The identification of runoff generating areas (RGAs) within a watershed is a difficult task because of their temporal and spatial behavior. A watershed was selected to investigate the RGAs to determine the factors affecting spatio-temporally in southern Ontario. The watershed was divided into 8 fields having a Wireless System Network (WSN) and a V-notch weir for flow and soil moisture measurements. The results show that surface runoff is generated by the infiltration excess mechanism in summer and fall, and the saturation excess mechanism in spring. The statistical analysis suggested that the amount of rainfall and rainfall intensity for summer (R 2 = 0.63, 0.82) and fall (R 2 = 0.74, 0.80), respectively, affected the RGAs. The analysis showed that 15% area generated 85% of surface runoff in summer, 100% of runoff in fall, and 40% of runoff in spring. The methodology developed has potential for identifying RGAs for protecting Ontario’s water resources.
Variable source areas (VSA) are hydrologically active, dynamic, sensitive, and have higher potential of generating runoff within a watershed in the event of a storm [
The theory of VSA hydrology expresses that runoff is generated when a thin layer of surface soil is saturated during rainfall over small parts of a watershed [
Non-point source pollution (NPS) is responsible for more than 50% of the total water quality impairment [
Best Management Practices (BMPs) can reduce the impacts of NPS on agricultural watersheds and water resources [
The identification of hydrologically sensitive areas within a watershed is a difficult task because of their spatial and temporal variabilities depending on differences in soils, topography, land use, storm size and intensity [
A study was conducted to examine the spatial patterns of storm runoff generation from eight small catchments as a function of antecedent moisture conditions in the Mont Saint-Hilaire, Quebec, Canada. They found that hydrologic response showed a strong nonlinear change with antecedent moisture conditions [
The experiments were carried out to identify the patterns of hydrologic connectivity with shallow groundwater fluctuations, and found that hydrologic connectivity between riparian and hill slope areas vary with season [
The researchers also used the variable source loading function (VSLF) watershed model to identify the fields at risk of generating runoff [
Since agricultural runoff is the main factor for transporting NPS, it is suggested that BMPs be targeted to the areas within a watershed that are most prone to generate surface runoff. To explore the importance of VSA in terms of managing water quantity and water quality, this study was conducted 1) to evaluate the dominance of runoff generation mechanisms in different seasons of the year in order to identify the hydrologically sensitive areas of the watershed, and 2) to determine the factors affecting spatio-temporally runoff generating areas (RGAs) in a small agricultural watershed in southern Ontario.
A 4.45 ha agricultural watershed, located on the Guelph Turfgrass Institute (GTI) of the University of Guelph, Guelph, between latitudes of 43˚32'42''N and 43˚32'50''N, and longitudes of 80˚12'17''W and 80˚12'30''W, was selected to study the seasonal dynamics of VSAs in southern Ontario (
In order to study the dynamics of VSAs, the GTI watershed was discretized into 8 homogenous fields based on soil, land use, and topography (
A V-notch weir was installed at the outlet of each field and the pressure sensor pipe was attached to the angle of the V-notch. A soil moisture sensor was placed into the ground beside the V-notch and were fed by a power source.
The rainfall data were collected from the GTI tipping bucket raingauge in time interval of 5 minutes. A total
Field | Length of Overland Flow (m) | Area (ha) | Mean Slope (%) |
---|---|---|---|
1 | 270 | 1.22 | 5.5 |
2 | 310 | 0.31 | 3.8 |
3 | 165 | 0.31 | 9.3 |
4 | 245 | 0.35 | 7.5 |
5 | 275 | 1.10 | 5.0 |
6 | 220 | 0.36 | 2.6 |
7 | 190 | 0.40 | 3.0 |
8 | 125 | 0.40 | 2.3 |
of 18 storms (10 in summer, 5 in fall, and 3 in spring) were recorded from July 2008 to April 2009 to investigate the seasonal variability of contributing areas of the watershed (
The maximum amount of rainfall (37.4 mm) occurred on August 5, 2008, with an average intensity of 0.16 mm−1・min−1 and return period of 19 years. The minimum amount of rainfall (1.4 mm) happened on August 2, 2008 with an intensity of 0.02 and return period of 1.1 years. The highest intensity was 0.2 mm/min, and it occurred on August 10, 2008, with 25.6 mm of rain and duration of 130 minutes. Overall, summer storms have higher intensities because of shorter rainfall durations, and spring storms have lower intensities because of longer rainfall durations.
