Articles | Volume 16, issue 20
https://doi.org/10.5194/gmd-16-5755-2023
© 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/gmd-16-5755-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
Anthony Schrapffer
CORRESPONDING AUTHOR
Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Centro de Investigaciones del Mar y la Atmósfera (CIMA), CONICET – Universidad de Buenos Aires, Buenos Aires, Argentina
Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI 3351 IFAECI), CNRS – IRD – CONICET – UBA, Buenos Aires, Argentina
EthiFinance, 11 Avenue Delcassé 75008, Paris, France
Jan Polcher
Laboratoire de Météorologie Dynamique (LMD), IPSL, CNRS, École Polytechnique, Palaiseau, France
Anna Sörensson
Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Centro de Investigaciones del Mar y la Atmósfera (CIMA), CONICET – Universidad de Buenos Aires, Buenos Aires, Argentina
Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI 3351 IFAECI), CNRS – IRD – CONICET – UBA, Buenos Aires, Argentina
Lluís Fita
Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Centro de Investigaciones del Mar y la Atmósfera (CIMA), CONICET – Universidad de Buenos Aires, Buenos Aires, Argentina
Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (UMI 3351 IFAECI), CNRS – IRD – CONICET – UBA, Buenos Aires, Argentina
Related authors
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
Short summary
Short summary
The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Rodrigo San Martin, Catherine Ottlé, Anna Sorenssön, Pradeebane Vattinada Ayar, Florent Mouillot, and Marielle Malfante
EGUsphere, https://doi.org/10.5194/egusphere-2025-3484, https://doi.org/10.5194/egusphere-2025-3484, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
We studied wildfires in the Gran Chaco, one of the world's largest dry forests, to understand why some fires grow larger than others. By analyzing fire size and weather conditions during burning, we found that strong winds and low humidity were key drivers of fire expansion. This work helps improve our understanding of extreme fire events and supports better fire risk management in dry ecosystems.
Detlef van Vuuren, Brian O'Neill, Claudia Tebaldi, Louise Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Benjamin Sanderson, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh Fall, Katja Frieler, Matthew Gidden, Laila Gohar, Andrew Jones, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camila Mathison, Shahbaz Mehmood, Luciana Prado, Qiang Zhang, Steven Rose, Alexander Ruane, Carl-Friederich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha Vishwanathan, Tokuta Yokohata, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3765, https://doi.org/10.5194/egusphere-2024-3765, 2025
Short summary
Short summary
We propose a set of six plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Laure Baratgin, Jan Polcher, Patrice Dumas, and Philippe Quirion
Hydrol. Earth Syst. Sci., 28, 5479–5509, https://doi.org/10.5194/hess-28-5479-2024, https://doi.org/10.5194/hess-28-5479-2024, 2024
Short summary
Short summary
Hydrological modeling is valuable for estimating the potential impact of climate change on hydropower generation. This study presents a comprehensive approach to modeling the management of hydroelectric reservoirs in hydrological models. The total power grid demand for hydropower is distributed to the various power plants to compute their release. The method is tested on the French national power grid, and it is demonstrated that it successfully reproduces the observed behavior of reservoirs.
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
Hydrol. Earth Syst. Sci., 28, 4455–4476, https://doi.org/10.5194/hess-28-4455-2024, https://doi.org/10.5194/hess-28-4455-2024, 2024
Short summary
Short summary
We conducted a high-resolution hydrological simulation from 1959 to 2020 across France. We used a simple trial-and-error calibration to reduce the biases of the simulated water budget compared to observations. The selected simulation satisfactorily reproduces water fluxes, including their spatial contrasts and temporal trends. This work offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
Short summary
Short summary
The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Phillip Papastefanou, Christian S. Zang, Zlatan Angelov, Aline Anderson de Castro, Juan Carlos Jimenez, Luiz Felipe Campos De Rezende, Romina C. Ruscica, Boris Sakschewski, Anna A. Sörensson, Kirsten Thonicke, Carolina Vera, Nicolas Viovy, Celso Von Randow, and Anja Rammig
Biogeosciences, 19, 3843–3861, https://doi.org/10.5194/bg-19-3843-2022, https://doi.org/10.5194/bg-19-3843-2022, 2022
Short summary
Short summary
The Amazon rainforest has been hit by multiple severe drought events. In this study, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon. Using nine different precipitation datasets and three drought indicators we find large differences in drought stress across the Amazon region. We conclude that future studies should use multiple rainfall datasets and drought indicators when estimating the impact of drought stress in the Amazon region.
