Droughts are worldwide the second most severe natural disaster beside floods. In Europe, droughts are the costliest natural disasters with average expenses of 621 million EUR per event. The last severe drought event took place in 2003. It induced an agro-economic loss of 1.5 billion EUR in Germany alone. Such economical losses emphasize the need of an operational system for monitoring agricultural droughts in order to mitigate their negative consequences.

Observation-based monitoring of agricultural droughts, which are characterized by soil moisture deficits, is technically and economically not feasible on regional to national scales. Hydrologic modeling is the prime alternative to estimate soil moisture availability on large spatial domains. Such models are driven by meteorological observations and predict hydrological fluxes and states, such as soil moisture or evapotranspiration. Predictions of hydrologic models underlie several sources of uncertainties. These uncertainties arise from input data, model structure, initial conditions, and model parameters. The implications of parametric uncertainty to hydrologic predictions are analyzed herein.

The main objective of this work is to develop a monitoring system for agricultural droughts in Germany. The development of such a system includes several challenges. First, a spatially continuous dataset of soil moisture for entire Germany is derived from modeling. The parametric uncertainty of such hydrologic predictions is taken into account. Second, the propagation of parametric uncertainty of soil moisture to the identification of drought characteristics is estimated in order to evaluate the uncertainty inherent to such a monitoring system. Third, an approach to reduce the parametric uncertainty by using satellite retrieved land surface temperature data is investigated. And forth, an operational system providing drought information in near-real time is developed and implemented.