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High-frequency water quality monitoring is increasingly used in examining the nutrient fluxes within catchments. Despite this, no studies have assessed the impact of monitoring frequency on the uncertainty of nitrate estimates obtained from distributed or semi-distributed catchment models. This study aims to evaluate the impacts of two different frequencies of nitrate sampling on the performance of a catchment hydrology model, including the uncertainty in both predictions and calibrated parameters. The investigation uses the HYPE model to simulate streamflow and nitrate concentrations (2010–2015) in the Selke catchment, a heterogeneous mesoscale catchment in central Germany. The Bayesian inference scheme of the DREAM code was employed for calibration and uncertainty analysis, and to explore differences between fortnightly and daily nitrate sampling strategies. The results indicate that: (a) the posterior uncertainty intervals of nitrogen-export process parameters were narrower when the model was calibrated to daily nitrate measurements, while similar maximum likelihood parameter values were obtained regardless of the sampling frequency; (b) the model calibrated using daily nitrate data better represented both daily and fortnightly nitrate measurements relative to that obtained using fortnightly sampling; (c) the daily nitrate dataset produced significantly smaller parametric prediction uncertainty, but only modest reduction in total prediction uncertainty, relative to the fortnightly nitrate dataset; (d) model structural error and measurement errors are the primary sources of total prediction uncertainty, and these combine to inhibit the benefits of high-frequency monitoring. We conclude that the adequacy of sampling frequency is dependent on model structure and measurement errors, such that higher-frequency nitrate monitoring may not markedly reduce the uncertainty of nutrient predictions, depending on other levels and sources of uncertainty.

There is significant debate about why less than half of European rivers and streams are in good ecological status, despite decades of intense regulatory efforts. Of the multiple stressors that are recognized as potential contributors to stream degradation, we focus on discharge from 26,500 European wastewater treatment plants (WWTPs). We tested the hypothesis that stream ecological status degradation across Europe is related to the local intensity of wastewater discharge, with an expected stream-order (ω) dependence based on the scaling laws that govern receiving stream networks. We found that ecological status in streams (ω≤3) declined consistently with increasing urban wastewater discharge fraction of stream flow (UDF) across river types and basins. In contrast, ecological status in larger rivers (ω≥4) was not related to UDF. From a continental-scale logistic regression model (accuracy 86%) we identified an ecologically critical threshold UDF = 6.5% ± 0.5. This is exceeded by more than one third of WWTPs in Europe, mostly discharging into smaller streams. Our results suggest that new receiving water-specific strategies for wastewater management are needed to achieve good ecological status in smaller streams.

Algae, as primary producers in riverine ecosystems, are found in two distinct habitats: benthic and pelagic algae typically prevalent in shallow/small and deep/large streams, respectively. Over an entire river continuum, spatiotemporal patterns of the two algal communities reflect specificity in habitat preference determined by geomorphic structure, hydroclimatic controls, and spatiotemporal heterogeneity in nutrient loads from point- and diffuse-sources. By representing these complex interactions between geomorphic, hydrologic, geochemical, and ecological processes, we present here a new river-network-scale dynamic model (CnANDY) for pelagic (A) and benthic (B) algae competing for energy and one limiting nutrient (phosphorus, P). We used the urbanized Weser River Basin in Germany (7th-order; ~8.4 million population; ~46 K km2) as a case study and analyzed simulations for equilibrium mass and concentrations under steady median river discharge. We also examined P, A, and B spatial patterns in four sub-basins. We found an emerging pattern characterized by scaling of P and A concentrations over stream-order ω, whereas B concentration was described by three distinct phases. Furthermore, an abrupt algal regime shift occurred in intermediate streams from B dominance in ω≤3 to exclusive A presence in ω≥6. Modeled and long-term basin-scale monitored dissolved P concentrations matched well for ω>4, and with overlapping ranges in ω

