Runoff as a factor in USLE/RUSLE Technology P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra • • • The USLE/RUSLE Model: A=RKLSCP R and K have.
Download ReportTranscript Runoff as a factor in USLE/RUSLE Technology P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra • • • The USLE/RUSLE Model: A=RKLSCP R and K have.
Slide 1
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
•
•
•
The USLE/RUSLE Model:
A=RKLSCP
R and K have units, L = S = C = P = 1 on UNIT PLOT
UNIT PLOT = 22.1 m long bare fallow on 9% slope
cultivation up and down the slope
The model works mathematically in 2 steps
A1 = R K
UNIT PLOT is the primary physical model
A = A1 L S C P
Factors are not independent in RUSLE and RUSLE2
L = (slope length / 22.1 )m where m varies with rill to interill ratio which
varies with soil properties and slope gradient
C varies with climate through interaction between temporal variations in
erosivity and crop growth
1/3
RUSLE2 Daily
Erodibility
EGU2014
Storm EI30, QR
and Erodibility
Predicted storm soil loss
by RUSLE2 and USLE-M
Slide 2
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
•
Soil loss depends on runoff but the R factor is based on event erosivity factor
that does not include runoff as an independent variable
Consequently, K, the soil loss per unit of R, for a soil with given physical
properties will be greater for wet climates than for dry climates.
•
•
In RUSLE2
Kj / Kn = 0.591 + 0.732 (Pj/Ps) – 0.324 (Tj/Ts)
Tj > 30oF
Kn = nomograph K, j = month,
Ts = average summer temp, Ps = average summer monthly rain
Base climate = Columbia, Missouri
2/3
RUSLE2 Daily
Erodibility
Storm EI30, QR
and Erodibility
Predicted storm soil loss
by RUSLE2 and USLE-M
Slide 3
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
•
•
Nomograph was developed from rainfall simulating experiments on 10.6 m
long plots using a sequence of runs – dry (giving Kd), wet (giving Kw), very
wet (giving Kvw)
K = (13 Kd + 4 Kw +3 Kvw) /20
(Dabney et al, 2004)
Tillage treatment
Kd
Conventional Till
0.33
No Till
0.37
K values in customary US units
Weighting for climate in central USA.
Kw
0.58
0.84
Kvw
0.76
0.89
3/4
RUSLE2 Daily
Erodibility
Storm EI30, QR
and Erodibility
Predicted storm soil loss
by RUSLE2 and USLE-M
Slide 4
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
•
•
•
•
RUSLE2 can predict soil losses for a set of representative storms
USLE-M includes runoff as a factor in the event erosivity index
A1 = QR EI30 KUM
QR = runoff ratio
KUM = soil erodibility associated with QR EI30 index
A1 = QR EI30 KUM = EI30 [QR KUM ]
[QR KUM ] = a runoff dependent erodibility factor associated with EI30
and can be compared with RUSLE2 Ks when applied to individual events
3/3
RUSLE2 Daily
Erodibility
Storm EI30, QR
and Erodibility
To 1
Predicted storm soil loss
by RUSLE2 and USLE-M
Slide 5
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
Daily soil erodibility
Daily temperature
Frozen
ground
in
Winter
35
30
daily temperature (oC)
25
20
15
10
5
0
Presque Isle
Bethnay
Macon
Tampa
-5
-10
-15
-20
1-Jan
1-Mar
30-Apr
29-Jun
date
Presque Isle, ME
Bethnay, MO
Macon, GA
Tampa, FL
28-Aug
27-Oct
North
South
26-Dec
Slide 6
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
EI30 and QR for storm sequence
Erodibilities associated with EI30
Slide 7
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
Erodibilities associated with EI30
Predicted event soil loss
The product of QR and KUM produces similar results to RUSLE2 Ks when applied RUSLE2 storms
Slide 8
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
Slide 9
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
•
•
•
The USLE/RUSLE Model:
A=RKLSCP
R and K have units, L = S = C = P = 1 on UNIT PLOT
UNIT PLOT = 22.1 m long bare fallow on 9% slope
cultivation up and down the slope
The model works mathematically in 2 steps
A1 = R K
UNIT PLOT is the primary physical model
A = A1 L S C P
Factors are not independent in RUSLE and RUSLE2
L = (slope length / 22.1 )m where m varies with rill to interill ratio which
varies with soil properties and slope gradient
C varies with climate through interaction between temporal variations in
erosivity and crop growth
1/3
RUSLE2 Daily
Erodibility
EGU2014
Storm EI30, QR
and Erodibility
Predicted storm soil loss
by RUSLE2 and USLE-M
Slide 2
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
•
Soil loss depends on runoff but the R factor is based on event erosivity factor
that does not include runoff as an independent variable
Consequently, K, the soil loss per unit of R, for a soil with given physical
properties will be greater for wet climates than for dry climates.
•
•
In RUSLE2
Kj / Kn = 0.591 + 0.732 (Pj/Ps) – 0.324 (Tj/Ts)
Tj > 30oF
Kn = nomograph K, j = month,
Ts = average summer temp, Ps = average summer monthly rain
Base climate = Columbia, Missouri
2/3
RUSLE2 Daily
Erodibility
Storm EI30, QR
and Erodibility
Predicted storm soil loss
by RUSLE2 and USLE-M
Slide 3
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
•
•
Nomograph was developed from rainfall simulating experiments on 10.6 m
long plots using a sequence of runs – dry (giving Kd), wet (giving Kw), very
wet (giving Kvw)
K = (13 Kd + 4 Kw +3 Kvw) /20
(Dabney et al, 2004)
Tillage treatment
Kd
Conventional Till
0.33
No Till
0.37
K values in customary US units
Weighting for climate in central USA.
Kw
0.58
0.84
Kvw
0.76
0.89
3/4
RUSLE2 Daily
Erodibility
Storm EI30, QR
and Erodibility
Predicted storm soil loss
by RUSLE2 and USLE-M
Slide 4
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
•
•
•
•
RUSLE2 can predict soil losses for a set of representative storms
USLE-M includes runoff as a factor in the event erosivity index
A1 = QR EI30 KUM
QR = runoff ratio
KUM = soil erodibility associated with QR EI30 index
A1 = QR EI30 KUM = EI30 [QR KUM ]
[QR KUM ] = a runoff dependent erodibility factor associated with EI30
and can be compared with RUSLE2 Ks when applied to individual events
3/3
RUSLE2 Daily
Erodibility
Storm EI30, QR
and Erodibility
To 1
Predicted storm soil loss
by RUSLE2 and USLE-M
Slide 5
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
Daily soil erodibility
Daily temperature
Frozen
ground
in
Winter
35
30
daily temperature (oC)
25
20
15
10
5
0
Presque Isle
Bethnay
Macon
Tampa
-5
-10
-15
-20
1-Jan
1-Mar
30-Apr
29-Jun
date
Presque Isle, ME
Bethnay, MO
Macon, GA
Tampa, FL
28-Aug
27-Oct
North
South
26-Dec
Slide 6
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
EI30 and QR for storm sequence
Erodibilities associated with EI30
Slide 7
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
Erodibilities associated with EI30
Predicted event soil loss
The product of QR and KUM produces similar results to RUSLE2 Ks when applied RUSLE2 storms
Slide 8
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra
Slide 9
Runoff as a factor in USLE/RUSLE Technology
P.I.A. Kinnell, Institute for Applied Ecology, University of Canberra