Articles | Volume 26, issue 10
https://doi.org/10.5194/hess-26-2715-2022
© 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-26-2715-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying multi-year hydrological memory with Catchment Forgetting Curves
Alban de Lavenne
CORRESPONDING AUTHOR
Swedish Meteorological and Hydrological Institute (SMHI), Hydrology Research Department, Norrköping, Sweden
Université Paris‐Saclay, INRAE, UR HYCAR, Antony, France
Vazken Andréassian
Université Paris‐Saclay, INRAE, UR HYCAR, Antony, France
Louise Crochemore
Swedish Meteorological and Hydrological Institute (SMHI), Hydrology Research Department, Norrköping, Sweden
INRAE, UR RiverLy, Lyon, France
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Göran Lindström
Swedish Meteorological and Hydrological Institute (SMHI), Hydrology Research Department, Norrköping, Sweden
Berit Arheimer
Swedish Meteorological and Hydrological Institute (SMHI), Hydrology Research Department, Norrköping, Sweden
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Samuel Morin, Hugues François, Marion Réveillet, Eric Sauquet, Louise Crochemore, Flora Branger, Étienne Leblois, and Marie Dumont
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Laurent Strohmenger, Eric Sauquet, Claire Bernard, Jérémie Bonneau, Flora Branger, Amélie Bresson, Pierre Brigode, Rémy Buzier, Olivier Delaigue, Alexandre Devers, Guillaume Evin, Maïté Fournier, Shu-Chen Hsu, Sandra Lanini, Alban de Lavenne, Thibault Lemaitre-Basset, Claire Magand, Guilherme Mendoza Guimarães, Max Mentha, Simon Munier, Charles Perrin, Tristan Podechard, Léo Rouchy, Malak Sadki, Myriam Soutif-Bellenger, François Tilmant, Yves Tramblay, Anne-Lise Véron, Jean-Philippe Vidal, and Guillaume Thirel
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Antoine Pelletier and Vazken Andréassian
Hydrol. Earth Syst. Sci., 26, 2733–2758, https://doi.org/10.5194/hess-26-2733-2022, https://doi.org/10.5194/hess-26-2733-2022, 2022
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A large part of the water cycle takes place underground. In many places, the soil stores water during the wet periods and can release it all year long, which is particularly visible when the river level is low. Modelling tools that are used to simulate and forecast the behaviour of the river struggle to represent this. We improved an existing model to take underground water into account using measurements of the soil water content. Results allow us make recommendations for model users.
H. E. Markus Meier, Madline Kniebusch, Christian Dieterich, Matthias Gröger, Eduardo Zorita, Ragnar Elmgren, Kai Myrberg, Markus P. Ahola, Alena Bartosova, Erik Bonsdorff, Florian Börgel, Rene Capell, Ida Carlén, Thomas Carlund, Jacob Carstensen, Ole B. Christensen, Volker Dierschke, Claudia Frauen, Morten Frederiksen, Elie Gaget, Anders Galatius, Jari J. Haapala, Antti Halkka, Gustaf Hugelius, Birgit Hünicke, Jaak Jaagus, Mart Jüssi, Jukka Käyhkö, Nina Kirchner, Erik Kjellström, Karol Kulinski, Andreas Lehmann, Göran Lindström, Wilhelm May, Paul A. Miller, Volker Mohrholz, Bärbel Müller-Karulis, Diego Pavón-Jordán, Markus Quante, Marcus Reckermann, Anna Rutgersson, Oleg P. Savchuk, Martin Stendel, Laura Tuomi, Markku Viitasalo, Ralf Weisse, and Wenyan Zhang
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Based on the Baltic Earth Assessment Reports of this thematic issue in Earth System Dynamics and recent peer-reviewed literature, current knowledge about the effects of global warming on past and future changes in the climate of the Baltic Sea region is summarised and assessed. The study is an update of the Second Assessment of Climate Change (BACC II) published in 2015 and focuses on the atmosphere, land, cryosphere, ocean, sediments, and the terrestrial and marine biosphere.
