G2 a new erosion model

1. G2a new erosion model towards a pan-European service for regional erosion monitoring

2. Acknowledgments • An invited lecture for • ITI premises, Thessaloniki, GR • 23 May 2012 • Special thanks to: • Director Prof. M. Petrou • Dr. I. Manakos

3. Christos G. Karydás Short CV • Christos G. Karydas has studied Agronomy/Land Reclamation (BSc/MSc) and Soil Resource Management (MSc) in the Aristotle University of Thessaloniki. His PhD was on automated rural landscape mapping using object-based image classification. He is a fellow researcher in the Lab of Forest Management and Remote Sensing of the Aristotle University of Thessaloniki, Greece. He teaches Remote Sensing and GIS in the university and other national and international institutes. Christos has been involved in many research and operational projects on crop mapping, precision agriculture, land-cover/use mapping, soil erosion and desertification, environmental risk and impact assessment. He has also contributed to many publications in peer review journals. Contact information • Aristotle University of Thessaloniki, • School of Forestry and Natural Environment, • Foinikas, Building B’, ground floor, office 7 • Tel: 2310992689 • E-mail: xkarydas@for.auth.gr,xkarydas@agro.auth.gr

4. Contributors Aristotle University of Thessaloniki – School of Forestry and Natural Environment – Lab of Forest Management and Remote Sensing • IoannisGitas • igitas@for.auth.gr • Christos Karydas • xkarydas@for.auth.gr Join Research Centre – Institute for Environment and Sustainability - Land management & Natural Hazards Unit • Luca Montanarela • luca.montanarella@jrc.ec.europa.eu  • PanosPanagos • panos.panagos@jrc.ec.europa.eu June 2011

5. Rain Terrain Rain Soil detachment Vegetation Runoff Soil movement Erosion by water • Erosion agents • Rain erosivity • Soil erodibility • Terrain shape • Land use

6. Erosion parameters Christos G. Karydas, Panos Panagos & Ioannis Z. Gitas (2012): A classification of water erosion models according to their geospatial characteristics, International Journal of Digital Earth, DOI:10.1080/17538947.2012.671380

7. Erosion and scale Christos G. Karydas, Panos Panagos & Ioannis Z. Gitas (2012): A classification of water erosion models according to their geospatial characteristics, International Journal of Digital Earth, DOI:10.1080/17538947.2012.671380

8. Erosion models Christos G. Karydas, Panos Panagos & Ioannis Z. Gitas (2012): A classification of water erosion models according to their geospatial characteristics, International Journal of Digital Earth, DOI:10.1080/17538947.2012.671380

9. G2 model features

10. G2 formula Wischmeier and Smith 1978 E=(R*V)*(S*T*I)

11. Study area • Soil erosion risk mapping • Scale 1:500,000 (pan-European) • monthly • Scale 1:50,000 (hot spots) • 3-4 months per year Strymonas river basin Hot spot area

12. R factor Wischmeier and Smith 1978 R=210+89*log[s*P/(d*h)] • R: rainfall erosivity of a specific month (MJ cm/ha h) • s: an empirical monthly storm factor (corresponding to Imax30 of USLE) • P: rainfall volume of the month (cm) • d: mean rainy days per month • h: mean rainy hours per day of the month

13. Storm factor ‘s’ • Expresses how more intensive are storms during a specific month in relation to the less intensive month of the year • Method for estimation • Calculation of EI values per month from available rain recording periods (e.g. 30-min, 1-h, etc.) using the original USLE formula • Averaging of calculated EI values per month • Normalization of the averaged EI value according to the minimum value in the set • Calibration according to measured data • The technique is based on the principle of cumulative EI figures developed in the framework of USLE

14. Erosivity calibration - example

15. Spatial distribution of ‘P’ • Rainfall of each month is tested across elevation and the coefficient of determination (r2) is recorded • Monthly rainfall maps are created using the most reliable function of P with elevation • Monthly rainfall prediction maps are created using Kriging interpolation • The two rainfall surfaces (from regression and interpolation) are weighty averaged according to the results of the regression • In cases where r2<0.10, the rainfall surface is set identical to the Kriging results

