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Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
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First Steps in Music
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A New Methodology for Identifying Ecologically Significant Groundwater Recharge Areas
1. 1
A new methodology for identifying
Ecologically Significant Groundwater
Recharge Areas
IAH 2013
M.A. Marchildon1, P.J. Thompson1, S.E. Cuddy2, K.N. Howson2,
Dirk Kassenaar1, E.J. Wexler1
¹Earthfx Incorporated, Toronto, Ontario, Canada
²Lake Simcoe Conservation Authority, Newmarket, Ontario, Canada
Presented by
Dirk Kassenaar
Earthfx Inc.
2. 2
Significant GW Recharge Areas (SGRA)
► Source Water Protection work in Ontario has broadly defined “SGRAs”
as areas of higher than average recharge
► “Ecologically Significant Groundwater Recharge Areas” (ESGRA) are
further defined as GW recharge areas that provide significant volumes
water to a wetland or stream reach
► Identifying ESGRA’s - Challenges include:
Need a model that can represents both recharge and eco discharge
Need to establish the link between the recharge area and the eco-feature
Need to assess the volume of recharge as significant
3. 3
ESGRA Modelling Challenge
► Model components:
Hydrology (recharge)
Complex shallow GW flow
systems
Detailed stream and
wetland hydraulics (head-
dependant leakage)
► In fact, we need
SW/GW/SW modelling
6. 6
Oro Moraine ESGRA Example
► Lake Simcoe Protection Act requires water budgets and
ESGRA assessment for all watersheds that contribute to
the lake
► Oro Moraine dominates the north-west portion of the lake
catchments
► Three part ESGRA assessment approach:
1. Build a fully-integrated GSFLOW model, representing the
hydrology, GW flow and stream and wetland hydraulics
2. Use Reverse Particle Tracking to link eco-feature to recharge area
3. Use Gaussian Kernel Density Function analysis to identify particle
endpoint clusters and significance
7. 7
Oro Moraine Study Area
Oro Moraine
Study
watersheds
► Three watersheds
contributing to the
northwestern shores
of Lake Simcoe
Oro North
Hawkstone
Oro South
8. 8
Hydrology: Precipitation
► Calibrated hourly NEXRAD
radar data provides the best
estimate of distributed
precipitation
► NEXRAD cell represented as
Virtual Climate Stations (VSCs)
spaced ~4.5 km apart across
the study area
8
NEXRAD VCS
9. 9
Hydrology: Land use
► Used a combination of land
use data to assign land use
and vegetative cover
properties
► LSRCA ELC is very detailed but
covers only the Oro and
Hawkestone watersheds
► SOLRIS v1.2 covers the
remaining area
10. 10
Hydrology: Topography and Runoff
► 50-m DEM used to
generate cascade flow
paths to route overland
runoff to streams
► Slope aspect used for
ET and snowmelt
modules
12. 12
Hydrogeology
► Too often, hydrogeologists have simplified the shallow aquifer systems
because of model stability and unsaturated model performance issues
The new MODFLOW-NWT sub-model in GSFLOW solves this problem!
► GSFLOW provides a GW model which can simulate seepage faces,
springs, and thin surficial sand deposits that are seasonally important
Particularly for important for vernal pools, wetlands and headwater creeks
13. 13
Hydraulics and Eco-Feature Representation
► Represent all streams,
down to the intermittent
Strahler Class 1 streams
► 85 Lakes, Ponds, and
Lake/Wetlands
► Wetlands accounted for
both hydraulically (LAKE)
and hydrologically (Soil
Moisture Accounting
package)
► Fully coupled GW/SW
interaction
13
Oro Moraine
14. 14
GSFLOW Streams
► Streams are represented as a
network of segments or channels
Streams can pick up precipitation,
runoff, interflow, groundwater and pipe
discharges
Stream losses to GW, ET, channel
diversions and pipelines
► GW leakage/discharge is
based on the dynamic head
difference between aquifer
and river stage elevation
Similar to MODFLOW rivers, but the
stage difference is based on total
flow river level
River Loss
River Pickup
15. 15
(Markstrom et.al., 2008)
GSFLOW: Stream Channel Geometry
► The Stream Flow Routing package (SFR2) represents stream channels using an
8-point cross-section in order to accommodate overbank flow conditions
Streamflow depths are solved using Manning’s equation
