Water area and volume calculation of two reservoirs in Central Cuba using Remote Sensing Methods. A new perspective

  1. Valero-Jorge, Alexey 1
  2. González-De Zayas, Roberto 2
  3. Alcántara-Martín, Anamaris 3
  4. Álvarez-Taboada, Flor 4
  5. Matos-Pupo, Felipe
  6. Brown-Manrique, Oscar 2
  1. 1 Swedish Meteorological and Hydrological Institute
    info

    Swedish Meteorological and Hydrological Institute

    Norrköping, Suecia

    ROR https://ror.org/00hgzve81

  2. 2 Universidad de Ciego de Ávila Máximo Gómez Báez
    info

    Universidad de Ciego de Ávila Máximo Gómez Báez

    Ciego de Ávila, Cuba

    ROR https://ror.org/00zhs8v21

  3. 3 Agencia provincial de GEOCUBA
  4. 4 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

Journal:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Year of publication: 2022

Issue: 60

Pages: 71-87

Type: Article

DOI: 10.4995/RAET.2022.17770 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista de teledetección: Revista de la Asociación Española de Teledetección

Abstract

The availability, quality and management of water constitute essential activities of national, regional and local governments and authorities. Historic annual rain (between 1961 and 2020) in Chambas River Basin (Central Cuba) was evaluated. Two remote sensing methods (Normalized Difference Water Index and RADAR images) were used to calculate the variation of water area and volumes of two reservoirs (Chambas II and Cañada Blanca) of Ciego de Ávila Province at end of wet and dry seasons from 2014-2021. The results showed that mean annual rain was 1330.9 ± 287.4 mm and it did not showed any significant tendency at evaluated period. For both reservoirs, mean water areas measured with two methods were 19 % and 8 % smaller than the mean water area reported by authorities for the same period. The static water storage capacity (water volume) of both reservoirs varied (as area) between seasons with the greatest volume in both reservoirs recorded in October of 2017 (30.5 million of m3 in Chambas II and 45.1 million of m3 in Cañada Blanca reservoir). Large deviations of water area and volumes occurred during the dry season (lower values) and the wet season of 2017 (influenced by rain associated to of Hurricane Irma) and wet season of 2020 (influenced by rain associated to tropical storm Laura). Calculated area – volume models with significant statistical correlation are another useful tool that could be used to improve water management in terms of accuracy and to increase reliable results in cases where gauge measurements are scarce or not available.

