Cartografía multitemporal y estrategias para la prevención de inundaciones en una cuenca con predominio agrícola de Argentina

Autores

  • María Ximena Solana Instituto de Investigaciones Marinas y Costeras, Consejo Nacional de Investigaciones Científicas y Técnicas, Facultad de Ciencias Exactas y Naturales; e Instituto de Geología de Costas y del Cuaternario, Universidad Nacional de Mar del Plata – Comisión de Investigaciones Científicas Provincia de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Mar del Plata, Argentina image/svg+xml https://orcid.org/0000-0002-4575-6990
  • Asunción Romanelli Instituto de Investigaciones Marinas y Costeras, Consejo Nacional de Investigaciones Científicas y Técnicas, Facultad de Ciencias Exactas y Naturales; e Instituto de Geología de Costas y del Cuaternario, Universidad Nacional de Mar del Plata – Comisión de Investigaciones Científicas Provincia de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Mar del Plata, Argentina. image/svg+xml https://orcid.org/0000-0002-9003-396X
  • Orlando Mauricio Quiroz Londoño Instituto de Investigaciones Marinas y Costeras, Consejo Nacional de Investigaciones Científicas y Técnicas, Facultad de Ciencias Exactas y Naturales; e Instituto de Geología de Costas y del Cuaternario, Universidad Nacional de Mar del Plata – Comisión de Investigaciones Científicas Provincia de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Mar del Plata, Argentina. image/svg+xml https://orcid.org/0000-0002-2817-9570

DOI:

https://doi.org/10.35305/curiham.v29i.e191

Palavras-chave:

Google Earth Engine, Imágenes Landsat, Índices Espectrales, Cartografía de inundaciones, Estrategias de Prevención a la Inundación

Resumo

En este estudio se propone un método semiautomático para la cartografía de la extensión y frecuencia de las inundaciones en una cuenca hidrográfica con predominio agrícola, seleccionándose la cuenca del Río Quequén Grande como caso de estudio. Mediante la combinación de índices diferenciales normalizadoscalculados a partir de imágenes Landsat y la aplicación del método de umbralización desarrollado por Otsu en el entorno de Google Earth Engine (GEE), se definieron dos categorías para las inundaciones: superficies de inundación abiertas (OFS) y vegetación inundada (FV). El análisis de la frecuencia histórica de las inundaciones permitió la propuesta de estrategias de prevención a las inundaciones dirigidas a ser implementadas en cada clase de frecuencia de inundación definida, siendo esencial para la mitigación de inundaciones en cuencas hidrográficas con predominio agrícola.

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Referências

Antolini, F., Tate, E., Dalzell, B., Young, N., Johnson, K. & Hawthorne, P. L. (2020). Flood risk reduction from agricultural best management practices. JAWRA Journal of the American Water Resources Association, 56(1), 161-179. https://doi.org/10.1111/1752-1688.12812

Aragón, R., Jobbágy, E. & Viglizzo, E. (2011). Surface and groundwater dynamics in the sedimentary plains of the Western Pampas (Argentina). Ecohydrology 4, 433–447. https://doi.org/10.1002/eco.149

Azedou, A., Khattabi, A. & Lahssini, S. (2022). Characterizing fluvial geomorphological change using Google Earth Engine (GEE) to support sustainable flood management in the rural municipality of El Faid. Arabian Journal of Geosciences, 15, 413. https://doi.org/10.1007/s12517-022-09674-3

Alderman, K., Turner, L. & Tong, S. (2012). Floods and human health: A systematic review. Environment International, 47, 37–47. https://doi.org/10.1016/j.envint.2012.06.003

Borro, M., Morandeira, N., Salvia, M., Minotti, P., Perna, P. & Kandus, P. (2014). Mapping shallow lakes in a large South American floodplain: A frequency approach on multitemporal Landsat TM/ETM data. Journal of Hydrolgy, 512, 39–52. https://doi.org/10.1016/j.jhydrol.2014.02.057

Campo de Ferreras, A. M. & Piccolo, M. C. (1999). Hidrogeomorfología de la cuenca del Río Quequén Grande, Argentina. Papeles de Geografía, 29. https://revistas.um.es/geografia/article/view/45221

Campos, J., Sillero, N. & Brito, J. (2012). Normalized difference water indexes have dissimilar performances in detecting seasonal and permanent water in the Sahara – Sahel transition zone. Journal of Hydrology 464-465, 438–446. https://doi.org/10.1016/j.jhydrol.2012.07.042

Dash, P. & Sar, J. (2020). Identification and validation of potential flood hazard area using GIS-based multicriteria analysis and satellite data-derived water index. Journal of Flood Risk Management, 13(3), 1-14. https://doi.org/10.1111/jfr3.12620

