Methodological projection of droughts in Bravo-Conchos, Mexico, using combined techniques
DOI:
https://doi.org/10.24850/j-tyca-2026-05-06Palabras clave:
drought, arid zones, water resources management, water balance, Svanidze method, Falkenmark index, CMIP5 models, water supply, river basins, mathematical models, climate change, bilateral relations, transboundary waters, MexicoResumen
Water scarcity is a critical issue exacerbated by population growth, climate change, and increasing water demand. The Bravo-Conchos hydrological region, located along the U.S.-Mexico border, faces severe drought conditions that threaten water availability and economic stability. This research applied a hybrid approach, combining the Svanidze method for synthetic data generation with traditional drought analysis to project future drought scenarios. The methodology includes assessing water stress using the Falkenmark Index, analyzing climate projections with CMIP5 models, and characterizing multiyear droughts. Results indicate a projected decrease in precipitation and rising temperatures, intensifying drought frequency and severity. The region should adapt to an average precipitation of approximately 243.35 mm, which is 78.82 % of the historical mean precipitation, considering future droughts and climate fluctuations. Findings highlight the urgent need for adaptive water management policies, cross-border cooperation, and infrastructure improvements to mitigate climate change impacts. This research also provides accessible and cost-effective tools for decision-making, supporting public policies and sustainable water resource management in data-scarce regions.
Referencias
Apurv, T., Sivapalan, M., & Cai, X. (2017). Understanding the role of climate characteristics in drought propagation. Water Resources Research, 53(9), 9304-9329. https://doi.org/10.1002/2017WR021445
Arganis, M., Domínguez, R., Mendoza, A., Osnaya, J., Carrizosa Elizondo, E., Macario, I., Valencia-Esteban, R., & Juan-Diego, E. (2023). Método de Svanidze modificado aplicado a volúmenes de ingreso a hidroeléctricas en cascada. En: Memorias de las VII Jornadas de Ingeniería del Agua (18-19 de octubre). Cartagena, España. https://repositorio.upct.es/server/api/core/bitstreams/6aeeb09f-a2d9-4300-8106-98a7a342019d/content
Conagua, Comisión Nacional del Agua. (2021). Estadísticas del agua en México 2021. Secretaría de Medio Ambiente y Recursos Naturales. https://files.conagua.gob.mx/conagua/publicaciones/Publicaciones/EAM%202021.pdf
Conde, A., & López, J. (2016). Variabilidad y cambio climático: impactos, vulnerabilidad y adaptación. Secretaría de Medio Ambiente y Recursos Naturales. https://biblioteca.semarnat.gob.mx/janium/Documentos/Ciga/Libros2013/CD002498.pdf
Cook, E. R., Seager, R., Cane, M. A., & Stahle, D. W. (2007). North American drought: Reconstructions, causes, and consequences. Earth-Science Reviews, 81(1-2), 93-134. https://doi.org/10.1016/j.earscirev.2006.12.002
Chetverikov, D., Liao, Z., & Chernozhukov, V. (2021). On cross-validated Lasso in high dimensions. The Annals of Statistics, 49(3), 1300-1317. https://doi.org/10.1214/20-AOS2000
Dai, A. (2011). Drought under global warming: A review. WIREs Climate Change, 2(1), 45-65. https://doi.org/10.1002/wcc.81
Dobler-Morales, C., & Bocco, G. (2021). Social and environmental dimensions of drought in Mexico: An integrative review. International Journal of Disaster Risk Reduction, 55, 102067. https://doi.org/10.1016/j.ijdrr.2021.102067
Dodig, A., Stankovic, V., Stankovic, L., & Stojkovic, M. (2024). Enhancement of hydrological time series prediction with real-world time series generative adversarial network-based synthetic data and deep learning models. SSRN. https://doi.org/10.2139/ssrn.5022039
Domínguez-Mora, R., Valencia, C., & Arganis-Juárez, M. L. (2005). Importancia de la generación de muestras sintéticas en el análisis del comportamiento de políticas de operación de presas. Ingeniería del Agua, 12(1), 1-14https://doi.org/10.4995/ia.2005.2548
Enqvist, J. P., & Ziervogel, G. (2019). Water governance and justice in Cape Town: An overview. WIREs Water, 6(4), e1354. https://doi.org/10.1002/wat2.1354
Escalante-Sandoval, C., & Reyes-Chávez, L. (2013). Meteorological drought analysis in northern Mexico. In: Proceedings of the 2013 IAHR World Congress (pp. 1-12). https://www.iahr.org/library/infor?pid=14746
Escalante-Sandoval, C., & Núñez-García, P. (2017). Meteorological drought features in northern and northwestern parts of Mexico under different climate change scenarios. Journal of Arid Land, 9(1), 65-75. https://doi.org/10.1007/s40333-016-0022-y
Estrada, F., Calderón-Bustamante, Ó., Raga, G., Torres, V., & Zavala-Hidalgo, J. (n.d.). Análisis del cambio climático observado y proyectado para México. Universidad Nacional Autónoma de México, Programa de Investigación en Cambio Climático (PINCC). https://datapincc.unam.mx/datapincc/
