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The implementation of the European Water Framework Directive requires reliable tools to predict the water quality situations in streams caused by planned land use changes at the scale of large regional river basins. This paper presents the results of modelling the in-stream nitrogen load and concentration within the macro-scale basin of the Saale river (24,167km2) using a semi-distributed process-based ecohydrological dynamic model SWIM (Soil and Water Integrated Model). The simulated load and concentration at the last gauge of the basin show that SWIM is capable to provide a satisfactory result for a large basin. The uncertainty analysis indicates the importance of realistic input data for agricultural management, and that the calibration of parameters can compensate the uncertainty in the input data to a certain extent. A hypothesis about the distributed nutrient retention parameters for macro-scale basins was tested aimed in improvement of the simulation results at the intermediate gauges and the outlet. To verify the hypothesis, the retention parameters were firstly proved to have a reasonable representation of the denitrification conditions in six meso-scale catchments. The area of the Saale region was classified depending on denitrification conditions in soil and groundwater into three classes (poor, neutral and good), and the distributed parameters were applied. However, the hypothesis about the usefulness of distributed retention parameters for macro-scale basins was not confirmed. Since the agricultural management is different in the sub-regions of the Saale basin, land use change scenarios were evaluated for two meso-scale subbasins of the Saale. The scenario results show that the optimal agricultural land use and management are essential for the reduction in nutrient load and improvement of water quality to meet the objectives of the European Water Framework Directive and in view of the regional development plans for future. [Copyright &y& Elsevier]

Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination ( E or R2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters. [ABSTRACT FROM AUTHOR]

Abstract: Recent trends in the management of water supply have increased the need for modelling techniques that can provide reliable, efficient, and accurate representation of the complex, non-linear dynamics of water quality within water distribution systems. Statistical models based on artificial neural networks (ANNs) have been found to be highly suited to this application, and offer distinct advantages over more conventional modelling techniques. However, many practitioners utilise somewhat heuristic or ad hoc methods for input variable selection (IVS) during ANN development. This paper describes the application of a newly proposed non-linear IVS algorithm to the development of ANN models to forecast water quality within two water distribution systems. The intention is to reduce the need for arbitrary judgement and extensive trial-and-error during model development. The algorithm utilises the concept of partial mutual information (PMI) to select inputs based on the analysis of relationship strength between inputs and outputs, and between redundant inputs. In comparison with an existing approach, the ANN models developed using the IVS algorithm are found to provide optimal prediction with significantly greater parsimony. Furthermore, the results obtained from the IVS procedure are useful for developing additional insight into the important relationships that exist between water distribution system variables. [Copyright &y& Elsevier]

The quality of drinking water generally deteriorates when it is delivered through a distribution system due to the decay of disinfectant, which subsequently allows the re-growth of microorganisms in the distribution system in addition to the formation of trihalomethane (THM). Therefore, a model which describes the changes that occur in the water quality in the distribution system is needed to determine whether to enhance the treatment processes or to improve the distribution system so that microbiological criteria are met. In this paper the chlorine decay kinetics and THM formation in treated water is modeled considering the reaction of chlorine with fast and slow reacting organic and nitrogenous compounds which are present in that water. The treated water was also passed through three types of resins to fractionate very hydrophobic acids (VHA), slightly hydrophobic acids (SHA), hydrophilic charged (CHA) and hydrophilic neutral (NEU) compounds which are present in the water. Chlorine decay tests were conducted on the effluents emerging from the resins to evaluate the chlorine demand and THM formation potential of those organic fractions. The model shows that the CHA presented in the waters has a very high THM formation potential (around 62% of the THM produced). VHA, NEU and CHA contributed to chlorine demand in the water. [ABSTRACT FROM AUTHOR]

Sensitivity analysis methods based on multiple simulations such as Monte Carlo Simulation (MCS) and Latin Hypercube Sampling (LHS) are very efficient, especially for complex computer models. The application of these methods involves successive runs of the model under investigation with different sampled sets of the uncertain model-input variables and (or) parameters. The subsequent statistical analysis based on regression and correlation analysis among the input variables and model output allows determination of the input variables or the parameters to which the model prediction uncertainty is most sensitive. The sensitivity effect of the model-input variables or parameters on the model outputs can be quantified by various statistical measures based on regression and correlation analysis. This paper provides a thorough review of these measures and their properties and develops a concept for selecting the most robust and reliable measures for practical use. The concept is demonstrated through the application of Latin Hypercube Sampling as the sensitivity analysis technique to the DUFLOW water-quality model developed for the Dender River in Belgium. The results obtained indicate that the Semi-Partial Correlation Coefficient and its rank equivalent the Semi-Partial Rank Correlation Coefficient can be considered adequate measures to assess the sensitivity of the DUFLOW model to the uncertainty in its input parameters. [Copyright &y& Elsevier]

