A study of anthropogenic and climatic disturbance of the New River Estuary using a Bayesian belief network

The present paper utilizes a Bayesian Belief Network (BBN) approach to intuitively present and quantify our current understanding of the complex physical, chemical, and biological processes that lead to eutrophication in an estuarine ecosystem (New River Estuary, North Carolina, USA). The model is further used to explore the effects of plausible future climatic and nutrient pollution management scenarios on water quality indicators. The BBN, through visualizing the structure of the network, facilitates knowledge communication with managers/stakeholders who might not be experts in the underlying scientific disciplines. Moreover, the developed structure of the BBN is transferable to other comparable estuaries. The BBN nodes are discretized exploring a new approach called moment matching method. The conditional probability tables of the variables are driven by a large dataset (four years). Our results show interaction among various predictors and their impact on water quality indicators. The synergistic effects caution future management actions.     

Mapping water quality and substrate cover in optically complex coastal and reef waters: an integrated approach

Sustainable management of coastal and coral reef environments requires regular collection of accurate information on recognized ecosystem health indicators. Satellite image data and derived maps of water column and substrate biophysical properties provide an opportunity to develop baseline mapping and monitoring programs for coastal and coral reef ecosystem health indicators. A significant challenge for satellite image data in coastal and coral reef water bodies is the mixture of both clear and turbid waters. A new approach is presented in this paper to enable production of water quality and substrate cover type maps, linked to a field based coastal ecosystem health indicator monitoring program, for use in turbid to clear coastal and coral reef waters. An optimized optical domain method was applied to map selected water quality (Secchi depth, Kd PAR, tripton, CDOM) and substrate cover type (seagrass, algae, sand) parameters. The approach is demonstrated using commercially available Landsat 7 Enhanced Thematic Mapper image data over a coastal embayment exhibiting the range of substrate cover types and water quality conditions commonly found in sub-tropical and tropical coastal environments. Spatially extensive and quantitative maps of selected water quality and substrate cover parameters were produced for the study site. These map products were refined by interactions with management agencies to suit the information requirements of their monitoring and management programs.     

Predicting ground water nitrate concentration from land use

Ground water nitrate concentrations on Nantucket Island, Massachusetts, were analyzed to assess the effects of land use on ground water quality. Exploratory data analysis was applied to historic ground water nitrate concentrations to determine spatial and temporal trends. Maximum likelihood Tobit and logistic regression analyses of explanatory variables that characterize land use within a 1000-foot radius of each well were used to develop predictive equations for nitrate concentration at 69 wells. The results demonstrate that historic nitrate concentrations downgradient from agricultural land are significantly higher than nitrate concentrations elsewhere. Tobit regression results demonstrate that the number of septic tanks and the percentages of forest, undeveloped, and high-density residential land within a 1000-foot radius of a well are reliable predictors of nitrate concentration in ground water. Similarly, logistic regression revealed that the percentages of forest, undeveloped, and low-density residential land are good indicators of ground water nitrate concentration > 2 mg/L. The methodology and results outlined here provide a useful tool for land managers in communities with shallow water tables overlain with highly permeable materials to evaluate potential effects of development on ground water quality.     

Ecosystem health assessment based on DPSIRM framework and health distance model in Nansi Lake,China

As lake ecosystem assessment is the foundation to achieve lake monitoring, environmental management and ecological restoration, a new concept of lake ecosystem health and driving force-pressure-state-impact-response-management framework was proposed to find out the causal relationship of the system and health distance model was taken to represent the health level of ecosystem. An assessment indicator system comprised of water quality, ecological and socio-economic criteria was established. The evaluation models were applied for the assessment of the ecosystem health level of a typical lake, Nansi Lake, China. Depends on the values of health distance, the heath level was described as 5°: very healthy, healthy, general healthy, sub-healthy and diseased. Using field investigation data and statistic data within the theory and applied models, the results of comprehensive assessment show that: (1) the health distances of water quality indicators, ecological indicators, socio-economic indicators and comprehensive health distance were 0.3989, 0.2495, 0.4983 and 0.4362, respectively. The health level was in general healthy condition. Ecological indicators were in healthy condition, which indicate that the stability was high. The distance of water quality had shown a tendency to approach general healthy level. As the health distance of socio-economic indicators have shown a bad impact form human beings, more effective measures need to be developed. (2) The results of a case study demonstrated that the methods in this paper provide a similar result corresponding with the actual lake health condition. Therefore, this paper shows that the proposed method is efficient and worths generalization.     

