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A New Methodology for Identifying Ecologically Significant Groundwater Recharge Areas

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A New Methodology for Identifying Ecologically Significant Groundwater Recharge Areas

  1. 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. 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. 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. 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. 5 GSFLOW SW/GW/SW Components ► Hydrology (PRMS) GW (MODFLOW-NWT) Hydraulics (SFR2)
  6. 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. 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. 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. 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. 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. 11 Hydrology: Average Recharge ► Average recharge from a 32-yr simulation ► Problem: Where are the ESGRA’s?? 11
  12. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22 ESGRA Eco-feature starting points ► Backward tracking from eco-features ► Streams: Red cells ► Wetlands: Green cells 22
  23. 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. 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
  25. 25. 25 Forward Tracking Confirmation 25 ► Topography and shallow aquifer layer pinching can drive water to surface
  26. 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. 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. 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. 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. 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. 31 ESGRA Methodology – Cluster Analysis Bivariate Kernel Density Function: in 2 dimensions Relative Frequency Diagram: Sum-volume under the surface = 1.0
  32. 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. 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. 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. 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. 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. 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. 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. 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. 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

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