Articles | Volume 28, issue 16
https://doi.org/10.5194/hess-28-3695-2024
© 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/hess-28-3695-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Aug 2024
Research article |
| 16 Aug 2024
| 16 Aug 2024
Drainage assessment of irrigation districts: on the precision and accuracy of four parsimonious models
Pierre Laluet
CORRESPONDING AUTHOR
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria
Luis Olivera-Guerra
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
now at: Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ-UPSACLAY, UMR 8212, IPSL, Gif-sur-Yvette, France
Víctor Altés
isardSAT, Doctor Trueta 113, Barcelona, Spain
Department of Environment and Soil Sciences, University of Lleida, Lleida, Spain
Vincent Rivalland
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Alexis Jeantet
UR HYCAR, University of Paris-Saclay, INRAE Jouy-en-Josas, Antony, France
Julien Tournebize
UR HYCAR, University of Paris-Saclay, INRAE Jouy-en-Josas, Antony, France
Omar Cenobio-Cruz
Observatori de l'Ebre, Universitat Ramon Llull – CSIC, Roquetes, Spain
Anaïs Barella-Ortiz
Observatori de l'Ebre, Universitat Ramon Llull – CSIC, Roquetes, Spain
Pere Quintana-Seguí
Observatori de l'Ebre, Universitat Ramon Llull – CSIC, Roquetes, Spain
Josep Maria Villar
Department of Environment and Soil Sciences, University of Lleida, Lleida, Spain
Olivier Merlin
Centre d'Etudes Spatiales de la Biosphère (CESBIO), Université de Toulouse, CNES, CNRS, IRD, UPS, Toulouse, France
Related authors
No articles found.
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).
Short summary
Short summary
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.
Alexis Jeantet, Jean-Pierre Vergnes, Simon Munier, and Florence Habets
EGUsphere, https://doi.org/10.5194/egusphere-2025-93, https://doi.org/10.5194/egusphere-2025-93, 2025
Short summary
Short summary
The AquiFR hydrogeological modelling plateform is forced by 36 climate projections in order to simulate future groundwater levels over France. The results show significant scatters between regional climate models and RCPs. Overall, a rise in groundwater levels, affecting most of the study area, is the dominant signal. Four storylines have been selected to to illustrate the impacts of worst-case scenarios and help decision-makers to adopt sustainable groundwater management policies.
Luis-Enrique Olivera-Guerra, Catherine Ottlé, Nina Raoult, and Philippe Peylin
Hydrol. Earth Syst. Sci., 29, 261–290, https://doi.org/10.5194/hess-29-261-2025, https://doi.org/10.5194/hess-29-261-2025, 2025
Short summary
Short summary
We assimilate the recent ESA-CCI land surface temperature (LST) product to optimize parameters of a land surface model (ORCHIDEE). We test different assimilation strategies to evaluate the best strategy over various in situ stations across Europe. We also provide advice on how to assimilate this LST product to better simulate LST and surface energy fluxes. Finally, we demonstrate the effectiveness of this optimization, which is essential to better simulate future projections.
Bruno J. Lemaire, Cédric Chaumont, Julien Tournebize, and Hocine Henine
Proc. IAHS, 385, 135–140, https://doi.org/10.5194/piahs-385-135-2024, https://doi.org/10.5194/piahs-385-135-2024, 2024
Short summary
Short summary
The potential of constructed wetland to remove nitrate and pesticides increases with the residence time. The aim of this work is to investigate how the hydraulic performance changes with the flow rate in a party vegetated wetland, using the 3D hydrodynamic model. It was calibrated on continuous outflow concentration and tracing experiment. The simulation matched satisfactorily with observation. Results showed a limited increase of the hydraulic performance with the flow rate.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Jacopo Dari, Luca Brocca, Sara Modanesi, Christian Massari, Angelica Tarpanelli, Silvia Barbetta, Raphael Quast, Mariette Vreugdenhil, Vahid Freeman, Anaïs Barella-Ortiz, Pere Quintana-Seguí, David Bretreger, and Espen Volden
Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, https://doi.org/10.5194/essd-15-1555-2023, 2023
Short summary
Short summary
Irrigation is the main source of global freshwater consumption. Despite this, a detailed knowledge of irrigation dynamics (i.e., timing, extent of irrigated areas, and amounts of water used) are generally lacking worldwide. Satellites represent a useful tool to fill this knowledge gap and monitor irrigation water from space. In this study, three regional-scale and high-resolution (1 and 6 km) products of irrigation amounts estimated by inverting the satellite soil moisture signals are presented.
