USGS Virginia Water Science Center: Scientific Support to the Virginia Department of Environmental Quality Groundwater Withdrawal Permitting Program

Transcription

1 USGS Virginia Water Science Center: Scientific Support to the Virginia Department of Environmental Quality Groundwater Withdrawal Permitting Program

2 Reported and Estimated Withdrawals for Virginia Coastal Plain Groundwater Model Withdrawal (Mgal/day) VA Reported VA Domestic MD Reported NC Reported Total Year

3 Cross Section of Coastal Plain Aquifer System B B

4 Potomac Aquifer Lithologic Heterogeneity WATER BEARING SAND SAND IMPERMEABLE CLAY CLAY INTERBED SAND

5 EXAMPLE PARTIAL LOG OVERLYING UNITS Geophysical Log Interpretation SAND CLAY SAND CLAY SAND CLAY 325 FEET SAND INTERVALS 1216 CLAY INTERVALS 2654 TOTAL INTERVALS SAND CLAY 530

6 Map of Model Area For Virginia Coastal Plain Groundwater Model Eastern Shore Model Grid Boundary B B Chesapeake Bay Impact Structure Virginia Coastal Plain Model Grid Boundary

7 Simulated Changes to Groundwater Storage Simulated 2008 Drawdown (ft) Drawdown in Potomac Aquifer from Pre Development to 2008

8 VCP Groundwater Model Groundwater levels and flows are currently simulated Model is SEAWAT and can simulate salinity changes Heywood and Pope, 2009

9 Clarke County/USGS Cooperative Project Clarke County BOS March 10, 2014

10 Relation between Precipitation and Water Budget Components from Data Network Evapotranspiration R 2 =0.32 R 2 =0.48 Recharge Runoff R 2 =0.71 Yellow grid is percentage of annual precipitation Red line is linear regression Grey area is 95% confidence interval

11 Spring source areas

12 Groundwater transit times

13 I. THE GREAT DISMAL SWAMP (RESILIENCY) Hydrologic Response to Increased Water Management Capability at the Great Dismal Swamp National Wildlife Refuge: Enhancing Resiliency for Wildlife and People Gary K. Speiran Collaborative effort among: U.S. Fish and Wildlife Service USGS Virginia Water Science Center The Nature Conservancy The City of Chesapeake Virginia Department of Conservation and Recreation, Division of Game and Inland Fisheries U.S. Army Corps of Engineers Study Area

14 I. THE GREAT DISMAL SWAMP (RESILIENCY) Hydrologic Response to Increased Water Management Capability at the Great Dismal Swamp National Wildlife Refuge: Enhancing Resiliency for Wildlife and People Objective: To assess the hydrologic response to increased water management and use the assessment to help design improved management strategies that (1) improve habitats by increasing the wetness and peat formation in the swamp, (2) reduce downstream flooding, and (3) reduce the risk of catastrophic fires

15 I. THE GREAT DISMAL SWAMP (RESILIENCY) Hydrologic Response to Increased Water Management Capability at the Great Dismal Swamp National Wildlife Refuge: Enhancing Resiliency for Wildlife and People Approach: Water-control structures are being installed and repaired on ditches to better manage the discharge of water from the peat. Water will be managed to enhance habitats, reduce downstream flooding, reduce the risk of catastrophic fire, and improve water quality. Groundwater and ditch levels, precipitation, soil-moisture, and water-quality will be monitored before structure installation. Survey peat thickness and vertical variations in peat characteristics.

16 II. THE GREAT DISMAL SWAMP (CARBON SEQUESTRATION) Ecosystem Services Assessment and Carbon Monitoring in Support of Land Management at Great Dismal Swamp, Pocosin Lakes, and Alligator River National Wildlife Refuges Collaborative effort among: USGS Climate and Land Use Change Program USGS Virginia Water Science Center USGS Wetlands Science Center USGS California Water Science Center U.S. Fish and Wildlife Service (FWS) The Nature Conservancy Virginia Department of Conservation and Recreation, Division of Natural Heritage Clemson University Numerous Area Stakeholders Study Area: Refuge-Wide

17 II. THE GREAT DISMAL SWAMP (CARBON SEQUESTRATION) Ecosystem Services Assessment and Carbon Monitoring in Support of Land Management at Great Dismal Swamp, Pocosin Lakes, and Alligator River National Wildlife Refuges Objective: To (1) characterize changes in potential carbon sequestration through the effects of groundwater levels and soil moisture on aboveground biomass, and peat thickness; (2) estimate the effects of refuge hydrologic management and restoration on carbon sequestration and maintaining resilient, target, plant communities; and (3) provide an assessment and valuation of select ecosystem services deemed important to FWS and other stakeholders.

