U.S. patent application number 15/653382 was filed with the patent office on 2018-01-18 for systems and methods for event-based modeling of runoff and pollutant benefits of sustainable stormwater management.
This patent application is currently assigned to 2NDNATURE Software Inc.. The applicant listed for this patent is 2NDNATURE Software Inc.. Invention is credited to Nicole G. Beck, Gary Conley, Nathaniel Evatt, Lisa Kanner, Margaret Mathias.
Application Number | 20180017710 15/653382 |
Document ID | / |
Family ID | 60942113 |
Filed Date | 2018-01-18 |
United States Patent
Application |
20180017710 |
Kind Code |
A1 |
Beck; Nicole G. ; et
al. |
January 18, 2018 |
Systems and Methods for Event-based Modeling of Runoff and
Pollutant Benefits of Sustainable Stormwater Management
Abstract
Systems and methods in accordance with embodiments of the
invention are software models that present information in a format
directly usable by stormwater managers to inform annual program
decisions and consistently evaluate the effectiveness of stormwater
management actions. Stormwater modeling systems in accordance with
many embodiments of the invention provide a tool that can be used
by stormwater managers to estimate load reductions. In a number of
embodiments, a user interface is provided that streamlines user
input data requirements. In this way, the stormwater modeling
system can extend the utility of event-based model inputs, generate
results that inform management decisions, and demonstrate progress
using a common scalable unit.
Inventors: |
Beck; Nicole G.; (Santa
Cruz, CA) ; Mathias; Margaret; (Santa Cruz, CA)
; Conley; Gary; (Santa Cruz, CA) ; Evatt;
Nathaniel; (Santa Cruz, CA) ; Kanner; Lisa;
(Fremont, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
2NDNATURE Software Inc. |
Santa Cruz |
CA |
US |
|
|
Assignee: |
2NDNATURE Software Inc.
Santa Cruz
CA
|
Family ID: |
60942113 |
Appl. No.: |
15/653382 |
Filed: |
July 18, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62363763 |
Jul 18, 2016 |
|
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|
62534173 |
Jul 18, 2017 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01V 99/005 20130101;
G01W 1/10 20130101; G06Q 50/26 20130101; G01W 1/14 20130101 |
International
Class: |
G01V 99/00 20090101
G01V099/00; G06Q 50/26 20120101 G06Q050/26 |
Claims
1. A method for managing water management features of a geographic
area, the method comprising: gathering spatial data describing a
set of land portions; gathering water management feature data for a
set of water management features; gathering land and feature
condition data describing a condition of at least one land portion
and at least one water management feature; calculating
precipitation level patterns; calculating an aggregate effect of
the set of water management features based on the spatial data, the
water management feature data, the land and feature condition data,
and the calculated precipitation level patterns; and managing water
management features to reduce the calculated aggregate effect of
the plurality of water management features.
2. The method of claim 1, wherein the spatial data comprises at
least one of land surface type, soil type, precipitation levels,
topography, hydrologic connection to receiving waters, traffic
levels, and land use type.
3. The method of claim 1, wherein the water management feature data
comprises at least one of the size of a water management feature,
types of outflow from the water management feature, construction
materials used to construct the water management feature, and
outflow rates from the water management feature.
4. The method of claim 1, wherein gathering land and feature
condition data comprises performing a set of standardized
assessment methods on each land portion in a geographic area.
5. The method of claim 1, wherein gathering land and feature
condition data comprises performing a set of standardized
assessment methods on each water management feature in a geographic
area.
6. The method of claim 1, wherein calculating the precipitation
level patterns comprises calculating an annualized statistical
distribution of rainfall events of a geographic area.
7. The method of claim 1, wherein calculating the expected
aggregate effect comprises discounting the effectiveness of a water
management feature based on a condition of the water management
feature from the land and feature condition data.
8. The method of claim 1, wherein calculating the aggregate effect
comprises: calculating an effect for each land portion; and
aggregating the effects of the land portions to calculate the
aggregate effect.
9. The method of claim 8, wherein aggregating the effects of the
land portions comprises: identifying flow routing data that
describes the flow of runoff between land portions; and calculating
the aggregate effect based on the effect for each land portion and
the flow routing data the portion and at least one neighboring land
portion.
10. A system for managing water management features of a geographic
area, the system comprising: a data gathering interface for
gathering baseline data and water management feature data for a
plurality of water management features; a stormwater modeling
system for calculating precipitation level patterns and calculating
an expected aggregate effect of the plurality of water management
features; and a system management interface for managing water
management features to reduce the expected aggregate effect of the
plurality of water management features.
11. The system of claim 10 further comprising a network, wherein a
set of devices communicate with the stormwater modeling system
through the data gathering interface
12. The system of claim 11, wherein the set of devices comprises at
least a database server, a personal computer, and a sensor for
measuring stormwater runoff.
13. The system of claim 10, wherein the system management interface
is configured to communicate with at least one controller for
modifying an element of the system.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The current application claims priority under 35 U.S.C.
119(e) to U.S. Provisional Patent Application Ser. No. 62/363,763,
entitled "Systems and Methods for Event-based Modeling of
Stormwater Runoff", filed Jul. 18, 2016 and U.S. Provisional Patent
Application Ser. No. 62/534,173, entitled "Systems and Methods for
Event-based Modeling of Runoff and Pollutant Benefits of
Sustainable Stormwater Management", filed Jul. 18, 2017. The
disclosures of U.S. Provisional Patent Application Ser. Nos.
62/363,763 and 62/534,173 are hereby incorporated by reference in
their entireties.
FIELD OF THE INVENTION
[0002] The present invention relates generally to geographic
information systems and more specifically to event-based modeling
of stormwater runoff and pollutants.
BACKGROUND
[0003] The hydrologic impacts associated with urban development are
well documented and include a decline in downstream receiving water
quality. Increased peak and total stormwater runoff volumes are the
result of impervious development and decreased potential for
surface infiltration. Additional water quality impairments are
linked to the elevated generation and surface water transport of
sediment, nutrients, bacteria, metals, pesticides, and other
chemicals derived from urban land uses.
[0004] Urban municipalities expend resources to reduce non-point
source urban pollutant loading to receiving waters and include a
suite of non-structural and structural best management practices
(BMPs). Non-structural BMPs focus on source control and pollution
prevention, including street sweeping programs and parcel runoff
controls like rain barrels or disconnected downspouts. Structural
BMPs are physical features installed on the landscape to reduce
stormwater runoff volumes and treat stormwater pollutants.
Structural BMPs include low impact developments (LIDs) and green
infrastructure BMPs such as infiltration or bio-retention features,
as well as larger scale centralized BMPs such as dry basins or
treatment vaults.
[0005] There are significant challenges in implementing an
appropriate experimental design and data analysis procedure to
confidently isolate pollutant load reductions attributable to a
single or a suite of conservation efforts. One challenge is related
to the lag time between the implementation of effective actions and
the measurable response in the receiving waters beyond hydrologic
variability. This lag time limits the immediate use of water
quality data to guide impending decisions and stormwater program
adjustments. The critical concept of maximizing the ability to make
inferences about surface water health and minimizing the influences
of natural seasonal or geographic variations are often overlooked.
Such oversights can elevate data collection, management, and
laboratory costs at the expense of developing a reliable and
rigorous sampling and post-sampling procedure. If not well planned,
sampling strategies can introduce ambiguity to measurements and
reduce confidence that changes in pollutant loads over time can be
attributed to management actions. Collection of water quality and
hydrologic data is costly, complicated, and inherently spatially
and temporally limited. Stormwater managers continue to struggle
with how to effectively incorporate monitoring data and results
into annual resource allocation decisions. Stormwater modeling
allows for the simulation of a range of potential hydrologic
conditions and the spatial aggregation of water quality benefits
from multiple structural and non-structural BMPs. The use of a wide
array of urban hydrology models to inform both short and long-term
stormwater programmatic planning decision is common.
SUMMARY OF THE INVENTION
[0006] Systems and methods for event-based modeling of runoff and
pollutants in accordance with embodiments of the invention are
illustrated. One embodiment includes a method for managing water
management features of a geographic area by gathering spatial data
describing a set of land portions, gathering water management
feature data for a set of water management features, gathering land
and feature condition data describing a condition of at least one
land portion and at least one water management feature, calculating
precipitation level patterns, calculating an aggregate effect of
the set of water management features based on the spatial data, the
water management feature data, the land and feature condition data,
and the calculated precipitation level patterns, and managing water
management features to reduce the calculated aggregate effect of
the plurality of water management features.
[0007] In another embodiment, the spatial data comprises at least
one of land surface type, soil type, precipitation levels,
topography, hydrologic connection to receiving waters, traffic
levels, and land use type.
[0008] In a further embodiment, the water management feature data
comprises at least one of the size of a water management feature,
types of outflow from the water management feature, construction
materials used to construct the water management feature, and
outflow rates from the water management feature.
[0009] In still another embodiment, gathering land and feature
condition data comprises performing a set of standardized
assessment methods on each land portion in a geographic area.
[0010] In yet another embodiment, gathering land and feature
condition data comprises performing a set of standardized
assessment methods on each water management feature in a geographic
area.
[0011] In another additional embodiment, calculating the
precipitation level patterns comprises calculating an annualized
statistical distribution of rainfall events of a geographic
area.
