Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3673-2025
© Author(s) 2025. 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-29-3673-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Aug 2025
Research article |
| 08 Aug 2025
| 08 Aug 2025
Catchment hydrological response and transport are affected differently by precipitation intensity and antecedent wetness
Julia L. A. Knapp
CORRESPONDING AUTHOR
Department of Earth Sciences, Durham University, Durham DH1 3LE, United Kingdom
Department of Environmental Systems Science, ETH Zürich, 8092 Zurich, Switzerland
Wouter R. Berghuijs
Department of Earth Sciences, Free University Amsterdam, 1081 HV Amsterdam, the Netherlands
Department of Environmental Systems Science, ETH Zürich, 8092 Zurich, Switzerland
Marius G. Floriancic
Department of Environmental Systems Science, ETH Zürich, 8092 Zurich, Switzerland
Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8092 Zurich, Switzerland
James W. Kirchner
Department of Environmental Systems Science, ETH Zürich, 8092 Zurich, Switzerland
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), 8903 Birmensdorf, Switzerland
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Nikos Theodoratos and James W. Kirchner
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We examine stream-power incision and linear diffusion landscape evolution models with and without incision thresholds. We present a steady-state relationship between curvature and the steepness index, which plots as a straight line. We view this line as a counterpart to the slope–area relationship for the case of landscapes with hillslope diffusion. We show that simple shifts and rotations of this line graphically express the topographic response of landscapes to changes in model parameters.
Scott T. Allen and James W. Kirchner
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-683, https://doi.org/10.5194/hess-2020-683, 2021
Revised manuscript not accepted
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Extracting water from plant stems can introduce analytical errors in isotope analyses. We demonstrate that sensitivities to suspected errors can be evaluated and that conclusions drawn from extracted plant water isotope ratios are neither generally valid nor generally invalid. Ultimately, imperfect measurements of plant and soil water isotope ratios can continue to support useful inferences if study designs are appropriately matched to their likely biases and uncertainties.
Cited articles
Bennett, B., Leonard, M., Deng, Y., and Westra, S.: An empirical investigation into the effect of antecedent precipitation on flood volume, J. Hydrol., 567, 435–445, https://doi.org/10.1016/j.jhydrol.2018.10.025, 2018. a
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J. W.: The Relative Importance of Different Flood-Generating Mechanisms Across Europe, Water Resour. Res., 55, 4582–4593, https://doi.org/10.1029/2019WR024841, 2019. a
Beven, K.: Interflow, Springer Netherlands, Dordrecht, 191–219, https://doi.org/10.1007/978-94-009-2352-2_7, 1989. a
Birkel, C., Geris, J., Molina, M. J., Mendez, C., Arce, R., Dick, J., Tetzlaff, D., and Soulsby, C.: Hydroclimatic controls on non-stationary stream water ages in humid tropical catchments, J. Hydrol., 542, 231–240, https://doi.org/10.1016/j.jhydrol.2016.09.006, 2016. a
Botter, G.: Catchment mixing processes and travel time distributions, Water Resour. Res., 48, 5545, https://doi.org/10.1029/2011WR011160, 2012. 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, 3514, https://doi.org/10.1029/2009WR008371, 2010. a, b
Carrillo, G., Troch, P. A., Sivapalan, M., Wagener, T., Harman, C., and Sawicz, K.: Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient, Hydrol. Earth Syst. Sci., 15, 3411–3430, https://doi.org/10.5194/hess-15-3411-2011, 2011. a
Detty, J. M. and McGuire, K. J.: Threshold changes in storm runoff generation at a till-mantled headwater catchment, Water Resour. Res., 46, 7525, https://doi.org/10.1029/2009WR008102, 2010. a
Floriancic, M. G., Stockinger, M. P., Kirchner, J. W., and Stumpp, C.: Monthly new water fractions and their relationships with climate and catchment properties across Alpine rivers, Hydrol. Earth Syst. Sci., 28, 3675–3694, https://doi.org/10.5194/hess-28-3675-2024, 2024. a
