Water Practice and Technology (Mar 2024)

Assessing the consequences of climate variability in the Wadi Saida watershed, Northwestern Algeria

  • Meriem Nadia Dahmani,
  • Kamila Baba-Hamed,
  • Abderrazak Bouanani

DOI
https://doi.org/10.2166/wpt.2024.058
Journal volume & issue
Vol. 19, no. 3
pp. 987 – 1002

Abstract

Read online

North Africa is identified as one of the most susceptible regions to the impacts of climate change. With potential consequences for food security, water supply, and extreme weather events, understanding climate variability is of paramount importance. This study delves into the Wadi Saida watershed in the Algerian highlands, a region vulnerable to such changes. The study employs a set of data and analytical tools to assess climate variability in the Wadi Saida watershed. These methods include the standardized precipitation index (SPI), climate moisture index (CMI), the Pettitt test, Bayesian modeling using the LEE and HEGHINIAN methods, and Hubert segmentation. Additionally, statistical tests for rupture detection are applied to identify abrupt changes in the climate data. The application of these methods and statistical tests has yielded noteworthy findings. Firstly, the study highlights climate variability characterized by alternating wet and dry periods. This fluctuation in precipitation and moisture conditions underscores the dynamic nature of the region's climate. Secondly, the study has successfully detected critical rupture points, particularly in the years 2002, 2005, and 2007. These years signify significant shifts in climate patterns and potentially hold the key to understanding the impacts on water resources and environmental stability in the region. HIGHLIGHT The study employs a set of data and analytical tools to assess climate variability in the Wadi Saida watershed (1 – the standardized precipitation index, 2 – climate moisture index. 3 – the Pettitt test. 4 – Bayesian modeling using the LEE and HEGHINIAN methods, and Hubert segmentation. 5 – statistical tests for rupture detection are applied to identify abrupt changes in the climate data).;

Keywords