To examine the effects of area and slope of the fields in addition to the way different fields respond to the storms in the watershed study, a Slope/Area Index was developed as follows:
where:
SAI = Slope/Area Index;
S = average slope of field (%);
A = area of field (ha).
The sensitivity of the fields was analyzed using the probability of each field responding to the recorded rainfall events. This probability (Pr) was calculated using the Equation (2) below:
Rainfall Event | Time | Total Rainfall (mm) | Duration Time (min) | Rainfall Intensity (mm/min) | Return Period (yr) |
---|---|---|---|---|---|
July 22, 2008 | 8:00 - 11:30 PM | 28.2 | 210 | 0.13 | 4.8 |
July 30, 2008 | 8:50 - 10:30 AM | 8.8 | 100 | 0.09 | 1.7 |
August 2, 2008 | 2.50 - 3.50 AM | 1.4 | 60 | 0.02 | 1.1 |
August 5, 2008 | 3:00 - 6:50 PM | 37.4 | 230 | 0.16 | 19.0 |
August 7, 2008 | 4:20 - 4:50 AM | 1.7 | 30 | 0.06 | 1.1 |
August 9, 2008 | 5:30 - 8:20 PM | 20.6 | 170 | 0.12 | 3.2 |
August 10, 2008 | 2:10 - 4:20 PM | 25.6 | 130 | 0.20 | 3.8 |
August 13, 2008 | 1:30 - 2:10 PM | 2.8 | 40 | 0.07 | 1.2 |
August 14, 2008 | 8:00 - 8:40 PM | 3.6 | 40 | 0.09 | 1.5 |
August 18, 2008 | 8:40 - 10:00 PM | 10.2 | 80 | 0.13 | 1.9 |
September 13, 2008 | 3:00 - 11:30 PM | 34.5 | 510 | 0.07 | 9.5 |
September 14, 2008 | 5:40 - 8:30 PM | 15.5 | 170 | 0.09 | 2.4 |
October 2, 2008 | 7:10 - 8:00 PM | 3.5 | 50 | 0.07 | 1.4 |
October 8, 2008 | 11:30 AM - 20:00 PM | 11.8 | 510 | 0.02 | 2.1 |
October 16, 2008 | 2:30 - 4:00 AM | 3.0 | 90 | 0.03 | 1.3 |
March 29, 2009 | 4:00 - 10:40 AM | 16.2 | 400 | 0.04 | 2.7 |
April 1, 2009 | 7:20 - 9:10 AM | 3.8 | 110 | 0.03 | 1.6 |
April 3, 2009 | 4:20 AM - 7:30 PM | 30.9 | 910 | 0.03 | 6.3 |
Each field of the watershed had eighteen possible numbers of responses (n) to the rainfall events, out of which the field has responded to some of the events (r).
The literature review for runoff generation has stated the importance of runoff coefficient in the evaluation of contributing areas [
In summer and fall, the average amount of soil moisture in the entire watershed was found to be lower than that of soil moisture in spring. Storms with different precipitation amounts increased the soil moisture in the field during the event. However, due to high soil-water storage capacity in summer and fall, the soil moisture never reached the saturation status. On the other hand, the soil was saturated or close to saturation in the entire watershed during spring. Therefore, even a small amount of rainfall generated runoff from all the fields.
