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Artificial tracers are often used for quantitative estimates of solute transport properties in rivers. However, single‐injection tracer tests give insights in transport characteristics limited to the ecohydrological conditions at the testing time. Series of time‐consuming and laborious tracer tests would be required to properly capture seasonal changes. The present study uses intrinsic diurnal fluctuations of electrical conductivity (EC) caused by discharge of treated wastewater as a tracer to evaluate solute transport processes along a 4.7‐km reach of the River Erpe, Germany. By reproducing the fluctuations recorded along the river using the solute transport model one‐dimensional transport with inflow and storage, this study investigated the long‐term dynamics in solute transport properties. Individual 48‐hr curves of EC were used in the steady state configuration of the model to gain 48‐hr‐integrated estimates of selected transport parameters. Using a sliding window approach in 1‐hr steps along the 2,270‐hr time series of EC the temporal variability of solute transport between April and June 2016 was assessed. To test the identifiability of parameters using the proposed method, sensitivity analyses and a breakthrough curve analysis of selected 48‐hr windows were implemented. With time advancing into the summer, a significant rising trend (Mann‐Kendall test p‐value < 0.05) of the cross sectional area of the channel was observed and attributed to the growth of macrophytes and a significant slightly decreasing trend for the storage rate was found. The presented method is of high value for river management, as promoting transient storage enhances biogeochemical cycling and benefits water quality. Key Points: Electrical conductivity fluctuations caused by discharge of treated wastewater into a river contain information about solute transportA solute transport model applied to recordings of solute fluctuations can be used to obtain long‐term time series of transport metricsDifferences in transport metrics in adjacent river sections were found; Mowing of macrophytes may increase the transient storage area [ABSTRACT FROM AUTHOR]

Improving nitrogen (N) status in European water bodies is a pressing issue. N levels depend not only on current but also past N inputs to the landscape, that have accumulated through time in legacy stores (e.g., soil, groundwater). Catchment‐scale N models, that are commonly used to investigate in‐stream N levels, rarely examine the magnitude and dynamics of legacy components. This study aims to gain a better understanding of the long‐term fate of the N inputs and its uncertainties, using a legacy‐driven N model (ELEMeNT) in Germany's largest national river basin (Weser; 38,450 km2) over the period 1960–2015. We estimate the nine model parameters based on a progressive constraining strategy, to assess the value of different observational data sets. We demonstrate that beyond in‐stream N loading, soil N content and in‐stream N concentration allow to reduce the equifinality in model parameterizations. We find that more than 50% of the N surplus denitrifies (1480–2210 kg ha−1) and the stream export amounts to around 18% (410–640 kg ha−1), leaving behind as much as around 230–780 kg ha−1 of N in the (soil) source zone and 10–105 kg ha−1 in the subsurface. A sensitivity analysis reveals the importance of different factors affecting the residual uncertainties in simulated N legacies, namely hydrologic travel time, denitrification rates, a coefficient characterizing the protection of organic N in source zone and N surplus input. Our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce the equifinality in water quality modeling. Plain Language Summary: Lowering nitrogen (N) amounts in European surface waters is a pressing issue. N levels largely result from fertilizer application in agricultural areas, and deposition of atmospheric N coming from fossil fuel combustion. These N inputs to the landscape can accumulate below the ground surface in so‐called legacy stores (including the soil and aquifer), from which they can be released progressively through time. Therefore, N levels depend not only on the recent N inputs, but also on their history. Our modeling study aims to improve our understanding of the long‐term fate of the N inputs and its uncertainties in Germany's largest national river basin (Weser) over the period 1960–2015. It suggests that more than 50% of the N inputs to land is lost to the atmosphere (denitrification, 1480–2210 kg ha−1) and the stream export amounts to around 18% (410–640 kg ha−1), leaving behind as much as around 16% (264–820 kg ha−1) in the landscape (legacy). However, the uncertainties in these estimates remain large, partly due to a lack of observational data on internal (legacy) components and uncertainties in N inputs. Overall, our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce them. Key Points: We use a parsimonious model to examine the long‐term (1960–2015) fate of nitrogen inputs to the landscape including the legacy buildupMore than 50% of the nitrogen surplus denitrifies and around 491 kg ha−1 accumulates in legacy stores in Germany's largest river basinHydrologic travel time and denitrification rates largely contribute to the residual uncertainty in the simulated nitrogen legacies [ABSTRACT FROM AUTHOR]

The quest for improved hydrological models is one of the big challenges in hydrology. When discrepancies are observed between simulated and measured discharge, it is essential to identify which algorithms may be responsible for poor model behavior. Particularly in complex hydrological models, different process representations may dominate at different moments and interact with each other, thus highly complicating this task. This paper investigates the analysis of the temporal dynamics of parameter sensitivity as a way to disentangle the simulation of a hydrological model and identify dominant parameterizations. Three existing methods (the Fourier amplitude sensitivity test, the extended Fourier amplitude sensitivity test, and Sobol's method) are compared by applying them to a TOPMODEL implementation in a small mountainous catchment in the tropics. For the major part of the simulation period, the three methods give comparable results, while the Fourier amplitude sensitivity test is much more computationally efficient. This method is also applied to the complex hydrological model WaSiM-ETH implemented in the Weisseritz catchment, Germany. A qualitative model validation was performed on the basis of the identification of relevant model components. The validation revealed that the saturation deficit parameterization of WaSiM-ETH is highly susceptible to parameter interaction and lack of identifiability. We conclude that temporal dynamics of model parameter sensitivity can be a powerful tool for hydrological model analysis, especially to identify parameter interaction as well as the dominant hydrological response modes. Finally, an open source implementation of the Fourier amplitude sensitivity test is provided. [ABSTRACT FROM AUTHOR]

In this paper we investigate the use of hydrological models as learning tools to help improve our understanding of the hydrological functioning of a catchment. With the model as a hypothetical conceptualization of how dominant hydrological processes contribute to catchment-scale response, we investigate three questions: (I) During which periods does the model (not) reproduce observed quantities and dynamics? (2) What is the nature of the error during times of bad model performance? (3) Which model components are responsible for this error? To investigate these questions, we combine a method for detecting repeating patterns of typical differences between model and observations (time series of grouped errors, TIGER) with a method for identifying the active model components during each simulation time step based on parameter sensitivity (temporal dynamics of parameter sensitivities, TEDPAS). The approach generates a time series of occurrence of dominant error types and time series of parameter sensitivities. A synoptic discussion of these time series highlights deficiencies in the assumptions about the functioning of the catchment. The approach is demonstrated for the Weisseritz headwater catchment in the eastern Ore Mountains. Our results indicate that the WaSiM-ETH complex grid-based model is not a sufficient working hypothesis for the functioning of the Weisseritz catchment and point toward future steps that can help improve our understanding of the catchment. [ABSTRACT FROM AUTHOR]