Estimating Total Maximum Daily Loads with the Stochastic Empirical Loading and Dilution Model

Abstract

The Massachusetts Department of Transportation (DOT) and the Rhode Island DOT are assessing and addressing roadway contributions to total maximum daily loads (TMDLs). Example analyses for total nitrogen, total phosphorus, suspended sediment, and total zinc in highway runoff were done by the U.S. Geological Survey in cooperation with FHWA to simulate long-term annual loads for TMDL analyses with the stochastic empirical loading and dilution model known as SELDM. Concentration statistics from 19 highway runoff monitoring sites in Massachusetts were used with precipitation statistics from 11 long-term monitoring sites to simulate long-term pavement yields (loads per unit area). Highway sites were stratified by traffic volume or surrounding land use to calculate concentration statistics for rural roads, low-volume highways, high-volume highways, and ultraurban highways. The median of the event mean concentration statistics in each traffic volume category was used to simulate annual yields from pavement for a 29- or 30-year period. Long-term average yields for total nitrogen, phosphorus, and zinc from rural roads are lower than yields from the other categories, but yields of sediment are higher than for the low-volume highways. The average yields of the selected water quality constituents from high-volume highways are 1.35 to 2.52 times the associated yields from low-volume highways. The average yields of the selected constituents from ultraurban highways are 1.52 to 3.46 times the associated yields from high-volume highways. Example simulations indicate that both concentration reduction and flow reduction by structural best management practices are crucial for reducing runoff yields.

Characterization of stormwater runoff from bridge decks in eastern Massachusetts, 2014–16

“…The comparable yields of TN, TP, and SS in this current bridge-runoff study (table 20) were about 1.3, 3.4, and 16 times the ultra-urban highway yields simulated by Granato and Jones (2017), respectively. This comparison indicates that bridge-deck yields are not representative of yields from the entire road network in Massachusetts; however, bridge-deck yields may be a substantial portion of annual yields from highways in a watershed because the bridge-deck yields were much higher than yields for all the road classes simulated by Granato and Jones (2017). Furthermore, many bridges are over and discharge to receiving waters, whereas State roadways typically discharge to the local land surface.…”

“…They used the average of precipitation statistics from 11 National Weather Service stations in eastern Massachusetts and Rhode Island. The percent differences between the runoff-event precipitation statistics used by Granato and Jones (2017) and the precipitation statistics used in this study were about 0.85 for event volume, 5.2 for event duration, and 1.3 for the number of hours between event midpoints. The comparable yields of TN, TP, and SS in this current bridge-runoff study (table 20) were about 1.3, 3.4, and 16 times the ultra-urban highway yields simulated by Granato and Jones (2017), respectively.…”

“…The percent differences between the runoff-event precipitation statistics used by Granato and Jones (2017) and the precipitation statistics used in this study were about 0.85 for event volume, 5.2 for event duration, and 1.3 for the number of hours between event midpoints. The comparable yields of TN, TP, and SS in this current bridge-runoff study (table 20) were about 1.3, 3.4, and 16 times the ultra-urban highway yields simulated by Granato and Jones (2017), respectively. This comparison indicates that bridge-deck yields are not representative of yields from the entire road network in Massachusetts; however, bridge-deck yields may be a substantial portion of annual yields from highways in a watershed because the bridge-deck yields were much higher than yields for all the road classes simulated by Granato and Jones (2017).…”

Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the stochastic empirical loading and dilution model

“…Estimated concentrations of SSC are calculated from TSS concentrations measured at highway-runoff monitoring sites in Oregon by using a regression equation developed by Granato and Jones (2017). They applied the maintenance of variance type 1 (MOVE.1) method to 90 paired TSS and SSC measurements from multiple sites from the HRDB (Granato and Jones, 2017). The SSC concentrations for sites in Oregon were then calculated by using TSS concentrations measured at sites in Oregon by using the MOVE.1 equation:…”

“…SELDM can be used to simulate one constituent as a dependent variable of another by using a regression equation with random variability (Granato, 2013;Granato and Jones, 2017). In this case, however, this MOVE.1 equation was used calculate the statistics in table 15 that will be used to directly simulate SSC values as a random variable.…”