Articles | Volume 27, issue 17
https://doi.org/10.5194/hess-27-3293-2023
© 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-27-3293-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Education and communication
| 
14 Sep 2023
Education and communication |
| 14 Sep 2023
| 14 Sep 2023
airGRteaching: an open-source tool for teaching hydrological modeling with R
Olivier Delaigue
CORRESPONDING AUTHOR
Université Paris-Saclay, INRAE, HYCAR, Antony, France
Pierre Brigode
Université Paris-Saclay, INRAE, HYCAR, Antony, France
Université Côte d'Azur, CNRS, OCA, IRD, Géoazur, Sophia Antipolis, France
Guillaume Thirel
Université Paris-Saclay, INRAE, HYCAR, Antony, France
Laurent Coron
EDF – PMC Hydrometeorological Center, Toulouse, France
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Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
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
Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023, https://doi.org/10.5194/hess-27-3375-2023, 2023
Short summary
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We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
EGUsphere, https://doi.org/10.5194/egusphere-2023-775, https://doi.org/10.5194/egusphere-2023-775, 2023
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We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows, but show poor performance outside the range of the targeted streamflows and are less robust. We show that no a priori assumption on transformations must be taken as warranted.
Paul C. Astagneau, Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven
Hydrol. Earth Syst. Sci., 25, 3937–3973, https://doi.org/10.5194/hess-25-3937-2021, https://doi.org/10.5194/hess-25-3937-2021, 2021
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The R programming language has become an important tool for many applications in hydrology. In this study, we provide an analysis of some of the R tools providing hydrological models. In total, two aspects are uniformly investigated, namely the conceptualisation of the models and the practicality of their implementation for end-users. These comparisons aim at easing the choice of R tools for users and at improving their usability for hydrology modelling to support more transferable research.
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Proc. IAHS, 383, 381–389, https://doi.org/10.5194/piahs-383-381-2020, https://doi.org/10.5194/piahs-383-381-2020, 2020
Louise J. Slater, Guillaume Thirel, Shaun Harrigan, Olivier Delaigue, Alexander Hurley, Abdou Khouakhi, Ilaria Prosdocimi, Claudia Vitolo, and Katie Smith
Hydrol. Earth Syst. Sci., 23, 2939–2963, https://doi.org/10.5194/hess-23-2939-2019, https://doi.org/10.5194/hess-23-2939-2019, 2019
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This paper explores the benefits and advantages of R's usage in hydrology. We provide an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology as well as a roadmap for R’s future within the discipline.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Preprint under review for HESS
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This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Ralph Bathelemy, Pierre Brigode, Vazken Andréassian, Charles Perrin, Vincent Moron, Cédric Gaucherel, Emmanuel Tric, and Dominique Boisson
Earth Syst. Sci. Data, 16, 2073–2098, https://doi.org/10.5194/essd-16-2073-2024, https://doi.org/10.5194/essd-16-2073-2024, 2024
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The aim of this work is to provide the first hydroclimatic database for Haiti, a Caribbean country particularly vulnerable to meteorological and hydrological hazards. The resulting database, named Simbi, provides hydroclimatic time series for around 150 stations and 24 catchment areas.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, and Antoinette Alias
Nat. Hazards Earth Syst. Sci., 24, 1163–1183, https://doi.org/10.5194/nhess-24-1163-2024, https://doi.org/10.5194/nhess-24-1163-2024, 2024
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High-resolution convection-permitting climate models (CPMs) are now available to better simulate rainstorm events leading to flash floods. In this study, two hydrological models are compared to simulate floods in a Mediterranean basin, showing a better ability of the CPM to reproduce flood peaks compared to coarser-resolution climate models. Future projections are also different, with a projected increase for the most severe floods and a potential decrease for the most frequent events.
Carlo Mologni, Marie Revel, Eric Chaumillon, Emmanuel Malet, Thibault Coulombier, Pierre Sabatier, Pierre Brigode, Hervé Gwenael, Anne-Lise Develle, Laure Schenini, Medhi Messous, Gourguen Davtian, Alain Carré, Delphine Bosch, Natacha Volto, Clément Ménard, Lamya Khalidi, and Fabien Arnaud
EGUsphere, https://doi.org/10.5194/egusphere-2024-310, https://doi.org/10.5194/egusphere-2024-310, 2024
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The reactivity of local to regional hydrology and soil to global changes remains understated in East African climatic models. This paper demonstrates the importance of studies on regional hydro-systems feedbacks to global atmospheric anomalies, to better understand and mitigate the sometimes catastrophic effects of global warming in extreme environments such as the Afar, especially in the context of current climate-induced food insecurity in East Africa and dire predictions for what is ahead.
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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We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
EGUsphere, https://doi.org/10.5194/egusphere-2023-775, https://doi.org/10.5194/egusphere-2023-775, 2023
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Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, and Pierre Javelle
Hydrol. Earth Syst. Sci., 26, 5793–5816, https://doi.org/10.5194/hess-26-5793-2022, https://doi.org/10.5194/hess-26-5793-2022, 2022
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Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
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Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Małgorzata Chmiel, Maxime Godano, Marco Piantini, Pierre Brigode, Florent Gimbert, Maarten Bakker, Françoise Courboulex, Jean-Paul Ampuero, Diane Rivet, Anthony Sladen, David Ambrois, and Margot Chapuis
Nat. Hazards Earth Syst. Sci., 22, 1541–1558, https://doi.org/10.5194/nhess-22-1541-2022, https://doi.org/10.5194/nhess-22-1541-2022, 2022
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On 2 October 2020, the French Maritime Alps were struck by an extreme rainfall event caused by Storm Alex. Here, we show that seismic data provide the timing and velocity of the propagation of flash-flood waves along the Vésubie River. We also detect 114 small local earthquakes triggered by the rainwater weight and/or its infiltration into the ground. This study paves the way for future works that can reveal further details of the impact of Storm Alex on the Earth’s surface and subsurface.
Thibault Lemaitre-Basset, Ludovic Oudin, Guillaume Thirel, and Lila Collet
Hydrol. Earth Syst. Sci., 26, 2147–2159, https://doi.org/10.5194/hess-26-2147-2022, https://doi.org/10.5194/hess-26-2147-2022, 2022
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Increasing temperature will impact evaporation and water resource management. Hydrological models are fed with an estimation of the evaporative demand of the atmosphere, called potential evapotranspiration (PE). The objectives of this study were (1) to compute the future PE anomaly over France and (2) to determine the impact of the choice of the method to estimate PE. Our results show that all methods present similar future trends. No method really stands out from the others.
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.
Alexis Jeantet, Hocine Henine, Cédric Chaumont, Lila Collet, Guillaume Thirel, and Julien Tournebize
Hydrol. Earth Syst. Sci., 25, 5447–5471, https://doi.org/10.5194/hess-25-5447-2021, https://doi.org/10.5194/hess-25-5447-2021, 2021
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The hydrological subsurface drainage model SIDRA-RU is assessed at the French national scale, using a unique database representing the large majority of the French drained areas. The model is evaluated following its capacity to simulate the drainage discharge variability and the annual drained water balance. Eventually, the temporal robustness of SIDRA-RU is assessed to demonstrate the utility of this model as a long-term management tool.
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.
