Natural Hazards and Earth System Sciences (Feb 2021)

A revision of the Combined Drought Indicator (CDI) used in the European Drought Observatory (EDO)

  • C. Cammalleri,
  • C. Arias-Muñoz,
  • P. Barbosa,
  • A. de Jager,
  • D. Magni,
  • D. Masante,
  • M. Mazzeschi,
  • N. McCormick,
  • G. Naumann,
  • J. Spinoni,
  • J. Vogt

DOI
https://doi.org/10.5194/nhess-21-481-2021
Journal volume & issue
Vol. 21
pp. 481 – 495

Abstract

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Building on almost 10 years of expertise and operational application of the Combined Drought Indicator (CDI), which is implemented within the European Commission's European Drought Observatory (EDO) for the purposes of early warning and monitoring of agricultural droughts in Europe, this paper proposes a revised version of the index. The CDI conceptualizes drought as a cascade process, where a precipitation shortage (WATCH stage) develops into a soil water deficit (WARNING stage), which in turn leads to stress for vegetation (ALERT stage). The main goal of the revised CDI proposed here is to improve the indicator's performance for those events that are currently not reliably represented, without altering either the modelling conceptual framework or the required input datasets. This is achieved by means of two main modifications: (a) use of the previously occurring CDI value to improve the temporal consistency of the time series and (b) introduction of two temporary classes – namely TEMPORARY RECOVERY for soil moisture and vegetation greenness, respectively – to avoid brief discontinuities in a stage. The efficacy of the modifications is tested by comparing the performances of the revised and currently implemented versions of the indicator for actual drought events in Europe during the last 20 years. The revised CDI reliably reproduces the evolution of major droughts, outperforming the current version of the indicator, especially for long-lasting events, and reducing the overall temporal inconsistencies in stage sequencing of about 70 %. Since the revised CDI does not need supplementary input datasets, it is suitable for operational implementation within the EDO drought monitoring system.