Impacto del riego estimado por satélite en la modelación hidrológica: análisis del balance hídrico y desempeño del modelo TETIS en la cuenca del río Po
Enviado: 25-09-2025
|Aceptado: 11-12-2025
|Publicado: 30-01-2026
Derechos de autor 2026 Ingeniería del Agua
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Descargas
Palabras clave:
riego, satélite, TETIS, río Po, modelación hidrológica, balance hídrico, caudal, evapotranspiración, humedad superficial
Agencias de apoyo:
Agencia Espacial Europea (ESA)
COLFUTURO
Generalitat Valenciana
Ministerio de Ciencia e Innovación
Resumen:
En muchas cuencas del planeta donde el riego constituye un elemento significativo del ciclo del agua, no existe información al respecto. Este trabajo evalúa los efectos de la inclusión del riego, estimado por satélite, en la modelación hidrológica de la cuenca del río Po. Se utilizó el modelo TETIS v9.1 para el periodo 2016-2021. El riego se incluyó sumándolo a la precipitación satelital y considerando sus abstracciones, extrayendo del caudal simulado un valor acorde con las demandas estimadas por la autoridad ambiental. Tras la incorporación del riego, el modelo muestra sensibilidad en el área irrigada y en toda la cuenca, dependiendo del experimento, reflejándose en el balance hídrico y en la mejora de la representación del caudal observado. Además, la evapotranspiración y humedad comparadas con datos satelitales sugieren una capacidad de mejora de la representación espaciotemporal del modelo, a pesar de las diferencias con los datos de referencia.
Citas:
ARPA Lombardia – Agenzia regionale per la protezione dell’ambiente della Lombardia. 2002. Form richiesta dati. https://www.arpalombardia.it/temi-ambientali/meteo-e-clima/form-richiesta-dati
ARPA Piemonte – Agenzia Regionale per la Protezione Ambientale del Piemonte. 2024. Dati meteoidrografici giornalieri – Richiesta automatica. https://webgis.arpa.piemonte.it/radar/open-scripts/richiesta_dati_gg_2024.php
ARPAE Emilia-Romagna – Agenzia regionale per la prevenzione, l’ambiente e l’energia dell’Emilia-Romagna. 2024. Dext3r, l’applicazione per l’estrazione in autonomia e completamente gratuita dei dati meteo registrati dalla rete di rilevamento regionale RIRER gestita da Arpae-SIMC. https://simc.arpae.it/dext3r/
Autorità di Bacino del Fiume Po. 2016. Piano del bilancio idrico per il distretto del fiume Po. https://pianobilancioidrico.adbpo.it/piano-del-bilancio-idrico/
Bozzola, M., Swanson, T. 2014. Policy implications of climate variability on agriculture: Water management in the Po River Basin, Italy. Environmental Science & Policy, 43, 26–38. https://doi.org/10.1016/j.envsci.2013.12.002
Brocca, L., Tarpanelli, A., Filippucci, P., Dorigo, W., Zaussinger, F., Gruber, A., Fernández-Prieto, D. 2018. How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products. International Journal of Applied Earth Observation and Geoinformation, 73, 752–766. https://doi.org/10.1016/j.jag.2018.08.023
Brocca, L., Zhao, W., Lu, H. 2023. High-resolution observations from space to address new applications in hydrology. The Innovation, 4(3). https://doi.org/10.1016/j.xinn.2023.100437
Brombacher, J., Silva, I.R. de O., Degen, J., Pelgrum, H. 2022. A novel evapotranspiration-based irrigation quantification method using the hydrological similar pixels algorithm. Agricultural Water Management, 267, 107602. https://doi.org/10.1016/j.agwat.2022.107602
Bussi, G., Francés, F., Montoya, J.J., Julien, P.Y. 2014. Distributed sediment yield modelling: Importance of initial sediment conditions. Environmental Modelling & Software, 58, 58–70. https://doi.org/10.1016/j.envsoft.2014.04.010
Camici, S., Dari, J., Filippucci, P., Massari, C., Mantovani, S., Avanzi, F., Delogu, F., Baez-Villanueva, O.M., Vreugdenhil, M., Volden, E., Fernandez, D.P., Brocca, L. 2025. High-resolution satellite observations for developing advanced decision support systems for water resources management in the Po River. Journal of Hydrology, 662, 134047. https://doi.org/10.1016/j.jhydrol.2025.134047
Centro Funzionale RAVDA – Centro Funzionale Regione Autonoma Valle d’Aosta. 2009. Dati osservati del Centro Funzionale RAVDA. https://presidi2.regione.vda.it/str_dataview_download
Copernicus Land Monitoring Service. 2020. CORINE Land Cover 2018 (raster 100 m), Europe, 6-yearly – Version 2020_20u1. European Environment Agency.
Cortés-Torres, N., Vignes, G., De León Pérez, D., Salazar, S., Francés, F. 2024. Influencia del reacondicionamiento y escalado espacial de parámetros geomorfológicos en modelación. In Memorias del XXXI Congreso Latinoamericano de Hidráulica (pp. 429–438). IAHR.