Paul C. Astagneau, Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven
Hydrol. Earth Syst. Sci., 25, 3937–3973, https://doi.org/10.5194/hess-25-3937-2021, https://doi.org/10.5194/hess-25-3937-2021, 2021
Short summary
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The R programming language has become an important tool for many applications in hydrology. In this study, we provide an analysis of some of the R tools providing hydrological models. In total, two aspects are uniformly investigated, namely the conceptualisation of the models and the practicality of their implementation for end-users. These comparisons aim at easing the choice of R tools for users and at improving their usability for hydrology modelling to support more transferable research.
Laurène J. E. Bouaziz, Fabrizio Fenicia, Guillaume Thirel, Tanja de Boer-Euser, Joost Buitink, Claudia C. Brauer, Jan De Niel, Benjamin J. Dewals, Gilles Drogue, Benjamin Grelier, Lieke A. Melsen, Sotirios Moustakas, Jiri Nossent, Fernando Pereira, Eric Sprokkereef, Jasper Stam, Albrecht H. Weerts, Patrick Willems, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, https://doi.org/10.5194/hess-25-1069-2021, 2021
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We quantify the differences in internal states and fluxes of 12 process-based models with similar streamflow performance and assess their plausibility using remotely sensed estimates of evaporation, snow cover, soil moisture and total storage anomalies. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Therefore, we invite modelers to evaluate their models using multiple variables and to rely on multi-model studies.
Manon Cassagnole, Maria-Helena Ramos, Ioanna Zalachori, Guillaume Thirel, Rémy Garçon, Joël Gailhard, and Thomas Ouillon
Hydrol. Earth Syst. Sci., 25, 1033–1052, https://doi.org/10.5194/hess-25-1033-2021, https://doi.org/10.5194/hess-25-1033-2021, 2021
Pierre Nicolle, François Besson, Olivier Delaigue, Pierre Etchevers, Didier François, Matthieu Le Lay, Charles Perrin, Fabienne Rousset, Dominique Thiéry, François Tilmant, Claire Magand, Timothée Leurent, and Élise Jacob
Proc. IAHS, 383, 381–389, https://doi.org/10.5194/piahs-383-381-2020, https://doi.org/10.5194/piahs-383-381-2020, 2020
Louise J. Slater, Guillaume Thirel, Shaun Harrigan, Olivier Delaigue, Alexander Hurley, Abdou Khouakhi, Ilaria Prosdocimi, Claudia Vitolo, and Katie Smith
Hydrol. Earth Syst. Sci., 23, 2939–2963, https://doi.org/10.5194/hess-23-2939-2019, https://doi.org/10.5194/hess-23-2939-2019, 2019
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This paper explores the benefits and advantages of R's usage in hydrology. We provide an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology as well as a roadmap for R’s future within the discipline.
Theano Iliopoulou, Cristina Aguilar, Berit Arheimer, María Bermúdez, Nejc Bezak, Andrea Ficchì, Demetris Koutsoyiannis, Juraj Parajka, María José Polo, Guillaume Thirel, and Alberto Montanari
Hydrol. Earth Syst. Sci., 23, 73–91, https://doi.org/10.5194/hess-23-73-2019, https://doi.org/10.5194/hess-23-73-2019, 2019
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We investigate the seasonal memory properties of a large sample of European rivers in terms of high and low flows. We compute seasonal correlations between peak and low flows and average flows in the previous seasons and explore the links with various physiographic and hydro-climatic catchment descriptors. Our findings suggest that there is a traceable physical basis for river memory which in turn can be employed to reduce uncertainty and improve probabilistic predictions of floods and droughts.
Léonard Santos, Guillaume Thirel, and Charles Perrin
Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, https://doi.org/10.5194/hess-22-4583-2018, 2018
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The Kling and Gupta efficiency (KGE) is a score used in hydrology to evaluate flow simulation compared to observations. In order to force the evaluation on the low flows, some authors used the log-transformed flow to calculate the KGE. In this technical note, we show that this transformation should be avoided because it produced numerical flaws that lead to difficulties in the score value interpretation.
Gaia Piazzi, Guillaume Thirel, Lorenzo Campo, and Simone Gabellani
The Cryosphere, 12, 2287–2306, https://doi.org/10.5194/tc-12-2287-2018, https://doi.org/10.5194/tc-12-2287-2018, 2018
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The study focuses on the development of a multivariate particle filtering data assimilation scheme into a point-scale snow model. One of the main challenging issues concerns the impoverishment of the particle sample, which is addressed by jointly perturbing meteorological data and model parameters. An additional snow density model is introduced to reduce sensitivity to the availability of snow mass-related observations. In this configuration, the system reveals a satisfying performance.
Léonard Santos, Guillaume Thirel, and Charles Perrin
Geosci. Model Dev., 11, 1591–1605, https://doi.org/10.5194/gmd-11-1591-2018, https://doi.org/10.5194/gmd-11-1591-2018, 2018
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Many rainfall–runoff models are based on stores. However, the differential equations that describe the stores' evolution are rarely presented in literature.
This represents an issue when the temporal resolution changes. In this work, we propose and evaluate a state-space version of a simple rainfall–runoff model within a robust resolution scheme. The results show that the proposed model performs equally well or slightly better than the original one and is independent of the temporal resolution.
Philippe Riboust, Nicolas Le Moine, Guillaume Thirel, and Pierre Ribstein
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-539, https://doi.org/10.5194/hess-2017-539, 2017
Revised manuscript not accepted
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In hydrological modelling complex forcing data are often needed to reproduce the energy balance, mainly for simulating snowmelt and evapotranspiration processes. Incoming radiation data are not widely measured and are often derived from reanalyses. We provide a method for simulating these radiations in mountainous areas using only daily temperature range data and a digital elevation model. The method has been validated on 105 weather stations and a simple snow surface temperature model.
Tanja de Boer-Euser, Laurène Bouaziz, Jan De Niel, Claudia Brauer, Benjamin Dewals, Gilles Drogue, Fabrizio Fenicia, Benjamin Grelier, Jiri Nossent, Fernando Pereira, Hubert Savenije, Guillaume Thirel, and Patrick Willems
Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017, https://doi.org/10.5194/hess-21-423-2017, 2017
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In this study, the rainfall–runoff models of eight international research groups were compared for a set of subcatchments of the Meuse basin to investigate the influence of certain model components on the modelled discharge. Although the models showed similar performances based on general metrics, clear differences could be observed for specific events. The differences during drier conditions could indeed be linked to differences in model structures.
Vazken Andréassian, Laurent Coron, Julien Lerat, and Nicolas Le Moine
Hydrol. Earth Syst. Sci., 20, 4503–4524, https://doi.org/10.5194/hess-20-4503-2016, https://doi.org/10.5194/hess-20-4503-2016, 2016
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We present a new method to derive the empirical (i.e., data-based) elasticity of streamflow to precipitation and potential evaporation. This method, which uses long-term hydrometeorological records, is tested on a set of 519 French catchments. We compare our method with the classical approach found in the literature and demonstrate its robustness and efficiency. Empirical elasticity is a powerful tool to test the extrapolation capacity of hydrological models.
