Articles | Volume 26, issue 23
https://doi.org/10.5194/hess-26-6227-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-6227-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Dec 2022
Research article |
| 12 Dec 2022
| 12 Dec 2022
Disentangling scatter in long-term concentration–discharge relationships: the role of event types
Felipe A. Saavedra
CORRESPONDING AUTHOR
Department Catchment Hydrology, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), 06120, Germany
Andreas Musolff
Department of Hydrogeology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, 04318, Germany
Jana von Freyberg
School of Architecture, Civil and Environmental Engineering, EPFL, 1015 Lausanne, Switzerland
Mountain Hydrology and Mass Movements, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), 8903 Birmensdorf, Switzerland
Ralf Merz
Department Catchment Hydrology, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), 06120, Germany
Stefano Basso
Department Catchment Hydrology, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), 06120, Germany
Larisa Tarasova
Department Catchment Hydrology, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), 06120, Germany
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EGUsphere, https://doi.org/10.5194/egusphere-2025-3532, https://doi.org/10.5194/egusphere-2025-3532, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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In this study, we simulated streamflow in German river catchments for climate projections using a deep learning model. Flood-generating processes were identified using explainable artificial intelligence. In the median, the models project mostly less rain-on-snow floods in Germany in the future and an overall lower importance of snowmelt. The average and strongest rain-on-snow floods will have a higher magnitude. The trends found for the individual climate models can vary considerably.
Pia Ebeling, Alexander Hubig, Alexander Wachholz, Ulrike Scharfenberger, Sarah Haug, Tam Nguyen, Fanny Sarrazin, Masooma Batool, Andreas Musolff, and Rohini Kumar
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-450, https://doi.org/10.5194/essd-2025-450, 2025
Preprint under review for ESSD
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This update of the water quality dataset QUADICA for 1386 German catchments adds new data on water quality, flow, and drivers. Additions include water temperature and concentrations of oxygen and others, as well as catchment-wise time series on pollution sources and improved integration with other relevant datasets. This expanded dataset will help researchers and practitioners better understand how human activities affect water quality and ecosystems, and make more informed decisions.
Pia Ebeling, Andreas Musolff, Rohini Kumar, Andreas Hartmann, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 29, 2925–2950, https://doi.org/10.5194/hess-29-2925-2025, https://doi.org/10.5194/hess-29-2925-2025, 2025
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Groundwater is a crucial resource at risk due to droughts. To understand drought effects on groundwater levels in Germany, we grouped 6626 wells into six regional and two national patterns. Weather explained half of the level variations with varied response times. Shallow groundwater responds fast and is more vulnerable to short droughts (a few months). Dampened deep heads buffer short droughts but suffer from long droughts and recoveries. Two nationwide trend patterns were linked to human water use.
Izabela Bujak-Ozga, Jana von Freyberg, Margaret Zimmer, Andrea Rinaldo, Paolo Benettin, and Ilja van Meerveld
Hydrol. Earth Syst. Sci., 29, 2339–2359, https://doi.org/10.5194/hess-29-2339-2025, https://doi.org/10.5194/hess-29-2339-2025, 2025
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Stream networks expand and contract, affecting the amount and quality of water in perennial streams. This study presents measurements of changes in water chemistry and the flowing portion of the drainage network during rainfall events in two neighboring catchments. Despite the proximity and similar size, soil, and bedrock, water chemistry and stream network dynamics differed substantially in the two catchments. These differences are attributed to the differences in the slope and channel network.
Hsing-Jui Wang, Ralf Merz, and Stefano Basso
Hydrol. Earth Syst. Sci., 29, 1525–1548, https://doi.org/10.5194/hess-29-1525-2025, https://doi.org/10.5194/hess-29-1525-2025, 2025
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Extreme floods are more common than expected. Knowing where these floods are likely to occur is key for risk management. Traditional methods struggle with limited data, causing uncertainty. We use common streamflow dynamics to indicate extreme flood propensity. Analyzing data from Atlantic Europe, northern Europe, and the US, we validate this novel approach and unravel intrinsic linkages between regional geographic patterns and extreme flood drivers.
Ralf Loritz, Alexander Dolich, Eduardo Acuña Espinoza, Pia Ebeling, Björn Guse, Jonas Götte, Sibylle K. Hassler, Corina Hauffe, Ingo Heidbüchel, Jens Kiesel, Mirko Mälicke, Hannes Müller-Thomy, Michael Stölzle, and Larisa Tarasova
Earth Syst. Sci. Data, 16, 5625–5642, https://doi.org/10.5194/essd-16-5625-2024, https://doi.org/10.5194/essd-16-5625-2024, 2024
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The CAMELS-DE dataset features data from 1582 streamflow gauges across Germany, with records spanning from 1951 to 2020. This comprehensive dataset, which includes time series of up to 70 years (median 46 years), enables advanced research on water flow and environmental trends and supports the development of hydrological models.
Christina Franziska Radtke, Xiaoqiang Yang, Christin Müller, Jarno Rouhiainen, Ralf Merz, Stefanie R. Lutz, Paolo Benettin, Hong Wei, and Kay Knöller
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-109, https://doi.org/10.5194/hess-2024-109, 2024
Revised manuscript not accepted
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Most studies assume no difference between transit times of water and nitrate, because nitrate is transported by water. With an 8-year high-frequency dataset of isotopic signatures of both, water and nitrate, and a transit time model, we show the temporal varying difference of nitrate and water transit times. This finding is highly relevant for applied future research related to nutrient dynamics in landscapes under anthropogenic forcing and for managing impacts of nitrate on aquatic ecosystems.
