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Characterising droughts in Central America with uncertain hydro-meteorological data

Research output: Contribution to journalArticle

  • B. Quesada-Montano
  • F. Wetterhall
  • I. K. Westerberg
  • H. G. Hidalgo
  • S. Halldin
Original languageEnglish
Pages (from-to)2125-2138
Number of pages14
JournalTheoretical and Applied Climatology
Volume137
Issue number3-4
Early online date6 Dec 2018
DOIs
DateAccepted/In press - 26 Nov 2018
DateE-pub ahead of print - 6 Dec 2018
DatePublished (current) - 1 Aug 2019

Abstract

Central America is frequently affected by droughts that cause significant socio-economic and environmental problems. Drought characterisation, monitoring and forecasting are potentially useful to support water resource management. Drought indices are designed for these purposes, but their ability to characterise droughts depends on the characteristics of the regional climate and the quality of the available data. Local comprehensive and high-quality observational networks of meteorological and hydrological data are not available, which limits the choice of drought indices and makes it important to assess available datasets. This study evaluated which combinations of drought index and meteorological dataset were most suitable for characterising droughts in the region. We evaluated the standardised precipitation index (SPI), a modified version of the deciles index (DI), the standardised precipitation evapotranspiration index (SPEI) and the effective drought index (EDI). These were calculated using precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), the CRN073 dataset, the Climate Research Unit (CRU), ECMWF Reanalysis (ERA-Interim) and a regional station dataset, and temperature from the CRU and ERA-Interim datasets. The gridded meteorological precipitation datasets were compared to assess how well they captured key features of the regional climate. The performance of all the drought indices calculated with all the meteorological datasets was then evaluated against a drought index calculated using river discharge data. Results showed that the selection of database was more important than the selection of drought index and that the best combinations were the EDI and DI calculated with CHIRPS and CRN073. Results also highlighted the importance of including indices like SPEI for drought assessment in Central America.

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Springer Nature at https://doi.org/10.1007/s00704-018-2730-z . Please refer to any applicable terms of use of the publisher.

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