Enhancing hydro-epidemiological modelling of nearshore coastal waters with source-receptor connectivity study.

Faecal Indicator Organism (FIO) concentrations in nearshore coastal waters may lead to significant public health concerns and economic loss. A three-dimensional numerical source-receptor connectivity study was conducted to improve the modelling of FIO transport and decay processes and identify major FIO sources impacting sensitive receptors (source apportionment). The study site was Swansea Bay, UK and the effects of wind, density, and tracer microbe (surrogate FIO) decay models were investigated by comparing the model simulations to microbial tracer field studies. The relevance of connectivity tests to source apportionment was demonstrated by hindcasting FIO concentration in Swansea Bay with the identified FIO source and the Impulse Response Function (IRF) in Control System theory. This is the first time the IRF approach has been applied for FIO modelling in bathing waters. Results show the importance of density, widely ignored in fully mixed water bodies, and the potential for biphasic decay models to improve prediction accuracy. The microbe-carrying riverine freshwater, having a smaller hydrostatic pressure, could not intrude on the heavier seawater and remained in the nearshore areas. The freshwater and the associated tracer microbes then travelled along the shoreline and reached bathing water sites. This effect cannot be faithfully modelled without the inclusion of the density effect. Biphasic decay models improved the agreement between measured and modelled microbe concentrations. The IRF hindcasted and measured FIO concentrations for Swansea Bay agreed reasonably, demonstrating the importance of connectivity tests in identifying key FIO sources. The findings of this study, namely enhancing hydro-epidemiological modelling and highlighting the effectiveness of connectivity studies in identifying key FIO sources, directly benefit hydraulics and water quality modellers, regulatory authorities, water resource managers and policy.

Fate and transport modelling for evaluating antibiotic resistance in aquatic environments: Current knowledge and research priorities.

Antibiotics have revolutionised medicine in the last century and enabled the prevention of bacterial infections that were previously deemed untreatable. However, in parallel, bacteria have increasingly developed resistance to antibiotics through various mechanisms. When resistant bacteria find their way into terrestrial and aquatic environments, animal and human exposures increase, e.g., via polluted soil, food, and water, and health risks multiply. Understanding the fate and transport of antibiotic resistant bacteria (ARB) and the transfer mechanisms of antibiotic resistance genes (ARGs) in aquatic environments is critical for evaluating and mitigating the risks of resistant-induced infections. The conceptual understanding of sources and pathways of antibiotics, ARB, and ARGs from society to the water environments is essential for setting the scene and developing an appropriate framework for modelling. Various factors and processes associated with hydrology, ecology, and climate change can significantly affect the fate and transport of ARB and ARGs in natural environments. This article reviews current knowledge, research gaps, and priorities for developing water quality models to assess the fate and transport of ARB and ARGs. The paper also provides inputs on future research needs, especially the need for new predictive models to guide risk assessment on AR transmission and spread in aquatic environments.

Water quality management at a critical checkpoint by coordinated multi-catchment urban-rural load allocation.

Improving river water quality at critical checkpoints, defined as locations with significant impacts on water use, to satisfy regulation standards is an important goal of sustainable catchment management. Challenges remain in investigating pollution hotspots, designing efficient target reduction, and evaluating management performance. To address these challenges, we develop a systems approach for water quality management that integrates natural physical processes with human activities and their environmental impacts. In this approach, we firstly expand the concepts of headroom (amount under a permitted value) and excess (amount exceeding a permit) onto the source, spatial, and temporal domains for water quality management. We evaluate system-wide pollution contributions by simulating physical processes in a semi-distributed integrated representation using the CatchWat-SD model. We apply the model to the Upper Thames River basin and validate it using available monitoring data. We then incorporate the evaluated headroom-excess into a coordinated load allocation to enhance the efficiency and feasibility of interventions. Load allocation scenarios where headroom-excess is coordinated at different domains are generated and simulated. Finally, we evaluate the performance of these scenarios using multi-criteria metrics to demonstrate the advantages of headroom-excess coordination. Results show that urban sources, downstream sub-catchments, and dry season flows are associated with excess, thus, enabling managers to identify which cases (pollution sources, locations, and times) to focus load reductions towards. The more a load allocation strategy coordinates headroom-excess across domains, the more target reduction is allocated to the cases with excess, and the better performance it obtains in all the criteria. The study emphasises the need to incorporate headroom-excess in load allocation, which helps to improve systems-level water quality performance more efficiently. The approach can be further expanded to water quality management at multiple checkpoints for sustainable management of regional water systems.