A recently developed approach to divide phytoplankton into functional groups was implemented in the biogeochemical lake model BELAMO and tested by comparing model results with data from three lakes of different trophic state. The groups are distinguished by different values of specific growth rate (k gro,ALG), half-saturation light intensity of primary production (K I,ALG), edibility (k gro,ZOO), sedimentation velocity (), and half-saturation concentration with respect to phosphate uptake (K P,ALG). After calibration, the occurrence pattern of the functional groups in the three lakes can qualitatively be represented by the model. However, some quantitative and systematic differences between model results and data remain. Furthermore, the division of total phytoplankton into functional groups makes these groups very sensitive to changes in kinetic model parameters. There is a very high sensitivity of each group not only to the parameters characterizing this group, but due to very strong interactions, also to the parameters determining the dynamics of the other groups. In contrast, the sensitivity of total phytoplankton to parameter changes remains at a similar level as in the aggregated model. The resulting high sensitivity of the composition of the phytoplankton community on model parameters indicates a poor predictability of the occurrence of functional groups in these lakes. Strong phosphorus limitation during the summer could be the cause for the better predictability of total phytoplankton concentrations in both, the aggregated and the functional group phytoplankton models. [Copyright &y& Elsevier]

Abstract: Harmful algal blooms, which are considered a serious environmental problem nowadays, occur in coastal waters in many parts of the world. They cause acute ecological damage and ensuing economic losses, due to fish kills and shellfish poisoning as well as public health threats posed by toxic blooms. Recently, data-driven models including machine-learning (ML) techniques have been employed to mimic dynamics of algal blooms. One of the most important steps in the application of a ML technique is the selection of significant model input variables. In the present paper, we use two extensively used ML techniques, artificial neural networks (ANN) and genetic programming (GP) for selecting the significant input variables. The efficacy of these techniques is first demonstrated on a test problem with known dependence and then they are applied to a real-world case study of water quality data from Tolo Harbour, Hong Kong. These ML techniques overcome some of the limitations of the currently used techniques for input variable selection, a review of which is also presented. The interpretation of the weights of the trained ANN and the GP evolved equations demonstrate their ability to identify the ecologically significant variables precisely. The significant variables suggested by the ML techniques also indicate chlorophyll-a (Chl-a) itself to be the most significant input in predicting the algal blooms, suggesting an auto-regressive nature or persistence in the algal bloom dynamics, which may be related to the long flushing time in the semi-enclosed coastal waters. The study also confirms the previous understanding that the algal blooms in coastal waters of Hong Kong often occur with a life cycle of the order of 1–2 weeks. [Copyright &y& Elsevier]

Abstract: In eutrophic sub-tropical coastal waters around Hong Kong and South China, algal blooms (more often called red tides) due to the rapid growth of microscopic phytoplankton are often observed. Under favourable environmental conditions, these blooms can occur and subside over rather short time scales—in the order of days to a few weeks. Very often, these blooms are observed in weakly flushed coastal waters under calm wind conditions—with or without stratification. Based on high-frequency field observations of harmful algal blooms at two coastal mariculture zones in Hong Kong, a mathematical model has been developed to forecast algal blooms. The model accounts for algal growth, decay, settling and vertical turbulent mixing, and adopts the same assumptions as the classical Riley, Stommel and Bumpus model (Riley, G.A., Stommel, H., Bumpus, D.F., 1949. Quantitative ecology of the plankton of the western North Atlantic. Bulletin of the Bingham Oceanographic Collection Yale University 12, 1–169). It is shown that for algal blooms to occur, a vertical stability criterion, E <4μl 2/π 2, must be satisfied, where E, μ, l are the vertical turbulent diffusivity, algal growth rate, and euphotic layer depth respectively. In addition, a minimum nutrient threshold concentration must be reached. Moreover, with a nutrient competition consideration, the type of bloom (caused by motile or non-motile species) can be classified. The model requires as input simple and readily available field measurements of water column transparency and nutrient concentration, and representative maximum algal growth rate of the motile and non-motile species. In addition, with the use of three-dimensional hydrodynamic circulation models, simple relations are derived to estimate the vertical mixing coefficient as a function of tidal range, wind speed, and density stratification. The model gives a quick assessment of the likelihood of algal bloom occurrence, and has been validated against field observations over a 4-year period. The model helps to explain the observed spatial and temporal patterns of bloom occurrences in relation to the vertical turbulence and nutrient condition. The success of the model points the way to the development of real time management models for disaster mitigation. [Copyright &y& Elsevier]

There are many sources of uncertainty in modelling systems including uncertainty due to parameter estimations, input data and structure of the system. In this paper, modelling system structure is understood to be the algorithms and equations used to describe and calculate processes. Specific attention is given to the uncertainty in the equations, which link models interactively in a modelling system and algorithms, which pass information between the models. This paper places emphasis on structural uncertainty in modelling systems since this topic has received very little attention in research compared to the wealth of literature on parameter and input data uncertainty. This imbalance is partly due to the difficulty in quantifying structural uncertainty and an example is given showing how this may be done. The modelling system used for this study incorporates the coupling of three models from the WASP5 package (Water quality Analysis Simulation Program) developed by the US Environmental Protection Agency: DYNHYD (hydrodynamics), EUTRO (dissolved oxygen, nutrient and phytoplankton dynamics) and TOXI (transport and transformation of sediments and micro-pollutants). The three models were coupled in the high level architecture (HLA) platform to enable interactions between the models during simulations. This platform allowed ease of implementing Monte Carlo simulation runs, which were used for the uncertainty analyses. The modelling system structure focuses on the exchange of data between EUTRO and TOXI in simulating two processes: (i) sorption of heavy metals to suspended solids as a function of the fraction of organic carbon constituting the solids and (ii) phytoplankton growth limitation by light extinction through the water column as a function of the concentration of suspended solids and chlorophyll a in the water. Both processes are described by empirical relationships using regression curves derived from field data. The variance in the y-axis interceptions of the curves (also called regression uncertainty) is taken to be the uncertainty bounds for the Monte Carlo simulations used for the uncertainty analyses. The results show that structural uncertainty can be more significant than parameter and input data uncertainty, especially if the processes are very sensitive to the state variables. [Copyright &y& Elsevier]

This paper describes the utility of QUAL2E as a modelling package in the evaluation of a water quality improvement programme. In this study, QUAL2E was applied to determine the pollution loads in the river Yamuna during its course through the national capital territory of Delhi, India. The study aimed at examining the influence of different scenarios on river water quality. Four different pollution scenarios were analysed besides the ‘business as usual’ situation. The study revealed that it was necessary to treat the discharge from drains to the river Yamuna and diversion of a substantial load to the Agra canal for further treatment was also essential. It was also established through this study that maintaining a flow rate of more than 10m3/s in the river could also help preserve the river''s water quality. To clearly display the model outcomes and demarcate polluted zones in the river stretch, model results were interfaced with a Geographical Information System (GIS) to produce cartographic outputs. In addition, uncertainty analysis in the form of first-order error analysis and Monte Carlo analysis was performed, to realise the effect of each model parameter on DO and BOD predictions. The uncertainty analysis gave satisfactory results on simulated data. [Copyright &y& Elsevier]

To achieve the objectives of the European Water Framework Directive (EWFD), the Seine basin Water Authority has constructed a number of prospective scenarios forecasting the impact of planned investments in water quality. Paris and its suburbs were given special attention because of their impact on the river Seine. Paris sewer system and overflow control is of major concern in future management plans. The composition and fate of the urban effluents have been characterized through numerous in situ samplings, laboratory experiments and modelling studies. The PROSE model was especially designed to simulate the impact on the river of both permanent dry-weather effluents and of highly transient Combined Sewer Overflow (CSO). It was also used to represent the impact of Paris at large spatial and temporal scales. In addition to immediate effects on oxygen levels, heavy particulate organic matter loads that settle downstream of the outlets contribute to permanent oxygen consumption. Until the late 90s, the 50 km long reach of the Seine inside Paris was permanently affected by high oxygen consumption accounting for 112% of the flux upstream of the city. 20% of this demand resulted from CSO. However, the oxygenation of the system is strong due to high phytoplankton activity. As expected, the model results predict a reduction of both permanent dry-weather effluents and CSOs in the future that will greatly improve the oxygen levels (concentrations higher than 7. 3 mgO2 L−1, 90% of the time instead of 4. 0 mgO2 L−1 in the late 90s). The main conclusion is that, given the spatial and temporal extent of the impact of many CSOs, water quality models should take into account the CSOs in order to be reliable. [Copyright &y& Elsevier]

Artificial neural networks (ANNs) have been applied successfully in various fields. However, ANN models depend on large sets of historical data. and are of limited use when only vague and uncertain information is available, which leads to difficulties in defining the model architecture and a low reliability of results. A conceptual fuzzy neural network (CFNN) is proposed and applied in a water quality model to simulate the Barra Bonita reservoir system, located in the southeast region of Brazil. The CFNN model consists of a rationally-defined architecture based on accumulated expert knowledge about variables and processes included in the model. A genetic algorithm is used as the training method |or finding the parameters of fuzzy inference and the connection weights. The proposed model may handle the uncertainties related to the system itself, model parameterization, complexity of concepts involved and scarcity and inaccuracy of data. The CFNN showed greater robustness and reliability when dealing with systems for which data are considered to be vague, uncertain or incomplete. The CFNN model structure is easier to understand and to define than other ANN-based models. Moreover, it can help to understand the basic behaviour of the system as a whole, being a successful example of cooperation between human and machine. [ABSTRACT FROM AUTHOR]

Summary: In a national effort, since 1972, the Swiss Government started the “National Long-term Monitoring of Swiss Rivers” (NADUF) program aimed at evaluating the chemical and physical states of major rivers leaving Swiss political boundaries. The established monitoring network of 19 sampling stations included locations on all major rivers of Switzerland. This study complements the monitoring program and aims to model one of the program’s catchments – Thur River basin (area 1700km2), which is located in the north-east of Switzerland and is a direct tributary to the Rhine. The program SWAT (Soil and Water Assessment Tool) was used to simulate all related processes affecting water quantity, sediment, and nutrient loads in the catchment. The main objectives were to test the performance of SWAT and the feasibility of using this model as a simulator of flow and transport processes at a watershed scale. Model calibration and uncertainty analysis were performed with SUFI-2 (Sequential Uncertainty FItting Ver. 2), which was interfaced with SWAT using the generic iSWAT program. Two measures were used to assess the goodness of calibration: (1) the percentage of data bracketed by the 95% prediction uncertainty calculated at the 2.5 and 97.5 percentiles of the cumulative distribution of the simulated variables, and (2) the d-factor, which is the ratio of the average distance between the above percentiles and the standard deviation of the corresponding measured variable. These statistics showed excellent results for discharge and nitrate and quite good results for sediment and total phosphorous. We concluded that: in watersheds similar to Thur – with good data quality and availability and relatively small model uncertainty – it is feasible to use SWAT as a flow and transport simulator. This is a precursor for watershed management studies. [Copyright &y& Elsevier]

Biodegradation of organic matter in the sediment affects both the level of dissolved oxygen (DO) in the overlaying water and material flux into the water body. In the present study, a mathematical model was developed to describe the DO dynamics and related organic transformation in the sediment after a pulsed load of rich organic, sediment. The model includes DO diffusion at the sediment-water interface and inside the sediment, diagenetic reactions of particulate organic to soluble organic, substances, biodegradation of soluble organic, and related DO consumption. The model can simulate sediment oxygen demand (SOD) in a dynamic system after a sudden organic load on the sediment and the flux of both biodegradable and nonbiodegradable organic can be predicted. In addition, laboratory experiments were conducted using SOD chambers to investigate the SOD dynamics and sediment material fluxes. The sediment of the batch SOD tests was loaded with fish food at various rates for the sudden organic input. Numerical simulations for SOD and organic fluxes compared well with the experimental measurements. The results demonstrate that the model can be used to predict the effect of sediment pollutants on the water quality of surface waters. [ABSTRACT FROM AUTHOR]

In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics. In the present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong. The study of the weights of the trained ANN and also the GP-evolved equations shows that they correctly identify the ecologically significant variables. Analysis of various ANN and GP scenarios indicates that good predictions of long-term trends in algal biomass can be obtained using only chlorophyll-a as input. The results indicate that the use of biweekly data can simulate long-term trends of algal biomass reasonably well, but it is not ideally suited to give short-term algal bloom predictions. [ABSTRACT FROM AUTHOR]