[0012] In a further additional embodiment, calculating the expected
aggregate effect comprises discounting the effectiveness of a water
management feature based on a condition of the water management
feature from the land and feature condition data.
[0013] In another embodiment again, calculating the aggregate
effect comprises calculating an effect for each land portion and
aggregating the effects of the land portions to calculate the
aggregate effect.
[0014] In a further embodiment again, aggregating the effects of
the land portions comprises identifying flow routing data that
describes the flow of runoff between land portions and calculating
the aggregate effect based on the effect for each land portion and
the flow routing data the portion and at least one neighboring land
portion.
[0015] Additional embodiments and features are set forth in part in
the description that follows, and in part will become apparent to
those skilled in the art upon examination of the specification or
may be learned by the practice of the invention. A further
understanding of the nature and advantages of the present invention
may be realized by reference to the remaining portions of the
specification and the drawings, which forms a part of this
disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 is a block diagram conceptually illustrating a
stormwater model for modeling stormwater in accordance with an
embodiment of the invention.
[0017] FIG. 2 is a flow chart conceptually illustrating an example
of a process for modeling stormwater in accordance with an
embodiment of the invention.
[0018] FIG. 3 is a system diagram illustrating an example of a
system for modeling stormwater runoff in accordance with an
embodiment of the invention.
[0019] FIG. 4 is a block diagram illustrating a stormwater modeling
system in accordance with an embodiment of the invention.
[0020] FIG. 5 illustrates a user interface for viewing inventoried
structural BMP assets.
[0021] FIG. 6 illustrates a view of a user interface for viewing
mapped baseline runoff impact results.
[0022] FIG. 7 illustrates a user interface for generating planning
scenarios in accordance with an embodiment of the invention.
[0023] FIG. 8 is a bar chart that illustrates annual baseline load,
current load, and current load reduction estimates for an urban
catchment.
[0024] FIG. 9 illustrates an example of an annualized summary of
the load reduction contributions of various water management
features in accordance with an embodiment of the invention.
DETAILED DESCRIPTION
[0025] Turning now to the drawings, stormwater runoff and pollutant
modeling systems and methods for modeling stormwater runoff in
accordance with various embodiments of the invention are
illustrated. Stormwater modeling systems and methods of modeling
stormwater runoff in accordance with various embodiments of the
invention are discussed further below. Before discussing these
systems, however, various characteristics of a model for modeling
stormwater runoff according to a number of embodiments of the
invention is described in further detail below.
Stormwater Model
[0026] A stormwater model in accordance with some embodiments of
the invention is used to model and predict the effectiveness of
stormwater management features in reducing stormwater runoff to a
specific receiving water, such as a stream, lake or river.
Alternatively, or conjunctively, the stormwater model is used to
model and predict the effectiveness of stormwater management
features in reducing pollutants that are introduced to the
receiving waters.
[0027] A stormwater model in accordance with various embodiments of
the invention is illustrated in FIG. 1. The example of FIG. 1
illustrates a stormwater modeling engine 140, along with various
inputs that are used for the modeling of stormwater according to
many embodiments of the invention. The inputs include spatial data
110, water management feature data 112, precipitation data 114, and
land and feature condition data 116.
[0028] Overly burdensome input data requirements for setup,
calibration, and validation of models is a barrier for appropriate
use by stormwater managers who are very often not hydrologic
modelling experts. Since catchment heterogeneity generally occurs
at spatial scales much finer than can be measured or represented in
a model, even the most detailed input data sets have important
limitations for characterizing catchments. In many embodiments, the
inputs that are required for the stormwater modeling engine are
greatly simplified and can be measured in an objective and
repeatable fashion. In several embodiments, the various inputs
(e.g., inputs 110-116 of FIG. 1) are gathered and collected from
various sources including, but not limited to, municipal databases,
satellite and aerial images, crowdsourced images, sensors, and
results captured through various standardized assessment
methods.
[0029] The inputs of the example described in FIG. 1 includes
spatial data 110. Spatial data in accordance with many embodiments
of the invention include various characteristics about the land
(e.g., catchments, roads, and water management features), including
(but not limited to) land surface type, soil type, precipitation
levels, topography, hydrologic connection to receiving waters,
traffic levels, and land use type. These characteristics are
grouped into discrete urban catchments that represent accurate
sub-drainages of urbanized areas. In some embodiments, the spatial
data is used by the stormwater model to quantify a baseline for
stormwater runoff and/or pollution generation based on the land
characteristics.
[0030] Stormwater models vary widely in terms of how the catchment
characteristics that generate the magnitude and timing of runoff
are defined. Land cover or land use inputs range from simple
distinctions between impervious and pervious land cover to
estimating the fraction of directly connected impervious surfaces
within multiple land use types. The stormwater model according to a
number of embodiments of the invention estimates stormwater runoff
volume on a land use basis using a standardized series of land use
types. For example, in certain embodiments, the land use types for
parcels of land include, but are not limited to, industrial use,
residential use, and cultivated use. Alternatively, or
conjunctively, roads may be classified based not only on their type
(e.g., paved, unpaved, gravel, etc.), but on the level of traffic
of the road (e.g., high, medium, low, etc.).
[0031] With regards to soil types, the stormwater model in
accordance with certain embodiments utilize specific soil
infiltration rates from hundreds of soil types defined by the
Natural Resources Conservation Service (NRCS) while, in other
embodiments, the stormwater model permits the selection of one of
four NRCS hydrologic soil groups.
[0032] In a number of embodiments, the spatial data is collected
from various public data sets. Alternatively, or conjunctively, the
spatial data is collected by analyzing various information. For
example, the spatial data according to certain embodiments of the
invention includes data that is gathered through machine vision
analyses of satellite imagery to determine such various
characteristics about the land.
[0033] In the example described with reference to FIG. 1, the
inputs further include water management feature data 112. In
several embodiments, water management feature data includes various
information about water management features that can be used to
calculate the effectiveness of each water management feature in
reducing stormwater runoff and/or reducing pollutants that flow to
receiving waters. Water management features, as used in this
application, is used to generally describe various practices,
structures, and/or other features of a geographic area that can be
built or applied in order to manage stormwater in the geographic
area. Water management features in accordance with many embodiments
of the invention include (but are not limited to) water management
policies, as well as structural and non-structural best management
practices (BMPs). Non-structural BMPs focus on source control and
pollution prevention, such as road street sweeping programs or
runoff generation reduction from urban parcels. Structural BMPs are
physical features specifically designed and installed to reduce
stormwater runoff volumes and to treat stormwater pollutants
generated from roads, parcels or larger mixed land use drainage
areas.
[0034] The water management feature data in accordance with several
embodiments of the invention includes various characteristics of a
BMP, including (but not limited to) the size of the BMP, the types
of outflow, construction materials, and/or outflow rates.
Stormwater modeling systems in accordance with a number of
embodiments of the invention can easily account for both structural
and nonstructural stormwater BMPs of various types, sizes, and
applications. In many embodiments, the stormwater model uses water
management feature data that incorporates a standardized sizing
regime and only requires the input of the fraction of impervious
area treated by BMPs for an entire catchment. Alternatively, or
conjunctively, the stormwater model may require specific BMP
dimensions using field measurements or storm size criteria. Beyond
the structural characteristics and original design specifications,
it is also possible to estimate the single and combined
effectiveness of these BMPs at infiltrating runoff and treating
pollutants in stormwater.
[0035] In the example described with reference to FIG. 1, the
inputs further include precipitation data 114. Precipitation data
in accordance with many embodiments of the invention includes
historic precipitation levels for a specified period of time, such
as, but not limited to a 30-year span. In some embodiments, the
historic precipitation data includes daily rainfall levels at a
specified geographic level (e.g., per catchment), allowing the
stormwater model to calculate percentile events for rainfall for
each geographic region. As many water management features are
designed to treat volumes generated from relatively high intensity
precipitation events, the effectiveness of a water management
features may not be linearly related to rainfall, but rather
dependent on the size of a storm. Accordingly, the stormwater model
in accordance with many embodiments of the invention calculates
annualized percentile rainfall events to more accurately compute
the baseline runoff levels and/or pollutant levels, as well as the
effects of water management features in these levels. While it is
important to use precipitation information that is relevant to a
specific catchment over space and time and obtain a reasonable
average annual runoff estimate, the stormwater model in accordance
with a number of embodiments of the invention does not account for
the nuances of seasonal and internal climate variability, as they
are not necessary to model the long-term benefit of effective
management actions.
[0036] The illustrated inputs to the stormwater model of FIG. 1
include land and feature condition data 116. Land and feature
condition data in accordance with several embodiments of the
invention describe the condition of the land and water management
features. While the spatial data and water management feature data
describes many of the unchanging characteristics of the land, the
land and feature condition data describes the condition of the land
and/or water management features.
[0037] In many embodiments, precise inspection procedures
consisting of standardized assessment methods are used to assess
parcel runoff potential, road pollutant generation potential, and
structural BMP performance relative to sustainable standards. The
standardized assessment methods in accordance with some embodiments
of the invention employ standardized sets of field observations to
ensure a consistent and repeatable measurement of each land area
and/or water management feature. Alternatively, or conjunctively,
the land and feature condition data includes information about the
land and/or water management features that is gathered remotely
through a variety of methods, such as, but not limited to, sensor
readings and image analysis of aerial and/or satellite imagery.