Kunlong He, Wei Zhao, Luca Brocca, and Pere Quintana-Seguí
Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, https://doi.org/10.5194/hess-27-169-2023, 2023
Short summary
Short summary
In this study, we developed a soil moisture-based precipitation downscaling (SMPD) method for spatially downscaling the GPM daily precipitation product by exploiting the connection between surface soil moisture and precipitation according to the soil water balance equation. Based on this physical method, the spatial resolution of the daily precipitation product was downscaled to 1 km and the SMPD method shows good potential for the development of the high-resolution precipitation product.
Jaime Gaona, Pere Quintana-Seguí, María José Escorihuela, Aaron Boone, and María Carmen Llasat
Nat. Hazards Earth Syst. Sci., 22, 3461–3485, https://doi.org/10.5194/nhess-22-3461-2022, https://doi.org/10.5194/nhess-22-3461-2022, 2022
Short summary
Short summary
Droughts represent a particularly complex natural hazard and require explorations of their multiple causes. Part of the complexity has roots in the interaction between the continuous changes in and deviation from normal conditions of the atmosphere and the land surface. The exchange between the atmospheric and surface conditions defines feedback towards dry or wet conditions. In semi-arid environments, energy seems to exceed water in its impact over the evolution of conditions, favoring drought.
Claire Pascal, Sylvain Ferrant, Adrien Selles, Jean-Christophe Maréchal, Abhilash Paswan, and Olivier Merlin
Hydrol. Earth Syst. Sci., 26, 4169–4186, https://doi.org/10.5194/hess-26-4169-2022, https://doi.org/10.5194/hess-26-4169-2022, 2022
Short summary
Short summary
This paper presents a new validation method for the downscaling of GRACE (Gravity Recovery and Climate Experiment) data. It measures the improvement of the downscaled data against the low-resolution data in both temporal and, for the first time, spatial domains. This validation method offers a standardized and comprehensive framework to interpret spatially and temporally the quality of the downscaled products, supporting future efforts in GRACE downscaling methods.
Yves Tramblay and Pere Quintana Seguí
Nat. Hazards Earth Syst. Sci., 22, 1325–1334, https://doi.org/10.5194/nhess-22-1325-2022, https://doi.org/10.5194/nhess-22-1325-2022, 2022
Short summary
Short summary
Monitoring soil moisture is important during droughts, but very few measurements are available. Consequently, land-surface models are essential tools for reproducing soil moisture dynamics. In this study, a hybrid approach allowed for regionalizing soil water content using a machine learning method. This approach proved to be efficient, compared to the use of soil property maps, to run a simple soil moisture accounting model, and therefore it can be applied in various regions.
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
Short summary
Short summary
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.
Cited articles
Abdi, D. E., Owen, J. S., Wilson, P. C., Hinz, F. O., Cregg, B., and Fernandez, R. T.: Reducing pesticide transport in surface and subsurface irrigation return flow in specialty crop production, Agr. Water Manage., 256, 107124, https://doi.org/10.1016/j.agwat.2021.107124, 2021.
Ale, S., Gowda, P. H., Mulla, D. J., Moriasi, D. N., and Youssef, M. A.: Comparison of the performances of DRAINMOD-NII and ADAPT models in simulating nitrate losses from subsurface drainage systems, Agr. Water Manage., 129, 21–30, https://doi.org/10.1016/j.agwat.2013.07.008, 2013.
Allen, R., Pereira, L., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, https://www.fao.org/4/X0490E/X0490E00.htm (last access: 11 August 2024 1998.
Altés, V., Bellvert, J., Pascual, M., and Villar, J. M.: Understanding Drainage Dynamics and Irrigation Management in a Semi-Arid Mediterranean Basin, Water, 15, 16, https://doi.org/10.3390/w15010016, 2022.
Amazirh, A., Merlin, O., Er-Raki, S., Bouras, E., and Chehbouni, A.: Implementing a new texture-based soil evaporation reduction coefficient in the FAO dual crop coefficient method, Agr. Water Manage., 250, 106827, https://doi.org/10.1016/j.agwat.2021.106827, 2021.
Bekele, E. G. and Nicklow, J. W.: Multi-objective automatic calibration of SWAT using NSGA-II, J. Hydrol., 341, 165–176, https://doi.org/10.1016/j.jhydrol.2007.05.014, 2007.
