---
title: "Sewer Infiltration"
canonical: "https://help.innovyze.com/space/xps/19663342/Sewer%20Infiltration"
format: markdown
---
**The Sewer Infiltration Global Data** dialog deals with data to estimate infiltration in a sewer system based upon existing information about the sewer, its surrounding soil and groundwater, and precipitation.
> ⚠️ 1. Sewer infiltration is added in sanitary mode.
> ⚠️ 2. In the [Sanitary Job Control](https://help-innovyze.atlassian.net/wiki/spaces/xps/pages/19663436) dialog, Sewer Infiltration should be checked and the appropriate sewer infiltration global database record should be selected.
> ⚠️ 3. The sewer infiltration flows are added to the upstream nodes of links that are active in the sanitary node.
Infiltration is classified into four categories:
1. miscellaneous sources causing a base dry weather inflow,
2. frozen residual moisture,
3. antecedent precipitation, and
4. high groundwater.
Infiltration is defined as:
QINF = DINFIL + RINFIL + SINFIL for low groundwater
QINF = GINFIL, for high groundwater
***Where***:
QINF = total infiltration
DINFIL = dry weather infiltration
RINFIL = wet weather infiltration
SINFIL = melting residual ice and snow infiltration
GINFIL = groundwater infiltration
The totals for the study area must be entered; the model then apportions these flows to each conduit. The criterion chosen for apportionment is an opportunity factor which represents the relative number and length of openings susceptible to infiltration. Pipe joints constitute the primary avenue for entry of infiltration. The number and length of joints is assumed to be proportional to the relative surface area of each conduit. The fraction of total infiltration is calculated as:
OPINF = Af * DIST / Σ (Af * DIST)
***Where:***
OPINF = fraction of total infiltration to a conduit
Af = cross-sectional area of conduit
DIST = conduit length
The summation in the denominator is for all conduits.
The apportioned infiltration enters the system at the node immediately upstream of the conduit. This procedure allocates the most infiltration to the largest and longest conduits. It is also possible to enter infiltrations directly at manholes, if local information dictates otherwise.
> ⚠️ When GINFIL is greater than 0 (high groundwater condition), DINFIL, RINFIL, and SINFIL (low groundwater condition) are ignored.
## Wet Weather Infiltration (Rinfil)
The rainwater infiltration, in cfs [cms]. This parameter depends on antecedent precipitation occurring within the nine day period prior to an estimate. If antecedent rainfall is unavailable or less than about 0.25 in. (6.4 mm), the rainwater contribution to total infiltration is usually small.
For larger antecedent rainfall contributions, regression techniques offer a suitable method for estimating this parameter. Three study areas in which sewer flow data were not affected by melting were shown to satisfy the following linear relationship:
RINFIL = ALF + SUM (ALF[n] * RN[n]), for n=0,9
= SWFLOW - DINFIL - SMMDWF
***where***
RINFIL = rainwater infiltration, gpm
ALF[n] = coefficient to rainfall for ‘n’ days prior to estimate, gpm/in
RN[n] = precipitation on ‘n’ days prior to estimate, in.
SWFLOW = daily average sewer flow excluding surface runoff, gpm
SMMDWF = otherwise accounted for sewage flow, gpm
DINFIL = dry weather infiltration, gpm
To determine the coefficients, a multiple linear regression should be run on existing flow and rainfall data. Some results are given in the following table (Lentz, 1963; Metcalf and Eddy et al., 1971):
| **Rainfall Coefficient** |
| --- |
| **Study Area** | **ALF ** | **ALF0 ** | **ALF1** | **ALF2** | **ALF3** | **ALF4** | **ALF5** | **ALF6** | **ALF7** | **ALF8** | **ALF9** |
| Brandenton, FL | 4.1 | 2.9 | 17.5 | 15.0 | 12.8 | 13.0 | 10.4 | 13.2 | 10.1 | 11.8 | 9.5 |
| Baltimore, MD | 2.4 | 11.3 | 11.6 | 5.5 | 6.4 | 4.8 | 3.6 | 1.0 | 1.5 | 1.4 | 1.9 |
| Springfield, MO | 2.0 | 18.3 | 13.9 | 8.9 | 5.5 | 6.7 | 16.0 | 5.2 | 4.6 | 4.4 | 1.3 |
## Residual Moisture Peak Contribution (RSMAX)
This infiltration arises from residual precipitation such as snow as it melts following cold periods. Published data in the form of degree-days (sum of deviations below 65 degrees fahrenheit) provide an excellent index as to the significance of this parameter. The onset and duration of melting can be estimated by noting the degree-days above and immediately below a value of 750.
The maximum contribution from residual moisture can be determined from previous gauging of the study area or local estimates. This parameter is used in the infiltration calculation as follows:
SINFIL = RSMAX * SIN (180 * (NDYUD-MLTBE)/(MLTEN-MLTBE))
or
SINFIL = 0.0, if NDYUD is not in melting period or if NDD never exceeds 750
***where***
SINFIL = melting residual ice and snow infiltration
RSMAX = residual moisture peak contribution, gpm
NDYUD = day on which infiltration estimate is required, day
MLTBE = day on which melting period begins, day
MLTEN = day on which melting period ends, day
NDD = number of degree-days for the month
Note that degree-day information is also required in calculating this infiltration.