Saúl Arciniega-Esparza, Christian Birkel, Andrés Chavarría-Palma, Berit Arheimer, and José Agustín Breña-Naranjo
Hydrol. Earth Syst. Sci., 26, 975–999, https://doi.org/10.5194/hess-26-975-2022, https://doi.org/10.5194/hess-26-975-2022, 2022
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In the humid tropics, a notoriously data-scarce region, we need to find alternatives in order to reasonably apply hydrological models. Here, we tested remotely sensed rainfall data in order to drive a model for Costa Rica, and we evaluated the simulations against evapotranspiration satellite products. We found that our model was able to reasonably simulate the water balance and streamflow dynamics of over 600 catchments where the satellite data helped to reduce the model uncertainties.
Paul Royer-Gaspard, Vazken Andréassian, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 25, 5703–5716, https://doi.org/10.5194/hess-25-5703-2021, https://doi.org/10.5194/hess-25-5703-2021, 2021
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Most evaluation studies based on the differential split-sample test (DSST) endorse the consensus that rainfall–runoff models lack climatic robustness. In this technical note, we propose a new performance metric to evaluate model robustness without applying the DSST and which can be used with a single hydrological model calibration. Our work makes it possible to evaluate the temporal transferability of any hydrological model, including uncalibrated models, at a very low computational cost.
Pierre Nicolle, Vazken Andréassian, Paul Royer-Gaspard, Charles Perrin, Guillaume Thirel, Laurent Coron, and Léonard Santos
Hydrol. Earth Syst. Sci., 25, 5013–5027, https://doi.org/10.5194/hess-25-5013-2021, https://doi.org/10.5194/hess-25-5013-2021, 2021
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In this note, a new method (RAT) is proposed to assess the robustness of hydrological models. The RAT method is particularly interesting because it does not require multiple calibrations (it is therefore applicable to uncalibrated models), and it can be used to determine whether a hydrological model may be safely used for climate change impact studies. Success at the robustness assessment test is a necessary (but not sufficient) condition of model robustness.
Marc Girons Lopez, Louise Crochemore, and Ilias G. Pechlivanidis
Hydrol. Earth Syst. Sci., 25, 1189–1209, https://doi.org/10.5194/hess-25-1189-2021, https://doi.org/10.5194/hess-25-1189-2021, 2021
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The Swedish hydrological warning service is extending its use of seasonal forecasts, which requires an analysis of the available methods. We evaluate the simple ESP method and find out how and why forecasts vary in time and space. We find that forecasts are useful up to 3 months into the future, especially during winter and in northern Sweden. They tend to be good in slow-reacting catchments and bad in flashy and highly regulated ones. We finally link them with areas of similar behaviour.
Matteo Giuliani, Louise Crochemore, Ilias Pechlivanidis, and Andrea Castelletti
Hydrol. Earth Syst. Sci., 24, 5891–5902, https://doi.org/10.5194/hess-24-5891-2020, https://doi.org/10.5194/hess-24-5891-2020, 2020
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This paper aims at quantifying the value of hydroclimatic forecasts in terms of potential economic benefit to end users in the Lake Como basin (Italy), which allows the inference of a relation between gains in forecast skill and gains in end user profit. We also explore the sensitivity of this benefit to both the forecast system setup and end user behavioral factors, showing that the estimated forecast value is potentially undermined by different levels of end user risk aversion.
