Globally, many rivers are experiencing declining water quality, for example, with altered levels of sediments, salts, and nutrients. Effective water quality management requires a sound understanding of how and why water quality differs across space, both within and between river catchments. Land cover, land use, land management, atmospheric deposition, geology and soil type, climate, topography, and catchment hydrology are the key features of a catchment that affect:(1) the amount of suspended sediment, nutrient, and salt concentrations in catchments (i.e., the source), (2) the mobilization ,and (3) the delivery of these constituents to receiving waters. There are, however, complexities in the relationship between landscape characteristics and stream water quality. The strength of this relationship can be influenced by the distance and spatial arrangement of constituent sources within the catchment, cross correlations between landscape characteristics, and seasonality. A knowledge gap that should be addressed in future studies is that of interactions and cross correlations between landscape characteristics. There is currently limited understanding of how the relationships between landscape characteristics and water quality responses can shift based on the other characteristics of the catchment. Understanding the many forces driving stream water quality and the complexities and interactions in these forces is necessary for the development of successful water quality management strategies. This knowledge could be used to develop predictive models, which would aid in forecasting of riverine water quality.

S. (2017). High-frequency monitoring of catchment nutrient exports reveals highly variable storm event responses and dynamic source zone activation. Abstract Storm events can drive highly variable behavior in catchment nutrient and water fluxes, yet short-term event dynamics are frequently missed by low-resolution sampling regimes. In addition, nutrient source zone contributions can vary significantly within and between storm events. Our inability to identify and characterize time-dynamic source zone contributions severely hampers the adequate design of land use management practices in order to control nutrient exports from agricultural landscapes. Here we utilize an 8 month high-frequency (hourly) time series of streamflow, nitrate (NO 3-N), dissolved organic carbon (DOC), and hydroclimatic variables for a headwater agricultural catchment. We identified 29 distinct storm events across the monitoring period. These events represented 31% of the time series and contributed disproportionately to nutrient loads (42% of NO 3-N and 43% of DOC) relative to their duration. Regression analysis identified a small subset of hydroclimatological variables (notably precipitation intensity and antecedent conditions) as key drivers of nutrient dynamics during storm events. Hysteresis analysis of nutrient concentration-discharge relationships highlighted the dynamic activation of discrete NO 3-N and DOC source zones, which varied on an event-specific basis. Our results highlight the benefits of high-frequency in situ monitoring for characterizing short-term nutrient fluxes and unraveling connections between hydroclimatological variability and river nutrient export and source zone activation under extreme flow conditions. These new process-based insights, which we summarize in a conceptual model, are fundamental to underpinning targeted management measures to reduce nutrient loading of surface waters.

Understanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NOx), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NOx and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.

This study uses water‐quality data collected over 20 years, from 102 predominantly rural sites across Victoria, Australia, to further our understanding of spatial variability in riverine water quality. We focus on concentrations of total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate/nitrite (NOx), and electrical conductivity. We used an exhaustive search approach to identify the linear models that best link catchment characteristics to time‐averaged constituent concentrations. We ran additional analyses to (1) assess the performance of these models under drought conditions, and (2) understand the key drivers of site‐level variability (standard deviations) of constituent concentrations. Natural catchment characteristics appear to have a greater effect on spatial differences in average constituent concentrations. Performance of the statistical models of time‐averaged constituent concentrations varied, and spatial variability in mean electrical conductivity levels could be more readily explained by catchment characteristics compared to more reactive nutrients. Notwithstanding, the models performed relatively well under varying hydrologic conditions for most constituents. As such, these models provide an insight into the key factors affecting spatial variability in average stream water‐quality conditions. We also identified that hydrologic, climatic, and topographic characteristics of the catchment helped explain the spatial variability in temporal changes in constituents. After calibration and validation, these models of both average water quality and variability in water quality could be used to forecast stream water‐quality responses to future land use, climate, or soil and land management changes.

