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Assessment of the potential implications of a 1.5°C versus higher global temperature rise for the Afobaka hydropower scheme in Suriname Article Type: S.I. : Small Island and 1.5 Degrees (Thomas)

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 14 m 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.

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Global warming is changing the magnitude and frequency of extreme precipitation events. This requires updating local rainfall intensity-duration-frequency (IDF) curves and flood hazard maps according to the future climate scenarios. This is, however, far from straightforward, given our limited ability to model the effects of climate change on the temporal and spatial variability of rainfall at small scales. In this study, we develop a robust method to update local IDF relations for sub-daily rainfall extremes using Global Climate Model (GCM) data, and we apply it to a coastal town in NW Spain. First, the relationship between large-scale atmospheric circulation, described by means of Lamb Circulation Type classification (LCT), and rainfall events with potential for flood generation is analyzed. A broad ensemble set of GCM runs is used to identify frequency changes in LCTs, and to assess the occurrence of flood generating events in the future. In a parallel way, we use this Weather Type (WT) classification and climate-flood linkages to downscale rainfall from GCMs, and to determine the IDF curves for the future climate scenarios. A hydrological-hydraulic modeling chain is then used to quantify the changes in flood maps induced by the IDF changes. The results point to a future increase in rainfall intensity for all rainfall durations, which consequently results in an increased flood hazard in the urban area. While acknowledging the uncertainty in the GCM projections, the results show the need to update IDF standards and flood hazard maps to reflect potential changes in future extreme rainfall intensities.