Soil erosion in the United States: Present and future (2020–2050)
Authors: Shahab A. Shojaeezadeh et al.
Links: [DOI Not Provided]
Why this paper matters
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- Ecosystem Impact: Accelerated soil erosion inflicts extreme changes in terrestrial and aquatic ecosystems [cite: 117]. [cite_start]
- Forecasting: Provides predictions for a probable future under different climate scenarios, which had not been fully surveyed at field scale previously [cite: 118].
Problem Formulation
Water-driven soil erosion (sheet and rill) needs to be estimated at a high resolution (30m) for the contiguous United States. [cite_start]Input Variables: Long-term precipitation, Climate scenarios, Land Use Land Cover (LULC) [cite: 120].
Methodology
The study integrates the G2 erosion model with Machine Learning and Remote Sensing techniques.
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- Climate Scenarios: CMIP6 Shared Socioeconomic Pathways (SSP-RCP) 2.6, 4.5, and 8.5 [cite: 119]. [cite_start]
- Resolution: Field-scale (30 meters) [cite: 118].
Results & Insights
[cite_start]The baseline model (2020) estimated soil erosion rates of 2.32 Mg ha$^{-1}$ yr$^{-1}$ under current conservation agriculture practices [cite: 121]. Future projections depend heavily on the selected SSP-RCP pathway.
Teaching Module
Concept 1: CMIP6 Scenarios
Exercise: Explain the difference between SSP-RCP 2.6 and SSP-RCP 8.5 regarding soil erosion drivers.
SSP-RCP 2.6 represents a "sustainability" pathway with low radiative forcing (aggressive mitigation), likely resulting in less extreme precipitation changes. SSP-RCP 8.5 is a "fossil-fueled development" pathway with high emissions, leading to more extreme weather events and likely higher rainfall erosivity.
Concept 2: The G2 Model
Exercise: How does the G2 model generally differ from the Universal Soil Loss Equation (USLE) in terms of data inputs?
While sharing principles, G2 is designed for digital soil mapping using standard GIS layers (Vegetation indices from RS, digital terrain analysis) and dynamic time-steps, whereas USLE was originally an empirical plot-based annual average model.