Sentence examples for erosion estimates from inspiring English sources

Models that convert 7Be inventory measurements to soil erosion estimates are all based on the observed depth distribution of 7Be, described by the relaxation mass depth (h0) parameter.

By coupling these erosion estimates with elastic displacement fault modeling, we can use the resulting topographic envelopes to constrain the rate and duration of deformation.

A comparative analysis between NDVI- and LSMA-derived C factors also proved that the latter produced a more detailed spatial variability, as well as generated more accurate erosion estimates when used as input to RUSLE model.

The objective of this work is to investigate how the erosion estimates are affected by the different sub-models and parameters of a CFD-based wear prediction model, so that its actual predictive capacity may be established.

Previous work, however, has not considered potential spatial variation in h0 linked to natural variability in soil physical properties, which could have major implications for the reliability of soil erosion estimates.

Combined water and tillage erosion estimates indicated a possible unsustainable increase in soil loss on some steep-slope fields within the last few decades that has resulted from shorter fallow periods, longer periods of cultivation before fallowing, and greater weed pressure.

The model was applied on 21 km of cliffs in Marine Corps Base Camp Pendleton, California considering sea level rise ranging from 0.5 to 2 m over 100 yrs using 207 profiles, sand budget deficits estimated from historical data, and sand inputs from terrestrial erosion estimated from a time series of lidar data.

We compare the distribution of glacial erosion (estimated from cirque floor elevations and last glacial maximum (LGM) equilibrium line altitude (ELA) reconstructions) and fault slip rate with three metrics of topography in each range: the along-strike maximum elevation swath profile, hypsometry, and slope-elevation profiles.

Association between USLE predicted risk and observed erosion, estimated using mixed effects logistic regression to control for within-site variability, correctly classified only 38% of sites into three degradation classes and model sensitivity for delineating regions of severe degradation was only 28%.

Data for soil erosion estimate is adopted from the author's previous work (Amdihun et al. [2014]).

Estimates of Hg emissions to the atmosphere are depicted in Figure 1, based on Holmes et al. Primary emissions include natural geogenic sources (volcanism and erosion; estimated at 500 Mg a 1) and anthropogenic sources (fuel combustion, waste incineration, and mining; 2100 Mg a 1).