Download Event soil loss

Prediction of Short Term Soil Losses
P.I.A. Kinnell
University of Canberra
USLE/RUSLE
Soil Loss = f (climate, soil, topography, landuse)
A
=
R
K
LS
C P
A = average annual soil loss
 Designed for looking at
long term average annual erosion
in field sized areas

Not for predicting soil losses by individual events or the
year by year variation in soil loss
Water quality concerns make the modelling of soil losses
at event and seasonal time scales desirable
P.I.A. Kinnell
University of Canberra
USLE/RUSLE
R = average annual sum of event energy (E) and the
maximum 30-minute intensity (I30)
Event soil loss from bare fallow area: Ae.1 = K (EI30)e
Event soil loss
 Under
predicts
large losses
 Over
predicts
small losses
P.I.A. Kinnell
University of Canberra
Event Soil Loss Prediction
Soil loss in terms of runoff and sediment concentration

Ae1 = Qe ce

Ae1 = K EI30 = Qe (K EI30/Qe)
Ae1 = unit plot event loss
Qe = event runoff
ce = sediment concentration for event
2.5

sed conc (t/ha/mm)


Morris Plot 5
y = 0.0009x
R2 = 0.3838
ce = f (rainfall intensity , energy per unit quantity of rain)
ce a
I30
E / rainfall
ce1 amount
= K [EI30/Qe]
Ae1 = k1 Qe (I30 E / rainfall amount)
2
1.5
1
0.5

0
ce1 = k1 [EI30/rain]
0
200
400
600
800
1000
1200
1400
EI30/Q (MJ/ha/h)
P.I.A. Kinnell
University of Canberra
Event Soil Loss Prediction
Erosion in terms of runoff and sediment concentration

Ae1 = Qe ce
Ae1 = unit plot event loss
Qe = event runoff
ce = sediment concentration for event
3
2.5


ce = f (rainfall intensity , energy per unit quantity of rain)
ce a
I30
E / rainfall amount
Ae1 = k1 Qe (I30 E / rainfall amount)
2.5
2
sed conc (t/ha/mm)
sed conc (t/ha/mm)

2
1.5
1
1.5
1
0.5
y = 0.0839x
R2 = 0.8642
0.5
0
0
0
5
10
15
20
25
EI30/rain (MJ/ha/mm)

Morris Plot 5
y = 0.0009x
R2 = 0.3838
Morris Plot 5
ce1 = k1 [EI30/rain]
P.I.A. Kinnell
30
35
0
200
400
600
800
1000
1200
EI30/Q (MJ/ha/h)
ce1 = K [EI30/Qe]
University of Canberra
1400
Event Soil Loss Prediction
Event soil loss
 Ae1
= k1 Q [ EI30/rain]
Predicts erosion more accurately
Approach used in USLE
variant called the USLE-M
Event soil loss
Ae1 = K EI30 
P.I.A. Kinnell
University of Canberra
Event Soil Loss Prediction
Event soil loss
Event soil loss
USLE - M
 top 5
Actual (t/ha)
177
 Lowest 10
P.I.A. Kinnell
0.83
USLE/RUSLE
USLE-M
164 (-7%)
1.12
USLE/RUSLE
123 (-31%)
25 (+2910%)
University of Canberra
Event Soil Loss Prediction
Event
erosion
 Ae1
= k1 Q [ EI30/rain]
Predicts erosion more accurately
k1 > K because EI30/rain < EI30/Q
while Ae1 remains the same
Event
erosion
Ae1 = K EI30 
USLE-M soil factor differs
from USLE/RUSLE soil factor
P.I.A. Kinnell
University of Canberra
Event Soil Loss Prediction

USLE-M Soil Factor (KUM) differs
from USLE/RUSLE Soil Factor (KU)
Soil
Bath
Caribou
Mexico
Monona
Honeye
Grenada
Shelby
Barnes
P.I.A. Kinnell
KUM
KU
KUM/KU
0.0088
0.0536
0.0728
0.0737
0.0836
0.0933
0.1228
0.1337
0.0031 (1)
0.0162 (2)
0.0327 (4)
0.0262 (3)
0.0390 (6)
0.0667( 8)
0.0619 (7)
0.0345 (5)
2.7
3.3
2.2
2.8
2.1
1.4
2.1
3.8
University of Canberra
USLE based on Unit Plot approach
Key issue = Unit Plot is



22 m long
9% slope gradient
Bare fallow (no vegetation), cultivation
up and down slope
L = S = C = P = 1.0
A=RK
P.I.A. Kinnell
University of Canberra
Unit Plot
22m long
Wheat Plot
9% slope
33m long
6% slope
 A1
= R K=10 t/ha
 AC
=A1 ( L
S
C
P)
 AC =10 (1.22 x 0.57 x 0.16 x 1.0)
= 1.1 t/ha
L, S, C and P are all ratios with respect to unit plot
conditions
The model operates in two stages
- predicts A1 then AC
P.I.A. Kinnell
University of Canberra
Event Soil Loss Prediction
Predicting event erosion on unit plot well
DOES NOT help predict erosion on cropped areas
Y = Event soil loss for
conventional corn
predicted by multiplying
event soil losses from a
nearby bare fallow plot by
fortnightly C factor values
Predicted vs
vs Observed
Observed event
event
Predicted
soil
loss from
cropped
plot
erosion
on cropped
plot
Observed = 0
P.I.A. Kinnell
X = Event soil losses observed for
conventional corn + 0.0001
University of Canberra
Event Soil Loss Prediction
Predicting event erosion on unit plot well
DOES NOT help predict erosion on cropped areas
Y = Event erosion for
conventional corn
predicted by multiplying
event soil losses from a
nearby bare fallow plot by
fortnightly C factor values
Erosion predicted
when none occurs
P.I.A. Kinnell
Predicted vs Observed event
erosion on cropped plot
Observed = 0
X = Event soil losses observed for
conventional corn + 0.0001
University of Canberra
Event Soil Loss Prediction