Reduction in water consumption in premise plumbing systems: Impacts on lead concentration under different water qualities.

Water conservation measures are increasing in response to regulatory requirements addressing the need for lower environmental footprint and in response to water shortages. In households with lead service lines (LSLs), lowering consumption can adversely impact lead release as it will increase stagnation. Using a lead dissolution model and data from extensive pilot studies on excavated LSLs, the impact of adaptation to different water conservation strategies on dissolved lead contamination at the kitchen tap is assessed under three water qualities and three LSL lengths (3, 14 and 30 m) using hydraulic and water quality modelling. Consumers' behavioural variability is also assessed based on integration of EPANET and results of the stochastic water demand model SIMDEUM. Demand reduction increased the dissolved lead concentrations (Pbdiss) at the end of the LSL with mean values ranging from 28.4 to 63.3 µg/L (without corrosion control) and from 4.6 to 9.9 µg/L with corrosion control (addition of orthophosphate and pH adjustment). Adding orthophosphate (1 mg P/L) to the water reduces the mean Pbdiss values at the kitchen tap from 7.1 µg/L to 1.2 µg/L for a high water demand scenario and from 31.2 to 4.9 µg/L for a low water demand scenario. Finally, the Integrated Exposure Uptake Biokinetic (IEUBK) model is used to predict the potential blood lead levels (BLLs) for children aged 0-84 months. Results showed that the orthophosphate addition of only 1 mg P/L can significantly decrease the proportion of children with a BLL >5 µg/dL, from 82 % to 17 %, under the most extreme water conservation scenario studied, using the 90th percentile of Pbdiss concentrations during usage at kitchen tap. Wide variations of Pbdiss concentrations at the kitchen tap were calculated at times of use over a week (up to 155 µg/L in lower demand scenarios, without corrosion control) showing evident limitations of single random daytime sampling.

Hierarchical systems integration for coordinated urban-rural water quality management at a catchment scale.

Managing river quality is important for sustainable catchment development. In this study, we present how catchment management strategies benefit from a coordinated implementation of measures that are based on understanding key drivers of pollution. We develop a modelling approach that integrates environmental impacts, human activities, and management measures as three hierarchical levels. We present a catchment water management model (CatchWat) that achieves all three hierarchical levels and is applied to the Cherwell Catchment, UK. CatchWat simulations are evaluated against observed river flow and pollutant data including suspended solids, total nitrogen, and total phosphorus. We compare three competing hypotheses, or framings, of the catchment representation (integrated, urban-only, and rural-only framings) to test the impacts of model boundaries on river water quality modelling. Scenarios are formulated to simulate separate, combined and coordinated implementation of fertiliser application reduction and enhanced wastewater treatment. Results show that models must represent both urban and rural pollution emissions to accurately estimate river quality. Agricultural activities are found to drive river quality in wet periods because runoff is the main pathway for rural pollutants. Meanwhile, urban activities are the key source of pollution in dry periods because effluent constitutes a larger percentage of river flow during this time. Based on this understanding, we identify a coordinated management strategy that implements fertiliser reduction measures to improve river quality during wet periods and enhanced wastewater treatment to improve river quality during dry periods. The coordinated strategy performs comparably to the combined strategy but with higher overall efficiency. This study emphasises the importance of systems boundaries in integrated water quality modelling and simulating the mechanisms of seasonal water quality behaviour. Our key recommendation is that incorporating these mechanisms is required to develop coordinated strategies for river water quality management, that can ultimately lead to more efficient and sustainable catchment management.

Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach.

