Elaboration of the soil erosion risk map of Sicily by ... · Elaboration of the soil erosion risk...
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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