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Future Scenarios of Soil Erosion in the Alps under Climate Change and Land Cover Transformations Simulated with Automatic Machine Learning

Authors: Gianinetto, M.; Aiello, M.; Vezzoli, R.; Polinelli, F.; Rulli, M.; Chiarelli, D.; Bocchiola, D.; +2 Authors

Future Scenarios of Soil Erosion in the Alps under Climate Change and Land Cover Transformations Simulated with Automatic Machine Learning

Abstract

Erosion is one of the major threats listed in the Soil Thematic Strategy of the European Commission and the Alps are one of the most vulnerable ecosystems, with one of the highest erosion rates of the whole European Union. This is the first study investigating the future scenarios of soil erosion in Val Camonica and Lake Iseo, which is one of the largest valleys of the central Italian Alps, considering both climate change and land cover transformations. Simulations were done with the Dynamic Revised Universal Soil Loss Equation (D-RUSLE) model, which is able to account also for snow cover and land cover dynamics simulated with automatic machine learning. Results confirm that land cover projections, usually ignored in these studies, might have a significant impact on the estimates of future soil erosion. Our scenario analysis for 2100 shows that if the mean annual precipitation does not change significantly and temperature increases no more than 1.5–2.0 °C, then the erosion rate will decrease by 67% for about half of the study area. At the other extreme, if the mean annual precipitation increases by more than 8% and the temperature increases by more than 4.0 °C, then about three-quarters of the study area increases the erosion rate by 92%. What clearly emerges from the study is that regions with higher erosion anomalies (positive and negative) are expected to expand in the future, and their patterns will be modulated by future land transformations.

Keywords

machine learning, climate change, soil erosion, machine learning; climate change; land cover changes; soil erosion; Alps, Alps, land cover changes

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    Top 10%
    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Average
Top 10%
Green
gold