Alessio Gentile, Jana von Freyberg, Davide Gisolo, Davide Canone, and Stefano Ferraris
Hydrol. Earth Syst. Sci., 28, 1915–1934, https://doi.org/10.5194/hess-28-1915-2024, https://doi.org/10.5194/hess-28-1915-2024, 2024
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Can we leverage high-resolution and low-cost EC measurements and biweekly δ18O data to estimate the young water fraction at higher temporal resolution? Here, we present the EXPECT method that combines two widespread techniques: EC-based hydrograph separation and sine-wave models of the seasonal isotope cycles. The method is not without its limitations, but its application in three small Swiss catchments is promising for future applications in catchments with different characteristics.
Matteo Pesce, Alberto Viglione, Jost von Hardenberg, Larisa Tarasova, Stefano Basso, Ralf Merz, Juraj Parajka, and Rui Tong
Proc. IAHS, 385, 65–69, https://doi.org/10.5194/piahs-385-65-2024, https://doi.org/10.5194/piahs-385-65-2024, 2024
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The manuscript describes an application of PArameter Set Shuffling (PASS) approach in the Alpine region. A machine learning decision-tree algorithm is applied for the regional calibration of a conceptual semi-distributed hydrological model. Regional model efficiencies don't decrease significantly when moving in space from catchments used for the regional calibration (training) to catchments used for the procedure validation (test) and, in time, from the calibration to the verification period.
Hsing-Jui Wang, Ralf Merz, Soohyun Yang, and Stefano Basso
Hydrol. Earth Syst. Sci., 27, 4369–4384, https://doi.org/10.5194/hess-27-4369-2023, https://doi.org/10.5194/hess-27-4369-2023, 2023
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Accurately assessing heavy-tailed flood behavior with limited data records is challenging and can lead to inaccurate hazard estimates. Our research introduces a new index that uses hydrograph recession to identify heavy-tailed flood behavior, compare severity, and produce reliable results with short data records. This index overcomes the limitations of current metrics, which lack physical meaning and require long records. It thus provides valuable insight into the flood hazard of river basins.
Michael Rode, Jörg Tittel, Frido Reinstorf, Michael Schubert, Kay Knöller, Benjamin Gilfedder, Florian Merensky-Pöhlein, and Andreas Musolff
Hydrol. Earth Syst. Sci., 27, 1261–1277, https://doi.org/10.5194/hess-27-1261-2023, https://doi.org/10.5194/hess-27-1261-2023, 2023
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Agricultural catchments show elevated phosphorus (P) concentrations during summer low flow. In an agricultural stream, we found that phosphorus in groundwater was a major source of stream water phosphorus during low flow, and stream sediments derived from farmland are unlikely to have increased stream phosphorus concentrations during low water. We found no evidence that riparian wetlands contributed to soluble reactive (SR) P loads. Agricultural phosphorus was largely buffered in the soil zone.
Carolin Winter, Tam V. Nguyen, Andreas Musolff, Stefanie R. Lutz, Michael Rode, Rohini Kumar, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 27, 303–318, https://doi.org/10.5194/hess-27-303-2023, https://doi.org/10.5194/hess-27-303-2023, 2023
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The increasing frequency of severe and prolonged droughts threatens our freshwater resources. While we understand drought impacts on water quantity, its effects on water quality remain largely unknown. Here, we studied the impact of the unprecedented 2018–2019 drought in Central Europe on nitrate export in a heterogeneous mesoscale catchment in Germany. We show that severe drought can reduce a catchment's capacity to retain nitrogen, intensifying the internal pollution and export of nitrate.
Jie Yang, Qiaoyu Wang, Ingo Heidbüchel, Chunhui Lu, Yueqing Xie, Andreas Musolff, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 26, 5051–5068, https://doi.org/10.5194/hess-26-5051-2022, https://doi.org/10.5194/hess-26-5051-2022, 2022
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We assessed the effect of catchment topographic slopes on the nitrate export dynamics in terms of the nitrogen mass fluxes and concentration level using a coupled surface–subsurface model. We found that flatter landscapes tend to retain more nitrogen mass in the soil and export less nitrogen mass to the stream, explained by the reduced leaching and increased potential of degradation in flat landscapes. We emphasized that stream water quality is potentially less vulnerable in flatter landscapes.
Pia Ebeling, Rohini Kumar, Stefanie R. Lutz, Tam Nguyen, Fanny Sarrazin, Michael Weber, Olaf Büttner, Sabine Attinger, and Andreas Musolff
Earth Syst. Sci. Data, 14, 3715–3741, https://doi.org/10.5194/essd-14-3715-2022, https://doi.org/10.5194/essd-14-3715-2022, 2022
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Environmental data are critical for understanding and managing ecosystems, including the mitigation of water quality degradation. To increase data availability, we present the first large-sample water quality data set (QUADICA) of riverine macronutrient concentrations combined with water quantity, meteorological, and nutrient forcing data as well as catchment attributes. QUADICA covers 1386 German catchments to facilitate large-sample data-driven and modeling water quality assessments.
Joni Dehaspe, Fanny Sarrazin, Rohini Kumar, Jan H. Fleckenstein, and Andreas Musolff
Hydrol. Earth Syst. Sci., 25, 6437–6463, https://doi.org/10.5194/hess-25-6437-2021, https://doi.org/10.5194/hess-25-6437-2021, 2021
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Increased nitrate concentrations in surface waters can compromise river ecosystem health. As riverine nitrate uptake is hard to measure, we explore how low-frequency nitrate concentration and discharge observations (that are widely available) can help to identify (in)efficient uptake in river networks. We find that channel geometry and water velocity rather than the biological uptake capacity dominate the nitrate-discharge pattern at the outlet. The former can be used to predict uptake.
