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Climate change affects both water resources and agricultural production. With rising temperatures and decreasing summer precipitation, it is expected that agricultural production will be increasingly limited by drought. Where surface- or groundwater resources are available for irrigation, an increase in water withdrawals for irrigation is to be expected. Therefore, quantitative approaches are required to anticipate and manage the expected conflicts related to increased water abstraction for irrigation. This project aims to investigate how agricultural production, water demand for irrigation, runoff and groundwater dynamics are affected by future climate change and how climate change impacts combined with changes in agricultural water use affect groundwater dynamics. To answer these research questions, a comprehensive, loosely coupled model approach was developed, combining models from three disciplines: an agricultural plant growth model, a hydrological model and a hydrogeological model. The model coupling was implemented and tested for an agricultural area located in Switzerland in which groundwater plays a significant role in providing irrigation water. Our suggested modelling approach can be easily adapted to other areas. The model results show that yield changes are driven by drought limitations and rising temperatures. However, an increase in yield may be realized with an increase in irrigation. Simulation results show that the water requirement for irrigation without climate protection (RCP8.5) could increase by 40% by the end of the century with an unchanged growing season and by up to 80% with varietal adaptations. With climate change mitigation (RCP2.6) the increase in water demand for irrigation would be limited to 7%. The increase in irrigation (+12 mm) and the summer decrease in recharge rates (~20 mm/month) with decreasing summer precipitation causes a lowering of groundwater levels (40 mm) in the area in the late summer and autumn. This impact may be accentuated by an intensification of irrigation and reduced by extensification., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Information on possible changes in future flood risk is essential for successful adaptation planning and risk management. However, various sources of uncertainty arise along the model chains used for the assessment of flood risk under climate change. Knowledge on the importance of these different sources of uncertainty can help to design future assessments of flood risk, and to identify areas of focus for further research that aims to reduce existing uncertainties. Here we investigate the role of four sources of epistemic uncertainty affecting the estimation of flood loss for changed climate conditions for a meso-scale, pre-alpine catchment. These are: the choice of a scenario-neutral method, climate projection uncertainty, hydrological model parameter sets, and the choice of the vulnerability function. To efficiently simulate a large number of loss estimates, a surrogate inundation model was used. 46,500 loss estimates were selected according to the change in annual mean precipitation and temperature of an ensemble of regional climate models, and considered for the attribution of uncertainty. Large uncertainty was found in the estimated loss for a 100-year flood event with losses ranging from a decrease of loss compared to estimations for present day climate, to more than a 7-fold increase. The choice of the vulnerability function was identified as the most important source of uncertainty explaining almost half of the variance in the estimates. However, uncertainty related to estimating floods for changed climate conditions contributed nearly as much. Hydrological model parametrisation was found to be negligible in the present setup. For our study area, these results highlight the importance of improving vulnerability function formulation even in a climate change context where additional major sources of uncertainty arise., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The Tambora volcano erupted in April 1815 caused many direct and indirect impacts on the climate system, as well as ecosystems and societies around the world. In Switzerland, the eruption contributed to the 1816 "Year Without a Summer", which is considered to be a key factor in generating the highest flooding ever documented of the Lake Constance (7th July 1817) and the flood of the Rhine in Basel. Snow was reported to remain during the summer of 1816, which laid the basis for a massive snow accumulation in the spring of 1817. The meltwater together with a triggering event led to the reported flooding. We aim to create a hydro-meteorological reconstruction of the 1816/1817 period in Switzerland to verify and quantify the historical sources and place them into present-day context. We used an analogue method that was based on historical measurements to generate temperature and precipitation fields for 1816/1817. These data drove a hydrological model that covers the Rhine Basin to Basel. We reproduced the reported features of the hydroclimate, especially in regards to the temperature and snow storage. We showed that the snow storage in spring 1816 and 1817 was substantial and attained the magnitude of a recent extreme, snow-rich winter (1999). However, simulations suggest that the snowfall alone in the spring of 1817, rather than the enduring snow from 1815/1816, led to the meltwater produced from the snow pack that contributed to the flooding in Lake Constance and Basel. These events were strongly underestimated, as the triggering rainfall event was reconstructed too weak. Artificial scenarios reveal that a precipitation amount with a magnitude higher than the largest recent flood (2005) was necessary to generate the documented flood levels. We conclude that these Tambora-following flood events were a product of an adverse combination of extreme weather within an extreme climate., (Copyright © 2018 Elsevier B.V. All rights reserved.)