Baseline data as source of uncertainty in large-scale hydrology - a case study

Global hydrological models (GHMs) supply key information for international stakeholders and policymakers, simulating the impacts of the water cycle associated with climate change. Uncertainty in simulation, e.g., linked to climate models, model structure and parameters, jeopardizes valuable decision support. Various scenario data sets have been used, and model‑intercomparison studies have been performed in climate change studies to account for uncertainty linked to climate models and model structure, respectively (Kundzewicz et al., 2018). However, uncertainty in baseline data, used (1) for parameter adjustment of GHMs, and (2) assessment of relative changes in future, has rarely been addressed. Here we show that neglecting the uncertainty related to baseline data can mislead decision-making when assessing the impacts of climate change. We found that three different calibrated versions of the GHM WaterGAP3 (using three different sources of baseline data, namely EWEMBI2b, E-OBS and German Weather Service) reveal contradicting results regarding future streamflow for the German part of the Danube basin. Whereas one data set shows a decreasing 90th percentile of streamflow, indicating less heavy flood occurrence, the other datasets show an increasing 90th percentile of streamflow, indicating the opposite. Although the impact of baseline data (and consecutive parameter estimation) is already present at the mesoscale (Remesan & Holman, 2015), it is often overlooked in climate change studies using GHMs. Our results demonstrate that the choice of baseline data must be considered a source of uncertainty for climate change studies using calibrated GHMs. We anticipate that our study will increase awareness of baseline data's importance and contribute to valuable decision support for international policy related to floods, drought, and human water management.