Articles | Volume 16, issue 4
https://doi.org/10.5194/essd-16-2073-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2073-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
30 Apr 2024
Data description paper |
| 30 Apr 2024
| 30 Apr 2024
Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti
Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, IRD, Géoazur, France
Université d’État d’Haïti, Faculté des Sciences, LMI CARIBACT, Urgéo, Haïti
Pierre Brigode
Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, IRD, Géoazur, France
Université Paris-Saclay, INRAE, HYCAR, Antony, France
Univ Rennes, CNRS, Géosciences Rennes – UMR 6118, Rennes, France
Vazken Andréassian
Université Paris-Saclay, INRAE, HYCAR, Antony, France
Charles Perrin
Université Paris-Saclay, INRAE, HYCAR, Antony, France
Vincent Moron
Aix Marseille University, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, France
Cédric Gaucherel
AMAP, INRAE, University of Montpellier, CNRS, IRD, Cirad, Montpellier, France
Emmanuel Tric
Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, IRD, Géoazur, France
Dominique Boisson
Université d’État d’Haïti, Faculté des Sciences, LMI CARIBACT, Urgéo, Haïti
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Taha-Abderrahman El Ouahabi, François Bourgin, Charles Perrin, and Vazken Andréassian
EGUsphere, https://doi.org/10.5194/egusphere-2025-3586, https://doi.org/10.5194/egusphere-2025-3586, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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To improve hydrological uncertainty estimation, recent studies have explored machine learning (ML)-based post-processing approaches. Among these, quantile random forests (QRF) are increasingly used for their balance between interpretability and performance. We develop a hydrologically informed QRF trained in a multi-site setting. Our results show that the regional QRF approach is beneficial, particularly in catchments where local information is insufficient.
Eric Sauquet, Guillaume Evin, Sonia Siauve, Ryma Aissat, Patrick Arnaud, Maud Bérel, Jérémie Bonneau, Flora Branger, Yvan Caballero, François Colléoni, Agnès Ducharne, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Benoît Hingray, Peng Huang, Tristan Jaouen, Alexis Jeantet, Sandra Lanini, Matthieu Le Lay, Claire Magand, Louise Mimeau, Céline Monteil, Simon Munier, Charles Perrin, Olivier Robelin, Fabienne Rousset, Jean-Michel Soubeyroux, Laurent Strohmenger, Guillaume Thirel, Flore Tocquer, Yves Tramblay, Jean-Pierre Vergnes, and Jean-Philippe Vidal
EGUsphere, https://doi.org/10.5194/egusphere-2025-1788, https://doi.org/10.5194/egusphere-2025-1788, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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The Explore2 project has provided an unprecedented set of hydrological projections in terms of the number of hydrological models used and the spatial and temporal resolution. The results have been made available through various media. Under the high-emission scenario, the hydrological models mostly agree on the decrease in seasonal flows in the south of France, confirming its hotspot status, and on the decrease in summer flows throughout France, with the exception of the northern part of France.
Olivier Delaigue, Guilherme Mendoza Guimarães, Pierre Brigode, Benoît Génot, Charles Perrin, Jean-Michel Soubeyroux, Bruno Janet, Nans Addor, and Vazken Andréassian
Earth Syst. Sci. Data, 17, 1461–1479, https://doi.org/10.5194/essd-17-1461-2025, https://doi.org/10.5194/essd-17-1461-2025, 2025
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This dataset covers 654 rivers all flowing in France. The provided time series and catchment attributes will be of interest to those modelers wishing to analyze hydrological behavior and perform model assessments.
Vazken Andréassian, Guilherme Mendoza Guimarães, Alban de Lavenne, and Julien Lerat
EGUsphere, https://doi.org/10.5194/egusphere-2025-414, https://doi.org/10.5194/egusphere-2025-414, 2025
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Using 4122 catchments from four continents, we investigate how annual streamflow depends on climate variables (rainfall and potential evaporation) and on the season when precipitation occurs, using and index representing the synchronicity between precipitation and potential evaporation. In all countries and under the main climates represented, synchronicity is, after precipitation, the second most important factor to explain annual streamflow variations.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 29, 683–700, https://doi.org/10.5194/hess-29-683-2025, https://doi.org/10.5194/hess-29-683-2025, 2025
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This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
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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 targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Pierre Brigode and Ludovic Oudin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-336, https://doi.org/10.5194/hess-2024-336, 2024
Revised manuscript accepted for HESS
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We analyzed how well two global climate datasets can simulate river flows across Europe over the last 150 years. Our results show good performance overall, revealing important long-term changes in water availability and extreme events, like floods, in different regions. This research helps us better understand past and future water trends, providing insights to manage resources and address the challenges posed by climate change.