Pierre Brigode, François Brissette, Antoine Nicault, Luc Perreault, Anna Kuentz, Thibault Mathevet, and Joël Gailhard
Clim. Past, 12, 1785–1804, https://doi.org/10.5194/cp-12-1785-2016, https://doi.org/10.5194/cp-12-1785-2016, 2016
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In this paper, we apply a new hydro-climatic reconstruction method on the Caniapiscau Reservoir (Canada), compare the obtained streamflow time series against time series derived from dendrohydrology by other authors on the same catchment, and study the natural streamflow variability over the 1881–2011 period. This new reconstruction is based on a historical reanalysis of global geopotential height fields and aims to produce daily streamflow time series (using a rainfall–runoff model).
Alban de Lavenne, Guillaume Thirel, Vazken Andréassian, Charles Perrin, and Maria-Helena Ramos
Proc. IAHS, 373, 87–94, https://doi.org/10.5194/piahs-373-87-2016, https://doi.org/10.5194/piahs-373-87-2016, 2016
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Developing modelling tools that help to understand the spatial distribution of water resources is a key issue for better management. Ideally, hydrological models which discretise catchment space into sub-catchments should offer better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However we demonstrate that those model raise other issues related to the calibration strategy and to the identifiability of the parameters.
P. Brigode, Z. Mićović, P. Bernardara, E. Paquet, F. Garavaglia, J. Gailhard, and P. Ribstein
Hydrol. Earth Syst. Sci., 17, 1455–1473, https://doi.org/10.5194/hess-17-1455-2013, https://doi.org/10.5194/hess-17-1455-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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A framework for parameter estimation, sensitivity analysis, and uncertainty analysis for holistic hydrologic modeling using SWAT+
On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions
Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations
Recent ground thermo-hydrological changes in a southern Tibetan endorheic catchment and implications for lake level changes
Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling
Modelling flood frequency and magnitude in a glacially conditioned, heterogeneous landscape: testing the importance of land cover and land use
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Direct integration of reservoirs' operations in a hydrological model for streamflow estimation: coupling a CLSTM model with MOHID-Land
Skill of seasonal flow forecasts at catchment-scale: an assessment across South Korea
Modelling the regional sensitivity of snowmelt, soil moisture, and streamflow generation to climate over the Canadian Prairies using a basin classification approach
To what extent does river routing matter in hydrological modeling?
Calibrating macroscale hydrological models in poorly gauged and heavily regulated basins
An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume
To Bucket or not to Bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 Large Ensemble
To What Extent Do Extreme Storm Events Change Future Flood Hazards?
Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation
Changes in Mediterranean flood processes and seasonality
Can the combining of wetlands with reservoir operation reduce the risk of future floods and droughts?
Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado
When best is the enemy of good – critical evaluation of performance criteria in hydrological models
The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
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We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
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Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
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We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
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Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Lillian M. McGill, E. Ashley Steel, and Aimee H. Fullerton
Hydrol. Earth Syst. Sci., 28, 1351–1371, https://doi.org/10.5194/hess-28-1351-2024, https://doi.org/10.5194/hess-28-1351-2024, 2024
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This study examines the relationship between air and river temperatures in Washington's Snoqualmie and Wenatchee basins. We used classification and regression approaches to show that the sensitivity of river temperature to air temperature is variable across basins and controlled largely by geology and snowmelt. Findings can be used to inform strategies for river basin restoration and conservation, such as identifying climate-insensitive areas of the basin that should be preserved and protected.
Stephanie R. Clark, Julien Lerat, Jean-Michel Perraud, and Peter Fitch
Hydrol. Earth Syst. Sci., 28, 1191–1213, https://doi.org/10.5194/hess-28-1191-2024, https://doi.org/10.5194/hess-28-1191-2024, 2024
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To determine if deep learning models are in general a viable alternative to traditional hydrologic modelling techniques in Australian catchments, a comparison of river–runoff predictions is made between traditional conceptual models and deep learning models in almost 500 catchments spread over the continent. It is found that the deep learning models match or outperform the traditional models in over two-thirds of the river catchments, indicating feasibility in a wide variety of conditions.
Dipti Tiwari, Mélanie Trudel, and Robert Leconte
Hydrol. Earth Syst. Sci., 28, 1127–1146, https://doi.org/10.5194/hess-28-1127-2024, https://doi.org/10.5194/hess-28-1127-2024, 2024
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Calibrating hydrological models with multi-objective functions enhances model robustness. By using spatially distributed snow information in the calibration, the model performance can be enhanced without compromising the outputs. In this study the HYDROTEL model was calibrated in seven different experiments, incorporating the SPAEF (spatial efficiency) metric alongside Nash–Sutcliffe efficiency (NSE) and root-mean-square error (RMSE), with the aim of identifying the optimal calibration strategy.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
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Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Louise Mimeau, Annika Künne, Flora Branger, Sven Kralisch, Alexandre Devers, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, https://doi.org/10.5194/hess-28-851-2024, 2024
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Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Elena Macdonald, Bruno Merz, Björn Guse, Viet Dung Nguyen, Xiaoxiang Guan, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 28, 833–850, https://doi.org/10.5194/hess-28-833-2024, https://doi.org/10.5194/hess-28-833-2024, 2024
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In some rivers, the occurrence of extreme flood events is more likely than in other rivers – they have heavy-tailed distributions. We find that threshold processes in the runoff generation lead to such a relatively high occurrence probability of extremes. Further, we find that beyond a certain return period, i.e. for rare events, rainfall is often the dominant control compared to runoff generation. Our results can help to improve the estimation of the occurrence probability of extreme floods.
Claire Kouba and Thomas Harter
Hydrol. Earth Syst. Sci., 28, 691–718, https://doi.org/10.5194/hess-28-691-2024, https://doi.org/10.5194/hess-28-691-2024, 2024
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In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, 5–6 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.
Yi Nan and Fuqiang Tian
Hydrol. Earth Syst. Sci., 28, 669–689, https://doi.org/10.5194/hess-28-669-2024, https://doi.org/10.5194/hess-28-669-2024, 2024
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This paper utilized a tracer-aided model validated by multiple datasets in a large mountainous basin on the Tibetan Plateau to analyze hydrological sensitivity to climate change. The spatial pattern of the local hydrological sensitivities and the influence factors were analyzed in particular. The main finding of this paper is that the local hydrological sensitivity in mountainous basins is determined by the relationship between the glacier area ratio and the mean annual precipitation.
Michael J. Vlah, Matthew R. V. Ross, Spencer Rhea, and Emily S. Bernhardt
Hydrol. Earth Syst. Sci., 28, 545–573, https://doi.org/10.5194/hess-28-545-2024, https://doi.org/10.5194/hess-28-545-2024, 2024
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Virtual stream gauging enables continuous streamflow estimation where a gauge might be difficult or impractical to install. We reconstructed flow at 27 gauges of the National Ecological Observatory Network (NEON), informing ~199 site-months of missing data in the official record and improving that accuracy of official estimates at 11 sites. This study shows that machine learning, but also routine regression methods, can be used to supplement existing gauge networks and reduce monitoring costs.
