Articles | Volume 27, issue 15
https://doi.org/10.5194/hess-27-2989-2023
© Author(s) 2023. 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-27-2989-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Aug 2023
Research article |
| 14 Aug 2023
| 14 Aug 2023
Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation
Arianna Borriero
CORRESPONDING AUTHOR
Department of Hydrogeology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Rohini Kumar
Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Tam V. Nguyen
Department of Hydrogeology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Jan H. Fleckenstein
Department of Hydrogeology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Bayreuth Centre of Ecology and Environmental Research, University of Bayreuth, Bayreuth, Germany
Stefanie R. Lutz
Copernicus Institute of Sustainable Development, Department of Environmental Sciences, Utrecht University, Utrecht, the Netherlands
Related authors
No articles found.
Katoria Lekarkar, Oldrich Rakovec, Rohini Kumar, Stefaan Dondeyne, and Ann van Griensven
EGUsphere, https://doi.org/10.5194/egusphere-2025-4526, https://doi.org/10.5194/egusphere-2025-4526, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Belgium has faced intense droughts in recent years, causing major losses across sectors. To assess their rarity, we used a hydrological model to reconstruct fifty years of soil moisture in the country. We show that 2011–2020 experienced the most severe droughts since 1971, with nearly 30 % of the decade under drought. We also show that rainfall-based indicators underestimate soil moisture droughts, so including soil moisture monitoring can give decision-makers a clearer picture of drought risks.
Vishal Thakur, Yannis Markonis, Rohini Kumar, Johanna Ruth Thomson, Mijael Rodrigo Vargas Godoy, Martin Hanel, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 29, 4395–4416, https://doi.org/10.5194/hess-29-4395-2025, https://doi.org/10.5194/hess-29-4395-2025, 2025
Short summary
Short summary
Understanding the changes in water movement in earth is crucial for everyone. To quantify this water movement there are several techniques. We examined how different methods of estimating evaporation impact predictions of various types of water movement across Europe. We found that, while these methods generally agree on whether changes are increasing or decreasing, they differ in magnitude. This means selecting the right evaporation method is crucial for accurate predictions of water movement.
Álvaro Pardo-Álvarez, Jan H. Fleckenstein, Kalliopi Koutantou, and Philip Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3521, https://doi.org/10.5194/egusphere-2025-3521, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
An upgraded version of a numerical solver is introduced to better capture the three-dimensional interactions between surface water and groundwater. Built using open-source software, it adds new features to handle the complexity of real environments, including the representation of subsurface geology and the simulation of diverse dynamic processes, such as solute transport and heat transfer, in both domains. A test case and a full description of the novel features are provided in this paper.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, Yannis Markonis, and Miroslav Trnka
Hydrol. Earth Syst. Sci., 29, 3341–3358, https://doi.org/10.5194/hess-29-3341-2025, https://doi.org/10.5194/hess-29-3341-2025, 2025
Short summary
Short summary
We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, with clusters of 775 and 630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
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
Short summary
Short summary
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.
Vinh Ngoc Tran, Tam V. Nguyen, Jongho Kim, and Valeriy Y. Ivanov
EGUsphere, https://doi.org/10.5194/egusphere-2025-769, https://doi.org/10.5194/egusphere-2025-769, 2025
Preprint archived
Short summary
Short summary
Our research questions whether machine learning models for predicting streamflow need to be trained on data from multiple basins at once. We compared three approaches: a global model trained on thousands of basins, regional models using hundreds of basins, and individual single-basin models. We found that regional and single-basin models often performed better than the global model. This suggests we should judge models by their actual performance rather than their training approach.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
Short summary
Short summary
Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Robert Reinecke, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
EGUsphere, https://doi.org/10.5194/egusphere-2025-1181, https://doi.org/10.5194/egusphere-2025-1181, 2025
Short summary
Short summary
Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
Masooma Batool, Fanny J. Sarrazin, and Rohini Kumar
Earth Syst. Sci. Data, 17, 881–916, https://doi.org/10.5194/essd-17-881-2025, https://doi.org/10.5194/essd-17-881-2025, 2025
Short summary
Short summary
Our paper presents a reconstruction and analysis of the gridded P surplus in European landscapes from 1850 to 2019 at a 5 arcmin resolution. By utilizing 48 different estimates, we account for uncertainties in major components of the P surplus. Our findings highlight substantial historical changes, with the total P surplus in the EU 27 tripling over 170 years. Our dataset enables flexible aggregation at various spatial scales, providing critical insights for land and water management strategies.