Accurate and effective prediction of polycyclic aromatic hydrocarbons (PAHs) in surface water remains a substantial challenge due to the limited understanding of the dynamic processes. To assist integrated surface water management, a novel hybrid surface water PAH prediction model based on a two-stage decomposition approach and deep learning algorithm was proposed. Specifically, a two-stage decomposition technique consisting of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) was first introduced to decompose the data into several subsequences to extract the main fluctuations and trends of the PAH sequence. Subsequently, the deep learning algorithm long short-term memory (LSTM) was employed to explore the latent dynamic characteristics of each subsequence. Finally, the predicted values of the subsequences were integrated to obtain the final predicted results. An empirical study was conducted based on PAH data of eight major rivers in Saxony, Germany. The empirical results proved that the CEEMDAN-VMD-LSTM model outperformed other benchmark data-driven methods in predicting PAHs in surface water because it combined the advantages of two-stage decomposition and deep learning methods. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) of the model were 27.89, 37.92 and 0.85, respectively. The proposed hybrid method can achieve effective and accurate water quality prediction and is an effective tool for surface water management.

Modelling the fate of micropollutants in integrated urban wastewater systems: Extending the applicability to pharmaceuticals.

Pharmaceutical active compounds (PhACs) are a category of micropollutants frequently detected across integrated urban wastewater systems. Existing modelling tools supporting the evaluation of micropollutant fate in such complex systems, such as the IUWS_MP model library (which acronym IUWS stands for Integrated Urban Wastewater System), do not consider fate processes and fractions that are typical for PhACs. This limitation was overcome by extending the existing IUWS_MP model library with new fractions (conjugated metabolites, sequestrated fraction) and processes (consumption-excretion, deconjugation). The performance of the extended library was evaluated for five PhACs (carbamazepine, ibuprofen, diclofenac, paracetamol, furosemide) in two different integrated urban wastewater systems where measurements were available. Despite data uncertainty and the simplicity of the modelling approach, chosen to minimize data requirements, model prediction uncertainty overlapped with the measurements ranges across both systems, stressing the robustness of the proposed modelling approach. Possible applications of the extended IUWS_MP model library are presented, illustrating how this tool can support urban water managers in reducing environmental impacts from PhACs discharges.

An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 1: Background and model description.

A new Model for the Agent-based simulation of Faecal Indicator Organisms (MAFIO) is developed that attempts to overcome limitations in existing faecal indicator organism (FIO) models arising from coarse spatial discretisations and poorly-constrained hydrological processes. MAFIO is a spatially-distributed, process-based model presently designed to simulate the fate and transport of agents representing FIOs shed by livestock at the sub-field scale in small (<10 km2) agricultural catchments. Specifically, FIO loading, die-off, detachment, surface routing, seepage and channel routing are modelled on a regular spatial grid. Central to MAFIO is that hydrological transfer mechanisms are simulated based on a hydrological environment generated by an external model for which it is possible to robustly determine the accuracy of simulated catchment hydrological functioning. The spatially-distributed, tracer-aided ecohydrological model EcH2O-iso is highlighted as a possible hydrological environment generator. The present paper provides a rationale for and description of MAFIO, whilst a companion paper applies the model in a small agricultural catchment in Scotland to provide a proof-of-concept.

An agent-based model that simulates the spatio-temporal dynamics of sources and transfer mechanisms contributing faecal indicator organisms to streams. Part 2: Application to a small agricultural catchment.

The new Model for the Agent-based simulation of Faecal Indicator Organisms (MAFIO) is applied to a small (0.42 km2) Scottish agricultural catchment to simulate the dynamics of E. coli arising from sheep and cattle farming, in order to provide a proof-of-concept. The hydrological environment for MAFIO was simulated by the "best" ensemble run of the tracer-aided ecohydrological model EcH2O-iso, obtained through multi-criteria calibration to stream discharge (MAE: 1.37 L s-1) and spatially-distributed stable isotope data (MAE: 1.14-3.02‰) for the period April-December 2017. MAFIO was then applied for the period June-August for which twice-weekly E. coli loads were quantified at up to three sites along the stream. Performance in simulating these data suggested the model has skill in capturing the transfer of faecal indicator organisms (FIOs) from livestock to streams via the processes of direct deposition, transport in overland flow and seepage from areas of degraded soil. Furthermore, its agent-based structure allowed source areas, transfer mechanisms and host animals contributing FIOs to the stream to be quantified. Such information is likely to have substantial value in the context of designing and spatially-targeting mitigation measures against impaired microbial water quality. This study also revealed, however, that avenues exist for improving process conceptualisation in MAFIO (e.g. to include FIO contributions from wildlife) and highlighted the need to quantitatively assess how uncertainty in the spatial extent of surface flow paths in the simulated hydrological environment may affect FIO simulations. Despite the consequent status of MAFIO as a research-level model, its encouraging performance in this proof-of-concept study suggests the model has significant potential for eventual incorporation into decision support frameworks.