Detrembleur, S., Stilmant, F., Dewals, B., Erpicum, S., Archambeau, P. & Pirotton, M. (2015). Impacts of climate change on future flood damage on the river Meuse, with a distributed uncertainty analysis. Natural Hazards, 77, 1533-1549. https://doi.org/10.1007/s11069-015-1661-6

DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. & Lang, M. (2020). Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240, 1-15. https://doi.org/10.1016/j.rse.2020.111664

Domenikiotis, C., Loukas, A. & Dalezios, N. (2003). The use of NOAA/AVHRR satellite data for monitoring and assessment of forest fires and floods. Natural Hazards and Earth System Sciences, 3, 115–128, https://doi.org/10.5194/nhess-3-115-2003

Du, S., Shi, P., Van Rompaey, A. & Wen, J. (2015). Quantifying the impact of impervious surface location on flood peak discharge in urban areas. Natural Hazards, 76, 1457–1471. https://doi.org/10.1007/s11069-014-1463-2

European Commission - Directorate-General for Environment. (2021). Strengthening the synergies betweenagriculture and flood risk management in the European Union. Publications Office of the European Union, 31 pp, https://doi.org/10.2779/128153

Fan, Y., Li, H. & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339, 940–943. https://doi.org/10.1126/science.1229881

Feloni, E., Mousadis, I. & Baltas, E. (2019). Flood vulnerability assessment using a GIS-based multi-criteria approach – The case of Attica region. Journal of Flood Risk Management, 13, 1-15. https://doi.org/10.1111/jfr3.12563

Foga, S., Scaramuzza, P., Guo, S., Zhu, Z., Dilley Jr, R., Beckmann, T., Schmidt, G., Dwyer, J., Hughes, M. & Laue, B.(2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. https://doi.org/10.1016/j.rse.2017.03.026

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/https://doi.org/10.1016/j.rse.2017.06.031

Haldane, J. (1948). Note on the Median of a Multivariate Distribution. Biometrika, 35, 414-417. https://doi.org/10.1093/biomet/35.3-4.414

Hawker, L., Neal, J., Tellman, B., Liang, J., Schumann, G., Doyle, C., Sullivan, J., Savage, J. & Tshimanga, R. (2020). Comparing earth observation and inundation models to map flood hazards. Environmental Research Letters, 15, 1-13. https://doi.org/10.1088/1748-9326/abc216

Houspanossian, J., Giménez, R., Whitworth-Hulse, J. I., Nosetto, M. D., Tych, W., Atkinson, P. M., Rufino, M. C. & Jobbágy, E. G. (2023). Agricultural expansion raises groundwater and increases flooding in the South American plains. Science, 380(6652), 1344-1348. https://doi.org/10.1126/science.add5462

Houspanossian, J., Kuppel, S., Nosetto, M., Di Bella, C., Oricchio, P., Barrucand, M., Rusticucci, M. & Jobbágy, E. (2018). Long-lasting floods buffer the thermal regime of the Pampas. Theoretical and Applied Climatology, 131, 111-120. https://doi.org/10.1007/s00704-016-1959-7

Jain, V. & Sinha, R. (2005). Response of active tectonics on the alluvial Baghmati River, Himalayan foreland basin, eastern India. Geomorphology, 70, 339–356. https://doi.org/10.1016/j.geomorph.2005.02.012

Ji, L., Zhang, L. & Wylie, B. (2009). Analysis of Dynamic Thresholds for the Normalized Difference Water Index. Photogrammetric Engineering & Remote Sensing, 75(11), 1307–1317. https://doi.org/10.14358/PERS.75.11.1307

Kumar, H., Karwariya, S. & Kumar, R. (2022). Google Earth Engine-Based Identification of Flood Extent and Flood-Affected Paddy Rice Fields Using Sentinel-2 MSI and Sentinel-1 SAR Data in Bihar State, India. Journal of the Indian Society of Remote Sensing, 50, 791-803. https://doi.org/10.1007/s12524-021-01487-3

Kuppel, S., Houspanossian, J., Nosetto, M. & Jobbágy, E. (2015). What does it take to flood the Pampas?: Lessons from a decade of strong hydrological fluctuations. Water Resources Research, 51, 2937–2950. https://doi.org/10.1002/2015WR016966

Lal, P., Prakash, A. & Kumar, A. (2020). Google Earth Engine for concurrent flood monitoring in the lower basin of Indo-Gangetic-Brahmaputra plains. Natural Hazards, 104, 1947-1952. https://doi.org/10.1007/s11069-020-04233-z

Marini, M. F. & Piccolo, M. C. (2005). Hidrogeomorfología de la cuenca del río Quequén Salado, Argentina. Investigaciones Geográficas, 37, 59-71. https://doi.org/10.14198/INGEO2005.37.04

Martínez, D. E., Solomon, K., Quiroz Londoño, O., Dapeña, C., Massone, H., Benavente, M., Panarello, H. & Grondona, S. (2010). Tiempo medio de residencia del flujo base en aguas superficiales de la llanura pampeana: aplicación de isótopos del agua, gases nobles y CFCs en el río Quequén Grande. In: I Congreso Internacional de Hidrología de Llanuras Azul, Buenos Aires, Argentina, 420-427.

