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

María Ximena Solana; Asunción Romanelli; Orlando Mauricio Quiroz Londoño

doi:10.35305/curiham.v29i.e191

Authors

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. 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. 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. https://orcid.org/0000-0002-2817-9570

DOI:

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

Keywords:

Google Earth Engine, Landsat Imagery, Spectral Indices, Flood Mapping, Flood Prevention Strategies

Abstract

This study presents a semi-automated approach for mapping the extent and frequency of floods in agriculturally dominated river watersheds, using the Quequén Grande River watershed as a case study. By the combination of normalized difference indices computed from Landsat imagery and the application of Otsu’s thresholding method in Google Earth Engine (GEE) environment, two flood categories were defined: Open Flood Surfaces (OFS) and Flooded Vegetation (FV). The analysis of historical flood frequency allowed the proposal of flood prevention strategies to be implemented in each defined flood frequency class, which is essential for flood mitigation in agriculturally dominated river watersheds.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

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