Storm Date | Soil Moisture (%) | ROMα | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Field 1 | Field 2 | Field 3 | Field 4 | Field 5 | Field 6 | Field 7 | Field 8 | ||||||||||
B† | D‡ | B | D | B | D | B | D | B | D | B | D | B | D | B | D | ||
Summer | |||||||||||||||||
07/22/08 | 16 | NR± | 15 | 18 | 13 | 15 | 11 | 16 | 12 | NR | 11 | NR | 10 | 14 | 17 | 23 | IEβ |
07/30/08 | 10 | NR | 8 | 9 | 9 | NR | 7 | 19 | 9 | NR | 9 | 10 | 9 | NR | 10 | NR | IE |
08/2/08 | 10 | NR | 8 | NR | 7 | NR | 7 | NR | 8 | NR | 9 | NR | 9 | NR | 9 | NR | --- |
08/5/08 | 10 | NR | 8 | 19 | 11 | NR | 10 | 18 | 10 | NR | 8 | 19 | 9 | NR | 8 | 15 | IE |
08/7/08 | 11 | NR | 10 | NR | 10 | NR | 8 | 15 | 10 | NR | 10 | 11 | 10 | 11 | 9 | NR | IE |
08/9/08 | 9 | NR | 14 | 18 | 9 | NR | 20 | 22 | 11 | NR | 11 | 14 | 12 | 15 | 13 | 16 | IE |
08/10/08 | 11 | 15 | 12 | 15 | 11 | 14 | 24 | 32 | 10 | 16 | 12 | 14 | 13 | 16 | 14 | 16 | IE |
08/13/08 | 11 | NR | 10 | NR | 12 | NR | 36 | 38 | 10 | NR | 9 | NR | 15 | 17 | 13 | 14 | IE |
08/14/08 | 11 | NR | 15 | 18 | 14 | NR | 18 | NR | 11 | NR | 11 | NR | 12 | NR | 13 | NR | IE |
08/18/08 | 10 | NR | 17 | 19 | 9 | NR | 23 | 37 | 16 | NR | 17 | 20 | 16 | 19 | 18 | 20 | IE |
Fall | |||||||||||||||||
09/13/08 | 10 | NR | 10 | NR | 11 | NR | 17 | NR | 9 | NR | 11 | NR | 10 | NR | 7 | NR | --- |
09/14/08 | 11 | 12 | 10 | 12 | 11 | 18 | 20 | 25 | 9 | 11 | 11 | 13 | 12 | 14 | 8 | 13 | IE |
10/2/08 | 6 | NR | 18 | 20 | 11 | NR | 19 | 22 | 8 | NR | 10 | NR | 11 | NR | 8 | NR | IE |
10/8/08 | 9 | NR | 9 | NR | 15 | NR | 16 | NR | 8 | NR | 10 | NR | 11 | NR | 8 | NR | --- |
10/16/08 | 13 | NR | 30 | NR | 24 | NR | 38 | 42 | 13 | NR | 11 | NR | 12 | NR | 13 | NR | IE |
Spring | |||||||||||||||||
03/29/09 | 45 | 54 | 41 | 43 | 41 | 42 | 43 | 51 | 47 | 52 | 36 | 48 | 33 | 47 | 31 | 48 | SEγ |
04/1/09 | 38 | 56 | 40 | 46 | 37 | 42 | 40 | 49 | 36 | 46 | 36 | 44 | 34 | 45 | 30 | 44 | SE |
04/3/09 | 43 | 54 | 43 | 49 | 36 | 47 | 48 | 50 | 40 | 44 | 35 | 46 | 35 | 46 | 33 | 46 | SE |
†Before the beginning of rainfall; ‡When runoff starts; ±No runoff ; αRunoff generation mechanism; βInfiltration excess; γSaturation excess.