Different roughness can be applied to in-channel and overbank regions
► SFR2 incorporates sub-daily 1D kinematic wave approximation if analysis of
longitudinal flood routing is required
16. 1616
Oro Aquifer Head vs. Stream Stage
• Groundwater
discharging to the
stream, except during
large events
• Hydrograph at Oro-Hawkstone stream gauge
17. 17
ESGRA Wetland Representation
► Wetlands have a wide range of water content (bogs, fens,
marshes, etc.), and can be represented in GSFLOW in
multiple zones
► Soil zone wetlands:
Partially or fully saturated soils, with surface ponding
Benefits – seasonal ET modelling, complex topography with
cascade overland flow and interflow, GW leakage or discharge
► Open water wetlands:
The portion of a wetland that generally has standing water
Represented as a lake that can penetrate one or more GW layers
Benefits: Dams, weirs, and control structures can all be simulated
18. 18
GW Discharge to Wetlands
Soil water
Unsaturated
zone
Precipitation
Evapotranspiration
StreamStream
Evaporation
Precipitation
Infiltration
Gravity drainage
Recharge
Ground-water flow
Soil-zone base
Surface Discharge
► Surface Discharge is the movement of water from the GW system to
the soil zone, where it can become interflow or surface runoff
► Saturated soils can reject recharge: groundwater feedback
19. 19
GSFLOW Lakes and Wetlands
► Wetlands and lakes can penetrate
multiple aquifer layers
► Outflow can be a fixed rate or
determined by stage-discharge
► Multiple inlets and outlets are allowed
20. 20
ESGRA Assessment Approach:
► Step 1: GSFLOW model construction - key points:
Hydrology: Need the best estimate of recharge and runoff
Hydrogeology: Need detailed simulation of the shallow subsurface
Hydraulics: Must represent stream routing and the variable head-
dependant leakage that governs stream-aquifer interaction
► Step 2: Use Particle Tracking to link eco-features to
recharge areas
► Step 3: Use Gaussian Kernel Density Function analysis to
identify particle endpoint clusters and significance
21. 21
► Particles released in the
wetland (green area)
► Particles tracked backwards
through the flow system
► Black dots show endpoints
where GW recharge occurred
► Select red lines illustrate flow
paths from wetland to
recharge area
► In this case, the wetland
received recharge from three
areas
21
ESGRA Assessment: Particle Tracking
Example of backward particle-tracking from a significant feature
(Bluffs Creek West Wetland, Oro Creeks North Subwatershed)
22. 22
ESGRA Eco-feature starting points
► Backward tracking
from eco-features
► Streams: Red cells
► Wetlands: Green cells
22
23. 2323
ESGRA Reverse Tracking Pathlines
► Three watersheds:
Three very different
track and recharge
patterns
► Oro North: regional
► Oro South: very local
► Stream: Red pathlines
► Wetland: Green pathlines
24. 2424
Forward Tracking Confirmation
• Radial flowpaths from
Moraine shown by forward
tracking
• Endpoints show that the
Moraine feeds headwater
streams and flanking
wetlands
• There are deep flow
pathways that emerge far
from the Moraine
26. 26
ESGRA Assessment Approach:
► Step 1: GSFLOW model construction - key points:
Hydrology: Need the best estimate of recharge and runoff
Hydrogeology: Need detailed simulation of the shallow subsurface
Hydraulics: Must represent stream routing and the variable head-
dependant leakage that governs stream-aquifer interaction
► Step 2: Use Particle Tracking to link eco-features to
recharge areas
► Step 3: Use Gaussian Kernel Density Function
analysis to identify particle endpoint clusters and
significance
27. 27
ESGRA Methodology – Cluster Analysis
► Purpose:
Need for a methodology to analyze particle endpoint clusters to delineate
Ecologically Significant Groundwater Recharge Areas (ESGRAs)
ESGRAs are defined as areas with a relatively high particle endpoint
density, where endpoint density is assumed to represent areas most likely
to contribute recharge to ecological systems of interest
The methodology must be automatic, objective, unbiased, consistent, and
transferable for use in other study areas
► Simple Approach:
Simply count endpoints that fall within a regular grid, identify a
count threshold for significance
