Runoff reaction from extreme rainfall events on natural hillslopes: a data set from 132 large-scale sprinkling experiments in south-western Germany

Abstract

Pluvial or flash floods generated by heavy precipitation events cause large economic damage and loss of life worldwide. As discharge observations from such extreme occurrences are rare, especially on the scale of small catchments or even hillslopes, data from artificial sprinkling experiments offer valuable information on runoff generation processes, overland and subsurface flow rates, and response times. We conducted 132 large-scale sprinkling experiments on natural hillslopes at 23 sites with different soil types and geology on pastures and arable land within the federal state of Baden-Württemberg in south-western Germany. The experiments were realized between 2016 and 2017. Simulated rainfall events of varying durations were based on (a) the site-specific 100-year return periods of rainfall with different durations and (b) the maximum rainfall intensity observed locally. The 100 m2 experimental area was divided into three individual plots, and overland and subsurface flow, soil moisture, and water level dynamics in the temporarily saturated soil zone were measured at 1 min resolution. Furthermore, soil characteristics were described in detail for each site. The data were carefully processed and corrected for measurement errors and combined into a consistent and easy-to-use database. The experiments revealed large variability in possible runoff responses to similar rainfall characteristics. In general, agricultural fields produced more overland flow than grassland. The latter generated hardly any runoff during the first simulated 100-year event on initially dry soils. The data set provides valuable information on runoff generation variability from natural hillslopes and may be used for the development and evaluation of hydrological models, especially those considering physical processes governing runoff generation during extreme precipitation events. The data set presented in this paper is freely available from the FreiDok plus data repository at https://doi.org/10.6094/UNIFR/151460 (Ries et al., 2019).