Bruno Basso Dept. Crop, Forest and Environmental Sciences University of Basilicata, Italy Contacts...
1
Bruno Basso Dept. Crop, Forest and Environmental Sciences University of Basilicata, Italy Contacts Joe T Ritchie: [email protected] Bruno Basso : [email protected] Joe T Ritchie Dept. Agricultural and Biological Engineering Rationale The Runoff Model Results and Discussion Incorporating Seasonal Rainfall Intensity and Soil Properties into a Daily Surface Runoff Model Runoff from cultivated land is a loss of water to the farmer and may contribute to soil erosion. There are three main mechanisms that produce runoff: saturation overland flow that occurs when a rising water table intersects the soil surface, generating exfiltration; Hortonian overland flow that occurs when the rainfall intensity exceeds the infiltration rate of the soil; and subsurface flow in which water flows laterally through a highly conductive soil profile. These mechanisms are part of a continuum of processes and may operate singularly, but more often in combination. Converting rainfall to runoff is at the center of surface water modeling. Estimating runoff when only daily total rainfall is known has been primarily estimated through the runoff curve approach developed by the USDA Soil Conservation Service. Such a method has to attempt to account for soil type, slope, and land management, but it does nnot distinguish betwen runoff mechanisms. For many years the curve number approach has been subject of support and criticism but it remains the primary method used in most daily incrementing models (Ponce and Hawkins, 1996). Mertens et al., (2002) incorporated rainfall intensity information in daily records using a frequency analysis of long-term rainfall data collected with a resolution of 10 minutes. The information derived was then used to model soil water infiltration on a short time scale using the Hydrus model based on Richards equation. The objectives of this study was to develop an alternative procedure to predict hortonian runoff that would be more sensitive to soil management and the known seasonal variations in rainfall intensity, yet only use the daily rainfall. Figure 3. Time to ponding estimate of runoff versus runoff modelled for various Ksat values Figure 2. Relantionship showing daily runoff versus runoff fraction of rain for various Ksat values used to develop model coefficients realization that soil hydraulic properties (Ksat) strongly in many locations varies throughout the year. The same amount s of runoff depending on time of the year. In many parts of the re is a marked difference in storm intensity between summer be shorter and more intense. were used with a time to ponding approach to estimate how much nfiltration capacity, represented by B in figure 1. ydraulic conductivity (Ksat). The model was evaluated using 1 to 36 cm d -1 . iation in the runoff fraction Rf follows: -e (-K*Rain) ) he fitted coefficient that is subsuquently related to maximum temperature as a surrugate for season of the year variations in storm lated as follow: r every site and Ksat values. For many sites in the USA the A This indicates that model developed is generally universal with the nfall intensities. REFERENCES Mertens J., D. Raes, and J. Feyen. Hydrol. Process. 2002. 16, 731-739 Ponce V. and R.H. Hawkins. J. Hydrol Engineering. 1996 1, 11-19 Aris Gerakis Dept. Crop and Soil Sciences Ian White Centre for Resources and Environmental Studies The fraction of rain resulting in runoff estimated from the time to ponding analysis resulted in a non linear relationship, especially for low daily rainfall (Fig. 2). An example of modelled runoff for Des Moines, IA, is shown In figure 3. A typical RMSE for runoff estimates were 2 mm day -1 . We found mark rainfall-runoff relationships between the warm and cool seasons for fixed Ksmacro and the Ksmacro values were very sensitive in determini relationship. Errors for larger Ksat values were greater because their dependance on storm intensity. We believe the method has promise when modeling t used with ponding capacity and known slopes. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 20 40 60 80 100 120 D ailyRainfall (mm) Runofffraction ofrain K sat=1 K sat=10 M odel 1 M odel 10 D esM oines, Iowa 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Tim eto pondingestim ateofrunoff(m m ) M odelled runoff(m m) K sat=1 cm /day K sat=3 cm /day K sat=6 cm /day K sat=10 cm /day '1:1 A B Precipitation Rate Cumulative Precipitatio KsTop Figure 1. Time to ponding curve
-
Upload
christian-bradford -
Category
Documents
-
view
241 -
download
7
Transcript of Bruno Basso Dept. Crop, Forest and Environmental Sciences University of Basilicata, Italy Contacts...