Gericke, O. J. and Smithers, J. C.: Revue des méthodes d'évaluation du temps de réponse d'un bassin versant pour l'estimation du débit de pointe, Hydrolog. Sci. J., 59, 1935–1971, https://doi.org/10.1080/02626667.2013.866712, 2014. a
Godsey, S. E., Aas, W., Clair, T. A., de Wit, H. A., Fernandez, I. J., Kahl, J. S., Malcolm, I. A., Neal, C., Neal, M., Nelson, S. J., Norton, S. A., Palucis, M. C., Skjelkvåle, B. L., Soulsby, C., Tetzlaff, D., and Kirchner, J. W.: Generality of fractal
Grathwohl, P., Rügner, H., Wöhling, T., Osenbrück, K., Schwientek, M., Gayler, S., Wollschläger, U., Selle, B., Pause, M., Delfs, J. O., Grzeschik, M., Weller, U., Ivanov, M., Cirpka, O. A., Maier, U., Kuch, B., Nowak, W., Wulfmeyer, V., Warrach-Sagi, K., Streck, T., Attinger, S., Bilke, L., Dietrich, P., Fleckenstein, J. H., Kalbacher, T., Kolditz, O., Rink, K., Samaniego, L., Vogel, H. J., Werban, U., and Teutsch, G.: Catchments as reactors: A comprehensive approach for water fluxes and solute turnover, Environ. Earth Sci., 69, 317–333, https://doi.org/10.1007/s12665-013-2281-7, 2013. a
Hagedorn, F., Schleppi, P., Waldner, P., and Flühler, H.: Export of dissolved organic carbon and nitrogen from Gleysol dominated catchments – The significance of water flow paths, Biogeochemistry, 50, 137–161, https://doi.org/10.1023/A:1006398105953, 2000. 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
Hill, T. and Neal, C.: Spatial and temporal variation in pH, alkalinity and conductivity in surface runoff and groundwater for the Upper River Severn catchment, Hydrol. Earth Syst. Sci., 1, 697–715, https://doi.org/10.5194/hess-1-697-1997, 1997. a
Hrachowitz, M., Soulsby, C., Tetzlaff, D., Dawson, J. J., Dunn, S. M., and Malcolm, I. A.: Using long-term data sets to understand transit times in contrasting headwater catchments, J. Hydrol., 367, 237–248, https://doi.org/10.1016/j.jhydrol.2009.01.001, 2009. 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., Ruiz, L., van der Velde, Y., and Wade, A. J.: Transit times–the link between hydrology and water quality at the catchment scale, Wiley Interdisciplinary Reviews: Water, 3, 629–657, https://doi.org/10.1002/wat2.1155, 2016. a
James, A. L. and Roulet, N. T.: Investigating hydrologic connectivity and its association with threshold change in runoff response in a temperate forested watershed, Hydrol. Process., 21, 3391–3408, https://doi.org/10.1002/hyp.6554, 2007. a
Jasechko, S., Kirchner, J. W., Welker, J. M., and McDonnell, J. J.: Substantial proportion of global streamflow less than three months old, Nat. Geosci., 9, 126–129, https://doi.org/10.1038/ngeo2636, 2016. a
Kirby, C., Newson, M., and Gilman, K. (Eds.): Plynlimon research: The first two decades. Wallingford, Institute of Hydrology, 188 pp., IH Report No.109, ISBN 0948540273, 1991. a
Kirchner, J. W.: A double paradox in catchment hydrology and geochemistry, Hydrol. Process., 17, 871–874, https://doi.org/10.1002/hyp.5108, 2003. a, b
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, b
Kirchner, J. W.: Impulse Response Functions for Nonlinear, Nonstationary, and Heterogeneous Systems, Estimated by Deconvolution and Demixing of Noisy Time Series, Sensors, 22, 3291, https://doi.org/10.3390/s22093291, 2022. a
Kirchner, J. W.: Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept, Hydrol. Earth Syst. Sci., 28, 4427–4454, https://doi.org/10.5194/hess-28-4427-2024, 2024a. a, b, c, d
Kirchner, J. W.: ERRA – an R script for Ensemble Rainfall-Runoff Analysis, EnviDat [code], https://doi.org/10.16904/envidat.529, 2024b. a
Kirchner, J. W. and Knapp, J. L. A.: Technical note: Calculation scripts for ensemble hydrograph separation, Hydrol. Earth Syst. Sci., 24, 5539–5558, https://doi.org/10.5194/hess-24-5539-2020, 2020a. a
Kirchner, J. W. and Knapp, J. L. A.: Ensemble hydrograph separation scripts, EnviDat [code], https://doi.org/10.16904/envidat.182, 2020b. a
Kirchner, J. W. and Neal, C.: Universal fractal scaling in stream chemistry and its implications for solute transport and water quality trend detection, P. Natl. Acad. Sci. USA, 110, 12213–12218, https://doi.org/10.1073/pnas.1304328110, 2013. a
Kirchner, J. W., Feng, X., and Neal, C.: Fractal stream chemistry and its implications for contaminant transport in catchments, Nature, 403, 524, https://doi.org/10.1038/35000537, 2000. a, b
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
Kirchner, J. W., Knapp, J. L. A., Schlumpf, A., Neal, C., and Neal, M.: Stable water isotopes in precipitation and streamflow at Plynlimon, Wales, UK, EnviDat [data set], https://doi.org/10.16904/envidat.82, 2019. a