Boris Sakschewski, Werner von Bloh, Markus Drüke, Anna Amelia Sörensson, Romina Ruscica, Fanny Langerwisch, Maik Billing, Sarah Bereswill, Marina Hirota, Rafael Silva Oliveira, Jens Heinke, and Kirsten Thonicke
Biogeosciences, 18, 4091–4116, https://doi.org/10.5194/bg-18-4091-2021, https://doi.org/10.5194/bg-18-4091-2021, 2021
Short summary
Short summary
This study shows how local adaptations of tree roots across tropical and sub-tropical South America explain patterns of biome distribution, productivity and evapotranspiration on this continent. By allowing for high diversity of tree rooting strategies in a dynamic global vegetation model (DGVM), we are able to mechanistically explain patterns of mean rooting depth and the effects on ecosystem functions. The approach can advance DGVMs and Earth system models.
Zun Yin, Catherine Ottlé, Philippe Ciais, Feng Zhou, Xuhui Wang, Polcher Jan, Patrice Dumas, Shushi Peng, Laurent Li, Xudong Zhou, Yan Bo, Yi Xi, and Shilong Piao
Hydrol. Earth Syst. Sci., 25, 1133–1150, https://doi.org/10.5194/hess-25-1133-2021, https://doi.org/10.5194/hess-25-1133-2021, 2021
Short summary
Short summary
We improved the irrigation module in a land surface model ORCHIDEE and developed a dam operation model with the aim to investigate how irrigation and dams affect the streamflow fluctuations of the Yellow River. Results show that irrigation mainly reduces the annual river flow. The dam operation, however, mainly affects streamflow variation. By considering two generic operation rules, flood control and base flow guarantee, our dam model can sustainably improve the simulation accuracy.
L. Cappelletti, A. Sörensson, R. Ruscica, M. M. Salvia, E. Jobbágy, S. Kuppel, and L. Fita
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 279–283, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-279-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-279-2020, 2020
Cited articles
Adler, B., Kalthoff, N., and Gantner, L.: The impact of soil moisture inhomogeneities on the modification of a mesoscale convective system: An idealised model study, Atmos. Res., 101, 354–372, https://doi.org/10.1016/j.atmosres.2011.03.013, 2011. a
Aires, F., Prigent, C., Fluet-Chouinard, E., Yamazaki, D., Papa, F., and Lehner, B.: Comparison of visible and multi-satellite global inundation datasets at high-spatial resolution, Remote Sens. Environ., 216, 427–441, https://doi.org/10.1016/j.rse.2018.06.015, 2018. a
Alsdorf, D., Han, S. C., Bates, P., and Melack, J.: Seasonal water storage on the Amazon floodplain measured from satellites, Remote Sens. Environ., 114, 2448–2456, https://doi.org/10.1016/j.rse.2010.05.020, 2010. a
ANA: Discharge used for Porto Murtinho, Brazilian National Water Agency (ANA) [data set], https://www.snirh.gov.br/hidroweb/, last access: 21 June 2021. a
Assine, M. L.: River avulsions on the Taquari megafan, Pantanal wetland, Brazil, Geomorphology, 70, 357–371, https://doi.org/10.1016/j.geomorph.2005.02.013, 2005. a
Barbosa da Silva, F. H., Nunes da Cunha, C., and Overbeck, G. E.: Seasonal dynamics of flooded tropical grassland communities in the Pantanal wetland, Wetlands, 40, 1257–1268, 2020. a
Barella-Ortiz, A., Polcher, J., Tuzet, A., and Laval, K.: Potential evaporation estimation through an unstressed surface-energy balance and its sensitivity to climate change, Hydrol. Earth Syst. Sci., 17, 4625–4639, https://doi.org/10.5194/hess-17-4625-2013, 2013. a
Barlage, M., Chen, F., Rasmussen, R., Zhang, Z., and Miguez-Macho, G.: The Importance of Scale-Dependent Groundwater Processes in Land-Atmosphere Interactions Over the Central United States, Geophys. Res. Lett., 48, e2020GL092171, https://doi.org/10.1029/2020GL092171, 2021. a