Bruno BassoDept. Crop, Forest and Environmental Sciences
University of Basilicata, Italy
Contacts
Joe T Ritchie: [email protected]
Bruno Basso : [email protected]
Joe T Ritchie
Dept. Agricultural and Biological Engineering
Rationale
The Runoff Model
Results and Discussion
Incorporating Seasonal Rainfall Intensity and Soil Properties into a Daily Surface Runoff Model
Runoff from cultivated land is a loss of water to the farmer and may contribute to soil erosion. There are three main mechanisms that produce runoff: saturation overland flowthat occurs when a rising water table intersects the soil surface, generating exfiltration; Hortonian overland flow that occurs when the rainfall intensity exceeds the infiltration rate of the soil; and subsurface flow in which water flows laterally through a highly conductive soil profile. These mechanisms are part of a continuum of processes and may operate singularly, but more often in combination.
Converting rainfall to runoff is at the center of surface water modeling. Estimating runoff when only daily total rainfall is known has been primarily estimated throughthe runoff curve approach developed by the USDA Soil Conservation Service. Such a method has to attempt to account for soil type, slope, and land management, but it does nnot distinguish betwen runoff mechanisms.
For many years the curve number approach has been subject of support and criticismbut it remains the primary method used in most daily incrementing models (Ponce and Hawkins, 1996).
Mertens et al., (2002) incorporated rainfall intensity information in daily records using a frequency analysis of long-term rainfall data collected with a resolution of 10 minutes. The information derived was then used to model soil water infiltration on a short time scale using the Hydrus model based on Richards equation.
The objectives of this study was to develop an alternative procedure to predict hortonian runoff that would be more sensitive to soil management and the known seasonal variations in rainfall intensity, yet only use the daily rainfall.
Figure 3. Time to ponding estimate of runoff versus runoff modelled for various Ksat values
Figure 2. Relantionship showing daily runoff versus runoff fraction of rain for various Ksat values used to develop model coefficients
The model development comes from the realization that soil hydraulic properties (Ksat) strongly influence runoff and storm intensity in many locations varies throughout the year. The same amount of rain may produce different amounts of runoff depending on time of the year. In many parts of theUSA and other temperate climates there is a marked difference in storm intensity between summer and winter. Summer storms tend to be shorter and more intense. Thirty years of hourly rainfall data were used with a time to ponding approach to estimate how muchof the daily rainfall exceeded the infiltration capacity, represented by B in figure 1. The procedure requires an input of hydraulic conductivity (Ksat). The model was evaluated using several Ksat values that varied from 1 to 36 cm d-1.
The equation used to express the variation in the runoff fraction Rf follows:
Rf= C (Ksat)* (i-e(-K*Rain))
where C is the maximum Rf and K is the fitted coefficient that is subsuquently related to maximum temperature as a surrugate for season of the year variations in storm intensity. Rf and K were found to
vary with Ksat (Fig. 2). K was calculated as follow:
K= A*(Temp-5)1.75
where A is the coefficient fitted for every site and Ksat values. For many sites in the USA the Acoefficient was relatively similar. This indicates that model developed is generally universal with theexpection of sites that have low rainfall intensities.
REFERENCES
Mertens J., D. Raes, and J. Feyen. Hydrol. Process. 2002. 16, 731-739Ponce V. and R.H. Hawkins. J. Hydrol Engineering. 1996 1, 11-19
Aris GerakisDept. Crop and Soil Sciences
Ian WhiteCentre for Resources and
Environmental Studies
The fraction of rain resulting in runoff estimated from the time to ponding analysis resulted in a non linear relationship, especially for low daily rainfall (Fig. 2).
An example of modelled runoff for Des Moines, IA, is shown In figure 3. A typical RMSE for runoff estimates were 2 mm day-1. We found marked differences in daily rainfall-runoff relationships between the warm and cool seasons for fixed Ksmacro and the Ksmacro values were very sensitive in determining to the rainfall-runoff relationship.
Errors for larger Ksat values were greater because their dependance on storm intensity. We believe the method has promise when modeling the soil water balance when used with ponding capacity and known slopes.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 20 40 60 80 100 120
Daily Rainfall (mm)
Run
off fra
ctio
n of
rai
n
Ksat=1
Ksat=10
Model 1
Model 10
Des Moines, Iowa
0
20
40
60
80
100
120
0 20 40 60 80 100 120Time to ponding estimate of runoff (mm)
Mod
elle
d ru
noff
(mm
)
Ksat=1 cm/ day
Ksat=3 cm/ day
Ksat=6 cm/ day
Ksat=10 cm/ day
'1:1
A
B
Pre
cipi
tatio
n R
ate
Cumulative Precipitation
KsTop
Figure 1. Time to ponding curve
