Victoria Naipal Max-Planck Institute for Meteorology Land Department; Vegetation Modelling Group Supervisor: Ch.Reick CO-Supervisor: J.Pongratz EGU, From Local to Global Scale Soil Erosion Modelling A Sensitivity Analysis of the RUSLE Model Photo: NRCS

Soil erosion and feedbacks 2  Estimations of present global soil erosion range between 20.5 – 201 Pg/yr  Current Dynamic Global Vegetation Models (DGVMs) do not account for soil erosion and do not treat soil as a dynamical sytem  This limits our capabilities to investigate the global carbon and nutrient cycles Land cover/ land use and soil erosion in Germany since the Early Middle Ages (data from Bork et al., 1989) Background Methods Results & Validation Analysis Conclusions Outlook

3 Why soil erosion modelling on global scale? Cumulative carbon emissions as a result of anthropogenic land-cover change in the non-alpine Rhine Basin (Hoffmann et al., 2013)  Sediment storage data indicates that hillslopes and floodplains could have sequestered an amount of OC similar to cumulative carbon emissions from anthropogenic land-cover change Objective: Investigate the effects of soil erosion on the global carbon cycle for different climate and land-use scenarios How to represent complex soil erosion processes, which occur on local scales, in a simplified but realistic way on global scale? Background Methods Results & Validation Analysis Conclusions Outlook

 Semi empirical/process-based model  Based on the largest set of experiments on soil erosion in the US  Parameterized for agriculture areas and the agriculture plot scale  Does not simulate deposition and sediment transport  Originally does not have a “limit to soil erosion” Tool – RUSLE Model (Renard, 1997) 4 Background Methods Results & Validation Analysis Conclusions Outlook

Input datasets Topography: 1 km global GTOPO DEM Climate: 0.25 degree monthly GPCC precipitation 1951 – 2000 Soil: 1km Harmonized World Soil Database Land cover: 0.05 degree MODIS NDVI Support practice: MODIS landcover data + Yang et al (2003) 5 Additional changes made to the RUSLE model: Implementation of gravel (from the soil database) as a limit to soil erosion Estimate the land-use/land-cover parameter based on NDVI Background Methods Results & Validation Analysis Conclusions Outlook

Mean global soil erosion rate: 7 tons ha-1 yr-1 Current global soil erosion rates 6 Background Methods Results & Validation Analysis Conclusions Outlook

Overestimation of soil erosion rates in mountainous areas – Cerdan et al. (2010) Underestimation of soil erosion rates on agricultural land - Van Oost et al.(2007), Doetterl et al.(2011) Validation Current StudyOthersReferenceMethod European9 ton ha -1 year -1 1 ton ha -1 year -1 Cerdan et al., 2010 Plot data &literature Global Agricultural 7 ton ha -1 year ton ha -1 year -1 Van Oost et al., 2007; Doetterl et al., 2011 Plot data & revised RUSLE 7 Background Methods Results & Validation Analysis Conclusions Outlook

Sensitivity analysis and scaling 8

The topographical factor of RUSLE is a function of the slope, and the slope is highly dependent on the spatial resolution of the DEM Topography generally obeys fractal statistics (Huang and Turcotte, 1989) Scaling slope – fractal method 9 Statistical variation of elevation between samples varies with the distance between them ( ex.: Klinkenberg and Goodchild, 1992) S = percentage slope α = fractal coefficient d = specified finer grid resolution D = fractal dimension Background Methods Results & Validation Analysis Conclusions Outlook DEM resolution (arc-minutes) Mean slope (degrees) Mean global slope versus DEM resolution

Implementing the fractal method 10 Slope derived from 1km DEM Slope estimated by the fractal method for 30m grid resolution Applying the fractal method for the scaling of slope from a 1km global DEM shows a significant increase in detail and improves the global mean Background Methods Results & Validation Analysis Conclusions Outlook

Large erosion amounts as modelled in mountainous areas can be related to biases in the erosivity factor Sensitivity of the erosivity 11 USLE Erosivity, NRI data Erosivity in current study Background Methods Results & Validation Analysis Conclusions Outlook

Validation of the erosivity 12 In the current study erosivity is largely overestimated for regions with small erosivity values in the NRI data High resolution PRISM precipitation data (800m) for the US was used to validate the global GPCC precipitation (0.25 degree) The good correlation between the precipitation data indicates that the spatial scale cannot be a reason for the large biases in the erosivity Background Methods Results & Validation Analysis Conclusions Outlook

Erosivity dependence on climate conditions 13 The biases in erosivity as computed with precipitation totals, could be attributed to large variability in precipitation intensities for different climatic conditions Temperate and tundra zones show the largest biases Background Methods Results & Validation Analysis Conclusions Outlook

 Implementing RUSLE on a global scale results in overestimating erosion rates on mountainous areas while underestimating erosion in agricultural/flatter areas  Soil erosion is highly dependent on the spatial resolution – use of scaling methods is essential  The fractal method can be used for improving the effect of topography on erosion in RUSLE  Improvement of the erosivity factor of the RUSLE model can remove biases in mountainous areas, on global scale Conclusions 14 Background Methods Results & Validation Analysis Conclusions Outlook

To estimate effects on the carbon cycle, other processes such as deposition and sediment transport have to be included A global approach needs therefore a simplified method that can capture the realistic behaviour of these processes Outlook: Sediment mass-balance model 15 from Hoffmann et al., 2013 (GBC) Background Methods Results & Validation Analysis Conclusions Outlook