Cortés-Torres, N., Salazar-Galán, S., Francés, F. 2025. Fractal dimension and multiscale analysis in geomorphological parameter assessment and hydrological modeling. EGUsphere. https://doi.org/10.5194/egusphere-egu25-11047
Danielson, J.J., Gesch, D.B. 2011. Global multi-resolution terrain elevation data 2010 (GMTED2010).
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., Volden, E. 2023. Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space. Earth System Science Data, 15(4), 1555–1575. https://doi.org/10.5194/essd-15-1555-2023
Dari, J., Quintana-Seguí, P., Barella-Ortiz, A., Rahmati, M., Saltalippi, C., Flammini, A., Brocca, L. 2024. Quantifying the hydrological impacts of irrigation on a Mediterranean agricultural context through explicit satellite-derived irrigation estimates. Water Resources Research, 60(5). https://doi.org/10.1029/2023WR036510
Duan, Q., Sorooshian, S., Gupta, V. 1992. Effective and efficient global optimization for conceptual rainfall–runoff models. Water Resources Research, 28(4), 1015–1031. https://doi.org/10.1029/91WR02985
Echeverría, C., Ruiz-Pérez, G., Puertes, C., Samaniego, L., Barrett, B., Francés, F. 2019. Assessment of remotely sensed nearsurface soil moisture for distributed eco-hydrological model implementation. Water, 11(12). https://doi.org/10.3390/w11122613
Federal Institute of Hydrology (BfG). 2025. Global Runoff Data Centre (GRDC). https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser#dataDownload/Subregions
Francés, F., Vélez, J.I., Vélez, J.J. 2007. Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology, 332(1–2), 226–240. https://doi.org/10.1016/j.jhydrol.2006.06.032
Francés, F., Vélez Upegui, J.I., Vélez, J., Múnera Estrada, J.C., Medici, C., Bussi, G., Puertes Castellano, C., Escamilla Cambres, V., Ruiz-Pérez, G., García-Arias, A., Gomis-Cebolla, J., Cortés-Torres, N. 2021. TETIS V9.1: Modelo hidrológico distribuido, conceptual. Zenodo. https://doi.org/10.5281/zenodo.17780029
García-García, A., Stradiotti, P., Di Paolo, F., Filippucci, P., Fischer, M., Orság, M., Brocca, L., Peng, J., Dorigo, W., Gruber, A., Droppers, B., Wanders, N., Haag, A., Weerts, A., Modiri, E., Rakovec, O., Francés, F., Dall’Amico, M., Anderson, M., … Samaniego, L. 2026. Intercomparison of Earth observation products for hyper-resolution hydrological modelling over Europe. Remote Sensing of Environment, 333, 115131. https://doi.org/10.1016/j.rse.2025.115131
Gomes, G., Thiemig, V., Skøien, J.O., Ziese, M., Rauthe-Schöch, A., Rustemeier, E., Rehfeldt, K., Walawender, J., Kolbe, C., Pichon, D., Schweim, C., Salamon, P. 2020. EMO: A high-resolution multivariable gridded meteorological data set for Europe. https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26
Gomis-Cebolla, J., García-Arias, A., Perpinyà-Vallès, M., Francés, F. 2022. Evaluation of Sentinel-1, SMAP and SMOS surface soil moisture products for distributed eco-hydrological modelling in Mediterranean forest basins. Journal of Hydrology, 608, 127569. https://doi.org/10.1016/j.jhydrol.2022.127569
Hengl, T., De Jesus, J.M., 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., Kempen, B. 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE, 12(2). https://doi.org/10.1371/journal.pone.0169748
Huscroft, J., Gleeson, T., Hartmann, J., Börker, J. 2018. Compiling and mapping global permeability of the unconsolidated and consolidated Earth: GLHYMPS 2.0. Geophysical Research Letters, 45(4), 1897–1904. https://doi.org/10.1002/2017GL075860
Jalilvand, E., Tajrishy, M., Ghazi Zadeh Hashemi, S.A., Brocca, L. 2019. Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region. Remote Sensing of Environment, 231, 111226. https://doi.org/10.1016/j.rse.2019.111226
Koch, J., Demirel, M.C., Stisen, S. 2018. The SPAtial EFficiency metric (SPAEF): Multiple-component evaluation of spatial patterns for optimization of hydrological models. Geoscientific Model Development, 11(5), 1873–1886. https://doi.org/10.5194/gmd-11-1873-2018
Lakshmi, V., Fang, B. 2023. SMAP-derived 1-km downscaled surface soil moisture product (Version 1) [Data set]. NASA National Snow and Ice Data Center Distributed Active Archive Center.
