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Climate change in combination with land use alterations may lead to significant changes in soil erosion and sediment fluxes in streams. Optical turbidity sensors can monitor with high frequency and can be used as a proxy for suspended sediment concentration (SSC) provided there is an acceptable calibration curve for turbidity measured by sensors and SSC from water samples. This study used such calibration data from 31 streams in 11 different research projects or monitoring programmes in six Northern European countries. The aim was to find patterns in the turbidity-SSC correlations based on stream characteristics such as mean and maximum turbidity and SSC, catchment area, land use, hydrology, soil type, topography, and the number and representativeness of the data that are used for the calibration. There were large variations, but the best correlations between turbidity and SSC were found in streams with a mean and maximum SSC of >30–200 mg/l, and a mean and maximum turbidity above 60–200 NTU/FNU, respectively. Streams draining agricultural areas with fine-grained soils had better correlations than forested streams draining more coarse-grained soils. However, the study also revealed considerable differences in methodological approaches, including analytical methods to determine SSC, water sampling strategies, quality control procedures, and the use of sensors based on different measuring principles. Relatively few national monitoring programmes in the six countries involved in the study included optical turbidity sensors, which may partly explain this lack of methodological harmonisation. Given the risk of future changes in soil erosion and sediment fluxes, increased harmonisation is highly recommended, so that turbidity data from optical sensors can be better evaluated and intercalibrated across streams in comparable geographical regions.

Despite an improvement of water quality across Europe there are many pollution hotspots for both nitrates and PPPs, mainly due to agricultural activities. The BMPs and MMs to reduce pollution from agriculture are well known, and there are policy instruments in place to ensure drinking water standards, but the current approach has not been efficient enough. Within the H2020 Water Protect project the premise was that there is a need for a multi-actor, participatory approach to tackle the issue from a new angle, and to assess why the uptake of known BMPs and MMs was not better among farmers. Seven “Action Labs” were selected that represent major physical, socio-economical, cultural and farming settings across Europe. A methodology of multi-actor engagement was chosen but with different approaches due to the local context. Initially the level of farmers' awareness about water quality issues was matched to the observed uptake rates of BMPs and MMs. In a second survey barriers hindering the uptake of measures were identified. The first survey revealed a low general awareness on the potential pollution to drinking water sources. Despite this, between 24% to 88% of the surveyed farmers per Action Lab were already voluntarily adopting one quarter of the selected BMPs and MMs. The second survey demonstrated the need to address organisational, legislative, sociological and technical barriers. The lack of coordination between different institutional bodies promoting measures and the financial incentives needed to invest and operate these often-costly measures need to be considered. The multi-actor, participatory approach with its improved awareness and collaboration made it possible to identify the crucial factors for improvement - to build a social acceptance among all actors and communicate the issues and solutions from the start.

Water and pollutant fluxes from combined sewer overflows (CSO) have a significant impact on receiving waters. The random nature of rainfall forcing dominates the variability of sewer discharges, pollutant loads, and concentrations. An analytical model developed here, shows how sewer network topology and rainfall properties variously impact the stochasticity of CSO functioning. Probability distributions of sewer discharge and concentration compare well with the results from a calibrated Storm Water Management Model in an application to a sewershed located in Dresden, Germany. The model is determined by only four parameters, three of which can be predicted a priori, two from the rainfall record and one from the network topology using geomorphological flow recession theory, while the fourth can be estimated from a short discharge time series. The sensitivity of CSO and wastewater treatment loads to network structure suggests simple topologies may be more vulnerable to poor performance. The analytical model is useful for evaluating various CSO management strategies to reduce adverse impacts on receiving waters in a probabilistic setting.

In this paper, we outline several recent insights for the priorities and challenges for future research for reducing phosphorus (P) based water eutrophication in the agricultural landscapes of Northwest Europe. We highlight that new research efforts best be focused on headwater catchments as they are a key influence on the initial chemistry of the larger river catchments, and here many management interventions are most effectively made. We emphasize the lack of understanding on how climate change will impact on P losses from agricultural landscapes. Particularly, the capability to disentangle current and future trends in P fluxes, due to climate change itself, from climate driven changes in agricultural management practices and P inputs. Knowing that, future climatic change trajectories for Western Europe will accelerate the release of the most bioavailable soil P. We stress the ambiguities created by the large varieties of sources and storage/transfer processes involved in P emissions in landscapes and the need to develop specific data treatment methods or tracers able to circumvent them, thereby helping catchment managers to identify the ultimate P sources that most contribute to diffuse P emissions. We point out that soil and aqueous P exist not only in various chemical forms, but also in range of less considered physical forms e.g., dissolved, nanoparticulate, colloidal and other particulates, all affected differently by climate as well as other environmental factors, and require bespoke mitigation measures. We support increased high resolution monitoring of headwater catchments, to not only help verify the effectiveness of catchments mitigation strategies, but also add data to further develop new water quality models (e.g., those include Fe-P interactions) which can deal with climate and land use change effects within an uncertainty framework. We finally conclude that there is a crucial need for more integrative research efforts to deal with our incomplete unde

