Computational Methods in Low Impact Development Stormwater Controls Part 1: Hydrology and Hydraulics Part 2: Case Studies and Models

Transcription

1 2011 Low Impact Development Symposium Computational Methods in Low Impact Development Stormwater Controls Part 1: Hydrology and Hydraulics Part 2: Case Studies and Models Bill Lucas Integrated Land Management Malvern, PA Dan Medina PBS & J Washington, DC Franco Montalto Drexel University Philadelphia, PA September 27, 2011

2 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 2

3 LID Goal Make this function like this 3

4 Fundamental Processes Rainfall Evapotranspiration Runoff Infiltration Storage, Routing & Conveyance Drainage & Conveyance Exfiltration 4

5 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 5

6 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 6

7 Rainwater Capture Storage Runoff Reduction Beneficial Reuse Source: American Rainwater Catchment Systems Association 7

8 Permeable Pavement Depression Storage Evapotranspiration Infiltration Storage Exfiltration Eco-Stone Grasspave 8

9 Vegetated Roofs Interception Depression Storage Evapotranspiration Storage Routing Maryland Department of the Environment (Baltimore, MD) 9

10 Biofiltration Swale Interception Depression storage Exfiltration Evapotranspiration Channel routing Storage routing (with check-dams) Source: Mike Clar, Ecosite, Inc. 10

11 Bioretention Interception Depression storage Infiltration Evapotranspiration Storage/Routing Exfiltration Maryland Department of the Environment (Baltimore, MD) 11

12 Infiltration Devices Storage Routing Exfiltration Boulder, CO Source: Roger Kilgore 12

13 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. 13

14 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 (K sat ), field capacity, wilting point, and suction wetting head (Ψ). The SPAW model at Saxton s web site recommended to be used to obtain K sat. Even when field tests available, SPAW provides more conservative results without having to use a safety factor, typically 2. 14

15 Infiltration- Parameter Estimation SPAW Pedotransfer Function Calculator computes K sat, Ψ, θ 33, and θ 1500 as a function of texture classification, density (compaction) and OM. Unsaturated values for K sat and Ψ also computed as function of θ. Source: SPAW Documentation (Saxton and Willey, 2005) 15

16 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) 16

17 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 evapotranspiration. This is applied to soil layers according to rooting distribution. Source: SPAW Documentation (Saxton and Willey, 2005) 17

18 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) 18

19 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 TRANSITION TO EMERGED FLOW EMERGED THICK BRUSH C Retardance (Ree & Palmer) SUBMERGED THICK BRUSH MANNING'S n 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 VR Source: DURMM Documentation (Lucas, 2005) PAVEMENT 19

20 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 20

21 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 21 21

22 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. 22

23 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? 23

24 Resolution at Scale 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? 24

25 Questions 25