Benedikt J. Werner, Oliver J. Lechtenfeld, Andreas Musolff, Gerrit H. de Rooij, Jie Yang, Ralf Gründling, Ulrike Werban, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 25, 6067–6086, https://doi.org/10.5194/hess-25-6067-2021, https://doi.org/10.5194/hess-25-6067-2021, 2021
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Export of dissolved organic carbon (DOC) from riparian zones (RZs) is an important yet poorly understood component of the catchment carbon budget. This study chemically and spatially classifies DOC source zones within a RZ of a small catchment to assess DOC export patterns. Results highlight that DOC export from only a small fraction of the RZ with distinct DOC composition dominates overall DOC export. The application of a spatial, topographic proxy can be used to improve DOC export models.
Jana von Freyberg, Julia L. A. Knapp, Andrea Rücker, Bjørn Studer, and James W. Kirchner
Hydrol. Earth Syst. Sci., 24, 5821–5834, https://doi.org/10.5194/hess-24-5821-2020, https://doi.org/10.5194/hess-24-5821-2020, 2020
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Automated water samplers are often used to collect precipitation and streamwater samples for subsequent isotope analysis, but the isotopic signal of these samples may be altered due to evaporative fractionation occurring during the storage inside the autosamplers in the field. In this article we present and evaluate a cost-efficient modification to the Teledyne ISCO automated water sampler that prevents isotopic enrichment through evaporative fractionation of the water samples.
Cited articles
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., Naik, V., Palmer, M., Plattner, G. K., Rogelj, J., and Rojas, M.: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Technical Summary, https://www.ipcc.ch/report/ar6/wg1/ (last access: 8 December 2022), 2021.
Basu, N. B., Thompson, S. E., and Rao, P. S. C.:
Hydrologic and biogeochemical functioning of intensively managed catchments: A synthesis of top-down analyses, Water Resour. Res., 47, W00J15, https://doi.org/10.1029/2011WR010800, 2011.
Bauwe, A., Tiemeyer, B., Kahle, P., and Lennartz, B.:
Classifying hydrological events to quantify their impact on nitrate leaching across three spatial scales, J. Hydrol., 531, 589–601, https://doi.org/10.1016/j.jhydrol.2015.10.069, 2015.
Benettin, P., Fovet, O., and Li, L.:
Nitrate removal and young stream water fractions at the catchment scale, Hydrol. Process., 34, 2725–2738, https://doi.org/10.1002/hyp.13781, 2020.
Bieroza, M. Z., Heathwaite, A. L., Bechmann, M., Kyllmar, K., and Jordan, P.:
The concentration–discharge slope as a tool for water quality management, Sci. Total Environ., 630, 738–749, https://doi.org/10.1016/j.scitotenv.2018.02.256, 2018.
Blume, T. and van Meerveld, H. J. (Ilja):
From hillslope to stream: methods to investigate subsurface connectivity, WIREs Water, 2, 177–198, https://doi.org/10.1002/wat2.1071, 2015.
Blume, T., Zehe, E., and Bronstert, A.:
Rainfall—runoff response, event-based runoff coefficients and hydrograph separation, Hydrolog. Sci. J., 52, 843–862, https://doi.org/10.1623/hysj.52.5.843, 2007.
Bowes, M. J., Jarvie, H. P., Naden, P. S., Old, G. H., Scarlett, P. M., Roberts, C., Armstrong, L. K., Harman, S. A., Wickham, H. D., and Collins, A. L.:
Identifying priorities for nutrient mitigation using river concentration–flow relationships: The Thames basin, UK, J. Hydrol., 517, 1–12, https://doi.org/10.1016/j.jhydrol.2014.03.063, 2014.
Bowes, M. J., Jarvie, H. P., Halliday, S. J., Skeffington, R. A., Wade, A. J., Loewenthal, M., Gozzard, E., Newman, J. R., and Palmer-Felgate, E. J.:
Characterising phosphorus and nitrate inputs to a rural river using high-frequency concentration–flow relationships, Sci. Total Environ., 511, 608–620, https://doi.org/10.1016/j.scitotenv.2014.12.086, 2015.
Bracken, L. J., Wainwright, J., Ali, G. A., Tetzlaff, D., Smith, M. W., Reaney, S. M., and Roy, A. G.:
Concepts of hydrological connectivity: Research approaches, pathways and future agendas, Earth-Sci. Rev., 119, 17–34, https://doi.org/10.1016/j.earscirev.2013.02.001, 2013.
Branco, P., Torgo, L., and Ribeiro, R.:
A Survey of Predictive Modelling under Imbalanced Distributions, arXiv [cs], arXiv:1505.01658, 2015.
Butturini, A., Gallart, F., Latron, J., Vazquez, E., and Sabater, F.:
Cross-site Comparison of Variability of DOC and Nitrate c–q Hysteresis during the Autumn–winter Period in Three Mediterranean Headwater Streams: A Synthetic Approach, Biogeochemistry, 77, 327–349, https://doi.org/10.1007/s10533-005-0711-7, 2006.
Cartwright, I.: Concentration vs. streamflow (C-Q) relationships of major ions in south-eastern Australian rivers: Sources and fluxes of inorganic ions and nutrients, Appl. Geochem., 120, 104680, https://doi.org/10.1016/j.apgeochem.2020.104680, 2020.
Casquin, A., Dupas, R., Gu, S., Couic, E., Gruau, G., and Durand, P.:
The influence of landscape spatial configuration on nitrogen and phosphorus exports in agricultural catchments, Landscape Ecol., 36, 3383–3399, https://doi.org/10.1007/s10980-021-01308-5, 2021.
Casson, N. J., Eimers, M. C., and Watmough, S. A.:
Sources of nitrate export during rain-on-snow events at forested catchments, Biogeochemistry, 120, 23–36, https://doi.org/10.1007/s10533-013-9850-4, 2014.