Blann, K., Anderson, J., Sands, G., and Vondracek, B.: Effects of Agricultural Drainage on Aquatic Ecosystems: A Review, Crit. Rev. Env. Sci. Tec., 39, 909–1001, https://doi.org/10.1080/10643380801977966, 2009.
Bouarfa, S. and Zimmer, D.: Water-table shapes and drain flow rates in shallow drainage systems, J. Hydrol., 235, 264–275, https://doi.org/10.1016/S0022-1694(00)00280-8, 2000.
Boussinesq, J.: Recherches théoriques sur l'écoulement des nappes d'eau infiltrées dans le sol et sur le débit des sources, J. Math. Pure. Appl., 10, 5–78, 1904.
Cavero, J., Barros, R., Sellam, F., Topcu, S., Isidoro, D., Hartani, T., Lounis, A., Ibrikci, H., Cetin, M., Williams, J. R., and Aragüés, R.: APEX simulation of best irrigation and N management strategies for off-site N pollution control in three Mediterranean irrigated watersheds, Agr. Water Manage., 103, 88–99, https://doi.org/10.1016/j.agwat.2011.10.021, 2012.
Cenobio-Cruz, O., Quintana-Seguí, P., Barella-Ortiz, A., Zabaleta, A., Garrote, L., Clavera-Gispert, R., Habets, F., and Beguería, S.: Improvement of low flows simulation in the SASER hydrological modeling chain, J. Hydrol. X, 18, 100147, https://doi.org/10.1016/j.hydroa.2022.100147, 2023.
Chang, X., Wang, S., Gao, Z., Chen, H., and Guan, X.: Simulation of Water and Salt Dynamics under Different Water-Saving Degrees Using the SAHYSMOD Model, Water, 13, 1939, https://doi.org/10.3390/w13141939, 2021.
Chelil, S., Oubanas, H., Henine, H., Gejadze, I., Malaterre, P. O., and Tournebize, J.: Variational data assimilation to improve subsurface drainage model parameters, J. Hydrol., 610, 128006, https://doi.org/10.1016/j.jhydrol.2022.128006, 2022.
Dari, J., Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., and Volden, E.: Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space, Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023.
David, C. H., Maidment, D. R., Niu, G.-Y., Yang, Z.-L., Habets, F., and Eijkhout, V.: River Network Routing on the NHDPlus Dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1, 2011.
Department of Climate Action, Food, and Rural Agenda of the Region of Catalonia: Crop type information, Department of Climate Action, Food, and Rural Agenda of the Region of Catalonia [data set], https://agricultura.gencat.cat/ca/ambits/desenvolupament-rural/sigpac/descarregues (last access: 11 August 2024), 2024.
De Schepper, G., Therrien, R., Refsgaard, J. C., and Hansen, A. L.: Simulating coupled surface and subsurface water flow in a tile-drained agricultural catchment, J. Hydrol., 521, 374–388, https://doi.org/10.1016/j.jhydrol.2014.12.035, 2015.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE T. Evolut. Comput., 6, 182–197, https://doi.org/10.1109/4235.996017, 2002.
FAO: The state of the world's land and water resources for food and agriculture, https://www.fao.org/documents/card/fr/c/cb7654en (last access: 20 April 2023), 2021.
Feng, G., Zhu, C., Wu, Q., Wang, C., Zhang, Z., Mwiya, R. M., and Zhang, L.: Evaluating the impacts of saline water irrigation on soil water-salt and summer maize yield in subsurface drainage condition using coupled HYDRUS and EPIC model, Agr. Water Manage., 258, 107175, https://doi.org/10.1016/j.agwat.2021.107175, 2021.
García-Garizábal, I. and Causapé, J.: Influence of irrigation water management on the quantity and quality of irrigation return flows, J. Hydrol., 385, 36–43, https://doi.org/10.1016/j.jhydrol.2010.02.002, 2010.
Gassman, P., Williams, J., Wang, X., Saleh, A., Osei, E., Hauck, L., Izaurralde, R, and Flowers, J.: The agricultural policy/environmental extender (APEX) model: an emerging tool for landscape and watershed environmental analyses, T. ASABE, 53, 711–740, https://elibrary.asabe.org/abstract.asp??JID=3&AID=30078&CID=t2010&v=53&i=3&T=1 (last access: 11 August 2024), 2010.
Generalitat de Catalunya: Meteorological data, Generalitat de Catalunya [data set], https://ruralcat.gencat.cat/agrometeo.estacions (last access: 11 August 2024), 2024.