Abstract: With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented. [Copyright &y& Elsevier]

Abstract: The transferability of a biogeochemical lake model developed for Lake Zürich was analyzed by confronting it with data from two other lakes. An iterative procedure of: (1) initial simulation, (2) lake-specific calibration, (3) joint calibration for all lakes and (4) sensitivity and identifiability analysis of model parameters was applied with the goal of finding a model with a minimum number of lake-specific parameter values. The application of this procedure led to a revised model structure with recalibrated values of some model parameters. The agreement of the model results with data from all lakes is good. The revised model needs only three parameters with lake-specific values. As the trophic states of the three lakes are significantly different, this demonstrates a remarkably high generality of the model. The main reasons for needing different values of these parameters were identified. They are the coarse spatial resolution of the sediment, the simplified description of mineralization processes in the sediment and the lack of a description of grazing of zooplankton by insect larvae and fish. Conclusions were drawn regarding which model extensions would make the model even more general. [Copyright &y& Elsevier]

Abstract: Understanding the process controls on episodic fluxes of faecal indicator organisms (FIOs) is becoming increasingly important for the sustainable management and accurate modelling of water quality in both recreational and shellfish harvesting waters. Both environments exhibit transitory non-compliance with microbiological standards after rainfall episodes despite significant expenditures on control of sewage derived pollutant loadings in recent years. This paper demonstrates the role of wave propagation in the entrainment of FIOs from river channel beds as a contributor to episodes of poor microbial water quality. Previously reported data is reviewed in the light of relationships between wave and mean water travel velocities. High flows and rapid changes in river flow, driven by releases of bacterially pure reservoir water, resulted in elevated FIO concentrations and transient peaks in concentration. The new interpretation of these data suggest three modes of entrainment: (i) immediate wave-front disturbance, (ii) wave propagation lift and post-wave transport at mean flow velocity, and (iii) stochastic erosional mechanisms that maintain elevated bacterial concentrations under steady high flow conditions. This is a significant advance on the previously proposed mechanisms. Understanding these mechanisms provides an aid to managing streams intended for recreational use and emphasises the need to control the timing of high flow generation prior to use of the water body for e.g. canoeing events. In addition the processes highlighted have relevance for the protection of shellfish nurseries, drinking water supply intakes and episodes of poor bathing water quality, and associated health risks. [Copyright &y& Elsevier]

Abstract: Harmful algal blooms (HAB) have been widely reported and have become a serious environmental problem world wide due to its negative impacts to aquatic ecosystems, fisheries, and human health. A capability to predict the occurrence of algal blooms with an acceptable accuracy and lead-time would clearly be very beneficial to fisheries and environmental management. In this study, we present the first real-time modelling and prediction of algal blooms using a data driven evolutionary algorithm, Genetic Programming (GP). The daily prediction of the algal blooms is carried out at Kat O station in Hong Kong using 3 years of high frequency (two-hourly) chlorophyll fluorescence and related hydro-meteorological and water quality data. The results for the prediction of chlorophyll fluorescence, a measure of algal biomass, are within reasonable accuracy for a lead-time of up to 1 day. The results generally concur with those obtained with artificial neural network. As compared to traditional data-driven models, GP has the advantage of evolving an equation relating input and output variables. A detailed analysis of the results of the GP models shows that GP not only correctly identifies the key input variables in accordance with ecological reasoning, but also demonstrates the relationship between the auto-regressive nature of bloom dynamics and flushing time. This study shows GP to be a viable alternative for algal bloom modelling and prediction; the interpretation of the results is greatly facilitated by the analytical form of the evolved equations. [Copyright &y& Elsevier]

Abstract: The estuary of the Swan and Canning Rivers in Western Australia is becoming increasingly prone to algal blooms, fish deaths and other biochemical problems that are thought to be associated with increasing eutrophication. Phosphorus and nitrogen enrichment are seen as the two most common causes of such eutrophication, with both elements being transported in streamflow and with concentrations strongly dependent upon land-use in the catchment. Many of the efforts to prevent and control eutrophication in the estuary are focused on managing land-use within the catchment. In this paper, the large-scale catchment model (LASCAM) is applied to Ellen Brook, a rural catchment located within the Swan River catchment, to simulate catchment exports of phosphorus and nitrogen, under a range of land cover scenarios that are designed to control the eutrophication. The scenarios, which are related to different management options for the catchment, are: (i) reforestation of agricultural land; (ii) reduction in fertiliser application; and (iii) urbanisation following a highway development. The model results show that: (i) full reforestation of agricultural land is expected to reduce phosphorus and nitrogen export by 50 and 85%, respectively; (ii) a proportionally greater reduction of phosphorus and nitrogen export occurs for smaller areas of reforestation than for larger areas; (iii) reduction in phosphorus fertiliser application produces a linear response with respect to phosphorus export; (iv) urbanisation increases runoff due to the larger impermeable areas causing an increase of overland flow during storms; and (v) phosphorus and nitrogen loads are expected to increase about 4 and 12%, respectively, during the 10 years following urbanisation. [Copyright &y& Elsevier]