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
Earth Syst. Sci. Data, 16, 4673–4708, https://doi.org/10.5194/essd-16-4673-2024, https://doi.org/10.5194/essd-16-4673-2024, 2024
Short summary
Short summary
Nitrogen (N) and phosphorus (P) contamination of water bodies is a long-term issue due to the long history of N and P inputs to the environment and their persistence. Here, we introduce a long-term and high-resolution dataset of N and P inputs from wastewater (point sources) for Germany, combining data from different sources and conceptual understanding. We also account for uncertainties in modelling choices, thus facilitating robust long-term and large-scale water quality studies.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Thomas Hermans, Pascal Goderniaux, Damien Jougnot, Jan H. Fleckenstein, Philip Brunner, Frédéric Nguyen, Niklas Linde, Johan Alexander Huisman, Olivier Bour, Jorge Lopez Alvis, Richard Hoffmann, Andrea Palacios, Anne-Karin Cooke, Álvaro Pardo-Álvarez, Lara Blazevic, Behzad Pouladi, Peleg Haruzi, Alejandro Fernandez Visentini, Guilherme E. H. Nogueira, Joel Tirado-Conde, Majken C. Looms, Meruyert Kenshilikova, Philippe Davy, and Tanguy Le Borgne
Hydrol. Earth Syst. Sci., 27, 255–287, https://doi.org/10.5194/hess-27-255-2023, https://doi.org/10.5194/hess-27-255-2023, 2023
Short summary
Short summary
Although invisible, groundwater plays an essential role for society as a source of drinking water or for ecosystems but is also facing important challenges in terms of contamination. Characterizing groundwater reservoirs with their spatial heterogeneity and their temporal evolution is therefore crucial for their sustainable management. In this paper, we review some important challenges and recent innovations in imaging and modeling the 4D nature of the hydrogeological systems.
Friedrich Boeing, Oldrich Rakovec, Rohini Kumar, Luis Samaniego, Martin Schrön, Anke Hildebrandt, Corinna Rebmann, Stephan Thober, Sebastian Müller, Steffen Zacharias, Heye Bogena, Katrin Schneider, Ralf Kiese, Sabine Attinger, and Andreas Marx
Hydrol. Earth Syst. Sci., 26, 5137–5161, https://doi.org/10.5194/hess-26-5137-2022, https://doi.org/10.5194/hess-26-5137-2022, 2022
Short summary
Short summary
In this paper, we deliver an evaluation of the second generation operational German drought monitor (https://www.ufz.de/duerremonitor) with a state-of-the-art compilation of observed soil moisture data from 40 locations and four different measurement methods in Germany. We show that the expressed stakeholder needs for higher resolution drought information at the one-kilometer scale can be met and that the agreement of simulated and observed soil moisture dynamics can be moderately improved.
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
Short summary
Short summary
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.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
Short summary
Short summary
Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Sadaf Nasreen, Markéta Součková, Mijael Rodrigo Vargas Godoy, Ujjwal Singh, Yannis Markonis, Rohini Kumar, Oldrich Rakovec, and Martin Hanel
Earth Syst. Sci. Data, 14, 4035–4056, https://doi.org/10.5194/essd-14-4035-2022, https://doi.org/10.5194/essd-14-4035-2022, 2022
Short summary
Short summary
This article presents a 500-year reconstructed annual runoff dataset for several European catchments. Several data-driven and hydrological models were used to derive the runoff series using reconstructed precipitation and temperature and a set of proxy data. The simulated runoff was validated using independent observed runoff data and documentary evidence. The validation revealed a good fit between the observed and reconstructed series for 14 catchments, which are available for further analysis.
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
Short summary
Short summary
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.
Guilherme E. H. Nogueira, Christian Schmidt, Daniel Partington, Philip Brunner, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 26, 1883–1905, https://doi.org/10.5194/hess-26-1883-2022, https://doi.org/10.5194/hess-26-1883-2022, 2022
Short summary
Short summary
In near-stream aquifers, mixing between stream water and ambient groundwater can lead to dilution and the removal of substances that can be harmful to the water ecosystem at high concentrations. We used a numerical model to track the spatiotemporal evolution of different water sources and their mixing around a stream, which are rather difficult in the field. Results show that mixing mainly develops as narrow spots, varying In time and space, and is affected by magnitudes of discharge events.
Robert Schweppe, Stephan Thober, Sebastian Müller, Matthias Kelbling, Rohini Kumar, Sabine Attinger, and Luis Samaniego
Geosci. Model Dev., 15, 859–882, https://doi.org/10.5194/gmd-15-859-2022, https://doi.org/10.5194/gmd-15-859-2022, 2022
Short summary
Short summary
The recently released multiscale parameter regionalization (MPR) tool enables
environmental modelers to efficiently use extensive datasets for model setups.
It flexibly ingests the datasets using user-defined data–parameter relationships
and rescales parameter fields to given model resolutions. Modern
land surface models especially benefit from MPR through increased transparency and
flexibility in modeling decisions. Thus, MPR empowers more sound and robust
simulations of the Earth system.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Katharina Blaurock, Burkhard Beudert, Benjamin S. Gilfedder, Jan H. Fleckenstein, Stefan Peiffer, and Luisa Hopp
Hydrol. Earth Syst. Sci., 25, 5133–5151, https://doi.org/10.5194/hess-25-5133-2021, https://doi.org/10.5194/hess-25-5133-2021, 2021
Short summary
Short summary
Dissolved organic carbon (DOC) is an important part of the global carbon cycle with regards to carbon storage, greenhouse gas emissions and drinking water treatment. In this study, we compared DOC export of a small, forested catchment during precipitation events after dry and wet preconditions. We found that the DOC export from areas that are usually important for DOC export was inhibited after long drought periods.