Golmohammadi, G., Rudra, R. P., Parkin, G. W., Kulasekera, P. B., Macrae, M., and Goel, P. K.: Assessment of Impacts of Climate Change on Tile Discharge and Nitrogen Yield Using the DRAINMOD Model, Hydrology, 8, 1, https://doi.org/10.3390/hydrology8010001, 2020.
Gowda, P., Mulla, D., Desmond, E., Ward, A., and Moriasi, D.: ADAPT: Model use, calibration and validation, T. ASABE, 55, 1345–1352, https://doi.org/10.13031/2013.42246, 2012.
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.
Hengl, T., Jesus, J. M. de, Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLOS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Henine, H., Jeantet, A., Chaumont, C., Chelil, S., Lauvernet, C., and Tournebize, J.: Coupling of a subsurface drainage model with a soil reservoir model to simulate drainage discharge and drain flow start, Agr. Water Manage., 262, 107318, https://doi.org/10.1016/j.agwat.2021.107318, 2022.
Houska, T., Kraft, P., Chamorro-Chavez, A., and Breuer, L.: SPOTting Model Parameters Using a Ready-Made Python Package, PLOS ONE, 10, e0145180, https://doi.org/10.1371/journal.pone.0145180, 2015 (code available at: https://pypi.python.org/pypi/spotpy/, last access: 11 August 2024).
Institute of Cartography of Catalonia: 2 m resolution DEM, Institute of Cartography of Catalonia [data set], https://www.icgc.cat/es/Datos-y-productos/Bessons-digitals-Elevacions/Modelo-de-elevaciones-del-terreno-de-2x2-m (last access: 11 August 2024), 2024.
Jahn, R., Blume, H. P., Asio, V., Spaargaren, O., and Schád, P.: FAO Guidelines for Soil Description, 4th ed., Food and Agriculture Organization of the United Nations: Rome, ISBN 92-5-105521-1, https://www.fao.org/3/a0541e/a0541e.pdf (last access: 11 August 2024), 2006.
Jarvis, N. and Larsbo, M.: MACRO (v5.2): Model Use, Calibration, and Validation, T. ASABE, 55, 1413–1423, https://doi.org/10.13031/2013.42251, 2012.
Jeantet, A., Henine, H., Chaumont, C., Collet, L., Thirel, G., and Tournebize, J.: Robustness of a parsimonious subsurface drainage model at the French national scale, Hydrol. Earth Syst. Sci., 25, 5447–5471, https://doi.org/10.5194/hess-25-5447-2021, 2021.
Jeantet, A., Thirel, G., Jeliazkov, A., Martin, P., and Tournebize, J.: Effects of Climate Change on Hydrological Indicators of Subsurface Drainage for a Representative French Drainage Site, Front. Environ. Sci., 10, 899226, https://doi.org/10.3389/fenvs.2022.899226, 2022.
Jiang, Q., Qi, Z., Lu, C., Tan, C. S., Zhang, T., and Prasher, S. O.: Evaluating RZ-SHAW model for simulating surface runoff and subsurface tile drainage under regular and controlled drainage with subirrigation in southern Ontario, Agr. Water Manage., 237, 106179, https://doi.org/10.1016/j.agwat.2020.106179, 2020.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Laluet, P., Olivera-Guerra, L., Rivalland, V., Simonneaux, V., Inglada, J., Bellvert, J., Er-raki, S., and Merlin, O.: A sensitivity analysis of a FAO-56 dual crop coefficient-based model under various field conditions, Environ. Modell. Softw., 160, 105608, https://doi.org/10.1016/j.envsoft.2022.105608, 2023.
Larsbo, M., Roulier, S., Stenemo, F., Kasteel, R., and Jarvis, N.: An Improved Dual-Permeability Model of Water Flow and Solute Transport in the Vadose Zone, Vadose Zone J., 4, 398–406, https://doi.org/10.2136/vzj2004.0137, 2005.
Lehmann, P., Merlin, O., Gentine, P., and Or, D.: Soil Texture Effects on Surface Resistance to Bare-Soil Evaporation, Geophys. Res. Lett., 45, 10398–10405, https://doi.org/10.1029/2018GL078803, 2018.
Lesaffre, B. and Zimmer, D.: Subsurface drainage peak flows in shallow soil, J. Irrig. Drain. E., 114, 387–397, https://doi.org/10.1061/(ASCE)0733-9437(1988)114:3(387), 1988.