Bazilian, M., Rogner, H., Howells, M., Hermann, S., Arent, D., Gielen, D., Steduto, P., Mueller, A., Komor, P., Tol, R. S., and Yumkella, K. K.: Considering the energy, water and food nexus: Towards an integrated modelling approach, Energy Policy, https://doi.org/10.1016/j.enpol.2011.09.039, 2011. a
Bergier, I.: Effects of highland land-use over lowlands of the Brazilian Pantanal, Sci. Total Environ., 463-464, 1060–1066, https://doi.org/10.1016/j.scitotenv.2013.06.036, 2013. a, b, c
Campoy, A., Ducharne, A., Cheruy, F., Hourdin, F., Polcher, J., and Dupont, J. C.: Response of land surface fluxes and precipitation to different soil bottom hydrological conditions in a general circulation model, J. Geophys. Res.-Atmos., 118, 10725–10739, https://doi.org/10.1002/jgrd.50627, 2013. a, b
Chaney, N. W., Torres-Rojas, L., Vergopolan, N., and Fisher, C. K.: HydroBlocks v0.2: enabling a field-scale two-way coupling between the land surface and river networks in Earth system models, Geosci. Model Dev., 14, 6813–6832, https://doi.org/10.5194/gmd-14-6813-2021, 2021. a, b, c
Collischonn, W., Allasia, D., da Silva, B. C., and Tucci, C. E.: The MGB-IPH model for large-scale rainfall–runoff modelling, Hydrol. Sci. J., 52, 878–895, https://doi.org/10.1623/HYSJ.52.5.878, 2010. a
Dadson, S. J., Ashpole, I., Harris, P., Davies, H. N., Clark, D. B., Blyth, E., and Taylor, C. M.: Wetland inundation dynamics in a model of land surface climate: Evaluation in the Niger inland delta region, J. Geophys. Res.-Atmos., 115, D23114, https://doi.org/10.1029/2010JD014474, 2010. a, b
Decharme, B., Delire, C., Minvielle, M., Colin, J., Vergnes, J. P., Alias, A., Saint-Martin, D., Séférian, R., Sénési, S., and Voldoire, A.: Recent Changes in the ISBA-CTRIP Land Surface System for Use in the CNRM-CM6 Climate Model and in Global Off-Line Hydrological Applications, J. Adv. Model. Earth Sy., 11, 1207–1252, https://doi.org/10.1029/2018MS001545, 2019. a, b, c
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
de Rosnay, P., Bruen, M., and Polcher, J.: Sensitivity of surface fluxes to the number of layers in the soil model used in GCMs, Geophys. Res. Lett., 27, 3329–3332, https://doi.org/10.1029/2000GL011574, 2000. a, b
de Rosnay, P., Polcher, J., Bruen, M., and Laval, K.: Impact of a physically based soil water flow and soil-plant interaction representation for modeling large-scale land surface processes, J. Geophys. Res.-Atmos., 107, ACL 3-1–ACL 3-19, https://doi.org/10.1029/2001jd000634, 2002. a, b
Diestel, R.: Graph theory, no. 173 in Graduate Texts in Mathematics, 4th edn., 2. corr. print edn., Springer, Heidelberg, oCLC: 820789409, 2012. a
Dirmeyer, P. A.: The terrestrial segment of soil moisture-climate coupling, Geophys. Res. Lett., 38, L16702, https://doi.org/10.1029/2011GL048268, 2011. a
ESA: ESA CCI land cover time-series v2.0.7 (1992-2015)., Tech. rep., European Space Agency-Climate Change Initiative, 2017. a
Fleischmann, A. S., Brêda, J. P., Passaia, O. A., Wongchuig, S. C., Fan, F. M., Paiva, R. C., Marques, G. F., and Collischonn, W.: Regional scale hydrodynamic modeling of the river-floodplain-reservoir continuum, J. Hydrol., 596, 126114, https://doi.org/10.1016/j.jhydrol.2021.126114, 2021. a
Frappart, F., Papa, F., Güntner, A., Werth, S., Santos da Silva, J., Tomasella, J., Seyler, F., Prigent, C., Rossow, W. B., Calmant, S., and Bonnet, M. P.: Satellite-based estimates of groundwater storage variations in large drainage basins with extensive floodplains, Remote Sens. Environ., 115, 1588–1594, https://doi.org/10.1016/J.RSE.2011.02.003, 2011. a, b
