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

hydrology and earth system sciences

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