Cited articles
Amogu, O., Descroix, L., Yéro, K. S., Breton, E. L., Mamadou, I., Ali,
A., Vischel, T., Bader, J.-C., Moussa, I. B., Gautier, E., Boubkraoui, S.,
and Belleudy, P.: Increasing River Flows in the Sahel?, Water, 2, 170–199,
https://doi.org/10.3390/w2020170, 2010. a
Andersson, L. and Arheimer, B.: Modelling of human and climatic impact on
nitrogen load in a Swedish river 1885-1994, Hydrobiologia, 497, 63–77,
https://doi.org/10.1023/a:1025409620738, 2003. a
Andréassian, V. and Perrin, C.: On the ambiguous interpretation of the
Turc-Budyko nondimensional graph, Water Resour. Res., 48,
https://doi.org/10.1029/2012wr012532, 2012. a
Andréassian, V., Coron, L., Lerat, J., and Le Moine, N.: Climate elasticity of streamflow revisited – an elasticity index based on long-term hydrometeorological records, Hydrol. Earth Syst. Sci., 20, 4503–4524, https://doi.org/10.5194/hess-20-4503-2016, 2016. a, b
Ballabio, C., Panagos, P., and Monatanarella, L.: Mapping topsoil physical
properties at European scale using the LUCAS database, Geoderma, 261,
110–123, https://doi.org/10.1016/j.geoderma.2015.07.006, 2016. a
Berghuijs, W. R. and Kirchner, J. W.: The relationship between contrasting ages
of groundwater and streamflow, Geophys. Res. Lett., 44, 8925–8935,
https://doi.org/10.1002/2017gl074962, 2017. a
Bierkens, M. F. P. and van Beek, L. P. H.: Seasonal Predictability of European
Discharge: NAO and Hydrological Response Time, J. Hydrometeorol.,
10, 953–968, https://doi.org/10.1175/2009jhm1034.1, 2009. a
Creutzfeldt, B., Ferré, T., Troch, P., Merz, B., Wziontek, H., and
Güntner, A.: Total water storage dynamics in response to climate variability
and extremes: Inference from long-term terrestrial gravity measurement,
J. Geophys. Res.-Atmos., 117, D08112,
https://doi.org/10.1029/2011jd016472, 2012. a, b
Delaigue, O., Génot, B., Lebecherel, L., Brigode, P., and Bourgin, P. Y.: Database of watershed-scale hydroclimatic observations in France, Université Paris-Saclay, INRAE, HYCAR Research Unit, Hydrology group, Antony, https://webgr.inrae.fr/base-de-donnees, last access: 29 March 2019. a
Descroix, L., Mahé, G., Lebel, T., Favreau, G., Galle, S., Gautier, E.,
Olivry, J.-C., Albergel, J., Amogu, O., Cappelaere, B., Dessouassi, R.,
Diedhiou, A., Breton, E. L., Mamadou, I., and Sighomnou, D.: Spatio-temporal
variability of hydrological regimes around the boundaries between Sahelian
and Sudanian areas of West Africa: A synthesis, J. Hydrol., 375,
90–102, https://doi.org/10.1016/j.jhydrol.2008.12.012, 2009. a
Dunn, S. M., Birkel, C., Tetzlaff, D., and Soulsby, C.: Transit time
distributions of a conceptual model: their characteristics and sensitivities,
Hydrol. Process., 24, 1719–1729, https://doi.org/10.1002/hyp.7560, 2010. a
Fowler, K., Knoben, W., Peel, M., Peterson, T., Ryu, D., Saft, M., Seo, K.-W.,
and Western, A.: Many commonly used rainfall-runoff models lack long, slow
dynamics: implications for runoff projections, Water Resour. Res., 56, e2019WR025286, https://doi.org/10.1029/2019wr025286, 2020. a, b
Gharari, S. and Razavi, S.: A review and synthesis of hysteresis in hydrology
and hydrological modeling: Memory, path-dependency, or missing physics?,
J. Hydrol., 566, 500–519, https://doi.org/10.1016/j.jhydrol.2018.06.037,
2018. 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 1/f scaling in catchment tracer time
series, and its implications for catchment travel time distributions,
Hydrol. Process., 24, 1660–1671, https://doi.org/10.1002/hyp.7677, 2010. a
Grigg, A. H. and Hughes, J. D.: Nonstationarity driven by multidecadal change
in catchment groundwater storage: A test of modifications to a common
rainfall-run-off model, Hydrol. Process., 32, 3675–3688,
https://doi.org/10.1002/hyp.13282, 2018. a, b
Harrigan, S., Prudhomme, C., Parry, S., Smith, K., and Tanguy, M.: Benchmarking ensemble streamflow prediction skill in the UK, Hydrol. Earth Syst. Sci., 22, 2023–2039, https://doi.org/10.5194/hess-22-2023-2018, 2018. a, b
Heidbüchel, I., Troch, P. A., Lyon, S. W., and Weiler, M.: The master transit
