Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios

Syed M.Touhidul Mustafa*, M. Moudud Hasan, Ajoy Kumar Saha, Rahena Parvin Rannu, Els Van Uytven, Patrick Willems, Marijke Huysmans

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Worldwide, groundwater resources are under a constant threat of overexploitation and pollution due to anthropogenic and climatic pressures. For sustainable management and policy making a reliable prediction of groundwater levels for different future scenarios is necessary. Uncertainties are present in these groundwater-level predictions and originate from greenhouse gas scenarios, climate models, conceptual hydro(geo)logical models (CHMs) and groundwater abstraction scenarios. The aim of this study is to quantify the individual uncertainty contributions using an ensemble of 2 greenhouse gas scenarios (representative concentration pathways 4.5 and 8.5), 22 global climate models, 15 alternative CHMs and 5 groundwater abstraction scenarios. This multi-model ensemble approach was applied to a drought-prone study area in Bangladesh. Findings of this study, firstly, point to the strong dependence of the groundwater levels on the CHMs considered. All groundwater abstraction scenarios showed a significant decrease in groundwater levels. If the current groundwater abstraction trend continues, the groundwater level is predicted to decline about 5 to 6 times faster for the future period 2026-2047 compared to the baseline period (1985-2006). Even with a 30% lower groundwater abstraction rate, the mean monthly groundwater level would decrease by up to 14m in the southwestern part of the study area. The groundwater abstraction in the northwestern part of Bangladesh has to decrease by 60% of the current abstraction to ensure sustainable use of groundwater. Finally, the difference in abstraction scenarios was identified as the dominant uncertainty source. CHM uncertainty contributed about 23% of total uncertainty. The alternative CHM uncertainty contribution is higher than the recharge scenario uncertainty contribution, including the greenhouse gas scenario and climate model uncertainty contributions. It is recommended that future groundwater-level prediction studies should use multi-model and multiple climate and abstraction scenarios.

Original languageEnglish
Pages (from-to)2279-2303
Number of pages25
JournalHydrology and Earth System Sciences
Volume23
Issue number5
DOIs
Publication statusPublished - 13 May 2019

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groundwater abstraction
global climate
climate modeling
groundwater
prediction
greenhouse gas
groundwater resource
policy making
recharge
drought
pollution
climate

ASJC Scopus subject areas

  • Water Science and Technology
  • Earth and Planetary Sciences (miscellaneous)

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Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios. / Mustafa, Syed M.Touhidul; Moudud Hasan, M.; Saha, Ajoy Kumar; Rannu, Rahena Parvin; Van Uytven, Els; Willems, Patrick; Huysmans, Marijke.

In: Hydrology and Earth System Sciences, Vol. 23, No. 5, 13.05.2019, p. 2279-2303.

Research output: Contribution to journalArticle

Mustafa, Syed M.Touhidul ; Moudud Hasan, M. ; Saha, Ajoy Kumar ; Rannu, Rahena Parvin ; Van Uytven, Els ; Willems, Patrick ; Huysmans, Marijke. / Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios. In: Hydrology and Earth System Sciences. 2019 ; Vol. 23, No. 5. pp. 2279-2303.
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abstract = "Worldwide, groundwater resources are under a constant threat of overexploitation and pollution due to anthropogenic and climatic pressures. For sustainable management and policy making a reliable prediction of groundwater levels for different future scenarios is necessary. Uncertainties are present in these groundwater-level predictions and originate from greenhouse gas scenarios, climate models, conceptual hydro(geo)logical models (CHMs) and groundwater abstraction scenarios. The aim of this study is to quantify the individual uncertainty contributions using an ensemble of 2 greenhouse gas scenarios (representative concentration pathways 4.5 and 8.5), 22 global climate models, 15 alternative CHMs and 5 groundwater abstraction scenarios. This multi-model ensemble approach was applied to a drought-prone study area in Bangladesh. Findings of this study, firstly, point to the strong dependence of the groundwater levels on the CHMs considered. All groundwater abstraction scenarios showed a significant decrease in groundwater levels. If the current groundwater abstraction trend continues, the groundwater level is predicted to decline about 5 to 6 times faster for the future period 2026-2047 compared to the baseline period (1985-2006). Even with a 30{\%} lower groundwater abstraction rate, the mean monthly groundwater level would decrease by up to 14m in the southwestern part of the study area. The groundwater abstraction in the northwestern part of Bangladesh has to decrease by 60{\%} of the current abstraction to ensure sustainable use of groundwater. Finally, the difference in abstraction scenarios was identified as the dominant uncertainty source. CHM uncertainty contributed about 23{\%} of total uncertainty. The alternative CHM uncertainty contribution is higher than the recharge scenario uncertainty contribution, including the greenhouse gas scenario and climate model uncertainty contributions. It is recommended that future groundwater-level prediction studies should use multi-model and multiple climate and abstraction scenarios.",
author = "Mustafa, {Syed M.Touhidul} and {Moudud Hasan}, M. and Saha, {Ajoy Kumar} and Rannu, {Rahena Parvin} and {Van Uytven}, Els and Patrick Willems and Marijke Huysmans",
note = "Acknowledgements. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. The fifth author obtained a PhD scholarship from the Fund for Scientific Research (FWO)-Flanders. This financial support is gratefully acknowledged. Data availability. The climate model data are publicly available through the website of the Earth System Grid Federation (https://esgf.llnl.gov, last access: 8 May 2019). Other data used in this study are summarized and presented in the figures, tables, references, and the Supplement. Additional data, model code and results are available upon request to the first (syed.mustafa@vub.be) and last (marijke.huysmans@vub.be) authors.",
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