Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis
The Morroa aquifer plays a crucial role supplying drinking water to around one million residents across Sucre, Córdoba, and Bolívar departments in Colombia. However, it faces severe water stress, ranking as the second most overexploited aquifer globally according to recent research using the Groundw...
- Autores:
-
Cohen-Manrique, Carlos S.
Solano-Correa, Yady Tatiana
Villa-Ramírez, Jose L.
Alvarez-Month, Alex A.
- Tipo de recurso:
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12729
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12729
- Palabra clave:
- Static Level
Aquifer
Planet
Remote Sensing
land cover
LEMB
- Rights
- closedAccess
- License
- http://purl.org/coar/access_right/c_14cb
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Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis |
| title |
Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis |
| spellingShingle |
Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis Static Level Aquifer Planet Remote Sensing land cover LEMB |
| title_short |
Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis |
| title_full |
Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis |
| title_fullStr |
Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis |
| title_full_unstemmed |
Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis |
| title_sort |
Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis |
| dc.creator.fl_str_mv |
Cohen-Manrique, Carlos S. Solano-Correa, Yady Tatiana Villa-Ramírez, Jose L. Alvarez-Month, Alex A. |
| dc.contributor.author.none.fl_str_mv |
Cohen-Manrique, Carlos S. Solano-Correa, Yady Tatiana Villa-Ramírez, Jose L. Alvarez-Month, Alex A. |
| dc.subject.keywords.spa.fl_str_mv |
Static Level Aquifer Planet Remote Sensing land cover |
| topic |
Static Level Aquifer Planet Remote Sensing land cover LEMB |
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LEMB |
| description |
The Morroa aquifer plays a crucial role supplying drinking water to around one million residents across Sucre, Córdoba, and Bolívar departments in Colombia. However, it faces severe water stress, ranking as the second most overexploited aquifer globally according to recent research using the Groundwater Footprint (GF) indicator. This situation threatens the sustainability of the aquifer and the well-being of the region's inhabitants who rely on it. To tackle this challenge, CARSUCRE, the entity responsible for aquifer management, has implemented various strategies. These include establishing a monitoring network with piezometers to track static and dynamic aquifer levels and conducting civil works to redirect rainfall runoff towards artificial recharge projects. Yet, the impact of vegetation variations in the recharge areas of the aquifer levels remains uncertain due to many different factors like drought, heavy rainfall, and economic changes. This research introduces a methodology that leverages remote sensing data, particularly high-resolution images from the Planet platform (3m), combined with land cover analysis in piezometer influence areas. The primary aim is to assess how changes in vegetation affect both static and dynamic levels of the Morroa Aquifer and then identify strategies to enhance land cover and improve water capture. The results obtained show a significant correlation between NDVI, EVI, and LULC for the aquifer recharge zone, with an average of 0.858 for all applied tools. These findings provide valuable information for the management and preservation of this vital water resource in the region. |
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2024 |
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2024-09-12T13:59:57Z |
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2024-09-12T13:59:57Z |
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2024-06-10 |
| dc.date.submitted.none.fl_str_mv |
2024-09-11 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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publishedVersion |
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C.S. Cohen-Manrique; Y. T. Solano-Correa; J.L. Villa-Ramírez; A.A. Alvarez-Month, "Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis," in Proc. SPIE 13037, Geospatial Informatics XIV, 1303704 (10 June 2024). DOI: https://doi.org/10.1117/12.3014190. |
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https://hdl.handle.net/20.500.12585/12729 |
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10.1117/12.3014190 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