Modifying SWAT-CS for simulating chloride dynamics in a Boreal Shield headwater catchment in south-central Ontario, Canada.

Rising chloride concentrations in surface water due to applications of deicing practices is proving detrimental to aquatic systems. In this study, a new chloride module is developed for a version of the Soil and Water Assessment Tool specially designed for Canadian Shield catchments (SWAT-CS) to model long-term chloride dynamics in a headwater catchment in south-central Ontario, Canada. In this modified model (SWAT-CS-CL; extended SWAT-CS model for chloride), chloride sources, sinks, internal storages or pools, and movement between these components are depicted. Performance of SWAT-CS-CL is assessed using a two-stage evaluation process based on the generalized likelihood uncertainty analysis (GLUE) framework. SWAT-CS-CL was found to perform moderately well, with simulated monthly chloride in streams and lake outflow following overall chloride trends and capturing regular chloride dynamics. However, simulations fail to consistently reproduce some instances of large or low chloride fluxes. Limitations in simulating large chloride fluxes may be attributed to the inadequate ability for SWAT-CS-CL to closely simulate snowpack and snowmelt processes. Parameter transferability among sub-catchments does suggest that there is a potential to extend SWAT-CS-CL to other Canadian Shield catchments for chloride modelling. Further improvements are needed through more trials to other catchments in a same or different landscape, and by modifying the simulation structure, especially representation of snow hydrology and chloride inputs.

Hydrological Simulation for Predicting the Future Water Quality of Adyar River, Chennai, India.

Just a few decades ago, Adyar River in India's city of Chennai was an important source of water for various uses. Due to local and global changes (e.g., population growth and climate change), its ecosystem and overall water quality, including its aesthetic value, has deteriorated, and the water has become unsuitable for commercial uses. Adverse impacts of excessive population and changing climate are expected to continue in the future. Thus, this study focused on predicting the future water quality of the Adyar river under "business as usual" (BAU) and "suitable with measures" scenarios. The water evaluation and planning (WEAP) simulation tool was used for this study. Water quality simulation along a 19 km stretch of the Adyar River, from downstream of the Chembarambakkam to Adyar (Bay of Bengal) was carried out. In this analysis, clear indication of further deterioration of Adyar water quality by 2030 under the BAU scenario was evidenced. This would be rendering the river unsuitable for many aquatic species. Due to both climate change (i.e., increased temperature and precipitation) and population growth, the WEAP model results indicated that by 2030, biochemical oxygen demand (BOD) and Escherichiacoli concentrations will increase by 26.7% and 8.3%, respectively. On the other hand, under the scenario with measures being taken, which assumes that "all wastewater generated locally will be collected and treated in WWTP with a capacity of 886 million liter per day (MLD)," the river water quality is expected to significantly improve by 2030. Specifically, the model results showed largely reduced concentrations of BOD and E.coli, respectively, to the tune of 74.2% and 98.4% compared to the BAU scenario. However, even under the scenario with measures being taken, water quality remains a concern, especially in the downstream area, when compared with class B (fishable surface water quality desirable by the national government). These results indicate that the current management policies and near future water resources management plan (i.e., the scenario including mitigating measures) are not adequate to check pollution levels to within the desirable limits. Thus, there is a need for transdisciplinary research into how the water quality can be further improved (e.g., through ecosystem restoration or river rehabilitation).

Water quality modelling under unsteady state analysis: Strategies for planning and management.

