant effluent, have been well documented, but other sources, particularly wet‐weather discharges from combined‐sewer‐overflow (CSO) and urban runoff, may also be major sources of OWCs. Samples of wastewater‐treatment‐plant (WWTP) effluent, CSO effluent, urban streams, large rivers, a reference (undeveloped) stream, and Lake Champlain were collected from March to August 2006. The highest concentrations of many OWCs associated with wastewater were in WWTP‐effluent samples, but high concentrations of some OWCs in samples of CSO effluent and storm runoff from urban streams subject to leaky sewer pipes or CSOs were also detected. Total concentrations and numbers of compounds detected differed substantially among sampling sites. The highest total OWC concentrations (10‐100 μg/l) were in samples of WWTP and CSO effluent. Total OWC concentrations in samples from urban streams ranged from 0.1 to 10 μg/l, and urban stream‐stormflow samples had higher concentrations than baseflow samples because of contributions of OWCs from CSOs and leaking sewer pipes. The relations between OWC concentrations in WWTP‐effluent and those in CSO effluent and urban streams varied with the degree to which the compound is removed through normal wastewater treatment. Concentrations of compounds that are highly removed during normal wastewater treatment [including caffeine, Tris(2‐butoxyethyl)phosphate, and cholesterol] were generally similar to or higher in CSO effluent than in WWTP effluent (and ranged from around 1 to over 10 μg/l) because CSO effluent is untreated, and were higher in urban‐stream stormflow samples than in baseflow samples as a result of CSO discharge and leakage from near‐surface sources during storms. Concentrations of compounds that are poorly removed during treatment, by contrast, are higher in WWTP effluent than in CSO, due to dilution. Results indicate that CSO effluent and urban stormwaters can be a significant major source of OWCs entering large water bodies such as Burlington Bay.     

INTEGRATION OF A BASIN-SCALE WATER QUALITY MODEL WITH GIS1

ABSTRACT: Geographic Information Systems (GIS) have been successfully integrated with distributed parameter, single-event, water quality models such as AGNPS (AGricultural NonPoint Source) and ANSWERS (Areal Nonpoint Source Watershed Environmental Response Simulation). These linkages proved to be an effective way to collect, manipulate, visualize, and analyze the input and output date of water quality models. However, for continuous-time, basin large-scale water quality models, collecting and manipulating the input data are more time-consuming and cumbersome due to the method of disaggregation (subdivisions are based on topographic boundaries). SWAT (Soil and Water Assessment Tool), a basin-scale water quality model, was integrated with a GIS to extract input data for modeling a basin. This paper discusses the detailed development of the integration of the SWAT water quality model with GRASS (Geographic Resources Analysis Support System) GIS, along with an application and advantages. The integrated system was applied to simulated a 114 sq. km upper portion of the Seco Creek Basin by subdividing it into 37 subbasins. The average monthly predicted streamflw is in agreement with measured monthly streamflw values.     

Use of GIS in siting stabilization pond facilities for domestic wastewater treatment

Geographic information systems (GIS) use is presented in the problem of sitting areas for construction of natural systems such as stabilization ponds (SPs) for domestic wastewater treatment. For this purpose, several variables, such as topography, land use, type of geological formation, distance to major rivers or lakes, distance to existing cities and villages, existence of environmentally protected areas, mean minimum monthly temperatures and required wastewater effluent characteristics were analyzed with the GIS, in order to accept or reject a particular area within a region. The method is applied in the region of Thrace (Northeast Greece) at the municipal level. The required area for SP systems was calculated in each of the 36 municipalities of Thrace (including two islands, Thassos and Samothraki) as a function of the population of each municipality, temperature and local wastewater effluent discharge criteria. Based on the GIS analysis, suitable locations were identified in each municipality first, and then the total required surface area of these systems was compared to the available surface area of each municipality, in order to decide whether SP systems could be a viable solution to the wastewater management problem in the particular region. In that way the present methodology offers a fast and simple method to check the suitability of new areas for construction of such systems.     

A river water quality management model for optimising regional wastewater treatment using a genetic algorithm

To achieve water quality goals and wastewater treatment cost optimisation in a river basin, a water quality management model has been developed through the integration of a genetic algorithm (GA) and a mathematical water quality model. The developed model has been applied to the Youngsan River, where water quality has decreased due to heavy pollutant loads from Kwangju City and surrounding areas. Pollution source, land use, geographic features and measured water quality data of the river basin were incorporated into the Arc/View geographic information system database. With the database, the management model calculated treatment type and treatment cost for each wastewater treatment plant in the river basin. Until now, wastewater treatment policy for polluted rivers in Korea has been, first of all, to construct secondary treatment plants for untreated areas, and secondarily, to construct advanced treatment plants for the river sections whose water quality is impaired and for which the water quality goal of the Ministry of Environment is not met. Four scenarios that do not use the GA were proposed and they were compared with the results of the management model using the GA. It became clear that the results based on the GA were much better than those for the other four scenarios from the viewpoint of the achievement of water quality goals and cost optimisation.     