Carlo Mologni, Marie Revel, Eric Chaumillon, Emmanuel Malet, Thibault Coulombier, Pierre Sabatier, Pierre Brigode, Gwenael Hervé, Anne-Lise Develle, Laure Schenini, Medhi Messous, Gourguen Davtian, Alain Carré, Delphine Bosch, Natacha Volto, Clément Ménard, Lamya Khalidi, and Fabien Arnaud
Clim. Past, 20, 1837–1860, https://doi.org/10.5194/cp-20-1837-2024, https://doi.org/10.5194/cp-20-1837-2024, 2024
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The reactivity of local to regional hydrosystems to global changes remains understated in East African climate models. By reconstructing a chronicle of seasonal floods and droughts from a lacustrine sedimentary core, this paper highlights the impact of El Niño anomalies in the Awash River valley (Ethiopia). Studying regional hydrosystem feedbacks to global atmospheric anomalies is essential for better comprehending and mitigating the effects of global warming in extreme environments.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
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The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
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
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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.
Olivier Delaigue, Pierre Brigode, Guillaume Thirel, and Laurent Coron
Hydrol. Earth Syst. Sci., 27, 3293–3327, https://doi.org/10.5194/hess-27-3293-2023, https://doi.org/10.5194/hess-27-3293-2023, 2023
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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.
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.
Alban de Lavenne, Vazken Andréassian, Louise Crochemore, Göran Lindström, and Berit Arheimer
Hydrol. Earth Syst. Sci., 26, 2715–2732, https://doi.org/10.5194/hess-26-2715-2022, https://doi.org/10.5194/hess-26-2715-2022, 2022
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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.
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.
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.
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.
Cited articles
Abbaspour, K. C., Faramarzi, M., Ghasemi, S. S., and Yang, H.: Assessing the impact of climate change on water resources in Iran, Water Resour. Res., 45, 16, https://doi.org/10.1029/2008WR007615, 2009.
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.
Alfieri, L., Lorini, V., Hirpa, F. A., Harrigan, S., Zsoter, E., Prudhomme, C., and Salamon, P.: A global streamflow reanalysis for 1980–2018, J. Hydrol. X, 6, 100049, https://doi.org/10.1016/j.hydroa.2019.100049, 2020.
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018.
Andréassian, V., Perrin, C., Michel, C., Usart-Sanchez, I., and Lavabre, J.: Impact of imperfect rainfall knowledge on the efficiency and the parameters of watershed models, J. Hydrol., 250, 206–223, https://doi.org/10.1016/S0022-1694(01)00437-1, 2001.
Andréassian, V., Perrin, C., and Michel, C.: Impact of imperfect potential evapotranspiration knowledge on the efficiency and parameters of watershed models, J. Hydrol., 286, 19–35, https://doi.org/10.1016/j.jhydrol.2003.09.030, 2004.
Bathelemy, R., Brigode, P., Boisson, D., and Tric, E.: Rainfall in the Greater and Lesser Antilles: Performance of five gridded datasets on a daily timescale, J. Hydrol.-Reg. Stud., 43, 101203, https://doi.org/10.1016/j.ejrh.2022.101203, 2022.
Bathelemy, R., Brigode, P., Andréassian, V., Perrin, C., Moron, V., Gaucherel, C., Tric, E., and Boisson, D.: Simbi database: historical hydro-meteorological time series and catchment attributes in Haiti 1905–2005, DataSuds [data set], https://doi.org/10.23708/02POK6, 2023.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Dijk, A. I. J. M. van, McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019.
Beirlant, J., Goegebeur, Y., Segers, J., and Teugels, J.: Statistics of extremes: theory and applications, Wiley: Hoboken, 522 pp., ISBN 978-0-471-97647-9, 2004.
