Modeling Flow through Wetlands

1. Modeling Flow through Wetlands Wayne Dodgens Chad E. Edwards Amy Gross

2. Modeling Flow Through Wetlands • Groundwater • Surfacewater • Groundwater-Surfacewater Interactions

3. Hydrologic Cycle

4. Groundwater • The water stored in interconnected pores located below the water table in an unconfined aquifer or located in a confined aquifer. (Fetter 2001) • That part of the subsurface water that is in the zone of saturation, including underground streams. (Glossary of Geology, 4th ed.)

5. Groundwater

6. Groundwater

7. Reasons groundwater is of concern to wetlands studies: • Ecological concerns • Can store and also filter contaminated fluids • Groundwater-surfacewater interactions

8. Animation from www.mhhe.com

9. Saltwater Intrusion www.mhhe.com

10. How do you model groundwater flow in wetlands? Treated the same as any groundwater investigation. -Surface mapping -Subsurface characterization: • Soils • Water -Modeling

11. Darcy’s Law Q = -k * i * a Q = discharge k = hydraulic conductivity i = hydraulic gradient a = area

12. Soils cont. • Data acquired from: • Soil borings • Well cuttings • Cores • Geophysical techniques

13. Soils

14. Hydraulic Conductivity (~Permeability) • Gravel – 10-2 – 1 cm/s * • Fine sand – 10-5 - 10-3 cm/s * • Clays – 10-9 - 10-6 cm/s * • Peat – 10-3 – 108 m/day †† *(Fetter 2001) †† (Wise et. al.)

15. Hydraulic Conductivity cont. • Flow rates from tests run during and after drilling of the monitoring wells • Inferred hydrologic parameters based on inspection of samples. • Assumed values for materials from published values in previous literature. • Estimates based upon the grain size distribution curve for samples run through a sieve analysis

16. Hydraulic Gradient • Monitoring wells • Piezometers • Hydraulic head values • Hydraulic gradient = change in head over distance or (Δh / Δl)

17. Wetland flow possibilities

18. Case Study

19. Study Area – Jensen Beach, Fla.

20. Study Area • Pine flatwoods of Savannas State Preserve • Circular shape – 60m diameter • USFWS designation: palustrine, persistent, emergent, nontidal and seasonally flooded wetland

21. Vegetation - upland Dahoon holly Wax myrtle Saw palmetto http://www.gillespiemuseum.stetson.edu/grounds/list.html

22. Vegetation - interior St. John’s Wort Blue Maidencane Duck potato Maidencane http://sofia.er.usgs.gov/virtual_tour/pgbigcypress.htmlhttp://www.gillespiemuseum.stetson.edu/grounds/list.html

23. Geology • Underlain by the surficial aquifer – Upper Miocene to Pleistocene 45-52m thick • Upper 12-18m = fine to coarse grained sand intermixed with shell beds • 3-6m layer of fine sand with a few shells • Lower layer of limestone and calcarenite mixed with shells and sand

24. Site Geometry • Sediment surface contouring during flooded conditions • 3m intervals along N-S & E-W, NW-SE & SW-NE transects • Peat thickness was measured by pushing 1cm rebar through the peat until higher resistance indicated the sand layer

25. Methods • The basic idea behind this study is to pump enough surfacewater from the wetland so that its relationship to the underlying aquifer can be assessed based on the rate at which the wetland levels recover due to groundwater seepage from below. • Monitoring of 6+ wells in the marsh interior, and 12+ wells outside the area, for initial head values and the lowering and subsequent rise of head values throughout the experiment.

26. WAIT – well transects

27. Results

28. Results

29. Conclusions • Model agrees with data for smaller time increments while extrapolation to longer periods may involve inclusion of more variables • WAIT quantifies the resistance to flow between wetland and aquifer • AWIT – to determine variability in the vertical hydraulic conductivity depending on direction

30. Computer Modeling • “Computer models are used to help hydrologists understand how flow systems work and sometimes to project how flow systems might be affected by changes in the hydrologic cycle. “ http://ut.water.usgs.gov/modelsb.html • More than 40 models have been developed or are being developed.

31. Modeling • Different programs solve for parameters dependant on the study design. • Current programs are combining the capabilities of existing software into packages that can deliver results or predictions for numerous parameters

32. GMS v.4.0 All images: http://www.ems-i.com/GMS/gms.html

33. Visual Modflow Pro v3.1 Animation:http://www.visual-modflow.com/html/visual_modflow.html

34. Modeling Surface Water Flow in Wetlands A non-mathematical explanation of a mathematical process

35. Development and evaluation of a mathematical model for surface-water flow within the Shark River Slough of the Florida Everglades Carl H. Bolster, James E. Saiers

36. Why develop a model for surface water flow through wetlands? • Wetlands are beginning to be appreciated for their value to society • The future management and restoration of wetlands relies on a quantitative understanding of surface water flows over vegetation

37. Over the last 50 years, 1000 miles of canals, 720 miles of levees, and nearly 200 water control measures have been implemented in the Florida Everglades

38. The restoration plan of $7.8 billion will include re-engineering the ecosystem to capture most of the water that is now being diverted to the ocean and use 80% of it for environmental restoration and the remaining 20% for society’s water needs

39. Planners need to be able to predict the effect on wetlands of actions such as: Removing canals and levees Removing dams Redirecting flow from canals to wetland sloughs

40. The model developed in this study is a two-dimensional model for surface water movement The model was tested against hydrologic data measured in Shark River Slough in the Fl. Everglades

41. Assumptions of the model include: • Uniform rates of evaporation • a constant ground surface slope • spatially homogenous vegetation cover • constant values for wetland porosity • exchanges between surface water and subsurface water are negligible

44. Overland flow models are determined by the properties of the wetland Bed shape irregularities (such as hummocks and depressions) and vegetation density control resistance to flow and the magnitude of the model’s friction coefficient

45. Variable data regarding the ground-surface slope represent the effects of gravity on the movement of water across the surface of the wetland Data on evapotranspiration , rainfall, and groundwater exchange also contribute to the designing of an accurate surface water flow model for wetlands

46. Field measurements of hydraulic head (water level) were obtained from databases operated by the USGS and Everglades National Park. Daily measurements were compiled by averaging 15-minute interval data

47. Results of the Shark River study • The model successfully predicted two observed decreases in hydrologic head occurring from Jan. 17,1998-July 29, 1998 and from Aug. 14, 1998-Dec. 30, 1998. • Also, the model coincides with rainfall-induced head oscillations recorded at the monitoring sites

48. The model is not perfect, however • Between May 1998 and July 1998 the model overestimated the observations at one recording station and underestimated the observations at another • This was presumed to have been caused by violations of the uniform wetland properties assumption

49. The model did predict accurately the temporal and spatial changes in surface water levels over a 27 km long area of Shark River Slough • Results suggest that good predictions of wetland flow over relatively large scales can be obtained with simple mathematical models, without allowing for varying wetland properties

50. The authors of the study conclude surface water flow for extended time periods , over larger expanses, can be predicted with reasonable accuracy without the need to model changes in wetland parameters