Sungwook Wi and Scott Steinschneider
Hydrol. Earth Syst. Sci., 28, 479–503, https://doi.org/10.5194/hess-28-479-2024, https://doi.org/10.5194/hess-28-479-2024, 2024
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We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
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Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-6, https://doi.org/10.5194/hess-2024-6, 2024
Revised manuscript accepted for HESS
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Climate and land-surface conditions influence the availability of fresh water resources. Their impact is quantified with data of 71 catchments in Saxony/Germany, for which distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff, that has not been seen in the observation records before.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Huy Dang and Yadu Pokhrel
EGUsphere, https://doi.org/10.5194/egusphere-2023-3158, https://doi.org/10.5194/egusphere-2023-3158, 2024
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By examining basin-wide simulations of the river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River Basin (MRB) over the long-term; however, dams have largely altered the seasonality of Mekong’s flow regime and annual flooding patterns at major downstream areas in recent years. These findings could help rethink the planning of future dams and water resource management in the MRB.
Salam A. Abbas, Ryan T. Bailey, Jeremy T. White, Jeffrey G. Arnold, Michael J. White, Natalja Čerkasova, and Jungang Gao
Hydrol. Earth Syst. Sci., 28, 21–48, https://doi.org/10.5194/hess-28-21-2024, https://doi.org/10.5194/hess-28-21-2024, 2024
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Research highlights.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Yuhang Zhang, Aizhong Ye, Bita Analui, Phu Nguyen, Soroosh Sorooshian, Kuolin Hsu, and Yuxuan Wang
Hydrol. Earth Syst. Sci., 27, 4529–4550, https://doi.org/10.5194/hess-27-4529-2023, https://doi.org/10.5194/hess-27-4529-2023, 2023
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Our study shows that while the quantile regression forest (QRF) and countable mixtures of asymmetric Laplacians long short-term memory (CMAL-LSTM) models demonstrate similar proficiency in multipoint probabilistic predictions, QRF excels in smaller watersheds and CMAL-LSTM in larger ones. CMAL-LSTM performs better in single-point deterministic predictions, whereas QRF model is more efficient overall.
Léo C. P. Martin, Sebastian Westermann, Michele Magni, Fanny Brun, Joel Fiddes, Yanbin Lei, Philip Kraaijenbrink, Tamara Mathys, Moritz Langer, Simon Allen, and Walter W. Immerzeel
Hydrol. Earth Syst. Sci., 27, 4409–4436, https://doi.org/10.5194/hess-27-4409-2023, https://doi.org/10.5194/hess-27-4409-2023, 2023
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Across the Tibetan Plateau, many large lakes have been changing level during the last decades as a response to climate change. In high-mountain environments, water fluxes from the land to the lakes are linked to the ground temperature of the land and to the energy fluxes between the ground and the atmosphere, which are modified by climate change. With a numerical model, we test how these water and energy fluxes have changed over the last decades and how they influence the lake level variations.
Diego Araya, Pablo A. Mendoza, Eduardo Muñoz-Castro, and James McPhee
Hydrol. Earth Syst. Sci., 27, 4385–4408, https://doi.org/10.5194/hess-27-4385-2023, https://doi.org/10.5194/hess-27-4385-2023, 2023
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Dynamical systems are used by many agencies worldwide to produce seasonal streamflow forecasts, which are critical for decision-making. Such systems rely on hydrology models, which contain parameters that are typically estimated using a target performance metric (i.e., objective function). This study explores the effects of this decision across mountainous basins in Chile, illustrating tradeoffs between seasonal forecast quality and the models' capability to simulate streamflow characteristics.
Pamela E. Tetford and Joseph R. Desloges
Hydrol. Earth Syst. Sci., 27, 3977–3998, https://doi.org/10.5194/hess-27-3977-2023, https://doi.org/10.5194/hess-27-3977-2023, 2023
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An efficient regional flood frequency model relates drainage area to discharge, with a major assumption of similar basin conditions. In a landscape with variable glacial deposits and land use, we characterize varying hydrological function using 28 explanatory variables. We demonstrate that (1) a heterogeneous landscape requires objective model selection criteria to optimize the fit of flow data, and (2) incorporating land use as a predictor variable improves the drainage area to discharge model.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn E. Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-258, https://doi.org/10.5194/hess-2023-258, 2023
Revised manuscript accepted for HESS
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Wouldn't it be nice to have both the accuracy of neural networks (NNs) and the interpretability of process-based models (PBMs)? Differentiable modeling gives you the best of both worlds by connecting NNs with PBMs. However, there was previously a major issue that iterative solution schemes would run into memory use trouble. This paper presents an operator called adjoint, which liberates all the iterative solvers. This is the first time adjoint is applied to large-scale hydrologic modeling.
Ana Ramos Oliveira, Tiago Brito Ramos, Lígia Pinto, and Ramiro Neves
Hydrol. Earth Syst. Sci., 27, 3875–3893, https://doi.org/10.5194/hess-27-3875-2023, https://doi.org/10.5194/hess-27-3875-2023, 2023
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This paper intends to demonstrate the adequacy of a hybrid solution to overcome the difficulties related to the incorporation of human behavior when modeling hydrological processes. Two models were implemented, one to estimate the outflow of a reservoir and the other to simulate the hydrological processes of the watershed. With both models feeding each other, results show that the proposed approach significantly improved the streamflow estimation downstream of the reservoir.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
EGUsphere, https://doi.org/10.5194/egusphere-2023-2169, https://doi.org/10.5194/egusphere-2023-2169, 2023
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (one or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Zhihua He, Kevin Shook, Christopher Spence, John W. Pomeroy, and Colin Whitfield
Hydrol. Earth Syst. Sci., 27, 3525–3546, https://doi.org/10.5194/hess-27-3525-2023, https://doi.org/10.5194/hess-27-3525-2023, 2023
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This study evaluated the impacts of climate change on snowmelt, soil moisture, and streamflow over the Canadian Prairies. The entire prairie region was divided into seven basin types. We found strong variations of hydrological sensitivity to precipitation and temperature changes in different land covers and basins, which suggests that different water management and adaptation methods are needed to address enhanced water stress due to expected climate change in different regions of the prairies.
Nicolás Cortés-Salazar, Nicolás Vásquez, Naoki Mizukami, Pablo A. Mendoza, and Ximena Vargas
Hydrol. Earth Syst. Sci., 27, 3505–3524, https://doi.org/10.5194/hess-27-3505-2023, https://doi.org/10.5194/hess-27-3505-2023, 2023
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This paper shows how important river models can be for water resource applications that involve hydrological models and, in particular, parameter calibration. To this end, we conduct numerical experiments in a pilot basin using a combination of hydrologic model simulations obtained from a large sample of parameter sets and different routing methods. We find that routing can affect streamflow simulations, even at monthly time steps; the choice of parameters; and relevant streamflow metrics.
Dung Trung Vu, Thanh Duc Dang, Francesca Pianosi, and Stefano Galelli
Hydrol. Earth Syst. Sci., 27, 3485–3504, https://doi.org/10.5194/hess-27-3485-2023, https://doi.org/10.5194/hess-27-3485-2023, 2023
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The calibration of hydrological models over extensive spatial domains is often challenged by the lack of data on river discharge and the operations of hydraulic infrastructures. Here, we use satellite data to address the lack of data that could unintentionally bias the calibration process. Our study is underpinned by a computational framework that quantifies this bias and provides a safe approach to the calibration of models in poorly gauged and heavily regulated basins.
