MEMORANDUM
To:
From:
Subject:
Date:
LID Computations Task Committee
Bill Lucas, co-chair, LID Computations Task Committee
LID Computations Task Committee Report Part 2
May 12, 2009
At our meeting of last May, we put together an ambitious program to compare models in terms of
their ability to project the performance of LID SMP designs. However, we have since
reconsidered the effort and feel that we ought to reexamine our approach. Recall that Part 1 was a
technical discussion of the “basics” of SMP processes and the mathematical computations
appropriate for these processes.
Therefore, it makes sense to first evaluate how the many different models out there represent
these processes, using a similar format. This is particularly relevant to “green” SMPs, where the
effects of plants and vadose zone processes are so important to the overall functioning of these
systems. Proper representation of these key processes is the fundamental basis to project the
response of green SMPs. This precept underlies our efforts in evaluating green SMP modeling
approaches. I present below the underlying rationale for this effort, even though we are all well
aware of the importance of the task at hand.
In addition to model representation, proper treatment of scale is another major element of this Part
2. There is a continuum of representation of processes ranging from the scale of a single green
SMP on the one hand, and how to aggregate such processes with tens of thousands of SMPs at the
city scale. This is particularly important with the current thrust to use green SMPs to reduce of
CSOs. This is element of this task has to be discussed at the outset so as to provide a better
understanding of the relative capabilities of different models.
Another very important element of this Part 2 will address the merits and drawbacks of
Continuous Simulation (CS) models versus Derived Distribution (DD) models versus Design
Storm (DS) models, versus simple spreadsheet (SS) methods. All of these methods have merits
depending upon the circumstances and scale. SS models are very useful at small scales for
individual SMPs, but they typically do not route flows dynamically. DS models have very
effective user interfaces and routing capabilities, but they cannot project cumulative responses
over time. DD models are a hybrid of CS and DS models that use a series of synthetic events,
instead of an historical record of rainfall. CS models are the most capable, but also the most
difficult to calibrate and use properly. CS models can be used to establish discharge criteria and
design storm characteristics most appropriate for the region where a DS model is used, as well as
design criteria for spreadsheet models. This element of the task will be discussed at length so as
to provide a basis for evaluating model capabilities.
This Part 2 Task is a critical need, given the proliferation of interest in LID projects. Especially
since proper representation of LID processes exceeds the capability of most models currently
utilized for the task. There are many model reviews that can generally be divided into two classes:
urban hydraulic routing models, and watershed response models. The hydraulic models originally
intended for routing purposes may be excellent tools to predict hydraulic responses of “grey”
SMPs, but they typically lack algorithms that represent green SMPs. The hydrologic models can
do an excellent job in predicting watershed responses, but they do not have routines intended to
represent green SMP responses, and they typically oversimplify hydraulic routing.
Green SMPs fall into both categories, as both hydrologic and hydraulic responses are involved.
So LID computations are thus anything but simple. In fact, their proper representation requires a
level of physical understanding and complexity often overlooked in LID designs. As a result,
systems are often either over- or under-designed. While over-design may be acceptable, it does
not use resources efficiently, so less runoff is treated for the same capital expenditure. Underdesigned systems do not attain design objectives. Providing tools that can assist in finding the
proper balance between these extremes is a primary objective of this task.
Furthermore, it is possible to manipulate many models so they can mimic the response expected
in green SMPs. This Part 2 is intended to provide modelers with methods by which many models
can be used to represent the processes that underlie green SMP responses. In essence, this task
will provide tools to both improve the modelers, as well as the models. By providing methods to
mimic green SMP responses in a complex hydraulic model, the precision of the model can be
applied to better represent the cumulative response of green SMPs. This is a very important
aspect of this Part 2.
The preceding discussion highlights how models first need to be evaluated from this qualitative
basis in order to properly understand their capabilities and limitations. Table 1 presents a list of
the parameters to be evaluated for each model. Given this framework, the final phase of Part 2 is
the step of actually comparing them to each other with the identical dataset format that we
formulated at our meeting. This phase will be revisited once we get through the current phase.
I propose that the heavy lifting of writing each model chapter be allocated to the authors of the
models where possible, and to experienced users in the remaining cases. I welcome any feedback
from members to add models I have overlooked in this brief compendium. As a very preliminary
cut, I allocated some of our group as follows in Table 2. I apologize for taking on more models
than others, but I have been doing a fair amount of work already in those models I allocated to
myself. As you can see, there are still many models that need volunteers.
