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|CSDMS meeting abstract presentation=In landscape evolution models, climate change is often assumed to be synonymous with changes in rainfall. In many climate changes, however, the dominant driver of landscape evolution is changes in vegetation cover. In this talk I review case studies that attempt to quantify the impact of vegetation changes on landscape evolution, including examples from hillslope/colluvial, fluvial, and aolian environments, spatial scales of ~10 m to whole continents, and time scales from decadal to millennial. Particular attention is paid to how to parameterize models using paleoclimatic and remote sensing data. | |CSDMS meeting abstract presentation=In landscape evolution models, climate change is often assumed to be synonymous with changes in rainfall. In many climate changes, however, the dominant driver of landscape evolution is changes in vegetation cover. In this talk I review case studies that attempt to quantify the impact of vegetation changes on landscape evolution, including examples from hillslope/colluvial, fluvial, and aolian environments, spatial scales of ~10 m to whole continents, and time scales from decadal to millennial. Particular attention is paid to how to parameterize models using paleoclimatic and remote sensing data. | ||
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Latest revision as of 16:34, 11 June 2025
Joint CSDMS-SEN annual meeting 2016: Capturing Climate Change
Modeling the Impact of Vegetation Changes on Erosion Rates and Landscape Evolution
Abstract
In landscape evolution models, climate change is often assumed to be synonymous with changes in rainfall. In many climate changes, however, the dominant driver of landscape evolution is changes in vegetation cover. In this talk I review case studies that attempt to quantify the impact of vegetation changes on landscape evolution, including examples from hillslope/colluvial, fluvial, and aolian environments, spatial scales of ~10 m to whole continents, and time scales from decadal to millennial. Particular attention is paid to how to parameterize models using paleoclimatic and remote sensing data.
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Of interest for:
