Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming

Abstract

Severe river floods often result in huge economic losses and fatalities. Since 1980, almost 1500 such events have been reported in Europe. This study investigates climate change impacts on European floods under 1.5, 2, and 3 K global warming. The impacts are assessed employing a multi-model ensemble containing three hydrologic models (HMs: mHM, Noah-MP, PCR-GLOBWB) forced by five CMIP5 general circulation models (GCMs) under three Representative Concentration Pathways (RCPs 2.6, 6.0, and 8.5). This multi-model ensemble is unprecedented with respect to the combination of its size (45 realisations) and its spatial resolution, which is 5 km over the entirety of Europe. Climate change impacts are quantified for high flows and flood events, represented by 10% exceedance probability and annual maxima of daily streamflow, respectively. The multi-model ensemble points to the Mediterranean region as a hotspot of changes with significant decrements in high flows from −11% at 1.5 K up to −30% at 3 K global warming mainly resulting from reduced precipitation. Small changes (< ±10%) are observed for river basins in Central Europe and the British Isles under different levels of warming. Projected higher annual precipitation increases high flows in Scandinavia, but reduced snow melt equivalent decreases flood events in this region. Neglecting uncertainties originating from internal climate variability, downscaling technique, and hydrologic model parameters, the contribution by the GCMs to the overall uncertainties of the ensemble is in general higher than that by the HMs. The latter, however, have a substantial share in the Mediterranean and Scandinavia. Adaptation measures for limiting the impacts of global warming could be similar under 1.5 K and 2 K global warming, but have to account for significantly higher changes under 3 K global warming.

Added Value of Large Ensemble Simulations for Assessing Extreme River Discharge in a 2 °C Warmer World

“…This is fundamentally different from observational records (which are subject to changing management conditions and transient climate) and transient model simulations. Finally, by combining multiple GCMs with multiple GHMs, the dominant sources of uncertainty in projections (meteorological variability, hydrological response, or model formulation) can be isolated (e.g., Marx et al, 2018;Thober et al, 2017). The design of the large ensembles used here, in combination with the empirical distribution approach, limits the need for (statistical) assumptions, descriptions, and corrections.…”

Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 °C

“…The aim is to provide a consistent framework using a compatible set of standardized forcings and initial conditions for the impact models to investigate low-flow changes under different levels of warming. This multi-model ensemble has recently being used by Thober et al (2017) to analyse projected changes in river floods and high flows in Europe.…”

“…The Noah-MP model was originally developed as the land surface component of the 5th generation mesoscale model MM5 to enable climate predictions with physically based ensembles and represents both the terrestrial water and energy cycle (Niu et al, 2011). The PCRaster global water balance model (PCR-GLOBWB) was developed to represent the terrestrial water cycle with a special focus on groundwater and modelling water resources under water stress (Van Beek and Bierkens, 2008;Wanders and Wada, 2015).…”

High‐Resolution Global Water Temperature Modeling

“…Both DynWat and PCR-GLOBWB 2 use ERA-40 and ERA-Interim forcing Dee et al, 2011;Uppala et al, 2005), with an elevation correction to account for spatial heterogeneity in the elevation (Sutanudjaja et al, 2018), to generate water temperature and runoff estimates for the period 1960-2014. The hydrological input from PCR-GLOBWB 2 has been extensively validated in multiple studies and shown to produce accurate estimates of daily discharge (Sutanudjaja et al, 2018;Van Beek et al, 2011), simulate hydrological extreme events (e.g., He et al, 2017;Marx et al, 2018;Thober et al, 2017;Wada et al, 2013;Wanders & Van Lanen, 2015), and reproduce decadal teleconnections (e.g., Wanders & Wada, 2015b). The river routing in DynWat is similar to that of PCR-GLOBWB and for the discharge simulation performance, we refer to Sutanudjaja et al (2018) for the latest evaluation.…”