Recent water resources planning and management strategies state that the concepts of risk and variable inputs should be appraised in order to comply with multiple conditions. This becomes evident especially in environments with diverse uses of water, land use and climate change. In such a context, modelling of discharges and concentrations in rivers are valuable strategies to predict different scenarios. This research proposes an integrated analysis for modelling of flow and contaminant transport in rivers, based on hydrodynamics, time series, and water quality simulations. The first module estimates water volume and velocity, that have direct impact in pollutants transport; time series of concentrations are generated as synthetic pollutographs, using techniques based on flow conditions, time and statistical factors of a historical monitoring dataset - the objective is to match temporal scales of boundary conditions, since water quality data is usually available as irregular samples; the third module solves the advection-dispersion-reaction equation, exploring the different synthetic series as input. Results evidence that the input pollutograph, usually not explored in similar studies, may have a significant role in simulations for transport of substance in rivers under unsteady state; as consequence, corroborate with better estimates for planning strategies where temporal dynamic is relevant. The contributions lay the basis for further assessment of riverine systems linked to watershed dynamics, with multiple scenarios of data availability and input conditions.

Predicting future river health in a minimally influenced mountainous area under climate change.

It has been shown that climate change impacts the overall health of a river's ecosystem. Although predicting river health under climate change would be useful for stakeholders to adapt to the change and better conserve river health, little research on this topic exists. This paper presents a methodology predicting river health under different climate change scenarios. First, a multi-source, distributed, time-variant gain hydrological model (MS-DTVGM) was used to predict the runoff from a mountainous river in eastern China using the data from three existing IPCC5 climate change models (RCP2.6, RCP4.5, and RCP8.4). Next, a model was developed to predict the river's water quality under these scenarios. Finally, a multidimensional response model utilizing hydrology, water quality, and biology was used to predict the river's biological status and ascertain the impact of climate change on its overall health. The river is in a mountainous area near Jinan City, one of China's first "pilot" cities recognized as a "healthy water ecological community." Our results predict that the overall health of the Yufu River, which is minimally influenced by human activities, will improve by 2030 due to the increased river flow due to an increase in rainfall frequency and subsequent peak runoff. However, the total nitrogen concentration is predicted to increase, which is a potential eutrophication risk. Therefore, effective control of nitrogen pollutants entering the river will be necessary. The increase in flow velocity (the annual average increase is ~0.5 m/s) is favorable for fish reproduction. Our methods and results will provide scientific guidance for policy makers and river managers and will help people to better understand how global climate change impacts river health.

Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters.

Accurate prediction of water quality parameters plays a crucial and decisive role in environmental monitoring, ecological systems sustainability, human health, aquaculture and improved agricultural practices. In this study a new hybrid two-layer decomposition model based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) and the variational mode decomposition (VMD) algorithm coupled with extreme learning machines (ELM) and also least square support vector machine (LSSVM) was designed to support real-time environmental monitoring of water quality parameters, i.e. chlorophyll-a (Chl-a) and dissolved oxygen (DO) in a Lake reservoir. Daily measurements of Chl-a and DO for June 2012-May 2013 were employed where the partial autocorrelation function was applied to screen the relevant inputs for the model construction. The variables were then split into training, validation and testing subsets where the first stage of the model testing captured the superiority of the ELM over the LSSVM algorithm. To improve these standalone predictive models, a second stage implemented a two-layer decomposition with the model inputs decomposed in the form of high and low frequency oscillations, represented by the intrinsic mode function (IMF) through the CEEMDAN algorithm. The highest frequency component, IMF1 was further decomposed with the VMD algorithm to segregate key model input features, leading to a two-layer hybrid VMD-CEEMDAN model. The VMD-CEEMDAN-ELM model was able to reduce the root mean square and the mean absolute error by about 14.04% and 7.12% for the Chl-a estimation and about 5.33% and 4.30% for the DO estimation, respectively, compared with the standalone counterparts. Overall, the developed methodology demonstrates the robustness of the two-phase VMD-CEEMDAN-ELM model in identifying and analyzing critical water quality parameters with a limited set of model construction data over daily horizons, and thus, to actively support environmental monitoring tasks, especially in case of high-frequency, and relatively complex, real-time datasets.

Assessment of the environmental health of an ecologically sensitive, semi-enclosed, basin - A water quality modelling approach.

Semi-enclosed basins are environmentally dynamic and some of the most anthropogenically affected components of the coastal realm. They can reflect various environmental impacts, thus qualifying as natural laboratories to study these impacts. The Gulf of Khambhat (GoK) is such a system where analysis of in situ parameters indicated polluted conditions. The sources of various contaminants were deciphered. Though there are considerable inputs of pollutants, the assimilative capacity of the GoK holds good with high Dissolved Oxygen (DO) (6-9.3 mg/L) content as revealed in situ and in silico. High Biochemical Oxygen Demand (BOD) and marginal ammonia contamination prevail in the region. Simulations revealed that the rivers bring in a considerable amount of nitrate, organic material and phosphate into the Gulf. Considering the prevailing environmental condition, the current study posits to have regular water quality monitoring; and the carrying capacity of the Gulf should be assessed before the authorization of anthropogenic activities.

Modelling impacts of climate change and socio-economic change on the Ganga, Brahmaputra, Meghna, Hooghly and Mahanadi river systems in India and Bangladesh.

The Ganga-Brahmaputra-Meghna (GBM) River System, the associated Hooghly River and the Mahanadi River System represent the largest river basins in the world serving a population of over 780 million. The rivers are of vital concern to India and Bangladesh as they provide fresh water for people, agriculture, industry, conservation and support the Delta System in the Bay of Bengal. Future changes in both climate and socio-economics have been investigated to assess whether these will alter river flows and water quality. Climate datasets downscaled from three different Global Climate Models have been used to drive a daily process based flow and water quality model. The results suggest that due to climate change the flows will increase in the monsoon period and also be enhanced in the dry season. However, once socio-economic changes are also considered, increased population, irrigation, water use and industrial development reduce water availability in drought conditions, threatening water supplies and posing a threat to river and coastal ecosystems. This study, as part of the DECCMA (Deltas, vulnerability and Climate Change: Migration and Adaptation) project, also addresses water quality issues, particularly nutrients (N and P) and their transport along the rivers and discharge into the Delta System. Climate will alter flows, increasing flood flows and changing pollution dilution factors in the rivers, as well as other key processes controlling water quality. Socio-economic change will affect water quality, as water diversion strategies, increased population and industrial development alter the water balance and enhance fluxes of nutrients from agriculture, urban centers and atmospheric deposition.

Risk-based cost-benefit analysis for evaluating microbial risk mitigation in a drinking water system.

Waterborne outbreaks of gastrointestinal diseases can cause large costs to society. Risk management needs to be holistic and transparent in order to reduce these risks in an effective manner. Microbial risk mitigation measures in a drinking water system were investigated using a novel approach combining probabilistic risk assessment and cost-benefit analysis. Lake Vomb in Sweden was used to exemplify and illustrate the risk-based decision model. Four mitigation alternatives were compared, where the first three alternatives, A1-A3, represented connecting 25, 50 and 75%, respectively, of on-site wastewater treatment systems in the catchment to the municipal wastewater treatment plant. The fourth alternative, A4, represented installing a UV-disinfection unit in the drinking water treatment plant. Quantitative microbial risk assessment was used to estimate the positive health effects in terms of quality adjusted life years (QALYs), resulting from the four mitigation alternatives. The health benefits were monetised using a unit cost per QALY. For each mitigation alternative, the net present value of health and environmental benefits and investment, maintenance and running costs was calculated. The results showed that only A4 can reduce the risk (probability of infection) below the World Health Organization guidelines of 10-4 infections per person per year (looking at the 95th percentile). Furthermore, all alternatives resulted in a negative net present value. However, the net present value would be positive (looking at the 50th percentile using a 1% discount rate) if non-monetised benefits (e.g. increased property value divided evenly over the studied time horizon and reduced microbial risks posed to animals), estimated at 800-1200 SEK (€100-150) per connected on-site wastewater treatment system per year, were included. This risk-based decision model creates a robust and transparent decision support tool. It is flexible enough to be tailored and applied to local settings of drinking water systems. The model provides a clear and holistic structure for decisions related to microbial risk mitigation. To improve the decision model, we suggest to further develop the valuation and monetisation of health effects and to refine the propagation of uncertainties and variabilities between the included methods.

Multi-criteria decision analysis for integrated water quality assessment and management support.