Francesco Fatone, Bartosz Szeląg, Przemysław Kowal, Arthur McGarity, Adam Kiczko, Grzegorz Wałek, Ewa Wojciechowska, Michał Stachura, and Nicolas Caradot
Hydrol. Earth Syst. Sci., 27, 3329–3349, https://doi.org/10.5194/hess-27-3329-2023, https://doi.org/10.5194/hess-27-3329-2023, 2023
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A novel methodology for the development of a stormwater network performance simulator including advanced risk assessment was proposed. The applied tool enables the analysis of the influence of spatial variability in catchment and stormwater network characteristics on the relation between (SWMM) model parameters and specific flood volume, as an alternative approach to mechanistic models. The proposed method can be used at the stage of catchment model development and spatial planning management.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2023-1980, https://doi.org/10.5194/egusphere-2023-1980, 2023
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Hydrological hybrid models merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using LSTM networks. We explored this method to evaluate if the flexibility given by LSTM overwrites the interpretability of the process-based part. We showed that if a well-tested model architecture is combined with an LSTM, the latter can learn to operate the process-based model consistently.
Florian Willkofer, Raul Roger Wood, and Ralf Ludwig
EGUsphere, https://doi.org/10.5194/egusphere-2023-2019, https://doi.org/10.5194/egusphere-2023-2019, 2023
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Severe flood events pose threat to riverine areas, yet robust estimates about the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses and benefits from data from a RCM SMILE to drive a high-resolution hydrological model for 98 catchments of the Hydrological Bavaria to exploit the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
EGUsphere, https://doi.org/10.5194/egusphere-2023-1969, https://doi.org/10.5194/egusphere-2023-1969, 2023
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Due to climate change, flooding is expected to become more frequent globally in the coming decades. Locally, storm-induced channel geometry changes can drastically affect flood hazards, yet rivers are mostly treated as static elements in flood studies. This study tried to gain an understanding of the effects of major storm events on future flood hazards, promoting a framework for incorporating channel conveyance adjustments into flood hazard assessment.
Siyuan Wang, Markus Hrachowitz, Gerrit Schoups, and Christine Stumpp
Hydrol. Earth Syst. Sci., 27, 3083–3114, https://doi.org/10.5194/hess-27-3083-2023, https://doi.org/10.5194/hess-27-3083-2023, 2023
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This study shows that previously reported underestimations of water ages are most likely not due to the use of seasonally variable tracers. Rather, these underestimations can be largely attributed to the choices of model approaches which rely on assumptions not frequently met in catchment hydrology. We therefore strongly advocate avoiding the use of this model type in combination with seasonally variable tracers and instead adopting StorAge Selection (SAS)-based or comparable model formulations.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emilio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
EGUsphere, https://doi.org/10.5194/egusphere-2023-1755, https://doi.org/10.5194/egusphere-2023-1755, 2023
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The authors compared different regionalization methods for river flow prediction in watersheds with few data. We collected data on precipitation, evapotranspiration, river flow, and geographical and climatic factors for 126 catchments in the Paraná state, Brazil. The regionalization method based on physiographic-climatic similarity showed to be the most robust for predicting daily and Q95 reference flow. We also found patterns in data, grouping locations based on similarities.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, https://doi.org/10.5194/hess-27-2989-2023, 2023
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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.
Yves Tramblay, Patrick Arnaud, Guillaume Artigue, Michel Lang, Emmanuel Paquet, Luc Neppel, and Eric Sauquet
Hydrol. Earth Syst. Sci., 27, 2973–2987, https://doi.org/10.5194/hess-27-2973-2023, https://doi.org/10.5194/hess-27-2973-2023, 2023
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Mediterranean floods are causing major damage, and recent studies have shown that, despite the increase in intense rainfall, there has been no increase in river floods. This study reveals that the seasonality of floods changed in the Mediterranean Basin during 1959–2021. There was also an increased frequency of floods linked to short episodes of intense rain, associated with a decrease in soil moisture. These changes need to be taken into consideration to adapt flood warning systems.
Yanfeng Wu, Jingxuan Sun, Boting Hu, Y. Jun Xu, Alain N. Rousseau, and Guangxin Zhang
Hydrol. Earth Syst. Sci., 27, 2725–2745, https://doi.org/10.5194/hess-27-2725-2023, https://doi.org/10.5194/hess-27-2725-2023, 2023
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Reservoirs and wetlands are important regulators of watershed hydrology, which should be considered when projecting floods and droughts. We first coupled wetlands and reservoir operations into a semi-spatially-explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. We found that, overall, the risk of future floods and droughts will increase further even under the combined influence of reservoirs and wetlands.
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
Short summary
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We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Guillaume Cinkus, Naomi Mazzilli, Hervé Jourde, Andreas Wunsch, Tanja Liesch, Nataša Ravbar, Zhao Chen, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 27, 2397–2411, https://doi.org/10.5194/hess-27-2397-2023, https://doi.org/10.5194/hess-27-2397-2023, 2023