Chan, S. C., Kendon, E. J., Berthou, S., Fosser, G., Lewis, E., and Fowler, H. J.:
Europe-wide precipitation projections at convection permitting scale with the Unified Model, Clim. Dynam., 55, 409–428, https://doi.org/10.1007/s00382-020-05192-8, 2020.
Chang, S., Zhang, Q., Byrnes, D., Basu, N., and Van Meter, K.:
Chesapeake legacies: The importance of legacy nitrogen to improving Chesapeake Bay water quality, Environ. Res. Lett., 16, 085002, https://doi.org/10.1088/1748-9326/ac0d7b, 2021.
Chen, X., Parajka, J., Széles, B., Valent, P., Viglione, A., and Blöschl, G.:
Impact of Climate and Geology on Event Runoff Characteristics at the Regional Scale, Water, 12, 3457, https://doi.org/10.3390/w12123457, 2020.
Cohen, J., Ye, H., and Jones, J.:
Trends and variability in rain-on-snow events, Geophys. Res. Lett., 42, 7115–7122, https://doi.org/10.1002/2015GL065320, 2015.
Cole, L. J., Stockan, J., and Helliwell, R.:
Managing riparian buffer strips to optimise ecosystem services: A review, Agriculture, Ecosystems and Environment, 296, 106891, https://doi.org/10.1016/j.agee.2020.106891, 2020.
Covino, T.:
Hydrologic connectivity as a framework for understanding biogeochemical flux through watersheds and along fluvial networks, Geomorphology, 277, 133–144, https://doi.org/10.1016/j.geomorph.2016.09.030, 2017.
Curtin, D., Beare, M. H., and Hernandez-Ramirez, G.:
Temperature and Moisture Effects on Microbial Biomass and Soil Organic Matter Mineralization, Soil Sci. Soc. Am. J., 76, 2055–2067, https://doi.org/10.2136/sssaj2012.0011, 2012.
Dai, A., Trenberth, K. E., and Qian, T.:
A Global Dataset of Palmer Drought Severity Index for 1870–2002: Relationship with Soil Moisture and Effects of Surface Warming, J. Hydrometeorol., 5, 1117–1130, https://doi.org/10.1175/JHM-386.1, 2004.
Dehaspe, J., Sarrazin, F., Kumar, R., Fleckenstein, J. H., and Musolff, A.:
Bending of the concentration discharge relationship can inform about in-stream nitrate removal, Hydrol. Earth Syst. Sci., 25, 6437–6463, https://doi.org/10.5194/hess-25-6437-2021, 2021.
Diamond, J. S. and Cohen, M. J.: Complex patterns of catchment solute–discharge relationships for coastal plain rivers, Hydrol. Process., 32, 388–401, https://doi.org/10.1002/hyp.11424, 2018.
Dupas, R., Jomaa, S., Musolff, A., Borchardt, D., and Rode, M.:
Disentangling the influence of hydroclimatic patterns and agricultural management on river nitrate dynamics from sub-hourly to decadal time scales, Sci. Total Environ., 571, 791–800, https://doi.org/10.1016/j.scitotenv.2016.07.053, 2016.
Dupas, R., Abbott, B. W., Minaudo, C., and Fovet, O.:
Distribution of Landscape Units Within Catchments Influences Nutrient Export Dynamics, Frontiers in Environmental Science, 7, 43, https://doi.org/10.3389/fenvs.2019.00043, 2019.
Dupas, R., Ehrhardt, S., Musolff, A., Fovet, O., and Durand, P.:
Long-term nitrogen retention and transit time distribution in agricultural catchments in western France, Environ. Res. Lett., 15, 115011, https://doi.org/10.1088/1748-9326/abbe47, 2020.
Ebeling, P., Kumar, R., and Musolff, A.: CCDB – catchment characteristics data base Germany,
HydroShare [data set],
https://doi.org/10.4211/hs.82f8094dd61e449a826afdef820a2c19,
2021.
Ebeling, P., Kumar, R., Weber, M., Knoll, L., Fleckenstein, J. H., and Musolff, A.:
Archetypes and Controls of Riverine Nutrient Export Across German Catchments, Water Resour. Res., 57, e2020WR028134, https://doi.org/10.1029/2020WR028134, 2021.
EEA: The European environment-State and outlook 2020, in: Knowledge for Transition to a Sustainable Europe; Publications Office of the European Union, Luxembourg, Brussels, Belgium, https://doi.org/10.2800/96749, 2019.
Fang, Z., Carroll, R. W. H., Schumer, R., Harman, C., Wilusz, D., and Williams, K. H.:
Streamflow partitioning and transit time distribution in snow-dominated basins as a function of climate, J. Hydrol., 570, 726–738, https://doi.org/10.1016/j.jhydrol.2019.01.029, 2019.
Fontrodona Bach, A., van der Schrier, G., Melsen, L. A., Klein Tank, A. M. G., and Teuling, A. J.:
Widespread and Accelerated Decrease of Observed Mean and Extreme Snow Depth Over Europe, Geophys. Res. Lett., 45, 12,312-12,319, https://doi.org/10.1029/2018GL079799, 2018.
Fovet, O., Humbert, G., Dupas, R., Gascuel-Odoux, C., Gruau, G., Jaffrezic, A., Thelusma, G., Faucheux, M., Gilliet, N., Hamon, Y., and Grimaldi, C.:
Seasonal variability of stream water quality response to storm events captured using high-frequency and multi-parameter data, J. Hydrol., 559, 282–293, https://doi.org/10.1016/j.jhydrol.2018.02.040, 2018.
