Computational Methods in Low Impact Development Stormwater Controls

Part 1: Hydrology and HydraulicsPart 2: Case Studies and Models

2011 Low Impact Development Symposium

Bill LucasIntegrated Land Management

Malvern, PA

Dan MedinaPBS & J

Washington, DC

Franco MontaltoDrexel UniversityPhiladelphia, PA

September 27, 2011

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Outline

Hydrologic & hydraulic processes & underlying mechanisms in LID stormwater controls

Overview of LID Stormwater Controls Discussion of Mechanisms of LID Controls Discussion of LID Computational approaches Problem set used for case studies Case Study summary and highlights

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Make this… function like this

LID Goal

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Fundamental Processes

Evapotranspiration

Drainage & Conveyance

Runoff

Infiltration

Exfiltration

Storage, Routing & Conveyance

Rainfall

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H&H Processes & Mechanisms

Rainfall Infiltration/Exfiltration– Matric Flow– Macropore Flow– Surface Sealing/Clogging– Bioturbation/Vegetation Effects

Evapotranspiration– Interception– Depression Storage– Surface Evaporation– Plant Transpiration

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H&H Processes & Mechanisms

Runoff Generation– Nearly all of above, and some of below

Runoff Conveyance– Vegetated Overland Flow– Channel and Pipe Flow

Runoff Detention– Ponding Storage– Storage Routing

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Rainwater Capture

Source: American Rainwater Catchment Systems Associationwww.arcsa-usa.org

StorageRunoff ReductionBeneficial Reuse

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Permeable Pavement

Grasspave

Eco-Stone

Depression Storage

Evapo-transpiration

InfiltrationStorageExfiltration

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Vegetated Roofs

Maryland Department of the Environment(Baltimore, MD)

InterceptionDepression

StorageEvapo-

transpirationStorageRouting

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Biofiltration Swale

Source: Mike Clar, Ecosite, Inc.

InterceptionDepression

storageExfiltrationEvapo-

transpirationChannel routingStorage routing

(with check-dams)

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Bioretention

Maryland Department of the Environment(Baltimore, MD)

InterceptionDepression

storage InfiltrationEvapo-

transpirationStorage/RoutingExfiltration

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Infiltration Devices

Boulder, COSource: Roger Kilgore

StorageRoutingExfiltration

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Infiltration- Mechanisms Effects of Vegetation (Ralston, 2004, Rachman et al 2004) .

– Vegetation remarkably effective in restoring and/or enhancing infiltration rates. Rates in undisturbed vegetated areas up to several orders of magnitude higher than matric flow.

– Vegetation roots penetrate confining layers, and provide habitat for worms and other fauna to create macropores, opening up soil structure. Root turnover promotes the formation of macropores.

– Native grass hedges in crop fields not only accumulate very substantial sediment (~70%), but their infiltration rate was nearly an order of magnitude higher than the adjacent cropped area. This even occurred in the depositional environment, where infiltration rates outside the hedges were half that of crops.

Effects of Organic Matter (Saxton and Rawls, 2004) . – Organic matter (OM) content can substantially increase infiltration rates, primarily

due to decreased bulk density. – This is largely due to the fact that soils high in OM cannot be compacted as much

as soils with less OM. – Intact mineral soils are typically 1-2% OM, and soils disturbed in development can

be substantially less. Soils can be amended to an OM content of 5-10%.– OM increases field capacity in sandy soils by approximately 10%.– OM promotes the microbial community, contributing to soil aggregate formation.

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Infiltration- Mechanisms Effects of Compaction (Pitt 1987; OCSCD 2001; Saxton and Rawls, 2004)

– Compaction can substantially reduce infiltration rates.– This is especially pronounced in sandy soils, where rates have been shown to decline from

HSG “A” to “D”.– This is primarily due to increased bulk density. – Compaction greatly inhibits the growth of plants, since roots cannot extend through the soil.– Compaction is not alleviated by freeze/thaw cycles- Chariot wheel tracks from roman times

are still visible in England. Pedotransfer Functions (PTFs) (Saxton and Rawls, 2004) .

