g2 a new erosion model

G2G2a new erosion modela new erosion modeltowards a pan-European servicefor regional erosion monitoring

AcknowledgmentsAcknowledgmentsAn invited lecture for

◦ ITI premises, Thessaloniki, GR◦ 23 May 2012

Special thanks to:◦ Director Prof. M. Petrou◦ Dr. I. Manakos

Christos G. KarydásChristos G. KarydásShort CVChristos 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 informationAristotle University of Thessaloniki,School of Forestry and Natural Environment, Foinikas, Building B’, ground floor, office 7Tel: 2310992689E-mail: xkarydas@for.auth.gr, xkarydas@agro.auth.gr

ContributorsContributorsAristotle University of Thessaloniki – School of Forestry and Natural Environment – Lab of Forest Management and Remote SensingIoannis Gitas

◦ igitas@for.auth.grChristos Karydas

◦ xkarydas@for.auth.gr

Join Research Centre – Institute for Environment and Sustainability - Land management & Natural Hazards UnitLuca Montanarela

◦ luca.montanarella@jrc.ec.europa.eu Panos Panagos

◦ panos.panagos@jrc.ec.europa.eu

June 2011

Erosion by waterErosion by waterRain

Runoff

Soil detachment

Rain

Soil movement

Terrain

Vegetation

Erosion agents•Rain erosivity•Soil erodibility•Terrain shape•Land use

Erosion parametersErosion 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

Erosion and scaleErosion 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

Erosion modelsErosion modelsChristos 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

G2 model featuresG2 model features

Erosion type Sheet – interril

Erosion features Soil loss (actual)

Erosion processes Splash, runoff

Spatial scale Landscape

Temporal scale Month (long term, averaged)

Mathematical basis Empirical (inherited from USLE)

Type of assessment Quantitative (t/ha)

G2 formulaG2 formula

E=(R*V)*(S*T*I)

E Actual soil loss (t/ha)

R Rainfall erosivity (modified from USLE by G2) DYNAMIC

FACTORSV Vegetation retention (developed by G2)

SSoil erodibility (modified from USLE by JRC, 2000-5)

STATIC FACTORS

TTopographic influence (USLE modifications, 1996)

IInterception of slope length (developed by G2)

Wischmeier and Smith 1978

Study areaStudy area

Soil erosion risk mapping◦Scale

1:500,000 (pan-European) monthly

◦Scale 1:50,000 (hot spots) 3-4 months per

year

Strymonasriver basin

Hot spotarea

R factorR factor

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 monthh: mean rainy hours per day of the month

Wischmeier and Smith 1978

Storm factor ‘s’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

Erosivity calibration - Erosivity calibration - exampleexample

Spatial distribution of ‘P’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

Spatial P - casesSpatial P - cases

Spatial interpolation Regression with elevation

R overviewR overview

V factorV factorV={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]

Panagos et al. 2011B

ioP

ar d

ata

SA

IL/P

RO

SP

EC

T m

od

el

BioPar data (geoland2 BioPar data (geoland2 CMS)CMS)

Fraction of soil Erosion

Vegetationstatus

Preparation of FSoil and LAI Preparation of FSoil and LAI gridsgridsQuality 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

S factorS factorInput 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

First approximationFirst approximation

Organic matterOrganic matter

Sc=So*e(-0.1013*OM)

◦Sc: corrected S

◦So: original S (before correction for organic matter

◦OM: content of organic matter per cent (%)

Panagos et al. 2011

Formula derived from USLE nomographs

T factorT factor

T=(As/22.13)0.4*(sinβ /0.0896)1.3

As: flow accumulation (m)β : slope steepness (rad)

Moore and Burch (1986)

T-calculation steps 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

I factorI factorAll 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

Panagos et al. 2011

I-factor estimation - I-factor estimation - exampleexample

Data sourcesData sources

Outputs Outputs Month-step erosion maps

◦Seasonal erosion maps◦Annual erosion maps

Month-step erosion profiles per land use

Seasonal maps - Seasonal maps - examplesexamples

Input and output parameter Input and output parameter trendstrends

Erosion trends per land Erosion trends per land useuse

All land uses per monthAll land uses per month

Local scaleLocal scale

Slope◦ ASTER DEM (30 m)

Rainfall erosivity◦ Hellenic National Meteo-

service Soil erodibility

◦ National physiographic map (Nakos )

• Vegetation status– (Euroland/Biopar products /

10 m)• Human management

– SPOT Image 2006 / 25 m

Quality assuranceQuality assurance

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

G2-model profileG2-model profileA generic model appropriate for implementation

throughout EuropeUse of harmonized standard input datasets Need for calibrationA dynamic model (takes into account seasonal

changes of rainfall erosivity and vegetation retention)

A simple model – low data demandA 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

G2 applicationsG2 applicationsMany institutes have

been interested for G2Currently 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

G2 spread outG2 spread out

LinksLinks◦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!!!Thanks for your attention!!!