Freitas, J. G., Furquim, S. A., Aravena, R., and Cardoso, E. L.: Interaction between lakes' surface water and groundwater in the Pantanal wetland, Brazil, Environ. Earth Sci., 78, 78, 1–15, https://doi.org/10.1007/S12665-019-8140-4, 2019. a
Getirana, A., Jung, H. C., Van Den Hoek, J., and Ndehedehe, C. E.: Hydropower dam operation strongly controls Lake Victoria's freshwater storage variability, Sci. Total Environ., 726, 138343, https://doi.org/10.1016/j.scitotenv.2020.138343, 2020. a
Getirana, A., Kumar, S. V., Konapala, G., and Ndehedehe, C. E.: Impacts of Fully Coupling Land Surface and Flood Models on the Simulation of Large Wetlands' Water Dynamics: The Case of the Inner Niger Delta, J. Adv. Model. Earth Sy., 13, e2021MS002463, https://doi.org/10.1029/2021ms002463, 2021. a, b
Girard, P., Da Silva, C. J., and Abdo, M.: River–groundwater interactions in the Brazilian Pantanal. The case of the Cuiabá River, J. Hydrol., 283, 57–66, https://doi.org/10.1016/S0022-1694(03)00235-X, 2003. a, b
Guerreiro, R. L., Bergier, I., McGlue, M. M., Warren, L. V., de Abreu, U. G. P., Abrahão, J., and Assine, M. L.: The soda lakes of Nhecolândia: A conservation opportunity for the Pantanal wetlands, Persp. Ecol. Conserv., 17, 9–18, https://doi.org/10.1016/J.PECON.2018.11.002, 2019. a
Guimberteau, M., Drapeau, G., Ronchail, J., Sultan, B., Polcher, J., Martinez, J.-M., Prigent, C., Guyot, J.-L., Cochonneau, G., Espinoza, J. C., Filizola, N., Fraizy, P., Lavado, W., De Oliveira, E., Pombosa, R., Noriega, L., and Vauchel, P.: Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets, Hydrol. Earth Syst. Sci., 16, 911–935, https://doi.org/10.5194/hess-16-911-2012, 2012. a
Guinaldo, T., Munier, S., Le Moigne, P., Boone, A., Decharme, B., Choulga, M., and Leroux, D. J.: Parametrization of a lake water dynamics model MLake in the ISBA-CTRIP land surface system (SURFEX v8.1), Geosci. Model Dev., 14, 1309–1344, https://doi.org/10.5194/gmd-14-1309-2021, 2021. a, b
Guion, A., Turquety, S., Polcher, J., Pennel, R., Bastin, S., and Arsouze, T.: Droughts and heatwaves in the Western Mediterranean: impact on vegetation and wildfires using the coupled WRF-ORCHIDEE regional model (RegIPSL), Clim. Dynam., 58, 2881–2903, 2022. a
Hallouin, T., Ellis, R. J., Clark, D. B., Dadson, S. J., Hughes, A. G., Lawrence, B. N., Lister, G. M. S., and Polcher, J.: UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python, Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, 2022. a
Hamilton, S. K.: Hydrological controls of ecological structure and function in the Pantanal wetland (Brazil), in: The ecohydrology of South American rivers and wetlands, edited by: McClain, M., IAHS (International Association of Hydrological Sciences, Manaus) Press, Wallingford, UK, IAHS Special Publication, 133–158, ISBN 1901502023, 2002. a, b, c, d, e, f, g
Hamilton, S. K., Sippel, S. J., and Melack, J.: Inundation patterns in the Pantanal Wetland of South America determined from passive microwave remote sensing, Archiv für Hydrobiologie, 137, 1–23, https://doi.org/10.1127/archiv-hydrobiol/137/1996/1, 1996. a
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7, 1–18, 2020. a
Howells, M., Hermann, S., Welsch, M., Bazilian, M., Segerström, R., Alfstad, T., Gielen, D., Rogner, H., Fischer, G., Van Velthuizen, H., Wiberg, D., Young, C., Alexander Roehrl, R., Mueller, A., Steduto, P., and Ramma, I.: Integrated analysis of climate change, land-use, energy and water strategies, 3, 621–626, https://doi.org/10.1038/nclimate1789, 2013. a
Hu, S., Niu, Z., and Chen, Y.: Global wetland datasets: a review, Wetlands, 37, 807–817, 2017. a
