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Rills often act as sediment sources and the dominant sediment and water transport mechanism for hillslopes. Six experiments were conducted on two soils and a uniform sand using three experimental methodologies. The results of this study challenge the assumption often used in hydrologic and erosion models that relationships derived for sheet flow or larger channel flow are applicable to actively eroding rills. Velocity did not vary with slope, and Reynolds number was not a consistent predictor of hydraulic friction. This result was due to interactions of slope gradient, flow rate, erosion, and the formation of rill roughness, bed structures, and head cuts. A relationship for rill flow velocities was proposed. Stream power was found to be a consistent and appropriate predictor for unit sediment load for the entire data set, while other hydraulic variables were not. The data for stream power and sediment load fit the form of a logistic curve (r 2 = 0.93), which is promising relative to recently proposed erosion models which are based on probabilistic particle threshold theory.

The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha−1 h−1 yr−1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

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Understanding and quantifying the large, unexplained variability in soil erosion data are critical for advancing erosion science, evaluating soil erosion models, and designing erosion experiments. We hypothesized that it is possible to quantify variability between replicated soil erosion field plots under natural rainfall, and thus determine the principal factor or factors which correlate to the magnitude of the variability. Data from replicated plot pairs for 2061 storms, 797 annual erosion measurements, and 53 multi‐year erosion totals were used. Thirteen different soil types and site locations were represented in the data. The relative differences between replicated plot pair data tended to be lesser for greater magnitudes of measured soil loss, thus indicating that soil loss magnitude was a principal factor for explaining variance in the soil loss data. Using this assumption, we estimated the coefficient of variation of within‐treatment, plot replicate values of measured soil loss. Variances between replicates decreased as a power function of measured soil loss, and were independent of whether the measurements were event‐, annual‐, or multi‐year values. Coefficients of variation ranged on the order of 14% for a measured soil loss of 20 kg/m2 to greater than 150% for a measured soil loss of less than 0.01 kg/m2 These results have important implications for both experimental design and for using erosion data to evaluate prediction capability for erosion models.

We assess the water balance of the Brazilian Cerrado based on remotely sensed estimates of precipitation (TRMM), evapotranspiration (MOD16), and terrestrial water storage (GRACE) for the period from 2003 to 2010. Uncertainties for each remotely sensed data set were computed, the budget closure was evaluated using measured discharge data for the three largest river basins in the Cerrado, and the Mann-Kendall test was used to evaluate temporal trends in the water balance components and measured river discharge. The results indicate an overestimation of discharge data, due mainly to the overestimation of rainfall by TRMM version 6. However, better results were obtained when the new release of TRMM 3B42 v7 was used instead. Our results suggest that there have been (a) significant increases in average annual evapotranspiration over the entire Cerrado of 51 6 15 mm yr 21 , (b) terrestrial water storage increases of 11 6 6 mm yr 21 in the northeast region of the Brazilian Cerrado, and (c) runoff decreases of 72 6 11 mm yr 21 in isolated spots and in the western part of the State of Mato Grosso. Although complete water budget closure from remote sensing remains a significant challenge due to uncertainties in the data, it provides a useful way to evaluate trends in major water balance components over large regions, identify dry periods, and assess changes in water balance due to land cover and land use change.