Badland and gully erosion assessment using remotely sensed data, non-invasive field techniques and stochastic modelling approaches

Mr. Michael Maerker, University of Pavia, Italy. Global Symposium on Soil Erosion (GSER19), 15 - 17 May 2019 at FAO HQ.

1. Badland and gully erosion assessment using remotely sensed data, non-invasive field techniques and stochastic modelling approaches Michael Maerker 1 , Alberto Bosino 1 , & Ulrike Hardenbicker 2 1 University of Pavia, Italy; 2 University of Regina, Canada 1

2. splash Spatio-temporal scales of soil erosion processes interrill rill gully fluvial after Renschler & Harbor (2002): Geomorphology 47, 189-209. badlands

3. Models often only address single processes and therefore are scale dependent (USLE, WEPP, Erosion 2D/3D, USPED) Models often are focussing only on laminar soil erosion (sheet/rill- interrill erosion) Integrated modelling is very complex Different soil erosion processes Parameter request Scale dependency (spatial/temporal) Spatio -temporal distribution Research Question: How to assess and quantify different soil erosion processes? 4 Challenges for soil erosion modeling : Märker et al. (2011): Geomorphology , 125(4), 530-540; Zakerinejad & Märker (2015): Natural Hazards , 79 (1), 25-50 Sidorchuk et al. (2003): Catena , 50, 507-525. Märker et al. (2001): Geografia Fisica e Dinamica Quaternaria , 24, 71-83

4. How to get Information about processes and forms? GSSI EMP 400 EM Conductivity Meter Fieldspectrometer Non invasive Field methods Local Information about substrates and soils

5. Multi & Hyperspectral Remotely Sensed Information and Derivatives TanDEM -X 90m, 30m, 12m Terrain Analysis & Topographic Indices High resolution DEM by Structure from Motion Spatial continuous information about forms, features and surface characteristics How to get Information about processes and forms?

6. 7 Badland & Gully Inventory Forms & Features Identification & Mapping Identification & Mapping

7. 8 Qualitative Assessment Maerker , M., Pelacani , S. & B. Schröder (2011): A functional entity approach to predict soil erosion processes in a small Plio -Pleistocene Mediterranean catchment in Northern Chianti, Italy . GEOMORPHOLOGY, vol. 125 (4), pp. 530-540

8. Susceptibility map MaxEnt stochastic approach to assess the driving factors for gully formation Variable importance Internal validation via ROC curves

9. 10 Initial phase ( 5%) Stati c p hase (95%) Sidorchuk (1999): Catena 37, 401-414 Sidorchuk , Maerker , Moretti & Rodolfi (2003) Catena 50, 507-525 Zakerinejad & Maerker (2015) Natural Hazards 79(1), 25-50 Quantitative Assessment Qualitative Assessment Maerker , M., Pelacani , S. & B. Schröder (2011): A functional entity approach to predict soil erosion processes in a small Plio -Pleistocene Mediterranean catchment in Northern Chianti, Italy . GEOMORPHOLOGY, vol. 125 (4), pp. 530-540 Gully Modelling

10. Conclusion Integrated method to assess gully and badland erosion susceptibilities over large areas using RS, fieldwork and stochastic modelling approaches. Spatial resolution of environmental data (DEM; RS-data) is increasing, however data need field validation Basis for process quantification and connectivity analysis on basin scale. 11