Analysis of uncertainties in the hydrological response of a model‐based climate change impact assessment in a subcatchment of the Spree River, Germany

Abstract

Climate change impact assessments form the basis for the development of suitable climate change adaptation strategies. For this purpose, ensembles consisting of stepwise coupled models are generally used [emission scenario → global circulation model → downscaling approach (DA) → bias correction → impact model (hydrological model)], in which every item is affected by considerable uncertainty. The aim of the current study is (1) to analyse the uncertainty related to the choice of the DA as well as the hydrological model and its parameterization and (2) to evaluate the vulnerability of the studied catchment, a subcatchment of the highly anthropogenically impacted Spree River catchment, to hydrological change. Four different DAs are used to drive four different model configurations of two conceptually different hydrological models (Water Balance Simulation Model developed at ETH Zürich and HBV-light). In total, 452 simulations are carried out. The results show that all simulations compute an increase in air temperature and potential evapotranspiration. For precipitation, runoff and actual evapotranspiration, opposing trends are computed depending on the DA used to drive the hydrological models. Overall, the largest source of uncertainty can be attributed to the choice of the DA, especially regarding whether it is statistical or dynamical. The choice of the hydrological model and its parameterization is of less importance when long-term mean annual changes are compared. The large bandwidth at the end of the modelling chain may exacerbate the formulation of suitable climate change adaption strategies on the regional scale. Figure 6. Frequency plot for daily (top, precipitation > 10 mm/day not displayed) and monthly (bottom) precipitation for the reference period (CCLM: COSMO model in climate mode; REMO: regional model; WettReg: weather-type regionalization method) 3990A. GÄDEKE ET AL. Figure 8. Comparison between the interannual course of measured and simulated (reference 1963-1992 and scenario period 2031-2060) temperatures for the Weißer Schöps River catchment (interpolation by the inverse distance method) (CCLM: COSMO model in climate mode; REMO: regional model; STAR: Statistical Regional model; WettReg: weather-type regionalization method) 3992 A. GÄDEKE ET AL.

Modeling Climate and Management Change Impacts on Water Quality and In-Stream Processes in the Elbe River Basin

“…The choice of a hydrological model is less important in terms of its contribution to uncertainty, especially when only the long-term mean annual changes are compared [77]. Often it was detected that results achieved with one hydrological model under two or more climate scenarios differ more than the results of different hydrological models achieved by using only one climate scenario [78,79].…”

“…However, there is no consistent opinion on the usefulness of bias correction for impact assessments. While Teutschbein and Seibert [82] recommend an application of bias correction, other authors complain about the lack of physical justifications of corrections damaging the physical consistency between the variables [77,83]. The latter do not appreciate this method as a "valid procedure", and complain that an additional uncertainty is added to the model chain.…”

Assessment of Impacts of Climate Change on the Water Resources of the Transboundary Jhelum River Basin of Pakistan and India

“…However, the outputs of these GCMs are coarse in spatial resolution [10,11] and might not be suitable at the basin level, especially for small basins, which require very fine spatial resolution [12,13]. To use the outputs of GCMs at the basin level, downscaling-dynamical and statistical-techniques have been developed [14].…”

An Integrated Modelling System to Predict Hydrological Processes under Climate and Land-Use/Cover Change Scenarios

“…The output of each of them is associated with uncertainty, and the cascade of uncertainty from climate models to hydrological impacts often results in excessive uncertainty, often referred to as "deep uncertainty". Many attempts have been made to reduce the uncertainties related to this cascade (for detail, see [21][22][23][24]) by employing higher resolution GCMs (e.g., [25]), advanced downscaling approaches (e.g., [26]) and physically-based distributed hydrological models (e.g., [9]). However, less attention has been given to incorporate the dynamics of land surface changes into the modelling framework for climate change impact assessment [27].…”