Cited articles
Abbaspour, K. C., Johnson, C. A., and van Genuchten, M. T.: Estimating
uncertain flow and transport parameters using a sequential uncertainty
fitting procedure, Vadose Zone J., 3, 1340–1352,
https://doi.org/10.2136/vzj2004.1340, 2004. a, b
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian
multimodel combination framework: Confronting input, parameter, and model
structural uncertainty in hydrologic prediction, Water Resour. Res., 43,
W01403, https://doi.org/10.1029/2005WR004745, 2007. a
Allen, S. T., Jasechko, S., Berghuijs, W. R., Welker, J. M., Goldsmith, G. R., and Kirchner, J. W.: Global sinusoidal seasonality in precipitation isotopes, Hydrol. Earth Syst. Sci., 23, 3423–3436, https://doi.org/10.5194/hess-23-3423-2019, 2019. a
Ambroise, B.: Variable “active” versus “contributing” areas or periods: a
necessary distinction, Hydrol. Process., 18, 1149–1155,
https://doi.org/10.1002/hyp.5536, 2004. a, b
Andersson, J. C. M., Arheimer, B., Traoré, F., Gustafsson, D., and Ali, A.:
Process refinements improve a hydrological model concept applied to the Niger
River basin, Hydrol. Process., 31, 4540–4554, https://doi.org/10.1002/hyp.11376,
2017. a
Asadollahi, M., Stumpp, C., Rinaldo, A., and Benettin, P.: Transport and water
age dynamics in soils: A comparative study of spatially integrated and
spatially explicit models, Water Resour. Res., 56, e2019WR025539,
https://doi.org/10.1029/2019WR025539, 2020. a
Benettin, P. and Bertuzzo, E.: tran-SAS v1.0 (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.1203600, 2018b. a
Benettin, P., van der Velde, Y., van der Zee, S. E. A. T. M., Rinaldo, A., and
Botter, G.: Chloride circulation in a lowland catchment and the formulation
of transport by travel time distributions, Water Resour. Res., 49,
4619–4632, https://doi.org/10.1002/wrcr.20309, 2013. a
Benettin, P., Bailey, S. W., Campbell, J. L., Green, M. B., Rinaldo, A.,
Likens, G. E., McGuire, J. K., and Botter, G.: Linking water age and solute
dynamics in streamflow at the Hubbard Brook Experimental Forest, NH, USA,
Water Resour. Res., 51, 9256–9272, https://doi.org/10.1002/2015WR017552,
2015a. a
Benettin, P., Kirchner, J. W., Rinaldo, A., and Botter, G.: Modeling chloride
transport using travel time distributions at Plynlimon, Wales, Water Resour.
Res., 51, 3259–3276, https://doi.org/10.1002/2014WR016600, 2015b. a
Bethke, C. M. and Johnson, T. M.: Groundwater age and groundwater age dating,
Annu. Rev. Earth Planet. Sci., 36, 121–152,
https://doi.org/10.1146/annurev.earth.36.031207.124210, 2008. a
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36,
https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006. a
Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty
estimation in mechanistic modelling of complex environmental systems using
the GLUE methodology, J. Hydrol., 249, 11–29,
https://doi.org/10.1016/S0022-1694(01)00421-8, 2001. a
Birkel, C. and Soulsby, C.: Advancing tracer-aided rainfall-runoff modelling: A
review of progress, problems and unrealised potential, Hydrol. Process., 29,
5227–5240, https://doi.org/10.1002/hyp.10594, 2015. a
Birkel, C., Dunn, S. M., Tetzlaff, D., and Soulsby, C.: Assessing the value of
high-resolution isotope tracer data in the stepwise development of a lumped
conceptual rainfall–runoff model, Hydrol. Process., 24, 2335–2348,
https://doi.org/10.1002/hyp.7763, 2010. a
Blume, T. and van Meerveld, H. J.: From hillslope to stream: methods to
investigate subsurface connectivity, WIREs Water, 2, 177–198,
https://doi.org/10.1002/wat2.1071, 2015. a
Borriero, A.: Hydroclimatic and isotope data – Upper Selke, Zenodo [data set], https://doi.org/10.5281/zenodo.8121108, 2022. a
Botter, G., Bertuzzo, E., Bellin, A., and Rinaldo, A.: On the Lagrangian
formulations of reactive solute transport in the hydrologic response, Water
Resour. Res., 41, W04008, https://doi.org/10.1029/2004WR003544, 2005. a
Botter, G., Bertuzzo, E., and Rinaldo, A.: Transport in the hydrologic
response: Travel time distributions, soil moisture dynamics, and the old
water paradox, Water Resour. Res., 46, W03514, https://doi.org/10.1029/2009WR008371,
2010. a
Botter, G., Bertuzzo, E., and Rinaldo, A.: Catchment residence and travel time
distributions: The master equation, Geophys. Res. Lett., 38, L11403,
https://doi.org/10.1029/2011GL047666, 2011. a
Buzacott, A. J. V., van der Velde, Y., Keitel, C., and Vervoort, R. W.:
Constraining water age dynamics in a south-eastern Australian catchment using
an age-ranked storage and stable isotope approach, Hydrol. Process., 34,
4384–4403, https://doi.org/10.1002/hyp.13880, 2020. a, b, c
Dai, A.: Erratum: Increasing drought under global warming in observations and
models, Nat. Clim. Change, 3, 171, https://doi.org/10.1038/nclimate1811, 2013. a
Danesh-Yazdi, M., Foufoula-Georgiou, E., Karwan, D. L., and Botter, G.:
Inferring changes in water cycle dynamics of intensively managed landscapes
via the theory of time-variant travel time distributions, Water Resour. Res.,
52, 7593–7614, https://doi.org/10.1002/2016WR019091, 2016. a
Danesh-Yazdi, M., Klaus, J., Condon, L. E., and Maxwell, R. M.: Bridging the
gap between numerical solutions of travel time distributions and analytical
storage selection functions, Hydrol. Process., 32, 1063–1076,
https://doi.org/10.1002/hyp.11481, 2018. a
Drever, M. C. and Hrachowitz, M.: Migration as flow: using hydrological
concepts to estimate the residence time of migrating birds from the daily
counts, Methods Ecol. Evol., 8, 1146–1157, https://doi.org/10.1111/2041-210X.12727,
2017. a, b
Dunn, S. M., Bacon, J. R., Soulsby, C., Tetzlaff, D., Stutter, M. I., Waldron,
S., and Malcolm, I. A.: Interpretation of homogeneity in δ18O
signatures of stream water in a nested sub-catchment system in north-east
Scotland, Hydrol. Process., 22, 4767–4782, https://doi.org/10.1002/hyp.7088, 2008. a
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. a
Dupas, R., Musolff, A., Jawitz, J. W., Rao, P. S. C., Jäger, C. G., Fleckenstein, J. H., Rode, M., and Borchardt, D.: Carbon and nutrient export regimes from headwater catchments to downstream reaches, Biogeosciences, 14, 4391–4407, https://doi.org/10.5194/bg-14-4391-2017, 2017. a
Ehrhardt, S., Kumar, R., Fleckenstein, J. H., Attinger, S., and Musolff, A.: Trajectories of nitrate input and output in three nested catchments along a land use gradient, Hydrol. Earth Syst. Sci., 23, 3503–3524, https://doi.org/10.5194/hess-23-3503-2019, 2019. a
Feng, X., Faiia, A. M., and Posmentier, E. S.: Seasonality of isotopes in
precipitation: A global perspective, J. Geophys. Res, 114, D08116,
https://doi.org/10.1029/2008JD011279, 2009. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Harman, C. J.: Time-variable transit time distributions and transport: Theory
and application to storage-dependent transport of chloride in a watershed,
Water Resour. Res., 51, 1–30, https://doi.org/10.1002/2014WR015707, 2015. a, b, c
Harman, C. J.: Age-Ranked Storage-Discharge Relations: A Unified Description of
Spatially Lumped Flow and Water Age in Hydrologic Systems, Water Resour.
Res., 55, 7143–7165, https://doi.org/10.1029/2017WR022304, 2019. a
Heidbüchel, I., Troch, P. A., and Lyon, S. W.: Separating physical and
meteorological controls of variable transit times in zero-order catchments,
Water Resour. Res., 49, 7644–7657, https://doi.org/10.1002/2012WR013149, 2013. a
Heidbüchel, I., Yang, J., Musolff, A., Troch, P., Ferré, T., and Fleckenstein, J. H.: On the shape of forward transit time distributions in low-order catchments, Hydrol. Earth Syst. Sci., 24, 2895–2920, https://doi.org/10.5194/hess-24-2895-2020, 2020. a
Holvoet, K. M. A., Seuntjens, P., and Vanrolleghem, P. A.: Monitoring and modeling
pesticide fate in surface waters at the catchment scale, Ecol. Model., 209,
53–64, https://doi.org/10.1016/j.ecolmodel.2007.07.030, 2007. a
Hrachowitz, M., Soulsby, C., Tetzlaff, D., Malcolm, I. A., and Schoups, G.:
Gamma distribution models for transit time estimation in catchments: Physical
interpretation of parameters and implications for time-variant transit time
assessment, Water Resour. Res., 46, W10536, https://doi.org/10.1029/2010WR009148,
2010. a
Hrachowitz, M., Soulsby, C., Tetzlaff, D., and Malcolm, I. A.: Sensitivity of
mean transit time estimates to model conditioning and data availability,
Hydrol. Process., 25, 980–990, https://doi.org/10.1002/hyp.7922, 2011. a
Hrachowitz, M., Savenije, H., Bogaard, T. A., Tetzlaff, D., and Soulsby, C.: What can flux tracking teach us about water age distribution patterns and their temporal dynamics?, Hydrol. Earth Syst. Sci., 17, 533–564, https://doi.org/10.5194/hess-17-533-2013, 2013. a
Hrachowitz, M., Benettin, P., van Breukelen, B. M., Fovet, O., Howden, N.
