Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments

Abstract

Projections of discharge are key for future water resources management. These projections are subject to uncertainties, which are difficult to handle in the decision process on adaptation strategies. Uncertainties arise from different sources such as the emission scenarios, the climate models and their postprocessing, the hydrological models, and the natural variability. Here we present a detailed and quantitative uncertainty assessment, based on recent climate scenarios for Switzerland (CH2011 data set) and covering catchments representative for midlatitude alpine areas. This study relies on a particularly wide range of discharge projections resulting from the factorial combination of 3 emission scenarios, 10-20 regional climate models, 2 postprocessing methods, and 3 hydrological models of different complexity. This enabled us to decompose the uncertainty in the ensemble of projections using analyses of variance (ANOVA). We applied the same modeling setup to six catchments to assess the influence of catchment characteristics on the projected streamflow, and focused on changes in the annual discharge cycle. The uncertainties captured by our setup originate mainly from the climate models and natural climate variability, but the choice of emission scenario plays a large role by the end of the 21st century. The contribution of the hydrological models to the projection uncertainty varied strongly with catchment elevation. The discharge changes were compared to the estimated natural decadal variability, which revealed that a climate change signal emerges even under the lowest emission scenario (RCP2.6) by the end of the century. Limiting emissions to RCP2.6 levels would nevertheless reduce the largest regime changes by the end of the century by approximately a factor of two, in comparison to impacts projected for the high emission scenario SRES A2. We finally show that robust regime changes emerge despite the projection uncertainty. These changes are significant and are consistent across a wide range of scenarios and catchments. We propose their identification as a way to aid decision making under uncertainty.

Mapping (dis)agreement in hydrologic projections

“…We showed that the uncertainty in the sign of change is more widespread for the mean runoff metric than for the discharge timing metric. Addor et al (2014) already showed that projected changes in the timing of discharge in Swiss catchments are significantly more robust (i.e. higher agreement among the ensemble members and greater deviation from the baseline) than changes in mean discharge.…”

“…A result of this conceptuality is that modellers make different decisions at different points in the model development process (Clark et al, 2011, producing models that have different portrayals of climate (Knutti and Sedláček, 2013; or hydrologic (Mendoza et al, 2015a;Addor et al, 2014) change. Hydrologic projections require a long chain of models, with each step along the chain introducing uncertainty into the projection Sonnenborg et al, 2015).…”

“…The representation of underlying (physical) principles of hydrological processes in the hydrologic model can thus have a profound effect on the results and conclusion of a study. Although uncertainty in hydrologic projections has already been discussed and investigated in the literature, studies usually focus on one source of uncertainty (Gutmann et al, 2014) or a limited number of catchments (Vidal et al, 2016;Addor et al, 2014;Dobler et al, 2012). Here, we investigate three sources of uncertainty (GCM forcing, hydrologic parameters, hydrologic model structure) in hydrologic projections for 605 basins throughout the contiguous US over a wide range of climates, in order to reveal spatial patterns in uncertainty.…”

Toward seamless hydrologic predictions across spatial scales

“…Richard P. Feynman Land surface and hydrologic models (LSMs/HMs) are currently used at diverse spatial resolutions ranging from 1 to 10 km in catchment-scale impact analysis and forecasting (Christensen and Lettenmaier, 2007;Addor et al, 2014) to over 50 km in global-scale climate change simulations to estimate land surface boundary conditions of key state variables (Haddeland et al, 2011;Bierkens, 2015;Wanders and Wada, 2015). The fundamental conditions behind the applicability of the same LSM/HM model structure at different spatial scales requires that the model parameterizations are scale invariant and that the model estimates similar fluxes across a range of spatial resolutions.…”

Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach

“…Several studies hence have pointed out that more physical methods should be favored over classical temperature index melt calculations in climate change impact studies (e. g., Farinotti et al, 2011;Huss et al, 2009;Radić et al, 2013;Viviroli et al, 2011). Some studies have for example applied enhanced temperature index methods that also take solar radiation into account for melt calculation (e. g., Addor et al, 2014;Bosshard et al, 2013;Fatichi et al, 2015;Finger et al, 2012), addressing the fact that glacier melt rates are especially sensitive to variations in solar radiation 5 (e. g., Huss et al, 2009;Ohmura et al, 2007). Only very few studies (e. g., Kobierska et al, 2013;Weber et al, 2010) however have applied full energy balance melt models for climate change impact assessment.…”