GEA: Waters in Germany: Status and assessment, German Environment Agency, Dessau–Roßlau, 2017.
Godsey, S. E., Kirchner, J. W., and Clow, D. W.:
Concentration–discharge relationships reflect chemostatic characteristics of US catchments, Hydrol. Process., 23, 1844–1864, https://doi.org/10.1002/hyp.7315, 2009.
Gorski, G. and Zimmer, M. A.: Hydrologic regimes drive nitrate export behavior in human-impacted watersheds, Hydrol. Earth Syst. Sci., 25, 1333–1345, https://doi.org/10.5194/hess-25-1333-2021, 2021.
Grayson, R. B., Western, A. W., Chiew, F. H. S., and Blöschl, G.:
Preferred states in spatial soil moisture patterns: Local and nonlocal controls, Water Resour. Res., 33, 2897–2908, https://doi.org/10.1029/97WR02174, 1997.
Guillemot, S., Fovet, O., Gascuel-Odoux, C., Gruau, G., Casquin, A., Curie, F., Minaudo, C., Strohmenger, L., and Moatar, F.:
Spatio-temporal controls of C–N–P dynamics across headwater catchments of a temperate agricultural region from public data analysis, Hydrol. Earth Syst. Sci., 25, 2491–2511, https://doi.org/10.5194/hess-25-2491-2021, 2021.
Guntiñas, M. E., Leirós, M. C., Trasar-Cepeda, C., and Gil-Sotres, F.:
Effects of moisture and temperature on net soil nitrogen mineralization: A laboratory study, Eur. J. Soil Biol., 48, 73–80, https://doi.org/10.1016/j.ejsobi.2011.07.015, 2012.
Hardie, M. A., Cotching, W. E., Doyle, R. B., Holz, G., Lisson, S., and Mattern, K.:
Effect of antecedent soil moisture on preferential flow in a texture-contrast soil, J. Hydrol., 398, 191–201, https://doi.org/10.1016/j.jhydrol.2010.12.008, 2011.
Häussermann, U., Klement, L., Breuer, L., Ullrich, A., Wechsung, G., and Bach, M.:
Nitrogen soil surface budgets for districts in Germany 1995 to 2017, Environmental Sciences Europe, 32, 109, https://doi.org/10.1186/s12302-020-00382-x, 2020.
Heathwaite, A. L. and Bieroza, M.:
Fingerprinting hydrological and biogeochemical drivers of freshwater quality, Hydrol. Process., 35, e13973, https://doi.org/10.1002/hyp.13973, 2021.
House, W. A., Leach, D. V., and Armitage, P. D.:
Study of dissolved silicon, and nitrate dynamics in a fresh water stream, Water Res., 35, 2749–2757, https://doi.org/10.1016/S0043-1354(00)00548-0, 2001.
Inamdar, S. P., O'Leary, N., Mitchell, M. J., and Riley, J. T.:
The impact of storm events on solute exports from a glaciated forested watershed in western New York, USA, Hydrol. Process., 20, 3423–3439, https://doi.org/10.1002/hyp.6141, 2006.
Jencso, K. G., McGlynn, B. L., Gooseff, M. N., Wondzell, S. M., Bencala, K. E., and Marshall, L. A.:
Hydrologic connectivity between landscapes and streams: Transferring reach- and plot-scale understanding to the catchment scale, Water Resour. Res., 45, W04428, https://doi.org/10.1029/2008WR007225, 2009.
Johannsen, A., Dähnke, K., and Emeis, K.:
Isotopic composition of nitrate in five German rivers discharging into the North Sea, Org. Geochem., 39, 1678–1689, https://doi.org/10.1016/j.orggeochem.2008.03.004, 2008.
Knapp, J. L. A., von Freyberg, J., Studer, B., Kiewiet, L., and Kirchner, J. W.:
Concentration–discharge relationships vary among hydrological events, reflecting differences in event characteristics, Hydrol. Earth Syst. Sci., 24, 2561–2576, https://doi.org/10.5194/hess-24-2561-2020, 2020.
Knoll, L., Breuer, L., and Bach, M.:
Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning, Environ. Res. Lett., 15, 064004, https://doi.org/10.1088/1748-9326/ab7d5c, 2020.
Koenig, L. E., Shattuck, M. D., Snyder, L. E., Potter, J. D., and McDowell, W. H.:
Deconstructing the Effects of Flow on DOC, Nitrate, and Major Ion Interactions Using a High-Frequency Aquatic Sensor Network, Water Resour. Res., 53, 10655–10673, https://doi.org/10.1002/2017WR020739, 2017.
Korom, S. F., Schuh, W. M., Tesfay, T., and Spencer, E. J.:
Aquifer denitrification and in situ mesocosms: Modeling electron donor contributions and measuring rates, J. Hydrol., 432–433, 112–126, https://doi.org/10.1016/j.jhydrol.2012.02.023, 2012.
Kruskal, W. H. and Wallis, W. A.:
Use of Ranks in One-Criterion Variance Analysis, J. Am. Stat. Assoc., 47, 583–621, https://doi.org/10.1080/01621459.1952.10483441, 1952.
Kumar, R., Livneh, B., and Samaniego, L.:
Toward computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme, Water Resour. Res., 49, 5700–5714, https://doi.org/10.1002/wrcr.20431, 2013.
Kunkel, R., Bach, M., Behrendt, H., and Wendland, F.:
Groundwater-borne nitrate intakes into surface waters in Germany, Water Sci. Technol., 49, 11–19, https://doi.org/10.2166/wst.2004.0152, 2004.
Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., and Garnier, J.:
50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland, Environ. Res. Lett., 9, 105011, https://doi.org/10.1088/1748-9326/9/10/105011, 2014.