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The Kling–Gupta Efficiency (KGE) is a performance criterion extensively used to evaluate hydrological models. We conduct a critical study on the KGE and its variant to examine counterbalancing errors. Results show that, when assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without an associated increase in model relevance. We suggest that one carefully choose performance criteria and use scaling factors.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 27, 2357–2373, https://doi.org/10.5194/hess-27-2357-2023, https://doi.org/10.5194/hess-27-2357-2023, 2023
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Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
Cited articles
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a
AghaKouchak, A., Nakhjiri, N., and Habib, E.: An educational model for ensemble streamflow simulation and uncertainty analysis, Hydrol. Earth Syst. Sci., 17, 445–452, https://doi.org/10.5194/hess-17-445-2013, 2013. a
Astagneau, P. C., Thirel, G., Delaigue, O., Guillaume, J. H. A., Parajka, J.,
Brauer, C. C., Viglione, A., Buytaert, W., and Beven, K. J.: Technical note:
Hydrology modelling R packages – a unified analysis of models and
practicalities from a user perspective, Hydrol. Earth Syst. Sci., 25, 3937–3973, https://doi.org/10.5194/hess-25-3937-2021, 2021. a, b
Baahmed, D., Oudin, L., and Errih, M.: Current runoff variations in the Macta
catchment (Algeria): is climate the sole factor? [Le facteur climatique
est-il la seule cause des modifications actuelles de l'écoulement dans le
bassin versant de la Macta (Algérie)?], Hydrolog. Sci. J., 60,
1331–1339, https://doi.org/10.1080/02626667.2014.975708, 2015. a
Belarbi, H., Touaibia, B., Boumechra, N., Amiar, S., and Baghli, N.: Drought
and modification of the rainfall-runoff relation: case of Wadi Sebdou basin
(western Algeria) [Sécheresse et modification de la relation pluie–débit: cas du bassin versant de l'Oued Sebdou (Algérie Occidentale)], Hydrolog. Sci. J., 62, 124–136, https://doi.org/10.1080/02626667.2015.1112394, 2017. a
Bezak, N., Jemec Auflič, M., and Mikoš, M.: Application of
hydrological modelling for temporal prediction of rainfall-induced shallow
landslides, Landslides, 16, 1273–1283, https://doi.org/10.1007/s10346-019-01169-9, 2019. a
Blöschl, G., Bierkens, M. F. P., Chambel, A., Cudennec, C., Destouni, G.,
Fiori, A., Kirchner, J. W., McDonnell, J. J., Savenije, H. H. G., Sivapalan,
M., Stumpp, C., Toth, E., Volpi, E., Carr, G., Lupton, C., Salinas, J.,
Széles, B., Viglione, A., Aksoy, H., et al.: Twenty-three unsolved problems in
hydrology (UPH) – a community perspective, Hydrolog. Sci. J., 64, 1141–1158, https://doi.org/10.1080/02626667.2019.1620507, 2019. a
Brigode, P., Lilas, D., Andréassian, V., Nicolle, P., Le Moine, N., Perrin,
C., Gremminger, S., and Augeard, B.: Une cartographie de l'écoulement des
rivières de Corse, La Houille Blanche, 1, 68–77, https://doi.org/10.1051/lhb/2019009, 2019. a
Brigode, P., Génot, B., Lobligeois, F., and Delaigue, O.: Summary sheets of
watershed-scale hydroclimatic observed data for France, Recherche Data Gouv [data set], https://doi.org/10.15454/UV01P1, 2020. a
Burt, T. and Butcher, D.: Stimulation from simulation? A teaching model of
hillslope hydrology for use on microcomputers, J. Geogr. High. Educ., 10, 23–39, https://doi.org/10.1080/03098268608708953, 1986. a
Carriba Demange, L., Chanoual, A., and Gazull, A.: Evaluation des logiciels,
modèles et packages disponibles pour l'enseignement de la modélisation
hydrologique, Projet d'ingénierie GE5, Polytech Nice Sophia, Université
Côte d'Azur, https://hal.science/hal-04191446 (last access: 20 July 2023), 2022. a
Cassagnole, M., Ramos, M.-H., Zalachori, I., Thirel, G., Garçon, R., Gailhard, J., and Ouillon, T.: Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach, Hydrology and Earth System Sciences, 25, 1033–1052,
https://doi.org/10.5194/hess-25-1033-2021, 2021. a
Ceola, S., Arheimer, B., Baratti, E., Blöschl, G., Capell, R., Castellarin, A., Freer, J., Han, D., Hrachowitz, M., Hundecha, Y., Hutton, C., Lindström, G., Montanari, A., Nijzink, R., Parajka, J., Toth, E., Viglione, A., and Wagener, T.: Virtual laboratories: new opportunities for collaborative water science, Hydrol. Earth Syst. Sci., 19, 2101–2117,
https://doi.org/10.5194/hess-19-2101-2015, 2015. a
Chang, W., Cheng, J., Allaire, J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., and Borges, B.: shiny: Web Application
Framework for R, R package version 1.7.2, https://CRAN.R-project.org/package=shiny (last access: 20 July 2023), 2022. a
Chauveau, M., Chazot, S., Perrin, C., Bourgin, P.-Y., Sauquet, E.,
Vidal, J.-P., Rouchy, N., Martin, E., David, J., Norotte, T.,
Maugis, P., and De Lacaze, X.: Quels impacts des changements climatiques
sur les eaux de surface en France à l´horizon 2070?, La Houille
Blanche, 4, 5–15, https://doi.org/10.1051/lhb/2013027, 2013. a
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple
working hypotheses for hydrological modeling, Water Resour. Res., 47,
W09301, https://doi.org/10.1029/2010WR009827, 2011. a
Coron, L., Delaigue, O., Thirel, G., Dorchies, D., Perrin, C., and Michel, C.: airGR: Suite of GR Hydrological Models for Precipitation-Runoff
Modelling, R package version 1.7.0, https://doi.org/10.15454/EX11NA, https://CRAN.R-project.org/package=airGR (last access: 5 August 2023), 2022. a, b, c
Delaigue, O., Thirel, G., Coron, L., and Brigode, P.: airGR and
airGRteaching: Two Open-Source Tools for Rainfall-Runoff Modeling and
Teaching Hydrology, in: HIC 2018, 13th International Conference on
Hydroinformatics, vol. 3 of EPiC Series in Engineering, edited by: La Loggia, G., Freni, G., Puleo, V., and De Marchis, M., EasyChair, 541–548, https://doi.org/10.29007/qsqj, 2018. a
Delaigue, O., Brigode, P., Andréassian, V., Perrin, C., Etchevers, P.,
Soubeyroux, J.-M., Janet, B., and Addor, N.: CAMELS-FR: A large sample
hydroclimatic dataset for France to explore hydrological diversity and
support model benchmarking, https://hal.inrae.fr/hal-03687235 (last access: 30 December 2022), 2022. a, b
Delaigue, O., Brigode, P., and Thirel, G.: airGRdatasets: Hydro-Meteorological Catchments Datasets for the “airGR” Packages, R package version 0.2.1, https://doi.org/10.57745/3SPJ4B, https://CRAN.R-project.org/package=airGRdatasets (last access: 5 August 2023), 2023a. a, b
Delaigue, O., Coron, L., Brigode, P., and Thirel, G.: airGRteaching: Teaching Hydrological Modelling with GR (Shiny Interface Included),
R package version 0.3.2, https://doi.org/10.15454/W0SSKT, https://CRAN.R-project.org/package=airGRteaching (last access: 5 August 2023), 2023b. a, b
de Lavenne, A., Andréassian, V., Thirel, G., Ramos, M.-H., and Perrin, C.: A
