Runoff Prediction Using Artificial Neural Network …
31
3.2 Methods
3.2.1 SCS-CN Method
The SCS-CN method, based on the water balance equation of the total precipitation
(P), is divided into three components (I) initial abstraction (I a ), (ii) cumulative infiltration excluding I a (F) and (iii) direct surface runoff (Q), which is given in Eq. 1
(all units are in volume occurring in t time interval)
P = I a + F + Q
(1)
I a = λS
(2)
For use in Indian conditions, λ = 0.1 and 0.3 subject to certain constraints of soil
type and AMC conditions. In this study, λ = 0.3 value has been used. To compute
the basin runoff (Q), Eq. 3
Q =
(P − I a )
2
P + (1 − λ)S
(3)
C N I =
C N I I
2.281 − 0.01281C N I I
(4)
C N I I I =
C N I I
0.427 + 0.00573C N I I
(5)
has been used, which combines two basic hypotheses, expressed in Eqs. 1 and 2.
The potential maximum retention (S) has been computed using Eq. 6 with the help
of a unique parameter, Curve number (CN) integrates the soil types, landcover,
and antecedent moisture conditions (AMC). Here, CN II indicates the curve number
corresponding to AMC-II condition. Equations 4 and 5 represent the curve number
corresponding to AMC-I and AMC-III condition.
S = 254
100
C N I I
− 1
(6)
This method considers four different hydrologic soil groups based on their runoff
potential. A, B, C, and D stand for low, moderately low, moderately high, and high
runoff potential. Details can be found in the literature [18].
31
3.2 Methods
3.2.1 SCS-CN Method
The SCS-CN method, based on the water balance equation of the total precipitation
(P), is divided into three components (I) initial abstraction (I a ), (ii) cumulative infiltration excluding I a (F) and (iii) direct surface runoff (Q), which is given in Eq. 1
(all units are in volume occurring in t time interval)
P = I a + F + Q
(1)
I a = λS
(2)
For use in Indian conditions, λ = 0.1 and 0.3 subject to certain constraints of soil
type and AMC conditions. In this study, λ = 0.3 value has been used. To compute
the basin runoff (Q), Eq. 3
Q =
(P − I a )
2
P + (1 − λ)S
(3)
C N I =
C N I I
2.281 − 0.01281C N I I
(4)
C N I I I =
C N I I
0.427 + 0.00573C N I I
(5)
has been used, which combines two basic hypotheses, expressed in Eqs. 1 and 2.
The potential maximum retention (S) has been computed using Eq. 6 with the help
of a unique parameter, Curve number (CN) integrates the soil types, landcover,
and antecedent moisture conditions (AMC). Here, CN II indicates the curve number
corresponding to AMC-II condition. Equations 4 and 5 represent the curve number
corresponding to AMC-I and AMC-III condition.
S = 254
100
C N I I
− 1
(6)
This method considers four different hydrologic soil groups based on their runoff
potential. A, B, C, and D stand for low, moderately low, moderately high, and high
runoff potential. Details can be found in the literature [18].