Elevated concentration and high variance of dissolved organic carbon (DOC) in surface waters are a challenge for the production of drinking water. Past studies have indicated a dominant role of wetlands in DOC mobilization, but have mainly been focused on boreal and oceanic catchments. Here we analyze the observational DOC time series from 89 temperate humid catchments which drain into German drinking water reservoirs. We characterize the DOC concentration median and variability and utilize partial least squares regression in order to quantify the relation to catchment characteristics such as land use, climate, and topography. We found that the long-term median DOC concentration in the catchment is well predicted by the 90th percentile of the distribution of the topographic wetness index (0.9P TWI) over the entire catchment area. The 0.9P TWI can be directly connected to the abundance of riparian wetlands in the catchments. DOC concentration variability (represented as the ratio of the interquartile range and the median concentrations) was also found to be well predictable. Concentration variability was highest in cold and wet catchments with a high 0.9P TWI. Here we also found stronger correlations between DOC concentrations and discharge, with positive concentration-discharge patterns. Catchments with elevated DOC-concentration variance also exhibited the most severe long-term increases in concentrations. Our results thus indicate that, in temperate climates, riparian wetlands can be the dominant source zones of DOC and control the hydrological mobilization and potentially also the spatial difference in long-term concentration trends observed in surface waters. We conclude that the dominance of topography and climatic conditions in controlling spatio-temporal patterns in DOC concentrations leads to very limited management options.

High-frequency sensor measurements enable calculation of continuous autotrophic nitrate uptake rate based on its intrinsic relationship with gross primary production (GPP). The spatiotemporally available data offer prospects to advance process understandings across scales. We used continuous 15-min data (2011–2015) from a forest upstream reach and an agricultural downstream reach of the Selke River, Germany. Based on the high-frequency data, we developed a parsimonious approach for regionalizing the autotrophic uptake rate, considering effects of global radiation and riparian shading. For networked modeling, we integrated this approach into the fully distributed mesoscale hydrological nitrate model (mHM-Nitrate). Daily GPP-based uptake rate calculations showed distinct seasonal patterns and ranges in the agricultural and forest streams (mean values were 80.9 and 15.5 m g N m − 2 d − 1 , respectively). Validation in the two streams showed acceptable performance (R2 = 0.47 and 0.45, respectively) and spatial transferability of the regionalization approach, given its parsimony. Networked modeling results showed high spatiotemporal variability in nitrate transport and uptake throughout the river network. The magnitude of gross uptake increased, whereas uptake efficiency decreased significantly along stream order. Longitudinal analysis in the main stem of the Selke River revealed that riparian shading and inter-annual hydrochemical variations strongly influenced daily dynamics of the uptake efficiency. This study provides a parsimonious and transferable procedure for regionalizing in-stream autotrophic nitrate uptake based on high-frequency data at reach scale. Integrating this approach in the mHM-Nitrate model allows detailed nitrate transport and in-stream uptake processes to be investigated throughout river networks.

Summary For capturing spatial variations of runoff and nutrient fluxes attributed to catchment heterogeneity, multi-site hydrological water quality monitoring strategies are increasingly put into practice. This study aimed to investigate the impacts of spatially distributed streamflow and streamwater Inorganic Nitrogen (IN) concentration observations on the identification of a continuous time, spatially semi-distributed and process-based hydrological water quality model HYPE (HYdrological Predictions for the Environment). A Bayesian inference based approach DREAM(ZS) (DiffeRential Evolution Adaptive Metrololis algorithm) was combined with HYPE to implement model optimisation and uncertainty analysis on streamflow and streamwater IN concentration simulations at a nested meso scale catchment in central Germany. To this end, a 10-year period (1994–1999 for calibration and 1999–2004 for validation) was utilised. We compared the parameters’ posterior distributions, modelling performance using the best estimated parameter set and 95% prediction confidence intervals at catchment outlet for the calibration period that were derived from single-site calibration (SSC) and multi-site calibration (MSC) modes. For SSC, streamflow and streamwater IN concentration observations at only the catchment outlet were used. While, for MSC, streamflow and streamwater IN concentration observations from both catchment outlet and two internal sites were considered. Results showed that the uncertainty intervals of hydrological water quality parameters’ posterior distributions estimated from MSC, were narrower than those obtained from SSC. In addition, it was found that the MSC outperformed SSC on streamwater IN concentration simulations at internal sites for both calibration and validation periods, while the influence on streamflow modelling performance was small. This can be explained by the “nested” nature of the catchment and high correlation between discharge observations from different sites. Results revealed, also, that 95% prediction confidence intervals of streamflow and streamwater IN concentration estimated from MSC were more credible compared with those estimated from SSC, which are reflected by narrower confidence intervals and higher percentage of observations bracketed in the estimated unit confidence intervals. The outcomes of this study pointed out the importance of spatially distributed hydrological water quality observations for improving model parameter identification and provided guidelines for choice of adequate calibration strategy and design of hydrological water quality monitoring campaign.