Martínez, D. E. & Bocanegra, E. M. (2002). Hydrogeochemistry and cation-exchange processes in the coastal aquifer of Mar Del Plata, Argentina. Hydrogeology Journal, 10, 393–408. https://doi.org/10.1007/s10040-002-0195-7

McFeeters, S. (1996). The Use of Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17(7), 1425–1432. https://doi.org/10.1080/01431169608948714

Mehmood, H., Conway, C. & Perera, D. (2021). Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform. Atmosphere, 12(7), 1-16. https://doi.org/10.3390/atmos12070866

Mohammadi, A., Costelloe, J. & Ryu, D. (2017). Application of time series of remotely sensed normalized difference water, vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone floodplains. Remote Sensing of Environment, 190, 70–82. https://doi.org/10.1016/j.rse.2016.12.003

Mora, D., Walker, E. & Venturini, V. (2021). Flood monitoring in Santa Fe using the Google Earth Engine platform. In: XIX Workshop on Information Processing and Control (RPIC), 1-6. https://doi.org/10.1109/RPIC53795.2021.9648518

Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62–66.

Pandey, A., Kaushik, K. & Parida, B. (2022). Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin. Sustainability, 14(7), 4210, 1-22. https://doi.org/10.3390/su14074210

Paruelo, J., Guerschman, J. & Verón, S. R. (2005). Expansión agrícola y cambios en el uso del suelo. Ciencia Hoy. 15(87), 14–23.

Policelli, F., Slayback, D., Brakenridge, B., Nigro, J., Hubbard, A., Zaitchik, B., Carroll, M. & Jung, H. (2017). The NASA Global Flood Mapping System. Remote Sensing of Hydrological Extremes, 47–63. https://doi.org/10.1007/978-3-319-43744-6_3

Powell, S., Jakeman, A. J. & Croke, B. (2014). Can NDVI response indicate the effective flood extent in macrophyte dominated floodplain wetlands? Ecological Indicators, 45, 486–493. https://doi.org/10.1016/j.ecolind.2014.05.009

Quiroz Londoño, O., Grondona, S., Massone, H., Farenga, M., Martínez, G. & Martínez, D. (2013). Modelo de anegamiento y estrategia de predicción–prevención del riesgo de inundación en áreas de llanura: el sudeste de la provincia de Buenos Aires como caso de estudio. GeoFocus. International Review of Geographical Information Science and Technology, 13_1, 76–98. https://www.geofocus.org/index.php/geofocus/article/view/262

Robinson, N., Allred, B., Jones, M., Moreno, A., Kimball, J., Naugle, D., Erickson, T. & Richardson, A. (2017). A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sensing, 9(8), 863,1-14. https://doi.org/10.3390/rs9080863

Rogers, A. S. & Kearney, M., (2004). Reducing signature variability in unmixing coastal marsh ThematicMapper scenes using spectral indices. International Journal of Remote Sensing, 25(12), 2317–2335.https://doi.org/10.1080/01431160310001618103

Roy, D., Kovalskyy, V., Zhang, H., Vermote, E., Yan, L., Kumar, S. & Egorov, A. (2016). Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185, 57-70. https://doi.org/10.1016/j.rse.2015.12.024

Rozenberg, J., Dborkin, D. V., Giuliano, F. M., Jooste, C., Mikou, M., Rodriguez Chamussy, L., Schwerhoff, G., Turner, S. D., Vezza, E. & Walsh, B. J. (2021). Argentina - Poverty and Macro Economic Impacts of Climate Shocks (English). Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/590371624981025569/Argentina-Poverty-and-Macro-EconomicImpacts-of-Climate-Shocks

Sandoval, V. & Sarmiento, J. (2020). A neglected issue: informal settlements, urban development, and disaster risk reduction in Latin America and the Caribbean. Disaster Prevention and Management, 29,731–745. https://doi.org/10.1108/DPM-04-2020-0115