Bibliographic References

  • Adediji, A., Ajibade, L.T. 2008. The change detection of major dams in Osun State, Nigeria using remote sensing (RS) and GIS techniques. Journal of Geography and Regional Planning, 1(6), 110-115.
  • Aguilar, C., Zinnert, J.C., Polo, M.J., Young, D.R. 2012. NDVI as an indicator for changes in water availability to woody vegetation. Ecological Indicators, 23, 290-300. https://doi.org/10.1016/j.ecolind.2012.04.008
  • Alarcón, D. 2021. Análisis de la disminución del cuerpo de agua de la laguna de Aculeo utilizando imágenes de radar de Sentinel -1A/1B y de Global Surface Water Explorer. Revista Cartógrafo, 1(1), 5-14.
  • Batista Silva, J.L. 2016. Evaluación de los recursos hídricos de Cuba. Revista Geográfica, 157, 73-83.
  • Chuvieco, E. 2008. Teledetección ambiental, la observación de la Tierra desde el espacio. 3ª edición. Editorial Ariel, Barcelona, España.
  • Cobos, M.E., Cruz, D.D., Hernández, M. 2016. Análisis multitemporal del Índice Normalizado de Diferencia de Vegetación (NDVI) en Cuba. Revista del Jardín Botánico Nacional, 37, 15-18.
  • Cruz Flores, D.D., Curbelo Benítez, E.A., Ferrer Sánchez, Y., Ávila, D.D. 2020. Variaciones espaciales y temporales en el Índice de Vegetación de Diferencia Normalizada en Cuba. Ecosistemas, 29(1), 1885. https://doi.org/10.7818/ECOS.1885
  • Denis Ávila, D. 2015. Análisis multitemporal de imágenes Landsat para evaluar las variaciones de la cobertura vegetal emergente en la laguna Leonero, Granma, Cuba. Revista del Jardín Botánico Nacional, 36, 47-53.
  • De Oliveira Xavier, G., de Almeida, T., Magno Moreira de Oliveira; C., Silva de Oliveira, P.D., Barros Costa; V.H., Moreira Alves Granado, L. 2020. Estimate and evaluation of reservoir metrics in Serra da Mesa dam (GO) using the Google Earth Engine platform. Ambiente & Água - An Interdisciplinary Journal of Applied Science, 15(5), e2584. https://doi.org/10.4136/ambi-agua.2584
  • Di Bella, C.M., Posse, G., Beget, M.E., Fischer, M.A., Mari, N., Veron, S. 2008. La teledetección como herramienta para la prevención, seguimiento y evaluación de incendios e inundaciones. Ecosistemas, 17(3), 39-52.
  • Döll, P., Fiedler, K., Zhang, J. 2009. Globalscale analysis of river flow alterations due to water withdrawals and reservoirs. Hydrology and Earth System Sciences, 13, 2413-2432. https://doi.org/10.5194/hess-13-2413-2009
  • Du, Z., Bin, L., Feng, L., Wenbo, L., Weidong, T., Hailei, W., Yuanmiao, G., Bingyu, S., Xiaoming, Z. 2012. Estimating surface water area changes using time-series Landsat data in the Qingjiang River Basin, China. Journal of Applied Remote Sensing, 6, https://doi.org/10.1117/1.JRS.6.063609
  • Escobar-Flores, J.G., Sandoval, S., Valdez, R., Shahriary, E., Torres, J., Álvarez-Cárdenas, S., Gallina-Tessaro, P. 2019. Waterhole detection using a vegetation index in desert bighorn sheep (Ovis Canadensis cremnobates) habitat. PLoS ONE, 14(1), e0211202. https://doi.org/10.1371/journal.pone.0211202
  • Favoreto da Cunha, C., Brandão Cardoso, S., Hideo Teramoto, E., Kiang Chang, H. 2020. Modelo áreavolume para a Represa Guarapiranga empregando o índice NDWI. Holos Environment, 20(1), 137-151. https://doi.org/10.14295/holos.v20i1.12370
  • Fuentes, I., Padarian, J., van Ogtrop, F., Vervoort, R.W. 2019. Comparison of Surface Water Volume Estimation Methodologies That Couple Surface Reflectance Data and Digital Terrain Models. Water, 11, 780. https://doi.org/10.3390/w11040780.
  • Haddeland, I., Skaugen, T., Lettenmaier, D.P. 2006. Anthropogenic impacts on continental surface water fluxes, Geophysical Research Letters, 33, L08406. https://doi.org/10.1029/2006GL026047
  • Hernández, M., Cruz, D. 2016. Cobertura de vegetación natural en Parques Nacionales de Cuba: análisis multitemporal y variación futura de las condiciones bioclimáticas. Revista del Jardín Botánico Nacional, 37, 93-102.