Lloyd, C. E. M., Freer, J. E., Johnes, P. J., and Collins, A. L.:
Using hysteresis analysis of high-resolution water quality monitoring data, including uncertainty, to infer controls on nutrient and sediment transfer in catchments, Sci. Total Environ., 543, 388–404, https://doi.org/10.1016/j.scitotenv.2015.11.028, 2016.
Lutz, S. R., Trauth, N., Musolff, A., Van Breukelen, B. M., Knöller, K., and Fleckenstein, J. H.:
How Important is Denitrification in Riparian Zones? Combining End-Member Mixing and Isotope Modeling to Quantify Nitrate Removal from Riparian Groundwater, Water Resour. Res., 56, e2019WR025528, https://doi.org/10.1029/2019WR025528, 2020.
Martin, C., Aquilina, L., Gascuel-Odoux, C., Molénat, J., Faucheux, M., and Ruiz, L.:
Seasonal and interannual variations of nitrate and chloride in stream waters related to spatial and temporal patterns of groundwater concentrations in agricultural catchments, Hydrol. Process., 18, 1237–1254, https://doi.org/10.1002/hyp.1395, 2004.
McGlynn, B. L. and Seibert, J.: Distributed assessment of contributing area and riparian buffering along stream networks, Water Resour. Res., 39, 1082, https://doi.org/10.1029/2002WR001521, 2003.
Meter, K. J. V. and Basu, N. B.:
Time lags in watershed-scale nutrient transport: an exploration of dominant controls, Environ. Res. Lett., 12, 084017, https://doi.org/10.1088/1748-9326/aa7bf4, 2017.
Meter, K. J. V., Basu, N. B., Veenstra, J. J., and Burras, C. L.:
The nitrogen legacy: emerging evidence of nitrogen accumulation in anthropogenic landscapes, Environ. Res. Lett., 11, 035014, https://doi.org/10.1088/1748-9326/11/3/035014, 2016.
Meybeck, M. and Moatar, F.:
Daily variability of river concentrations and fluxes: indicators based on the segmentation of the rating curve, Hydrol. Process., 26, 1188–1207, https://doi.org/10.1002/hyp.8211, 2012.
Minaudo, C., Dupas, R., Gascuel-Odoux, C., Roubeix, V., Danis, P.-A., and Moatar, F.:
Seasonal and event-based concentration–discharge relationships to identify catchment controls on nutrient export regimes, Adv. Water Resour., 131, 103379, https://doi.org/10.1016/j.advwatres.2019.103379, 2019.
Moatar, F., Abbott, B. W., Minaudo, C., Curie, F., and Pinay, G.:
Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment, and major ions, Water Resour. Res., 53, 1270–1287, https://doi.org/10.1002/2016WR019635, 2017.
Mulholland, P. J., Helton, A. M., Poole, G. C., Hall, R. O., Hamilton, S. K., Peterson, B. J., Tank, J. L., Ashkenas, L. R., Cooper, L. W., Dahm, C. N., Dodds, W. K., Findlay, S. E. G., Gregory, S. V., Grimm, N. B., Johnson, S. L., McDowell, W. H., Meyer, J. L., Valett, H. M., Webster, J. R., Arango, C. P., Beaulieu, J. J., Bernot, M. J., Burgin, A. J., Crenshaw, C. L., Johnson, L. T., Niederlehner, B. R., O'Brien, J. M., Potter, J. D., Sheibley, R. W., Sobota, D. J., and Thomas, S. M.:
Stream denitrification across biomes and its response to anthropogenic nitrate loading, Nature, 452, 202–205, https://doi.org/10.1038/nature06686, 2008.
Musolff, A.
WQQDB – water quality and quantity data base Germany,
HydroShare [data set],
https://doi.org/10.4211/hs.a42addcbd59a466a9aa56472dfef8721, 2020.
Musolff, A., Fleckenstein, J. H., Rao, P. S. C., and Jawitz, J. W.:
Emergent archetype patterns of coupled hydrologic and biogeochemical responses in catchments: Emergence of Archetype C–Q Patterns, Geophys. Res. Lett., 44, 4143–4151, https://doi.org/10.1002/2017GL072630, 2017.
Musolff, A., Fleckenstein, J., Opitz, M., Büttner, O., Kumar, R., and Tittel, J.:
Spatio-temporal controls of dissolved organic carbon stream water concentrations, J. Hydrol., 566, 205–215, https://doi.org/10.1016/j.jhydrol.2018.09.011, 2018.
Musolff, A., Zhan, Q., Dupas, R., Minaudo, C., Fleckenstein, J., Rode, M., Dehaspe, J., and Rinke, K.:
Spatial and Temporal Variability in Concentration–Discharge Relationships at the Event Scale, Water Resour. Res., 57, https://doi.org/10.1029/2020WR029442, 2021.
Ocampo, C. J., Sivapalan, M., and Oldham, C.:
Hydrological connectivity of upland-riparian zones in agricultural catchments: Implications for runoff generation and nitrate transport, J. Hydrol., 331, 643–658, https://doi.org/10.1016/j.jhydrol.2006.06.010, 2006.
Ortmeyer, F., Begerow, D., Guerreiro, M. A., Wohnlich, S., and Banning, A.:
Comparison of Denitrification Induced by Various Organic Substances—Reaction Rates, Microbiology, and Temperature Effect, Water Resour. Res., 57, https://doi.org/10.1029/2021WR029793, 2021.
Outram, F. N., Cooper, R. J., Sünnenberg, G., Hiscock, K. M., and Lovett, A. A.:
Antecedent conditions, hydrological connectivity and anthropogenic inputs: Factors affecting nitrate and phosphorus transfers to agricultural headwater streams, Sci. Total Environ., 545–546, 184–199, https://doi.org/10.1016/j.scitotenv.2015.12.025, 2016.