Groundwater Modeling in Integrated Water Resources Management—Visions for 2020

Groundwater modeling is undergoing a change from traditional stand-alone studies toward being an integrated part of holistic water resources management procedures. This is illustrated by the development in Denmark, where comprehensive national databases for geologic borehole data, groundwater-related geophysical data, geologic models, as well as a national groundwater-surface water model have been established and integrated to support water management. This has enhanced the benefits of using groundwater models. Based on insight gained from this Danish experience, a scientifically realistic scenario for the use of groundwater modeling in 2020 has been developed, in which groundwater models will be a part of sophisticated databases and modeling systems. The databases and numerical models will be seamlessly integrated, and the tasks of monitoring and modeling will be merged. Numerical models for atmospheric, surface water, and groundwater processes will be coupled in one integrated modeling system that can operate at a wide range of spatial scales. Furthermore, the management systems will be constructed with a focus on building credibility of model and data use among all stakeholders and on facilitating a learning process whereby data and models, as well as stakeholders' understanding of the system, are updated to currently available information. The key scientific challenges for achieving this are (1) developing new methodologies for integration of statistical and qualitative uncertainty; (2) mapping geological heterogeneity and developing scaling methodologies; (3) developing coupled model codes; and (4) developing integrated information systems, including quality assurance and uncertainty information that facilitate active stakeholder involvement and learning.     

Strategy for management of lake-catchment system integrated with natural and anthropogenic factors in China

Lake eutrophication that has caused severe damages to aquatic ecosystems is a hot issue in the field of water pollution control, so analyzing its process and driving mechanism is of great significance. In this study, we detected its major driving factors based on an observed dataset and assessed the impacts of these factors. Firstly, empirical econometric models were used to ascertain the Per capita GDP and the percentage of impervious surface, which are significantly related with the eutrophication level. Eutrophication is slightly affected by anthropogenic disturbance, but is negatively related to the supply coefficient. Secondly, all driving factors are divided into two categories, including the stresses caused by human activities and the comprehensive health status, which are expressed as the two coordinate axes of an indicator-matrix. Thirdly, lake classification, which is a decision-making tool for management and control of eutrophication, suggests a breakdown in communication between researchers and water managers. Finally, corresponding management strategies are recommended, and the classification system can be used to simplify management by grouping lakes that may be managed by similar strategies. The classification management may enlighten and guide the appropriate policy implementation for lake eutrophication management.     

Application of the soil and water assessment tool model on the Lower Porsuk Stream Watershed

Watershed models that combine hydrology and water quality are being widely used in integrated watershed management for the determination of best water management practices. In this study, the hydrology of the Lower Porsuk Stream Watershed in Turkey has been modelled with the Soil and Water Assessment Tool to determine optimal water management strategies. The calibration and the validation process have been accomplished using data from two monitoring stations. The model has been run for the 1978–2009 period, and while the 1998–2004 period has been used for calibration, the validation has spanned the whole period. The SWATCup calibration and uncertainty program has been used for this purpose. No significant differences have been detected among different iteration numbers in the calibration period. The monthly Nash–Sutcliffe and R² performance indicators for the upstream Esenkara station have been 0.74 and 0.88, respectively, for the calibration period, and 0.87 and 0.87, respectively, for the validation period. The Kiranharmani station, which is located close to the watershed outlet, has shown values of 0.59 and 0.72, respectively, for the calibration period, and 0.44 and 0.56, respectively, for the validation period. There are uncertainties in the abstracted irrigation and groundwater quantities that have reflected in the results in the Kiranharmani station, which is more affected as it lies downstream of the irrigation areas. The effects of different irrigation practices on the flow regime have been also investigated. A scenario has been implemented in which drip irrigation wholly replaces conventional furrow and sprinkler irrigation. The scenario has shown increases in stream flows by 87% for the whole year. The adoption of more efficient irrigation practices thus results in reducing the water stress induced by irrigation demands. With this study, a modelling framework has been founded to aid water management applications in the Lower Porsuk Stream Watershed by generating scenarios for best management practices. Copyright © 2012 John Wiley & Sons, Ltd.     