[0038] The stormwater model according to several embodiments of the
invention calculates a score or description of the condition of
each land area (e.g., parcels and catchments) and/or water
management feature. An urban parcel is determined to be `runoff
neutral` when standardized visual observations indicate that much
of the precipitation falling on the parcel does not leave as
runoff. The assessment of the condition of a land area may also
include other elements such as (but not limited to) trash levels
and road conditions. In some embodiments, a standardized assessment
method (e.g., Parcel RAM.TM.) is used to assess and document land
areas (e.g., to identify runoff neutral parcels, trash levels,
etc.). Parcel RAM.TM. is a standardized geospatial data collection,
management and reporting tool for assessing the condition of land
parcels and is described in greater detail in "ParceIRAM: Technical
and User Guidance Document v1" (available at
https://2ntelr.com/parcelram/dist/prod/data/ParceIRAMTechDoc_Web.pdf),
which is submitted herewith via Information Disclosure Statement
and incorporated herein by reference.
[0039] In many embodiments, standardized assessment methods (e.g.,
Road RAM.TM.) are used to assess and document the relative
pollutant generation threat of urban roads. Road RAM.TM. is a
standardized geospatial data collection, management and reporting
tool for assessing the condition of roads.
[0040] In several embodiments, standardized assessment methods
(e.g., BMP RAM.TM.) are used to assess and document the performance
and relative maintenance urgency of implemented water management
features. In a number of embodiments, the land and feature
condition data for the water management features serve as measures
of the localized effectiveness of the implemented stormwater
management runoff and pollutant controls over time and space. BMP
RAM.TM. is a standardized geospatial data collection, management
and reporting tool for assessing the condition of BMPs and is
described in further detail in "BMP RAM: User Guidance v3.2"
(available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMUserGuida-
nce3.1.pdf), "BMP RAM: Technical Document v3.1" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/11/BMPRAMv3-1_Technic-
alDoc_Nov2016. pdf), and "BMP RAM: Field Protocols v3.1" (available
at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMFieldProtoco-
ls3.1.pdf), which are submitted herewith via Information Disclosure
Statement and incorporated herein by reference. Additional
embodiments may utilize other assessment methods and inputs as
appropriate to the particular application.
[0041] In the example of FIG. 1, the various data 110-116 serve as
inputs to stormwater modeling engine 140. Stormwater modeling
engine 140 includes a baseline load calculator 150, managed load
calculator 160, and receiving water reduction calculator 170.
[0042] Baseline load calculator 150 in accordance with many
embodiments of the invention calculates a baseline (or unmodified)
load to a set of receiving waters. In certain embodiments, the
baseline load calculator defines the fraction of flow that
infiltrates over pervious surfaces and the fraction of overland
runoff that is eventually discharged to the receiving waters. In
many embodiments, the baseline load includes runoff and/or
pollutant loads that make it into the receiving waters based on
various characteristics of the land surrounding the receiving
waters including (but not limited to) land surface type, soil type,
precipitation levels, topography, hydrologic connection to
receiving waters, and land use type. These characteristics are
grouped into discrete urban catchments that represent accurate
sub-drainages of urbanized areas. In some embodiments, the
underlying urban geography and stormwater volumes are used to
quantify baseline stormwater runoff and pollutant generation of a
given urban drainage, delineated into smaller urban catchments on
the order of 100 acres.
[0043] Stormwater volume metrics can serve as cost-effective
proxies for pollutant loading in urban landscapes. While pollutant
concentrations in urban catchments may depend on factors such as
antecedent rainfall conditions, storm duration, intensity, etc.,
urban pollutant loads have been shown to depend primarily on runoff
event volumes. Since the annual runoff is a summation of events
throughout the year, it is reasonable to infer dependence of the
annual loading on annual runoff volumes in urban catchments, just
as it has been shown in other types of catchments.
[0044] In the example of FIG. 1, baseline load calculator 150
includes spatial engine 152 and precipitation engine 154. Spatial
engine 152 in accordance with some embodiments of the invention
models the effects of the input spatial data collected regarding
the land area and water management features. The spatial data
regarding the land area can be used to determine a baseline load
for runoff and/or pollutants based on characteristics of the land
itself, while the spatial data regarding the water management
features can be used to determine the effect of the water
management features on reducing the baseline load.
[0045] In various embodiments, spatial engine 152 uses water
management feature data to compute the effects of large scale
centralized structural BMPs (e.g., treatment vaults, infiltration
basins, or dry basins), which typically treat stormwater runoff
from mixed land use catchments and have treatment capacities on the
order of an acre-foot. Stormwater can exit a centralized BMP in one
of three ways: soil infiltration, through a treatment aperture, or
via bypass where no treatment or detention occurs. Some models also
include evaporative losses, but given proper functioning,
structural BMPs should have drawdown times on the order of hours
and evaporation can be assumed negligible. The relative components
of volume loss depend on the BMP type and design specifics. For
example, an infiltration BMP has only infiltrated and bypassed
volumes, while a treatment vault has only treated and bypassed
volumes (no infiltration).
[0046] Precipitation engine 154 in accordance with a number of
embodiments of the invention uses precipitation data to compute
predicted rainfall levels for the land area. In many embodiments,
precipitation engine 154 uses an approach that brackets the
seasonal and inter-annual variability demonstrated by historic
precipitation data from any climatic region in order to focus on
long-term average annual runoff volumes. The long-term average
annual runoff volumes allow the stormwater modeling engine in
accordance with several embodiments to provide a comparison of
various water management plans by using a set of fixed
precipitation inputs for multiple scenarios over a period of
multiple years.
[0047] For example, in several embodiments, precipitation engine
154 uses a probabilistic approach to determine local precipitation
patterns to estimate average daily runoff from multiple 24-hr
events rather than using a single rainfall-runoff ratio. To isolate
the water quality benefit signal as a result of management actions
(e.g. structural and non-structural BMPs), the same precipitation
inputs are used in all of the modeled scenarios for each urban
catchment. Additional embodiments may utilize other inputs and
methods for calculating baseline loads as appropriate to the
particular application.
[0048] Managed load calculator 160 in accordance with some
embodiments is used to calculate the load at the catchment level,
based on the spatial features and precipitation levels calculated
by the baseline load calculator 150. In the example of FIG. 1,
managed load calculator 160 includes water management feature
module 162 and drainage routing module 164. The water management
feature module in accordance with various embodiments of the
invention is used for calculating the effects of water management
features in reducing the calculated baseline load, which is
calculated based on calculated precipitation levels and effects of
the various land areas.
[0049] In certain embodiments, the water management feature module
models water management features, such as (but not limited to)
centralized BMPs, using the USDA TR-55 (1986) methodology for
estimating peak inflow and peak outflow. Calculations for
infiltrated, treated, and bypassed stormwater runoff volumes are
completed for each prescribed 24-hr percentile storm event. Average
annual infiltrated, treated, and bypassed stormwater volumes are
estimated using the trapezoid rule and the average number of rain
days per year. In some embodiments, the water management feature
module calculates the effects based on the characteristics of the
water management features, including (but not limited to) the size
of the water management feature, whether the feature filters the
water, the types of outflow, construction materials, and/or outflow
rates. In some embodiments, the effects of the water management
features are calculated for portions (e.g., parcels and/or
catchments) of a geographic region that feed into one or more
receiving waters.
[0050] Spatially distributed models can incorporate flow routing
across different land use types rather than lumping similar land
use types within a catchment. Drainage routing module 164
calculates receiving runoff based on interactions of calculated
loads (e.g., stormwater runoff and/or pollutant generation) between
the portions and/or water management features of the geographic
region. Draining routing module 164 in accordance with a number of
embodiments of the invention calculates the routing of stormwater
through a sequence of catchments and their associated water
management features. For example, the simulation of flow routing
through structural BMPs can vary widely depending on the
characteristics of the BMP, including (but not limited to) the size
of the BMP, the types of outflow, construction materials, and/or
outflow rates. Drainage routing modules in accordance with a number
of embodiments of the invention can account for both structural and
nonstructural stormwater BMPs of various types, sizes, and
applications. In some embodiments, the drainage routing module
allocates the calculated loads (e.g., runoff and/or pollution) to
the receiving waters in manner that is proportional to the various
catchment's contributions, allowing for a user to view the paths
and sources of load contributions to the receiving waters at the
catchment level. Additional embodiments may utilize other inputs
and methods for calculating managed loads as appropriate to the
particular application.
[0051] Stormwater modeling engine 140 also includes a receiving
water reduction calculator 170 for calculating the cumulative
contributions from precipitation levels, land surfaces, and/or
water management features to the various receiving waters in a
geographic area. In certain embodiments, the receiving water
reduction calculator 170 aggregates the calculations of the
baseline load calculator 150 and the managed load calculator 160
for all of the urban catchments of a region to calculate the
results of various analyses performed by the stormwater modeling
engine, including (but not limited to) the cumulative effects to
the receiving waters, the effects of various water management
planning scenarios (e.g., the implementation of various water
management policies, installation of new water management features,
etc.), and forecasts for future stormwater and/or pollutant
loads.