Riverine dissolved organic carbon (DOC) supports the production of estuaries and coastal ecosystems, constituting one of the most actively recycled pools of the global carbon cycle. A substantial proportion of DOC entering oceans is highly aged, but its origins remain unclear. Significant fluxes of old DOC have never been observed in temperate headwaters where terrestrial imports take place. Here, we studied the radiocarbon age of DOC in three streams draining forested headwater catchments of the river Mulde (Ore Mountains, Germany). In a 4 week summer precipitation event DOC aged at between 160 and 270 years was delivered into the watershed. In one stream, the DOC was modern but depleted in radiocarbon compared to other hydrological conditions. The yield was substantial and corresponded to 20–52 % of the annual DOC yields in wet and dry years, respectively. The analysis of long-term data suggested that the DOC export in extreme precipitation events added to the annual yield and was not compensated for by lower exports in remaining periods. We conclude that climate change, along with additional processes associated with human activities, channels old soil carbon into more rapidly cycled carbon pools of the hydrosphere.

Reservoirs can be viewed as sentinels of their catchments and a detailed monitoring of reservoir systems informs about biogeochemical and hydrological processes at the catchment scale. We developed a comprehensive online monitoring system at Rappbode reservoir, the largest drinking water reservoir in Germany, and its inflows. The Rappbode Reservoir Observatory comprises of a set of online-sensors for the measurement of physical, chemical, and biological variables and is complemented by a biweekly limnological sampling schedule. Measurement stations are deployed at the four major inflows into the system, at the outlets of all pre-reservoirs, as well as in the main reservoir. The newly installed monitoring system serves both scientific monitoring and process studies, as well as reservoir management. Particular emphasis is paid to the monitoring of short-term dynamics and many variables are measured at high temporal resolution. As an example, we quantitatively documented a flood event which mobilised high loads of dissolved organic carbon and changed the characteristics of the receiving reservoir from eutrophic to dystrophic within a few days. This event could have been completely missed by conventional biweekly sampling programs, but is relevant for biogeochemical fluxes at the catchment scale. We also show that the high frequency data provide a deeper insight into ecosystem dynamics and lake metabolism. The Rappbode Reservoir Observatory; moreover, offers a unique study site to apply, validate, and develop state of the art lake models to improve their predictive capabilities.

An assessment of water quality measurements during a long-lasting low water period in the Elbe River is presented. Weekly samples were taken from May to December 2003 at a sampling site in the middle part of the Elbe River. For multivariate data analysis, 34 parameters of 46 samplings were considered. As a result of this analysis, 78% of the variance of the data set is explained by five factors. They can be assigned to the following latent variables: season (37.5%) > tributaries (12.7%) > re-suspension (10.4%) > discharge (9.4%) > complexation (8.5%). For the investigated sampling site, two key processes were identified as dominating factors on the water quality during low water conditions. First, seasonal phytoplankton development caused changes in redox conditions with consequences for re-solution of pollutants from sediments. Second, tributaries have a higher impact on the main stream, due to changes in mixing processes. Therefore, in addition to flood investigations, monitoring strategies, and management plans should be developed in order to survey changes in water quality during low water conditions.

Extreme fluvial floods may cause severe contamination with fuel oil and diesel, originating from gasoline pipes and tanks in private households and industrial areas, respectively. Geo-referenced oil spills in the region of Bitterfeld (Germany) after extreme flood events, such as in August 2002, were simulated using the two-dimensional (2D) Finite Element model system Telemac2D, which is subdivided into a hydrodynamic (Telemac-2D) and a transport module (Subief2D). Fuel oil settled via adhesion showed a thickness of less than 1.0 mm. Fuel oil concentrations on the flood wave amounted up to 80 g m -3 in the vicinity of the point sources. At a distance of several hundred meters downstream of the point sources, the fuel oil concentrations were calculated to be zero. Settled areas were only partially contaminated with fuel oil. While one village experienced severe oil contamination, the town of Bitterfeld was almost unaffected by oil spills. It was demonstrated that the 2D transport model applied is capable of simulating fuel oil spills during extreme high waters in the terrestrial environment. Such simulations of fuel oil spills will feed into a GIS-based decision support system of flood protection.

