High-Resolution Climate Change Impact Analysis on Medium-Sized River Catchments in Germany: An Ensemble Assessment

Abstract

The impact of climate change on three small-to medium-sized river catchments (Ammer, Mulde, and Ruhr) in Germany is investigated for the near future (2021-50) following the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario. A 10-member ensemble of hydrological model (HM) simulations, based on two high-resolution regional climate models (RCMs) driven by two global climate models (GCMs), with three realizations of ECHAM5 (E5) and one realization of the Canadian Centre for Climate Modelling and Analysis version 3 (CCCma3; C3) is established. All GCM simulations are downscaled by the RCM Community Land Model (CLM), and one realization of E5 is downscaled also with the RCM Weather Research and Forecasting Model (WRF). This concerted 7-km, high-resolution RCM ensemble provides a sound basis for runoff simulations of small catchments and is currently unique for Germany. The hydrology for each catchment is simulated in an overlapping scheme, with two of the three HMs used in the project. The resulting ensemble hence contains for each chain link (GCM-realization-RCM-HM) at least two members and allows the investigation of qualitative and limited quantitative indications of the existence and uncertainty range of the change signal. The ensemble spread in the climate change signal is large and varies with catchment and season, and the results show that most of the uncertainty of the change signal arises from the natural variability in winter and from the RCMs in summer.

HESS Opinions "Should we apply bias correction to global and regional climate model data?"

“…With respect to uncertainty quantification, many projects such as ENSEMBLES (Christensen et al, 2008), PRUDENCE and among many others, Wilby (2010), Ott et al (2012), Schädler et al (2012) or Sun et al (2011) promote the use of model ensembles to avoid non-representativeness of the sample. Currently within the Coordinated Regional climate Downscaling Experiment (CORDEX) (Giorgi et al, 2009) In short, nested approaches can help to reduce the bias; multimodel ensembles can help to quantify the uncertainty associated with CCIS results.…”

Floods and climate: emerging perspectives for flood risk assessment and management

“…The typical approach for deriving future flood hazard scenarios under climate change is to implement model chains consisting of the following elements: "emission scenario → general circulation model (GCM) → downscaling, possibly including bias correction → hydrological catchment model → flood frequency analysis" (e.g. Dankers and Feyen, 2009;Kay et al, 2009;Ott et al, 2013), with the emission scenarios stemming from scenarios of future economic and social development (Nakicenovic and Swart, 2000). The IPCC has changed the climate change impact modelling concept slightly with the introduction of the concept of representative concentration pathways (Moss et al, 2008), but this will not affect the above-mentioned modelling chain except for replacing the initial element from "emission scenario" to "representative concentration pathways" (Moss et al, 2010).…”

“…In the last two decades, flood change research has been dominated by studies looking at changes in flood hazard, for instance due to human-induced climate change (e.g. Feyen et al, 2012;Ott et al, 2013, Ward et al, 2014a, land-use change or river training (e.g. Bronstert et al, 2007).…”

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

“…Therefore, further bias correction is often required. The impacts of biases on hydrological and agriculture modeling has been studied extensively (e.g., Kunstmann et al, 2004;Baigorria et al, 2007;Ghosh and Mujumdar, 2009;Ott et al, 2013). Precipitation is an important parameter in climate studies (Schmidli et al, 2006).…”