A univariate model of river water nitrate time series

Four time series were taken from three catchments in the North and South of England. The sites chosen included two in predominantly agricultural catchments, one at the tidal limit and one downstream of a sewage treatment works. A time series model was constructed for each of these series as a means of decomposing the elements controlling river water nitrate concentrations and to assess whether this approach could provide a simple management tool for protecting water abstractions. Autoregressive (AR) modelling of the detrended and deseasoned time series showed a “memory effect”. This memory effect expressed itself as an increase in the winter–summer difference in nitrate levels that was dependent upon the nitrate concentration 12 or 6 months previously. Autoregressive moving average (ARMA) modelling showed that one of the series contained seasonal, non-stationary elements that appeared as an increasing trend in the winter–summer difference. The ARMA model was used to predict nitrate levels and predictions were tested against data held back from the model construction process – predictions gave average percentage errors of less than 10%. Empirical modelling can therefore provide a simple, efficient method for constructing management models for downstream water abstraction.     

Clustering and forecasting of dissolved oxygen concentration on a river basin

The aim of this contribution is to combine statistical methodologies to geographically classify homogeneous groups of water quality monitoring sites based on similarities in the temporal dynamics of the dissolved oxygen (DO) concentration, in order to obtain accurate forecasts of this quality variable. Our methodology intends to classify the water quality monitoring sites into spatial homogeneous groups, based on the DO concentration, which has been selected and considered relevant to characterize the water quality. We apply clustering techniques based on Kullback Information, measures that are obtained in the state space modelling process. For each homogeneous group of water quality monitoring sites we model the DO concentration using linear and state space models, which incorporate tendency and seasonality components in different ways. Both approaches are compared by the mean squared error (MSE) of forecasts.     

Modelling groundwater level variations by learning from multiple models using fuzzy logic

Modelling time series of groundwater levels is investigated by three fuzzy logic (FL) models, Sugeno (SFL), Mamdani (MFL) and Larsen (LFL), using data from observation wells. One novelty in the study is the re-use of these three models as multiple models through the following strategies: (a) simple averaging, (b) weighted averaging and (c) committee machine techniques; these are implemented using artificial neural networks (ANN). These strategies provide some evidence that (i) multiple models improve on the performance of individual models and those using committee machines perform better than the other two options; and (ii) committee machine models produce defensible modelling results to develop management scenarios. The study investigates water table declines through management scenarios and shows that in this aquifer water use has higher impacts on water table variations than climatic variations. This provides evidence of the need for planned management in the study area.     

Integrating water management,habitat modelling and water quality at the basin scale and environmental flow assessment: case study of the Tormes River,Spain

Abstract

Multidisciplinary models are useful for integrating different disciplines when addressing water planning and management problems. We combine water resources management, water quality and habitat analysis tools that were developed with the decision support system AQUATOOL at the basin scale. The water management model solves the allocation problem through network flow optimization and considers the environmental flows in some river stretches. Once volumes and flows are estimated, the water quality model is applied. Furthermore, the flows are evaluated from an ecological perspective using time series of aquatic species habitat indicators. This approach was applied in the Tormes River Water System, where agricultural demands jeopardize the environmental needs of the river ecosystem. Additionally, water quality problems in the lower part of the river result from wastewater loading and agricultural pollution. Our methodological framework can be used to define water management rules that maintain water supply, aquatic ecosystem and legal standards of water quality. The integration of ecological and water management criteria in a software platform with objective criteria and heuristic optimization procedures allows realistic assessment and application of environmental flows to be made. Here, we improve the general methodological framework by assessing the hydrological alteration of selected environmental flow regime scenarios.

Editor D. Koutsoyiannis; Guest editor M. Acreman

Citation Paredes-Arquiola, J., Solera, A., Martinez-Capel, F., Momblanch, A., and Andreu, J., 2014. Integrating water management, habitat modelling and water quality at the basin scale and environmental flow assessment: case study of the Tormes River, Spain. Hydrological Sciences Journal, 59 (3–4), 878–889.     