time distribution of variable flow systems, Water Resour. Res., 48,
W06520, https://doi.org/10.1029/2011wr011293, 2012. a
Hirpa, F. A., Gebremichael, M., and Over, T. M.: River flow fluctuation
analysis: Effect of watershed area, Water Resour. Res., 46, W12529,
https://doi.org/10.1029/2009wr009000, 2010. 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., Fovet, O., Ruiz, L., and Savenije, H. H. G.: Transit time
distributions, legacy contamination and variability in biogeochemical
1/fα scaling: how are hydrological response dynamics linked to water
quality at the catchment scale?, Hydrol. Process., 29, 5241–5256,
https://doi.org/10.1002/hyp.10546, 2015. 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, WIRES Water, 3, 629–657, https://doi.org/10.1002/wat2.1155, 2016. a
Hughes, J. D., Petrone, K. C., and Silberstein, R. P.: Drought, groundwater
storage and stream flow decline in southwestern Australia, Geophys. Res. Lett., 39, L03408, https://doi.org/10.1029/2011gl050797, 2012. a
Hurst, H. E.: Long-term storage capacity of reservoirs, Trans. Amer. Soc. Civil
Eng., 116, 770–799, 1951. a
Ilampooranan, I., Van Meter, K. J., and Basu, N. B.: A Race Against Time:
Modeling Time Lags in Watershed Response, Water Resour. Res., 55,
3941–3959, https://doi.org/10.1029/2018WR023815, 2019. a
Iliopoulou, T., Aguilar, C., Arheimer, B., Bermúdez, M., Bezak, N., Ficchì, A., Koutsoyiannis, D., Parajka, J., Polo, M. J., Thirel, G., and Montanari, A.: A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers, Hydrol. Earth Syst. Sci., 23, 73–91, https://doi.org/10.5194/hess-23-73-2019, 2019. a, b, c, d
Johansson, B.: Estimation of areal precipitation for hydrological modelling in
Sweden, Doctoral Thesis, University of Gothenburg, Gothenburg,
http://hdl.handle.net/2077/15575 (last access: 29 April 2022), 2002. 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, 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
Klemeš, V., Srikanthan, R., and McMahon, T. A.: Long-memory flow models
in reservoir analysis: What is their practical value?, Water Resour. Res., 17, 737–751, https://doi.org/10.1029/wr017i003p00737, 1981. a
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019. a
Leleu, I., Tonnelier, I., Puechberty, R., Gouin, P., Viquendi, I., Cobos, L.,
Foray, A., Baillon, M., and Ndima, P.-O.: Re-founding the national
information system designed to manage and give access to hydrometric data, La
Houille Blanche, 25–32, https://doi.org/10.1051/lhb/2014004, 2014(in French). a
Lins, H. F.: Interannual streamflow variability in the United States based on
principal components, Water Resour. Res., 21, 691–701,
https://doi.org/10.1029/wr021i005p00691, 1985. a
Lo, M.-H. and Famiglietti, J. S.: Effect of water table dynamics on land
surface hydrologic memory, J. Geophys. Res., 115,
https://doi.org/10.1029/2010jd014191, 2010. a
Girons Lopez, M., Crochemore, L., and Pechlivanidis, I. G.: Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden, Hydrol. Earth Syst. Sci., 25, 1189–1209, https://doi.org/10.5194/hess-25-1189-2021, 2021. a, b, c
Marshall, A.: Principles of Economics, Palgrave Macmillan, London, 802 pp., https://doi.org/10.1057/9781137375261, 1890. a
McDonnell, J. J.: Beyond the water balance, Nat. Geosci., 10, 396–396,
https://doi.org/10.1038/ngeo2964, 2017. 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
Merz, B., Nguyen, V. D., and Vorogushyn, S.: Temporal clustering of floods in
Germany: Do flood-rich and flood-poor periods exist?, J. Hydrol.,
541, 824–838, https://doi.org/10.1016/j.jhydrol.2016.07.041, 2016. a, b
Montanari, A., Rosso, R., and Taqqu, M. S.: Fractionally differenced ARIMA
models applied to hydrologic time series: Identification, estimation, and
simulation, Water Resour. Res., 33, 1035–1044,
https://doi.org/10.1029/97wr00043, 1997. a
Mudelsee, M.: Long memory of rivers from spatial aggregation, Water Resour. Res., 43, https://doi.org/10.1029/2006wr005721, 2007. a, b, c
Nippgen, F., McGlynn, B. L., Emanuel, R. E., and Vose, J. M.: Watershed memory
