EOC Green Roof Seattle, Washington

Dataset Description

The EOC Green Roof is located on the top of the Emergency Operations Center (EOC) building complex in Seattle, Washington. The vegetated area of the EOC green roof is 7,480 square feet/685 square meter (Taylor Associates, 2012). The roof only intercepts precipitation that falls upon it; no additional runoff is received from adjacent areas. Outflow moves through the granular storage layer along the bottom of the green roof and ultimately out through a roof drain, where flow was measured during the monitoring study (2008-2011; Cardno TEC, 2012). All water from the roof that is not captured by the vegetation leaves the installation through the roof drains and drainage system. A flow monitoring station measured flow using a Unidata tipping bucket gauge for low flow rates and an electromagnetic flowmeter (a 2-inch Unimag magmeter) for higher flow rates (Taylor Associates, 2012). Precipitation was measured by a tipping bucket rain gauge on the adjacent fire station roof.

The green roof is filled with two inches of granular stone and four inches of growing media. Vegetation types are listed in Taylor Associates (2012). The media for the 2-inch/5.08 cm drainage layer is Roofmeadow® Type A Granular Drainage Media. The growing media is “Roofmeadow Type M3”, a proprietary soil mix described as moderately coarse with low silt content and moderately high moisture content at field capacity (Cardno TEC, 2012). The underlying drainage layer is described as a granular media with a high permeability and porosity greater than 0.2.

LID Photograph and Schematic

Figure 1 - Photograph of the EOC Seattle Green Roof(Taylor Associates, 2012)

1

Outflow

Figure 2 - Schematic of a Green Roof(Taylor, 2008)

Model Configuration

The model configuration used to represent this LID included a single subcatchment, with the same area as the green roof. In the LID Usage Editor, the green roof was allocated to occupy the entire subcatchment. Rainfall was modeled with a rain gage using a five-minute intensity time series derived from tipping bucket rainfall data. Rainfall was the only inflow received by the LID. The drainage from the underdrain runoff was routed to an outfall where it could be compared to the measured drain flow values. Figure 3, below, depicts this model configuration.

Figure 3 - SWMM5 model configuration

Rain

LID

2

Data Transformations

SWMM version 5.1.10 was used to complete this analysis. The rainfall events were provided in a tipping bucket rain data format (in), which were converted to intensity values (in/hr) using the estimation method described in Wang et al. (2008). This in/hr data file was then were input into SWMM via a rain gauge. The provided outflow data was in five-minute time series format, therefore, all SWMM simulations were also reported in a five-minute time step. All drainage outflow comparisons were completed in in/hr. The measured runoff values were reported in gallons per minute GPM, therefore, to compare measured values to the SWMM generated outflow, the measured values were normalized by the area of the roof (7480ft2) and converted to in/hr. Lastly, in order to calculate the total measured outflow from the green roof (in), the measured outflow time series (in/hr) was summed, multiplied by the five-minute time step, and divided by 60 minutes, resulting in inches of outflow.

3

Model Inputs Table 1: SWMM 5 Input Parameters

Type ValueRange

(if estimated) Data SourceSubcatchmentTotal Area (ac) 0.172 LID StudyPercent Slope 4.1 LID StudyPercent Impervious 100 LID StudyGreen Roof UsageArea (ft2) 7480 LID StudyWidth (ft.) 100.8 30-110 EstimatedInitial Saturation (%) 2.82 0.01-25 EstimatedSurface LayerBerm Height (in) 0.10 0.10-1.0 EstimatedVeg. Volume Fraction 0.05 0.01-0.5 EstimatedSurface Roughness 0.41 0.01-0.70 EstimatedSurface Slope (%) 4.17 1.0-5.0 EstimatedSoil LayerThickness (in) 4.00 LID StudyPorosity (v. fraction) 0.70 0.34-0.70 EstimatedField Capacity (v. fraction)

0.33 0.30-0.33 Estimated

Wilting Point (v. fraction) 0.22 0.09-0.29 EstimatedConductivity (in/hr) 3.00 0.60-45.0 EstimatedConductivity Slope 0.50 0.01-20.0 EstimatedSuction Head (in) 4.00 3.0-8.0 EstimatedDrainage Mat LayerDrain Mat Thickness (in) 2.00 LID StudyDrain Mat Void Ratio 0.50 0.20-0.50 EstimatedDrain Mat Roughness 6.99 0.01-10.0 Estimated

Table 1 lists the parameters required by the SWMM model unique to the green roof. Parameters used in this evaluation were either listed in the original study or were estimated using PEST (Doherty, 2005). The best fit parameters determined using PEST are listed in Table 1, above.

