A global erodibility index to represent sediment production potential of different rock types

Abstract

Erosion drives landscape evolution and is fundamental for understanding the interplay between climate and tectonics as well as land-ocean matter fluxes. Large scale erosion models are an important part of the suite of Earth system models. However, their representation of the sediment production potential of different rock types is very limited. A new global erodibility index is proposed herein. It depicts the relative erosion potential of different lithological classes in the Global Lithological Map (GLiM). The erodibility index was developed by correlating observational hillslope data against climatic and tectonic setting. Hillslope data were compared for different lithological classes in regions of similar uplift across a range of landscapes. An erodibility index was defined for each lithological class, based on the relative hillslope compared to that of acid plutonic rocks in the respective area, which are here used as base lithology. The erodibility indices of different lithological classes fall within three groups: low erodibility - 1.0 for acid plutonic rocks, metamorphic rocks, and carbonate sedimentary rocks, 1.1 for acid volcanic rocks, 1.2 for mixed sedimentary rocks; medium erodibility - 1.5 for basic plutonic rocks and siliciclastic rocks of all grain sizes, 1.4 for basic volcanic rocks; high erodibility – 3.2 for unconsolidated sediments. This erodibility index is the first estimate of sediment production potential of different rock types aimed at regional to global scale application. The erodibility index presented here can help improve our understanding of global erosion and landscape evolution. A new lithology erodibility layer, the global erodibility index dataset (GEroID), is produced and used as input in a global sediment flux model (WBMsed) to demonstrate a potential utilization of the new dataset. Using an updated lithological map paired with the new erodibility index yields improved regional model results compared to the original model input.