Bendjoudi, H. and Hubert, P.: Le coefficient de compacité de Gravelius: analyse critique d'un indice de forme des bassins versants, Hydrol. Sci. J., 47, 921–930, https://doi.org/10.1080/02626660209493000, 2002.
Benoit, L., Sichoix, L., Nugent, A. D., Lucas, M. P., and Giambelluca, T. W.: Stochastic daily rainfall generation on tropical islands with complex topography, Hydrol. Earth Syst. Sci., 26, 2113–2129, https://doi.org/10.5194/hess-26-2113-2022, 2022.
Boisson, D. and Pubellier, M.: Carte géologique à 1/250 000 de la République d’Haïti [Geologic map of the Republic of Haiti at 1/250,000] BME, IMAGEO, CNRS, Paris, https://books.openedition.org/iheal/5618?lang=en (last access: 25 April 2024), 1987.
Bonhomme, V., Frelat, R., and Gaucherel, C.: Application of elliptical Fourier analysis to watershed boundaries: a case study in Haiti, Géomorphologie: relief, processus, Environnement, 19, 17–26, https://doi.org/10.4000/geomorphologie.10100, 2013.
Brigode, P., Brissette, F., Nicault, A., Perreault, L., Kuentz, A., Mathevet, T., and Gailhard, J.: Streamflow variability over the 1881–2011 period in northern Québec: comparison of hydrological reconstructions based on tree rings and geopotential height field reanalysis, Clim. Past, 12, 1785–1804, https://doi.org/10.5194/cp-12-1785-2016, 2016.
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.
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W.: SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations, Earth Syst. Sci. Data, 11, 1583–1601, https://doi.org/10.5194/essd-11-1583-2019, 2019.
Burgess, C. P., Taylor, M. A., Spencer, N., Jones, J., and Stephenson, T. S.: Estimating damages from climate-related natural disasters for the Caribbean at 1.5°C and 2°C global warming above preindustrial levels, Reg. Environ. Change, 18, 2297–2312, https://doi.org/10.1007/s10113-018-1423-6, 2018.
Burnash, R. J. C.: The NWS River Forecast System-catchment modeling, Computer models of watershed hydrology, edited by: Singh, V. P., Water Resources Publications, 311–366, ISBN 978-0-918334-91-6, 1995.
Butterlin, J.: Geologie generale et regionale de la Republique d'Haiti, Éditions de l'IHEAL, Paris, 194 pp., https://doi.org/10.4000/books.iheal.5606, 1960.
Caillouet, L., Vidal, J.-P., Sauquet, E., Devers, A., and Graff, B.: Ensemble reconstruction of spatio-temporal extreme low-flow events in France since 1871, Hydrol. Earth Syst. Sci., 21, 2923–2951, https://doi.org/10.5194/hess-21-2923-2017, 2017.
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.
Chokkavarapu, N. and Mandla, V. R.: Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation, SN Appl. Sci., 1, 1698, https://doi.org/10.1007/s42452-019-1764-x, 2019.
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer, London, 209 pp., https://doi.org/10.1007/978-1-4471-3675-0, 2001.
Coron, L., Andréassian, V., Perrin, C., and Le Moine, N.: Graphical tools based on Turc-Budyko plots to detect changes in catchment behaviour, Hydrol. Sci. J., 60, 1394–1407, https://doi.org/10.1080/02626667.2014.964245, 2015.
Coron, L., Thirel, G., Delaigue, O., Perrin, C., and Andréassian, V.: The suite of lumped GR hydrological models in an R package, Environ. Modell. Softw., 94, 166–171, https://doi.org/10.1016/j.envsoft.2017.05.002, 2017.
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 1.7.6., https://doi.org/10.15454/EX11NA, 2020.
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020.
Cressie, N.: The origins of kriging, Math. Geol., 22, 239–252, https://doi.org/10.1007/BF00889887, 1990.
Croley, T. E. and Hartmann, H. C.: Resolving Thiessen polygons, J. Hydrol., 76, 363–379, https://doi.org/10.1016/0022-1694(85)90143-X, 1985.