[0052] The receiving water module, in accordance with several
embodiments of the invention, uses a coupled mass balance and
hydrologic routing approach to aggregate the runoff and pollutant
generation benefits of non-structural BMPs at the road and parcel
source, followed by volume and load reductions achieved via
detention or retention by structural BMPs prior to the volume and
load eventually being discharged to the receiving waters. In a
number of embodiments, the receiving water module combines the
fundamental urban hydrology calculations from the U.S. Department
of Agriculture with a hydrograph separation approach to handle flow
partitioning and estimate runoff reductions achieved by a range of
large-scale, centralized structural BMP types. Additional
embodiments may utilize other inputs and methods for calculating
receiving water reductions as appropriate to the particular
application.
[0053] In several embodiments, the stormwater modeling engine
includes a display engine (not shown) that automatically generates
results in standardized formats by mapping where actions are
implemented and quantifying the relative effectiveness of those
actions, providing an objective and transparent approach to urban
land management accounting.
[0054] While spatially distributed models often include detailed
physical process representation, they don't necessarily offer the
most useful outputs for users. Uncertainty commonly associated with
complex stormwater models can make comparisons over time or testing
heuristic scenarios difficult since the results depend strongly on
model parameter values that may be poorly defined, and vary over
time and space. Even where good hydrological data are available,
they are often only sufficient to support reliable calibration of
models of very limited complexity.
[0055] Many existing models for measuring stormwater runoff
struggle to determine the specific effectiveness of actions taken
to manage stormwater runoff. A model that does not exhibit
hydrologic sensitivity to water management features is of little
use to stormwater managers who need to use it as a planning,
reporting and decision making tool. Inclusion of extraneous model
components or parameters that do not result in a measurable output
response can fortify a model against discerning hydrologic changes
in a catchment over time. Models such as the widely used stormwater
management model (SWMM) with numerous free parameters requiring
user calibration often only include a few key input variables that
contribute significantly to the outputs, which translates to
greater uncertainty. This uncertainty reduces confidence and
precision that the results are sensitive to the effect of BMPs
rather than error or variability contained within the model
algorithms. Imprecise models are of little use to stormwater
managers who must know where stormwater flows, where their features
are located, if their features require maintenance, and what
progress they are making toward improvement goals.
[0056] It is often difficult to identify a convincing demonstration
of load reductions that result from effective management actions
and involve a degree of change that can be detected above other
sources of variability. When only short-term monitoring data is
available, such changes are often difficult to detect to a high
level of confidence. Current approaches to tracking and reporting
these practices have yet to show compelling evidence of widespread
changes in receiving water quality throughout the nation despite
significant investments. To improve receiving water quality and
restore associated ecological functions, the limitations of the
current trajectory need to be acknowledged so that available
resources can be more efficiently used to implement sustainable
practices with a new degree of focus, transparency, and
accountability.
[0057] In many embodiments, the stormwater modeling engine is
useful in identifying areas for improvement in a geographic area.
The stormwater modeling engine in accordance with several
embodiments of the invention provides a detailed breakdown of the
calculated load contributions at various levels, including (but not
limited to) by catchment, parcel, receiving water, municipality,
road, and water management feature.
[0058] Stormwater modeling engines in accordance with a number of
embodiments of the invention function on the urban catchment scale,
which is an appropriate unit for stormwater managers who need to
evaluate and compare results to inform decisions and track
environmental benefits. The urban catchment spatial scale allows
users to easily set up scenarios for multiple catchments with batch
uploads of tables from a geographic information system (GIS) that
specifies the catchment characteristics that are utilized as model
inputs. In many embodiments, various characteristics of the
catchments, such as (but not limited to) boundaries and land use
type, are readily available data that can be collected from data
stores of a municipality.
[0059] At the catchment scale, the stormwater modeling engine in
accordance with various embodiments of the invention is `lumped`
rather than distributed, meaning that calculations are performed
for the catchment rather than within spatially referenced grid
cells. This approach greatly limits the uncertainty in parameter
calibration associated with a distributed model, and allows for the
gathering of the required inputs for multiple catchments en masse
using geographic information system (GIS) spatial analysis tools.
In some embodiments, at least a portion of the required inputs are
gathered from satellite imagery of a geographic area. The use of
the catchment spatial scale aligns with manageable drainage areas
where water quality improvement actions can be planned and
effective actions can have measurable reductions on the quantified
loading to the receiving waters on annual time scales.
[0060] With a parsimonious approach and minimal parameterization,
systems and methods in accordance with several embodiments of the
invention reduce uncertainty from insensitive model components to
create a tool that will be appropriate for testing water management
feature implementation scenarios over time and across multiple
catchments. By keeping different water management feature
implementation schemes and other elements of a scenario fixed, the
model exhibits measurable changes in the predicted hydrologic
response. The ability to forecast detailed effects of water
management features across several different scenarios allows the
stormwater modeling engine to provide valuable insights with
regards to stormwater runoff and pollutant loading. In some
embodiments, the stormwater modeling engine provides insight into
the expected benefits of various stormwater management scenarios,
allowing a user to identify the greatest reductions to stormwater
runoff and/or pollutant loads. The stormwater modeling engine of
certain embodiments provides the user with costs associated with
the various stormwater management solutions, allowing the user to
maximize a budget and/or minimize the costs required to meet
various guidelines and regulations. Methods and systems for
modeling stormwater effects according to a number of embodiments of
the invention are described in further detail below.
Methods for Modeling Stormwater Runoff
[0061] A process for managing stormwater runoff environments is
conceptually illustrated in FIG. 2. To inform stormwater management
and be usable by the municipal stormwater community, inputs to a
stormwater model according to various embodiments of the invention
are specified with commonly available data sources and capture the
need for on-going maintenance and management to achieve sustained
water quality benefits. In addition to being computationally
simpler, the process for stormwater modeling in accordance with
various embodiments of the invention is designed to have lower
input data requirements than more sophisticated alternatives, with
the aim that users can spend less time gathering, processing, and
managing data. With limited inputs, it is easier to provide users
of stormwater modeling systems a clear, standardized process for
creating input data and running simulations. This consistency in
data input generation translates to increased consistency and
comparability of model results amongst users with varying levels of
modeling expertise. The input formats and modeling procedure are
structured to enable multiple users to represent the same features
(e.g., water management features, catchments, parcels, roads, etc.)
with the same available data in the same way. The results can then
be used to compare area normalized runoff volumes across catchments
to inform priorities where stormwater actions are likely to provide
the greatest benefits to the receiving waters.
[0062] The process according to some embodiments of the invention
gathers (210) spatial data that can be used to generate a
description of various physical features, such as (but not limited
to) land portions (e.g., urban catchments and parcels) and roads
for a particular analysis. The spatial data according to several
embodiments of the invention includes various characteristics of
the geographic area including (but not limited to) parcel
information, land use distribution, soil type, parcel condition,
road information, road conditions, topography, imperviousness,
existing stormwater infrastructure (e.g., water management
features), and catchment connectivity. Some models incorporate a
standardized sizing regime and the only user input is the fraction
of impervious area treated for the entire catchment.
[0063] In some embodiments, the boundaries of each portion of land
(e.g., catchments, parcels, etc.) are defined by a municipality in
terms of geographic coordinates (e.g., latitude and longitude).
Examples of delineating urban catchments are described in further
detail in "Delineate Urban Catchments: Guidance for Creating
Catchment Boundaries and Attributes v3.1" (available at
http://www.2ndnaturellc.com/documents/MS4_Mapping_Guidance.pdf),
which is submitted herewith via Information Disclosure Statement
and incorporated by reference herein. In several embodiments,
catchment boundaries and hydrologic routing are primarily
determined by intersecting local stormwater infrastructure data
layers and a high-resolution digital elevation model (DEM). Large
drainages are split into smaller, approximately 100-acre catchments
using basic flow routing principles. Often there are a series of
catchments that drain to the same receiving water, and together
they form a hydrologic unit that we refer to as an urban drainage.
Once the catchment boundaries are finalized, a series of catchment
attributes are generated using a variety of freely available
spatially referenced datasets and GIS tools, and the data are
organized into standardized templates and ready for upload to the
stormwater modeling system. The defined boundaries are then used in
various assessments and/or automated processes to classify and
measure the imperviousness of each catchment. Example methods for
such assessment methods are described in greater detail in
"ParceIRAM: Technical and User Guidance Document v1" (available at
https://2ntelr.com/parcelram/dist/prod/data/ParceIRAMTechDoc_Web.pdf),
"BMP RAM: User Guidance v3.2" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMUserGuidance-
3.1.pdf), "BMP RAM: Technical Document v3.1" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/11/BMPRAMv3-1_Technic-
alDoc_Nov2016. pdf), and "BMP RAM: Field Protocols v3.1" (available
at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMFieldProtoco-
ls3.1.pdf), which are submitted herewith via Information Disclosure
Statement and incorporated herein by reference.
[0064] In certain embodiments, the imperviousness of the land cover
and soil type for each land portion are critical inputs for
estimating stormwater runoff for a given location. The
imperviousness of the land cover measures the volume of water that
is infiltrated by the land cover, based on the fractional area
available for stormwater infiltration (1-% impervious) and the
relative ability of the soil in pervious areas to absorb and
infiltrate water. In many cases, the percent impervious (PIA) of
any land use type varies within and across municipal separate storm
sewer systems (MS4s). The process according to some embodiments of
the invention accounts for PIA variability by creating a consistent
and repeatable approach to estimate the PIA for each land use type
via a series of empirical equations.