To predict event erosion from vegetated area
must use runoff from vegetated area in calculation
of event erosivity index for the USLE-M
Ae = [QR EI30] KUM L S CUM.e PUM.e

Thus, values for crop and conservation factors for
USLE-M differ from those used in the USLE
because erosivity index is not directed at
predicting soil loss from unit plot
P.I.A. Kinnell
University of Canberra
Event Soil Loss Prediction

CUM: USLE-M C factor (annual values)
P.I.A. Kinnell
Location
Bethany, Missouri
Crop
alfalfa
corn
corn/meadow/wheat
CUM
0.008
0.674
0.188
CU CUM/CU
0.002
4.0
0.628
1.1
0.106
1.8
Clarinda, Iowa
corn
corn/oats/meadow
0.634
0.424
0.316
0.168
2.0
2.5
Guthrie, Oklahoma
cotton
Bermuda grass
wheat/clover/cotton
2.435
0.064
0.913
1.357
0.002
0.344
1.8
32.3
2.7
LaCrosse, Wisconsin corn
0.527
0.469
1.1
Madison, S.Dakota
corn(ploughed)
corn(mulch till)
0.486
0.384
0.337
0.250
1.4
1.5
Morris, Minnesota
corn
meadow/corn/oats
0.520
0.046
0.434
0.010
1.2
4.6
Presque Isle, Maine
potatoes
0.634
0.316
2.0
corn (mean)
0.569
0.437
1.3
Corn
University of Canberra
Event Soil Loss Prediction

CUM: USLE-M C factor (annual values)
P.I.A. Kinnell
Location
Bethany, Missouri
Crop
alfalfa
corn
corn/meadow/wheat
CUM
0.008
0.674
0.188
CU CUM/CU
0.002
4.0
0.628
1.1
0.106
1.8
Clarinda, Iowa
corn
corn/oats/meadow
0.634
0.424
0.316
0.168
2.0
2.5
Guthrie, Oklahoma
cotton
Bermuda grass
wheat/clover/cotton
2.435
0.064
0.913
1.357
0.002
0.344
1.8
32.3
2.7
LaCrosse, Wisconsin corn
0.527
0.469
1.1
Madison, S.Dakota
corn(ploughed)
corn(mulch till)
0.486
0.384
0.337
0.250
1.4
1.5
Morris, Minnesota
corn
meadow/corn/oats
0.520
0.046
0.434
0.010
1.2
4.6
Presque Isle, Maine
potatoes
0.634
0.316
2.0
corn (mean)
0.569
0.437
1.3
Bermuda
grass
Corn
University of Canberra
Event Soil Loss Prediction




With the need to consider short term (daily)
sediment loads in rivers, NPS pollution models
need to predict erosion on a daily basis.
In some models, long term average annual
erosion values are disaggregated to give daily
erosion
Alternatively, modelling daily erosion directly
is seen as the better approach
The development of the USLE-M is
consistent with that objective
P.I.A. Kinnell
University of Canberra
Event Soil Loss Prediction

The Modified Universal Soil Loss Equation
The MUSLE - developed by Williams (1975)

Replaces EI30 with a(Qe qp)0.56
Qe = event runoff amount
qp = peak runoff rate
a = empirical coefficient

Used in SWAT
P.I.A. Kinnell
University of Canberra
The Modified
Universal Soil Loss Equation

Uses USLE K, L, S, C, P factors in event approach
Ae = [a(Qe qp)0.56 ] X
K L S Ce Pe

USLE K should NOT be used.
KMUSLE should be calculated for the fact that
Re is not equal to EI30
K =
N
 Ae.1
e=1
——————
N
 (EI30)e
e=1
P.I.A. Kinnell
KMUSLE =
N
 Ae.1
e=1
——————
N
 (a(Q qp)0.56 )e
e=1
University of Canberra
The Modified
Universal Soil Loss Equation

Uses USLE K, L, S, C, P factors in event approach
Ae = [a(Qe qp)0.56 ] X
K L SX
CeX
Pe

USLE K should NOT be used.
KMUSLE should be calculated for the fact that Re is
not equal to EI30

C and P are influenced by runoff in the USLE.
They too need to be calculated for for the fact that
Re is not equal to EI30
P.I.A. Kinnell
University of Canberra
The Modified
Universal Soil Loss Equation
 Ae
= [a(Qe qp)0.56 ] K L S Ce Pe
INVALID
variant of the
USLE/RUSLE model
P.I.A. Kinnell
University of Canberra
Agricultural pollution
model
Scientific shortcomings in the modelling
of short term erosion in catchments
Need to overcome them in order to
develop more scientifically robust aids to
making decisions on land management
P.I.A. Kinnell
University of Canberra