The eight fields of the study watershed had different soil moisture status before the beginning of rainfall storms, even though the soil and land use are uniform over the entire watershed (
Three fields were randomly selected (
The amount of soil moisture in fall was slightly lower than that of summer before starting rainfall; however it did not significantly change during runoff generation period due to light rain in the fall season (
The time series output with depth of surface runoff were analyzed for eighteen recorded rainfall events. If the node at the field outlet registered any positive value except zero from the pressure sensor, the entire area of that field was considered to be RGA. The percentage of the contributing area was then computed for each rainfall storm to study its variability in time and space (
Figures 5-7 demonstrate the spatial and temporal variability of RGAs for summer (August 10, 2008), fall (October 2, 2008) and spring (April 1, 2009). The percentage of RGAs in summer increased rapidly to the maximum value (100% for the rainfall of August 10, 2008) due to higher rainfall intensity; however, these areas started disappearing gradually with time (
Time (min) | Percentage of RGA | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
07/22/08 | 07/30/08 | 08/2/08 | 08/5/08 | 08/7/08 | 08/9/08 | 08/10/08 | 08/13/08 | 08/14/08 | 08/18/08 | 09/13/08 | 09/14/08 | 10/2/08 | 10/8/08 | 10/16/08 | 03/29/09 | 04/1/09 | 04/3/09 | |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
30 | 24 | 0 | 41 | 26 | 66 | 15 | 17 | 7 | 41 | 100 | 15 | 8 | 15 | 0 | 0 | |||
60 | 31 | 0 | 41 | 8 | 48 | 100 | 17 | 0 | 17 | 100 | 15 | 0 | 31 | 24 | 0 | |||
120 | 33 | 15 | 25 | 8 | 15 | 31 | 8 | 8 | 48 | 8 | 100 | 17 | 24 | |||||
240 | 22 | 0 | 17 | 0 | 8 | 17 | 0 | 0 | 32 | 0 | 100 | 100 | 100 | |||||
360 | 8 | 8 | 8 | 8 | 24 | 100 | 100 | 100 | ||||||||||
480 | 0 | 8 | 0 | 8 | 17 | 100 | 24 | 100 | ||||||||||
600 | 8 | 0 | 8 | 100 | 0 | 100 | ||||||||||||
840 | 8 | 0 | 48 | 100 | ||||||||||||||
1080 | 0 | 24 | 100 | |||||||||||||||
1320 | 0 | 100 | ||||||||||||||||
1560 | 100 | |||||||||||||||||
1680 | 100 | |||||||||||||||||
1800 | 83 | |||||||||||||||||
1920 | 48 | |||||||||||||||||
2040 | 24 | |||||||||||||||||
2160 | 0 |
August 10, 2008 Storm August 10, 2008 Storm August 10, 2008 Storm
to low amount of rainfall (1.4 mm) with very low average intensity (0.02 mm・min−1) as shown in
October 2, 2008 Storm October 2, 2008 Storm October 2, 2008 Storm
April 1, 2009 Storm April 1, 2009 Storm April 1, 2009 Storm
The minimum soil moisture for the RGAs in the watershed for summer and fall was 7%, and the maximum area generated runoff in summer (66%) was higher than that of fall (48%). The exception was the storms of August 10, 2008, and September 14, 2008, which contributed 100% due to the occurrence of heavy rainfall a day before those storms. The minimum and maximum RGAs were 15% and 83%, respectively, in spring regardless of the 100% contribution for all three rainfall events.
Overall,
The analysis of persistence of 100% runoff generating status for the five rainfall events for summer, fall and spring illustrates that the 100% runoff generating status occurred sooner in summer and fall than spring (
The spatio-temporal assessment of the factors governing the variability of RGAs, include; total rainfall, average rainfall duration, average rainfall intensity, average five-day antecedent rainfall, and average soil moisture. Statistical analysis (coefficient of correlation) indicated that there is no linear relationship between any single factor and RGAs in summer and fall, indicating that increasing and decreasing of these factors may not necessarily increase or decrease the RGAs (
RGA (%) | Seasons | n† | Coefficient of Correlation | ||||
---|---|---|---|---|---|---|---|
P‡ (mm) | D± (hr) | Iα (mm/hr) | APβ (mm) | Ɵγ (%) | |||
Summer | 10 | 0.63 | 0.47 | 0.82 | 0.51 | 0.71 | |
Fall | 5 | 0.74 | 0.15 | 0.80 | 0.97 | 0.92 | |
Spring | 3 | 0.99 | 0.99 | −0.11 | 0.12 | 0.54 |
†Number of storms; ‡Total rainfall; ±Rainfall duration; αRainfall intensity; β5-day antecedent rainfall; γSoil moisture.