Junk, W. J. and Wantzen, K. M.: The flood pulse concept: new aspects, approaches and applications-an update, in: Second international symposium on the management of large rivers for fisheries, 117–149, Food and Agriculture Organization and Mekong River Commission, FAO Regional, 2004. a
Junk, W. J., Da Cunha, C. N., Wantzen, K. M., Petermann, P., Strüssmann, C., Marques, M. I., and Adis, J.: Biodiversity and its conservation in the Pantanal of Mato Grosso, Brazil, in: Aquatic Sciences, https://doi.org/10.1007/s00027-006-0851-4, 2006. a
Karabulut, A., Egoh, B. N., Lanzanova, D., Grizzetti, B., Bidoglio, G., Pagliero, L., Bouraoui, F., Aloe, A., Reynaud, A., Maes, J., Vandecasteele, I., and Mubareka, S.: Mapping water provisioning services to support the ecosystem-water-food-energy nexus in the Danube river basin, Ecosyst. Services, 17, 278–292, https://doi.org/10.1016/j.ecoser.2015.08.002, 2016. a
Krause, S., Bronstert, A., and Zehe, E.: Groundwater–surface water interactions in a North German lowland floodplain – Implications for the river discharge dynamics and riparian water balance, J. Hydrol., 347, 404–417, https://doi.org/10.1016/J.JHYDROL.2007.09.028, 2007. a, b
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biochem. Cycles, 19, 1–33, https://doi.org/10.1029/2003GB002199, 2005. a
Lauerwald, R., Regnier, P., Camino-Serrano, M., Guenet, B., Guimberteau, M., Ducharne, A., Polcher, J., and Ciais, P.: ORCHILEAK (revision 3875): a new model branch to simulate carbon transfers along the terrestrial–aquatic continuum of the Amazon basin, Geosci. Model Dev., 10, 3821–3859, https://doi.org/10.5194/gmd-10-3821-2017, 2017. a
Lee, H., Beighley, R. E., Alsdorf, D., Jung, H. C., Shum, C. K., Duan, J., Guo, J., Yamazaki, D., and Andreadis, K.: Characterization of terrestrial water dynamics in the Congo Basin using GRACE and satellite radar altimetry, Remote Sens. Environ., 115, 3530–3538, https://doi.org/10.1016/j.rse.2011.08.015, 2011. a
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, Eos, 89, 93–104, https://doi.org/10.1029/2008EO100001, 2008. a
Louzada, R. O., Bergier, I., and Assine, M. L.: Landscape changes in avulsive river systems: Case study of Taquari River on Brazilian Pantanal wetlands, Sci. Total Environ., 723, 138067, https://doi.org/10.1016/j.scitotenv.2020.138067, 2020. a
Lucas-Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P., Lemonsu, A., Kotlarski, S., and Caillaud, C.: Convection-permitting modeling with regional climate models: Latest developments and next steps, WIREs Clim. Change, 12, e731, https://doi.org/10.1002/wcc.731, 2021. a
Makungu, E. and Hughes, D. A.: Understanding and modelling the effects of wetland on the hydrology and water resources of large African river basins, J. Hydrol., 603, 127039, https://doi.org/10.1016/J.JHYDROL.2021.127039, 2021. a
Marthews, T. R., Dadson, S. J., Clark, D. B., Blyth, E. M., Hayman, G. D., Yamazaki, D., Becher, O. R. E., Martínez-de la Torre, A., Prigent, C., and Jiménez, C.: Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations, Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, 2022. a, b
Munier, S. and Decharme, B.: River network and hydro-geomorphological parameters at ∘ resolution for global hydrological and climate studies, Earth Syst. Sci. Data, 14, 2239–2258, https://doi.org/10.5194/essd-14-2239-2022, 2022. a
Ngo-Duc, T., Laval, K., Ramillien, G., Polcher, J., and Cazenave, A.: Validation of the land water storage simulated by Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) with Gravity Recovery and Climate Experiment (GRACE) data, Water Resour. Res., 43, W04427, https://doi.org/10.1029/2006WR004941, 2007. a