  • Instituto Nacional de Recursos Hidráulicos (INRH). 2018. Boletín Hidrológico. Octubre 2018. Servicio Hidrológico y Disponibilidad. pp 16.
  • Jakovljevic, G., Govedarica, M., Álvarez-Taboada, F. 2018. Waterbody mapping: a comparison of remotely sensed and GIS open data sources, International Journal of Remote Sensing, 40(8), 2936-2964. https://doi.org/10.1080/01431161.2018.1538584
  • Jin, Y.Q., Yan, F. 2007. A change detection algorithm for terrain surface moisture mapping based on multiyear passive microwave remote sensing (Examples of SSM/I and TMI Channels). Hydrological Processes, 21, 1918-1924. https://doi.org/10.1002/hyp.6401
  • Karran, D.J., Westbrook, C.J., Wheaton, J.M., Johnston, C.A., Bedard-Haughn, A. 2016. Rapid surface water volume estimations in beaver ponds. Hydrology and Earth System Sciences Discuss., 352, 1-26. https://doi.org/10.5194/hess-2016-352.
  • Kendall, M. 1975. Multivariate Analysis. Charles Griffin & Company, London.
  • Lee, J.S. 1980. Digital Image Enhancement and Noise Filtering by use of Local Statistics. IEE Transactions on Pattern Analysis and Machine Intelligence, 2, 165-168. https://doi.org/10.1109/TPAMI.1980.4766994
  • Lee, J.S. 1981a. Refined Filtering of Image Noise Using Local Statistics. Computer Graphics and Image Processing, 15(4), 380-389. https://doi.org/10.1016/S0146-664X(81)80018-4
  • Lee, J.S. 1981b. Speckle Analysis and Smoothing of Synthetic Aperture Radar Images. Computer Graphics and Image Processing, 17(1), 24-32. https://doi.org/10.1016/S0146-664X(81)80005-6
  • Lehner, B., Döll, P. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology, 296(1-4), 1-22. https://doi.org/10.1016/j.jhydrol.2004.03.028
  • Lehner, B., Liermann, C.R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rödel, R., Sindorf, N., Wisser, D. 2011. Global reservoir and dam database. Version 1.1 (GRanDv1): Dams, Revision 01. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). pp. 12.
  • Lopes, F.B., Barbosa, C.C., Novo, E M., Andrade, E.M.D., Chaves, L.C. 2014. Modelagem da qualidade das águas a partir de sensoriamento remoto hiperespectral. Revista Brasileira de Engenharia Agrícola e Ambiental, 18, 13-19. https://doi.org/10.1590/1807-1929/agriambi.v18nsupps13-s19
  • Machado, M.T. de S.; Baptista, G.M. de M. 2016. Sensoriamento remoto como ferramenta de monitoramento da qualidade da água do Lago Paranoá (DF). Engenharia Sanitária e Ambiental, 21(2), 357-365. http://doi.org/10.1590/s1413-41522016141970
  • Mann H.B. 1945. Nonparametric tests against trend. Econometrica, 13, 245-259. https://doi.org/10.2307/1907187
  • Marchionni, D.S., Cavayas, F. 2014. La Teledetección por Radar como fuente de Información Litológica Y Estructural. Análisis Espacial de Imágenes SAR de Radarsat-1, GEOACTA, 39(1), 62-89.
  • Martínez, J. 2005. Percepción Remota "Fundamentos de Teledetección Espacial". Comisión Nacional del Agua, CNA. México.
  • Martínez, M., Martínez, M.E., Martínez, E., Renza, D. 2017. Detection of Changes in Natural Aquifer Reservoirs based on the Index of Drought. IEEE Latin America Transactions, 15(11), 2059-2063. https://doi.org/10.1109/TLA.2017.8070408
  • McFeeter, S.K. 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17, 1425-1432. https://doi.org/10.1080/01431169608948714
  • Morejón, M., Vega, M., Escarré, A., Gómez, R., Febles, J.M. 2010. Clasificación de la vegetación en los sectores superiores de cuencas de la región occidental de Pinar del Rio utilizando la teledetección. Ciencias de la Tierra y el Espacio, 11, 60-68.
  • Mulligan, M., van Soesbergen, A., Sáenz, L. 2020. GOODD, a global dataset of more than 38000 georeferenced dams. Scientific Data, 7, 31. https://doi.org/10.1038/s41597-020-0362-5
  • Muro, J., Strauch, A., Fitoka, E., Tompoulidou, M., Thonfeld, F. 2019. Mapping wetland dynamics with SAR-based change detection in the cloud. Geoscience and Remote Sensing Letters IEEE, 1-4. https://doi.org/10.1109/LGRS.2019.2903596
