Soil Erosion Modelling

JRC Ispra

20-21-22 March 2017

Elaboration of the soil erosion risk map of Sicily

by calibration and validation of an USLE model

Maria Fantappiè, CREA-ABP, Italy

Materials and study area

2100 data on absence of soil erosion 4050

data on presence of soil erosion

Calibration erosivity factor R

(Ferro et al.

1999)

(Arnoldous

1977)

(Yu and

Rosewell 1996)

(Renard and

Freimund 1994) (Arnoldous 1980)

571.0732 943.7909 1749.863 2414.327 3309.005

Root Mean Squared Errors of R (Mj mm ha-1 h-1 y-1) estimated with 5 different formula,

against R measured at 5 meteorological stations by Agnese et al. (2006)

dove

12

1

2

i

i

P

PF

N

j

j

FN

FF

1

12

1

2

i j

ij

jP

PF

59.15249.0 FFR 93.1

302.0 FR 41.182.3 FR 847.1

739.0 FR 02.17*152*17.4 FR

Pi monthly mean precipitations (mm) of the ith month.

Pij monthly mean precipitations (mm) of the ith month of the jth year.

Pj yearly mean precipitations (mm) of the jth year.

FF mean of Fj for a period of N years.

Methods adopted for LS and K factors

5.0*8.16 senS

S factor, as McCool et al. (1987)

The chosen formula gives negative values

for slope gradients <3%, so that it is

possible to delineate flat and depositional

areas

L factor, as McCool et al. (1989) m

slL

13.22 1m

56.03086.08.0 sensen

2 22tan pspssl

θ is the slope gradient expressed as radians.

sl is the slope length expressed as meters,

ps is the pixel size expressed as meters.

0.0277 0.0316 Clay

0.0342 0.0356 Silty clay

0.0277 0.0277 Sandy clay

0.0395 0.0461 Silty clay loam

0.0369 0.0435 Clay loam

0.0263 0.0263 Sandy clay loam

0.0514 0.0561 Silt

0.0487 0.0540 Silt loam

0.0342 0.0448 Loam

0.0158 0.0184 Sandy loam

0.0053 0.0066 Loamy sand

0.0013 0.0040 Sand

More

than

2%

Less

than

2%

Organic Matter

Content USDA Soil

Texture Classes

K factor, with Stone and Hilborn

(2012) coefficients, converted to

tons hour MJ-1 mm-1

K factor set at

0.08 for volcanic

soils following

Van der Knijff et

al. (1999)

1004.0 cRe

K factor correction

for gravel content

with Poesen et al.

(1994) .

Rc (%) is the

gravel content,

stoniness and

rockiness.

Elaboration of potential soil erosion Ep

Calibration of land cover factor C Definition: the ratio of soil loss from land cropped under specified conditions to the corresponding loss from clean-tilled, continuous fallow. This factor measures the combined effect of all the interrelated cover and conventional management variables (but excluding the adoption of specific soil protection measures, which constitute the P factor)

0L

tLL

Ep

EC

CL is the C factor calibrated for each one of the 9 groups (L) of land use considered;

μEpL0 is the mean value (μ) of potential soil erosion (Ep) calculated for each land use group (L), on the

base of the punctual Ep values estimated at each one of the 2100 field evidences of soil erosion absence;

EtL is the actual soil erosion, assumed to be 2 ton ha-1 y-1 at the 2100 sites with absence of soil erosion.

This values is considered as a treshold for ‘tolerable soil erosion rate’ (a), therefore constitutes a soil

erosion rate presumably not visible to the naked eye.

(a) Jones, A., et al. (2012). The state of soil in Europe. A contribution of the JRC to the EEA Environment State and Outlook Report - SOER 2010. Report EUR 25185 EN. ISBN 978-92-79-22806-3. DOI:10.27 88/77361. Office for Official Publications of the European Communities, Brussels, Luxembourg, 76 pp. (online) http://ec.europa.eu/dgs/jrc/downloads/jrc_reference_report_2012_02_soil.pdf.

Result: the calibrated C factors

Corine Land Cover codes Decoding C factors

211, 212, 213 Arable crops 0.197

242, 243 Complex cultivations 0.212

221 Vineyards 0.542

323, 324, 333, 334 Shrublands and post fire vegetation 0.090

223, 222, 224

Olive groves, fruit trees, Eucalyptus

plantations 0.272

231, 321, 322 Pastures and natural grasslands 0.074

312 Coniferous forests 0.056

311, 313 Broad leaved and mixed forests 0.051

2223 Citrus 0.253

Elaboration of actual soil erosion Ea

PCEpE

The concept of soil erosion risk We calculated the risk as years necessary to completely lose the soil cover up to

the effective rooting depth. The concept is that risk is harsher on thinnest soils.

E

QsY

where Qs is the mass of soil cover to the effective rooting depth (tons ha-1) calculated as

DBQs

where µB is the mean bulk density (g dm-3), and µD is the mean effective rooting depth

(dm) of the soils in each delineation of the Soil Map of Sicily.

Four empirical erosion risk classes were defined,

considering how much it could affect a human life span:

(i) Low risk or not appreciable soil erosion > than 500 years;

(ii) Moderate risk, 100-500 years;

(iii) High risk, 10-100 years;

(iv) Very high risk, < 10 years.

The map of soil erosion risk of Sicily published on

JOURNALS OF MAPS

Fantappiè M., Priori S., Costantini E.A.C., (2014). Soil erosion risk, Sicilian Region (1:250,000 scale). Journal of Maps, Taylor and Francis. DOI: 10.1080/17445647.2014.956349

Bayesan validation Applying the Bayes theorem it is possible to calculate the positive (pred+) and negative (pred-) predictivity,

that is the probability of occurance of the investigated phenomena in case the model estimated its occurance, and

the probability of not occurance in case the model estimated its not occurance.

)1(*)1(*

*

prevSprevSe

prevSepred

p

prevSeprevSp

prevSppred

*)1()1(*

)1(*

toty

okyysensitivitSe

_

_)(

totn

oknyspecificitSp

_

_)(

totntoty

toyprevalenceprev

__

_)(

Where y_ok is the number of occurences correctly predicted, n_ok is the number of not occurances

correclty predicted, y_tot is the number of real occurences, n_tot is the number of real not occurences.

Results

Prev 0.659

Se 0.782

Sp 0.657

Pred+ 0.815

Pred- 0.610