Nguyen-Quang, T., Polcher, J., Ducharne, A., Arsouze, T., Zhou, X., Schneider, A., and Fita, L.: ORCHIDEE-ROUTING: revising the river routing scheme using a high-resolution hydrological database, Geosci. Model Dev., 11, 4965–4985, https://doi.org/10.5194/gmd-11-4965-2018, 2018. a, b
Nobre, A. D., Cuartas, L. A., Hodnett, M., Rennó, C. D., Rodrigues, G., Silveira, A., Waterloo, M., and Saleska, S.: Height Above the Nearest Drainage – a hydrologically relevant new terrain model, J. Hydrol., 404, 13–29, https://doi.org/10.1016/j.jhydrol.2011.03.051, 2011. a
Padovani: Dinâmica Espaço-Temporal das Inundações do Pantanal, PhD thesis, Piracicaba: Escola Superior de Agricultura Luiz de Queiroz, Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, http://www.teses.usp.br/teses/disponiveis/91/91131/tde-14022011-170515/pt-br.php (last access: 12 October 2023), 2010. a, b, c, d, e, f, g, h, i, j
Paiva, R. C., Collischonn, W., and Tucci, C. E.: Large scale hydrologic and hydrodynamic modeling using limited data and a GIS based approach, J. Hydrol., 406, 170–181, https://doi.org/10.1016/j.jhydrol.2011.06.007, 2011. a
Penatti, N. C., de Almeida, T. I. R., Ferreira, L. G., Arantes, A. E., and Coe, M. T.: Satellite-based hydrological dynamics of the world's largest continuous wetland, Remote Sens. Environ., 170, 1–13, https://doi.org/10.1016/j.rse.2015.08.031, 2015. a, b
Pinzon, J. E., Pak, E. W., Tucker, C. J., Bhatt, U. S., Frost, G. V., and Macander, M. J.: Global Vegetation Greenness (NDVI) from AVHRR GIMMS-3G+, 1981–2022, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/2187, 2023. a
Polcher, J., Scrapffer, A., and Rinchiuso, L.: The pre-processor for ORCHIDEE's routing scheme (Version used for Polcher et al. 2022, GMD), Zenodo [code], https://doi.org/10.5281/zenodo.7058895, 2022. a
Polcher, J., Schrapffer, A., Dupont, E., Rinchiuso, L., Zhou, X., Boucher, O., Mouche, E., Ottlé, C., and Servonnat, J.: Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water, Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, 2023. a, b, c, d, e, f, g
Pontes, P. R. M., Fan, F. M., Fleischmann, A. S., de Paiva, R. C. D., Buarque, D. C., Siqueira, V. A., Jardim, P. F., Sorribas, M. V., and Collischonn, W.: MGB-IPH model for hydrological and hydraulic simulation of large floodplain river systems coupled with open source GIS, Environ. Model. Softw., 94, 1–20, https://doi.org/10.1016/j.envsoft.2017.03.029, 2017. a, b
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., Van Lipzig, N. P., and Leung, R.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
Prigent, C., Jimenez, C., and Bousquet, P.: Satellite-Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS-2), J. Geophys. Res.-Atmos., 125, e2019JD030711, https://doi.org/10.1029/2019JD030711, 2020. a, b, c, d
Reynolds, C. A., Jackson, T. J., and Rawls, W. J.: Estimating soil water-holding capacities by linking the Food and Agriculture Organization soil map of the world with global pedon databases and continuous pedotransfer functions, Water Resour. Res., 36, 3653–3662, https://doi.org/10.1029/2000WR900130, 2000. a
Schmidt, R., Flechtner, F., Meyer, U., Neumayer, K. H., Dahle, C., König, R., and Kusche, J.: Hydrological signals observed by the GRACE satellites, 29, 319–334, https://doi.org/10.1007/s10712-008-9033-3, 2008. a
Schneider, U., Finger, P., Meyer-Christoffer, A., Rustemeier, E., Ziese, M., and Becker, A.: Evaluating the Hydrological Cycle over Land Using the Newly-Corrected Precipitation Climatology from the Global Precipitation Climatology Centre (GPCC), Atmosphere, 8, 52, https://doi.org/10.3390/atmos8030052, 2017. a
