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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 m 2 plots) and concentrated overland-flow runoff and erosion rates (∼9 m 2 plots), along with collective rates for these same processes combined over the patch scale (13 m 2 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: 10.15482/USDA.ADC/1504518; Pierson et al., 2019). [ABSTRACT FROM AUTHOR]