Global water resources modeling with an integrated model of the social-economic-environmental system

Awareness of increasing water scarcity has driven efforts to model global water resources for improved insight into water resources infrastructure and management strategies. Most water resources models focus explicitly on water systems and represent socio-economic and environmental change as external drivers. In contrast, the system dynamics-based integrated assessment model employed here, ANEMI, incorporates dynamic representations of these systems, so that their broader changes affect and are affected by water resources systems through feedbacks. Sectors in ANEMI therefore include the global climate system, carbon cycle, economy, population, land use and agriculture, and novel versions of the hydrological cycle, global water use and water quality. Since the model focus is on their interconnections through explicit nonlinear feedbacks, simulations with ANEMI provide insight into the nature and structure of connections between water resources and socio-economic and environmental change. Of particular interest to water resources researchers and modelers will be the simulated effects of a new water stress definition that incorporates both water quality and water quantity effects into the measurement of water scarcity. Five simulation runs demonstrate the value of wastewater treatment and reuse programs and the feedback-effects of irrigated agriculture and greater consumption of animal products.     

Development of an inexact two-stage stochastic model with downside risk control for water quality management and decision analysis under uncertainty

Water quality management along rivers involves making water-allocation plans, establishing water quality goals, and controlling pollutant discharges, which is complicated itself but further challenged by existence of uncertainties. In this study, an inexact two-stage stochastic downside risk-aversion programming (ITSDP) model is developed for supporting regional water resources allocation and water quality management problems under uncertainties. The ITSDP method is a hybrid of interval-parameter programming, two-stage stochastic programming, and downside risk measure to tackle uncertainties described in terms of interval values and probability distributions. A water quality simulation model was provided for reflecting the relationship between the water resources allocation, wastewater discharge, and environmental responses. The proposed approach was applied to a hypothetical case for a shared stream water quality management with one municipal, three industrial and two agricultural sectors. A number of scenarios corresponding to different river inflows and risk levels were examined. The results demonstrated that the model could effectively communicate the interval-format and random uncertainties, and risk-aversion into optimization process, and generate a trade-off between the system economy and stability. They could be helpful for seeking cost-effective management strategies under uncertainties, and gaining an in-depth insight into the water quality management system characteristics, and make cost-effective decisions.     

Water quality model of the Danube in its Bulgarian part

Models for predicting river water quality could assist protection and utilization of water resources under alternative management strategies. The model of the water quality characteristics of the Danube is based on an ecosystem approach. Considered are all waste water discharges and inflow of tributaries among the catchment area. The identification of model parameters is realized according to the method of integral transformation. For the performance of the model a computer program was elaborated. It is applied in predicting the water quality of the Danube considering almost 20 indicators for the abiotic and biotic parameters of river ecosystem.     

Investigating evidential reasoning for the interpretation of microbial water quality in a distribution network

Total coliforms are used as indicators for evaluating microbial water quality in distribution networks. However, total coliform provides only a weak “evidence” of possible fecal contamination because pathogens are subset of total coliform and therefore their presence in drinking water is not necessarily associated with fecal contamination. Heterotrophic plate counts are also commonly used to evaluate microbial water quality in the distribution networks, but they cover even a wider range of organisms. As a result, both of these indicators can provide incomplete and highly uncertain bodies of evidence when used individually. In this paper, it is shown that combing these two sources of information by an appropriate data fusion technique can provide improved insight into microbial water quality within distribution networks. Approximate reasoning methods like fuzzy logic and probabilistic reasoning are commonly used for data fusion where knowledge is uncertain (i.e., ambiguous, incomplete, and/or vague). Traditional probabilistic frameworks like Bayesian analysis, reasons through conditioning based on prior probabilities (which are hardly ever available). The Dempster–Shafer (DS) theory generalizes the Bayesian analysis without requiring prior probabilities. The DS theory can efficiently deal with the difficulties related to the interpretation of overall water quality where the redundancy of information is routinely observed and the credibility of available data continuously changes. In this paper, the DS rule of combination and its modifications including Yager’s modified rule, Dubois–Prade disjunctive rule and Dezert–Smarandache rule are described using an example of microbial water quality in a distribution network.     