Crooks, S. M. and Kay, A. L.: Simulation of river flow in the Thames over 120 years: Evidence of change in rainfall-runoff response?, J. Hydrol.-Reg. Stud., 4, 172–195, https://doi.org/10.1016/j.ejrh.2015.05.014, 2015.
Dewandel, B., Lachassagne, P., Bakalowicz, M., Weng, P., and Al-Malki, A.: Evaluation of aquifer thickness by analysing recession hydrographs. Application to the Oman ophiolite hard-rock aquifer, J. Hydrol., 274, 248–269, https://doi.org/10.1016/S0022-1694(02)00418-3, 2003.
Dewandel, B., Lachassagne, P., and Qatan, A.: Spatial measurements of stream baseflow, a relevant method for aquifer characterization and permeability evaluation. Application to a hard-rock aquifer, the Oman ophiolite, Hydrol. Process., 18, 3391–3400, https://doi.org/10.1002/hyp.1502, 2004.
Di Piazza, A., Conti, F. L., Noto, L. V., Viola, F., and La Loggia, G.: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy, Int. J. Appl. Earth Observ. Geoinform., 13, 396–408, https://doi.org/10.1016/j.jag.2011.01.005, 2011.
Fowler, A.: Assessment of the validity of using mean potential evaporation in computations of the long-term soil water balance, J. Hydrol., 256, 248–263, 2002.
Fowler, K. J. A., Acharya, S. C., Addor, N., Chou, C., and Peel, M. C.: CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia, Earth Syst. Sci. Data, 13, 3847–3867, https://doi.org/10.5194/essd-13-3847-2021, 2021.
Gaucherel, C., Frelat, R., Lustig, A., Rouy, B., Chéry, Y., and Hubert, P.: Time–frequency analysis to profile hydrological regimes: application to Haiti, Hydrol. Sci. J., 61, 274–288, https://doi.org/10.1080/02626667.2015.1006231, 2016.
Gaucherel, C., Frelat, R., Salomon, L., Rouy, B., Pandey, N., and Cudennec, C.: Regional watershed characterization and classification with river network analyses, Earth Surf. Process. Landf., 42, 2068–2081, https://doi.org/10.1002/esp.4172, 2017.
Gaucherel, C., Frelat, R., Polidori, L., El Hage, M., Cudennec, C., Mondesir, P., and Moron, V.: Weak relationships between landforms and hydro-climatologic processes: a case study in Haiti, Hydrol. Res., 50, 744–760, https://doi.org/10.2166/nh.2018.041, 2018.
Gudmundsson, L. and Seneviratne, S. I.: Observation-based gridded runoff estimates for Europe (E-RUN version 1.1), Earth Syst. Sci. Data, 8, 279–295, https://doi.org/10.5194/essd-8-279-2016, 2016.
Gudmundsson, L., Do, H. X., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018, 2018.
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.
Han, D. and Bray, M.: Automated Thiessen polygon generation, Water Resour. Res., 42, 5, https://doi.org/10.1029/2005WR004365, 2006.
Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data, 12, 2043–2060, https://doi.org/10.5194/essd-12-2043-2020, 2020.
Hedges, S. B., Cohen, W. B., Timyan, J., and Yang, Z.: Haiti's biodiversity threatened by nearly complete loss of primary forest, P. Natl. Acad. Sci. USA, 115, 11850–11855, https://doi.org/10.1073/pnas.1809753115, 2018.
Höge, M., Kauzlaric, M., Siber, R., Schönenberger, U., Horton, P., Schwanbeck, J., Floriancic, M. G., Viviroli, D., Wilhelm, S., Sikorska-Senoner, A. E., Addor, N., Brunner, M., Pool, S., Zappa, M., and Fenicia, F.: CAMELS-CH: hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland, Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, 2023.
Horn, B. K.: Hill shading and the reflectance map, P. IEEE, 69, 14–47, 1981.
Jenkinson, A. F.: The frequency distribution of the annual maximum (or minimum) values of meteorological elements, Q. J. Roy. Meteorol. Soc., 81, 158–171, https://doi.org/10.1002/qj.49708134804, 1955.
Jones, P. D. and Lister, D. H.: Riverflow reconstructions for 15 catchments over England and Wales and an assessment of hydrologic drought since 1865, Int. J. Climatol., 18, 999–1013, https://doi.org/10.1002/(SICI)1097-0088(199807)18:9<999::AID-JOC300>3.0.CO;2-8, 1998.
