Sabine Attinger (Universität Potsdam)
Anthropogenic warming is anticipated to impact the hydrological cycle tremendously in the future. However, projections are accompanied by large uncertainty due to varying estimates of future warming but also due to hydrological model uncertainties.
In a recent publication (Samaniego et al, 2018) we presented for example hydrological projections using an ensemble of hydrological and land-surface models, forced with bias-corrected downscaled general circulation model output. We estimated the impacts of 1–3 K global mean temperature increases on soil moisture droughts in Europe. Our results clearly show that in the absence of effective mitigation, Europe will therefore face unprecedented increases in soil moisture drought, presenting new challenges for adaptation across the continent.
These findings underpin the urgent need for hydrological models that are predictive in a high spatial and temporal resolution and that can help to develop appropriate adaptation measures.
Even if hydrological model development has pushed hard during the past decades to embrace enough physical complexity to be predictive, while still stay operational at regional or even global scales without prohibitive computational costs, we are still not there (Bierkens 2015). This statement holds true for the data side too, even though the amount of available data for model parametrization has grown exponentially during the past decades (Blöschl et al. 2013).
The main challenge is to find a good comprise between accuracy and robustness of the hydrological model employed to serve the needs of long-term predictions of water and matter flows under global change processes.
In this presentation, I will present a series of modeling concepts, methods and tools for model scaling, parameter regionalization and uncertainty quantification which have been professionally implemented into the UFZ mesoscale hydrological model mHM (www.ufz.de/mhm/) to achieve model robustness and accuracy as much as possible.
Finally, I will discuss the potential to include data assimilation methods into our hydrological model framework mHM.
