SVAT modelling in support to flood risk assessment in Bulgaria

This study explores the benefit that can be drawn from incorporating the diagnosis of initial soil moisture of the top vegetation/soil layer and its anomalies as parameters in support of operational weather forecasting. For that purpose, a 1D vertical numerical land surface scheme, referred to as Soil Vegetation Transfer Model (‘SVAT_bg’) has been developed to simulate the soil–vegetation–atmosphere mass and energy transfer, accounting for local soil/climate features. The model is run daily for estimating soil moisture content and on this basis, a biogeophysical index designating Soil Moisture Availability Index (SMAI) to vegetation land cover is derived. SMAI is introduced as a measure of the proportion between the energy and water balances and their anomalies at different weather/climate conditions through a 6-level threshold scheme of land surface moistening. To facilitate the use of SMAI as a diagnostic tool for operational forecasting purposes, it is generated on a daily basis and visualised by colour-coded maps, covering the main administrative regions of Bulgaria in combination with a numerical part, which indicates the required flood-producing rainfall quantities (specific for each region). In case of overmoistening conditions, the numerical part denotes the rainfall excess above the soil saturation moisture content. ility of this approach is illustrated in two case studies of severe weather produced by deep convection and a rapid cyclogenesis developed at initial ‘dry’/‘wet’ soil moisture anomalies, respectively. The thermodynamic conditions and space–time structure of the rainfall are analysed by NWP output fields and satellite information. udy contributes to a better definition of the role of vegetation–soil moistening in flood risk forecasting within strong synoptic scale forcing regimes. The utility of the results comes also from the recognition of soil moisture as a meteorological forcing factor, which may affect both severity and frequency of extreme weather events.