as well as the importance of other factors that may affect this phenomenon which are not evaluated in this study. In spring, the total rainfall and rainfall duration linearly affect the RGA’s variability.
Total amount of rainfall, average rainfall intensity, and soil moisture showed higher correlation coefficients for summer; rainfall intensity, five-day antecedent rainfall and soil moisture for fall; and soil moisture, total rainfall amount, and rainfall duration have higher correlations with RGAs for spring (
Multiple regression analysis was performed for the factors with higher correlation coefficients with RGA except for spring season due to the low number of storms. For the spring season, two simple regression models were chosen since the low number (n = 3) of rainfall storms did not allow for multiple regressions analysis.
The outflow hydrographs at the outlets of each field was used to compute the total flow generated by the different field in the study watershed.
The average percentage of runoff volumes generated by each field was ranked based on the average percentage of total volume of runoff generated by each field.
The eight fields in the study watershed were then ranked based on percentage of the number of responses to the 18 rainfall events on another sensitivity map.
Season | Model | R2 | Significant Level |
---|---|---|---|
Summer | RGA = −56.65 − 0.23 P‡ + 427.41 Iα + 4.24Ɵγ | 0.76 | 5% |
Fall | RGA = −243.04 + 7.16 I − 0.079 APβ + 25.22Ɵ | 0.92 | 10% |
Spring | RGA = 14.62 + 2.18 P | 0.99 | 5% |
RGA = 17.03 + 0.07 D± | 0.99 | 5% |
‡Total rainfall; αRainfall intensity; γSoil moisture; β5-day antecedent rainfall; ±Rainfall duration.
Season | Storm | Depth of Runoff (mm) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Field 1 | Field 2 | Field 3 | Field 4 | Field 5 | Field 6 | Field 7 | Field 8 | Entire Watershed | ||
Summer 2008 | July 22 | 0 | 2.85(68)† | 0.31 (8) | 0.65 (16) | 0 | 0 | 0.19 (4.5) | 0.18 (4.5) | 0.27 |
July 30 | 0 | 1.52 (74) | 0 | 0.53 (26) | 0 | 0 | 0 | 0 | 0.13 | |
August 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
August 5 | 0 | 1.90 (45) | 0 | 1.18 (27) | 0 | 0.61 (15) | 0 | 0.53 (13) | 0.31 | |
August 7 | 0 | 0 | 0 | 0.76 (95) | 0 | 0.01 (1) | 0.03 (4) | 0 | 0.01 | |
August 9 | 0 | 1.02 (66) | 0 | 0.15 (10) | 0 | 0.11 (7) | 0.20 (13) | 0.06 (4) | 0.11 | |
August 10 | 0.08 (1) | 3.20 (47) | 0.34 (5) | 1.80 (26) | 0.11 (2) | 0.80 (11) | 0.71 (10) | 0.57 (8) | 0.60 | |
August 13 | 0 | 0 | 0 | 0.85 (90) | 0 | 0 | 0.07 (7) | 0.03 (3) | 0.07 | |
August 14 | 0 | 0.22(100) | 0 | 0 | 0 | 0 | 0 | 0 | 0.01 | |
August 18 | 0 | 0.55 (53) | 0 | 0.27 (26) | 0 | 0.03 (3) | 0.18 (17) | 0.01 (1) | 0.08 | |
Fall 2008 | September 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
September 14 | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
October 2 | 0 | 1.37 (99) | 0 | 0.01 (1) | 0 | 0 | 0 | 0 | 0.05 | |
October 8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
October 16 | 0 | 0 | 0 | 0.48(100) | 0 | 0 | 0 | 0 | 0.03 | |
Spring 2009 | March 29 | 2.10 (6) | 7.10 (20) | 2.42 (7) | 2.85 (8) | 0.32 (2.5) | 12.43 (30.5) | 4.60 (12.5) | 3.71 (10) | 4.02 |
April 1 | 0.01 (1) | 0.54 (34) | 0.11 (7) | 0.66 (47) | 0.01 (1) | 0.11 (7) | 0.02 (1) | 0.01 (1) | 0.19 | |
April 3 | 7 (16) | 18.6 (43) | 2.73 (6) | 6.23 (14) | 0.75 (2) | 6.4 (14) | 1.8 (4) | 0.7 (2) | 13.98 | |
Average contribution (%) | (1.4) | (38.2) | (1.9) | (28.6) | (0.5) | (5.2) | (4.3) | (2.7) |
†The value in bracket is the percentage of contribution of the field to generate runoff as a fraction of total surface runoff depth.