J. K., Ruiz, L van der Velde, Y., and Wade, A. J.: Transit times – the link
between hydrology and water quality at the catchment scale, WIREs Water, 3,
629–657, https://doi.org/10.1002/wat2.1155, 2016. a, b
Huang, T., Pang, Z., Li, J., Xiang, Y., and Zhao, Z.: Mapping groundwater
renewability using age data in the Baiyang alluvial fan, NW, China, Hydrogeol.
J., 25, 743–755, https://doi.org/10.1007/s10040-017-1534-z, 2017. a
Jasechko, S.: Global isotope hydrogeology – review, Rev. Geophys.,
57, 835–965, https://doi.org/10.1029/2018RG000627, 2019. a
Jasechko, S., Wassenaar, L. I., and Mayer, B.: Isotopic evidence for widespread
cold-season-biased groundwater recharge and young streamflow across central
Canada, Hydrol. Process., 31, 2196–2209, https://doi.org/10.1002/hyp.11175, 2017. a
Jiang, S., Jomaa, S., and Rode, M.: Modelling inorganic nitrogen leaching in
nested mesoscale catchments in central Germany, Ecohydrol., 7, 1345–1362,
https://doi.org/10.1002/eco.1462, 2014. a
Jing, M., Heße, F., Kumar, R., Kolditz, O., Kalbacher, T., and Attinger, S.: Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions, Hydrol. Earth Syst. Sci., 23, 171–190, https://doi.org/10.5194/hess-23-171-2019, 2019. a
Kim, M. and Troch, P. A.: Transit time distributions estimation exploiting
flow-weighted time: Theory and proof-of-concept, Water Resour. Res., 56,
e2020WR027186, https://doi.org/10.1029/2020WR027186, 2020. a
Kim, M., Pangle, L. A., Cardoso, C., Lora, M., Volkmann, T. H. M., Wang, Y.,
Harman, C. J., and Troch, P. A.: Transit time distributions and StorAge
Selection functions in a sloping soil lysimeter with time-varying flow paths:
Direct observation of internal and external transport variability, Water
Resour. Res., 52, 7105–7129, https://doi.org/10.1002/2016WR018620, 2016. a
Kirchner, J. W.: Getting the right answers for the right reasons: Linking
measurements, analyses, and models to advance the science of hydrology, Water
Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006. a
Kirchner, J. W.: Aggregation in environmental systems – Part 1: Seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments, Hydrol. Earth Syst. Sci., 20, 279–297, https://doi.org/10.5194/hess-20-279-2016, 2016. a
Kirchner, J. W.: Quantifying new water fractions and transit time distributions using ensemble hydrograph separation: theory and benchmark tests, Hydrol. Earth Syst. Sci., 23, 303–349, https://doi.org/10.5194/hess-23-303-2019, 2019. a
Kirchner, J. W., Feng, X., and Neal, C.: Fractal stream chemistry and its
implications for contaminant transport in catchments, Nature, 403, 524–527,
https://doi.org/10.1038/35000537, 2000. a
Kirchner, J. W., Feng, X., and Neal, C.: Catchment-scale advection and
dispersion as a mechanism for fractal scaling in stream tracer
concentrations, J. Hydrol., 254, 82–101,
https://doi.org/10.1016/S0022-1694(01)00487-5, 2001. a, b
Kirchner, J. W., Feng, X., Neal, C., and Robson, A. J.: The fine structure of
water-quality dynamics: the (high-frequency) wave of the future, Hydrol.