We are indeed fortunate in having the designers of many of these models on our committee. We
are a remarkably well qualified group and I trust we can leverage this effort to make a substantial
contribution to the knowledge base of green SMP design.
Table 1: List of Parameters to be evaluated by the Modelers
Parameter
SCALE
Watershed/City
Project/Site
Building/ SMP
TYPE
Continuous Simulation
Derived Distribution
Design Storm
Simple Spreadsheet
HYDROLOGIC PROCESSES
Rainfall Infiltration
Evapotranspiration
Soil Moisture Accounting
Depression Storage
Surface Evaporation
Runoff Generation
Impervious Area Disconnection
Subsurface Flow
HYDRAULIC PROCESSES
Kinematic Wave Routing
Full Dynamic Wave Routing
Sheet Flow, Concentrated Flow
Unit Hydrograph Method
Muskingum-Cunge Routing
Linear Reservoir Routing
Detention Routing
Storage Infiltration
LID SMP DESIGN
Rainwater Harvesting
Soil Engineering
Filter Strips
Biofiltration Swales
Bioretention- no underdrain
Bioretention- with underdrain
Green Roofs
Constructed Wetlands
Pervious Pavement
Infiltration Trenches
Extended Detention Basins
Brief Description of Most Important Processes
Many nodes, very experienced users
Fewer nodes, experienced users
Few nodes, inexperienced users
Most data needed, very experienced users
Less data needed, experienced users
Little data needed, experienced users
Little data needed, inexperienced users
Horton, Green-Ampt, CN Method, Other
Types of PET and AET Methods Used
Water Balance Effect on Infiltration Response
Water Budget during Precipitation Events
Effects upon Depression Storage and Interception
Precipitation less Infiltration and Depression Storage
Reductions in Runoff Volume over Pervious Surfaces
Delays Discharge of Infiltrated Runoff
St. Venant Equations without the inertia term.
St. Venant Equations with the inertia term.
NRCS runoff routing methods
Applied to hydrograph convolution
Empirical Channel Flow Routing Method
Theoretical Channel Flow Routing Method
Storage Indication Routing
Darcy’s Law and Green-Ampt Processes
Effects of Interception and Reuse upon Runoff Volume
Effects of Improved Infiltration upon Disconnection
Effects of slope, roughness and disconnection
Effects of slope, width, roughness and disconnection
Effects of media and soil infiltration responses
Effects of infiltration responses, plus dynamic routing
Effects of Unsaturated Flow, Darcy Flow through media
Storage Indication Routing, Water Budgeting
Infiltration into soil, Routing from Media
Infiltration into soil, Routing from Media
Storage Indication Routing, Water Budgeting
Table 2: List of Runoff Models and Potential TC Member Contributions
Model Name
LID Computation TC Member (or Affiliate)
CS MODELS- FULL SCALE
LIFE
Dan Medina
SUSTAIN
Leslie Shoemaker and Khalid Alvy
BMPDSS
Tham Saranavapavan and Jenny Zhen
HEC-HMS
Rob Traver
SWMM
Bill Lucas, Lew Rossman, Franco Montalto
PCSWMM
Bill James, Rob James
XPSWMM
Rob Dickinson
HSPF
Jill Bicknell
WWHM
Doug Beyerlein, Tracy Tackett
QualHYMO
Charles Rowney
STORMSHED 3G
Lenny Kong
MUSIC
Tony Wong, ??
GSSHA
James Doan, ??
Mike-URBAN
??
Mike-SHE
??
InfoWorks CS
??
P8-UCM
??
WMS
??
SWAT
??
STORM
??
DR3M-QUAL
??
Q-ILLUDAS
??
CS MODELS- SMP SCALE
SPAW
Bill Lucas, Keith Saxton
HYDRUS 2D
Bill Lucas, ??
RECARGA
John Gulliver
DD MODELS
WinSLAMM
Bob Pitt and John Vorhees
IDEAL
Billy Barfield and John Hayes
CN Method (many events)
Bill Lucas, Pete Hawkins
Yu Method (many events)
Bill Lucas, Bofu Yu, Calvin Rose
DS MODELS
TR-55-revised
Rick McCuen
HydroCAD
Bill Lucas
MIDUSS
Alan Smith
Hastead Methods (Bentley)
??
Intellisolve (AutoDesk)
??
VTPSUHM
??
SS MODELS
DURMM
Bill Lucas
LID Quicksheet
Paul Koch
Simple Method
??