18 II. THE GREAT DISMAL SWAMP (CARBON SEQUESTRATION) Ecosystem Services Assessment and Carbon Monitoring in Support of Land Management at Great Dismal Swamp, Pocosin Lakes, and Alligator River National Wildlife Refuges Approach (1): The primary sites are at the Great Dismal Swamp (GDS); secondary sites are at Pocosin Lakes. The effects of groundwater levels and soil moisture on (1) carbon storage in plant biomass and peat and (2) carbon (carbon dioxide, methane, and dissolved organic carbon) export directly to the atmosphere and to ditches through groundwater are being studied at three local, primary sites in each of three target plant communities.

19 Subsidence Monitoring Planning Cooperator: Hampton Roads Planning District Commission (HRPDC) Project Chief: Jack Eggleston Photograph by The N. Pham, The Virginian- Pilot, February 2013, used with permission

20 The Problem Why it is Important High Rates of Relative Sea- Level Rise Very High Vulnerability to Sea-Level Rise NOAA Sea-Level Trend Data (Zervas, 2009) Thieler and Hammar-Klose, 1999, USGS, OFR

21 What is Affected by Sea-Level Rise in HRDPC? 1.7 Million people Norfolk Naval Base Photo courtesy of The Virginian-Pilot Jamestown Fort Coastal Marsh Photo courtesy of Colonial Williamsburg Foundation

22 USGS Subsidence Circular Sets stage for current project Summarizes available data Emphasizes need for data

23 Subsidence Monitoring Project Project Goals Provide forum for stakeholders to collaborate and learn about subsidence monitoring Summarize available subsidence monitoring options Provide guidance on matching monitoring methods with intended uses of data Estimate costs Summarize findings in memo or OFR

24 Pumping Induced Aquifer Compaction Spatially variable controls on Subsidence: Compressibility Sediment thickness Water levels Cumulative Compaction (mm) Extensometer Records Suffolk 3.7 mm/yr Franklin 1.6 mm/yr Pope and Burbey, 2004, Ground Water, 42(1) p

25 Monitoring Locations

26 Land Subsidence Monitoring Methods Method Type of data Measures Aquifer Compaction Spatial detail Temporal detail Borehole extensometer Aquifer-system thickness Yes Low High Tidal station Sea elevation No Low High Geodetic surveying Land elevations at one or several locations No Low to moderate Low to high Remote sensing (InSAR) Land elevations over wide area No High Low to Moderate

27 National hazards, risk, and resilience assessments Peak flow statistics Drought response Flood warning

28 Characterizing the response of stream low-flows to precipitation and climate. Samuel H. Austin A cooperative project with

29 Purpose. Build upon our low-flow analyses from 2010 to... Extend the lead time for drought response. Improve and extend DEQ s drought response information and services throughout Virginia. Deepen our understandings of interactions among precipitation, low-flow, and other basin variables.

30 Concept: Effective recharge window. We began to appreciate the idea that... Rainfall during the N-D-J-F recharge months (before leaf-out ), is linked to summer stream flow. Recharge during this critical time may drive water availability during summer low-flow months.

31 Active Study All these insights have lead us to our current emphasis... Predicting the likelihood of future low-flows as a function of precipitation during recharge months. where: Mean monthly flow is used as a surrogate for precipitation. and, Maximum likelihood logistic regression is used to determine July, August, and September flow probabilities as functions of November, December, January, and February mean monthly flow.

32 Our current emphasis: Maximum likelihood logistic regressions. Example maximum likelihood probability plots describing the chance that average daily flow will exceed median August POR return flow as a function of February mean monthly flow, and the average of combined N-D-J-F mean monthly flows for station number , Appomattox River near Farmville, Virginia. August as F(February) Probability Plot August as F(Ave. Combined N-D-J-F ) Probability Plot YES YES 0.25 NO NO 662

33 Our current emphasis: Maximum likelihood logistic regressions. Active Study Example maximum likelihood probability plots describing the chance that average monthly flow will exceed median August POR return flow as a function of February mean monthly flow, and the average of combined N-D-J-F mean monthly flows for station number , Appomattox River near Farmville, Virginia. August as F(February) Probability Plot August as F(Ave. Combined N-D-J-F ) Probability Plot YES 0.75 YES NO NO 160

34 Future Directions Sea Level Rise/Subsidence Urban Hydrology Continuous Water Quality Water Availability

35 Questions? Future Directions Sea Level Rise/Subsidence Urban Hydrology Continuous Water Quality Water Availability