Process., 18, 1353–1359, https://doi.org/10.1002/hyp.5537, 2004. a
Kolbe, T., de Dreuzy, J. R., Abbot, B. W., Aquilina, L., Babey, T., Green, C. T., Fleckenstein, J. H., Labasque, T., Laverman, A. M., Marçais, J., Peiffer, S., Thomas, Z., and Pinay, G.: Stratification of reactivity determines nitrate removal in
groundwater, P. Natl. Acad. Sci. USA, 116,
2494–2499, https://doi.org/10.1073/pnas.1816892116, 2019. a
Kumar, R., Samaniego, L., and Attinger, S.: The effects of spatial discretization and
model parameterization on the prediction of extreme runoff characteristics,
J. Hydrol., 392, 54–69, https://doi.org/10.1016/j.jhydrol.2010.07.047, 2010. a
Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed
hydrologic model parameterization on water fluxes at multiple scales and
locations, Water Resour. Res., 49, 360–379, https://doi.org/10.1029/2012WR012195,
2013. a
Kumar, R., Heße, F., Rao, P. S. C., Musolff, A., Jawitz, J. W., Sarrazin,
F., Samaniego, L., Fleckenstein, J. H., Rakovec, O., Thober, S., and
Attinger, S.: Strong hydroclimatic controls on vulnerability to subsurface
nitrate contamination across Europe, Nat. Commun., 11, 6302,
https://doi.org/10.1038/s41467-020-19955-8, 2020. a, b, c
Le Gal La Salle, C., Marlin, C., Leduc, C., Taupin, J. D., Massault, M., and
Favreau, G.: Renewal rate estimation of groundwater based on radioactive
tracers (3H, 14C) in an unconfined aquifer in a semi-arid area,
Iullemeden Basin, Niger, J. Hydrol., 254, 145–156,
https://doi.org/10.1016/S0022-1694(01)00491-7, 2001. a
Leu, C., Singer, H., Stamm, C., Muller, S. R., and Schwarzenbach, R. P.:
Simultaneous Assessment of Sources, Processes, and Factors Influencing
Herbicide Losses to Surface Waters in a Small Agricultural Catchment,
Environ. Sci. Technol., 38, 3827–3834, https://doi.org/10.1021/es0499602, 2004. a
Lutz, S. R., van Meerveld, H. J., Waterloo, M. J., Broers, H. P., and van Breukelen, B. M.: A model-based assessment of the potential use of compound-specific stable isotope analysis in river monitoring of diffuse pesticide pollution, Hydrol. Earth Syst. Sci., 17, 4505–4524, https://doi.org/10.5194/hess-17-4505-2013, 2013. a
Lutz, S. R., Velde, Y. V. D., Elsayed, O. F., Imfeld, G., Lefrancq, M., Payraudeau, S., and van Breukelen, B. M.: Pesticide fate on catchment scale: conceptual modelling of stream CSIA data, Hydrol. Earth Syst. Sci., 21, 5243–5261, https://doi.org/10.5194/hess-21-5243-2017, 2017. a, b
Lutz, S. R., Krieg, R., Müller, C., Zink, M., Knöller, K., Samaniego, L., and
Merz, R.: Spatial patterns of water age: using young water fractions to
improve the characterization of transit times in contrasting catchments,
Water Resour. Res., 54, 4767–4784, https://doi.org/10.1029/2017WR022216, 2018. a, b, c
Lutz, S. R., Ebeling, P., Musolff, A., Nguyen, T. V., Sarrazin, F. J.,
Van Meter, K. J., Basu, N. B., Fleckenstein, J. H., Attinger, S., and Kumar,
R.: Pulling the rabbit out of the hat: Unravelling hidden nitrogen legacies
in catchment-scale water quality models, Hydrol. Processes, 36, e14682,
https://doi.org/10.1002/hyp.14682, 2022. a
McDonnell, J. J., McGuire, K., Aggarwal, P., Beven, K. J., Biondi, D., Destouni, G., Dunn, S., James, A., Kirchner, J., Kraft, P., Lyon, S., Maloszewski, P., Newman, B., Pfister, L., Rinaldo, A., Rodhe, A., Sayama, T., Seibert, J., Solomon, K., Soulsby, C., Stewart, M., Tetzlaff, D., Tobin, C., Troch, P., Weiler, M., Western, A., Wörman, A., and Wrede, S.: How old is streamwater? Open questions in catchment transit time
conceptualization, modelling and analysis, Hydrol. Process., 24, 1745–1754,
https://doi.org/10.1002/hyp.7796, 2010. a, b
McGuire, K. J. and McDonnel, J. J.: A review and evaluation of catchment
transit time modeling, J. Hydrol., 330, 543–563,
https://doi.org/10.1016/j.jhydrol.2006.04.020, 2006. a, b, c
McKay, M. D., Beckman, R. J., and Conover, W. J.: A Comparison of Three Methods
for Selecting Values of Input Variables in the Analysis of Output from a
Computer Code, Technometrics, 21, 239–245, https://doi.org/10.2307/1268522, 1979. a
Morgenstern, U., Daughney, C. J., Leonard, G., Gordon, D., Donath, F. M., and Reeves, R.: Using groundwater age and hydrochemistry to understand sources and dynamics of nutrient contamination through the catchment into Lake Rotorua, New Zealand, Hydrol. Earth Syst. Sci., 19, 803–822, https://doi.org/10.5194/hess-19-803-2015, 2015. a
Nguyen, T. V., Sarrazin, F. J., Ebeling, P., Musolff, A., Fleckenstein, J. H.,
and Kumar, R.: Toward Understanding of Long-Term Nitrogen Transport and
Retention Dynamics Across German Catchments, Geophys. Res. Lett.,
49, e2022GL100278, https://doi.org/10.1029/2022GL100278, 2022. a
Niemi, A. J.: Residence time distributions of variable flow processes, Int. J. Appl. Radiat. Is., 28, 855–860,
https://doi.org/10.1016/0020-708X(77)90026-6, 1977. a
Opazo, T., Aravena, R., and Parker, B.: Nitrate distribution and potential
attenuation mechanisms of a municipal water supply bedrock aquifer, Appl.