C.S. Cohen-Manrique; Y. T. Solano-Correa; J.L. Villa-Ramírez; A.A. Alvarez-Month, "Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis," in Proc. SPIE 13037, Geospatial Informatics XIV, 1303704 (10 June 2024). DOI: https://doi.org/10.1117/12.3014190. 10.1117/12.3014190 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12729 |
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eng |
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eng |
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9 páginas |
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application/pdf |
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Cartagena de Indias |
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Ciencias Básicas |
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Proceedings Volume 13037, Geospatial Informatics XIV |
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Universidad Tecnológica de Bolívar |
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Cohen-Manrique, Carlos S.ce2bc8d8-f621-40d8-8865-bc95904772d4Solano-Correa, Yady Tatiana64432ee7-11fa-4bfb-b643-143125ef82c1Villa-Ramírez, Jose L.7f7b0659-aaa4-4271-8f18-51fd41c9ec25Alvarez-Month, Alex A.2d6be155-d904-4cc3-99a4-1566244f17822024-09-12T13:59:57Z2024-09-12T13:59:57Z2024-06-102024-09-11C.S. Cohen-Manrique; Y. T. Solano-Correa; J.L. Villa-Ramírez; A.A. Alvarez-Month, "Impact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysis," in Proc. SPIE 13037, Geospatial Informatics XIV, 1303704 (10 June 2024). DOI: https://doi.org/10.1117/12.3014190.https://hdl.handle.net/20.500.12585/1272910.1117/12.3014190Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe Morroa aquifer plays a crucial role supplying drinking water to around one million residents across Sucre, Córdoba, and Bolívar departments in Colombia. However, it faces severe water stress, ranking as the second most overexploited aquifer globally according to recent research using the Groundwater Footprint (GF) indicator. This situation threatens the sustainability of the aquifer and the well-being of the region's inhabitants who rely on it. To tackle this challenge, CARSUCRE, the entity responsible for aquifer management, has implemented various strategies. These include establishing a monitoring network with piezometers to track static and dynamic aquifer levels and conducting civil works to redirect rainfall runoff towards artificial recharge projects. Yet, the impact of vegetation variations in the recharge areas of the aquifer levels remains uncertain due to many different factors like drought, heavy rainfall, and economic changes. This research introduces a methodology that leverages remote sensing data, particularly high-resolution images from the Planet platform (3m), combined with land cover analysis in piezometer influence areas. The primary aim is to assess how changes in vegetation affect both static and dynamic levels of the Morroa Aquifer and then identify strategies to enhance land cover and improve water capture. The results obtained show a significant correlation between NDVI, EVI, and LULC for the aquifer recharge zone, with an average of 0.858 for all applied tools. These findings provide valuable information for the management and preservation of this vital water resource in the region.9 páginasapplication/pdfengProceedings Volume 13037, Geospatial Informatics XIVImpact of land cover variations on the Morroa aquifer (Colombia) static and dynamic levels through remote sensing analysisinfo:eu-repo/semantics/lectureinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_c94fhttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_8544Static LevelAquiferPlanetRemote Sensingland coverLEMBinfo:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbCartagena de IndiasCiencias BásicasInvestigadoresAranguren-Díaz, Y., Galán-Freyle, N. J., Guerra, A., Manares-Romero, A., Pacheco-Londoño, L. C., & Machado- Sierra, E. Aquifers and Groundwater: Challenges and Opportunities in Water Resource Management in Colombia. Water, 16(5), 685. (2024).Cardona-Almeida, C., & Suárez, A. Integrated Water Resources Management in Colombia: A Historical Perspective. Ambiente & Sociedade, 27. (2024).Henao, C., Lis-Gutiérrez, J. P., Lis-Gutiérrez, M., & Ariza-Salazar, J. Determinants of efficient water use and conservation in the Colombian manufacturing industry using machine learning. Humanities and Social Sciences Communications, 11(1), 1-11. (2024).Mirumachi, N., Duda, A., Gregulska, J., & Smetek, J. The human