Joseph, A.: Caractérisation et modélisation des écoulements de crue: application aux inondations de la ville de Cavaillon en Haiti, PhD, UCL – Université Catholique de Louvain, http://hdl.handle.net/2078.1/264969, 2019.
Joseph, A., Gonomy, N., Zech, Y., and Soares-Frazão, S.: Modelling and analysis of the flood risk at Cavaillon City, Haiti, La Houille Blanche, 104, 68–75, https://doi.org/10.1051/lhb/2018020, 2018.
Khouakhi, A., Villarini, G., and Vecchi, G. A.: Contribution of Tropical Cyclones to Rainfall at the Global Scale, J. Climate, 30, 359–372, https://doi.org/10.1175/JCLI-D-16-0298.1, 2017.
Klemeš, V.: Operational testing of hydrological simulation models, Hydrol. Sci. J., 31, 13–24, https://doi.org/10.1080/02626668609491024, 1986.
Klingler, C., Schulz, K., and Herrnegger, M.: LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, 2021.
Kribèche, R.: Etude de la sensibilité d’un modèle pluie-débit à l’exactitude de l’évaporation (modèle GR4J), DEA Thésis, Université Paris XII, Créteil, 42 pp., 1994.
Krige, D. G.: A statistical approach to some mine valuation and allied problems on the Witwatersrand, M.Sc, Engineering, University of the Witwatersrand, 62 pp., http://hdl.handle.net/10539/17975 (last access: 24 April 2024), 1951.
Mathieu, G.: Développement d’une méthodologie pour la cartographie du risque d’inondation: application à la rivière de Cavaillon en Haïti, Ph.D., UCL – Université Catholique de Louvain, Belgium, 221 pp., https://hdl.handle.net/2078.1/273984 (last access: 24 April 2024), 2023.
Mompremier, R., Her, Y., Hoogenboom, G., and Song, J.: Effects of deforestation and afforestation on water availability for dry bean production in Haiti, Agr. Ecosyst. Environ., 325, 107721, https://doi.org/10.1016/j.agee.2021.107721, 2022.
Moron, V., Frelat, R., Jean-Jeune, P. K., and Gaucherel, C.: Interannual and intra-annual variability of rainfall in Haiti (1905–2005), Clim. Dynam., 45, 915–932, https://doi.org/10.1007/s00382-014-2326-y, 2015.
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, 2006.
Oriani, F., Stisen, S., Demirel, M. C., and Mariethoz, G.: Missing Data Imputation for Multisite Rainfall Networks: A Comparison between Geostatistical Interpolation and Pattern-Based Estimation on Different Terrain Types, J. Hydrometeorol., 21, 2325–2341, https://doi.org/10.1175/JHM-D-19-0220.1, 2020.
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.
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, 2020.
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.
Peterson, T. C., Taylor, M. A., Demeritte, R., Duncombe, D. L., Burton, S., Thompson, F., Porter, A., Mercedes, M., Villegas, E., Fils, R. S., Tank, A. K., Martis, A., Warner, R., Joyette, A., Mills, W., Alexander, L., and Gleason, B.: Recent changes in climate extremes in the Caribbean region, J. Geophys. Res.-Atmos., 107, ACL16-1–ACL16-9, https://doi.org/10.1029/2002JD002251, 2002.
Pouyaud, B. and Hoepffner, M.: Rapport d’expertise hydrologique: appui au Service National des Ressources en Eau de la République d’Haïti, ORSTOM, Centre ORSTOM Montpellier, 35 pp., https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers12-05/010018883.pdf (last access: 24 April 2024), 1987.
Prakash, S.: Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India, J. Hydrol., 571, 50–59, https://doi.org/10.1016/j.jhydrol.2019.01.036, 2019.
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: 24 April 2024), 2022.
Reuter, H. I., Nelson, A., and Jarvis, A.: An evaluation of void-filling interpolation methods for SRTM data, Int. J. Geogr. Inf. Sci., 21, 983–1008, https://doi.org/10.1080/13658810601169899, 2007.
