relationship between volumetric runoff coefficient...

Relationship between volumetric runoff coefficient and imperviousness using gauged streamflow and rainfall: Case study in New Jersey

Derek Caponigro and Kirk Barrett Department of Civil and Environmental Engineering, Manhattan College Bronx, NY With funding from the Mahony Family

• imperviousness (%I): percentage of land covered by pavement and rooftops that are impervious to rain and snowmelt.

• volumetric runoff coefficient (Rv): fraction of rainfall converted into direct runoff (aka stormflow)

• as %I increases, infiltration decreases and Rv increases

• relationship between Rv and %I is not as simple as sometimes assumed; wide variations in Rv for different storms in the same watershed

Relationship between Rv and %I has important implications in society

Several states use %I as the basis for computed the volume of runoff and/or pollutant load that must be treated by a new development Some states specify Rv by land-use type Others specify an equation relating Rv to %I

Graph and equation were developed by T. Schueler in 1987 report for the Metropolitan Washington (DC) Council of

Governments.

Figure 2.2 From NY State DEC Stormwater Management Design Manual (also used by MD, NH and MN

Rv = 0.05 + 0.009*%I (N=47, R2=0.71)

each point represents mean Rv for one site for multiple storms

• Most of the data (44 of 47 sites) is the USEPA’s Nationwide Urban Runoff Program” (NURP) in the 1970s, from 16 cities scattered throughout the USA

• But, NURP and Schueler excluded data from 16

additional sites in 4 other cities because their data did not conform with this Rv vs. %I relationship

• Rainfall patterns (intensity and duration), vegetation types and soil types vary markedly across the nation.

• Could they not cause a different Rv vs. %I relationship in different regions?

• Decided to try it for NJ, using USGS data from NWIS

Why NJ? • USGS has lots of continuous stream monitoring

stations in NJ, like in most states

• A small percentage have rainfall gauges collocated with stream gauges (but data available for only last 120 days)

• NJ has excellent, state-wide, high-resolution,

imperviousness data

11 test watersheds

1 2

4

10

7

11 6

5

9

3

30 60

km

Sta-tion num Station name

Percent imper- vious

Basin area,

km2

1 Flat Brook 0.6 165.92 Papakating Cr 2.2 41.03 Salem River 2.4 37.84 North Branch 6.0 67.95 Pike Run 7.2 13.96 West Branch 11.7 5.27 Stony Brook 12.9 14.38 Deep Run 14.4 41.59 Shabakunk Cr 20.8 10.310 Peckman River 26.9 20.311 Bound Brook 29.4 125.5

Hydrograph and Hyetograph for a station, full record

Baseflow computed with Web-based Hydrograph Analysis Tool by Lim et al.

Hydrograph and Hyetograph for a single storm

• Area between streamflow and baseflow curves represents volume of stormflow

• Depth of stormflow (cm) = Volume (m^3) / Watershed area (m^2) • Rv = depth of stormflow / depth of rainfall

Distribution of rainfall for each station

Distribution of Rv for each station

Schueler: Rv = 0.05 + 0.009*%I R2 = .77

Rv = 0.12 + 0.001*%I R2 = .05

Regression based on mean Rv

Schueler: Rv = 0.05 + 0.0090*%I R2 = .77 Median Rv = 0.12 + 0.0010*%I R2 = .05

Mean Rv = 0.20 + 0.0032*%I R2 = .30

Regression based on overall Rv

Schueler: Rv = 0.05 + 0.0090*%I R2 = .77 Median Rv = 0.12 + 0.0010*%I R2 = .05

Mean Rv = 0.20 + 0.0032*%I R2 = .30 Overall Rv = 0.20 + 0.0032*%I R2 = .30

Regression based on all storms

Schueler: Rv = 0.05 + 0.0090*%I R2 = .77 Median Rv = 0.12 + 0.0010*%I R2 = .05

Mean Rv = 0.20 + 0.0032*%I R2 = .30 Overall Rv = 0.20 + 0.0032*%I R2 = .30 All storms Rv = 0.15 + 0.0020*%I R2 = .01

Why are the correlations so poor? Why are the regression equations so

different from Schueler’s? • key assumption: rainfall at a single point represents

rainfall over the entire watershed • increasing inaccurate as the watershed size increases • NURP watersheds were all small (< 3 km2), where this

study’s ranged from 5.2 to 166 km2

• expected that median, mean and/or overall Rv would “averaged out” the differences in point vs. watershed rainfall for individual storms.

Why are the correlations so poor? Why are the regression equations so

different from Schueler’s? • Analyzed only low to medium imperviousness (~30%

maximum) watersheds • while NURP/Schueler watershed had %I up to ~100%,

with about half above 30%. • Low impervious watersheds may have a high percentage

of their imperviousness disconnected from the stormwater drainage system, reducing Rv.

Why are the correlations so poor? Why are the regression equations so

different from Schueler’s? • We examined only storms that occurred in the spring and

summer (because the rainfall only went back 120 days.) Storms during these periods would show a smaller Rv because evapotranspiration is high, reducing soil moisture and increasing infiltration

Conclusions • Poor correlation between Rv and %I • Regression equations much different than Schueler’s • may not be feasible to use USGS NWIS streamflow and

rainfall to develop regional Rv vs. %I equations, because • Most gauged watersheds are too large to be

characterized by rainfall measured at a single point • gauges are seldom located in highly impervious

watersheds • Developing a region-specific Rv vs. %I equation would

likely require a special monitoring program like NURP focused on very small watersheds including some highly impervious ones.