沙漠研究 22-1, 95 -98 (2012 )"ICAL 1 / DT X"

Journal of Arid Land Studies－Refereed Paper－Estimating the Volume of Surface Runoff from in Situ Measured Soil Sorptivity
Fumio WATANABE*1), Yukimitsu KOBAYASHI2), Shinji SUZUKI1), Tomoki HOTTA2) and Satoru TAKAHASHI1)
Abstract: There are many surface water collecting technologies such as water harvesting, micro catchment and spate irrigation. It
is still not possible to apply the technologies due to inaccurate estimations of the volume of runoff water. For this reason, weexamined the possibility of estimating the volume of runoff using field measured sorptivity. Our experimental field was located in
the Oromia region of central Ethiopia. Sorptivity values of different soil moisture contents were measured by a disc tension
permeameter. Meteorological data such as rainfall, air temperature, relative humidity, sunshine radiation and wind speed were alsomeasured in the experimental field. A small reservoir was constructed for collecting the surface runoff water from a watershedadjacent to the experimental field. A pressure sensor in the reservoir was installed for observing the volume of actual runoff water.
At first, we calculated the volume of runoff with several rainfall events using the Curve Number method introduced by Soil
Conservation Services, USDA. Secondly, we estimated the runoff volume for each rainfall event using the measured sorptivity
values. Finally, parameters of Chong’s model were optimized using the observed runoff volume for each rainfall event resulting in
better estimation of runoff volume.
Key Words: Curve Number method, Disc permeameter, Ethiopia, Infiltration, Water harvesting

2. Methodology and Study Site
1. IntroductionWater availability is the main limiting factor in dry-land
agriculture, throughout arid and semi-arid regions, due to lowannual rainfall depth and its non-uniform temporal and spatialdistribution. Water harvesting has been used for thousands ofyears to supplement scarce water resources in dry areas(Sharma et al., 1986). The major advantages of water
harvesting are that it is simple, cheap, replicable, efficient andadaptable. Water harvesting can improve soil moisturestorage, prolong the period of moisture availability, andenhance growth of agricultural, horticultural and forest crops(Carter and Miller, 1991; Li et al., 2000).
On the other hand, Kitanaka et al. (2010) conductedirrigation water balance simulation for crop cultivation withconsecutive irrigation reservoir system composed ofhierarchical reservoirs under unstable rainfall conditions inEthiopia. These reservoirs using water harvesting techniquewere used to collect and store precipitation surface runoff sothat stored water could be used for supplemental irrigation
during long dry seasons. However, the application of water
harvesting technologies is still limited due to inaccurateestimating of the volume of surface runoff water. For thisreason, we examined the possibility of estimating the volumeof surface runoff using field measured sorptivity.

2.1. Evaluation of the volume of Surface Runoff using theCurve Number (CN) method
Runoff estimates are often needed for ungauged
watersheds for engineering design of hydraulic structures,watershed the U.S. Department of Agriculture (USDA) - Soil
Conservation Service (SCS) developed a method for
estimating rainfed runoff volume based on measured total
rainfall and direct runoff, and physical watershed features (SCS,1972). This method is simple to use and requires basic
descriptive inputs that are converted to numeric values for
estimation of watershed direct-runoff volume. The curve
number (CN) method is widely used by engineers andhydrologists as a simple watershed model, and as therunoff-estimating component in more complex, watershed
models. The method depends on using measured watershed
runoff and rainfall data to develop a CN value that reflects theCN value that should be developed from measured data.
The maximum potential retention(S) can be calculated
from the CN value which is able to be determined inconsidering hydrological, soil property, land use and surface
conditions and soil moisture content before runoff occurs(Mishra and Singh, 2003). However, the CN method does
not consider rainfall intensity and there are questions as towhether it is applicable for areas outside of the United States(Yamashita et al., 2006).
On the other hand, Chong et al. (1983) introduced thefollowing Eq.(1) which combining with the SCS rainfall-runoff* Corresponding Author: f-nabe@nodai.jp
1-1-1, Sakuragaoka, Setagaya, Tokyo, Japan, 156-8502
1) Faculty of Regional Environmental Science, Tokyo University of Agriculture2) NTC International Co., Ltd.