Ma, L., Ahuja, L., Nolan, B. T., Malone, R., Trout, T., and Qi, Z.: Root zone water quality model (RZWQM2): Model use, calibration and validation, T. ASABE, 55, 1425–1446, https://doi.org/10.13031/2013.42252, 2012.
Massari, C., Modanesi, S., Dari, J., Gruber, A., De Lannoy, G. J. M., Girotto, M., Quintana-Seguí, P., Le Page, M., Jarlan, L., Zribi, M., Ouaadi, N., Vreugdenhil, M., Zappa, L., Dorigo, W., Wagner, W., Brombacher, J., Pelgrum, H., Jaquot, P., Freeman, V., Volden, E., Fernandez Prieto, D., Tarpanelli, A., Barbetta, S., and Brocca, L.: A Review of Irrigation Information Retrievals from Space and Their Utility for Users, Remote Sensing, 13, 4112, https://doi.org/10.3390/rs13204112, 2021.
Merlin, O., Stefan, V. G., Amazirh, A., Chanzy, A., Ceschia, E., Er-Raki, S., Gentine, P., Tallec, T., Ezzahar, J., Bircher, S., Beringer, J., and Khabba, S.: Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta-analysis approach, Water Resour. Res., 52, 3663–3684, https://doi.org/10.1002/2015WR018233, 2016.
Moursi, H., Youssef, M. A., and Chescheir, G. M.: Development and application of DRAINMOD model for simulating crop yield and water conservation benefits of drainage water recycling, Agr. Water Manageme., 266, 107592, https://doi.org/10.1016/j.agwat.2022.107592, 2022.
Muma, M., Rousseau, A. N., and Gumiere, S. J.: Modeling of subsurface agricultural drainage using two hydrological models with different conceptual approaches as well as dimensions and spatial scales, Can. Water Resour. J., 42, 38–53, https://doi.org/10.1080/07011784.2016.1231014, 2017.
Negm, L. M., Youssef, M. A., and Jaynes, D. B.: Evaluation of DRAINMOD-DSSAT simulated effects of controlled drainage on crop yield, water balance, and water quality for a corn-soybean cropping system in central Iowa, Agr. Water Manage., 187, 57–68, https://doi.org/10.1016/j.agwat.2017.03.010, 2017.
Nousiainen, R., Warsta, L., Turunen, M., Huitu, H., Koivusalo, H., and Pesonen, L.: Analyzing subsurface drain network performance in an agricultural monitoring site with a three-dimensional hydrological model, J. Hydrol., 529, 82–93, https://doi.org/10.1016/j.jhydrol.2015.07.018, 2015.
Olivera-Guerra, L., Merlin, O., and Er-Raki, S.: Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region, Remote Sens. Environ., 239, 111627, https://doi.org/10.1016/j.rse.2019.111627, 2020.
Olivera-Guerra, L.-E., Laluet, P., Altés, V., Ollivier, C., Pageot, Y., Paolini, G., Chavanon, E., Rivalland, V., Boulet, G., Villar, J.-M., and Merlin, O.: Modeling actual water use under different irrigation regimes at district scale: Application to the FAO-56 dual crop coefficient method, Agr. Water Manage., 278, 108119, https://doi.org/10.1016/j.agwat.2022.108119, 2023.
Oosterbaan, R. J.: SAHYSMOD (version 1.7 a), Description of principles, user manual and case studies, International Institute for Land Reclamation and Improvement, Wageningen, the Netherlands, 140, https://waterlog.info/pdf/sahysmod.pdf (last access: 11 August 2024), 2005.
Ouaadi, N., Jarlan, L., Khabba, S., Ezzahar, J., Le Page, M., and Merlin, O.: Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region, Remote Sensing, 13, 2667, https://doi.org/10.3390/rs13142667, 2021.
Pereira, L. S., Paredes, P., Hunsaker, D. J., López-Urrea, R., and Jovanovic, N.: Updates and advances to the FAO-56 crop water requirements method, Agr. Water Manageme., 248, 106697, https://doi.org/10.1016/j.agwat.2020.106697, 2021.
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021 (data available at: https://maps.isric.org/, last access: 11 August 2024).
Puy, A., Beneventano, P., Levin, S. A., Lo Piano, S., Portaluri, T., and Saltelli, A.: Models with higher effective dimensions tend to produce more uncertain estimates, Science Advances, 8, eabn9450, https://doi.org/10.1126/sciadv.abn9450, 2022.