Geochem., 73, 157–168, https://doi.org/10.1016/j.apgeochem.2016.08.010, 2016. a
Queloz, P., Carraro, L., Benettin, P., Botter, G., Rinaldo, A., and Bertuzzo,
E.: Transport of fluorobenzoate tracers in a vegetated hydrologic control
volume: 2. Theoretical inferences and modeling, Water Resour. Res, 51,
2793–2806, https://doi.org/10.1002/2014WR016508, 2015. a, b
Rinaldo, A. and Marani, M.: Basin Scale Model of Solute Transport, Water
Resour. Res., 23, 2107–2118, https://doi.org/10.1029/WR023i011p02107, 1987. a
Rinaldo, A., Botter, G., Bertuzzo, E., Uccelli, A., Settin, T., and Marani, M.: Transport at basin scales: 1. Theoretical framework, Hydrol. Earth Syst. Sci., 10, 19–29, https://doi.org/10.5194/hess-10-19-2006, 2006. a
Rinaldo, A., Benettin, P., Harman, C. J., Hrachowitz, M., McGuire, K. J., ven der Velde, Y., Bertuzzo, E., and Botter, G.: Storage selection functions: A coherent framework
for quantifying how catchments store and release water and solutes, Water
Resour. Res., 51, 4840–4847, https://doi.org/10.1002/2015WR017273, 2015. a
Rodriguez, N. B., McGuire, K. J., and Klaus, J.: Time-Varying Storage–Water
Age Relationships in a Catchment With a Mediterranean Climate, Water Resour.
Res., 54, 3988–4008, https://doi.org/10.1029/2017WR021964, 2018. a
Rodriguez, N. B., Pfister, L., Zehe, E., and Klaus, J.: A comparison of catchment travel times and storage deduced from deuterium and tritium tracers using StorAge Selection functions, Hydrol. Earth Syst. Sci., 25, 401–428, https://doi.org/10.5194/hess-25-401-2021, 2021. a
Rodríguez-Cruz, M. S., Jones, J. E., and Bending, G. D.: Field-scale study of
the variability in pesticide biodegradation with soil depth and its
relationship with soil characteristics, Soil Biol. Biochem., 38,
2910–2918, https://doi.org/10.1016/j.soilbio.2006.04.051, 2006. a
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. a
Schoups, G., van de Giesen, N. C., and Savenije, H. H. G.: Model complexity
control for hydrologic prediction, Water Resour. Res., 44, W00B03,
https://doi.org/10.1029/2008WR006836, 2008. a
Seeger, S. and Weiler, M.: Reevaluation of transit time distributions, mean transit times and their relation to catchment topography, Hydrol. Earth Syst. Sci., 18, 4751–4771, https://doi.org/10.5194/hess-18-4751-2014, 2014. a
Soulsby, C. and Tetzlaff, D.: Towards simple approaches for mean residence time
estimation in ungauged basins using tracers and soil distributions, J.
Hydrol., 363, 60–74, https://doi.org/10.1016/j.jhydrol.2008.10.001, 2008. a
Stewart, M. K., Morgenstern, U., and McDonnell, J. J.: Truncation of stream
residence time: How the use of stable isotopes has skewed our concept of
streamwater age and origin, Hydrol. Process., 24, 1646–1659,
https://doi.org/10.1002/hyp.7576, 2010. a
Stewart, M. K., Morgenstern, U., McDonnell, J. J., and Pfister, L.: The
“hidden streamflow” challenge in catchment hydrology: A call to action
for stream water transit time analysis, Hydrol. Process., 26, 2061–2066,
https://doi.org/10.1002/hyp.9262, 2012. a
Stockinger, M. P., Lücke, A., McDonnell, J. J., Diekkrüger, B., Vereecken,
H., and Bogena, H. R.: Interception effects on stable isotope driven
streamwater transit time estimates, Geophys. Res. Lett., 42, 5299–5308,
https://doi.org/10.1002/2015GL064622, 2015. a
Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018. a
Svensson, T., Lovett, G. M., and Likens, G. E.: Is chloride a conservative ion
in forest ecosystems?, Biogeochemistry, 107, 125–134,
https://doi.org/10.1007/s10533-010-9538-y, 2012. a
Tetzlaff, D., Piovano, T., Ala-Aho, P., Smith, A., Carey, S. K., Marsh, P., Wookey,
P. A., Street, L. E., and Soulsby, C.: Using stable isotopes to estimate
travel times in a data-sparse Arctic catchment: Challenges and possible
solutions, Hydrol. Process., 32, 1936–1952, https://doi.org/10.1002/hyp.13146, 2018. a
Thiemig, V., Rojas, R., Zombrano-Bigiarini, M., and De Roo, A.: Hydrological
evaluation of satellite-based rainfall estimates over the Volta and
Baro-Akobo Basin, J. Hydrol., 499, 324–338,
https://doi.org/10.1016/j.jhydrol.2013.07.012, 2013. a
Van der Velde, Y., De Rooij, G. H., Rozemeijer, J. C., Van Geer, F. C., and
Broers, H. P.: Nitrate response of a lowland catchment: On the relation
between stream concentration and travel time distribution dynamics, Water
Resour. Res., 46, W11534, https://doi.org/10.1029/2010WR009105, 2010. a
van der Velde, Y., Torfs, P. J. J. F., van der Zee, S. E. A. T. M., and