[0065] Alternatively, or conjunctively, the process according to
some embodiments of the invention gathers spatial data, such as
(but not limited to) the percent impervious of a catchment (PIAC),
from images of the catchment, such as (but not limited to)
satellite and/or aerial imagery. Determining the PIAC using such
imagery has two benefits. First, images that capture such
impervious coverage data is widely available, can be easily
accessed by any municipality, and provides consistency across
municipalities. Second, catchment images incorporate the urban tree
canopy to estimate overall impervious area, allowing for the
incorporation of the benefits that urban trees provide in the
actual rainfall-runoff transformation in urban drainages. In some
embodiments, any of a variety of machine vision and machine
learning methods are applied to the images for any of a variety of
applications, including (but not limited to) the identification of
catchments, determination of the PIAC for each catchment, and the
classification of land use for each catchment.
[0066] The process according to a number of embodiments gathers
spatial data related to land use using a standardized series of
land use types. For example, in certain embodiments, the land use
types for parcels of land include, but are not limited to,
industrial use, residential use, and cultivated use. Alternatively,
or conjunctively, roads may be classified based not only on their
type (e.g., paved, unpaved, gravel, etc.), but on the level of
traffic of the road (e.g., high, medium, low, etc.). In some
embodiments, land use types are used to generate a baseline level
of pollution load for a geographic area.
[0067] In addition to properties of the individual catchments,
catchment connectivity is a critical element of generating reliable
estimates of average annual runoff and loading derived from an
urban catchment and delivered to a receiving water. Catchment
connectivity is defined as the proportion of stormwater discharging
from a catchment discharge point that reaches the receiving water
and is not diverted in some way. The process according to many
embodiments of the invention categorizes the catchment connectivity
between catchments based on an amount of flow that enters a
receiving water during different precipitation events (e.g., a
storm within the xth percentile of a peak daily runoff).
[0068] In several embodiments, the process adjusts all catchment
runoff and loading estimates based on the relative hydrologic
connectivity of each catchment to the receiving water. The process
according to many embodiments of the invention uses a systematic
and consistent process to determine the relative hydrologic
connectivity of a catchment to receiving waters based on the
distance, substrate and visual characteristics of the flow path
that physically connects the discharge point of a specific
catchment to the receiving water. Examples of such processes are
described in "ParceIRAM: Technical and User Guidance Document v1"
(available at
https://2ntelr.com/parcelram/dist/prod/data/ParceIRAMTechDoc_Web.pdf),
"BMP RAM: User Guidance v3.2" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMUserGuidance-
3.1. pdf), "BMP RAM: Technical Document v3.1" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/11/BMPRAMv3-1_Technic-
alDoc_Nov2016. pdf), and "BMP RAM: Field Protocols v3.1" (available
at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMFieldProtoco-
ls3.1.pdf), which are submitted herewith via Information Disclosure
Statement and incorporated herein by reference. Working inland from
the receiving waters, all catchments that drain into another
catchment inherit the same connectivity as the downstream
catchment, unless credible evidence suggests some surface volume
loss occurs between the two catchments.
[0069] The process 200 also gathers (212) water management feature
data. The water management feature data according to some
embodiments includes data for existing water management features
and/or planned water management features that have not yet been
implemented. In several embodiments, the water management feature
data includes various characteristics of water management features
(e.g., BMPs), including (but not limited to) the size of the water
management feature, the types of outflow, construction materials,
and/or outflow rates. The process according to certain embodiments
of the invention gather the water management feature data based on
structural characteristics and/or the original design
specifications for each water management feature. The water
management feature data according to some embodiments of the
invention allows the process to estimate the single and combined
effectiveness of these water management features at infiltrating
runoff and treating pollutants in stormwater.
[0070] In some embodiments, the process gathers information about
the type of each water management feature and/or the effects of
various characteristics of a water management feature on the way
that the water management feature is able to manage the flow of
stormwater. For example, the characteristics that are measured in
accordance with certain embodiments include (but are not limited
to) whether the feature filters the water, a water capacity of the
feature, as well as the size and surface material of the
feature.
[0071] In many embodiments of the invention, the process gathers
feature data for the proposed water management features based on
the performance of similar water management features and their
effects in other areas. In certain embodiments, the process applies
various machine learning techniques to previously collected data
regarding the effectiveness of other water management features, in
conjunction with the proposed water management features, in order
to predict the effectiveness of the proposed water management
features.
[0072] The quantification of non-structural BMPs aligns with common
practices implemented on parcels and roads. On parcels, common
`non-structural` practices are those that most importantly reduce
the amount of runoff leaving a parcel. These urban parcel site
design elements are common in low impact development and green
infrastructure designs and include practices such as reduced use of
concrete or asphalt for walkways, patios, driveways, etc.,
downspout disconnection, installation of French drains, routing of
impervious surface runoff to pervious areas, green roofs, onsite
rain capture and reuse, etc. Effective implementation of any
combination of non-structural BMPs on specific parcel can
effectively achieve the desired parcel condition where surface
water does not exit the parcel.
[0073] The process according to many embodiments of the invention
gathers (214) land and feature condition data that describes the
condition of the various features (e.g., water management features,
land parcels, catchments, roads, etc.). In some embodiments, the
land and feature condition data is generated according to
customized, standardized assessment methods (e.g., 2N RAMs.TM.)
used to quantify the performance of land portions (e.g.,
catchments, parcels, etc.) as well as existing water management
features (e.g., structural and non-structural BMPs) implemented
throughout the MS4. The assessment methods according to various
embodiments of the invention are based on the knowledge of experts
in sustainable land management and available monitoring data. In
many embodiments of the invention, the assessment methods are
repeatable, objective assessment tools whose data are direct inputs
to annual runoff and loading reduction estimates.
[0074] The assessment results eliminate any assumptions regarding
the effectiveness of specific actions or practices. Rather, the
assessment methods provide directly observable evidence that roads,
parcels and/or structural BMPs conditions are represented in the
model at current conditions. This focus on water management
features as assets that need to be managed will concentrate
attention on the importance of effective maintenance actions
towards sustaining the water quality benefits of these investments
year after year.
[0075] In a number of embodiments, the gathered condition data is
generated by a variety of assessment methods which employ
standardized protocols for field observations. Examples of such
protocols are described in further detail in "ParcelRAM: Technical
and User Guidance Document v1" (available at
https://2ntelr.com/parcelram/dist/prod/data/ParceIRAMTechDoc_Web.pdf),
"BMP RAM: User Guidance v3.2" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMUserGuidance-
3.1. pdf), "BMP RAM: Technical Document v3.1" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/11/BMPRAMv3-1_Technic-
alDoc_Nov2016. pdf), and "BMP RAM: Field Protocols v3.1" (available
at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMFieldProtoco-
ls3.1.pdf), which are submitted herewith via Information Disclosure
Statement and incorporated herein by reference. In some
embodiments, the process gathers the data for water management
features based on an image analysis of images (e.g., satellite
imagery, aerial imagery, etc.) that capture the condition of BMPs
for a geographic area. Alternatively, or conjunctively, the process
of many embodiments gathers data from remote sensors (or computing
devices connected to such sensors) that provide additional
information regarding a condition or state (e.g., water level) of a
water management feature.
[0076] In many embodiments, the process 200 calculates (216)
precipitation level patterns for the geographic area under
consideration. Precipitation is an important factor in a stormwater
model because it defines the total amount of rainfall that reaches
the catchment and, in turn, is directly linked to the total amount
of estimated runoff. Typically, stormwater models use one of two
approaches for modeling precipitation levels: a single storm event
methodology or a multi-year, high-resolution (daily or sub-daily)
continuous simulation. Each approach has its advantages and
disadvantages. Event-based approaches are programmatically simple
but were originally designed to simulate runoff for a single storm
event size. They have also been used to estimate long term average
annual runoff by modeling one average 24-hour event and
extrapolating to the entire year. Continuous simulations are better
able to capture the dynamic range of seasonal precipitation events,
storing and applying antecedent conditions. Continuous, rather than
event, simulations are generally thought to represent catchment
rainfall-runoff response to the best extent practical, but these
models can be computationally burdensome and costly to develop and
maintain.
[0077] Consistent with the development objective of a
computationally simple and robust model, the process according to
many embodiments of the invention utilizes an event-based approach
to calculating the expected precipitation levels, but estimates
average daily runoff from multiple 24-hr events rather than using a
single rainfall-runoff ratio. The process according to many
embodiments of the invention calculates the precipitation level
patterns by building a frequency distribution of 24-hr rainfall
depths (24-hr event frequencies), and uses the average annual
number of days with rain to generate the average annual runoff
estimates. In certain embodiments, event-based runoff is calculated
for each land use type and then aggregated using a set of storm
frequency intervals to generate average annual runoff estimates.
Effectively, the model of the expected precipitation levels
functions as an event-based model, but uses a set of inputs
designed to be representative of the distribution of the potential
range of inputs used by a continuous model.
[0078] The goal in processing the precipitation data is to
adequately represent the rainfall distribution with a simplified
set of inputs that closely approximate the mean annual rainfall
calculated for the entire dataset. In a number of embodiments, the
hydrologic estimation approach utilized by the stormwater modeling
systems is able to predict average annual runoff within 10% of
widely accepted models that have more complex data entry
requirements.
