Identifying runoff processes on the plot and catchment scale
- 1Insitute of Environmental Engineering, ETH Zurich, ETH Hönggerberg, 8093 Zürich, Switzerland
- 2Swiss Federal Institute for Forest, Snow and Landscape Research, WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
- 3Scherrer AG Hydrologie und Hochwasserschutz, Stockackerstrasse 25, 4153 Reinach, Schwitzerland
Abstract. Rainfall-runoff models that adequately represent the real hydrological processes and that do not have to be calibrated, are needed in hydrology. Such a model would require information about the runoff processes occurring in a catchment and their spatial distribution. Therefore, the aim of this article is (1) to develop a methodology that allows the delineation of dominant runoff processes (DRP) in the field and with a GIS, and (2) to illustrate how such a map can be used in rainfall-runoff modelling.
Soil properties were assessed of 44 soil profiles in two Swiss catchments. On some profiles, sprinkling experiments were performed and soil-water levels measured. With these data, the dominant runoff processes (DRP) were determined using the Scherrer and Naef (2003) process decision scheme. At the same time, a simplified method was developed to make it possible to determine the DRP only on the basis of maps of the soil, topography and geology. In 67% of the soil profiles, the two methods indicated the same processes; in 24% with minor deviations.
By transforming the simplified method into a set of rules that could be introduced into a GIS, the distributions of the different DRPs in two catchments could be delineated automatically so that maps of the dominant runoff processes could be produced. These maps agreed well with manually derived maps and field observations.
Flood-runoff volumes could be quite accurately predicted on the basis of the rainfall measured and information on the water retention capacity contained in the DRP map. This illustrates the potential of the DRP maps for defining the infiltration parameters used in rainfall-runoff models.