Abstract: The dynamic behaviour of pesticides in river systems strongly depends on varying climatological conditions and agricultural management practices. To describe this behaviour at the river-basin scale, integrated hydrological and water quality models are needed. A crucial step in understanding the various processes determining pesticide fate is to perform a sensitivity analysis. Sensitivity analysis for hydrology and pesticide supply in SWAT (Soil and Water Assessment Tool) will provide useful support for the development of a reliable hydrological model and will give insight in which parameters are most sensitive concerning pesticide supply towards rivers. The study was performed on the Nil catchment in Belgium. In this study we utilised an LH-OAT sensitivity analysis. The LH-OAT method combines the One-factor-At-a-Time (OAT) design and Latin Hypercube (LH) sampling by taking the Latin Hypercube samples as initial points for an OAT design. By means of the LH-OAT sensitivity analysis, the dominant hydrological parameters were determined and a reduction of the number of model parameters was performed. Dominant hydrological parameters were the curve number (CN2), the surface runoff lag (surlag), the recharge to deep aquifer (rchrg_dp) and the threshold depth of water in the shallow aquifer (GWQMN). Next, the selected parameters were estimated by manual calibration. Hereby, the Nash–Sutcliffe coefficient of efficiency improved from an initial value of −22.4 to +0.53. In the second part, sensitivity analyses were performed to provide insight in which parameters or model inputs contribute most to variance in pesticide output. The results of this study show that for the Nil catchment, hydrologic parameters are dominant in controlling pesticide predictions. The other parameter that affects pesticide concentrations in surface water is ‘apfp_pest’, which meaning was changed into a parameter that controls direct losses to the river system (e.g., through the clean up of spray equipment, leaking tools, processing of spray waste on paved surfaces). As a consequence, it is of utmost importance that hydrology is well calibrated while––in this case––a correct estimation of the direct losses is of importance as well. Besides, a study of only the pesticide related parameters, i.e. application rate (kg/ha), application time (day), etc., reveals that the application time has much more impact than the application rate, which has itself a higher impact than errors in the daily rainfall observations. [Copyright &y& Elsevier]

Many catchments in Australia have experienced increased sediment and nutrient loadings and concomitant declines in water quality and increases in problematic blue–green algal blooms over the past several decades. This paper describes an integrated hydrologic, sediment and nutrient export modelling framework known as catchment-scale management of diffuse sources (CatchMODS). The modelling framework is designed to simulate and assess catchment-scale land and water management activities designed to reduce nutrient and sediment delivery to receiving waters. The Ben Chifley Dam Catchment in New South Wales, Australia is used as a case study for describing the development of the framework. Improving water quality through reduction of sediment and nutrient loads will reduce the probability of algal bloom occurrence in the dam. The innovation of the system is the integration of otherwise separate modelling approaches to enable biophysical and economic assessment of different management options. The outputs from the research are used to improve and focus on-ground remediation, targeted to specific stream reaches or subcatchment areas, as well as to encourage sustainable management practices more broadly in the catchment. With minimal modification, the methods developed are applicable elsewhere to address water quality decline. [Copyright &y& Elsevier]

A Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg–Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir. [ABSTRACT FROM AUTHOR]

The water quality management tool RIONET for river basins has been developed with regard to the EU Water Framework Directive. The management tool can simulate the water quality in catchment basins not only in the dimension of a single river but in whole river networks. A submodel of the IWA River Water Quality Model No. 1 is used in RIONET. The river model is based on the assumption that self purification processes in the river takes place both in the benthic biofilm and the bulk water phase. Laboratory experiments with sediment cores underline the major role of the benthic biofilm. The input parameters of the management tool such as volumetric flow rates from waste water treatment plants and flow velocities and discharge in the main river and its tributaries can be loaded directly from geographic information systems (GIS). The subcatchment basin of the river Bode in Saxon Anhalt was used for test runs of RIONET. [ABSTRACT FROM AUTHOR]