of bulk density and hydraulic conductivity could also be responsible for variability of field responses to rainfall storms.
Slope-Area AnalysisThe analysis of the Slope/Area Index (SAI) of the eight fields for the eighteen rainfall events indicated a logarithmic relationship with the sensitivity of the fields. The relationship between the sensitivity of runoff generating areas alongside SAI with R2 value of 0.88 conforms the Topographic Index (TI), which is the most widely used index for the identification of saturated areas (
Fields 7, 6, 8, 5, and 1 were placed after Field 2 in this ranking, respectively. Field 3 did not show any relationship with Slope/Area Index, even though it had the highest value of the index. This could be due to sensor misreading or different soil compaction in this field, resulting in a different pattern of soil moisture status. These results indicate that area and slope of the fields have significant effects on the contribution of fields to runoff. This should be considered along with the soil moisture to predict the sensitivity of the field to contribute to runoff.
These data combined with the data in
It was also confirmed by the SAI that the hot spots of the watershed were successfully highlighted. Therefore, the SAI method could be effectively used for the identification of RGAs in the study watershed for the purposes of nutrient management to reduce the risk of water pollution as proficiently as possible.
The watershed outflow data were analyzed in order to compute the flow characteristics of the rainfall events (
Storm | Runoff generating Areas (%) | Rainfall (mm) | Peak Flow (L/S) | Time to Peak (min) | Volume of Runoff (m3) | Depth of Runoff (mm) | Runoff Coefficient (%) | Return Period (year) |
---|---|---|---|---|---|---|---|---|
July 22, 2008 | 33 | 28.2 | 3.40 | 140 | 12 | 0.30 | 1 | 2.8 |
July 30, 2008 | 8 | 8.8 | 0.50 | 120 | 6 | 0.14 | 1.5 | 1.3 |
August 2, 2008 | 0 | 1.4 | 0.00 | 0 | 0 | 0.00 | 0 | 0.0 |
August 5, 2008 | 41 | 37.4 | 6.10 | 50 | 14 | 0.40 | 1 | 3.5 |
August 7, 2008 | 26 | 1.7 | 0.20 | 70 | 0.7 | 0.05 | 6 | 1.2 |
August 9, 2008 | 66 | 20.6 | 3.00 | 30 | 5 | 0.15 | 1 | 2.3 |
August 10, 2008 | 100 | 25.6 | 7.10 | 110 | 26 | 0.60 | 2.5 | 4.7 |
August 13, 2008 | 17 | 2.8 | 0.15 | 40 | 4 | 0.10 | 3.5 | 1.1 |
August 14, 2008 | 7 | 3.6 | 0.45 | 30 | 0.6 | 0.02 | 0.5 | 1.3 |
August 18, 2008 | 41 | 10.2 | 2.20 | 60 | 4 | 0.10 | 1 | 2.0 |
September 13, 2008 | 0 | 34.5 | 0.00 | 0 | 0 | 0.00 | 0 | 0.0 |
September 14, 2008 | 100 | 15.5 | NA | NA | NA | NA | NA | NA |
October 2, 2008 | 15 | 3.5 | 0.85 | 40 | 2.2 | 0.06 | 2 | 1.6 |
October 8, 2008 | 0 | 11.8 | 0.00 | 0 | 0 | 0.00 | 0 | 0.0 |
October 16, 2008 | 7 | 3.0 | 0.50 | 40 | 1.2 | 0.04 | 1.5 | 1.4 |
March 29, 2009 | 48 | 16.2 | 13.50 | 150 | 178 | 4.00 | 24 | 7 |
April 1, 2009 | 24 | 3.8 | 1.30 | 150 | 8.3 | 0.20 | 7 | 1.8 |
April 3, 2009 | 83 | 30.9 | 19.50 | 820 | 623 | 14.0 | 45 | 14 |
Season | Correlation Coefficient (R2) | |
---|---|---|
Rainfall | Runoff | |
Summer | 0.42 | 0.83 |
Fall | 0.45 | 0.81 |
Spring | 0.96 | 0.99 |
peak flow, total flow, runoff coefficient, and return period were selected for further analysis for summer, fall, and spring seasons.
