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

     Groundwater

     Surfacewater

     Groundwater-
      Surfacewater
       Interactions
Hydrologic Cycle
                 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.)
Groundwater
Groundwater
Reasons groundwater is of concern
      to wetlands studies:

                    Ecological concerns

                     Can store and also filter
                     contaminated fluids

                    Groundwater-
                     surfacewater
                     interactions
Animation from www.mhhe.com
Saltwater Intrusion




                  www.mhhe.com
     How do you model groundwater
           flow in wetlands?
Treated the same as any groundwater investigation.
-Surface mapping
-Subsurface characterization:
1.   Soils
2.   Water


-Modeling
    Darcy’s Law
      Q = -k * i * a

      Q = discharge
k = hydraulic conductivity
  i = hydraulic gradient
         a = area
                        Soils cont.
   Data acquired from:
       Soil borings
       Well cuttings
       Cores
       Geophysical
        techniques
Soils
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.)
       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
          Hydraulic Gradient
   Monitoring wells

   Piezometers

   Hydraulic head values

   Hydraulic gradient = change in head over
    distance or (Δh / Δl)
Wetland flow possibilities
Case Study
Study Area – Jensen Beach, Fla.
                Study Area
   Pine flatwoods of Savannas State
    Preserve

   Circular shape – 60m diameter

   USFWS designation: palustrine,
    persistent, emergent, nontidal and
    seasonally flooded wetland
      Vegetation - upland

                   Dahoon holly




                             Wax myrtle



Saw palmetto
               http://www.gillespiemuseum.stets
                     on.edu/grounds/list.html
Vegetation - interior
 St. John’s Wort



     Blue Maidencane




                               Duck potato




                   Maidencane
                        http://sofia.er.usgs.gov/virtual_tour/pgbigcypress.html

                       http://www.gillespiemuseum.stetson.edu/grounds/list.html
                    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
               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
                    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.
WAIT – well transects
Results
Results
                 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
                   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.
                 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
               GMS v.4.0




All images: http://www.ems-i.com/GMS/gms.html
Visual Modflow Pro v3.1




           Animation:http://www.visual-modflow.com/html/visual_modflow.html
Modeling Surface Water
Flow in Wetlands
A non-mathematical explanation of a
mathematical process
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
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
   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
   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
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
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
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
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
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
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
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
   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
   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
   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
A 2-dimensional, diffusion-based wetland flow model
(WETFLOW) Ke Feng and F.J. Molz


Two cases are presented in this study: a laboratory testing of
the model and the model applied to a wetland pond in
Talladega National Forest near Moundville, AL


This model was developed to be applied to a general wetland
type
This small wetland was created when beavers
dammed a perennial stream




This is a Riparian wetland, one that is
adjacent to a body of water and is flooded
on a regular basis
Flow domain boundaries (outlines of study area) and
outlines of the islands must be defined
The varying boundaries of a wetland provide a problem to
the mathematical modeling of surface water flow
The boundaries of a wetland may change with time, due to
flooding events and drought
This model has many positive
attributes
   The model allows for variations in wetland
    characteristics
   The model applies to both 1-D and 2-D flow
    fields (evidenced by the laboratory study and
    the wetland study)
   During drought and flood events, the model
    can identify changing wetland boundaries
   This model can be used (as a
    hydrodynamic basis) for wetland research
    involving transport, chemistry, and biology
   The authors of the study concluded that
    micro-topography and the distribution of
    flow resistance are the two parameters that
    must be measured in detail, and not
    assumed, in order to build an accurate
    model
Numerical Representation of
dynamic flow and transport at the
Everglades/Florida bay interface
Dr. Eric Swain USGS
   Southern Inland and Coastal Systems
    Numerical Model (SICS)