Regularization Approach to Improve the Sequential Calibration of a
Semidistributed Hydrological Model, Water Resour. Res., 55, 8821–8839, https://doi.org/10.1029/2018WR024266, 2019. a
Desclaux, T., Lemonnier, H., Genthon, P., Soulard, B., and Gendre, R. L.:
Suitability of a lumped rainfall–runoff model for flashy tropical
watersheds in New Caledonia, Hydrolog. Sci. J., 63, 1689–1706,
https://doi.org/10.1080/02626667.2018.1523613, 2018. a
Dorchies, D., Thirel, G., Jay-Allemand, M., Chauveau, M., Dehay, F., Bourgin,
P.-Y., Perrin, C., Jost, C., Rizzoli, J.-L., Demerliac, S., and Thépot, R.:
Climate change impacts on multi-objective reservoir management: case study on
the Seine River basin, France, Int. J. River Basin Manage., 12, 265–283, https://doi.org/10.1080/15715124.2013.865636, 2014. a
Dorchies, D., Delaigue, O., and Thirel, G.: airGRiwrm: “airGR” Integrated Water Resource Management, R package version 0.6.1, https://doi.org/10.15454/3CVD1I, https://CRAN.R-project.org/package=airGRiwrm (last access: 5 August 2023), 2022. a
Elshorbagy, A.: Learner-centered approach to teaching watershed hydrology using system dynamics, Int. J. Eng. Educ., 21, 1203–1213, 2005. a
Ficchì, A., Perrin, C., and Andréassian, V.: Hydrological modelling at
multiple sub-daily time steps: Model improvement via flux-matching, J. Hydrol., 575, 1308–1327, https://doi.org/10.1016/j.jhydrol.2019.05.084, 2019. a
Fiering, M. B.: Streamflow Synthesis, Harvard University Press, Cambridge, Mass., ISBN 9780674189270, 1967. a
Fuka, D., Walter, M., Archibald, J., Steenhuis, T., and Easton, Z.:
EcoHydRology: A Community Modeling Foundation for Eco-Hydrology, R package
version 0.4.12.1, CRAN, https://CRAN.R-project.org/package=EcoHydRology (last access: 20 July 2023), 2018. a
Furusho, C., Perrin, C., Viatgé, J., Lamblin, R., and Andréassian, V.:
Synergies entre acteurs opérationnels et scientifiques au service de
l'amélioration de la prévision des crues, La Houille Blanche, 4, 5–10, https://doi.org/10.1051/lhb/2016033, 2016. a
García Hernández, J., Paredes Arquiola, J., Foehn, A., Roquier, B., and Fluixá-Sanmartín, J.: RS MINERVE – Technical Manual
v2.25, Tech. rep., RS MINERVE Group, Sion, Switzerland, https://crealp.ch/wp-content/uploads/2021/09/rsminerve_technical_manual_v2.25.pdf (last access: 30 August 2023),
2020. a, b
GEBCO Bathymetric Compilation Group 2021: The GEBCO_2021 Grid – a
continuous terrain model of the global oceans and land, ERC EDS British Oceanographic Data Centre NOC [data set], https://doi.org/10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f, 2021. 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
Hall, C. A., Saia, S. M., Popp, A. L., Dogulu, N., Schymanski, S. J., Drost,
N., van Emmerik, T., and Hut, R.: A hydrologist's guide to open science,
Hydrol. Earth Syst. Sci., 26, 647–664, https://doi.org/10.5194/hess-26-647-2022, 2022. a
Hutton, C., Wagener, T., Freer, J., Han, D., Duffy, C., and Arheimer, B.: Most computational hydrology is not reproducible, so is it really science?, Water Resour. Res., 52, 7548–7555, https://doi.org/10.1002/2016WR019285, 2016. a, b
Irving, K., Kuemmerlen, M., Kiesel, J., Kakouei, K., Domisch, S., and Jähnig,
S. C.: A high-resolution streamflow and hydrological metrics dataset for
ecological modeling using a regression model, Sci. Data, 5, 180224, https://doi.org/10.1038/sdata.2018.224, 2018. a
Kay, D., Kay, N., and McDonald, A.: Teaching Catchment Hydrology: Two
Dynamic Models for Classroom Use, Teach. Geogr., 7, 118–124, 1982. a
Kirkby, M. and Naden, P.: The use of simulation models in teaching geomorphology and hydrology, J. Geogr. High. Educ., 12, 31–49, https://doi.org/10.1080/03098268808709023, 1988. a
Klemeš, V.: Operational testing of hydrological simulation models, Hydrolog. Sci. J., 31, 13–24, https://doi.org/10.1080/02626668609491024, 1986. a, b
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a
Knoben, W. J. M. and Spieler, D.: Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise, Hydrol. Earth Syst. Sci., 26, 3299–3314,
https://doi.org/10.5194/hess-26-3299-2022, 2022. a
Kouassi, A., Koffi, Y., Kouame, K., Lasm, T., and Biemi, J.: Modeling of annual flows using a conceptual model and an artificial neural network model in the N'zi-Bandama watershed (Côte d'Ivoire), Agris On-line Papers in Economics and Informatics, 2, 2082–2094, 2012. a
Lehner, B. and Grill, G.: Global river hydrography and network routing:
baseline data and new approaches to study the world's large river systems,
Hydrol. Process., 27, 2171–2186, https://doi.org/10.1002/hyp.9740, 2013. a
Le Moine, N.: Le bassin versant de surface vu par le souterrain: une voie
d’amélioration des performances et du réalisme des modèles pluie-débit?, PhD thesis, Université Pierre et Marie Curie, Paris 6,
https://hal.science/tel-02591478
(last access: 30 December 2022), 2008. a
Marchane, A., Tramblay, Y., Hanich, L., Ruelland, D., and Jarlan, L.: Climate
change impacts on surface water resources in the Rheraya catchment (High
Atlas, Morocco), Hydrolog. Sci. J., 62, 979–995,
https://doi.org/10.1080/02626667.2017.1283042, 2017. a
Marshall, J. A., Castillo, A. J., and Cardenas, M. B.: The Effect of
Modeling and Visualization Resources on Student Understanding of
Physical Hydrology, J. Geosci. Educ., 63, 127–139, https://doi.org/10.5408/14-057.1, 2015. a
Martel, J.-L., Demeester, K., Brissette, F., Poulin, A., and Arsenault, R.:
HMETS – A simple and efficient hydrology model for teaching hydrological
modelling, flow forecasting and climate change impacts, Int. J. Eng. Educ., 33, 1307–1316, 2017. a
Mathevet, T.: Quels modèles pluie-débit globaux au pas de temps horaire?
Développements empiriques et comparaison de modèles sur un large échantillon de bassins versants, PhD thesis, ENGREF, Paris,
https://hal.science/tel-02587642v1
(last access: 30 December 2022), 2005. a
MATLAB: 9.7.0.1190202 (R2019b), The MathWorks Inc., Natick, Massachusetts,
https://www.mathworks.com (last access: 30 August 2023), 2018. a
McConnell, S.: Code complete, 2nd Edn., Microsoft Press, Redmond, Wash.
ISBN-13 9780735619678, 2004. a
Mendez, M. and Calvo-Valverde, L.: Development of the HBV-TEC Hydrological Model, Proced. Eng., 154, 1116–1123,
https://doi.org/10.1016/j.proeng.2016.07.521, 2016. a
Merwade, V. and Ruddell, B. L.: Moving university hydrology education forward
with community-based geoinformatics, data and modeling resources, Hydrol.