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Abstract. The application of the hydrological process-oriented model J2000 (J2K) is part of a cooperation project between the Thuringian Environmental Agency (Thüringer Landesanstalt für Umwelt und Geologie – TLUG) and the Department of Geoinformatics of the Friedrich-Schiller-University Jena focussing on the implementation of the EU water framework directive (WFD). In the first project phase J2K was parametrised and calibrated for a mesoscale catchment to quantify if it can be used as hydrological part of a multi-objective tool-box needed for the implementation of the WFD. The main objectives for that pilot study were: The development and application of a suitable distribution concept which provide the spatial data basis for various tasks and which reflects the specific physiogeographical variability and heterogeneity of river basins adequately. This distribution concept should consider the following constraints: The absolute number of spatial entities, which forms the basis for any distributive modelling should be as small as possible, but the spatial distributed factors, which controls quantitative and qualitative hydrological processes should not be generalised to much. The distribution concept of hydrological response units HRUs (Flügel, 1995) was selected and enhanced by a topological routing scheme (Staudenrausch, 2001) for the simulation of lateral flow processes. J2K should be calibrated for one subbasin of the pilot watershed only. Then the parameter set should be used on the other subbasins (referred as transfer basins) to investigate and quantify the transferability of a calibrated model and potential spatial dependencies of its parameter set. In addition, potential structural problems in the process description should be identified by the transfer to basins which show a different process dominance as the one which was used for calibration does. Model calibration and selection of efficiency criteria for the quantification of the model quality should be based on a comprehensive sensitivity and uncertainty analysis (Bäse, 2005) and multi-response validations with independent data sets (Krause and Flügel, 2005) carried out in advance in the headwater part of the calibration basin. To obtain good results in the transfer basins the calibrated parameter set could be adjusted slightly. This step was considered as necessary because of specific constraints which were not of significant importance in the calibration basin. This readjustment should be carried out on parameters which show a sensitive reaction on the identified differences in the environmental setup. Potential scaling problems of the process description, distribution concept or model structure should be identified by the comparison of the modelling results obtained in a small headwater region of the calibration basin with observed streamflow to find out if the selected efficiency measures show a significant change.

Automated high frequency nutrient analysers have recently become available for in-stream monitoring of freshwater ecosystems. These instruments permit observation of nutrients at the same temporal frequency as discharge measurements. In principle this development will overcome some of the limitations of current water quality sampling and enable a better understanding of coupled terrestrial and aquatic environmental systems. This paper presents a systematic approach to choosing such instruments for research applications and informing the design of prescribed water quality monitoring. The instruments considered are ion-selective electrodes, wet chemistry analysers and ultraviolet/visible light spectrophotometers. Before committing to a new technology, investigators should evaluate instrument related considerations and complementary, often project-specific factors, in a structured way. The instrument related considerations are the ability of the instrument to measure the required nutrient parameters, the temporal resolution, the detection limits and range of individual measurements, required accuracy and operating temperatures as well as the overall cost. The complementary factors to consider are the maintenance effort, operating conditions, major service expenses and special consideration associated with individual instruments. This evaluation is presented for a range of available instruments across the three instrument types. As supplementary material a tabular approach that combines these factors is proposed and illustrated with a case study where instruments were selected for researching nutrient movement in a catchment in northern Tasmania, Australia. Few of the instruments can provide all the essential requirements of the case study and significant compromise of maintenance costs and functionality was necessary. The approach is readily adaptable to choices of instruments for a wide range of investigations concerning aquatic water quality. Clearly the outcome of the choice process is likely to be different for different applications, locations and environments.

For effective water quality management and policy development, spatial variability in the mean concentrations and dynamics of riverine water quality needs to be understood. Using water chemistry (calcium, electrical conductivity, nitrate‐nitrite, soluble reactive phosphorus, total nitrogen, total phosphorus and total suspended solids) data for up to 578 locations across the Australian continent, we assessed the impact of climate zones (arid, Mediterranean, temperate, subtropical, tropical) on (i) inter‐annual mean concentration and (ii) water chemistry dynamics as represented by constituent export regimes (ratio of the coefficients of variation of concentration and discharge) and export patterns (slope of the concentration‐discharge relationship). We found that inter‐annual mean concentrations vary significantly by climate zones and that spatial variability in water chemistry generally exceeds temporal variability. However, export regimes and patterns are generally consistent across climate zones. This suggests that intrinsic properties of individual constituents rather than catchment properties determine export regimes and patterns. The spatially consistent water chemistry dynamics highlights the potential to predict riverine water quality across the Australian continent, which can support national riverine water quality management and policy development.