Abstract
Abstract. This paper presents a first attempt to estimate future groundwater levels by applying extreme value statistics on predictions from a hydrological model. Climate scenarios for the future period, 2081–2100, are represented by projections from nine combinations of three global climate models and six regional climate models, and downscaled (including bias correction) with two different methods. An integrated surface water/groundwater model is forced with precipitation, temperature, and potential evapotranspiration from the 18 models and downscaling combinations. Extreme value analyses are performed on the hydraulic head changes from a control period (1991–2010) to the future period for the 18 combinations. Hydraulic heads for return periods of 21, 50 and 100 yr (T21–100) are estimated. Three uncertainty sources are evaluated: climate models, downscaling and extreme value statistics. Of these sources, extreme value statistics dominates for return periods higher than 50 yr, whereas uncertainty from climate models and extreme value statistics are similar for lower return periods. Uncertainty from downscaling only contributes to around 10% of the uncertainty from the three sources.
Highlights
Climate change adaptation is an increasingly recognized component in planning of infrastructure development
This resolution is too coarse for further application in hydrological models (Fowler et al, 2007), downscaling to a more local scale is necessary either by dynamical downscaling to regional climate models (RCMs) or by statistical downscaling
Results from the Delta Change (DC) and Distribution Based Scaling (DBS) climate ensembles are compared with observations from the baseline period (1991– 2010) for precipitation, temperature, and reference evapotranspiration (Fig. 7)
Summary
Climate change adaptation is an increasingly recognized component in planning of infrastructure development Infrastructures, such as roads, are designed to be able to withstand extreme hydrological events. Estimates of future temperature and precipitation can be generated by global climate models (GCMs) with grid resolutions of typically 200 km. This resolution is too coarse for further application in hydrological models (Fowler et al, 2007), downscaling to a more local scale is necessary either by dynamical downscaling to regional climate models (RCMs) or by statistical downscaling.
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