– Preceding effects of compaction and organic matter can greatly affect the underlying textural class properties of the mineral soils.

– PTF equations can predict soil properties affecting infiltration rates such as saturated hydraulic conductivity (Ksat), field capacity, wilting point, and suction wetting head (Ψ).

– The SPAW model at Saxton’s web site recommended to be used to obtain Ksat. – Even when field tests available, SPAW provides more conservative results without

having to use a safety factor, typically 2.

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Infiltration- Parameter Estimation SPAW Pedotransfer Function Calculator computes Ksat, Ψ, θ33, and θ1500 as a

function of texture classification, density (compaction) and OM. Unsaturated values for Ksat and Ψ also computed as function of θ.

Source: SPAW Documentation

(Saxton and Willey, 2005)

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Evapotranspiration- Mechanisms

Moisture stress then used to project actual vs. potential transpiration.

Note that proportionate AET increases under less PET demand.

Source: SPAW Documentation (Saxton and Willey, 2005)

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Evapotranspiration- Mechanisms Interception (including

evaporation from depression storage) comes off the top, then soil evaporation is computed as affected by canopy cover. Excess radiation applied to plants.

Phenology and energy interactions then used to project actual evapo-transpiration. This is applied to soil layers according to rooting distribution.

Source: SPAW Documentation (Saxton and Willey, 2005)

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Evapotranspiration- Mechanisms

Phenology determines how canopy interception, activity, root growth vary through the year. Data used to compute interception, soil exposure, and moisture transpiration.

Source: SPAW Documentation (Saxton and Willey, 2005)

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Conveyance- Mechanisms Flows are very shallow in LID controls such as biofiltration

swales, where Manning’s n plays a very important role in computations. Manipulation of swale flows very effective method of extending Tc to reduce peak flows.

RELATIONSHIP OF MANNING'S n TO VR

0.01

0.10

1.00

0.01 0.10 1.00VR

MA

NN

ING

'S n

.

EMERGED THICK BRUSH

C Retardance (Ree & Palmer)

SUBMERGED THICK BRUSH

Kuo & Barfield: s=.02, Med. Stiff

EMERGED DENSE GRASS

D Retardance (Ree & Palmer)

SUBMERGED DENSE GRASS

Kuo & Barfield: s=.02, Soft

SHORT GRASS

E Retardance (Ree & Palmer)

SUBMERGED SHORT GRASS

Filter Strip, Abu-Zreig et al, 2001

STONE

PAVEMENT

TRANSITION TO EMERGED FLOW

Source: DURMM Documentation (Lucas, 2005)

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Methods of the TC

Establish a series of LID modeling problem sets.

Engage the modeling community to take on these problems.

Document the modeler’s process:– How does the modeler incorporate LID into

the model?– What H&H processes can and cannot be

simulated?Wh t d t d d ti d t

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Problem set guidelines

Source areas:– Flat and pitched roofs– Parking lots– Swales & lawns

LID facilities/approaches:– Impervious area disconnection– Rain gardens (no underdrain)– Bioretention facilities (w/ underdrain)– Porous pavement– Green roofs– Grass bioswales

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Models Evaluated

Simulations performed on the following models:

Continuous simulation, large scale.– SWMM- New LID extension very comprehensive

for H & H.– IDEAL- Comprehensive pollutant modeling

algorithms– WWHM- Comprehensive watershed modeling

algorithms.– WinSLAMM- Comprehensive for SCM

interactions. – SUSTAIN- Extensive SCM optimization

algorithms.

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Discussion: Scale & uncertainty issues

Individual control scale (site to block)– How do we select model parameters given actual

heterogeneous site conditions?– How well do different modeling tools represent H&H

process fundamentals ? Urban scale (block to watershed)

– How significant are the errors associated with upscaling the uncertainty inherent in the control scale models?

– How do we know which LID technologies are going to appear where in the watershed?

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• Urban watersheds Large and heterogeneous• GI technologies Small and decentralized• Urban watershed models Require some level of

aggregation

How do model scale and resolution effect GI

effectiveness predictions?

Resolution at Scale

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Questions