[0079] Based on historic daily rainfall data, the process according
to various embodiments of the invention calculates, d, the average
number of rain days per water year when daily rainfall exceeds 0.01
inches and, PPT(x), various 24-hr event frequency estimates, where
PPT is the 24-hr rainfall (inches) for the xth percentile event. In
many embodiments, the rainfall data is evaluated on a water year
basis. In some embodiments, the process applies the trapezoid rule
to estimate the integral of the 24-hr event frequency distribution
and obtains a long-term average 24-hr rainfall volume for days when
it rains. The process according to many embodiments of the
invention approximates the integral using the following equation
for non-uniform intervals of x:
.intg. 0 100 PPT ( x ) dx .apprxeq. 1 2 k = 1 N ( x k + 1 - x k ) *
( PPT ( x k + 1 ) + PPT ( x k ) ) ##EQU00001##
where x is a number between 0 and 100, exclusive, k is number in
the sequence of total, N, percentile events used to estimate the
integral. To obtain a long-term average 365-day rainfall volume,
PPT365, the process according to several embodiments of the
invention multiplies the 24-hr average by the number of rain days
per year, d:
PPT.sub.365=d*.intg.PPT(x)dx
[0080] In some embodiments, the process calculates (218) the
aggregate effect of water management features on the stormwater
runoff and pollutant load to a set of receiving waters. In many
embodiments, the stormwater model uses the gathered spatial data,
water management feature data, and land and feature condition data
to estimate annual runoff and pollutant loads. In some embodiments,
the process also accounts for a condition of the water management
feature. For example, in certain embodiments, the effectiveness of
a water management feature decreases as time passes or based on the
physical condition of the water management feature. The process
according to several embodiments of the invention calculates the
aggregate effect and/or the effects at each individual land portion
based on a common scaleable unit (e.g., annual runoff, total
suspended solids (TSS)). The common scaleable unit can serve as a
standardized accounting unit to prioritize and track stormwater
improvement actions in reducing pollutant loads to a receiving
water between regions and over various periods of time.
[0081] In a number of embodiments of the invention, runoff
estimates are driven by the rainfall inputs and the catchment
attributes (land use, impervious area, hydrologic connectivity,
soils, etc.) for rainfall-runoff transformation. The process
according to several embodiments of the invention preserves
hydrologic routing on the urban drainage scale and ensures
consistent aggregation over hydrologically linked catchments.
[0082] In many embodiments, the stormwater modeling system relies
on the Soil Conservation Service (SCS) curve number (CN) method and
the approach detailed in Technical Release 55 (TR-55) to estimate
runoff from small urban catchments, the disclosure of which is
hereby incorporated by reference herein in its entirety. The SCS
runoff equation is:
Q LU = ( PPT - I a ) 2 ( PPT - I a ) + S ##EQU00002##
where QLU is the runoff depth (inches) for each land use, PPT is
the 24-hr rainfall volume (inches), S is the potential maximum
retention after runoff begins (inches), and I.sub.a is the initial
abstraction (inches). The initial abstraction incorporates all
losses before runoff begins, including water retained in surface
depressions, water intercepted by vegetation, evaporation, and
infiltration. Runoff does not begin until the initial abstraction
has been met. I.sub.a is variable across the landscape but is
highly correlated to the curve number. Curve numbers range from 30
to 98 and lower numbers indicate low potential runoff whereas
higher numbers indicate increasing runoff potential. The major
factors that determine SCS curve numbers are the soil permeability
and infiltration classified into the NRCS HSGs, the land use
(specifically, the percent impervious of the land use), and the
hydrologic condition.
[0083] To simply account for variations in soil permeability and
infiltration, the NRCS has classified soils into four hydrologic
soil groups (HSGs). Stormwater modeling systems in accordance with
several embodiments of the invention assume that all land uses have
poor hydrologic condition when no parcel-scale BMPs are
implemented.
[0084] Hydrologic computations according to various embodiments of
the invention combine a set of metrics that describe a 30-year
rainfall distribution with well-tested USDA algorithms for
rainfall-runoff transformation and routing to generate average
annual runoff estimates for each catchment. In several embodiments,
decentralized and non-structural BMPs effects on runoff and
pollutant generation are based on best available science and
datasets. A hydrograph separation approach in accordance with many
embodiments of the invention quantifies the water quality benefits
of centralized, large-scale structural BMPs. In some embodiments,
the process calculates the effectiveness of structural water
management features based on the functions specific to each
feature.
[0085] The process according to various embodiments of the
invention calculates the aggregate effects of water management
features over a range of precipitation conditions. Structural
stormwater best management practices (BMPs) are designed to treat
volumes generated from relatively high intensity precipitation
events, but in reality accept and treat stormwater and associated
pollutants across a range of runoff event magnitudes, intensities
and durations. For example, the process according to many
embodiments calculates the infiltrated, treated, and bypassed
volumes for a water management feature using graphical methods.
Separation of the infiltration volume is determined by drawing a
flat line across the hydrograph at the infiltration flow rate
(cfs), calculated as the product of the infiltration rate (in/hr)
and the basin footprint (sq ft) with proper unit conversion.
Separation of the treatment volume is defined by drawing a flat
line across the hydrograph at the treatment flow rate (cfs), and
depending on the BMP type, can be estimated as quotient of the
treatment capacity (ac-ft) and the drawdown time (hr) with proper
unit conversion. Both the infiltration volume and the treatment
volume are calculated as the area of the outflow hydrograph under
the respective flow rates down to zero. If the sum of the
infiltrated and treated volumes is less than the total outflow
volume, then the remaining volume is allocated to bypass. If the
sum of the infiltrated and treated volumes is greater than the
total outflow volume, then the treatment volume is reduced to
accommodate the difference and the volumetric balance between
inflow and outflow is retained.
[0086] In several embodiments, the stormwater modeling system can
operate in either of two scenarios: baseline (without BMP
implementation) and current (with BMP implementation and
performance) to estimate stormwater reductions that result of
cumulative BMP implementation. In both scenarios, the precipitation
inputs are constant and represent the degree of hydrologic
variability present in the historical record. Changes from one
scenario to another for a given catchment are primarily associated
with the implementation of BMPs. Runoff reductions are also
normalized by catchment size for standardized comparison between
catchment results within a municipality to inform spatial
priorities.
[0087] In many embodiments, the calculated aggregate effect is
presented as a score or description of each parcel's effect on a
receiving water. Parcel effect according to certain embodiments of
the invention is a measure of runoff neutrality. A parcel is
determined to be `runoff neutral` when standardized visual
observations indicate that much of the precipitation falling on the
parcel does not leave as runoff. There are many combinations of
actions or practices that can be implemented on an urban parcel to
achieve runoff neutrality. Parcel improvements are practical to
implement and, if implemented over high density of parcels within a
catchment, they can begin to restore the natural hydrograph of the
landscape by diffusing stormwater infiltration throughout a
catchment.
[0088] Many of the examples and steps are described with reference
to stormwater runoff volumes. However, in many embodiments, similar
calculations are used to measure pollutant load. Particulates are
modelled via land-use based characteristic runoff concentrations to
represent the hydrophobic urban-derived pollutants. The process of
many embodiments can accommodate multi-pollutant modeling, focused
on stormwater runoff volumes (average annual runoff (volume per
year)) and particulate pollutant loads (mass per year). The
prominent role that stormwater surface runoff has on both the
hydrology and pollutant loading to receiving waters makes the
focus, quantification and tracking of effective urban land
management actions in the context of surface runoff reductions
extremely powerful and informative. In addition, modeling and
monitoring of stormwater volumes can be done more accurately and
more precisely at orders of magnitude less cost than the cost to
sample, analyze, manage and report on a pollutant by pollutant
basis.
[0089] The process according to some embodiments of the invention
calculates a total suspended solids (TSS) value as a surrogate for
particulate pollutants. Most urban pollutants of concern are
hydrophobic, and their fate and transport in the environment is
similar to particulates rather than dissolved pollutants.
Hydrophobic pollutants include, but are not limited to, trace
metals (e.g., zinc, copper, iron, arsenic, etc.); hydrocarbons
(e.g., oil and grease, benzene, toluene, xylene, etc.); total and
dissolved phosphorous; and pathogens (e.g., fecal coliform, total
coliform, etc.). Since pollutant loads are strongly dependent upon
volumes, attempting to capture the short-term variability in
pollutant concentrations is less important for the process
according to many embodiments of the invention. In several
embodiments, the process uses static runoff concentrations for
different land-uses.
[0090] The process according to some embodiments calculates the
particulate catchment loads for the baseline and mitigated
scenarios as the product of the stormwater volume and a pollutant
concentration using the following general equation:
Pollutant Load ( mass time ) = stormwater runoff ( volume time ) *
pollutant concentration ( mass volume ) ##EQU00003##
[0091] In some embodiments, the process uses a single
characteristic runoff concentration (CRC) to describe the pollutant
concentration for each land use, consistent with the approaches
that have been successfully employed by other regional stormwater
mitigation tracking models. A CRC is defined as the expected
average annual pollutant concentration generated from a land use in
a particular condition across a range of event types. While similar
to event mean concentration (EMC) values commonly applied in
stormwater modeling, CRCs are intended to be an annual volume
weighted average of EMC values. The use of CRCs simplifies and
focuses the pollutant generation algorithms in a manner that aligns
with the need to isolate the signal of effective management
practices in a manner that can be objectively measured and
verified.