Model calibration is the most important step in the overall model development. Genetic algorithm (GA) was used in this study to optimise model parameters of river water quality models. In general, the efficiency of using GA depends on the proper selection of GA operators, which are the components that make up the overall GA process. A comprehensive investigation on the importance of GA operators on model parameter optimisation was conducted in this study (and presented in this paper) based on the hypothesis that the selection of GA operators depends on the sensitivity of model parameters to model output. A numerical experiment on GA operators was conducted first considering a hypothetical river network water quality model with both insensitive and sensitive model parameters and were later validated using Yarra River Water Quality Model (YRWQM). It was found that a robust GA operator set obtained from the literature was capable of achieving a near-optimum model parameter set for the sensitive model but not for the insensitive model. However, due to insensitivity of water quality model parameters on model output, the ‘not so’ near-optimum parameter set did not contribute a great difference to the overall water quality predictions in the insensitive model. Therefore, based on these numerical experiments, it was concluded that a GA operator set obtained from the literature is adequate for calibration of model parameters of river water quality model using GA. [Copyright &y& Elsevier]

Water quality models generally require a relatively large number of parameters to define their functional relationships, and since prior information on parameter values is limited, these are commonly defined by fitting the model to observed data. In this paper, the identifiability of water quality parameters and the associated uncertainty in model simulations are investigated. A modification to the water quality model ‘Quality Simulation Along River Systems’ is presented in which an improved flow component is used within the existing water quality model framework. The performance of the model is evaluated in an application to the Bedford Ouse river, UK, using a Monte-Carlo analysis toolbox. The essential framework of the model proved to be sound, and calibration and validation performance was generally good. However some supposedly important water quality parameters associated with algal activity were found to be completely insensitive, and hence non-identifiable, within the model structure, while others (nitrification and sedimentation) had optimum values at or close to zero, indicating that those processes were not detectable from the data set examined. [Copyright &y& Elsevier]

Uncertainty analysis in hydrology and water quality modelling is an important issue. Various methods have been proposed to estimate uncertainties on model results based on given uncertainties on model parameters. Among these methods, the mean-value first-order second-moment (MFOSM) method and the advanced mean-value first-order second-moment (AFOSM) method are the most common ones. This paper presents a method based on a second-order approximation of a model output function. The application of this method requires the estimation of first- and second-order derivatives at a mean-value point in the parameter space. Application to a Streeter–Phelps prototype model is presented. Uncertainties on two and six parameters are considered. Exceedance probabilities (EP) of dissolved oxygen concentrations are obtained and compared with EP computed using Monte Carlo, AFOSM and MFOSM methods. These results show that the mean-value second-order method leads to better estimates of EP. [Copyright &y& Elsevier]

An artificial neural network (ANN), a data driven modelling approach, is proposed to predict the algal bloom dynamics of the coastal waters of Hong Kong. The commonly used back-propagation learning algorithm is employed for training the ANN. The modeling is based on (a) comprehensive biweekly water quality data at Tolo Harbour (1982–2000); and (b) 4-year set of weekly phytoplankton abundance data at Lamma Island (1996–2000). Algal biomass is represented as chlorophyll-a and cell concentration of Skeletonema at the two locations, respectively. Analysis of a large number of scenarios shows that the best agreement with observations is obtained by using merely the time-lagged algal dynamics as the network input. In contrast to previous findings with more complicated neural networks of algal blooms in freshwater systems, the present work suggests the algal concentration in the eutrophic sub-tropical coastal water is mainly dependent on the antecedent algal concentrations in the previous 1–2 weeks. This finding is also supported by an interpretation of the neural networks’ weights. Through a systematic analysis of network performance, it is shown that previous reports of predictability of algal dynamics by ANN are erroneous in that ‘future data’ have been used to drive the network prediction. In addition, a novel real time forecast of coastal algal blooms based on weekly data at Lamma is presented. Our study shows that an ANN model with a small number of input variables is able to capture trends of algal dynamics, but data with a minimum sampling interval of 1 week is necessary. However, the sufficiency of the weekly sampling for real time predictions using ANN models needs to be further evaluated against longer weekly data sets as they become available. [Copyright &y& Elsevier]