Delineation and Validation of River Network Spatial Scales for Water Resources and Fisheries Management

Identifying appropriate spatial scales is critically important for assessing health, attributing data, and guiding management actions for rivers. We describe a process for identifying a three-level hierarchy of spatial scales for Michigan rivers. Additionally, we conduct a variance decomposition of fish occurrence, abundance, and assemblage metric data to evaluate how much observed variability can be explained by the three spatial scales as a gage of their utility for water resources and fisheries management. The process involved the development of geographic information system programs, statistical models, modification by experienced biologists, and simplification to meet the needs of policy makers. Altogether, 28,889 reaches, 6,198 multiple-reach segments, and 11 segment classes were identified from Michigan river networks. The segment scale explained the greatest amount of variation in fish abundance and occurrence, followed by segment class, and reach. Segment scale also explained the greatest amount of variation in 13 of the 19 analyzed fish assemblage metrics, with segment class explaining the greatest amount of variation in the other six fish metrics. Segments appear to be a useful spatial scale/unit for measuring and synthesizing information for managing rivers and streams. Additionally, segment classes provide a useful typology for summarizing the numerous segments into a few categories. Reaches are the foundation for the identification of segments and segment classes and thus are integral elements of the overall spatial scale hierarchy despite reaches not explaining significant variation in fish assemblage data.     

A procedure for incorporating spatial variability in ecological risk assessment of Dutch river floodplains

Floodplain soils along the river Rhine in the Netherlands show a large spatial variability in pollutant concentrations. For an accurate ecological risk characterization of the river floodplains, this heterogeneity has to be included into the ecological risk assessment. In this paper a procedure is presented that incorporates spatial components of exposure into the risk assessment by linking geographical information systems (GIS) with models that estimate exposure for the most sensitive species of a floodplain. The procedure uses readily available site-specific data and is applicable to a wide range of locations and floodplain management scenarios. The procedure is applied to estimate exposure risks to metals for a typical foodweb in the Afferdensche and Deestsche Waarden floodplain along the river Waal, the main branch of the Rhine in the Netherlands. Spatial variability of pollutants is quantified by overlaying appropriate topographic and soil maps resulting in the definition of homogeneous pollution units. Next to that, GIS is used to include foraging behavior of the exposed terrestrial organisms. Risk estimates from a probabilistic exposure model were used to construct site-specific risk maps for the floodplain. Based on these maps, recommendations for future management of the floodplain can be made that aim at both ecological rehabilitation and an optimal flood defense.     

Comparative Evaluation of Three River Water Quality Models1

Abstract: Selection of a biochemical oxygen demand (BOD) reaction model to incorporate into dissolved oxygen (DO) water quality models is an overlooked choice available to river water quality modelers. Data from rivers can serve in screening methods to discriminate between competing water quality models. In this study, 15 published BOD and DO datasets based on a 7 year long study of the Bormida River in Italy are used to calibrate three‐multiorder BOD models: first‐order, three‐halves order, and second‐order, which are then included in three corresponding DO models which incorporate these BOD models. The adequacy of the first‐order, three‐halves order and second‐order BOD models was evaluated by calculating the root mean square error between a model and data. A similar procedure was followed to evaluate three DO models, each of which incorporated one of the three BOD models. The first‐order BOD model most frequently fit the river data best, followed by the three‐halves order and the second‐order BOD models. The DO model incorporating a first‐order BOD model most frequently fit the data best, followed by the DO order incorporating second‐order BOD and the DO model incorporating three‐halves order BOD.     

地理信息系统与海上溢油模型集成研究

将地理信息系统(GIS)技术与溢油模型进行集成,其优点是能发挥GIS在模型参数获取、空间数据分析与管理、模拟结果可视化等方面的优势,使整个溢油建模过程更为便捷。文中介绍了GIS与溢油模型集成的概念,分析了GIS技术在海上溢油模型中的应用实例,给出了一种GIS与溢油模型的紧密集成方案,讨论了两者集成的发展趋势。     

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.     

Assessing the application of an additive model to estimate toxicity of a complex effluent

A number of industries monitor levels of chemicals in their effluent, but few have undertaken prolonged biological monitoring of this wastewater. The focus of the present study was to determine whether past chemical data for effluent from a lead smelter could be used to estimate its past toxicity. Since the interactive effects of metals in effluents are often assumed to be additive, it was hypothesized that an additive model, 100/[sigma(metal concentration in effluent/EC50 for individual metal)], could be used to generate an EC50 from chemical data (where EC50 is the concentration of test material that affects 50% of the test organisms). To test the approach, a larval development toxicity test with the marine polychaete, Galeolaria caespitosa, was used to test 26 separate samples of effluent from a lead smelter, generating empirical EC50 values. EC50 values for each individual metal in the effluent were also generated using the larval development toxicity test. The concentrations of trace metals in each effluent sample were determined and, using the additive model, EC50 values were calculated. For the majority of effluent samples tested, the additive model underestimated toxicity, suggesting the presence of additional unidentified contaminants in the effluent samples. Additionally, a nonlinear rather than linear regression curve was found to best describe the relationship between the model and empirically derived EC50 values. This relationship was then used to estimate past trends in toxicity of the smelter effluent. Forty-eight percent of the variability in measured toxicity was explained by the model, with the model underestimating toxicity in the majority of samples.     

再生水补给城市河道的生态服务价值预测

为了充分认识再生水用于城市河道补水后的潜在正面环境价值,构建了案例区规划水系的水量水质模型,并筛选了7项景观生态河流的生态服务价值指标,对案例区的河流生态服务价值进行了评估。以规划补水量为基础条件,规划水系的潜在服务价值总量约为1.995亿元,每增加1倍补水量可增加约0.4~0.5亿元的服务价值。气候调节、洪水调蓄和水量存贮是总量中占比最高的前三位指标,在规划情景下分别占总量的43.2%、41.6%、15.0%。生态服务价值总量和前三位指标的计算结果均能与再生水补水量拟合形成较好的响应关系,因此研究结果可以为预测评估不同补水量条件下的潜在河流生态服务价值提供一定的依据。