Scarpati, O. E. & Capriolo, A. D (2013). Droughts and floods in Buenos Aires province (Argentina) and their space and temporal distribution. Investigaciones Geográficas, 82, 38–51. https://doi.org/10.14350/rig.31903

Sethre, P., Rundquist, B. & Todhunter, P. (2005). Remote Detection of Prairie Pothole Ponds in the Devils Lake Basin, North Dakota. GIScience& Remote Sensing, 42(4), 277–296. https://doi.org/10.2747/1548-1603.42.4.277

Shrestha, R., Di, L., Yu, E., Kang, L., Shao, Y. & Bai, Y. (2017). Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer. Journal of Integrative Agriculture, 16(2), 398–407. https://doi.org/10.1016/S2095-3119(16)61502-2

Solana, M. X., Quiroz Londoño, O. M., Weinzettel, P. & Donna, F. (2021a). Contributions to the conceptual hydrogeological model of the Quequén Grande River Basin at its southwestern limit. Boletín Geológico y Minero, 132(1-2): 197-205. https://doi.org/10.21701/bolgeomin.132.1-2.020

Solana, M. X., Quiroz Londoño, O. M, Romanelli, A., Donna, F., Martínez, D. & Weinzettel, P. (2021b). Connectivity of temperate shallow lakes to groundwater in the Pampean Plain, Argentina: A remote sensing and multi-tracer approach. Groundwater for Sustainable Development, 13, 1-10. https://doi.org/10.1016/j.gsd.2021.100556

Tellman, B., Sullivan, J. A. & Doyle, C. S. (2021). Global Flood Observation with Multiple Satellites. In: Global Drought and Flood (eds. H. Wu, D.P. Lettenmaier, Q. Tang and P.J. Ward), 99–121. https://doi.org/10.1002/9781119427339.ch5

Teruggi, L., Martínez, G., Billi, P. & Preciso, E. (2005). Geomorphologic units and sediment transport in a very low relief basin: Rio Quequén Grande, Argentina. IAHS Publication, 299, 154–160.

Tsyganskaya, V., Martinis, S., Marzahn, P. & Ludwig, R. (2018). SAR-based detection of flooded vegetation – a review of characteristics and approaches. International Journal of Remote Sensing, 39(8), 2255–2293. https://doi.org/10.1080/01431161.2017.1420938

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote

Sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0

UN-OCHA (2020). Natural disasters in Latin American and the Caribbean, 2000–2019. New York: United Nations Office for the Coordination of Humanitarian Affairs. https://www.unocha.org/publications/report/world/natural-disasters-latin-america-and-caribbean-2000-2019

United State Geological Survey. (s.f.). Landsat Known Issues.https://www.usgs.gov/core-sciencesystems/nli/landsat/landsat-known-issues

Vanama, V. S. K., Mandal, D. & Rao, Y. S. (2020). GEE4FLOOD: Rapid mapping of flood areas using temporal Sentinel 1 SAR images with Google Earth Engine cloud platform. Journal of Applied Remote Sensing, 14(3), 1-23. https://doi.org/10.1117/1.JRS.14.034505

Wannous, C. & Velasquez, G. (2017). United Nations Office for Disaster Risk Reduction (UNISDR)—UNISDR’s Contribution to Science and Technology for Disaster Risk Reduction and the Role of the International Consortium on Landslides (ICL). In: Sassa, K., Mikoš, M., Yin, Y. (eds) Advancing Culture of Living with Landslides, 109-115. https://doi.org/10.1007/978-3-319-59469-9_6

Warner, B. P., Schattman, R. E. & Hatch, C. E. (2017). Farming the floodplain: Ecological and agricultural tradeoffs and opportunities in river and stream governance in New England’s changing climate. Case Studies in the Environment, 1(1), 1-9. https://doi.org/10.1525/cse.2017.sc.512407

World Bank (2021). Argentina: Valuing Water -Brief for Policy Makers. Water Security Diagnostic, 1-27. https://documents1.worldbank.org/curated/en/945671624438916229/pdf/Argentina-Water-Security-ValuingWater-Brief-for-Policy-Makers.pdf

Wulder, M. A. & Coops, N. C. (2014). Satellites: Make Earth observations open access. Nature, 513, 30–31. https://doi.org/10.1038/513030a

Xu, H. (2006). Modification of Normalized Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27(14), 3025–3033.https://doi.org/10.1080/01431160600589179

Publicado

2023-12-11

Como Citar

Solana, M. X., Romanelli, A., & Quiroz Londoño, O. M. (2023). Cartografía multitemporal y estrategias para la prevención de inundaciones en una cuenca con predominio agrícola de Argentina. Cuadernos Del CURIHAM, 29, 191. https://doi.org/10.35305/curiham.v29i.e191

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