Probabilistic forecasting of antecedent soil moisture condition as flash flood precursor variables

Flood forecasting is a rather complicated task, particularly in those catchments which are prone to flash flood formation or which the response time is of the order of few hours and, even brief anticipation are important and welcomed. In this context, some kind of hydrological precursors can be considered to improve the effectiveness of the emergency actions (i.e. early flood warning). Now, in literature has been widely recognized how soil moisture is an important factor in flood formation, because the runoff generation is strongly influenced by the antecedent soil moisture conditions of the catchment. The basic idea of the work here presented is to use soil moisture conditions in a probabilistic framework to define a first alert phase in a flash flood forecasting chain. For the soil moisture conditions modelling, the IHACRES model which is a spatially-lumped rainfall-runoff model that can be employed to reproduce the continuous daily response of the catchment was used to derive a series of wetness indexes. Soil moisture conditions are defined using an Antecedent Moisture Conditions index (AMC) similar to this widely used for the implementation of the Soil Conservation Service – Curve Number methodology. Instead, for the soil moisture conditions forecasting a Markov chain model has been implemented and tested. Application of the proposed methodology has been carried out with reference to a river basin in Cyprus.