Beside damages of infrastructure in industrial regions, extreme floods can cause contamination with particle-bound pollutants, e.g., due to erosion of soils and sediments. In order to predict contamination with inorganic pollutants, the transport and fate of arsenic, lead, and mercury during a fictive flood event of River Vereinigte Mulde in the region of Bitterfeld (Germany) with 200 years recurrence time was modeled. The finite element model system Telemac2D, which is subdivided into a hydrodynamic (Telemac-2D), a transport (Subief-2D), and a water quality module (wq2subief) was applied. The transport and water quality model models were calibrated using results of sediment trap exposures in the floodplain of River Vereinigte Mulde. Model results exhibited that the spatial patterns of particle-associated arsenic and lead concentrations significantly changed. Extended, mostly agriculturally used areas showed arsenic and lead concentrations between 150 and 200 mg kg -1 and 250 and 300 mg kg -1 , respectively, while urban areas were to a great extent spared from high contamination with arsenic and lead. Concentrations of particle-associated mercury showed a pattern distinct from those of arsenic and lead. Outside of small patches with concentrations up to 63 mg kg -1 , concentrations of particle-associated mercury remained close to zero. Differences in the spatial patterns of the three pollutants regarded mainly arise from significantly different initial and boundary conditions. Sensitivity analyses of initial and boundary conditions revealed a high sensitivity of particle-bound pollutant concentrations, whereas the sensitivities of concentrations of suspended sediments and soluble pollutants were mediocre to negligible.

The objective of this case study was to calibrate and verify a detailed sediment transport model in a 4-kilometre stretch of the middle Elbe floodplains in Germany. The hydraulic RMA-2 model and the SED2d-WES sediment transport model were used. The sediment transport model was verified with a flood event by detailed measurements of surface water levels, flow velocities at six profiles, suspended sediment concentration and sediment deposition (by means of 10 sediment traps). Discharge was modelled for three steady-state conditions. Surface water levels could be calculated to an accuracy of less than 5 cm compared to measurements. The differences between calculated and measured velocities were with one exception smaller than 0.2 m/s (measured range 0.1...1.0 m/s). Simulated average sediment input amount to 35 g/(m 2 d) for the given flood event. The highest calculated sedimentation rates of 700 g/(m 2 d) (dry density 90 kg/m 2 ) took place in backwaters and abandoned channels. Twenty-five percent of the deposited sediment settled in the backwaters (which only account for 13 percent of the area). The most sensitive parameters of the sediment transport model were the settling velocity and critical shear stress. The modelling approach allowed a realistic spatial distributed calculation of sediment inputs into the floodplain of the Elbe River.

Dissolved organic carbon (DOC) transported by rivers represents an important link between carbon pools of terrestrial and oceanic ecosystems. However, it is unclear how frequent DOC must be sampled to obtain reasonable load estimates. Here, we used continuous records of the specific UV absorption coefficient (SAC) and discharge from a headwater stream at the Ore Mountains (Germany) to calculate load errors depending on DOC sampling frequency. SAC was used as a proxy for DOC. The results show that the load was underestimated by 13-19% with monthly, 10-13% with bi-weekly and 7-9% with weekly DOC samplings, respectively. We conclude that collecting additional data from high discharge events decrease the error significantly.

An exact morphometric description of Mining Lake 111, Brandenburg, Germany, was obtained after a new survey in 1996 by the staff of the water research department. Volume and surface area of ML111 were calculated based on values of this survey. An actual bathymetric map was created from the datapool which was the basis for the selection of sampling sites where we obtained sediment cores to describe the geochemistry of the lake sediments. Principal Component Analysis (PCA) was carried out to examine patterns and similarities between concentrations of different heavy metals. The patterns are different for the three basins within the lake. The spatial distributions of 12 elements in the sediment surface were estimated with a geostatistical procedure (Ordinary Kriging) as well as with a conventional interpolation method. The structure found with PCA was confirmed by the plots from spatial interpolation. Due to the complexity of the lake morphometry and bathymetry 66 sediment cores taken at 47 sampling sites were not sufficient to reduce the spatial variance distributions to acceptable values.