16. Spatial P - cases Spatial interpolation Regression with elevation

17. R overview

18. V factor Panagos et al. 2011 V={Fsoil+[Fsoil/(LAI+1)]}/2 • V: vegetation retention (a normalised monthly vegetation parameter) • FSoil: fraction of soil that is visible in the vertical direction, sunlit or shaded from the canopy • expresses percentage of soil in the surface unit (cell) • range: [0,1] • LAI: total one-sided area of leaf tissue per unit ground surface area • expresses vegetation density • unit: m2/m2 • range: [0,6] BioPar data SAIL/PROSPECT model

19. Erosion Fraction of soil Vegetation status BioPar data (geoland2 CMS)

20. Preparation of FSoil and LAI grids • Quality assessment of the available grids; exclusion of scenes/areas with • clouds • shadows • Temporal integration of the selected grids • Targeted date: the 15th of each month • Input from different years (minimum: 3) • Linear temporal interpolation of grids

21. S factor • Input parameters • First approximation by • Soil texture class • Corrections by • Crusting property • Double-application of low pass filter 3x3 • Organic matter content Van der Knijff et al. 2000 Le Bissonnais et al. 1998

22. First approximation

23. Organic matter Panagos et al. 2011 • Sc=So*e(-0.1013*OM) • Sc: corrected S • So: original S (before correction for organic matter • OM: content of organic matter per cent (%) Formula derived from USLE nomographs

24. T factor Moore and Burch (1986) T=(As/22.13)0.4*(sinβ /0.0896)1.3 • As: flow accumulation (m) • β: slope steepness (rad)

25. T-calculation steps • Calculation of flow accumulation grid (As) • Values 0 in the flow accumulation grid are reset to 1 • Slope steepness β is calculated in degrees • Slope steepness β in degrees is converted to radians • As is multiplied by the cell size in m • T>10 is set equal to 10

26. I factor Panagos et al. 2011 • All anti-erosion measures target to intercept rainfall runoff by reducing the slope length • Steps: • Sobel* filter 3x3 on NIR-band of SPOT (25m) • Resampling to 300m • Conversion of Sobel values into I values • Formula: I=1-√(Sf/255) *non-directional edge detection filter

27. I-factor estimation - example

28. Data sources

29. Outputs • Month-step erosion maps • Seasonal erosion maps • Annual erosion maps • Month-step erosion profiles per land use

30. Seasonal maps - examples

31. Input and output parameter trends

32. Erosion trends per land use

33. All land uses per month

34. Local scale • Vegetation status • (Euroland/Biopar products / 10 m) • Human management • SPOT Image 2006 / 25 m • Slope • ASTER DEM (30 m) • Rainfall erosivity • Hellenic National Meteo-service • Soil erodibility • National physiographic map (Nakos )

35. Quality assurance

36. Modifications in the new version • E=(R/V)*S*(T/I) • V=2*SQRT(LAI)-LN(FSoil) • T:same, new condition: T<=4 • I=EXP(2.5*Sf/255)

37. G2-model profile • A generic model appropriate for implementation throughout Europe • Use of harmonized standard input datasets • Need for calibration • A dynamic model (takes into account seasonal changes of rainfall erosivity and vegetation retention) • A simple model – low data demand • A realistic model (preliminary validation with experimental erosion measurements in the cross-border river basin of Strymonas/Struma in Greece and Bulgaria) • A feasible, data-driven model

38. G2 applications • Many institutes have been interested for G2 • Currently the new modified G2 is implemented: • In a river basin of Albania (MSc thesis in IAMB) • In the whole of Greece (together with a new sediment yield module) on a watershed scale

39. G2 spread out

40. Links • International Journal of Digital Earth • http://dx.doi.org/10.1080/17538947.2011.587897 • www:: http://eusoils.jrc.it • http://www.gmes-geoland.info/ Thanks for your attention!!!