Calibration and Testing

Four storms were used in this analysis to represent the green roof’s hydraulic activity. In order to identify the best fit parameters, an iterative PEST calibration was completed for the unknown variables for each chosen storm. During a calibration trial, PEST was executed for the desired storm using the originally estimated parameter values as a start point. When an optimal set of parameters was converged upon, PEST stopped running SWMM and output the new parameter estimations. These output parameters were then indicated as the new start baseline, and PEST was executed again. If the second PEST trial generated the same numbers as were input, then it could be concluded that an optimal set of parameters was attained. To prohibit over-calibrating to any one storm, a limit of three PEST iterations was put in place for a given set of baseline parameters. For each storm calibration, several varying baseline sets of parameters were used, allowing PEST to explore a wide range of estimation space. The Nash-Sutcliffe value was calculated for each calibration iteration. The estimated parameter set with the highest obtained Nash-Sutcliffe value was selected as the overall optimal set of parameters for that storm event.

4

The estimated optimal parameters were then substituted into the other three storm simulations to complete three trials. All variables, excluding initial saturation, were held constant for the testings to see how SWMM would perform under the conditions determined in the calibration. Because the initial saturation of the LID was unique to each storm, it was not possible for this value to be held constant among all four storm events. The Nash-Sutcliffe values for the trials were calculated and recorded in Table 2.

Nash-Sutcliffe values were used as an indicator of goodness of fit between the modeled output and the measured output. The calibration/testing that produced the highest overall average Nash-Sutcliffe value was selected as the true optimum set of parameters. The selected calibration storm was the 4/12/2009 event, which generated an overall Nash-Sutcliffe average 0.937. Table 3 gives a performance summary for the calibration run and three trials. Table 2: Calibration Method

Calibration Storm 5/5/2009

12/11/2010 4/12/2009 10/9/2010 AVG

5/5/2009 0.977 0.825 0.775 0.901 0.87012/11/2010 0.582 0.890 0.729 0.893 0.7734/12/2009 0.970 0.853 0.988 0.939 0.93710/9/2010 0.969 0.856 0.967 0.941 0.933

Table 3: Performance Summary

Run Storm ID Storm Date

Total Inflow

(in)

Total Observed Outflow

(in)

Total Simulated Outflow

(in)

N-S Value*

R2

Value

% Change Outflow Volume

Initial Deficit

Calibration 137 4/12/2009 0.750 0.367 0.370 0.988 0.99 0.82 2.82Test 1 140 12/11/2010 3.586 2.53 3.21 0.853 0.97 26.9 2.82Test 2 138 5/5/2009 0.862 0.667 0.720 0.970 0.97 7.95 11.6Test 3 139 10/9/2010 1.625 1.09 1.20 0.939 0.94 10.1 2.82

*Nash-Sutcliffe coefficient of efficiency

The figures below include calibration hydrograph plots as well as correlations plots. The hydrograph plots depict the inflow hydrograph to the LID practice, the actual outflow as documented in the study research, and the outflow as reported by the SWMM 5.1.10 program. Both inflow and outflow are presented in in/hr on the left axis. The correlation plots compare the observed outflow to the SWMM 5.1.10 simulated outflow.

