Search

Summary Characterizing the spatial and temporal variability of small scale runoff responses is essential to distributed hydrologic modeling. To explore the variability of runoff responses, we analyzed surface runoff hydrographs from 12 neighboring hillslopes in central Iowa, USA that were observed for 72 runoff events over a four-year period. These agricultural experimental hillslopes receive various prairie filter strip treatments and drain areas ranging from 0.48 to 3.19 ha. The distances between them vary from tens of meters to about 3 km. We compared the hydrographs from the remaining 11 hillslopes to the hydrograph at the benchmark hillslope (i.e., hillslope B6 with no treatment). The results showed that: (1) for any individual event in which noticeable surface runoff occurred, the hydrographs from these hillslopes had similar shapes but different magnitudes; (2) for any paired hillslopes, the shape similarity persisted, but the scaling factor (the regression slope between two flow series) changed across events; and (3) for any runoff event, no simple relationship exists between the spatial variation of the scaling factor and the slope, slope length, area, and prairie strip width at the footslope of the hillslopes. Interestingly, we found that for 9 out of the 11 paired hillslopes, 40–70% of the temporal variation in the scaling factors can be explained by the antecedent wetness condition and the maximum hourly rain accumulation. These results suggest that the small-scale surface runoff responses are spatially variable but organized linearly, i.e., shape similarity (or linearity) in space is another feature of the small-scale runoff process. This phenomenon seems to result from the spatial vicinity and small-scale spatial variability of rainfall intensity and antecedent soil moisture. [ABSTRACT FROM AUTHOR]

• Antecedent SMAP soil moisture (SM) has a significant relationship with runoff ratio. • Probability of higher runoff ratios increases as event total rainfall increase. • SM-deficit-normalized rainfall has a better predictive power than SM and rainfall. • Vegetation has significant effect on runoff in an agricultural region. Following results by Crow et al. (2017) [Geophys. Res. Lett. 44, 5495–5503] on the impact of antecedent soil moisture on runoff production, we investigate total runoff production during individual rainfall-runoff events in agricultural landscapes as a function of antecedent soil moisture, total rainfall, and vegetation cover for catchments with drainage areas ranging from 80 to 1000 km 2 in the state of Iowa, USA. For our study, we use Enhanced SMAP soil moisture estimates, the MODIS enhanced vegetation index (EVI), gauge-corrected Stage IV radar rainfall, and USGS streamflow data. We analyze the event runoff ratio as a function of event-scale rainfall, antecedent SMAP soil moisture and soil-moisture-deficit-normalized rainfall for the events in a period from March 31, 2015 to October 31, 2018. Our goal is to confirm the relationships identified by Crow et al. (2017) in heavily managed agricultural landscapes and to refine some of their methodological steps to quantify the role of additional variables controlling runoff production. To this end, we define three different strategies to identify rainfall-runoff events and add a baseflow separation step to better insulate event scale stormflow runoff. We test the effects of antecedent soil moisture, rainfall, and vegetation on the event-scale runoff ratio. The antecedent SMAP soil moisture and event-scale rainfall are found to have significant predictive power in estimating event runoff ratio. Soil moisture deficit-normalized rainfall, introduced as the ratio of event-scale rainfall to available space in top soil before initiation of the event, exhibited a more distinct relationship with runoff ratio. The long-term analysis of runoff ratio, rainfall, and MODIS EVI indicated that, in an agricultural region, vegetation plays a significant role in determining event-scale runoff ratios. The methodology and outcome of our study have direct implications on real-time flood forecasting and long-term hydrologic assessments. [ABSTRACT FROM AUTHOR]