Rapid underway profiling of water quality in Queensland estuaries

We present an overview of a portable underway water quality monitoring system (RUM-Rapid Underway Monitoring), developed by integrating several off-the-shelf water quality instruments to provide rapid, comprehensive, and spatially referenced 'snapshots' of water quality conditions. We demonstrate the utility of the system from studies in the Northern Great Barrier Reef (Daintree River) and the Moreton Bay region. The Brisbane dataset highlights RUM's utility in characterising plumes as well as its ability to identify the smaller scale structure of large areas. RUM is shown to be particularly useful when measuring indicators with large small-scale variability such as turbidity and chlorophyll-a. Additionally, the Daintree dataset shows the ability to integrate other technologies, resulting in a more comprehensive analysis, whilst sampling offshore highlights some of the analytical issues required for sampling low concentration data. RUM is a low cost, highly flexible solution that can be modified for use in any water type, on most vessels and is only limited by the available monitoring technologies.     

城镇化下河流水质变化及其与景观格局关系分析——以太湖流域苏州市为例

以河流近域景观格局与水环境质量间的关系为研究对象,基于2001年及2010年两期土地利用类型以及7条典型河流的溶解氧、氨氮、高锰酸盐指数、总磷、总氮5项水质指标,分析城镇化下苏州市河流水质与景观格局之间的关系及其变化规律.结果表明:(1)苏州市河流水质状况总体较差,但呈现一定的好转趋势.(2)河流水质受到城镇用地、旱地及水田的综合影响,并表现出尺度效应.其中城镇用地与旱地对河流水质恶化具有明显的作用,水田则反之.2001-2010年城镇用地及水田对水质的影响程度有所减弱,旱地则增强.(3)蔓延度指数、最大面积斑块比例对河流水质呈现负相关,斑块数量、斑块密度、香农均匀度指数、香农多样性指数则与水质呈现正相关.景观格局对于水质的影响在大范围缓冲区更为显著.研究结果可为苏州水环境管理及太湖流域城市空间开发提供一定的参考依据.     

Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management

There is increasing demand for models that can accurately predict river temperature at the large spatial scales appropriate to river management. This paper combined summer water temperature data from a strategically designed, quality controlled network of 25 sites, with recently developed flexible spatial regression models, to understand and predict river temperature across a 3,000 km² river catchment. Minimum, mean and maximum temperatures were modelled as a function of nine potential landscape covariates that represented proxies for heat and water exchange processes. Generalised additive models were used to allow for flexible responses. Spatial structure in the river network data (local spatial variation) was accounted for by including river network smoothers. Minimum and mean temperatures decreased with increasing elevation, riparian woodland and channel gradient. Maximum temperatures increased with channel width. There was greater between‐river and between‐reach variability in all temperature metrics in lower‐order rivers indicating that increased monitoring effort should be focussed at these smaller scales. The combination of strategic network design and recently developed spatial statistical approaches employed in this study have not been used in previous studies of river temperature. The resulting catchment scale temperature models provide a valuable quantitative tool for understanding and predicting river temperature variability at the catchment scales relevant to land use planning and fisheries management and provide a template for future studies.     

Data mining methods for hydroclimatic forecasting

Skillful streamflow forecasts at seasonal lead times may be useful to water managers seeking to provide reliable water supplies and maximize hydrosystem benefits. In this study, a class of data mining techniques, known as tree-structured models, is investigated to address the nonlinear dynamics of climate teleconnections and screen promising probabilistic streamflow forecast models for river–reservoir systems. In a case study of the Lower Colorado River system in central Texas, a number of potential predictors are evaluated for forecasting seasonal streamflow, including large-scale climate indices related to the El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and others. Results show that the tree-structured models can effectively capture the nonlinear features hidden in the data. Skill scores of probabilistic forecasts generated by both classification trees and logistic regression trees indicate that seasonal inflows throughout the system can be predicted with sufficient accuracy to improve water management, especially in the winter and spring seasons in central Texas.