The analyses of peak flow and total flow at the outlet of the study watershed indicated that there is a linear relationship between peak flow and RGA, and between total flow and RGA for summer, fall, and spring (
Southern Ontario experiences more intense storms in summer than in the fall and spring; therefore, the responses of the watersheds to the storms are very rapid with sharper peak flows in summer than in the fall and spring. However, due to low soil moisture and in turn high soil storage, most of the produced runoff will enter the soil, resulting in lower total flow in summer. In contrast, early fall experiences fewer storms with lower intensities and soil moisture condition is almost similar to summer. Therefore, the contribution of the watershed to runoff is lower, resulting in reduced peak flow and total flow due to higher available storage. As a result, both total flow and peak flow can be assessed as good indicator for the fall season. The intensity of storm events is very low during spring and they rarely produce very sharp peak flows as observe during summer storms. Also, due to soil saturation, most of the rainfall events contribute runoff, resulting in high total flows, during the spring season. This clearly implies that total flow is a more reasonable indicator of RGAs for spring than peak flow.
Quantitative comparison of peak flow and total flow with RGAs during summer, fall, and spring, the 50th percentile value of runoff generating area was calculated (data not shown). The data indicated that if 50% of the area of the study watershed contributes to runoff, the peak flows of 4, 2.8, and 11 L・s−1; and the total flows of 13, 8.6, and 240 m3 would be generated during summer, fall, and spring, respectively. These results depict that the study watershed contributes a considerable amount of runoff during spring while it only contributes a slight amount of runoff during fall. Therefore, spring is a critical season for manure/fertilizer application due to high risk of nutrient transport with water. Consequently, early fall may be the best time of the year for agricultural activities in the study watershed.
Runoff coefficient analysis explains the variability of RGA extremely well during spring and fall by indicating that the watershed runoff contribution pattern follows storm characteristics, i.e., intense storms result higher runoff coefficients and larger size of RGAs. Such a relationship is not visible in summer. The average seasonal runoff coefficients for the three seasons show a nonlinear relationship alongside RGA with R2 of 0.96. Seasonal runoff coefficients were found to be very low in fall, increasing sharply in summer and increasing moderately in spring (
Evaluation of RGAs revealed that the return is more closely correlated to RGAs for all seasons than the rainfall return period (
The average values of RGA and the factors influencing its spatial and temporal variability including the amount of rainfall, rainfall duration, rainfall intensity, five-day antecedent rainfall, and soil moisture at the beginning of the rainfall event were examined for nutrient management (
For summer and fall seasons the magnitude of RGAs is more influenced by five days antecedent rainfall followed by soil moisture and the amount of rainfall in that season. For the spring season the magnitude of RGAs is
influenced by soil moisture and the amount of rainfall in spring.