at the Coweeta Hydrologic Laboratory: The effect of past precipitation and
storage on hydrologic response, Water Resour. Res., 52, 1673–1695,
https://doi.org/10.1002/2015wr018196, 2016. a
O'Connell, P., Koutsoyiannis, D., Lins, H. F., Markonis, Y., Montanari, A., and
Cohn, T.: The scientific legacy of Harold Edwin Hurst
(1880–1978), Hydrolog. Sci. J., 61, 1571–1590,
https://doi.org/10.1080/02626667.2015.1125998, 2016. a
Orth, R. and Seneviratne, S. I.: Propagation of soil moisture memory to streamflow and evapotranspiration in Europe, Hydrol. Earth Syst. Sci., 17, 3895–3911, https://doi.org/10.5194/hess-17-3895-2013, 2013. a, b
Oudin, L., Hervieu, F., Michel, C., Perrin, C., Andréassian, V., Anctil,
F., and Loumagne, C.: Which potential evapotranspiration input for a lumped
rainfall–runoff model? Part 2 – Towards a simple and efficient
potential evapotranspiration model for rainfall–runoff modelling, J. Hydrol., 303, 290–306, https://doi.org/10.1016/j.jhydrol.2004.08.026, 2005. a
Pechlivanidis, I. G., Crochemore, L., Rosberg, J., and Bosshard, T.: What Are
the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts?,
Water Resour. Res., 56, e2019WR026987, https://doi.org/10.1029/2019wr026987, 2020. a
Pelletier, A. and Andréassian, V.: Hydrograph separation: an impartial parametrisation for an imperfect method, Hydrol. Earth Syst. Sci., 24, 1171–1187, https://doi.org/10.5194/hess-24-1171-2020, 2020a. a, b, c
Pelletier, A. and Andréassian, V.: Caractérisation de la
mémoire des bassins versants par approche croisée entre
piézométrie et séparation d′hydrogramme,
La Houille Blanche, 106, 30–37, https://doi.org/10.1051/lhb/2020032,
2020b. a
Pelletier, A., Andréassian, V., and Delaigue, O.: baseflow: Computes
Hydrograph Separation, https://doi.org/10.15454/Z9IK5N, r package version 0.13.2, 2021. a
Quinn, D. F., Murphy, C., Wilby, R. L., Matthews, T., Broderick, C., Golian,
S., Donegan, S., and Harrigan, S.: Benchmarking seasonal forecasting skill
using river flow persistence in Irish catchments, Hydrolog. Sci. J., https://doi.org/10.1080/02626667.2021.1874612, 2021. a, b
Rao, A. and Bhattacharya, D.: Hypothesis testing for long-term memory in
hydrologic series, J. Hydrol., 216, 183–196,
https://doi.org/10.1016/s0022-1694(99)00005-0, 1999. a
Risbey, J. S. and Entekhabi, D.: Observed Sacramento Basin streamflow response
to precipitation and temperature changes and its relevance to climate impact
studies, J. Hydrol., 184, 209–223,
https://doi.org/10.1016/0022-1694(95)02984-2, 1996. a, b
Schaake, J. and Liu, C.: Development and application of simple water balance models to understand the relationship between climate and water resources, in: New Directions for Surface Water Modeling (Proceedings of the Baltimore Symposium), edited by: IAHS Publication, 181, 343–352, ISBN 0-947571-96-5, 1989. a, b
Shukla, S., Sheffield, J., Wood, E. F., and Lettenmaier, D. P.: On the sources of global land surface hydrologic predictability, Hydrol. Earth Syst. Sci., 17, 2781–2796, https://doi.org/10.5194/hess-17-2781-2013, 2013. a
SMHI: Vattenwebb, https://vattenweb.smhi.se/station/, last access: 26 June 2019. a
Sprenger, M., Stumpp, C., Weiler, M., Aeschbach, W., Allen, S. T., Benettin,
P., Dubbert, M., Hartmann, A., Hrachowitz, M., Kirchner, J. W., McDonnell,
J. J., Orlowski, N., Penna, D., Pfahl, S., Rinderer, M., Rodriguez, N.,
Schmidt, M., and Werner, C.: The Demographics of Water: A Review of Water
Ages in the Critical Zone, Rev. Geophys., 57, 800–834,
https://doi.org/10.1029/2018rg000633, 2019. a
Staudinger, M., Stoelzle, M., Seeger, S., Seibert, J., Weiler, M., and Stahl,
K.: Catchment water storage variation with elevation, Hydrol. Process.,
31, 2000–2015, https://doi.org/10.1002/hyp.11158, 2017. a, b
Svensson, C.: Seasonal river flow forecasts for the United Kingdom using
persistence and historical analogues, Hydrolog. Sci. J., 61,
19–35, https://doi.org/10.1080/02626667.2014.992788, 2015. a, b
Szolgayova, E., Laaha, G., Blöschl, G., and Bucher, C.: Factors influencing
long range dependence in streamflow of European rivers, Hydrol.
