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Performance of Swales/Drainage Ditches in Infiltrating Stormwater

May 2012 (volume 7 - issue 4)

Contributed by Farzana Ahmed, Graduate Research Assistant and PhD Candidate, St. Anthony Falls Laboratory, University of Minnesota; Advised by: Professors John S. Gulliver and John L. Nieber,

Funded by the Local Road Research Board of Minnesota.

The term swale (grassed channel, dry swale, wet swale, biofilter, or bioswale) refers to a vegetated, open-channel management practice designed specifically to treat and attenuate stormwater runoff for a given water quality volume (EPA, 2012). Roadside swales (drainage ditches) are an attractive best management practice for agencies like departments of transportation because they can be installed easily on two sides and in the median of highways, and are well-suited for treating highway or residential road runoff. A well-maintained grassed swale can reduce the runoff volume and improve the water quality by removing suspended solids, nutrients, toxic metals and petroleum hydrocarbons. Volume reduction occurs primarily through infiltration into the soil, either as the water flows over the slide slope perpendicular to the roadway into the swale or down the length of the swale parallel to the roadway. Suspended solids can be removed from stormwater runoff flowing into the swale by filtration through vegetation and sedimentation that is captured on the bottom of the swale (Backstrom 2002, Schueler 1987, Yu et al. 2001). Because volume reduction of storm water occurs through infiltration, it is an important factor affecting the performance of a swale. A low infiltration rate can damage vegetation because of standing water. Particulate metals are filtered and aqueous phase metals are adsorbed in soil matrix, so if the infiltration rate is low then the pollutant removal efficiency will be also be low. In this project five swales were selected from the Twin Cities metro area. Then, 15-24 infiltration measurements were taken on each swale to characterize the infiltration capacity of these swales.

Infiltration measurements were taken using Modified Philip Dunne (MPD) infiltrometer. This is a new falling head single ring infiltrometer developed with funding from the Metropolitan Council and the Minnesota Pollution Control Agency. The geometric mean and co-efficient of variation were calculated for each site:

Geometric Mean
Arithmetic Mean
Standard Deviation
Coefficient of Variation

where: n = number of measurements, and a_i = Ksat at a specific location.

Figure 1: Performing MPD infiltrometer testing on swale located in Hwy 212.

Following are the results of the statistical analysis on the infiltration data:

Table 1: Statistical analysis of infiltration data.

Location	Number of Infiltration Measurements	Geometric Mean of Ksat (cm/hr)	Coefficient of Variation	Soil Type	Typical Ksat for this soil type
Highway 47	18	2.14	1.54	Loamy sand	2.99
				Sandy loam	1.09
Highway 212	24	0.96	1.90	Silt loam	0.65
				Loam	0.34
Highway 13	18	2.22	1.26	Loam	0.34
				Sandy clay loam	0.15
				Silty loam	0.65
Highway 77	15	2.83	0.89	Loamy sand	2.99
Highway 51	19	3.33	1.06	Loam	0.34
				Sandy loam	1.09

From the result it was observed that:

1. Coefficient of variation is very high which indicates that there are very high spatial variations of Ksat value in these swales.
2. Sometimes the geometric mean of Ksat is very similar to the Ksat of that type of soil (i.e., Hwy 77) and sometimes the geometric mean is not at all close to the typical Ksat of that soil type (i.e., Hwy 13). The reason of these discrepancies may be because of the presence of macropores or cracks in field soil, heterogeneity of soil, vegetation and soil compaction.  In addition, it is not unusual for the measured hydraulic conductivity to be substantially different from the “typical” hydraulic conductivity for a soil type.

The results obtained from an MPD infiltrometer are estimates of the saturated hydraulic conductivity and soil suction at the wetting front. These two parameters can be used to calculate the infiltration capacity of the soil in an infiltration practice and thereby facilitate determination of runoff from the practice. The field data from Hwy 212 have been used to calculate the infiltration capacity of that swale for a three-month 24-hour rainfall event (Huff and Angel, 1992) of 2.5 cm in the Minneapolis-St. Paul area (shown in Table 2). Figure 2 shows the spatial variation of Ksat at different spots of the swale located at Hwy 212. The land area of this swale is 10% of the contributing two way highway. A specific section was chosen in the swale to take the measurements and then 24 measurements were taken 5ft apart from each other both in horizontal and vertical directions.

Figure 2: Spatial variation of Ksat values at the swale located in Hwy 212.

A grid was assumed in that area so that each box is 5’ by 5’, keeping the infiltration measurement spot at the center of each box. It is assumed that the Ksat value over the area of each box will be the same as the Ksat value at the center of that box. The Green Ampt method (Mays, 2005) has been used to compute infiltration rate into the soil of the swale. For a specific rainfall intensity, starting from either side of cross section across the swale, the amount of infiltration and runoff of the grid box closest to the road was calculated and then the runoff was passed on to the downhill grid box. Then a similar process was followed and any remaining runoff was passed on to the downhill box. Thus the amount of infiltration and ponding was calculated at each cross section for each rainfall intensity. The resulting average of the three longitudinal cells are shown in Figure 3.  After summing up total infiltration and runoff it was found that the whole amount of surface runoff infiltrated into the side slope of the swales. For this storm at this location, there was no ponding in the swale. Barrett (1999) also found that most of the pollutant removal and infiltration occurs on the side slope. The reason may be that most sediment deposition occurs at the center of the swale. Clogging of soil pores decreases infiltration rates and increases the quantity overland swale flow leading to diminished swale efficiency (Deletic et al. 2006).

Figure 3: Predicted total average infiltration into Hwy 212 swale locations with the saturated hydraulic conductivities given in Figure 2 for a 2.5 cm 24 hour storm generating 25 cm of sheet flow into cells 1 and cells 8. The center of the swale, cells 4 and 5, were not predicted to have any water reach them for this storm.

References

• Backstrom, M., 2002b. Grassed swales for urban storm drainage, 2002. Doctoral thesis, Division of Sanitary Engineering Lulea University of Technology, Lulea, Sweden.
• Barrett M.E., Walsh P.M., Malina Jr J.F., Charbenueau R.J. (1999), “Performance of vegetative controls for treating highway runoff”, Journal of Environmeental Engineering, Vol. 124, No 11, P 1121-1128.
• Deletic A., Fletcher T.D., (2006), “Performance of grass filters used for stormwater treatment – a field and modeling study”, Journal of Hydrology, 317, P261-275.
• Huff, F.  A.,  Angel J.  R.( 1992) Rainfall Frequency Atlas of the Midwest. Illinois State Water Survey, Champaign, Bulletin 71.
  Mays L.W. (2005), Water resources engineering, John Wiley and Sons, Inc., 111 River Street, Hoboken, NJ07030, USA.
• Schueler, T. R. (1987). Controlling urban runoff; “A practical manual for planning and designing urban BMP’S”, Dept. of Environmental Programs, Metropolitan Washington Council of Governments, Washington, D.C.
• Yu S.L, Kuo J., Fassman E. A., Pan H., (2001), “Field test of Grassed swale performance in removing runoff pollution, Journal of water resource planning and management”, Vol 127, No 3, P 168-171.

We want to hear from you!!!

Let us know your thoughts, experiences, and questions by posting a comment. To get you thinking, here are a few questions:

• These swales have been shown to have a hydraulic conductivity that will allow intense storms to infiltrate. Can you think of two reasons that swales would fail to infiltrate an intense storm?
• Do you see a pattern to the ksat values given in Figure 2, or does the pattern look random?