Project start: 01.01.2016

Duration: 3 years

Funding: Bayerisches Staatsministerium für Umwelt und Verbraucherschutz (StMUV)

Leitung: Harald Kunstmann

Beteiligte Wissenschaftler: Michael Warscher, Sven Wagner, Manuel Lorenz, Florian Marshall (Universität Augsburg), Patrick Laux, Jakob Garvelmann, Gerhard Smiatek (KIT Campus Alpin – IMK-IFU)


        Ulrich Strasser, Sascha Bellaire, Universität Innsbruck

        Nationalparkverwaltung Berchtesgaden



High-resolution regional climate simulations are indispensable for the regional assessment of the effects of climate change. This applies in particular to complex, climate-sensitive regions such as the Alpine region and to the estimation of future expected changes in the water balance and their effects on ecosystems. For the new IPCC report (AR5), global scenario runs based on improved emission estimates (RCPs) have been performed with the current model generations. However, due to their coarse spatial resolution and their sometimes large errors in the reproduction of the present-day climate, these are not suitable for regional climate impact analysis. They must be spatially refined and corrected with statistical methods.

Stochastic bias correction of precipitation

The aim of the Bias II project is to dynamically regionalize the latest global climate scenarios in very high spatial resolution (5-10 km) and to develop multivariate stochastic bias correction methods for all relevant hydrometeorological variables. Precipitation, temperature, radiation, humidity and wind speed should be corrected as physically as possible. In addition, snow dynamics and transport processes in the subsurface are investigated by means of combined hydrological model and process analyzes and integrated into an extended hydrological model so that more robust climate impact analyzes, especially for mountain regions, are possible. The challenge for the target region Berchtesgaden National Park consists in the complex hydrogeology, the extremely steep terrain, the large small-scale climatic gradients and the high importance of snow dynamics for the water balance. For improved process modeling, stable water isotopes and newly developed SnoMoS sensors are used as innovative validation methods.