Paerl, H. W.:
Coastal eutrophication and harmful algal blooms: Importance of atmospheric deposition and groundwater as “new” nitrogen and other nutrient sources, Limnol. Oceanogr., 42, 1154–1165, https://doi.org/10.4319/lo.1997.42.5_part_2.1154, 1997.
Pohle, I., Baggaley, N., Palarea-Albaladejo, J., Stutter, M., and Glendell, M.: A Framework for Assessing Concentration‐Discharge Catchment Behavior From Low‐Frequency Water Quality Data, Water Resour. Res., 57, e2021WR029692, https://doi.org/10.1029/2021WR029692, 2021.
Puckett, L. J., Tesoriero, A. J., and Dubrovsky, N. M.:
Nitrogen Contamination of Surficial Aquifers—A Growing Legacy, Environ. Sci. Technol., 45, 839–844, https://doi.org/10.1021/es1038358, 2011.
Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., and Gratzki, A.:
A Central European precipitation climatology – Part I: Generation and validation of a high-resolution gridded daily data set (HYRAS), Meteorol. Z., 235–256, https://doi.org/10.1127/0941-2948/2013/0436, 2013.
Rode, M., Halbedel née Angelstein, S., Anis, M. R., Borchardt, D., and Weitere, M.:
Continuous In-Stream Assimilatory Nitrate Uptake from High-Frequency Sensor Measurements, Environ. Sci. Technol., 50, 5685–5694, https://doi.org/10.1021/acs.est.6b00943, 2016.
Rose, L. A., Karwan, D. L., and Godsey, S. E.:
Concentration–discharge relationships describe solute and sediment mobilization, reaction, and transport at event and longer timescales, Hydrol. Process., 32, 2829–2844, https://doi.org/10.1002/hyp.13235, 2018.
Sabater, S., Butturini, A., Clement, J.-C., Burt, T., Dowrick, D., Hefting, M., Matre, V., Pinay, G., Postolache, C., Rzepecki, M., and Sabater, F.:
Nitrogen Removal by Riparian Buffers along a European Climatic Gradient: Patterns and Factors of Variation, Ecosystems, 6, 0020–0030, https://doi.org/10.1007/s10021-002-0183-8, 2003.
Samaniego, L., Kumar, R., and Attinger, S.:
Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010.
Schwientek, M., Osenbrück, K., and Fleischer, M.:
Investigating hydrological drivers of nitrate export dynamics in two agricultural catchments in Germany using high-frequency data series, Environ. Earth Sci., 69, 381–393, https://doi.org/10.1007/s12665-013-2322-2, 2013.
Seibert, J., Grabs, T., Köhler, S., Laudon, H., Winterdahl, M., and Bishop, K.:
Linking soil- and stream-water chemistry based on a Riparian Flow-Concentration Integration Model, Hydrol. Earth Syst. Sci., 13, 2287–2297, https://doi.org/10.5194/hess-13-2287-2009, 2009.
Stieglitz, M., Shaman, J., McNamara, J., Engel, V., Shanley, J., and Kling, G. W.:
An approach to understanding hydrologic connectivity on the hillslope and the implications for nutrient transport, Global Biogeochem. Cy., 17, 1105, https://doi.org/10.1029/2003GB002041, 2003.
Stumpf, R. P., Johnson, L. T., Wynne, T. T., and Baker, D. B.:
Forecasting annual cyanobacterial bloom biomass to inform management decisions in Lake Erie, J. Great Lakes Res., 42, 1174–1183, https://doi.org/10.1016/j.jglr.2016.08.006, 2016.
Tarasova, L.: Classified runoff events, Zenodo [data set], https://doi.org/10.5281/zenodo.3575024, 2019.
Tarasova, L., Basso, S., Zink, M., and Merz, R.:
Exploring Controls on Rainfall-Runoff Events: 1. Time Series-Based Event Separation and Temporal Dynamics of Event Runoff Response in Germany, Water Resour. Res., 54, 7711–7732, https://doi.org/10.1029/2018WR022587, 2018.
Tarasova, L., Basso, S., Wendi, D., Viglione, A., Kumar, R., and Merz, R.:
A Process-Based Framework to Characterize and Classify Runoff Events: The Event Typology of Germany, Water Resour. Res., 56, e2019WR026951, https://doi.org/10.1029/2019WR026951, 2020.
Taszarek, M., Kendzierski, S., and Pilguj, N.:
Hazardous weather affecting European airports: Climatological estimates of situations with limited visibility, thunderstorm, low-level wind shear and snowfall from ERA5, Weather and Climate Extremes, 28, 100243, https://doi.org/10.1016/j.wace.2020.100243, 2020.
Tesoriero, A. J., Duff, J. H., Saad, D. A., Spahr, N. E., and Wolock, D. M.:
Vulnerability of Streams to Legacy Nitrate Sources, Environ. Sci. Technol., 47, 3623–3629, https://doi.org/10.1021/es305026x, 2013.
Thompson, S. E., Basu, N. B., Lascurain Jr., J., Aubeneau, A., and Rao, P. S. C.:
Relative dominance of hydrologic versus biogeochemical factors on solute export across impact gradients, Water Resour. Res., 47, W00J05, https://doi.org/10.1029/2010WR009605, 2011.
van Grinsven, H. J. M., ten Berge, H. F. M., Dalgaard, T., Fraters, B., Durand, P., Hart, A., Hofman, G., Jacobsen, B. H., Lalor, S. T. J., Lesschen, J. P., Osterburg, B., Richards, K. G., Techen, A.-K., Vertès, F., Webb, J., and Willems, W. J.:
Management, regulation and environmental impacts of nitrogen fertilization in northwestern Europe under the Nitrates Directive; a benchmark study, Biogeosciences, 9, 5143–5160, https://doi.org/10.5194/bg-9-5143-2012, 2012.
