Mapping catchment-scale unmonitored groundwater abstractions: Approaches based on soft data

H. M. Vu, M. Shanafield, T. T. Nhat, D. Partington, O. Batelaan

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Study region: The ungauged, agriculturally dominated La Vi River Basin, Vietnam. Study focus: Groundwater abstraction for food and industrial production is increasing globally, putting pressure on groundwater resources and associated ecosystems. In many countries, monitoring of abstraction is poorly organised, resulting in a paucity of data, particularly in developing regions. Therefore, alternative approaches to estimate groundwater withdrawals are necessary. In this study, two soft-data approaches for indirect catchment-scale groundwater abstraction estimation are developed using: (1) local knowledge through a qualitative field survey of groundwater level fluctuations and groundwater withdrawals, and (2) land use data combined with local knowledge on cropping and irrigation practices. New hydrological insights for the region: The approaches are tested and applied for the La Vi River Basin, for the 2016 dry season. Total dry season estimated abstractions of 31.07 × 106 m3 and 36.19 × 106 m3 resulted from the two approaches. The advantage of the second approach is the spatial distribution of the estimated groundwater abstraction, aligning highly intensive abstractions with intensive agricultural areas. Despite high uncertainty in both estimates, this quantitative estimate gives valuable information for water managers, and the relatively good agreement between the methods provides trust in the estimates. The approaches are cost-effective and computationally simple solutions for estimating groundwater abstraction in data-poor regions.

Original languageEnglish
Article number100695
Number of pages18
JournalJournal of Hydrology: Regional Studies
Publication statusPublished - Aug 2020

Bibliographical note

© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (


  • Domestic consumption
  • Groundwater abstraction
  • Irrigation
  • Soft data


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