Knapp, J. L. A., Neal, C., Schlumpf, A., Neal, M., and Kirchner, J. W.: New water fractions and transit time distributions at Plynlimon, Wales, estimated from stable water isotopes in precipitation and streamflow, Hydrol. Earth Syst. Sci., 23, 4367–4388, https://doi.org/10.5194/hess-23-4367-2019, 2019. a, b, c, d
Laudon, H. and Sponseller, R. A.: How landscape organization and scale shape catchment hydrology and biogeochemistry: insights from a long-term catchment study, Wiley Interdisciplinary Reviews: Water, 5, e1265, https://doi.org/10.1002/wat2.1265, 2018. a
Li, L., Knapp, J. L. A., Lintern, A., Ng, G.-H. C., Perdrial, J., Sullivan, P. L., and Zhi, W.: River water quality shaped by land–river connectivity in a changing climate, Nat. Clim. Change, 14, 225–237, https://doi.org/10.1038/s41558-023-01923-x, 2024. a
Małoszewski, P. and Zuber, A.: Determining the turnover time of groundwater systems with the aid of environmental tracers: 1. Models and their applicability, J. Hydrol., 57, 207–231, https://doi.org/10.1016/0022-1694(82)90147-0, 1982. a, b
McDonnell, J. J.: A Rationale for Old Water Discharge Through Macropores in a Steep, Humid Catchment, Water Resour. Res., 26, 2821–2832, https://doi.org/10.1029/WR026i011p02821, 1990. a
McDonnell, J. J. and Beven, K.: Debates–The future of hydrological sciences: A (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph, Water Resour. Res., 50, 5342–5350, https://doi.org/10.1002/2013WR015141, 2014. a
Meyer, A., Fleischmann, A. S., Collischonn, W., Paiva, R., and Jardim, P.: Empirical assessment of flood wave celerity–discharge relationships at local and reach scales, Hydrolog. Sci. J., 63, 2035–2047, https://doi.org/10.1080/02626667.2018.1557336, 2018. a
Mindham, D., Beven, K., and Chappell, N.: Rainfall–streamflow response times for diverse upland UK micro-basins: quantifying hydrographs to identify the nonlinearity of storm response, Hydrol. Res., 54, 233–244, https://doi.org/10.2166/nh.2023.115, 2023. a, b
Neal, C. and Rosier, P. T. W.: Chemical studies of chloride and stable oxygen isotopes in two conifer afforested and moorland sites in the British uplands, J. Hydrol., 115, 269–283, https://doi.org/10.1016/0022-1694(90)90209-G, 1990. a
Nippgen, F., McGlynn, B. L., Marshall, L. A., and Emanuel, R. E.: Landscape structure and climate influences on hydrologic response, Water Resour. Res., 47, 12528, https://doi.org/10.1029/2011WR011161, 2011. a
Penna, D., Tromp-van Meerveld, H. J., Gobbi, A., Borga, M., and Dalla Fontana, G.: The influence of soil moisture on threshold runoff generation processes in an alpine headwater catchment, Hydrol. Earth Syst. Sci., 15, 689–702, https://doi.org/10.5194/hess-15-689-2011, 2011. a
Peskett, L. M., Heal, K. V., MacDonald, A. M., Black, A. R., and McDonnell, J. J.: Land cover influence on catchment scale subsurface water storage investigated by multiple methods: Implications for UK Natural Flood Management, J. Hydrol. Regional Studies, 47, 101398, https://doi.org/10.1016/j.ejrh.2023.101398, 2023. a
Post, D. A. and Jakeman, A. J.: Relationships between catchment attributes and hydrological response characteristics in small Australian Mountain Ash catchments, Hydrol. Process., 10, 877–892, https://doi.org/10.1002/(SICI)1099-1085(199606)10:6<877::AID-HYP377>3.0.CO;2-T, 1996. a
Rasmussen, T. C., Baldwin, R. H., Dowd, J. F., and Williams, A. G.: Tracer vs. Pressure Wave Velocities through Unsaturated Saprolite, Soil Sci. Soc. Am. J., 64, 75–85, https://doi.org/10.2136/SSSAJ2000.64175X, 2000. a
Rinderer, M., van Meerveld, H. J., and Seibert, J.: Topographic controls on shallow groundwater levels in a steep, prealpine catchment: When are the TWI assumptions valid?, Water Resour. Res., 50, 6067–6080, https://doi.org/10.1002/2013WR015009, 2014. a
Rodhe, A., Nyberg, L., and Bishop, K.: Transit Times for Water in a Small Till Catchment from a Step Shift in the Oxygen 18 Content of the Water Input, Water Resour. Res., 32, 3497–3511, https://doi.org/10.1029/95WR01806, 1996. a
Scaini, A., Audebert, M., Hissler, C., Fenicia, F., Gourdol, L., Pfister, L., and Beven, K. J.: Velocity and celerity dynamics at plot scale inferred from artificial tracing experiments and time-lapse ERT, J. Hydrol., 546, 28–43, https://doi.org/10.1016/j.jhydrol.2016.12.035, 2017. a