In order to evaluate the results of the spatial-temporal variability of the RGA study, the findings were compared to Dickinson and Whiteley’s moisture index [
where SI is storm index is in inch (2.54 mm), A is antecedent rainfall in inch (2.54 mm), and S is total storm rainfall in inch (2.54 mm).
The variability of contributing areas in summer, fall and spring were plotted versus these two indices.
different during spring (
Saturation by groundwater does not occur in the study watershed since the soil of the watershed belongs to a well-drained soil series so that the depth of groundwater generally remains at 0.5 m below the soil surface even during March to May, and is below 2 m during August to October [
The following conclusions have been drawn from the current study:
・ The results show that due to low soil moisture, runoff is generated by the infiltration excess mechanism in summer and fall, while the saturation excess mechanism dominated in the entire watershed in spring is due to the high initial moisture content of the soil.
・ Runoff generating areas (RGAs) are highly dynamic and vary within a storm, storm to storm, as well as seasonally within a watershed. Out of 18 studied rainfall storm events, only five events were able to produce 100% contribution from the fields (one summer storm, one fall storm, and three spring storms). Also, the 100% runoff generating status occurred sooner in summer and fall than spring. RGA is relatively more responsive during spring than in fall and summer due to high soil moisture content in the field.
・ The nature of a field responds differently to various rainfall events. This is evident when a field with low soil moisture generated surface runoff for a rainfall event, while the same field with higher soil water content does not generate any runoff for some other events. This implies that only soil moisture is not sufficient to study the variability of runoff generating areas. The statistical analysis showed that the amount of rainfall and rainfall intensity for summer (R2 = 0.63, 0.82) and fall (R2 = 0.74, 0.80), respectively, affected the RGAs. For spring, the amount and duration of rainfall (R2 = 0.99) were found to be effective parameters in generating runoff.
・ The field scale analysis developed a sensitivity map and slope-area method which showed that 15% area in the study watershed generated 85% of surface runoff in summer, 100% of runoff in fall, and 40% of runoff in spring. Analysis on annual basis also indicated that only the 15% area in the study watershed contributed runoff by an average of 75%. These data indicated that these hot spots should be given serious attention for agricultural activities in the study watershed.
・ The statistical analysis at watershed scale indicated that the relationship between RGA and peak flow (R2 of 0.70) is a better indicator than the total flow (R2 of 0.63) for summer. In addition, the peak flow (R2 = 0.97) and total flow (R2 = 0.98) are equally good indicators for the variability of RGAs during fall and spring seasons. No relationship was found between RGA and runoff coefficient for the summer season; however, runoff coefficient showed a linear relationship with RGA for fall (R2 = 0.90) and spring (R2 = 0.99), respectively.
・ The database and the Slope/Area Index method developed in this research have potential for identifying RGAs at watershed scale. These fields can be marked for the purposes of BMPs for non-point source pollution controls, protecting source waters, and nutrient management.
・ The runoff sensors and wireless system use very sensitive electronic devices which are affected by climatic characteristics, animals in the field, and other field operations. Therefore, development of a robust system and an appropriate housing is essential for their short-term and long term use.
・ Manually recording of surface runoff and soil moisture are recommended for some randomly selected rainfall events to evaluate the function of pressure and soil moisture sensors during field experiment.
・ The developed sensors do not perform well during the growing season when the plants are tall. Therefore, further research is needed to develop an antenna system capable of delivering signals from the surface runoff sensor to the base station for any height of crop. Also, further research is needed for comprehensive evaluation of the developed monitoring system in heterogeneous watersheds exhibiting spatial variations in soil surface, subsurface, land use and topographic characteristics.
The authors gratefully acknowledge the useful contributions to this paper of Dr. Stefano Gregori, Dr. Amanjot Singh, and Steven P. Poret, of the School of Engineering, University of Guelph.