equation and the maximum potential retention(S) of awatershed in order to estimate the value of sorptivity. Thisequation shows that it is possible to estimate the volume ofrainfall runoff from a watershed, if there is a relationshipbetween sorptivity values and initial soil moisture contents.
−1 / 2[Sp(θ )] ⋅ ⋅ ⋅ (1)S = 1 Ri
K Sat
2
where S is the maximum potential retention. Sp(θ) is soil
sorptivity, Ksat is saturated soil hydraulic conductivity, Ri:
rainfall intensity. The term “sorptivity”was introduced byPhilip (1957) in his well-known two-term infiltration equation.
As described by Philip, sorptivity, Sp(θ), is a measure of theuptake of water by soil without gravitational effects.
According to the Philip two-term equation，this coefficient is
one of the most important soil parameters governing the early
portion of infiltration.
Therefore, we tried to clarify the relationship between
sorptivity values and soil moisture contents and estimated themaximum potential retention using equation (1) with rainfall
intensity and saturated soil hydraulic conductivity. Finally,
the estimated surface runoff volumes (Qsp) for each rainfall
event were calculated using sorptivity. On the other hand,surface runoff volumes (QCN) using the CN method were also
estimated in order to compare the Qsp.
−1 / 222.2. Study site and data collection
Our study site (Fig. 1) is located in Adami Tule
Agricultural Research Center (hereafter ATARC) in theOromia region, Ethiopia. A water reservoir to measure
rainfall runoff volume from a catchment area was constructedin an experimental field with covered plastic film sheet toprevent percolation into soils. In order to measure the waterlevel of this reservoir, a water pressure sensor with a datalogger was installed at the bottom of the reservoir. At thesame time, we measured the atmospheric pressure using
another pressure sensor with a data logger so that we can get awater depth in the reservoir to calculate a difference value ofboth pressure sensors. Other meteorological data such as air
temperature, relative humidity, wind speed, solar radiation andrainfall were also collected in the experimental field. The
catchment area is approximately 4400 m2 (Fig. 2). Theobservation period was from June to December, 2008.
2.3. Measurement of sorptivity using the disc tension
permeameter
We fabricated a disc tension permeameter (Smettem andClothier, 1989) in order to measure water infiltration in the soil,
which is characterized by in situ saturated and unsaturated soil
hydraulic properties. It is mainly used to provide estimates ofsorptivity and the hydraulic conductivity of the soil nearFig. 1. Location map of Adami-Tule agricultural research station.

Fig. 2. Layout plan of experimental reservoir, catchment area, canals
and experiment field.

saturation. In order to clarify the relationship between
sorptivity values and initial moisture contents in soils in thecatchment area, we carried out an experiment using the disctension permeameter near the catchment area located in
ATRAC. The steps for measuring sorptivity are as follows:Firstly, surface top soils of in 2 to 3 cm thickness are movedout and a metal cylinder of diameter 15 cm is verticallyinserted into soils. The disc tension permeameter with 4 cm
suction is installed on the cylinder. After starting infiltration
into soils of the metal cylinder, accumulated infiltration
amounts at each elapsed time are measured. We also supplied
water to surface soil close to the cylinder in order to change
soil moisture content of top soils so that sorptivity values under
different soil moisture conditions could be measured.
Undisturbed soil cores were collected using a 100 cm3 soil
sampler to measure the moisture content of the soil surface
close to the cylinder so that the relationship between sorptivity
values and initial soil moisture content could be clarified foreach sorptivity measurement. Table 1 shows basic soil
properties in the catchment area.
Soil texture was sandy clay loam using the textural triangle
classification of the International Society of Soil Science.
Table 1. Soil properties in catchment area.

Fig. 3. Relationship between soil moisture content and sorptivity incatchment area.

Fig. 4. Comparison between measured (Qobs) and estimated (Qsp)surface runoff volume using sorptivity values.

3. Results and Discussion
3.1. Relationship between soil moisture content andsorptivity value
Figure 3 shows the relationship between soil sorptivity values
and moisture contents which was measured in the study site.
Sorptivity values decrease with increasing soil moisture
content. The following regression Eq. (2) can be used todetermine the value of sorptivity when rainfall event occurs.
Sp (θ ) = 5.2239θ −0.831 ⋅ ⋅ ⋅ ( 2)where Sp(θ) is sorptivity, θis volumetric soil moisture
content.
3.2. Comparison between measured and estimatedsurface runoff volume using sorptivity values
Figure 4 shows the comparison between measured andestimated surface runoff volume using sorptivity values.
There is a tendency between the observed volume of
surface runoff and the estimated volume using sorptivity values.
However, the method using sorptivity values overestimated theQsp values of surface runoff and these were four times larger
than the Qobs. This led us to develop a more accurate
equation to determine maximum potential retention (Eq. (1))
by using the optimal parameter values.
3.3. Comparison of measured and estimated surface
runoff volume using the CN method
Figure 5 shows the comparison of measured and estimatedsurface runoff volume using the CN method. There is large
variation in the relationship between the measured and theFig. 5. Comparison of measured and estimated surface runoff
volume using the CN method.

estimated surface runoff volume using the CN method.
The measured runoff volume was 136 m3 during the 32
rainfall events. On the other hand, the estimated volume is698 m3 which is five times or more than that observed.
Therefore, we should consider optimizing the parameter values
in Equation (1) in order to optimize the result. Chong andTeng (1986) and Gan (2002) determined the maximum
potential retention (S) in order to optimize the parameters inChong’s equation (1). According to their method, we canrecalculate the maximum potential retention (S) using
optimized parameter values (a, b, c, d) in Eq. (1). The resultof recalculation is as follows in Eq. (3);0.5
S = 0.0012 K Sat
R 0.7 Sp (θ )2.25

⋅ ⋅ ⋅ (3)where S is the maximum potential retention, Sp(θ) is soil
sorptivity, Ksat is saturated soil hydraulic conductivity, Ri:
rainfall intensity. Consequently, substituting each value ofKsat, Ri and Sp into the above equation gives the value of S foreach rainfall event.
3.4. Comparison of maximum potential retention using
observed runoff volume and maximum potential
retention using optimized parameters
Figure 6 shows the comparison of maximum potential
retention using observed runoff volume S (mm) and maximum
potential retention using Eq. (3). Here there is little scatter
about the line of equivalent. This result shows good
agreement between the observed and the estimated maximum
potential retention values. The Residual Mean Square Error(RMSE) has the dimensions of the observations and so thesmaller the RSME the better the prediction.