5

4/12/09 3:21 4/12/09 15:21 4/13/09 3:21 4/13/09 15:210

0.05

0.1

0.15

0.2

0.25

Observed Inflow Observed Outflow Simulated Outflow

Date

Flow

(in/

hr)

Figure 4A: Calibration Hydrograph

0 0.01 0.02 0.03 0.04 0.05 0.060

0.01

0.02

0.03

0.04

0.05

0.06

Simulated Outflow (in/hr)

Mea

sure

d O

utflo

w (i

n/hr

)

Figure 4B: Correlation Plot for Calibration

6

12/11/10 12:00 12/12/10 0:00 12/12/10 12:00 12/13/10 0:000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Observed Inflow Observed Outflow Simulated Outflow

Date

Flow

(in/

hr)

Figure 5A: Test 1 Hydrograph

0 0.05 0.1 0.15 0.2 0.250

0.05

0.1

0.15

0.2

0.25

Simulated Outflow (in/hr)

Mea

sure

d O

utflo

w (i

n/hr

)

Figure 5B: Correlation Plot for Test 1

7

5/4/09 23:31 5/5/09 5:31 5/5/09 11:31 5/5/09 17:31 5/5/09 23:310

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Observed Inflow Observed Outflow Simulated Outflow

Date

Flow

(in/

hr)

Figure 6A: Test 2 Hydrograph

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Simulated Outflow (in/hr)

Mea

sure

d O

utflo

w (i

n/hr

)

Figure 6B: Correlation Plot for Test 2

8

10/9/10 11:31 10/9/10 18:43 10/10/10 1:55 10/10/10 9:07 10/10/10 16:190

0.05

0.1

0.15

0.2

0.25

Observed Inflow Observed Outflow Simulated Outflow

Date

Flow

(in/

hr)

Figure 7A: Test 3 Hydrograph

0 0.02 0.04 0.06 0.08 0.1 0.120

0.02

0.04

0.06

0.08

0.1

0.12

Simulated Outflow (in/hr)

Mea

sure

d O

utflo

w (i

n/hr

)

Figure 7B: Correlation Plot for Test 3

9

Sensitivity Analysis

A sensitivity analysis was completed by PEST as part of the parameter estimation process. With each model run, PEST recorded the composite sensitivity of each parameter by calculating the magnitude of the Jacobian matrix column pertaining to a specific parameter, modulating it by the weight of the respective observation, and dividing that figure by the total number of observations.

The surface layer parameters (storage, vegetation volume, surface roughness, and surface slope) as well as three soil layer properties (conductivity, conductivity slope, and suction head) returned zero sensitivity figures, indicating that their values had no impact on the drainage outflow or total outflow. These values were therefore not included in the sensitivity analysis in Table 4, below. The reported sensitivity analysis was obtained from the calibration storm, event 4/12/2009, using the optimal parameters converged upon in the calibration/testing process.

Table 4: Parameter Sensitivity

Parameter Estimated Value Sensitivity Rank

LID Usage

Width (ft.) 100.8 0.000003 7Initial Saturation 2.82 0.000068 5

SoilPorosity 0.70 0.000318 4

Field Capacity 0.33 0.017666 2Wilting Point 0.22 0.023487 1

Drainage Mat

Void Ratio 0.50 0.000423 3Roughness 6.99 0.000050 6

From Table 4, wilting point and field capacity parameters were the most sensitive, respectively, with the drainage mat void ratio being the third most sensitive. The soil parameters were highly sensitive since they indicated how quickly water infiltrated through the medium to the drainage layer as well as how much water could be retained in the soil layer. The drainage mat void ratio had a similar effect on the results, as it dictated how quickly as well as how much water could flow to the outlet in the roof.

Discussion

Figure 4A depicts the hydrograph for the calibration storm, the 4/12/2009 event. From Table 3, this event can be seen with both the highest N-S value and R2 values of all four storms, 0.988 and 0.99, respectively. SWMM over-predicted the total outflow volume by less than one percent, nearly perfectly matching the observed dataset. From Figure 4A, the main visible discrepancies between the measure and simulated datasets occur in the beginning portions of outflow and in peak flow rates. The modeled initial flow lagged behind the observed dataset. This was most likely a result of the low estimated initial deficit value allowing excess water retention to be simulated within the LID. SWMM over-predicted the first peak flow rate slightly, while under-estimating the second peak by an equally small margin.

