Characterisation and prediction of meteorological drought using stochastic models in the semi-arid Chéliff–Zahrez basin (Algeria)

Brahim Habibi*, Mohamed Meddi, Paul J.J.F. Torfs, Mohamed Remaoun, Henny A.J. Van Lanen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

Study region: North Algeria. Study focus: The semi-arid to arid Chéliff–Zahrez basin faced several droughts with severe impacts on agriculture due to the high temporal and spatial distribution of rainfall. We explored the potential of the Standardized Precipitation Index (SPI), Markov chain models, the Drought Index and time series modelling to characterize meteorological drought. Time series of annual precipitation (1960–2010) from 65 meteorological stations across the basin were used. The basin was subdivided into five subbasins to account for spatial variability. New hydrological insights for the regions: The analysis of the Standardized Precipitation Index showed few droughts in the period 1960–1970, whereas in the 1990s a multi-year drought occurred with SPIs as low as −2 (extremely dry) in many subbasins. The Markov chain analysis learnt that the probability of having two consecutive drought years appears to be higher in the southern subbasins. The Drought Index derived from transition probabilities indicates that the southern and the southwestern parts of the Chéliff–Zahrez basin are most drought prone. Time series modelling was applied to compute the SPI for different return periods (6‐17 years). Eleven models were tested and it appeared that the Asymmetric Power Autoregressive Conditional Heteroskedasticity (APARCH) approach was best performing based on several information criteria. For a return period of 17 years, the SPI is lower than −1.5 (severely dry) in many subbasins.
Original languageEnglish
Pages (from-to)15-31
JournalJournal of Hydrology: Regional Studies
Volume16
DOIs
Publication statusPublished - 1 Apr 2018

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drought
prediction
basin
time series
Markov chain
return period
temporal distribution
modeling
index
spatial distribution
agriculture
rainfall

Keywords

  • Chéliff–Zahrez basin
  • Drought maps
  • Markov chain
  • Meteorological drought
  • SPI
  • Time series modelling

Cite this

@article{c796f7cedff5430cbb53de85820d96f1,
title = "Characterisation and prediction of meteorological drought using stochastic models in the semi-arid Ch{\'e}liff–Zahrez basin (Algeria)",
abstract = "Study region: North Algeria. Study focus: The semi-arid to arid Ch{\'e}liff–Zahrez basin faced several droughts with severe impacts on agriculture due to the high temporal and spatial distribution of rainfall. We explored the potential of the Standardized Precipitation Index (SPI), Markov chain models, the Drought Index and time series modelling to characterize meteorological drought. Time series of annual precipitation (1960–2010) from 65 meteorological stations across the basin were used. The basin was subdivided into five subbasins to account for spatial variability. New hydrological insights for the regions: The analysis of the Standardized Precipitation Index showed few droughts in the period 1960–1970, whereas in the 1990s a multi-year drought occurred with SPIs as low as −2 (extremely dry) in many subbasins. The Markov chain analysis learnt that the probability of having two consecutive drought years appears to be higher in the southern subbasins. The Drought Index derived from transition probabilities indicates that the southern and the southwestern parts of the Ch{\'e}liff–Zahrez basin are most drought prone. Time series modelling was applied to compute the SPI for different return periods (6‐17 years). Eleven models were tested and it appeared that the Asymmetric Power Autoregressive Conditional Heteroskedasticity (APARCH) approach was best performing based on several information criteria. For a return period of 17 years, the SPI is lower than −1.5 (severely dry) in many subbasins.",
keywords = "Ch{\'e}liff–Zahrez basin, Drought maps, Markov chain, Meteorological drought, SPI, Time series modelling",
author = "Brahim Habibi and Mohamed Meddi and Torfs, {Paul J.J.F.} and Mohamed Remaoun and {Van Lanen}, {Henny A.J.}",
year = "2018",
month = "4",
day = "1",
doi = "10.1016/j.ejrh.2018.02.005",
language = "English",
volume = "16",
pages = "15--31",
journal = "Journal of Hydrology: Regional Studies",
issn = "2214-5818",
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}

Characterisation and prediction of meteorological drought using stochastic models in the semi-arid Chéliff–Zahrez basin (Algeria). / Habibi, Brahim; Meddi, Mohamed; Torfs, Paul J.J.F.; Remaoun, Mohamed; Van Lanen, Henny A.J.