right to drinking water: Impact of large-scale agriculture and industry. Policy Department for External Relations, Directorate General for External Policies of the Union, European Parliament. (2021).Caro, M. A. T., Vargas, R. D. S., & Otero, C. Qualitative indicators for community water resilience in floodable areas: Agricultural pantry of La Mojana, Colombia. Economia agro-alimentare, (2023/1). (2023).INCA. Informe nacional de Calidad del Agua para Consumo Humano. Ministerio de Salud y Protección Social de Colombia. URL: https://www.minvivienda.gov.co/sites/default/files/documentos/informe-nacional-de-calidad-delagua- para-consumo-humano-inca-2021.pdf (2024).Carsucre. Estudio Técnico del Acuífero de Morroa: Grupo de Aguas CARSUCRE. Colombia. (2021).Pérez, A. J., Hurtado-Patiño, J., Herrera, H. M., Carvajal, A. F., Pérez, M. L., Gonzalez-Rojas, E., & Pérez-García, J. Assessing sub-regional water scarcity using the groundwater footprint. Ecological indicators, 96, 32-39. (2019).Adeyeri, O. E., Folorunsho, A. H., Ayegbusi, K. I., Bobde, V., Adeliyi, T. E., Ndehedehe, C. E., & Akinsanola, A. A., Land surface dynamics and meteorological forcings modulate land surface temperature characteristics. Sustainable Cities and Society, 101, 105072. (2024).Arrechea-Castillo, D. A., Solano-Correa, Y. T., Muñoz-Ordóñez, J. F., Pencue-Fierro, E. L., & Figueroa-Casas, A. Multiclass land use and land cover classification of andean sub-basins in Colombia with sentinel-2 and deep learning. Remote Sensing, 15(10), 2521. (2023).Xie, Z., Phinn, S. R., Game, E. T., Pannell, D. J., Hobbs, R. J., Briggs, P. R., & McDonald-Madden, E. Using Landsat observations (1988–2017) and Google Earth Engine to detect vegetation cover changes in rangelands-A first step towards identifying degraded lands for conservation. Remote Sensing of Environment, 232, 111317. (2019).Yang, X., & Zhang, Z. A CNN-LSTM model based on a meta-learning algorithm to predict groundwater level in the middle and lower reaches of the Heihe River, China. Water, 14(15), 2377. (2022).Ahmadi, A., Olyaei, M., Heydari, Z., Emami, M., Zeynolabedin, A., Ghomlaghi, A. & Sadegh, M. Groundwater level modeling with machine learning: a systematic review and meta-analysis. Water, 14(6), 949. (2022)Jeong, J., Park, E., Chen, H., Kim, K. Y., Han, W. S., & Suk, H. Estimation of groundwater level based on the robust training of recurrent neural networks using corrupted data. Journal of Hydrology, 582, 124512. (2020).Manrique, C. C., Villa, J. L., Month, A. A., & Velilla, G. P. Application of Artificial Intelligence Tools, Data Processing, and Analysis in the Forecasting of Level and Flow Variables in Wells with Little Data from the Morroa Aquifer. In Workshop on Engineering Applications (pp. 228-239). Cham: Springer Nature Switzerland. (2023).Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM)., “Estudio Nacional del Agua (ENA)”, < http://www.ideam.gov.co/web/agua/estudio-nacional-del-agua/ (29 March 2024).Navarro Mercado, J. L. Monitoreo de las obras piloto de recarga artificial en el acuífero Morroa, departamento de Sucre, Colombia. (2020).Solano-Correa, Y. T., Bovolo, F., Bruzzone, L., & Fernández-Prieto, D. Automatic derivation of cropland phenological parameters by adaptive non-parametric regression of Sentinel-2 NDVI time series. In IGARSS 2018- 2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 1946-1949). IEEE. (2018)Liang, J., Ren, C., Li, Y., Yue, W., Wei, Z., Song, X., & Lin, X. Using enhanced gap-filling and whittaker smoothing to reconstruct high spatiotemporal resolution NDVI time series based on Landsat 8, Sentinel-2, and MODIS imagery. ISPRS International Journal of Geo-Information, 12(6), 214. (2023).Li, X., Peng, Q., Zheng, Y., Lin, S., He, B., Qiu, Y., & Yuan, W. Incorporating environmental variables into spatiotemporal fusion model to reconstruct high-quality vegetation index data. IEEE Transactions on Geoscience and Remote Sensing. (2024).Karfs, R., Holloway, C., Pritchard, K., & Resing, J. Land condition photo standards for the Burdekin dry tropics rangelands: a guide for practitioners. Burdekin Solutions Ltd and Queensland Department of Primary Industries and Fisheries: Townsville. (2009).Zhang, T., Su, J., Xu, Z., Luo, Y., & Li, J. Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Applied Sciences, 11(2), 543. (2022).Hoorman, J. J. Using cover crops to improve soil and water quality. Lima, Ohio: Agriculture and Natural Resources, The Ohio State University Extension. 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