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

Abstract

Abstract. There is growing evidence that climate change will alter water availability in Europe. Here, we investigate how hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K with respect to the pre-industrial period) in rivers with a contributing area of more than 1000 km 2 . The analysis is based on a multi-model ensemble of 45 hydrological simulations based on three representative concentration pathways (RCP2.6, RCP6.0, RCP8.5), five Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) and three state-of-theart hydrological models (HMs: mHM, Noah-MP, and PCR-GLOBWB). High-resolution model results are available at a spatial resolution of 5 km across the pan-European domain at a daily temporal resolution. Low river flow is described as the percentile of daily streamflow that is exceeded 90 % of the time. It is determined separately for each GCM/HM combination and warming scenario. The results show that the low-flow change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean region, while they increase in the Alpine and Northern regions. In the Mediterranean, the level of warming amplifies the signal from −12 % under 1.5 K, compared to the baseline period 1971-2000, to −35 % under global warming of 3 K, largely due to the projected decreases in annual precipitation. In contrast, the signal is amplified from +22 (1.5 K) to +45 % (3 K) in the Alpine region due to changes in snow accumulation. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. However, it is not possible to distinguish climate-induced differences in low flows between 1.5 and 2 K warming because of (1) the large inter-annual variability which prevents distinguishing statistical estimates of periodaveraged changes for a given GCM/HM combination, and (2) the uncertainty in the multi-model ensemble expressed by the signal-to-noise ratio. The contribution by the GCMs to the uncertainty in the model results is generally higher than the one by the HMs. However, the uncertainty due to HMs cannot be neglected. In the Alpine, Northern, and Mediterranean regions, the uncertainty contribution by the HMs is partly higher than those by the GCMs due to different representations of processes such as snow, soil moisture and evapotranspiration. Based on the analysis results, it is recommended (1) to use multiple HMs in climate impact studies and (2) to embrace uncertainty information on the multimodel ensemble as well as its single members in the adaptation process.

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.…”

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.…”

High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 2. Impact of Climate and Land Cover Change

“…In order to systematically attribute the hydrological changes over the Sanjiangyuan region, both hydrological variables/parameters (e.g., ground temperature, soil frozen duration, streamflow, TWS) and the hydrological extremes (i.e., low flows and high flows) were investigated in this study. Low flows and high flows were defined as 5 and 95% percentile for daily streamflow time series in a specific year, where this volume-based definition was widely used in trend analysis (Burn, 2008;Hodgkins & Dudley, 2016;Marx et al, 2018). As introduced in Marx et al (2018), low flows could cause shortage of surface or subsurface water resources, which has the potential to impact hydrological drought.…”

“…Low flows and high flows were defined as 5 and 95% percentile for daily streamflow time series in a specific year, where this volume-based definition was widely used in trend analysis (Burn, 2008;Hodgkins & Dudley, 2016;Marx et al, 2018). As introduced in Marx et al (2018), low flows could cause shortage of surface or subsurface water resources, which has the potential to impact hydrological drought. High flows are indicators for flooding (Lee et al, 2015), and are usually related to heavy rainfall (Groisman et al, 2001).…”