Volume XV |

HYDROBOD: obtaining a GIS-based hydrological soil database and a runoff coefficient calculator for Lower Austria

Abstract: In the State of Lower Austria, rainfall-runoff models it is an acknowledged method used when estimating flood peak discharges for small catchments where there are no direct gauging observations. An important input parameter for these models is the volumetric runoff coefficient, which was estimated by rather simple methods until now (for instance the CN-method of the U.S.G.S), which did not provide very reliable results.
The project HYDROBOD intends to provide a solid and homogeneous database of some basic soil hydraulic parameters over the whole state area (over 19.000 km²) and contains a hydrological model for estimation of these runoff coefficients which takes into account some relevant input variables.
In a first step (HYDROBOD I), hydraulic soil parameters are calculated by regionalization methods and assembled for the whole area of Lower Austria, using a GIS-database (ESRI ArcGIS 10.2; at a 50 x 50 m grid). They include soil layer depth, storage capacity, saturated vertical conductivity, plus a classification of the soil reaction types referring to storm events. These data are now available for three soil layers, from top soil down to 1 m below surface. In a second step (HYDROBOD II), a vertical one-dimensional event model was set up which allows to calculate storm event runoff coefficients on a cell-by-cell basis for any given area in Lower Austria.
This model uses the hydraulic soil parameters obtained from HYDROBOD I, plus an estimation of unsaturated vertical pore flux and a soil water storage model with several modules. This model needs the following input parameters: a shape-file with the catchment area, and pairs of rainfall data (duration + rainfall depth).
Results of a calculation process are: runoff coefficients (as an average over the catchment area) for each pair of rainfall data, and for different initial wetness scenarios (from “dry” to “saturated”). Validation of the model is promising.