Vaughan, M. C. H., Bowden, W. B., Shanley, J. B., Vermilyea, A., Sleeper, R., Gold, A. J., Pradhanang, S. M., Inamdar, S. P., Levia, D. F., Andres, A. S., Birgand, F., and Schroth, A. W.:
High-frequency dissolved organic carbon and nitrate measurements reveal differences in storm hysteresis and loading in relation to land cover and seasonality, Water Resour. Res., 53, 5345–5363, https://doi.org/10.1002/2017WR020491, 2017.
Veith, T. L., Preisendanz, H. E., and Elkin, K. R.:
Characterizing transport of natural and anthropogenic constituents in a long-term agricultural watershed in the northeastern United States, J. Soil Water Conserv., 75, 319–329, https://doi.org/10.2489/jswc.75.3.319, 2020.
Vervloet, L. S. C., Binning, P. J., Børgesen, C. D., and Højberg, A. L.:
Delay in catchment nitrogen load to streams following restrictions on fertilizer application, Sci. Total Environ., 627, 1154–1166, https://doi.org/10.1016/j.scitotenv.2018.01.255, 2018.
von Freyberg, J., Radny, D., Gall, H. E., and Schirmer, M.:
Implications of hydrologic connectivity between hillslopes and riparian zones on streamflow composition, J. Contam. Hydrol., 169, 62–74, https://doi.org/10.1016/j.jconhyd.2014.07.005, 2014.
Weitere, M., Altenburger, R., Anlanger, C., Baborowski, M., Bärlund, I., Beckers, L.-M., Borchardt, D., Brack, W., Brase, L., Busch, W., Chatzinotas, A., Deutschmann, B., Eligehausen, J., Frank, K., Graeber, D., Griebler, C., Hagemann, J., Herzsprung, P., Hollert, H., Inostroza, P. A., Jäger, C. G., Kallies, R., Kamjunke, N., Karrasch, B., Kaschuba, S., Kaus, A., Klauer, B., Knöller, K., Koschorreck, M., Krauss, M., Kunz, J. V., Kurz, M. J., Liess, M., Mages, M., Müller, C., Muschket, M., Musolff, A., Norf, H., Pöhlein, F., Reiber, L., Risse-Buhl, U., Schramm, K.-W., Schmitt-Jansen, M., Schmitz, M., Strachauer, U., von Tümpling, W., Weber, N., Wild, R., Wolf, C., and Brauns, M.:
Disentangling multiple chemical and non-chemical stressors in a lotic ecosystem using a longitudinal approach, Sci. Total Environ., 769, 144324, https://doi.org/10.1016/j.scitotenv.2020.144324, 2021.
Wendland, F., Blum, A., Coetsiers, M., Gorova, R., Griffioen, J., Grima, J., Hinsby, K., Kunkel, R., Marandi, A., Melo, T., Panagopoulos, A., Pauwels, H., Ruisi, M., Traversa, P., Vermooten, J. S. A., and Walraevens, K.:
European aquifer typology: a practical framework for an overview of major groundwater composition at European scale, Environ. Geol., 55, 77–85, https://doi.org/10.1007/s00254-007-0966-5, 2008.
Winter, C., Lutz, S. R., Musolff, A., Kumar, R., Weber, M., and Fleckenstein, J. H.:
Disentangling the Impact of Catchment Heterogeneity on Nitrate Export Dynamics From Event to Long-Term Time Scales, Water Resour. Res., 57, e2020WR027992, https://doi.org/10.1029/2020WR027992, 2021.
Winter, C., Tarasova, L., Lutz, S., Musolff, A., Kumar, R., and Fleckenstein, J.:
Explaining the Variability in High-Frequency Nitrate Export Patterns Using Long-Term Hydrological Event Classification, Water Resour. Res., 58, e2021WR030938, https://doi.org/10.1002/essoar.10507676.1, 2022.
Yang, J., Heidbüchel, I., Musolff, A., Reinstorf, F., and Fleckenstein, J. H.:
Exploring the Dynamics of Transit Times and Subsurface Mixing in a Small Agricultural Catchment, Water Resour. Res., 54, 2317–2335, https://doi.org/10.1002/2017WR021896, 2018.
Zhang, X., Yang, X., Jomaa, S., and Rode, M.:
Analyzing impacts of seasonality and landscape gradient on event-scale nitrate-discharge dynamics based on nested high-frequency monitoring, J. Hydrol., 591, 125585, https://doi.org/10.1016/j.jhydrol.2020.125585, 2020.
Zhi, W., Li, L., Dong, W., Brown, W., Kaye, J., Steefel, C., and Williams, K. H.:
Distinct Source Water Chemistry Shapes Contrasting Concentration-Discharge Patterns, Water Resour. Res., 55, 4233–4251, https://doi.org/10.1029/2018WR024257, 2019.
Zink, M., Kumar, R., Cuntz, M., and Samaniego, L.:
A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty, Hydrol. Earth Syst. Sci., 21, 1769–1790, https://doi.org/10.5194/hess-21-1769-2017, 2017.
Short summary
Nitrate contamination of rivers from agricultural sources is a challenge for water quality management. During runoff events, different transport paths within the catchment might be activated, generating a variety of responses in nitrate concentration in stream water. Using nitrate samples from 184 German catchments and a runoff event classification, we show that hydrologic connectivity during runoff events is a key control of nitrate transport from catchments to streams in our study domain.
Nitrate contamination of rivers from agricultural sources is a challenge for water quality...