[0092] By focusing computations on runoff reductions from
sustainable and effective improvements on parcels and pollutant
concentration reductions on paved roads, the process according to
certain embodiments aligns the land use conditions and their
associated influence on the average annual catchment loads. This
approach also allows for the verification of model inputs using
land use condition observations. If non-structural BMPs are
effectively implemented over the majority of the urban land area
(and these improved land conditions can be observed and verified)
within an urban catchment, they are anticipated to have substantial
and measurable runoff and/or load reduction at the catchment
discharge point.
[0093] The process according to certain embodiments of the
invention manages (220) the stormwater effects based on the
calculated effects of the water management features. Each year,
municipalities can track and communicate estimated reductions
delivered to receiving waters because of management actions. Mapped
comparisons of current stormwater runoff and pollutant loading
rates across catchments inform the current water quality
improvement opportunities and identify changing patterns as
management actions are implemented. This information helps local
governments iteratively manage infrastructure and plan for capital
improvement investments on time scales that align with their
budgets and decision-making processes.
[0094] In many embodiments, the process manages the stormwater
effects by managing the condition of land areas and/or water
management features. For example, the condition of a land area may
be managed through management practices that reduce stormwater
effects, such as (but not limited to) redirecting water outputs to
lawns, building structural BMPs, and installing rain barrels.
Reductions in the water quality impacts of paved roadways can be
achieved by effective street sweeping programs, maintenance of
pavement condition and other water quality minded road management
practices. In some embodiments, the benefits of effective paved
road condition improvements in the process are quantified by
adjusting road characteristic runoff concentration (CRC) values,
which represent a concentration for a pollutant of concern in
stormwater runoff from a specific urban land use and its associated
condition. The condition of roads in accordance with some
embodiments of the invention is sustained by effective management
practices, such as (but not limited to) modifying street sweeping
schedules, organizing trash pickups, and repairing road surfaces,
which reduce pollutant accumulation and subsequent transport.
[0095] In some embodiments, the process manages the stormwater
runoff by providing various recommendations for water management
based on the calculated runoff volumes including, but not limited
to water management features to be installed, schedules for street
sweeping, and policies to be enforced. The process according to
several embodiments of the invention iteratively calculates the
aggregate effects of different sets of water management features to
identify effective strategies for reducing stormwater runoff. In
certain such embodiments, the process is provided with a cost
constraint and costs are associated with a cost to implement. The
process according to some such embodiments then identifies a best
set of water management features to maximize the stormwater runoff
reduction within a given cost constraint.
[0096] The process according to certain embodiments of the
invention provides a user of the system with a visual, spatial
representation of the sources of runoff at varying levels of
specificity. The visual representation according to certain
embodiments provides a tool that allows the user to identify runoff
sources based on a receiving water, catchment, and/or parcel.
[0097] In many embodiments, the process manages the stormwater
runoff directly by modifying different water management features
directly. For example, in some embodiments, the process manages the
stormwater runoff by using remote controllers and communication
systems to modify existing water management features. While a
specific example of a process for modeling stormwater runoff is
described above, one of ordinary skill in the art can appreciate
that various steps of the process can be performed in different
orders and that certain steps may be optional according to some
embodiments of the invention. As such, it should be clear that the
various steps of the process could be used as appropriate to the
requirements of specific applications.
Systems for Modeling Stormwater Runoff
[0098] The system for modeling stormwater runoff in accordance with
many embodiments of the invention allows simple and consistent
management, storage and instant recall of BMP implementation,
effectiveness and associated catchment results. The platform allows
users to easily assess stormwater runoff and particulate pollutant
load changes at different spatial scales. Municipal users can also
access and export their data at any time for additional management
and reporting needs.
[0099] An example of a system for managing stormwater runoff
environments is illustrated in FIG. 3. Use of stormwater modelling
systems in accordance with certain embodiments of the invention can
provide information concerning the aggregated effects of various
water management features that affect the stormwater runoff levels
of a particular geographic area. Water management features
according to various embodiments of the invention include various
elements and/or processes that affect stormwater runoff levels
including (but not limited to) structural stormwater best
management practices (BMPs). System 300 includes various data
gathering elements 310, a stormwater modeling system 320, and
management elements 330. The data gathering elements 310,
stormwater modeling system 320, and management elements 330 are
connected by a network 350.
[0100] Network 350 can be, but is not limited to, the Internet, a
local area network, a wireless local area network, wide area
network, a software defined network, and/or any other type or
combination of types of network as appropriate to the requirements
of a given application. Although the example of FIG. 3 shows a
single network 350, multiple networks may be used for
communications between various elements of the system. For example,
in some embodiments, stormwater modeling system 320 communicates
with data gathering elements 310 through a first network and
communicates with management elements 330 through a different
second network. Network communications may also include
communications with other elements, such as (but not limited to)
external data sources for municipal data.
[0101] Data gathering elements 310 according to several embodiments
of the invention include various types of devices for gathering
data (e.g., regarding the water management features, land parcels,
precipitation levels, etc.) for the modeling of stormwater rainfall
runoff. In some embodiments, data gathering elements 310 include
various computing devices such as (but not limited to) mobile
phones, tablet computers, desktop computers, and laptop computers.
Alternatively, or conjunctively, the data gathering elements 310
according to some embodiments include recording devices and
sensors, such as (but not limited to) cameras and water level
sensors, that can be used to automate and/or standardize the
capture of data.
[0102] Stormwater modeling system 320 according to some embodiments
of the invention performs various methods for modeling the
stormwater runoff of geographic areas. Stormwater modeling systems
in accordance with many embodiments of the invention are
implemented using computing systems that can take any of a variety
of forms from personal computers to cloud based services. The
stormwater modeling systems of some embodiments include a data
storage for storing data gathered from the data gathering elements,
as well as other data generated by the stormwater modeling system,
such as (but not limited to) predicted precipitation levels,
projected stormwater runoff, projected pollutant levels, and water
management proposals.
[0103] Management elements 330 are configured include various
devices to provide the results of the stormwater modeling system
320 according to various embodiments of the invention. The results
can be used by a variety of different users, such as (but not
limited to) regulators, city managers, and maintenance crews for
the water management features. In the example of FIG. 3, management
elements 330 also include a controller that can be used to directly
modify characteristics of a water management feature in some
embodiments. For example, in some embodiments, the controller
modifies a BMP based on the expected runoff.
[0104] Although the data gathering elements 310, stormwater
modeling system 320, and management elements 330 are shown as
separate elements in this particular example, the distinction
between the various roles is not necessarily so distinct. For
example, in some embodiments, the management elements 330 also
operate as data gathering elements. The management elements 330 in
accordance with embodiments perform at least a portion of the data
modeling of the stormwater runoff system. In this example,
stormwater modeling system 320 is illustrated as a single system,
however, the runoff modeling system 320 according to other
embodiments of the system is a distributed system with processing
and storage elements that are distributed across several locations,
such as (but not limited to) the cloud, server clusters, and other
data gathering and/or management devices operated in the field.
Stormwater modeling systems in accordance with several embodiments
of the invention are implemented as web-based applications that
communicate with a complete data management system to simplify use
and automatically generate map based results.
[0105] While a specific example of a system for modeling stormwater
runoff is described above, one of ordinary skill in the art can
appreciate that many different configurations or devices could be
used as appropriate to the requirements of specific applications.
Various elements of an example of a stormwater modeling system are
described in greater detail below.
[0106] A stormwater modeling system in accordance with an
embodiment of the invention is conceptually illustrated in FIG. 4.
The goal of stormwater modeling systems implemented in this manner
is to provide an easy to use platform for stormwater managers to
prioritize stormwater reduction actions, efficiently manage inputs
and results, and track and report estimated benefits of actions
implemented across the urban landscape over time. The stormwater
modeling system 400 includes a processor 410 and a memory 420.
Memory 420 includes a stormwater modeling application 422 and a
measurement data storage 424. The stormwater modeling application
422 according to some embodiments executes on processor 410 to
model stormwater runoff by gathering data in measurement data
storage 424 and analyzing the data in various ways including (but
not limited to) modeling future precipitation levels, modeling the
effectiveness of various water management features, identifying
priorities for maintaining and instituting water management
features, and modifying water management features to reduce
stormwater runoff. As can readily be appreciated, the specific
computing system utilized to implement a stormwater modeling system
will typically depend upon the requirements of a given
application.
[0107] The system according to several embodiments of the invention
provides a user interface that is spatially based, using urban
catchments to communicate how stormwater runoff is generated and
routed through an MS4. Seamless integration with a stormwater BMP
inventory and tracking system provides municipalities a stormwater
quality asset management system that facilitates consistent
quantification of water quality benefits of program actions. The
user interface according to various embodiments of the invention
provide spatial outputs that clearly communicate patterns of
stormwater impacts to identify the greatest volume and load
reduction opportunities within the urban area and allow managers to
objectively prioritize actions. The annual results explicitly
incorporate the need for maintenance, creating an information
feedback loop that facilitates effective asset management over
time. The stormwater suite allows users to compare alternatives
during the planning phase to inform the tradeoffs and benefits of
various strategies to reduce urban impacts to receiving water
quality. The intuitive interface simplifies website navigation and
improves consistency and repeatability across users who are not
modelling experts. In many embodiments, the system automatically
generates results in standardized formats by mapping where actions
are implemented and quantifying the relative effectiveness of those
actions, providing an objective and transparent approach to urban
land management accounting.
