Relationship between volumetric runoff coefficient and imperviousness using gauged streamflow and rainfall: Case study in New Jersey
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1 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
2 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
3 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
4 Figure 2.2 From NY State DEC Stormwater Management Design Manual (also used by MD, NH and MN R v = *%I (N=47, R 2 =0.71) each point represents mean Rv for one site for multiple storms Graph and equation were developed by T. Schueler in 1987 report for the Metropolitan Washington (DC) Council of Governments.
5 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
6 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
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9 11 test watersheds Station num Station name Percent impervious Basin area, km 2 1 Flat Brook Papakating Cr Salem River North Branch Pike Run West Branch Stony Brook Deep Run Shabakunk Cr Peckman River Bound Brook km
10 Hydrograph and Hyetograph for a station, full record Baseflow computed with Web-based Hydrograph Analysis Tool by Lim et al.
11 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
12 Distribution of rainfall for each station
13 Distribution of Rv for each station Rv = *%I R 2 =.05 Schueler: Rv = *%I R 2 =.77
14 Regression based on mean Rv Schueler: Rv = *%I Median Rv = *%I R 2 =.77 R 2 =.05 Mean Rv = *%I R 2 =.30
15 Regression based on overall Rv Schueler: Rv = *%I Median Rv = *%I R 2 =.77 R 2 =.05 Mean Rv = *%I R 2 =.30 Overall Rv = *%I R 2 =.30
16 Regression based on all storms Schueler: Rv = *%I R 2 =.77 Median Rv = *%I R 2 =.05 Mean Rv = *%I R 2 =.30 Overall Rv = *%I R 2 =.30 All storms Rv = *%I R 2 =.01
17 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 km 2 ), where this study s ranged from 5.2 to 166 km 2 expected that median, mean and/or overall Rv would averaged out the differences in point vs. watershed rainfall for individual storms.
18 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.
19 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
20 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.
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