  • Muro, J., Varea, A., Strauch, A., Guelmami, A., Fitoka, E., Thonfeld, F. 2020. Multitemporal optical and radar metrics for wetland mapping at national level in Albania. Heliyon, 6(8), e04496. https://doi.org/10.1016/j.heliyon.2020.e04496
  • Naba Sayl, K., Muhammad, N.S., El-Shafie, A. 2017. Optimization of area-volume-elevation curve using GIS-SRTM method for rainwater harvesting in arid areas. Environmental Earth Sciences, 76, 368. https://doi.org/10.1007/s12665-017-6699-1
  • Nandi, D., Chowdhury, R., Mohapatra, J., Mohanta, K., Ray, D. 2018. Automatic Delineation of Water Bodies Using Multiple Spectral Indices. International Journal of Scientific Research in Science, Engineering and Technology, 4(4), 498-512.
  • Nhat Quang, D., Khanh, L.N., Tam, H.S., Trung Viet, N. 2021. Remote sensing applications for reservoir water level monitoring, sustainable water surface management, and environmental risks in Quang Nam province, Vietnam. Journal of Water and Climate Change, 12(7), 3045. https://doi.org/10.2166/wcc.2021.347
  • Park, J.W., Korosov, A., Babiker, M., Sandven, S., Won, J.S. 2018. Efficient Thermal Noise Removal for Sentinel -1 TOPSAR Cross-Polarization Channel. IEEE Transactions on Geoscience and Remote Sensing, 56(3), 24-41. https://doi.org/10.1109/TGRS.2017.2765248
  • Pohlert,T. 2016. Non-parametric Trend Tests and Change-Point Detection. CC BY-ND, 4.
  • Ponvert-Delisles, B. 2016. Algunas consideraciones sobre el comportamiento de la sequía agrícola en la agricultura de Cuba y el uso de imágenes por satélites en su evaluación. Revista Cultivos Tropicales, 37(3), 22-41.
  • Polo, P.L. 2004. Aplicaciones de imágenes de radar, en la generación de información para la mitigación de riesgos naturales. Dialogo Andino, 23, 36-43.
  • Rodrigues, L.N., Sano, E.E., Steenhuis, T.S., Passo, D.P. 2012. Estimation of small reservoir storage capacities with remote sensing in the Brazilian Savannah region. Water Resources Management, 26, 873-882. https://doi.org/10.1007/s11269-011-9941-8
  • Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS (Earth Resources Technology Satellite). In Proceedings of Third Earth Resources Technology Satellite Symposium, Greenbelt, ON, Canada, 10-14 December 1973; Volume SP-351, pp. 309-317.
  • Schwatke, C., Scherer, D., Dettmering, D. 2019. Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2. Remote Sensing, 11, 1010. https://doi.org/10.3390/rs11091010
  • Suleiman, Y.M. 2014. The role of rain variability in reservoir storage management at Shiroro Hydropower Dam, Nigeria. AFRREV STECH. An International Journal of Science and Technology, 3(2), 18-30. https://doi.org/10.4314/stech.v3i2.2
  • Valero, A., Matos-Pupo, F., Hernández, S. 2021. Uso de Dashboard y SIG en servicios climáticos de Ciego de Ávila: Nueva propuesta metodológica. Universidad&Ciencia, 10(2), 196-211.
  • Van Bemmelen, C.W.T., Mann, M., De Ridder, M.P., Rutten, M.M., Van De Giesen, N.C. 2016. Determining water reservoir characteristics with global elevation data. Geophysical Research Letters, 43(21), 11-278. https://doi.org/10.1002/2016GL069816
  • Van Den Hoek, J., Getirana, A., Chul Jung, H., Modurodoluwa A., Okeowo, M., Hyongki L. 2019. Monitoring Reservoir Drought Dynamics with Landsat and Radar/Lidar Altimetry Time Series in Persistently Cloudy Eastern Brazil. Remote Sensing, 11, 827. https://doi.org/10.3390/rs11070827
  • 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, 3025-3033. https://doi.org/10.1080/01431160600589179
  • Wang, X., Yan, Ch. Song, L., Chen, X., Xie, H., Liu, L. 2013. Analysis of lengths, water areas and volumes of the Three Gorges Reservoir at different water levels using Landsat images and SRTM DEM data. Quaternary International, 304, 115-125. https://doi.org/10.1016/j.quaint.2013.03.041
  • Wilson, E.H., Sader, S.A. 2002. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing Environment, 80, 385-396. https://doi.org/10.1016/S0034-4257(01)00318-2
  • https://doi.org/10.1016/S0034-4257(01)00318-2