Comparación de índices de sequía univariables y multivariables basados en datos satelitales para la monitorización de sequías hidrológicas en el ARA Sur, Mozambique

  1. Araneda-Cabrera, Ronnie J. 1
  2. Bermúdez, María 2
  3. Puertas, Jerónimo 1
  4. Penas, Víctor 3
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

  2. 2 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

  3. 3 Augas de Galicia
Mostrar afiliaciones +
Revista:
Ingeniería del agua

ISSN: 1134-2196

Año de publicación: 2022

Volumen: 26

Número: 3

Páginas: 217-229

Tipo: Artículo

DOI: 10.4995/IA.2022.18037 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Ingeniería del agua

Resumen

La sequía es un fenómeno natural que afecta a los sistemas socioeconómicos y medioambientales por lo que su monitorización es clave para minimizar sus impactos. En Mozambique, en el sur de África el 70% de la población depende la agricultura para sobrevivir, y el agua para esta actividad se extrae mayoritariamente directo de los ríos. En este trabajo hemos comparado varios índices de sequía univariables y multivariables calculados con variables provenientes de bases de datos satelitales para definir uno que mejor se ajuste a las condiciones de sequía hidrológica en las cuencas hidrográficas del ARA Sur de Mozambique. Las condiciones hidrológicas se definieron con el Índice Estandarizado de Escorrentía acumulado 3 meses (SRI-3). Mediante relaciones cruzadas y modelos de regresión lineales y no lineales se encontró que el Índice Estandarizado de Precipitación acumulado 3 meses (SPI-3) podría usarse para monitorizar las sequías hidrológicas en esta región en tiempo (casi) real.