## Dry weather infiltration
The total base infiltration for the study area in dry weather, cfs [cms]. If the study area has been gauged, base dry-weather infiltration can be taken from inspection of the flow data. In the absence of flow data, an estimate of the unit infiltration rate (gpm per inch-diameter per mile) must be obtained from local professionals. From data in this form, the following equation can be used:
DINFIL = XLOCAL * DIAM * PLEN
***where***
DINFIL = dry weather infiltration, gpm
DIAM = average sewer diameter, inch
PLEN = pipe length, mile
XLOCAL = unit infiltration rate, gpm/inch-diameter/mile
Values of XLOCAL range from 250 to 600 gpm/in-diam/day and may be even higher for laterals with many stubs. The importance of local data cannot be over-emphasized.
## Groundwater infiltration
That component of total infiltration originating from groundwater, cfs [cms]. For locations and times of the year where the groundwater table is above the sewer invert, groundwater infiltration supersedes contribution from all other sources. Groundwater infiltration can be determined from historical sewer flow data, by inspection, or regression analysis. For example, a regression analysis could involve determination of the BETA coefficient in the following formulation:
GINFIL = BETA + BETA1*GWHD + BETA2*GWHD^2 + BETA3*GWHD^0.5
***where***
GINFIL = groundwater infiltration.
GWHD = groundwater table elevation above sewer invert
BETAn = coefficient for term n
## Degree Days
The sum of deviations of temperatures below 65 degrees fahrenheit. Only deviations below 65 degrees are counted. Figures for various cities in the United States can be obtained from the “Heating, Ventilating, Air Conditioning Guide”, American Society of Heating and Air Conditioning Engineers. Sample values for various cities are shown in the following table, from that source:
| **State ** | **Station ** | **Jul ** | **Aug ** | **Sep ** | **Oct ** | **Nov ** | **Dec ** | **Jan ** | **Feb ** | **Mar ** | **Apr ** | **May ** | **Jun** |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Ala | Montgomery | 0 | 0 | 0 | 55 | 267 | 458 | 483 | 360 | 265 | 66 | 0 | 0 |
| Ariz | Phoenix | 0 | 0 | 0 | 13 | 182 | 360 | 425 | 275 | 175 | 62 | 0 | 0 |
| Calif | Los Angeles | 0 | 0 | 17 | 41 | 140 | 253 | 328 | 244 | 212 | 129 | 68 | 19 |
| D.C. | Washington | 0 | 0 | 32 | 231 | 510 | 831 | 884 | 770 | 606 | 314 | 80 | 0 |
| Fla | Tampa | 0 | 0 | 0 | 0 | 60 | 163 | 201 | 148 | 102 | 0 | 0 | 0 |
| Ill | Chicago | 0 | 0 | 90 | 350 | 765 | 1147 | 1243 | 1053 | 868 | 507 | 229 | 58 |
| Kan | Wichita | 0 | 0 | 32 | 219 | 597 | 915 | 1023 | 778 | 619 | 280 | 101 | 7 |
| La | New Orleans | 0 | 0 | 0 | 5 | 141 | 283 | 341 | 223 | 163 | 19 | 0 | 0 |
| Mass | Boston | 0 | 7 | 77 | 315 | 618 | 998 | 1113 | 1002 | 849 | 534 | 236 | 42 |
| Mich | Detroit City | 0 | 8 | 96 | 381 | 747 | 1101 | 1203 | 1972 | 927 | 558 | 251 | 60 |
| Nev | Reno | 27 | 61 | 165 | 443 | 744 | 986 | 1048 | 804 | 756 | 519 | 318 | 165 |
| N.Y. | New York | 0 | 0 | 31 | 250 | 552 | 902 | 1001 | 910 | 747 | 435 | 130 | 7 |
| Ohio | Cleveland | 0 | 9 | 60 | 311 | 636 | 995 | 1101 | 977 | 846 | 510 | 223 | 49 |
| Ore | Portland | 13 | 14 | 85 | 280 | 534 | 701 | 791 | 594 | 515 | 347 | 199 | 70 |
| Tenn | Memphis | 0 | 0 | 13 | 98 | 392 | 639 | 716 | 574 | 423 | 131 | 20 | 0 |
| Texas | Houston | 0 | 0 | 0 | 0 | 162 | 303 | 378 | 240 | 166 | 27 | 0 | 0 |
| Wyo | Cheyenne | 33 | 39 | 241 | 577 | 897 | 1125 | 1225 | 1044 | 1029 | 717 | 315 | 100 |
## Pollutant Concentration
This dialog provides for the input of constant concentrations of pollutants in inflows. Although infiltration is often assumed to be "clean" due to its origin in the soil layers, in-conduit measurements usually indicate non-zero levels of most parameters.
***Pollutant Name***
Pollutant name reference. These pollutants are defined from the 'Pollutant List' item in Job Control.
***Pollutant Concentration***
The constant concentration of the given pollutant in the inflow from infiltration. The concentration is given in units consistent with the pollutant, as defined in the Pollutants Global Database.
> Macro (__confluenceADFMigrationUnsupportedContentInternalExtension__)