Quintana-Seguí, P., Turco, M., Herrera, S., and Miguez-Macho, G.: Validation of a new SAFRAN-based gridded precipitation product for Spain and comparisons to Spain02 and ERA-Interim, Hydrol. Earth Syst. Sci., 21, 2187–2201, https://doi.org/10.5194/hess-21-2187-2017, 2017.
Román Dobarco, M., Cousin, I., Le Bas, C., and Martin, M. P.: Pedotransfer functions for predicting available water capacity in French soils, their applicability domain and associated uncertainty, Geoderma, 336, 81–95, https://doi.org/10.1016/j.geoderma.2018.08.022, 2019.
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.
Schultz, B., Zimmer, D., and Vlotman, W. F.: Drainage under increasing and changing requirements, Irrig. Drain., 56, S3–S22, https://doi.org/10.1002/ird.372, 2007.
Shafii, M. and De Smedt, F.: Multi-objective calibration of a distributed hydrological model (WetSpa) using a genetic algorithm, Hydrol. Earth Syst. Sci., 13, 2137–2149, https://doi.org/10.5194/hess-13-2137-2009, 2009.
Simonneaux, V., Lepage, M., Helson, D., Metral, J., Thomas, S., Duchemin, B., Cherkaoui, M., Kharrou, H., Berjami, B., and Chehbouni, A.: Estimation spatialisée de l'évapotranspiration des cultures irriguées par télédétection: application à la gestion de l'irrigation dans la plaine du Haouz (Marrakech, Maroc), Science et changements planétaires/Sécheresse, 20, 123–130, https://doi.org/10.1684/sec.2009.0177, 2009.
Singh, A.: Environmental problems of salinization and poor drainage in irrigated areas: Management through the mathematical models, J. Clean. Prod., 206, 572–579, https://doi.org/10.1016/j.jclepro.2018.09.211, 2019.
Skaggs, R. W., Youssef, M., and Chescheir, G. M.: DRAINMOD: model use, calibration, and validation, T. ASABE, 55, 1509–1522, https://doi.org/10.13031/2013.42259, 2012.
Tournebize, J., Kao, C., Nikolic, N., and Zimmer, D.: Adaptation of the STICS model to subsurface drained soils, Agronomie, 24, 305–313, https://doi.org/10.1051/agro:2004030, 2004.
Turunen, M., Warsta, L., Paasonen-Kivekäs, M., Nurminen, J., Myllys, M., Alakukku, L., Äijö, H., Puustinen, M., and Koivusalo, H.: Modeling water balance and effects of different subsurface drainage methods on water outflow components in a clayey agricultural field in boreal conditions, Agr. Water Manage., 121, 135–148, https://doi.org/10.1016/j.agwat.2013.01.012, 2013.
van Dam, J. C., Groenendijk, P., Hendriks, R. F. A., and Kroes, J. G.: Advances of Modeling Water Flow in Variably Saturated Soils with SWAP, Vadose Zone J., 7, 640–653, https://doi.org/10.2136/vzj2007.0060, 2008.
Vergnes, J.-P. and Habets, F.: Impact of river water levels on the simulation of stream–aquifer exchanges over the Upper Rhine alluvial aquifer (France/Germany), Hydrogeol. J., 26, 2443–2457, https://doi.org/10.1007/s10040-018-1788-0, 2018.
Wen, Y., Shang, S., Rahman, K. U., Xia, Y., and Ren, D.: A semi-distributed drainage model for monthly drainage water and salinity simulation in a large irrigation district in arid region, Agr. Water Manage., 230, 105962, https://doi.org/10.1016/j.agwat.2019.105962, 2020.
Xian, C., Qi, Z., Tan, C. S., and Zhang, T.-Q.: Modeling hourly subsurface drainage using steady-state and transient methods, J. Hydrol., 550, 516–526, https://doi.org/10.1016/j.jhydrol.2017.05.016, 2017.
Zimmer, D., Tournebize, J., Bouarfa, S., Kao, C., and Lesaffre, B.: Land drainage functioning and hydrological impacts in rural catchments: model development and field experiments, C. R. Géosci., 355, 1–19, https://doi.org/10.5802/crgeos.194, 2023.
Short summary
Monitoring agricultural drainage flow in irrigated areas is key to water and soil management. In this paper, four simple drainage models are evaluated on two irrigated sub-basins where drainage flow is measured daily. The evaluation of their precision shows that they simulate drainage very well when calibrated with drainage data and that one of them is slightly better. The evaluation of their accuracy shows that only one model can provide rough drainage estimates without calibration data.
Monitoring agricultural drainage flow in irrigated areas is key to water and soil management. In...
