Abstract. Rainfall simulation and overland-flow experiments enhance
understanding of surface hydrology and erosion processes, quantify runoff
and erosion rates, and provide valuable data for developing and testing
predictive models. We present a unique dataset (1021 experimental plots) of
rainfall simulation (1300 plot runs) and overland-flow (838 plot runs)
experimental plot data paired with measures of vegetation, ground cover, and
surface soil physical properties spanning point to hillslope scales. The
experimental data were collected at three sloping sagebrush (Artemisia spp.) sites in
the Great Basin, USA, each subjected to woodland encroachment and with
conditions representative of intact wooded shrublands and 1–9 years following
wildfire, prescribed fire, and/or tree cutting and shredding tree-removal
treatments. The methodologies applied in data collection and the cross-scale
experimental design uniquely provide scale-dependent, separate measures of
interrill (rain splash and sheet flow processes, 0.5 m2 plots) and
concentrated overland-flow runoff and erosion rates (∼9 m2 plots), along with collective rates for these same processes
combined over the patch scale (13 m2 plots). The dataset provides a
valuable source for developing, assessing, and calibrating/validating runoff
and erosion models applicable to diverse plant community dynamics with
varying vegetation, ground cover, and surface soil conditions. The
experimental data advance understanding and quantification of surface
hydrologic and erosion processes for the research domain and potentially for
other patchy-vegetated rangeland landscapes elsewhere. Lastly, the unique
nature of repeated measures spanning numerous treatments and timescales
delivers a valuable dataset for examining long-term landscape vegetation,
soil, hydrology, and erosion responses to various management actions, land
use, and natural disturbances. The dataset is available from the US
Department of Agriculture National Agricultural Library at
https://data.nal.usda.gov/search/type/dataset (last access: 7 May 2020) (doi: https://doi.org/10.15482/USDA.ADC/1504518; Pierson et al., 2019).