€ Ver financiación

Información de financiación

Financiadores

Referencias bibliográficas

  • Abatzoglou, J.T., Dobrowski, S.Z., Parks, S.A., Hegewisch, K.C. 2018. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci. Data, 5, 1–12. https://doi.org/10.1038/sdata.2017.191
  • AghaKouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D., Hain, C.R. 2015. Remote sensing of drought: Progress, challenges and opportunities. Rev. Geophys., 53, 452–480. https://doi.org/10.1002/2014RG000456
  • Agutu, N.O., Awange, J.L., Zerihun, A., Ndehedehe, C.E., Kuhn, M., Fukuda, Y. 2017. Assessing multi-satellite remote sensing, reanalysis, and land surface models’ products in characterizing agricultural drought in East Africa. Remote Sens. Environ., 194, 287–302. https://doi.org/10.1016/j.rse.2017.03.041
  • Araneda-Cabrera, R.J., Bermudez, M., Puertas, J. 2021a. Índices de precipitación y vegetación estandarizados bivariables para evaluar y monitorear sequías agrícolas. Rev. Hidrolatinoamericana, 5, 27–30.
  • Araneda-Cabrera, R.J., Bermudez, M., Puertas, J. 2021b. Revealing the spatio-temporal characteristics of drought in Mozambique and their relationship with large-scale climate variability. J. Hydrol. Reg. Stud., 38, 100938. https://doi.org/10.1016/j.ejrh.2021.100938
  • Araneda-Cabrera, R.J., Bermúdez, M., Puertas, J. 2020. Unified framework for drought monitoring and assessment in a transboundary river basin, in: River Flow 2020; Uijttewaal, W, Franca M, Valero D, Chavarrias V, Arbós C, Schielen R and Crosato A, Eds. Taylor & Francis Group, London, pp. 1081–1086. https://doi.org/10.1201/b22619
  • Conselho de Ministros, 2020. BR No 160 de 20.08.20, Boletim da República - I Serie. Publicação oficial da República de Moçambique. Maputo, Mozambique. https://www.inm.gov.mz/pt-br/content/br-n%C2%BA-160-de-200820-boletim-darep%C3%BAblica-i-serie.
  • Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., Ceccato, P. 2018. Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q. J. R. Meteorol. Soc., 144(51), 292–312. https://doi.org/10.1002/qj.3244
  • Dorigo, W.A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., Van Oevelen, P., Robock, A., Jackson, T. 2011. The International Soil Moisture Network: A data hosting facility for global in situ soil moisture measurements. Hydrol. Earth Syst. Sci., 15, 1675–1698. https://doi.org/10.5194/hess-15-1675-2011
  • Easterling, D.R. 2013. Global Data Sets for Analysis of Climate Extremes., in: Water Science and Technology Library (Ed.), Global Data Sets for Analysis of Climate Extremes. Springer, Dordrecht, pp. 347–361. https://doi.org/10.1007/978-94-007-4479-0_12
  • Eriksen, S., Silva, J.A. 2009. The vulnerability context of a savanna area in Mozambique: household drought coping strategies and responses to economic change. Environ. Sci. Policy, 12, 33–52. https://doi.org/10.1016/j.envsci.2008.10.007
  • Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., Michaelsen, J. 2015. The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Sci. Data 2, 1–21. https://doi.org/10.1038/sdata.2015.66
  • Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., Verdin, A.P. 2014. A Quasi-Global Precipitation Time Series for Drought Monitoring. U.S. Geol. Surv. Data Ser. 832, 4.
  • Golnaraghi, M., Etienne, C., Sapir, D.G., Below, R. 2014. Atlas of Mortality and Economic Losses From Weather, Climate and Water Extremes (1970-2012), WMO-No. 1123, World Meteorological Organization, Geneva, Switzerland. https://www.preventionweb.net/files/38413_wmo1123atlas120614.pdf.
  • Gringorten, I.I. 1963. A Plotting Rule for Extreme Probability Paper. J. Geophys. Res., 68, 813–814. https://doi.org/doi:10.1029/JZ068i003p00813
  • Guttman, N.B. 1998. Comparing the palmer drought index and the standardized precipitation index. J. Am. Water Resour. Assoc. 34, 113–121. https://doi.org/10.1111/j.1752-1688.1998.tb05964.x
  • Guttman, N.B. 1999. Accepting the Standardized Precipitation Index: a Calculation Algorithm. JAWRA J. Am. Water Resour. Assoc., 35, 311–322. https://doi.org/10.1111/j.1752-1688.1999.tb03592.x
  • Hagenlocher, M., Meza, Isabel, Carl Anderson, Annika Min, Fabrice G. Renaud, Y., Walz, S.S., Sebesvari, Z. 2019. Drought vulnerability and risk assessments: state of the art, persistent gaps, and research agenda. Environ. Res. Lett., 4. https://doi.org/10.1088/1748-9326/ab225d
  • Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. 1998. Multivariate Data Analysis, 7th ed, Pearson. Prentice Hall, Englewood Cliffs, New Jersey, USA.
  • Hao, Z., AghaKouchak, A. 2013. Multivariate Standardized Drought Index: A parametric multi-index model. Adv. Water Resour., 57, 12–18. https://doi.org/10.1016/j.advwatres.2013.03.009
  • IPCC, 2014. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, Cambridge University Press. Cambridge, United Kingdom and New York, NY, USA.
  • Kogan, F.N. 1995. Application of vegetation index and brightness temperature for drought detection. Adv. Sp. Res., 15, 91–100. https://doi.org/10.1016/0273-1177(95)00079-T
  • Kumar, N.M., Murthy, C.S., Sesha Sai, M.V.R., Roy, P.S. 2009. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Meteorol. Appl., 16, 381–389. https://doi.org/10.1002/met.136
  • McKee, T.B., Doesken, N.J., Kleist, J. 1993. The Relationship of Drought Frequency and Duration to Time Scales, Paper Presented at 8th Conference on Applied Climatology. American Meteorological Society, Anaheim, CA.
  • Ministério da Agricultura e Segurança Alimentar, 2015. Anuário de Estatísticas Agrárias 2015.
  • Mo, K.C., Lettenmaier, D.P. 2013. Objective Drought Classification Using Multiple Land Surface Models. J. Hydrometeorol., 15, 990–1010. https://doi.org/10.1175/jhm-d-13-071.1
  • MunichRE, 2018. NatCatSERVICE.
  • Niemeyer, S. 2008. New drought indices. Options Méditerranéennes, 80, 267–274.
  • Sepulcre-Canto, G., Horion, S., Singleton, A., Carrao, H., Vogt, J. 2012. Development of a Combined Drought Indicator to detect agricultural drought in Europe. Nat. Hazards Earth Syst. Sci., 12, 3519–3531. https://doi.org/10.5194/nhess-12-3519-2012
  • Shukla, S., Wood, A.W. 2008. Use of a standardized runoff index for characterizing hydrologic drought. Geophys. Res. Lett., 35, 1–7. https://doi.org/10.1029/2007GL032487
  • Svodova, M., Funchs, B.A., Integrated Drought Management Programme (IDMP), 2016. Handbook of drought indicators and indices, Drought Mitigation Center Faculty Publications. 117. https://doi.org/10.1007/s00704-016-1984-6
  • Vicente-Serrano, S.M., Beguería, S., Gimeno, L., Eklundh, L., Giuliani, G., Weston, D., El Kenawy, A., López-Moreno, J.I., Nieto, R., Ayenew, T., Konte, D., Ardö, J., Pegram, G.G.S. 2012. Challenges for drought mitigation in Africa: The potential use of geospatial data and drought information systems. Appl. Geogr., 34, 471–486. https://doi.org/10.1016/j.apgeog.2012.02.001
  • Wilhite, D.A., Glantz, M.H. 1985. Understanding: The drought phenomenon: The role of definitions. Water Int., 10, 111–120. https://doi.org/10.1080/02508068508686328
  • World Meteorological Organization and Global Water Partnership, 2016. Handbook of Drought Indicators and Indices (M. Svoboda and B.A. Fuchs). Integrated Drought Management Programme (IDMP), Integrated Drought Management Tools and Guidelines Series 2, Geneva.
  • Wu, J., Chen, X., Yao, H., Gao, L., Chen, Y., Liu, M. 2017. Non-linear relationship of hydrological drought responding to meteorological drought and impact of a large reservoir. J. Hydrol., 551, 495–507. https://doi.org/10.1016/j.jhydrol.2017.06.029
  • Yue, S., Ouarda, T.B.M.J., Bobée, B., Legendre, P., Bruneau, P. 1999. The Gumbel mixed model for flood frequency analysis. J. Hydrol., 226, 88–100. https://doi.org/10.1016/S0022-1694(99)00168-7
  • Zhang, F., Zhou, G., Nilsson, C. 2013. Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China. J. Plant Ecol., 8, 429–435. https://doi.org/10.1093/jpe/rtu027
Fuente de los datos: Dialnet