Uijlenhoet, R.: Quantifying catchment-scale mixing and its effect on
time-varying travel time distributions, Water Resour. Res., 48, W06536,
https://doi.org/10.1029/2011WR011310, 2012. a
Van Meter, K. J., Basu, N. B., and Van Cappellen, P.: Two centuries of nitrogen
dynamics: legacy sources and sinks in the Mississippi and Susquehanna River
Basins, Global Biogeochem. Cy., 31, 2–23, https://doi.org/10.1002/2016GB005498,
2017. a
Visser, A., Broers, H. P., Purtschert, R., Sültenfuß, J., and de Jonge, M.:
Groundwater age distributions at a public drinking water supply well field
derived from multiple age tracers (85Kr, 3H
Visser, A., Thaw, M., Deinhart, A., Bibby, R., Safeeq, M., Conklin, M., Esser,
B., and van der Velde, Y.: Cosmogenic isotopes unravel the hydrochronology
and water storage dynamics of the Southern Sierra critical zone, Water
Resour. Res., 55, 1429–1450, https://doi.org/10.1029/2018WR023665, 2019. a, b
von Freyberg, J., Studer, B., and Kirchner, J. W.: A lab in the field: high-frequency analysis of water quality and stable isotopes in stream water and precipitation, Hydrol. Earth Syst. Sci., 21, 1721–1739, https://doi.org/10.5194/hess-21-1721-2017, 2017. a
von Freyberg, J., Allen, S. T., Seeger, S., Weiler, M., and Kirchner, J. W.: Sensitivity of young water fractions to hydro-climatic forcing and landscape properties across 22 Swiss catchments, Hydrol. Earth Syst. Sci., 22, 3841–3861, https://doi.org/10.5194/hess-22-3841-2018, 2018. a, b
von Freyberg, J., Rücker, A., Zappa, M., Schlumpf, A., Studer, B., and
Kirchner, J. W.: Four years of daily stable water isotope data in stream
water and precipitation from three Swiss catchments, Sci. Data, 9, 46,
https://doi.org/10.1038/s41597-022-01148-1, 2022. a
Wang, S., Hrachowitz, M., Schoups, G., and Stumpp, C.: Stable water isotopes and tritium tracers tell the same tale: No evidence for underestimation of catchment transit times inferred by stable isotopes in SAS function models, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2022-400, in review, 2022. a
Wilusz, D. C., Harman, C. J., and Ball, W. P.: Sensitivity of Catchment Transit
Times to Rainfall Variability Under Present and Future Climates, Water
Resour. Res., 53, 10231–10256, https://doi.org/10.1002/2017WR020894, 2017. a, b, c, d
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, 2020. a, b
Winter, C., Nguyen, T. V., Musolff, A., Lutz, S. R., Rode, M., Kumar, R., and Fleckenstein, J. H.: Droughts can reduce the nitrogen retention capacity of catchments, Hydrol. Earth Syst. Sci., 27, 303–318, https://doi.org/10.5194/hess-27-303-2023, 2023.
a
Wollschläger, U., Attinger, S., Borchardt, D., Brauns, M., Cuntz, M., Dietrich, P., Fleckenstein, J. H., Friese, K., Friesen, J., Harpke, A., Hildebrandt, A., Jäckel, G., Kamjunke, N., Knöller, K., Kögler, S., Kolditz, O., Krieg, R., Kumar, R., Lausch, A., Liess, M., Marx, A., Merz, R., Mueller, C., Musolff, A., Norf, H., Oswald, S. E., Rebmann, C., Reinstorf, F., Rode, M., Rink, K., Rinke, K., Samaniego, L., Vieweg, M., Vogel, H. J., Weitere, M., Werban, U., Zink, M., and Zacharias, S.: The Bode hydrological observatory: a platform for
integrated, interdisciplinary hydro-ecological research within the TERENO
Harz/Central German Lowland Observatory, Environ. Earth Sci., 76,
29, https://doi.org/10.1007/s12665-016-6327-5, 2017. a
Xu, G., Magen, H., Tarchitzky, J., and Kafkafi, U.: Advances in Chloride
Nutrition of Plants, Adv. Agron., 68, 97–150,
https://doi.org/10.1016/S0065-2113(08)60844-5, 1999. a
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. a
Yang, X., Seifeddine, J., Zink, M., Fleckenstein, J. H., Borchardt, D., and
Rode, M.: A New Fully Distributed Model of Nitrate Transport and Removal at
Catchment Scale, Water Resour. Res., 54, 5856–5877,
https://doi.org/10.1029/2017WR022380, 2018. a, b
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. a
Short summary
We analyzed the uncertainty of the water transit time distribution (TTD) arising from model input (interpolated tracer data) and structure (StorAge Selection, SAS, functions). We found that uncertainty was mainly associated with temporal interpolation, choice of SAS function, nonspatial interpolation, and low-flow conditions. It is important to characterize the specific uncertainty sources and their combined effects on TTD, as this has relevant implications for both water quantity and quality.
We analyzed the uncertainty of the water transit time distribution (TTD) arising from model...