FAO, Food and Agriculture Organization of the United Nations. (2025). Water scarcity means less water for agriculture production, which in turn means less food available, threatening food security and nutrition. https://www.fao.org/newsroom/detail/water-scarcity-means-less-water-for-agriculture-production-which-in-turn-means-less-food-available-threatening-food-security-and-nutrition/es
Hernández-Romero, P., & Patiño-Gómez, C. (2017). Perspectiva actual y futura de los recursos hídricos de la cuenca del río Bravo. Entorno UDLAP, 5, 42-51. https://entorno.udlap.mx/perspectiva-actual-y-futura-de-los-recursos-hidricos-en-la-cuenca-del-rio-bravo/
IBWC, International Boundary and Water Commission. (2022). Treaty between the United States and Mexico relating to the utilization of waters of the Colorado and Tijuana Rivers and of the Rio Grande. https://www.ibwc.gov/wp-content/uploads/2022/11/1944Treaty.pdf
INEGI, Instituto Nacional de Estadística y Geografía. (2020). Censo de población y vivienda 2020. https://www.inegi.org.mx/programas/ccpv/2020/
Jain, M., Singh, V., & Kumar, P. (2021). Water management challenges in Chennai: A case of a rapidly urbanizing city. Urban Water Journal, 18(4), 313-323. https://doi.org/10.1080/1573062X.2020.1862622
James, R., Washington, R., Schleussner, C., Rogelj, J., & Conway, D. (2017). Characterizing half-a-degree difference: A review of methods for identifying regional climate responses to global warming targets. WIREs Climate Change, 8(2), e457. https://doi.org/10.1002/wcc.457
Karimanzira, D. (2024). Mass conservative time-series GAN for synthetic extreme flood-event generation: Impact on probabilistic forecasting models. Stats, 7(3), 808-826. https://doi.org/10.3390/stats7030049
Ledolter, J. (1976). ARIMA models and their use in modelling hydrologic sequences. Institute for Applied Systems Analysis. https://pure.iiasa.ac.at/id/eprint/615/1/RM-76-069.pdf
Liu, Z., Cui, Y., Ding, C., Gan, Y., Luo, J., Luo, X., & Wang, Y. (2024). The characteristics of ARMA (ARIMA) model and some key points to be noted in application: A case study of Changtan Reservoir, Zhejiang Province, China. Sustainability, 16(18), 7955. https://doi.org/10.3390/su16187955
Liu, J., Yang, H., Gosling, S. N., Kummu, M., Flörke, M., Pfister, S., Hanasaki, N., Wada, Y., Zhang, X., Zheng, C., Alcamo, J., & Oki, T. (2017). Water scarcity assessments in the past, present, and future. Earth’s Future, 5(6), 545-559. https://doi.org/10.1002/2016EF000518
López-García, T., Manzano, M., & Ramírez, A. I. (2017). Disponibilidad hídrica bajo escenarios de cambio climático en el Valle de Galeana, Nuevo León, México. Tecnología y ciencias del agua, 8(1), 105-114. https://doi.org/10.24850/j-tyca-2017-01-08
Marusov, A., Grabar, V., Maximov, Y., Sotiriadi, N., Bulkin, A., & Zaytsev, A. (2024). Long-term drought prediction using deep neural networks based on geospatial weather data. Environmental Modelling & Software, 179, 106127. https://doi.org/10.1016/j.envsoft.2024.106127
Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1-2), 202-216. https://doi.org/10.1016/j.jhydrol.2010.07.012
Nguyen, D. T., & Chen, S. T. (2022). Generating continuous rainfall time series with high temporal resolution by using a stochastic rainfall generator with a copula and modified Huff rainfall curves. Water, 14(13), 2123. https://doi.org/10.3390/w14132123
Ortiz-Gómez, R., Cardona-Díaz, J. C., Ortiz-Robles, F. A., & Alvarado-Medellín, P. (2018). Characterization of droughts by comparing three multiscale indices in Zacatecas, Mexico. Tecnología y Ciencias del Agua, 9(3), 47-91. https://doi.org/10.24850/j-tyca-2018-03-03
Ramírez-Abundis, Y. (2024). Sequía e inundaciones: dos caras ante el cambio climático en México [tesis doctoral inédita]. Universidad Nacional Autónoma de México.
Shepard, D. (1968). A two-dimensional interpolation function for irregularly spaced data. In: Proceedings of the 1968 ACM National Conference (pp. 517-524). https://doi.org/10.1145/800186.810616
Semarnat, Secretaría de Medio Ambiente y Recursos Naturales. (2021). Programa hídrico regional 2021–2024. Secretaría de Medio Ambiente y Recursos Naturales. https://files.conagua.gob.mx/conagua/generico/PNH/PHR_2021-2024_RHA_VI_R_o_Bravo_.pdf
Soto, P. (2024). Impacto de la sequía en las aguas subterráneas. Revista IC Colegio de Ingenieros Civiles de México, 652, 12-16. https://www.researchgate.net/publication/381923026
Svanidze, G. G. (1980). Mathematical modeling of hydrologic series. Water Resources Publications.
Villazón-Bustillos, D., Rubio-Arias, H., & Ortega-Gutiérrez, J. A. (2015). Análisis en series de tiempo para el pronóstico de sequía en la región noroeste del estado de Chihuahua. Ecosistemas y Recursos Agropecuarios, 3(9), 307-315. https://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S2007-90282016000300307
Wang, H. R., Wang, C., Lin, X., & Kang, J. (2014). An improved ARIMA model for hydrological simulations. Nonlinear Processes in Geophysics Discussions, 1(2), 841-876. https://doi.org/10.5194/npgd-1-841-2014
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