Process., 28, 1573–1586, https://doi.org/10.1002/hyp.9694, 2013. a, b, c, d
Tetzlaff, D., Soulsby, C., Hrachowitz, M., and Speed, M.: Relative influence of
upland and lowland headwaters on the isotope hydrology and transit times of
larger catchments, J. Hydrol., 400, 438–447,
https://doi.org/10.1016/j.jhydrol.2011.01.053, 2011. a
Tomasella, J., Hodnett, M. G., Cuartas, L. A., Nobre, A. D., Waterloo, M. J.,
and Oliveira, S. M.: The water balance of an Amazonian micro-catchment: the
effect of interannual variability of rainfall on hydrological behaviour,
Hydrol. Process., 22, 2133–2147, https://doi.org/10.1002/hyp.6813, 2008. a
Trask, J. C., Fogg, G. E., and Puente, C. E.: Resolving hydrologic water
balances through a novel error analysis approach, with application to the
Tahoe basin, J. Hydrol., 546, 326–340,
https://doi.org/10.1016/j.jhydrol.2016.12.029, 2017. a
van Dijk, A. I. J. M., Peña-Arancibia, J. L., Wood, E. F., Sheffield, J.,
and Beck, H. E.: Global analysis of seasonal streamflow predictability using
an ensemble prediction system and observations from 6192 small catchments
worldwide, Water Resour. Res., 49, 2729–2746,
https://doi.org/10.1002/wrcr.20251, 2013. a, b
Van Meter, K. J., 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. a
Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux,
J.-M.: A 50-year high-resolution atmospheric reanalysis over France with the
Safran system, Int. J. Climatol., 30, 1627–1644,
https://doi.org/10.1002/joc.2003, 2010. a, b
Vogel, R. M., Tsai, Y., and Limbrunner, J. F.: The regional persistence and
variability of annual streamflow in the United States, Water Resour. Res., 34, 3445–3459, https://doi.org/10.1029/98wr02523, 1998. a
Wang, W., Van Gelder, P. H. A. J. M., Vrijling, J. K., and Chen, X.: Detecting long-memory: Monte Carlo simulations and application to daily streamflow processes, Hydrol. Earth Syst. Sci., 11, 851–862, https://doi.org/10.5194/hess-11-851-2007, 2007.
a
Yang, Y., McVicar, T. R., Donohue, R. J., Zhang, Y., Roderick, M. L., Chiew,
F. H., Zhang, L., and Zhang, J.: Lags in hydrologic recovery following an
extreme drought: Assessing the roles of climate and catchment
characteristics, Water Resour. Res., 53, 4821–4837,
https://doi.org/10.1002/2017wr020683, 2017. a
Yossef, N. C., Winsemius, H., Weerts, A., van Beek, R., and Bierkens, M. F. P.:
Skill of a global seasonal streamflow forecasting system, relative roles of
initial conditions and meteorological forcing, Water Resour. Res., 49,
4687–4699, https://doi.org/10.1002/wrcr.20350, 2013. a
Yuan, X. and Zhu, E.: A First Look at Decadal Hydrological Predictability by
Land Surface Ensemble Simulations, Geophys. Res. Lett., 45,
2362–2369, https://doi.org/10.1002/2018gl077211, 2018. a
Zambrano-Bigiarini, M. and Rojas, R.: A model-independent Particle Swarm
Optimisation software for model calibration, Environ. Modell. Softw., 43, 5–25, https://doi.org/10.1016/j.envsoft.2013.01.004, 2013. a
Short summary
A watershed remembers the past to some extent, and this memory influences its behavior. This memory is defined by the ability to store past rainfall for several years. By releasing this water into the river or the atmosphere, it tends to forget. We describe how this memory fades over time in France and Sweden. A few watersheds show a multi-year memory. It increases with the influence of groundwater or dry conditions. After 3 or 4 years, they behave independently of the past.
A watershed remembers the past to some extent, and this memory influences its behavior. This...