Rohde, R., Muller, R. A., Jacobsen, R., Muller, E., Perlmutter, S., Rosenfeld, A., Wurtele, J., Groom, D., and Wickham, C.: A new estimate of the average Earth surface land temperature spanning 1753 to 2011, Geoinfor. Geostat: An Overview, 1, 1, https://doi.org/10.4172/2327-4581.1000101, 2013.
Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J., Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A., Crouthamel, R., Domínguez-Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan, A., Kubota, H., Blancq, F. L., Lee, T.-C., Lorrey, A., Luterbacher, J., Maugeri, M., Mock, C. J., Moore, G. W. K., Przybylak, R., Pudmenzky, C., Reason, C., Slonosky, V. C., Smith, C. A., Tinz, B., Trewin, B., Valente, M. A., Wang, X. L., Wilkinson, C., Wood, K., and Wyszyñski, P.: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system, Q. J. Roy. Meteorol. Soc., 145, 2876–2908, https://doi.org/10.1002/qj.3598, 2019.
Smith, K. A., Barker, L. J., Tanguy, M., Parry, S., Harrigan, S., Legg, T. P., Prudhomme, C., and Hannaford, J.: A multi-objective ensemble approach to hydrological modelling in the UK: an application to historic drought reconstruction, Hydrol. Earth Syst. Sci., 23, 3247–3268, https://doi.org/10.5194/hess-23-3247-2019, 2019.
Strohmenger, L., Sauquet, E., Bernard, C., Bonneau, J., Branger, F., Bresson, A., Brigode, P., Buzier, R., Delaigue, O., Devers, A., Evin, G., Fournier, M., Hsu, S.-C., Lanini, S., de Lavenne, A., Lemaitre-Basset, T., Magand, C., Mendoza Guimarães, G., Mentha, M., Munier, S., Perrin, C., Podechard, T., Rouchy, L., Sadki, M., Soutif-Bellenger, M., Tilmant, F., Tramblay, Y., Véron, A.-L., Vidal, J.-P., and Thirel, G.: On the visual detection of non-natural records in streamflow time series: challenges and impacts, Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023, 2023.
Tarboton, D. G., Watson, D. W., Wallace, R., Schreuders, K. A. T., and Neff, J.: Terrain Analysis Using Digital Elevation Models, Utah State University, 48 pp., https://hydrology.usu.edu/taudem (last access: 24 April 2024), 2005.
Tarter, A., Freeman, K. K., Ward, C., Sander, K., Theus, K., Coello, B., Fawaz, Y., Miles, M., and Ahmed, T. T. G.: Charcoal in Haiti: A National Assessment of Charcoal Production and Consumption Trends, World Bank, Washington, DC, https://doi.org/10.1596/31257, 2018.
Terrier, M., Bialkowski, A., Nachbaur, A., Prépetit, C., and Joseph, Y. F.: Revision of the geological context of the Port-au-Prince metropolitan area, Haiti: implications for slope failures and seismic hazard assessment, Nat. Hazards Earth Syst. Sci., 14, 2577–2587, https://doi.org/10.5194/nhess-14-2577-2014, 2014.
Terrier, M., Rançon, J.-P., Bertil, D., Chêne, F., Desprats, J.-F., Lecacheux, S., Le Roy, S., Stollsteiner, P., Bouc, O., and Raynal, M.: Atlas des menaces naturelles en Haïti, Comité Interministériel d’Aménagement du Territoire., Bradley Lyon, Rafael Van der Borght, Haïti, 114 pp., ISBN 978-99970-4-871-4, 2017.
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hydrol., 456–457, 12–29, https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012.
Tramblay, Y., Rouché, N., Paturel, J.-E., Mahé, G., Boyer, J.-F., Amoussou, E., Bodian, A., Dacosta, H., Dakhlaoui, H., Dezetter, A., Hughes, D., Hanich, L., Peugeot, C., Tshimanga, R., and Lachassagne, P.: ADHI: the African Database of Hydrometric Indices (1950–2018), Earth Syst. Sci. Data, 13, 1547–1560, https://doi.org/10.5194/essd-13-1547-2021, 2021.
Short summary
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.
The aim of this work is to provide the first hydroclimatic database for Haiti, a Caribbean...
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