Lehner, B., Grill, G. 2013. Global river hydrography and network routing: Baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15), 2171–2186. https://doi.org/10.1002/hyp.9740
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., … Brocca, L. 2021. A review of irrigation information retrievals from space and their utility for users. Remote Sensing, 13(20). https://doi.org/10.3390/rs13204112
McDermid, S., Nocco, M., Lawston-Parker, P., Keune, J., Pokhrel, Y., Jain, M., Jägermeyr, J., Brocca, L., Massari, C., Jones, A.D., Vahmani, P., Thiery, W., Yao, Y., Bell, A., Chen, L., Dorigo, W., Hanasaki, N., Jasechko, S., Lo, M.H., … Yokohata, T. 2023. Irrigation in the Earth system. Nature Reviews Earth & Environment, 4(7), 435–453. https://doi.org/10.1038/s43017-023-00438-5
McInerney, D., Thyer, M., Kavetski, D., Githui, F., Thayalakumaran, T., Liu, M., Kuczera, G. 2018. The importance of spatiotemporal variability in irrigation inputs for hydrological modeling of irrigated catchments. Water Resources Research, 54(9), 6792–6821. https://doi.org/10.1029/2017WR022049
Medici, C., Butturini, A., Bernal, S., Vázquez, E., Sabater, F., Vélez, J.I., Francés, F. 2008. Modelling the non-linear hydrological behaviour of a small Mediterranean forested catchment. Hydrological Processes, 22(18), 3814–3828. https://doi.org/10.1002/hyp.6991
Montanari, A. 2012. Hydrology of the Po River: Looking for changing patterns in river discharge. Hydrology and Earth System Sciences, 16(10), 3739–3747. https://doi.org/10.5194/hess-16-3739-2012
Montanari, A., Nguyen, H., Rubinetti, S., Ceola, S., Galelli, S., Rubino, A., Zanchettin, D. 2023. Why the 2022 Po River drought is the worst in the past two centuries. Science Advances, 9(32).
Naciones Unidas. 2024. Agua para la prosperidad y la paz: Informe mundial de las Naciones Unidas sobre el desarrollo de los recursos hídricos 2024 (UNESCO, Ed.). https://unesdoc.unesco.org/ark:/48223/pf0000391195
Orozco, I., Francés, F., Mora, J. 2019. Parsimonious modeling of snow accumulation and snowmelt processes in high mountain basins. Water, 11(6). https://doi.org/10.3390/w11061288
Puertes, C., Bautista, I., Lidón, A., Francés, F. 2021. Best management practices scenario analysis to reduce agricultural nitrogen loads and sediment yield to the semiarid Mar Menor coastal lagoon (Spain). Agricultural Systems, 188, 103029. https://doi.org/10.1016/j.agsy.2020.103029
Puertes, C., Sepúlveda, J.F., Lidón, A., Francés, F. 2024. Análisis de actuaciones en la zona agrícola de las cuencas Sur del Mar Menor con el objetivo de mejorar el estado ecológico de la laguna. Ingeniería del Agua, 28(3), 153–168. https://doi.org/10.4995/ia.2024.21575
Romero-Hernández, C.P. 2022. Análisis del impacto del crecimiento de las megaciudades sobre el ciclo hidrológico bajo escenarios de cambio climático. Aplicación a la cuenca del río Bogotá (Colombia) [Tesis doctoral, Universitat Politècnica de València]. https://doi.org/10.4995/Thesis/10251/191025
Running, S., Mu, Q., Zhao, M. 2021. MODIS/Terra net evapotranspiration 8-day L4 global 500 m SIN grid V061 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center.
Shibuo, Y., Jarsjö, J., Destouni, G. 2007. Hydrological responses to climate change and irrigation in the Aral Sea drainage basin. Geophysical Research Letters, 34(21). https://doi.org/10.1029/2007GL031465
Tóth, B., Weynants, M., Pásztor, L., Hengl, T. 2017. 3D soil hydraulic database of Europe at 250 m resolution. Hydrological Processes, 31(14), 2662–2666. https://doi.org/10.1002/hyp.11203
Valt, M. 2016. Densità della neve fresca sulle Alpi italiane. Neve e Valanghe – Rivista dell’AINEVA, 87, 32–39.
Voit, P., Francke, T., Bronstert, A. 2023. Accounting for operational irrigation options in mesoscale hydrological modelling of dryland environments. Hydrological Sciences Journal, 68(5), 670-684. https://doi.org/10.1080/02626667.2023.2187296
WWAP. 2019. United Nations world water development report 2019: Leaving no one behind (UNESCO World Water Assessment Programme, Ed.). UNESCO.
Xia, Q., Liu, P., Fan, Y., Cheng, L., An, R., Xie, K., Zhou, L. 2022. Representing Irrigation Processes in the Land Surface-Hydrological Model and a Case Study in the Yangtze River Basin, China. Journal of Advances in Modeling Earth Systems, 14(7). https://doi.org/10.1029/2021MS002653
Zappa, L., Schlaffer, S., Bauer-Marschallinger, B., Nendel, C., Zimmerman, B., Dorigo, W. 2021. Detection and quantification of irrigation water amounts at 500 m using Sentinel-1 surface soil moisture. Remote Sensing, 13(9). https://doi.org/10.3390/rs13091727
