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Excessive nutrient loadings into rivers are a well-known ecological problem. Implemented mitigation measures should ideally be cost-effective, but perfectly ranking alternative nutrient mitigation measures according to cost-effectiveness is a difficult methodological challenge. Furthermore, a particularly practical challenge is that cost-effective measures are not necessarily favoured by local stakeholders, and this may impede their successful implementation in practice. The objective of this study was to evaluate the cost-effectiveness of mitigation measures using a methodology that includes a participatory process and social learning to ensure their successful implementation. By combining cost data, hydrological modelling and a bottom-up approach for three different European catchment areas (the Latvian Berze, the Swedish Helge and the German Selke rivers), the cost-effectiveness of 16 nutrient mitigation measures were analysed under current conditions as well as under selected scenarios for future climate and land-use changes. Fertiliser reduction, wetlands, contour ploughing and municipal wastewater treatment plants are the measures that remove nutrients with the highest cost-effectiveness in the respective case study context. However, the results suggest that the cost-effectiveness of measures not only depends on their design, specific location and the conditions of the surrounding area, but is also affected by the future changes the area may be exposed to. Climate and land-use changes do not only affect the cost-effectiveness of measures, but also shape the overall nutrient loads and potential target levels in a catchment., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Excessive dissolved inorganic nitrogen (DIN) added to urban river systems by point-source (PS) inputs, including raw sewage and wastewater treatment plant (WWTP) effluent, constitutes a water-quality problem of growing concern worldwide. However, the quantification of their impacts on DIN retention capacity and pathways in receiving water still remains partial. In this study, a spatially intensive water quality monitoring campaign was conducted to support the application of a water quality model to a PS-impacted urban river in Hefei City, China. The DIN retention capacities and pathway of a reference upstream Reach A, a raw-sewage-impacted Reach B and a WWTP-effluent-dominated Reach C were quantified using the model results after a Bayesian approach for parameter estimation and uncertainty analysis. The results showed that the raw sewage discharge elevated the assimilatory uptake rate but lowered its efficiency in Reach B, while the WWTP effluent discharge elevated both the denitrification rate and efficiency and made Reach C a denitrification hotspot with an increased nitrate concentration and hypoxic environment. The effects of the PS inputs on the DIN retention pathways (assimilatory uptake vs. denitrification) were regulated by their impacts on river metabolism. Despite different pathways, the total DIN retention ratios of Reaches A, B and C under low-flow conditions were 30.3% km-1, 14.3% km-1 and 6.5% km-1, respectively, which indicated that the instream DIN retention capacities were significantly impaired by the PS inputs. This result suggests that the DIN discharged from PS inputs to urban rivers will be transported with the potential to create long-term ecological implications not only locally but also more distant downstream.

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 mgNm -2 d -1 , respectively). Validation in the two streams showed acceptable performance (R 2  = 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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Despite extensive efforts to reduce nitrate transfer in agricultural areas, limited response is often observed in the nitrate concentration in rivers. To investigate the reasons for this limited response, nitrate dynamics in a 100km(2) agricultural catchment in eastern Germany was analysed from sub-hourly to decadal time-scales. Sub-hourly analysis of storm event dynamics during a typical hydrological year (2005-2006) was performed to identify periods of the year with high leaching risk and to link the latter to agricultural management practices in the catchment. Dynamic Harmonic Regression analysis of a 32-year (1982-2014) record of nitrate and discharge revealed that i) the long-term trend in nitrate concentration was closely related to that in discharge, suggesting that large-scale weather and climate patterns were masking the effect of improved nitrogen management on nitrate trends; ii) a persistent seasonal pattern with winter concentration maxima and summer minima could be observed, which was interpreted in terms of a dynamic nitrate concentration profile in the soil and subsoil; and iii) the catchment progressively changed from chemodynamic to more chemostatic behaviour over the three decades of study, which is a sign of long-term homogenisation of nitrate concentrations distribution over depth. This study shows that detailed physical understanding of nitrate dynamics across time scales can be obtained only through combined analysis of long-term records and high-resolution sensor data. Hence, a joint effort is advocated between environmental authorities, who usually perform long-term monitoring, and scientific programmes, which usually perform high-resolution monitoring., (Copyright © 2016 Elsevier B.V. All rights reserved.)

New scientific understanding is catalyzed by novel technologies that enhance measurement precision, resolution or type, and that provide new tools to test and develop theory. Over the last 50 years, technology has transformed the hydrologic sciences by enabling direct measurements of watershed fluxes (evapotranspiration, streamflow) at time scales and spatial extents aligned with variation in physical drivers. High frequency water quality measurements, increasingly obtained by in situ water quality sensors, are extending that transformation. Widely available sensors for some physical (temperature) and chemical (conductivity, dissolved oxygen) attributes have become integral to aquatic science, and emerging sensors for nutrients, dissolved CO 2 , turbidity, algal pigments, and dissolved organic matter are now enabling observations of watersheds and streams at time scales commensurate with their fundamental hydrological, energetic, elemental, and biological drivers. Here we synthesize insights from emerging technologies across a suite of applications, and envision future advances, enabled by sensors, in our ability to understand, predict, and restore watershed and stream systems.

River networks play an important role in nutrient transport, retention and export from terrestrial to coastal ecosystems through complex interaction of hydrological, geomorphological and biogeochemical processes. Our physical understanding of the interactions between river geomorphology and nutrient retention in river networks is still limited. To investigate the relationship between nutrient retention and stream morphology (sinuosity and stream width), the fully-distributed catchment nitrate model (mHM-Nitrate) was applied in the Bode catchment (3300 km2 Germany). The stream width represented in the mMH-Nitrate model was modified to simulate natural cross section area. The sinuosity of Bode river network was divided into four classes (1-1.5, 1.5-2, 2-2.5, and >3). The baseline situation of the stream sinuosity was dominated by a first-class sinuosity (1-1.5). Different scenarios of stream sinuosity and stream width were generated considering the land use, soil type and slope. Then, the mHM-Nitrate model was used to quantify the nitrate retention between the different scenarios compared with the baseline conditions. Preliminary results showed that different combinations of sinuosity and stream width result in different uptake fractions from the total nitrate load. In addition, how much the effects of sinuosity and stream width are controlled by other factors (such as transit time, soil type and land use), were investigated using the mHM-Nitrate model. This kind of study, will definitely allow us to gain further insights on how effective restoration and nutrient mitigation management can be reached at the catchment scale. [ABSTRACT FROM AUTHOR]