[0108] In several embodiments, the stormwater runoff management
system provides user interfaces for gathering data about the land
and water management features, as well as for reporting and
displaying the results of the runoff modeling system. A user
interface according to various embodiments of the invention for a
customized, web-based, asset management tool for stormwater
managers to inventory and evaluate the relative conditions of
structural BMPs is illustrated in FIG. 5. In this example, user
interface 500 includes a map 510 and a structural BMP inventory
520. The map according to some embodiments of the invention
illustrates the geographic location of various structural BMPs
within a specified area. In some embodiments, structural BMPs are
shown differently based on a type, condition, and/or status (i.e.,
whether the structural BMP has been assessed) of the structural
BMP. In some embodiments, the user interface allows a user to
navigate to any catchment (e.g., from a map view or a tabular view)
to review the specific model inputs for the years of record and
view the spatial locations and respective conditions of the
implemented BMPs.
[0109] The visualization of existing water features allows for the
condition of the existing water features to be reviewed and edited
so that users can view the mapped attributes for each catchment. In
several embodiments, the user interface allows a user to toggle
views of urban drainages, catchment soil types, land use
distribution, and catchment imperviousness. These maps provide
additional information to stormwater managers as they plan
implementation strategies in various areas within their MS4. In
many embodiments, the map view can be adjusted to view structural
BMPs at various zoom levels, or at various hierarchical levels
including (but not limited to) a municipality, a receiving water, a
catchment, and a parcel. Structural BMP inventory 520 according to
several embodiments of the invention provides a tabular view of the
structural BMPs displayed in the map 510.
[0110] In some embodiments, the user interface allows a user to
determine the maintenance urgency to ensure that the water quality
benefit is being sustained at an acceptable level over time.
Assessment methods for evaluating the condition of water management
features facilitate the rapid field evaluation of any structural
BMP or water management feature. In many cases, the capability of a
structural BMP to perform its treatment function and provide water
quality benefits will typically decrease over time unless
maintenance actions are performed. For example, at a certain
threshold of pollutant accumulation, which varies from one
structural BMP to another, the functional efficiency of passive
treatment processes in the structural BMP will be drastically
decreased. Poorly maintained structural BMPs have been observed to
become so degraded that they simply provide a temporary storage of
pollutants of concern where they can be easily entrained by
subsequent storm flows.
[0111] In order to address the effects of the condition of a water
management feature, assessment methods in accordance with various
embodiments of the invention provide a complete and consistent
field evaluation and data management tool for jurisdictions to
determine the urgency of maintenance, track condition over time,
and maintain the intended water quality benefits of structural
BMPs.
[0112] User interface 500, in accordance with several embodiments,
allows users to quickly inventory, determine, and track the
condition of structural BMPs. Assessment results can be used to
track structural BMP distribution and conditions over time,
prioritize BMPs for maintenance, and report programmatic progress.
In many embodiments, the standardized and efficient methods for
gathering and inputting structural BMP data are directly applicable
for stormwater managers to evaluate and track the condition of
stormwater structural BMPs in any location with minimal subjective
decision making or local calibration.
[0113] The interface and description described above are directed
to the inventory and evaluation of structural BMPs. Similar
interfaces can be applied to other elements of the stormwater
model, including (but not limited to) land parcels, non-structural
BMPs, roads, and trash. As can readily be appreciated, the specific
assessment methods and user interfaces utilized to assess and
gather data various types of water management features will
typically depend upon the requirements of a given application.
[0114] Once data for the water management features has been
gathered, the system according to several embodiments of the
invention provides various reports and visualizations to inform
water management strategies and priorities going forward. An
example of a municipal view 600 that displays priority mapping
results is provided in FIG. 6, which illustrates catchments with
relatively higher stormwater volume delivery to the receiving
waters based on current conditions.
[0115] In the municipal view 600, the color-coded runoff and
particulate maps shows the results (baseline or current) normalized
by the area of the catchment for direct comparisons of runoff and
loading rates across catchments. The baseline and current volumes
and loads are also used to provide users with information to inform
spatial priorities within their municipality. The population of the
runoff and loading rates are ranked and evenly distributed across
five categories and mapped using standardized color gradations.
[0116] In several embodiments, catchments are presented with
different appearances (e.g., different colors, textures, borders,
etc.) to indicate which the surface runoff of the various
catchments to the receiving waters on an average annual basis. In
this example, the darker shades of color indicate that a greater
proportion of surface runoff (e.g., the highest runoff rate per
unit area) to receiving waters on an average annual basis. These
catchments present opportunities for the municipality to implement
actions where the greatest receiving water benefits are expected.
In many embodiments, the user interface of municipal view 600
allows users to easily toggle between the baseline and current
priority maps for all saved years of record. The ability to view
results spatially and by receiving water provides additional
functionality for managers to intersect the stormwater modeling
results with other spatial datasets to inform more strategic
planning efforts. Examples of other complementary spatial datasets
include (but are not limited to) locations of groundwater
overdraft, groundwater recharge zones, downstream biological
impairments, funding opportunities, etc.
[0117] The system according to some embodiments of the invention
provides various tools for planning and evaluating plans for
managing stormwater runoff for a municipality. Exemplary user
interfaces for generating and evaluating planning scenarios
according to several embodiments of the invention are described
below.
[0118] A user interface 700 for generating planning scenarios with
various water management features and land modifications is
illustrated in FIG. 7. Planning scenarios can be completed for any
catchment via manual entry of parcel, road and structural BMP
design characteristics and conditions. Predicted loading results
are automatically generated based on user inputs of runoff neutral
parcels, road condition, distribution and condition of runoff and
particulate decentralized BMPs, and sizing and condition of
centralized BMPs. Catchment runoff and loading estimates from
different water quality improvement scenarios can be compared to
assess the relative runoff and pollutant loading mitigation effects
of various design alternatives. In some embodiments, user interface
for generating planning scenarios allows for viewing an estimated
cost for the planning scenario and/or an allowed budget for the
planning scenario.
[0119] The user interface in accordance with various embodiments of
the invention allow for the tracking of progress in a manner that
is consistent and standardized, making the determination of a
planning scenario's effectiveness easy to communicate and
understand. In many embodiments, the stormwater modeling system
provides visualizations of proposed water management features and
resulting projected runoff load reductions generated by a
stormwater modeling system.
[0120] A bar chart 800 that provides the annual baseline load,
current load and current load reduction estimates for a catchment
is illustrated in FIG. 8. Baseline refers to the average annual
runoff (volume per year) or particulates (mass per year) delivered
from the respective catchment(s) to receiving waters with no BMPs
present. The baseline volumes and loads will increase when new
development occurs and/or impervious area within the MS4 boundary
is increased, requiring the user to upload the revised spatial
information. Current runoff and loading estimates are made using
the same catchment characteristics and precipitation inputs as the
baseline estimates, but with the inclusion of water management
features, such as (but not limited to) non-structural and
structural BMPs.
[0121] In the bar chart 800, all catchment results are aggregated
to provide a measure of the stormwater program progress over time.
In many embodiments, this progress is quantified as average annual
reductions in surface runoff and particulate pollutant loads to
receiving waters. An effective stormwater program will have
increasingly lighter colored bars over time, representing
successful load reduction progress. The historic and forward
looking views allow a user to document progress of various water
management features and communicates programmatic priorities for a
municipality or receiving water.
[0122] An annualized summary of the load reduction contributions of
various elements of a planning scenario is illustrated in FIG. 9.
The summary 900 of this example shows the relative reductions
resulting from improvements parcels and roads, as well as from
decentralized and centralized structural BMPs. Additional examples,
screenshots and descriptions of user interfaces according to
several embodiments of the invention are provided in "ParcelRAM:
Technical and User Guidance Document v1" (available at
https://2ntelr.com/parcelram/dist/prod/data/ParceIRAMTechDoc_Web.pdf),
"BMP RAM: User Guidance v3.2" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMUserGuidance-
3.1. pdf), "BMP RAM: Technical Document v3.1" (available at
http://www.2ndnaturellc.com/wp-content/uploads/2016/11/BMPRAMv3-1_Technic-
alDoc_Nov2016. pdf), and "BMP RAM: Field Protocols v3.1" (available
at
http://www.2ndnaturellc.com/wp-content/uploads/2016/09/BMPRAMFieldProtoco-
ls3.1.pdf), which are submitted herewith via Information Disclosure
Statement and incorporated herein by reference.
[0123] While the above description contains descriptions of many
specific systems and methods for modeling stormwater runoff in
accordance with various embodiments of the invention, these should
not be construed as limitations on the scope of the invention, but
rather as an example of one embodiment thereof. Additional details,
examples, and embodiments are described in "Stormwater Tool to
Estimate Load Reductions: Final Technical Document v1.1" (available
at http://www.2ndnaturellc.com/documents/swTELR_TechDoc.pdf), which
is submitted herewith via Information Disclosure Statement and
incorporated herein by reference. Accordingly, the scope of the
invention should not be limited to the discussion of any specific
embodiment that is illustrated are described.
* * * * *
References