Earth Syst. Sci., 16, 2393–2404, https://doi.org/10.5194/hess-16-2393-2012, 2012. a
Michel, C.: How to use single-parameter conceptual model in hydrology?, La
Houille Blanche, 69, 39–44, https://doi.org/10.1051/lhb/1983004, 1983. a
Michel, C.: Hydrologie appliquée aux petits bassins ruraux, Cemagref, Antony,
https://belinrae.inrae.fr/index.php?lvl=notice_display&id=225112 (last access: 1 August 2023), 1991. a
Mouelhi, S., Michel, C., Perrin, C., and Andréassian, V.: Linking stream flow
to rainfall at the annual time step: The Manabe bucket model revisited,
J. Hydrol., 328, 283–296, https://doi.org/10.1016/j.jhydrol.2005.12.022, 2006a. a
Mouelhi, S., Michel, C., Perrin, C., and Andréassian, V.: Stepwise development of a two-parameter monthly water balance model, J. Hydrol., 318, 200–214, https://doi.org/10.1016/j.jhydrol.2005.06.014, 2006b. a
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Neumann, J. L., Arnal, L., Emerton, R. E., Griffith, H., Hyslop, S.,
Theofanidi, S., and Cloke, H. L.: Can seasonal hydrological forecasts inform
local decisions and actions? A decision-making activity, Geosci. Commun., 1, 35–57, https://doi.org/10.5194/gc-1-35-2018, 2018. a
Nicolle, P., Pushpalatha, R., Perrin, C., François, D., Thiéry, D.,
Mathevet, T., Le Lay, M., Besson, F., Soubeyroux, J.-M., Viel, C., Regimbeau,
F., Andréassian, V., Maugis, P., Augeard, B., and Morice, E.: Benchmarking hydrological models for low-flow simulation and forecasting on French catchments, Hydrol. Earth Syst. Sci., 18, 2829–2857,
https://doi.org/10.5194/hess-18-2829-2014, 2014. a
Oudin, L., Andréassian, V., Mathevet, T., Perrin, C., and Michel, C.: Dynamic
averaging of rainfall-runoff model simulations from complementary model
parameterizations, Water Resour. Res., 42, W07410, https://doi.org/10.1029/2005WR004636, 2006. a
Paquet, E., Garavaglia, F., Garçon, R., and Gailhard, J.: The SCHADEX
method: A semi-continuous rainfall–runoff simulation for extreme flood
estimation, J. Hydrol., 495, 23–37, https://doi.org/10.1016/j.jhydrol.2013.04.045, 2013. a
Pérez-Sánchez, J., Senent-Aparicio, J., and Jimeno-Sáez, P.: The application of spreadsheets for teaching hydrological modeling and climate change impacts on streamflow, Comput. Appl. Eng. Educ., 30, 1510–1525, https://doi.org/10.1002/cae.22541, 2022. a
Perrin, C., Michel, C., and Andréassian, V.: Improvement of a parsimonious
model for streamflow simulation, J. Hydrol., 279, 275–289,
https://doi.org/10.1016/S0022-1694(03)00225-7, 2003. a, b, c
Piazzi, G. and Delaigue, O.: airGRdatassim: Suite of Tools to Perform
Ensemble-Based Data Assimilation in GR Hydrological Models, R package version 0.1.3, https://doi.org/10.15454/WEYYVZ, https://CRAN.R-project.org/package=airGRdatassim (last access: 5 August 2023) 2021. a
Piazzi, G., Thirel, G., Perrin, C., and Delaigue, O.: Sequential Data
Assimilation for Streamflow Forecasting: Assessing the Sensitivity
to Uncertainties and Updated Variables of a Conceptual Hydrological
Model at Basin Scale, Water Resour. Res., 57, e2020WR02839, https://doi.org/10.1029/2020WR028390, 2021. a
Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T., and Andréassian, V.:
A downward structural sensitivity analysis of hydrological models to improve
low-flow simulation, J. Hydrol, 411, 66–76, https://doi.org/10.1016/j.jhydrol.2011.09.034, 2011. a
Ramos, M. H., van Andel, S. J., and Pappenberger, F.: Do probabilistic
forecasts lead to better decisions?, Hydrol. Earth Syst. Sci., 17, 2219–2232, https://doi.org/10.5194/hess-17-2219-2013, 2013. a
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/, last access: 20 July 2023. a
Riboust, P., Thirel, G., Moine, N. L., and Ribstein, P.: Revisiting a Simple Degree-Day Model for Integrating Satellite Data: Implementation of Swe-Sca Hystereses, J. Hydrol. Hydromech., 67, 70–81, https://doi.org/10.2478/johh-2018-0004, 2019. a, b
Richmond, B., Aspinwall, D., Vescuso, P., Peterson, S., and High Performance
Systems, Inc.: STELLA, High Performance, Lyme, NH, OCLC: 14639320,
https://www.iseesystems.com (last access: 1 August 2023), 1985. a
Roux, Q. and Brigode, P.: How long would we have to wait before (re)filling the Malpasset dam reservoir? An example of a teaching project done using R and airGR modeling packages, https://hal.science/hal-03020769 (last access: 20 July 2023), 2018. a
Sanchez, C. A., Ruddell, B. L., Schiesser, R., and Merwade, V.: Enhancing the
T-shaped learning profile when teaching hydrology using data, modeling, and
visualization activities, Hydrol. Earth Syst. Sci., 20, 1289–1299, https://doi.org/10.5194/hess-20-1289-2016, 2016. a
Santos, L., Thirel, G., and Perrin, C.: Technical note: Pitfalls in using
log-transformed flows within the KGE criterion, Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, 2018. a
Seibert, J. and Vis, M. J. P.: Teaching hydrological modeling with a
user-friendly catchment-runoff-model software package, Hydrol. Earth Syst. Sci., 16, 3315–3325, https://doi.org/10.5194/hess-16-3315-2012, 2012. a
Seibert, J., Uhlenbrook, S., and Wagener, T.: Preface “Hydrology education in a changing world”, Hydrol. Earth Syst. Sci., 17, 1393–1399,
https://doi.org/10.5194/hess-17-1393-2013, 2013. a
Shmueli, G.: To Explain or to Predict?, Stat. Sci., 25, 289–310, https://doi.org/10.1214/10-STS330, 2010. a
Slater, L. J., Thirel, G., Harrigan, S., Delaigue, O., Hurley, A., Khouakhi, A., Prosdocimi, I., Vitolo, C., and Smith, K.: Using R in hydrology: a review of recent developments and future directions, Hydrol. Earth Syst. Sci., 23, 2939–2963, https://doi.org/10.5194/hess-23-2939-2019, 2019.
a, b, c
Tarboton, D., Idaszak, R., Horsburgh, J., Heard, J., Ames, D., Goodall, J.,
Band, L., Merwade, V., Couch, A., Arrigo, J., Hooper, R., Valentine, D., and
Maidment, D.: HydroShare: Advancing Collaboration through Hydrologic
Data and Model Sharing, in:7th International Congress on Environmental Modelling and Software - San Diego, California, USA, 15–19 June 2014,
https://scholarsarchive.byu.edu/iemssconference/2014/Stream-A/7 (last access: 20 July 2023), 2014. a
Toum, E., Masiokas, M. H., Villalba, R., Pitte, P., and Ruiz, L.: The
HBV.IANIGLA Hydrological Model, R J., 13, 378–395, 2021. a
Valéry, A., Andréassian, V., and Perrin, C.: `As simple as possible but not simpler': what is useful in a temperature-based snow-accounting routine? Part 2 – Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments, J. Hydrol., 517, 1176–1187, https://doi.org/10.1016/j.jhydrol.2014.04.058, 2014. a, b
Vanderkam, D., Allaire, J., Owen, J., Gromer, D., and Thieurmel, B.: dygraphs: Interface to 'Dygraphs' Interactive Time Series Charting Library,
R package version 1.1.1.6, https://CRAN.R-project.org/package=dygraphs
(last access: 20 July 2023), 2018. a
Vidal, J., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.: 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
Wi, S., Ray, P., Demaria, E. M. C., Steinschneider, S., and Brown, C.: A
user-friendly software package for VIC hydrologic model development, Environ. Model. Softw., 98, 35–53, https://doi.org/10.1016/j.envsoft.2017.09.006, 2017. a
Zimmerman, W. B. J.: Multiphysics Modeling with Finite Element Methods, in: vol. 18 of eries on Stability, Vibration and Control of
Systems, Series A, World Scientific, https://doi.org/10.1142/6141, 2006. a
Zipper, S., Albers, S., and Prosdocimi, I.: CRAN Task View: Hydrological Data and Modeling, https://cran.r-project.org/view=Hydrology (last access: 1 August 2023), 2022. a
Short summary
Teaching hydrological modeling is an important, but difficult, matter. It requires appropriate tools and teaching material. In this article, we present the airGRteaching package, which is an open-source software tool relying on widely used hydrological models. This tool proposes an interface and numerous hydrological modeling exercises representing a wide range of hydrological applications. We show how this tool can be applied to simple but real-life cases.
Teaching hydrological modeling is an important, but difficult, matter. It requires appropriate...
