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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.

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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

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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

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USGS-GSFLOW

S o i l w a t e r

U n s a t u r a t e d

z o n e P r e c i p i t a t i o n

E v a p o t r a n s p i r a t i o n

S t r e a m S t r e a m

E v a p o r a t i o n

P r e c i p i t a t i o n

I n f i l t r a t i o n

G r a v i t y d r a i n a g e

R e c h a r g e

G r o u n d - w a t e r f l o w

Zone 1: Hydrology (PRMS)

Zone 3: Hydraulics (MODFLOW SFR2 and

Lake7)

Zone 3: Groundwater (MODFLOW-NWT)

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► 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

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GSFLOW SW/GW/SW Components

► Hydrology (PRMS) GW (MODFLOW-NWT) Hydraulics (SFR2)

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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

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Oro Moraine Study Area

Oro Moraine

Study watersheds

► Three watersheds contributing to the northwestern shores of Lake Simcoe

Oro North

Hawkstone

Oro South

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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

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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

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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

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Hydrology: Average Recharge

► Average recharge from a 32-yr simulation

► Problem: Where are the ESGRA’s??

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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

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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

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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

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(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

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Oro Aquifer Head vs. Stream Stage

• Groundwater discharging to the stream, except during large events

• Hydrograph at Oro-Hawkstone stream gauge

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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

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GW Discharge to Wetlands

S o i l w a t e r

U n s a t u r a t e d

z o n e P r e c i p i t a t i o n

E v a p o t r a n s p i r a t i o n

S t r e a m S t r e a m

E v a p o r a t i o n

P r e c i p i t a t i o n

I n f i l t r a t i o n

G r a v i t y d r a i n a g e

R e c h a r g e

G r o u n d - w a t e r f l o w

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

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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

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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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► 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)

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ESGRA Eco-feature starting points

► Backward tracking from eco-features

► Streams: Red cells

► Wetlands: Green cells

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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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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 2𝜋𝑒

−𝑥−𝜇 2

2𝜎2

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ESGRA Methodology – Cluster Analysis

Kernel Density Function:

ℎ smoothing factor 𝑑𝑖 distance from particle tracking endpoint 𝑛 total number of endpoints

𝑓 𝐻 𝑥 =1

𝑛ℎ 2𝜋 𝑒

−12

𝑑𝑖ℎ

2𝑛

𝑖=1

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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

𝑓 𝐻 𝑥 =1

𝑛ℎ 2𝜋 𝑒

−12

𝑑𝑖ℎ

2𝑛

𝑖=1

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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:

𝑓 𝐻 𝑥 =1

𝑛ℎ 2𝜋 𝑒

−12

𝑑𝑖ℎ

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

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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)

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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

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ESGRA Assessment

►Final ESGRA mapping

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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

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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

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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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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

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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

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