► Results highly dependant number of particles released and cell size
► Selected Approach:
Assume each pathline endpoint is representative of a normally distributed
recharge feature, as outlined below…
27
28. 28
ESGRA Methodology – Cluster Analysis
Gaussian (Normal) Distribution:
• Standard normal distribution:
• Mean (𝜇) = 0.0
• Variance (𝜎2) = 1.0
• Tails continue on to infinity
• Sum under the curve =
100% probability
𝑓 𝑥; 𝜇, 𝜎2
=
1
𝜎 2𝜋
𝑒
−
𝑥−𝜇 2
2𝜎2
28
29. 29
ESGRA Methodology – Cluster Analysis
Kernel Density Function:
ℎ smoothing factor
𝑑𝑖 distance from particle tracking endpoint
𝑛 total number of endpoints
𝑓𝐻 𝑥 =
1
𝑛ℎ 2𝜋
𝑒
−
1
2
𝑑 𝑖
ℎ
2𝑛
𝑖=1
29
30. 30
ESGRA Methodology – Cluster
Analysis
Kernel Density Function:
Sum of all individual Gaussian curves
Provides consistent
results:
• Invariant to origin
• Invariant to choice of
bin size
𝑓𝐻 𝑥 =
1
𝑛ℎ 2𝜋
𝑒
−
1
2
𝑑 𝑖
ℎ
2𝑛
𝑖=1
30
31. 31
ESGRA Methodology – Cluster Analysis
Bivariate Kernel Density Function: in 2 dimensions
Relative Frequency Diagram: Sum-volume under the
surface = 1.0
32. 32
ℎ = 0.2
ℎ = 0.1
ESGRA Methodology – Cluster Analysis
ℎ = 0.05
Smoothing Factor (ℎ) is analogous to standard deviation
- Provides a means of extrapolation
Kernel Density Function:
𝑓𝐻 𝑥 =
1
𝑛ℎ 2𝜋
𝑒
−
1
2
𝑑 𝑖
ℎ
2𝑛
𝑖=1
ℎ smoothing factor (or bandwidth)
𝑑𝑖 distance to particle tracking endpoint
𝑑𝑖 = 𝑥 − 𝑥𝑖
𝑛 total number of endpoints
Particle endpoint
32
33. 33
ESGRA Methodology – Cluster Analysis
Bivariate Kernel Density Function: Selection of ℎ
𝒉 = 𝟏𝟎𝟎𝒎
► Kernel density processing
converts endpoints (black
dots) into a continuous
“Cluster frequency
distribution”
► Cluster frequency
distribution can be
processed at various h
threshold levels
(h=100 m shown)
34. 34
ESGRA Methodology – Cluster Analysis
Bivariate Kernel Density Function: Selection of ℎ
𝒉 = 𝟓𝟎𝒎
► Cluster frequency
distribution at h=50 m
► Optimum h value
determined from
sensitivity analysis
35. 35
► Delineated ESGRAs
with Endpoints
► h = 25m, ɛ=200
► Optimal values determined
through sensitivity analysis
► All Points (stream and
wetland endpoints
considered)
► 96.2% of points (~920,000)
captured by delineation
35
ESGRA Assessment
36. 36
ESGRA Assessment
► Final ESGRA
mapping
36
Subwatershed 1/ε = 0.005
Oro North 22.6%
Hawkestone 26.1%
Oro South 14.6%
Total 21.4%
Area outside of study
area
2.2 km2
Percentage of subwatersheds
covered by potential ESGRAs
37. 37
ESGRA Assessment
► Comparison of
ESGRA and SGRA
(2010) mapping
► SGRAs (in green) represent
areas of high volume recharge
► ESGRAs (in red) further identify
areas of important local eco-
recharge
► SGRAs miss areas of local
significance
37
38. 38
Conclusions
► Integrated GW/SW modelling for eco-assessment is an important
emerging area
ESGRA analysis is an ideal application to understand flow system linkages
and volumetric recharge
With integrated total flow stream routing, future applications include flow
regime assessment
► Existing uncoupled GW and SW models need to be upgraded
Original conceptualizations may be too simplified in the critical shallow
interface zone
► Integrated models such as GSFLOW can represent:
Hydrology: ET processes, GW feedback and rejected recharge
Hydrogeology: Shallow variably saturated layers
Hydraulics: Stream routing, variation in stream stage, vernal pools
38
39. 39
ESGRA Methodology
► Particle tracking is a power means to link the recharge
area to the feature and therefore assign eco-significance
► The Kernel Density Function approach is useful to convert
endpoints into a distributed, mappable parameter
The function is independent of how the particle end points are
generated
39
40. 40
ESGRA Findings
► ESGRA Analysis has identified both ecologically significant high volume
recharge areas, as well as lower rate recharge areas that also support
eco-features.
► Particle tracking provides visual insights into both the shallow and deep
flow system. Two apparently similar watersheds (Oro North and
South) have significantly different flow systems.
► Drought simulations further demonstrate that streams fed by deep
regional flow are less sensitive to drought conditions
► Special Thanks: This ESGRA assessment methodology was developed
with the support of the Lake Simcoe Conservation Authority and
Ontario MNR
40