Schleppi, P., Muller, N., Feyen, H., Papritz, A., Bucher, J. B., and Flühler, H.: Nitrogen budgets of two small experimental forested catchments at Alptal, Switzerland, Forest Ecol. Manag., 101, 177–185, https://doi.org/10.1016/S0378-1127(97)00134-5, 1998. a
Schoener, G. and Stone, M. C.: Impact of antecedent soil moisture on runoff from a semiarid catchment, J. Hydrol., 569, 627–636, https://doi.org/10.1016/j.jhydrol.2018.12.025, 2019. 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
Shand, P., Haria, A. H., Neal, C., Griffiths, K. J., Gooddy, D. C., Dixon, A. J., Hill, T., Buckley, D. K., and Cunningham, J. E.: Hydrochemical heterogeneity in an upland catchment: further characterisation of the spatial, temporal and depth variations in soils, streams and groundwaters of the Plynlimon forested catchment, Wales, Hydrol. Earth Syst. Sci., 9, 621–644, https://doi.org/10.5194/hess-9-621-2005, 2005. a
Soulsby, C., Piegat, K., Seibert, J., and Tetzlaff, D.: Catchment-scale estimates of flow path partitioning and water storage based on transit time and runoff modelling, Hydrol. Process., 25, 3960–3976, https://doi.org/10.1002/hyp.8324, 2011. a
Stähli, M., Seibert, J., Kirchner, J. W., von Freyberg, J., and van Meerveld, I.: Hydrological trends and the evolution of catchment research in the Alptal valley, central Switzerland, Hydrol. Process., 35, e14113, https://doi.org/10.1002/hyp.14113, 2021. a
Staudinger, M., Kauzlaric, M., Mas, A., Evin, G., Hingray, B., and Viviroli, D.: The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context, Nat. Hazards Earth Syst. Sci., 25, 247–265, https://doi.org/10.5194/nhess-25-247-2025, 2025. a
Sulis, M., Paniconi, C., Rivard, C., Harvey, R., and Chaumont, D.: Assessment of climate change impacts at the catchment scale with a detailed hydrological model of surface-subsurface interactions and comparison with a land surface model, Water Resour. Res., 47, 1513, https://doi.org/10.1029/2010WR009167, 2011. a
Torres, R., Dietrich, W. E., Montgomery, D. R., Anderson, S. P., and Loague, K.: Unsaturated zone processes and the hydrologic response of a steep, unchanneled catchment, Water Resour. Res., 34, 1865–1879, https://doi.org/10.1029/98WR01140, 1998. a
van der Velde, Y., Torfs, P. J., van der Zee, S. E., 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 Meerveld, H. J., Fischer, B. M. C., Rinderer, M., Stähli, M., and Seibert, J.: Runoff generation in a pre-alpine catchment: a discussion between a tracer and a shallow groundwater hydrologist, Cuadernos de Investigación Geográfica, 44, 429–452, https://doi.org/10.18172/cig.3349, 2018. a, b
van Verseveld, W. J., Barnard, H. R., Graham, C. B., McDonnell, J. J., Brooks, J. R., and Weiler, M.: A sprinkling experiment to quantify celerity–velocity differences at the hillslope scale, Hydrol. Earth Syst. Sci., 21, 5891–5910, https://doi.org/10.5194/hess-21-5891-2017, 2017. a
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
Weiler, M., McGlynn, B. L., McGuire, K. J., and McDonnell, J. J.: How does rainfall become runoff? A combined tracer and runoff transfer function approach, Water Resour. Res., 39, 1315, https://doi.org/10.1029/2003WR002331, 2003. a, b
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
Zehe, E., Becker, R., Bárdossy, A., and Plate, E.: Uncertainty of simulated catchment runoff response in the presence of threshold processes: Role of initial soil moisture and precipitation, J. Hydrol., 315, 183–202, https://doi.org/10.1016/j.jhydrol.2005.03.038, 2005. a
Zobrist, J., Schoenenberger, U., Figura, S., and Hug, S. J.: Long-term trends in Swiss rivers sampled continuously over 39 years reflect changes in geochemical processes and pollution, Environ. Sci. Pollut. R., 25, 16788–16809, https://doi.org/10.1007/s11356-018-1679-x, 2018. a
Short summary
This study explores how streams react to rain and how water travels through the landscape to reach them, two processes rarely studied together. Using detailed data from two temperate areas, we show that streams respond to rain much faster than rainwater travels to them. Wetter conditions lead to stronger runoff by releasing older stored water, while heavy rainfall moves newer rainwater to streams faster. These findings offer new insights into how water moves through the environment.
This study explores how streams react to rain and how water travels through the landscape to...
