Groundwater recharge estimation and its spatial distribution in arid regions using GIS: a case study from Qatar karst aquifer

  • Husam Musa Baalousha*
  • , Nicolas Barth
  • , Fanilo H. Ramasomanana
  • , Said Ahzi
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Groundwater recharge from rainfall is a major input into aquifers, especially in arid and semi-arid areas, where little or no surface water exist. However, estimation of rainfall recharge is not an easy task as it is highly variable spatially and temporally. In arid areas, recharge occurs indirectly after rainfall-runoff accumulates in low lands such as Wadis and percolates the aquifer. The method proposed in this study to estimate rainfall recharge is based on soil–water budget model and utilizing geoprocessing tools in GIS. It uses digital elevation model (DEM), land-cover and rainfall distribution to estimate runoff accumulation in low lands. The soil–water budget model is then applied to estimate the groundwater recharge in areas of runoff accumulation. The proposed methodology was applied on Qatar karst aquifer as a case study. The spatial resolution of raster maps was 350 by 350 m and the temporal resolution is one day. Results obtained in this study for the hydrological year 2013/2014 show the total groundwater recharge is approximately 14 million m3, and concentrated more in the northern part of Qatar. The method can be applied on any arid region.

Original languageEnglish
Pages (from-to)1319-1329
Number of pages11
JournalModeling Earth Systems and Environment
Volume4
Issue number4
DOIs
StatePublished - 1 Dec 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018, Springer Nature Switzerland AG.

Keywords

  • Arid areas
  • GIS
  • Groundwater recharge
  • Karst
  • Qatar
  • Runoff
  • Soil–water budget

ASJC Scopus subject areas

  • General Environmental Science
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Computers in Earth Sciences

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