Abstract. Surface runoff generation on arable fields is an important driver of flooding, on-site and off-site damages by erosion, and of nutrient and agrochemical transport. In general, three different processes generate surface runoff (Hortonian runoff, saturation excess runoff, and return of subsurface flow). Despite the developments in our understanding of these processes it remains difficult to predict which processes govern runoff generation during the course of an event or throughout the year, when soil and vegetation on arable land are passing many states. We analysed the results from 317 rainfall simulations on 209 soils from different landscapes with a resolution of 14 286 runoff measurements to determine temporal and spatial differences in variables governing surface runoff, and to derive and test a statistical model of surface runoff generation independent from an a priori selection of modelled process types. Measured runoff was related to 20 time-invariant soil properties, three variable soil properties, four rain properties, three land use properties and many derived variables describing interactions and curvilinear behaviour. In an iterative multiple regression procedure, six of these properties/variables best described initial abstraction and the hydrograph. To estimate initial abstraction, the percentages of stone cover above 10 % and of sand content in the bulk soil were needed, while the hydrograph could be predicted best from rain depth exceeding initial abstraction, rainfall intensity, soil organic carbon content, and time since last tillage. Combining the multiple regressions to estimate initial abstraction and surface runoff allowed modelling of event-specific hydrographs without an a priori assumption of the underlying process. The statistical model described the measured data well and performed equally well during validation. In both cases, the model explained 71 and 58 % of variability in accumulated runoff volume and instantaneous runoff rate (RSME: 5.2 mm and 0.23 mm min −1 , respectively), while RMSE of runoff volume predicted by the curve number model was 50 % higher (7.7 mm). Stone cover, if it exceeded 10 %, was most important for the initial abstraction, while time since tillage was most important for the hydrograph. Time since tillage is not taken into account either in typical lumped hydrological models (e.g. SCS curve number approach) or in more mechanistic models using Horton, Green and Ampt, or Philip type approaches to address infiltration although tillage affects many physical and biological soil properties that subsequently and gradually change again. This finding should foster a discussion regarding our ability to predict surface runoff from arable land, which seemed to be dominated by agricultural operations that introduce man-made seasonality in soil hydraulic properties.

Abstract. Distributed flood control measures such as landuse changes or differing soil tillage practices which affect the runoff generation process, are hard to simulate physically based due to a high degree of uncertainty with regard to soil parameterisation. In this study the physically based rainfall runoff model WaSiM-ETH (Version 8.4.2) was used with a multi-layered vegetation and soil parameterisation. The modelling area was the meso-scaled and rurally characterised Windach catchment. In addition, soil measurement datasets were compared to demonstrate the uncertainties in soil parameterisation of physically based models. The datasets were gained from the hillslope scale at the Scheyern research farm with similar soil conditions to the Windach catchment. While parameterising and calibrating the model, seven different pedotransfer functions were used with strong influence on the simulated hydrographs. The differing bulk densities of soils depending on land-use and soil tillage must be taken into consideration due to their high impact on modelling results, and they also offer a comprehensive way to model distributed flood control measures. These measures have noticeable effects on flood events under HQ 10 , especially if the land-use type which is affected by the distributed flood control measure is the dominating land-use form in the catchment area. To account for the variability of soils in the investigation area of Scheyern, different approaches were applied to estimate soil hydraulic properties and saturated hydraulic conductivity, and were compared to field measurements.

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A high degree of uncertainty with regard to soil parameterisation limits the significance of physically-based simulation of distributed flood control measures, which affect the runoff generation process, such as land-use changes or differing soil tillage practices. In this study, the soil measurement data from the hillslope scale at the Scheyern research farm were compared to demonstrate this uncertainty. To account for the spatial variability of soils in the investigation area of Scheyern, different approaches were applied to estimate soil hydraulic properties and saturated hydraulic conductivity, and were compared to field measurements. KEY WORDS: hydraulic conductivity, pedotransfer function, hood infiltrometer, uncertainty, distributed flood control measures.