In: Journal of Hydrology: Regional Studies, Vol. 16, 01.04.2018, p. 15-31.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Characterisation and prediction of meteorological drought using stochastic models in the semi-arid Chéliff–Zahrez basin (Algeria)

AU - Habibi, Brahim

AU - Meddi, Mohamed

AU - Torfs, Paul J.J.F.

AU - Remaoun, Mohamed

AU - Van Lanen, Henny A.J.

PY - 2018/4/1

Y1 - 2018/4/1

N2 - Study region: North Algeria. Study focus: The semi-arid to arid Chéliff–Zahrez basin faced several droughts with severe impacts on agriculture due to the high temporal and spatial distribution of rainfall. We explored the potential of the Standardized Precipitation Index (SPI), Markov chain models, the Drought Index and time series modelling to characterize meteorological drought. Time series of annual precipitation (1960–2010) from 65 meteorological stations across the basin were used. The basin was subdivided into five subbasins to account for spatial variability. New hydrological insights for the regions: The analysis of the Standardized Precipitation Index showed few droughts in the period 1960–1970, whereas in the 1990s a multi-year drought occurred with SPIs as low as −2 (extremely dry) in many subbasins. The Markov chain analysis learnt that the probability of having two consecutive drought years appears to be higher in the southern subbasins. The Drought Index derived from transition probabilities indicates that the southern and the southwestern parts of the Chéliff–Zahrez basin are most drought prone. Time series modelling was applied to compute the SPI for different return periods (6‐17 years). Eleven models were tested and it appeared that the Asymmetric Power Autoregressive Conditional Heteroskedasticity (APARCH) approach was best performing based on several information criteria. For a return period of 17 years, the SPI is lower than −1.5 (severely dry) in many subbasins.

AB - Study region: North Algeria. Study focus: The semi-arid to arid Chéliff–Zahrez basin faced several droughts with severe impacts on agriculture due to the high temporal and spatial distribution of rainfall. We explored the potential of the Standardized Precipitation Index (SPI), Markov chain models, the Drought Index and time series modelling to characterize meteorological drought. Time series of annual precipitation (1960–2010) from 65 meteorological stations across the basin were used. The basin was subdivided into five subbasins to account for spatial variability. New hydrological insights for the regions: The analysis of the Standardized Precipitation Index showed few droughts in the period 1960–1970, whereas in the 1990s a multi-year drought occurred with SPIs as low as −2 (extremely dry) in many subbasins. The Markov chain analysis learnt that the probability of having two consecutive drought years appears to be higher in the southern subbasins. The Drought Index derived from transition probabilities indicates that the southern and the southwestern parts of the Chéliff–Zahrez basin are most drought prone. Time series modelling was applied to compute the SPI for different return periods (6‐17 years). Eleven models were tested and it appeared that the Asymmetric Power Autoregressive Conditional Heteroskedasticity (APARCH) approach was best performing based on several information criteria. For a return period of 17 years, the SPI is lower than −1.5 (severely dry) in many subbasins.

KW - Chéliff–Zahrez basin

KW - Drought maps

KW - Markov chain

KW - Meteorological drought

KW - SPI

KW - Time series modelling

U2 - 10.1016/j.ejrh.2018.02.005

DO - 10.1016/j.ejrh.2018.02.005

M3 - Article

VL - 16

SP - 15

EP - 31

JO - Journal of Hydrology: Regional Studies

JF - Journal of Hydrology: Regional Studies

SN - 2214-5818

ER -