Schrapffer, A., Polcher, J., Sörensson, A., and Fita, L.: Experiment for the validation and evaluation of the floodplains scheme in ORCHIDEE, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7761859, 2023b. a
Seneviratne, S. I. and Stöckli, R.: The Role of Land–Atmosphere Interactions for Climate Variability in Europe, in: Climate Variability and Extremes during the Past 100 years, edited by: Brönnimann, S., Luterbacher, J., Ewen, T., Diaz, H. F., Stolarski, R. S., and Neu, U., Springer Dordrecht, 1st edn., https://doi.org/10.1007/978-1-4020-6766-2, 2008. a
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture-climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010. a
Stephens, G., Polcher, J., Zeng, X., van Oevelen, P., Poveda, G., Bosilovich, M., Ahn, M.-H., Balsamo, G., Duan, Q., Hegerl, G., Jakob, C., Lamptey, B., Leung, R., Piles, M., Su, Z., Dirmeyer, P., Findell, K. L., Verhoef, A., Ek, M., L'Ecuyer, T., Roca, R., Nazemi, A., Dominguez, F., Klocke, D., and Bony, S.: The First 30 Years of GEWEX, B. Am. Meteorol. Soc., 104, E126–E157, https://doi.org/10.1175/BAMS-D-22-0061.1, 2023. a, b
Taylor, C. M.: Feedbacks on convection from an African wetland, Geophys. Res. Lett., 37, L05406, https://doi.org/10.1029/2009GL041652, 2010. a
Taylor, C. M., Prigent, C., and Dadson, S. J.: Mesoscale rainfall patterns observed around wetlands in sub-Saharan Africa, Q. J. Roy. Meteor. Soc., 144, 2118–2132, https://doi.org/10.1002/qj.3311, 2018. a
Thielen, D., Schuchmann, K. L., Ramoni-Perazzi, P., Marquez, M., Rojas, W., Quintero, J. I., and Marques, M. I.: Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature, PLoS ONE, 15, e0227437, https://doi.org/10.1371/journal.pone.0227437, 2020. a
Vishwakarma, B. D., Zhang, J., and Sneeuw, N.: Downscaling GRACE total water storage change using partial least squares regression, Sci. Data, 8, 95, https://doi.org/10.1038/s41597-021-00862-6, 2021. a
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.: The WFDEI meteorological forcing data set: WATCH Forcing data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014. a
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based description of floodplain inundation dynamics in a global river routing model, Water Resour. Res., 47, W04501, https://doi.org/10.1029/2010WR009726, 2011. a
Yamazaki, D., De Almeida, G. A., and Bates, P. D.: Improving computational efficiency in global river models by implementing the local inertial flow equation and a vector-based river network map, Water Resour. Res., 49, 7221–7235, https://doi.org/10.1002/wrcr.20552, 2013. a, b
Yamazaki, D., Sato, T., Kanae, S., Hirabayashi, Y., and Bates, P. D.: Regional flood dynamics in a bifurcating mega delta simulated in a global river model, Geophys. Res. Lett., 41, 3127–3135, https://doi.org/10.1002/2014GL059744, 2014. a
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017. a
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., and Pavelsky, T.: MERIT Hydro: A high‐resolution global hydrography map based on latest topography datasets, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019WR024873, 2019. a, b, c
Zhou, X., Prigent, C., and Yamazaki, D.: Toward improved comparisons between land‐surface‐water‐area estimates from a global river model and satellite observations, Water Resour. Res., 57, e2020WR029256, https://doi.org/10.1029/2020WR029256, 2021. a, b, c
Short summary
The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
The present paper introduces a floodplain scheme for a high-resolution land surface model river...