    This model was developed by starting with the
    USGS Swift 2-D model, and was then modified to
    make it applicable to the Everglades
Model input data
   The model area is characterized by:
    topography, vegetation, wind friction
    coefficient, and bathymetry
   Hydrologic data is then incorporated: rainfall,
    evapotranspiration, salinity time series data,
    and water discharge at the boundaries of the
    study area
   There must be observed data on hand to
    compare to the results of the model: the
    amount of water discharged at coastal creeks,
    at the boundaries, and within the study area
   Calibration data
The Southern Inland and Coastal Research
  Systems (SICS) will be discussed more by the
  next presenter (groundwater and surface water
  interactions)
Several papers were researched in studying
  modeling surface water flow through
  wetlands, most of these papers deal with the
  mathematical equations of the models
The models usually contain a series of
  differential equations that work together
There will be separate equations for different
  aspects of wetland hydrology
conclusions
 There are a few mathematical models used for
  modeling surface water flow through wetlands
 These models may be modified to apply to a
  particular type of wetland: Everglades,
  riparian, etc.
 All of these models attempt to provide a
  relatively simple means to model wetland flow
  without the need to account for minor changes
  in topography, porosity,etc.
Overall, the authors of the papers presented report
 relatively successful models, that have correctly
 predicted observed changes in the surface water
 flows in the wetlands studied
It is important to have reliable models that allow us
 to understand and predict changes that may occur
 in surface water flows in a wetland due to human
 intervention; whether those changes are for better
 (tearing down control structures) or for worse
 (building structures that resist wetland flow)
References
   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. Journal of Hydrology 259 (2002)221-235
 A 2-D, diffusion-based, wetland flow model. Ke
    Feng, F.J. Molz. Journal of Hydrology 196 (1997)
    230-250
   Numerical representation of dynamic flow and
    transport at the Everglades/Florida Bay interface. Dr.
    Eric D. Swain, USGS
Ground and Surface Water
Interaction
   Examine the effects of fluxes in water
    between the ground and surface
   Study the effects of these movements on
    solutes: Organic (carbon), inorganic
    (nitrogen), pollution (mercury)
Ground and Surface Water
Interaction
   Ecological effects: salinity front movements
   Used to study the effects of management
    practices on hydrology
QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.
Interactions Between Groundwater
and Surface Water Models
   Case Study:
   The Tides and Inflows in the
   Mangroves of the Everglades (TIME)
   And
   Southern Inland and Coastal Systems
   (SICS)
Introduction
   A critical goal of the Comprehensive
    Everglades Restoration Plan (CERP) is to
    restore and preserve the hydrology of the
    predrainage ecosystem to provide ecological
    conditions that are consistent with habitat
    requirements.
Introduction
   SICS will investigate wetland response to
    freshwater inflows and to compute resultant
    salinity patterns and concentrations in the
    subtidal embayments of Florida Bay as
    functions of freshwater inflows
SICS Study Area
The dynamic
surface-water
model is
connected to a
three-
dimensional
ground -water
model
SICS
   What effects hydrologic changes to Taylor
    Slough and C-111 will have on:
   Hydroperiods and Hydropatterns
   Quantity, timing, and location of freshwater
    flow
   Development of hypersaline conditions and
    excess nutrients and contaminants
SICS
   An existing, generic, two-dimensional
    surface-water flow and transport model was
    coupled to a fully developed, generic, three-
    dimensional variable-density ground-water
    flow and solute-transport model
QuickTime™ and a Cinepak decompressor are needed to see this picture.
TIME
   TIME is an investigation into the interacting
    effects of freshwater inflows and coastal
    driving forces in and along the mangrove
    ecotone of southern Florida within Everglades
    National Park
Satellite image of
south Florida covering
Everglades National
Park, 1:500,000 scale
Satellite image showing
TIME model boundary
Scale 1:500,000
Development of the TIME Model
   An extension of the SICS model westward
   Required many new, high resolution data sets
    to be created including, topography,
    vegetation, and other hydrographic data
Primary Objectives of the TIME
Project
   Develop, implement, and use a mathematical
    model to study the interaction of overland sheet
    flow and dynamic tidal forces
   Including flow exchanges and salinity fluxes
    between the surface- and ground-water systems
   In the mangrove-dominated transition zone
    between the Everglades wetlands and adjacent
    coastal-marine ecosystems
Goals of the TIME project
to provide
 new scientific insight,

 additional quantitative information,

 more comprehensive data

 a refined hydrodynamic model to help guide
   and assess restoration and management
   decisions for this critical ecosystem.
Questions Addressed by TIME
   How do the Everglades freshwater-wetland
    and coastal-marine ecosystems respond
    concurrently, both hydrologically and
    ecologically, to regulation of inflow?
   Will upland restoration actions affect the
    transformation of freshwater wetlands to
    brackish and marine marshes and
    subsequently to mangrove marsh ecotones?
Questions Addressed by TIME
   How will changes in inflows act in concert with
    predicted increases in sea level to affect migration of
    the freshwater/saltwater interface within the surface
    and subsurface flow systems?
   What key factors influence salt concentrations in the
    coastal mixing zone and how do these factors
    interact to affect wildlife habitat areas?
Questions Addressed by TIME
   How will external dynamic forcing factors,
    such as sea level rise or meteorological
    effects, adversely affect upland regulatory
    plans?
   What concurrent changes in wetland
    hydroperiods and coastal salinities are likely
    to occur in response to various proposed
    restoration and management plans?
Data sets used in model
   vegetation characteristics
   aquifer properties
   surface-water levels,
   ground-water heads,
   flow velocities,
   structure discharges,
   tidal fluctuations,
   salt concentrations,
   Rainfall events,
   and meteorological conditions
Findings to Date
   Water management has increased recharge
    and discharge in the north-central Everglades
    above pre-drainage conditions
   Mercury is being recharged from surface
    water to groundwater and stored in the
    surficial aquifer
Findings to Date
   Ungaged freshwater flows discharging from
    groundwater into Taylor Slough were
    quantified for the first time
   Significant recharge and discharge occurs by
    vertical flow through Everglades peat in areas
    that are far from boundaries with levees and
    canals
Findings to Date
   Discharge of deep groundwater from relict
    seawater origin beneath WCAs cannot explain
    the contaminant-level concentrations of
    sulfate in Water Conservation Areas
Conclusions
   Models are very useful and powerful tools:
   Predict effects of management practices
   Allow officials to make management
    decisions based on more than speculation
   Predict effects of natural phenomena

				
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