Figure 5A depicts the hydrograph of the 12/11/2010 event, which received the lowest N-S value of the four tests, 0.853, and an R2 value of 0.97. The hydrograph depicts how the model over-predicted outflow rates for much of the storm, while also miss-timing the outflow start time. The model accurately simulated peak flow rate timing however, despite the discrepancy in flow magnitude. Because of SWMM’s consistent outflow over-estimation, the simulated dataset modeled 26.9% more outflow than was observed.

10

Figure 6A, the 5/5/2009 event, performed the second best overall, receiving an N-S value of 0.97 and an R2 of 0.97 as well. The simulated dataset followed the same trends as seen in the calibration storm, with discrepancies in the early outflow activity and a slight over-estimation on the descending arm of the hydrograph. SWMM modeled the peak outflow rate timing and magnitude nearly perfectly for this event. In the first section of the hydrograph, the modeled dataset is seen simulating declining initial outflow, while the observed dataset indicated increasing initial outflow. This is most likely a result of the high initial saturation, causing drainage from the LID to be simulated rather than water storage at the start of the rain event. The descending side of the hydrograph was simulated as a smoother curve than occurred in the measured dataset. This difference indicated that some form of lateral exfiltration or evaporation most likely occurred on the green roof that was not simulated. SWMM over-prediction of total outflow volume by 7.95% for this event.

Lastly, Figure 7A illustrates the 10/9/2010 event and displayed similar trends to those seen in Test 1, consistently over-estimating the intensity of outflow from the green roof. SWMM again simulated an outflow start time later than the observed dataset due to the low initial saturation value estimated by PEST. The peak flow timing was accurately simulated despite the intensity differences between the two datasets. This simulation generated an N-S value of 0.939 and an R2 of 0.94 while over-estimating the total outflow volume by 10.1%.

Conclusion

There is an apparent conflict with initial saturation estimation resulting in a consistently low initial outflow rate simulation by SWMM, as seen in the Calibration, Test 1, and Test 3 hydrographs. This result may be a consequence of how the Test trials were completed using PEST to estimate for the initial saturation. Because all other parameters were affixed at the values converged upon in the calibration trials, PEST may have accommodated for the inability to change these parameters by under-estimating the initial saturation value. This low initial saturation would allow for more water storage to occur later in the storm event, supplementing parameters of the soil that could not be changed. Tests 1 and 3 also displayed similar consistent outflow rate over-predictions, indicating that there was a discrepancy in storage volume available in the LID. Because these two storms occurred nearly a year after the others, it is possible that factors such as wind erosion could have decreased the density of the green roof soil and drainage medias, resulting in more storage space for water in the LID.

Aside from these two flaws, SWMM could simulate peak outflow rates and timing with exceptional accuracy for this LID application, attaining both N-S and R2 values in the mid to high 0.90 for all but one value. The model also simulated descending segments of all four hydrographs with relatively high precision. Because of this high performance in the testing process, SWMM’s performance with this LID application was given a rating of “excellent”.

References

Cardno TEC. (2012). Green Roof Performance Study: Seattle Public Utilities. http://www.seattle.gov/dpd/GreenBuilding/docs/dpdp022445.pdf.

Doherty, J. (2005). PEST Model-Independent Parameter Estimation User Manual: 5th Edition. 333.

Roofmeadow (2013). http://www.roofmeadow.com/ accessed August 21, 2018.

Taylor Associates. (2012). Fire Station 10 / Emergency Operations Center Green Roof Performance Study Implementation Plan. Prepared for Seattle Public Utilities.

11

Taylor, B.L. (2008). The Stormwater Control Potential of Green Roofs in Seattle. Low Impact Development for Urban Ecosystem and Habitat Protection: pp. 1-10. American Society of Civil Engineers.

Wang, J. X., B. L. Fisher and D. B. Wolff (2008). "Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements." Journal of Atmospheric and Oceanic Technology 25(1): 43-56.

12