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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

4. 4
USGS-GSFLOW
Soil water
Unsaturated
zone
Precipitation
Evapotranspiration
StreamStream
Evaporation
Precipitation
Infiltration
Gravity drainage
Recharge
Ground-water flow
Zone 1: Hydrology (PRMS)
Zone 3: Hydraulics
(MODFLOW SFR2 and
Lake7)
Zone 3: Groundwater (MODFLOW-NWT)
1
2 3
► Hydrology: USGS PRMS (Precipitation-Runoff Modelling System)
► GW Flow: MODFLOW-NWT: (A new version of MODFLOW optimized for
shallow variably saturated (wet/dry) layers
► Hydraulics: Lake and SFR2 River Routing Package

5. 5
GSFLOW SW/GW/SW Components
► Hydrology (PRMS) GW (MODFLOW-NWT) Hydraulics (SFR2)

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
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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

11. 11
Hydrology: Average Recharge
► Average recharge
from a 32-yr
simulation
► Problem: Where are
the ESGRA’s??
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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
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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

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► 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
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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
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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

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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

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Forward Tracking
Confirmation
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► Topography and shallow aquifer layer
pinching can drive water to surface

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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

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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…
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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
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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

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ESGRA Methodology – Cluster Analysis
Bivariate Kernel Density Function: in 2 dimensions
Relative Frequency Diagram: Sum-volume under the
surface = 1.0

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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
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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

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► 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
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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
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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