Hydrology of Alpine catchments

The Alpine region is exposed to a high frequency of extreme precipitation events and is particularly vulnerable to their secondary effects like floods, landslides and erosion, which endanger environment, inhabitants and infrastructure. To mitigate the consequences of such events, reliable quantitative precipitation and flood forecasts with sufficient lead time are needed. The production of hydrological forecasts with sufficient lead time requires the use of coupled hydrometeorological models. However, numerically simulated precipitation (needed as input) contains large uncertainties. The latter arise from the use of imperfect numerical models and from the chaotic nature of the atmospheric dynamics, and can effectively limit the skill of a deterministic hydrological forecast. This effect can be partly encompassed by following a probabilistic approach based on ensemble forecasts.

Within the scope of external pageCOST 731call_made we try to assess the propagation of uncertainty in advanced meteo-hydrological forecast systems, especially investigating the benefits of applying a coupled meteorological-hydrological ensemble prediction system. The employed hydrological model is the semi-distributed hydrological model external pagePREVAHcall_made. The model is applied to the Alpine tributaries of the Rhine river basin (see Fig.1). The meteorological inputs come a) from observations, to provide the initial conditions of the hydrological model b) from deterministic meteorological forecasts (from the NWP model external pageCOSMOcall_made of MeteoSwiss) and c) from a meteorological ensemble prediction system (external pageCOSMO-LEPScall_made). This setup was applied for several case studies characterized by heavy precipitation events (May 1999, November 2002, August 2005: see publication list below and Fig.2). We found a good hindcast performance of the applied coupled meteorological-hydrological ensemble forecast system. The additional probabilistic information helps to classify the deterministic forecast and provides useful information about its uncertainty. To further assess the applicability of such a system, a study providing two years of continuous probabilistic hindcasts is currently in progress.

We are also producing real-time hydrological ensemble forecasts. This work takes place in the context of the MAP-D-PHASE program.