We tested an empirical modeling approach using relatively low-cost continuous records of turbidity and discharge as proxies to estimate phosphorus (P) concentrations at a subhourly time step for estimating loads. The method takes into account nonlinearity and hysteresis effects during storm events, and hydrological conditions variability. High-frequency records of total P and reactive P originating from four contrasting European agricultural catchments in terms of P loads were used to test the method. The models were calibrated on weekly grab sampling data combined with 10 storms surveyed subhourly per year (weekly+ survey) and then used to reconstruct P concentrations during all storm events for computing annual loads. For total P, results showed that this modeling approach allowed the estimation of annual loads with limited uncertainties (≈ −10% ± 15%), more reliable than estimations based on simple linear regressions using turbidity, based on interpolated weekly+ data without storm event reconstruction, or on discharge weighted calculations from weekly series or monthly series. For reactive P, load uncertainties based on the nonlinear model were similar to uncertainties based on storm event reconstruction using simple linear regression (≈ 20% ± 30%), and remained lower than uncertainties obtained without storm reconstruction on weekly or monthly series, but larger than uncertainties based on interpolated weekly+ data (≈ −15% ± 20%). These empirical models showed we could estimate reliable P exports from noncontinuous P time series when using continuous proxies, and this could potentially be very useful for completing time-series data sets in high-frequency surveys, even over extended periods.

For ecosystem modelling of the Boreal forest it is important to include processes associated with low soil temperature during spring-early summer, as these affect the tree water uptake. The COUP model, a physically based SVAT model, was tested with 2 years of soil and snow physical measurements and sap flow measurements in a 70-year-old Scots pine stand in the boreal zone of northern Sweden. During the first year the extent and duration of soil frost was manipulated in the field. The model was successful in reproducing the timing of the soil warming after the snowmelt and frost thaw. A delayed soil warming, into the growing season, severely reduced the transpiration. We demonstrated the potential for considerable overestimation of transpiration by the model if the reduction of the trees' capacity to transpire due to low soil temperatures is not taken into account. We also demonstrated that the accumulated effect of aboveground conditions could be included when simulating the relationship between soil temperature and tree water uptake. This improved the estimated transpiration for the control plot and when soil warming was delayed into the growing season. The study illustrates the need of including antecedent conditions on root growth in the model in order to catch these effects on transpiration. The COUP model is a promising tool for predicting transpiration in high-latitude stands., QC 20100525

A physically based SVAT-model was rested with soil and snow physical measurements, as well as runoff data fr om an 8600 m(2) catchment in northern Sweden in order to quantify the influence of soil frost on spring snowmelt runoff in a moderately sloped, boreal forest. The model was run as an array of connected profiles cascading to the brook. For three winter seasons (1995-98) it was able to predict the onset and total accumulation of the runoff with satisfactory accuracy. Surface runoff was identified as only a minor fraction of the total runoff occurring during short periods in connection with ice blocking of the water-conducting pores. Little surface runoff, though, does not mean that soil frost is unimportant for spring runoff. Simulations without frost routines systematically underestimated the total accumulated runoff. The possibility of major frost effects appearing in response to specific combinations of weather conditions were also tested. Different scenarios of critical initial conditions for the winter, e.g, high water saturation and delayed snow accumulation leading to an increased frost penetration, were tested. These showed that under special circumstances there is potential for increased spring runoff due to soil frost., QC 20100525

It is a major challenge in modem science to decrease the uncertainty in predictions of global climate change. One of the largest uncertainties in present-day global climate models resides with the understanding of processes in the soil-vegetation-atmosphere-transfer (SVAT) system. Continuous, long-term data are needed to correctly quantify balances of water, energy and CO2 in this system and to correctly model them. It is the objective of this paper to demonstrate how a combined system of existing sensor, computer, and network technologies could be set up to provide continuous and reliable long-term SVAT-process data from an agricultural site under almost all weather conditions. A long-term climate-monitoring system within the framework of NOPEX was set up in 1993-1994 at the Marsta Meteorological Observatory (MMO). It is situated in a flat agricultural area where annual crops are cultivated on a heavy clay soil. It has successfully monitored relevant states and fluxes in the system, such as atmospheric fluxes of momentum, heat, water vapour and CO2, atmospheric profiles of wind speed, direction, and temperature, short- and long-wave radiation, soil temperature, soil-water contents, groundwater levels, and rainfall and snow depth. System uptime has been more than 90% for most of its components during the first 5 years of operation. Results from the first 5 years of operation has proven MMO to be an ideal site for intercomparison and intercalibration of radiometers and fast turbulence sensors, and for evaluation of other sensors, e.g., rain gauges. The long time series of radiation data have been valuable to establish numerical limits for a set of quality-control flags. MMO has served as a boundary-layer research station and results from NOPEX campaigns show how the dimensionless wind gradient depends not only on the traditional stability parameter z/L but also on the height of the convective boundary layer. Measurements at the observatory grounds and a neighbouring