In densely populated areas, surface waters are affected by many sources of pollution. Besides classical pollutants like nutrients and organic matter that lead to eutrophication, micropollutants from various point- and non-point sources are getting more attention by water quality managers. For cost-effective management an integrated assessment is needed that takes into account all relevant pollutants and all sources of pollution within a catchment. Due to the difficulty of identifying and quantifying sources of pollution and the need for considering long-term changes in boundary conditions, typically substantial uncertainty exists about the consequences of potential management alternatives to improve surface water quality. We therefore need integrated assessment methods that are able to deal with multiple objectives and account for various sources of uncertainty. This paper aims to contribute to integrated, prospective water management by combining a) multi-criteria decision support methods to structure the decision process and quantify preferences, b) integrated water quality modelling to predict consequences of management alternatives accounting for uncertainty, and c) scenario planning to consider uncertainty from potential future climate and socio-economic developments, to evaluate the future cost-effectiveness of water quality management alternatives at the catchment scale. It aims to demonstrate the usefulness of multi-attribute value functions for water quality assessment to i) propagate uncertainties throughout the entire assessment procedure, ii) facilitate the aggregation of multiple objectives while avoiding discretization errors when using categories for sub-objectives, iii) transparently communicate the results. We show how to use such multi-attribute value functions for model-based decision support in water quality management. We showcase the procedure for the Mönchaltorfer Aa catchment on the Swiss Plateau. We evaluate ten different water quality management alternatives, including current practice, that tackle macro- and micropollutants from a wide spectrum of agricultural and urban sources. We evaluate costs and water quality effects of the alternatives under four different socio-economic scenarios for the horizon 2050 under present and future climate projections and visualize their uncertainty. While the performance of alternatives is catchment specific, the methods can be transferred to other places and other management situations. Results confirm the need for cross-sectoral coordination of different management actions and interdisciplinary collaboration to support the development of prospective strategies to improve water quality.

Evaluating the relationship between temporal changes in land use and resulting water quality.

Changes in land use have a direct impact on receiving water quality. Effective mitigation strategies require the accurate prediction of water quality in order to enhance community well-being and ecosystem health. The research study employed Bayesian Network modelling to investigate the validity of using cross-sectional and longitudinal data on water quality and land use for predicting water quality in a mixed use catchment and the role it plays in the generation of blue-green algae in the receiving marine environment. Bayesian Network modelling showed that cross-sectional and longitudinal data analyses generate contrasting information about the influence of different land uses on surface water pollution. The modelling outcomes highlighted the lack of reliability in cross-sectional data analysis, based on the indication of spurious relationships between water quality and land use. On the other hand, the longitudinal data analysis, which accounted for changes in water quality and land use over a ten-year period, informed how catchment water quality varies in response to temporal changes in land use. The longitudinal data analysis further revealed that the types of anthropogenic activities have a more significant influence on pollutant generation than the change in the area extent of different land uses over time. Therefore, the careful interpretation of the findings derived solely from cross-sectional data analysis is important in the design of long-term strategies for pollution mitigation.

Modelling the effects of water diversion and combined sewer overflow on urban inland river quality.

In order to assist and optimize the operation of a clean water diversion project for the medium-sized inland rivers in Chaohu, China, an integrated hydrodynamic and water quality model was used in this study. Sixteen diversion scenarios and five sewage interception scenarios were defined to assess the improvement of water quality parameters including ammonia nitrogen (NH3-N), total phosphorus (TP) and chemical oxygen demand (COD) under different diverted water flows, diverting times, diverting points, diverting routines and sewage interception proportions. An index of pollutant removal rate per unit diverted water flow (PRUWF) was proposed to evaluate the effect of the clean water diversion. Results show that operating conditions played important roles in water quality improvement of medium-sized inland rivers. The optimal clean water diversion was operated under the conditions of a flow rate of 5 m3/s for 48 h with an additional constructed bridge sluice. A global sensitivity analysis using the Latin Hypercube One-Factor-at-a-Time (LH-OAT) method was conducted to distinguish the contributions of various driving forces to inland river water restoration. Results show that sewage interception was more important than diverted water flow and diverting time with respect to water quality improvement, especially for COD.