Fig. 6. Comparison of maximum potential retention using theobserved runoff volume and maximum potential retention
after optimizing parameters (RMSE = 10.5 mm).

its practical field measurement will be very useful in estimatingsurface runoff volume in this study area. A growing interestin characterizing spatial and temporal variability of soils incatchment areas will likely encourage the use of sorptivity
methods which are sufficiently simple and economical to makepossible the extensive sampling required.
Fig. 7. Comparison of the estimated Qobs and the estimated Qsp after
optimizing parameter vales.

Acknowledgement
The authors would like to thank Mr. Kenji Nakamura withNTC International for his data collection works in ATARC.
References

Fig. 8. Comparisons of the observed and the estimated volumeduring the 32 rainfall events.

3.5. Comparison of the estimated Qobs and the estimated
Qsp after optimizing parameter values
Figure 7 shows the comparison between the estimated Qobs
and the estimated Qsp after optimizing parameter values.
This result shows good agreement between the observed
and the estimated surface runoff volume. After optimizing
the parameter vales in Eq. (3), we recalculated the surface
runoff volume using two methods which are the CN method
and sorptivity value method. Figure 8 describes thecomparisons to the observed Qobs and the estimated surface
runoff volume QSp for the 32 rainfall events during theobservation period. It is seen that the estimated result using
the sorptivity value agrees well with the observed surface
runoff volume. But the result Qcp using the CN method isless accurate in comparing with using the sorptivity values.

4. Conclusion
It was recognized that there was a relationship between soil
sorptivity and soil moisture content in the catchment area.
The volumes of surface runoff from each rainfall event could
be estimated using sorptivity values measured in theexperimental field, however the values were five times larger
than those observed. After optimizing parameter values, it
was clear that there was a good agreement with between theestimated volume of surface runoff and the observed. Our
proposed method which was used by in situ measured
sorptivity value will be expected to apply to estimations of thesurface runoff volume for the water harvesting system inEthiopia. We also conclude that the concept of sorptivity andCarter D.C., Miller S. (1991): Three years experience with
on-farm water catchment water harvesting system inBotswana. Agric. Water Manage, 19: 191 - 203.
Chong S.K., Green R.E. (1983): Sorptivity measurement andits application, Proc. National Conference on Advances ininfiltration. ASAE Publication, Michigan, 82-91.
Chong S.K., Teng T.M. (1986): Relationship between theRunoff curve number and hydrologic soil properties. Journalof Hydrology, 84: 1-7.
Gan T.Y. (2002): Behavior of theoretical curve number withrespect to soil and rainfall properties. Lowland TechnologyInternational, 4: 21 - 36.
Kitanaka M., Hotta T., Nakamura K., Suzuki S., Nishimaki R.,Takahashi S. (2010): Verification study on water collecting
characteristic of water-harvesting technique in Ethiopia Consecutive
irrigation
reservoirsystemwithwater-harvesting for NERICA in semi-arid area (2) (Japanese). Journal of Arid Land Studies, 20(3):151-158.
Li X.Y., Gong J.D., Wei X.H. (2000): In situ rainwater
harvesting and gravel mulch combination for corn
production in the dry semi-arid region of China. J. AridEnviron., 46: 371-382.
Mishra S.K., Singh V.P. (2003): Soil conservation service
Curve Number (SCS-CN) methodology. Kluwer AcademicPublishers.
Philip J.R. (1957): The theory of infiltration: 4. Sorptivity andalgebraic infiltration equations. Soil Science, 84: 257-264.
Sharma K.D., Pareek O.P., Singh H.P. (1986):
Micro-catchment water harvesting for raising jujube
orchards in an arid climate. Trans. Am. Soc. Agric. Eng., 29:644 - 649.
Soil Conservation Services (SCS) (1972): Section 4,Hydrology. National Engineering Handbook, U.S.
Department of Agriculture, Washimgton, D.C.
Smettem K.R.J., Clothier B.E. (1989): Measuring unsaturated
sorptivity and hydraulic conductivity using multiple discpermeameters. European Journal of Soil Science, 40:563-568.
Yamashita M., Ichikawa A., Satoh F., Shibata H. (2006):
Quantitative approach and problems of river hydrologicalsimulation models. Japanese Journal of Limnology, 67: 267- 280