Annual technical report

Florida Water Resources Research Center
Annual Technical Report
FY 2006

Introduction

The mission of the Florida Water Resources Research Center at the University of Florida is to facilitate
communication and collaboration between Florida's Universities and the state agencies that are
responsible for managing Florida's water resources. A primary component of this collaborative effort is
the development of graduate training opportunities in critical areas of water resources that are targeted to
meet Florida's short- and long-term needs.

Under the direction of Dr. Kirk Hatfield, the Florida Water Resources Research Center is working to
maximize the amount of graduate student funding available to the state of Florida under the provisions of
section 104 of the Water Resources Research Act of 1984. Over the past year total funding through the
Center was $1,157,652, including agreements with four of Florida's universities (Florida Atlantic
University, Florida State University, University of South Florida, and the University of Florida) and four
state agencies (South Florida Water Management District, Southwest Florida Water Management District,
St. Johns River Water Management District, and the Florida Geological Survey) and has supported the
research of 13 Ph.D. students and 3 Masters students focusing on water resources issues.

During FY 2006, along with providing support to graduate students within the state of Florida, the Center
also facilitated development of research at both the state and national level and produced 23 peer reviewed
publications some of which received international recognition (Best Technology Paper published in
ES&T, 2006). The Center is also a state repository for water resource related publications. Final project
reports for Center funded research efforts are available free of charge and can be requested through the
WRRC website (WRRC Website.

Research Program

During FY 2006 the Water Resources Research Center supported eight 104B research projects and one
104G project. The supported research projects considered a wide range of water resource related issues
while maintaining focus on topics specific to Florida. These topics include investigation of the
geochemical processes that control the mobilization of arsenic during aquifer storage recovery (ASR),
comparing widely used procedures by which radar- and gauge-derived rainfall are optimally combined for
water management and regulatory decisions, investigating the measurement of evapotranspiration,
recharge, and runoff in shallow water table environments characteristic of the Gulf of Mexico coastal
plain, studying the measurement of erosion around and flow through hydraulic structures and culverts, and
developing software for quantifying the impacts of saltwater up-coning and well field pumping.

Space-based monitoring of wetland surface flow

Basic Information

Title: Space-based monitoring of wetland surface flow

Project Number: 2004FL76G

Start Date: 9/1/2004

End Date: 8/31/2007

Funding Source: 104G

Congressional District: 18

Research Category: Not Applicable

Focus Category: Wetlands, Surface Water, Hydrology

Descriptors:

Principal Investigators: Shimon Wdowinski

Publication

1. Bieler B., R. Garcia-Martine, S. Wdowinski and F. Miralles- Wilhelm, Modeling Water Flow in the
Water Conservation Area 1 Using Interferometric Synthetic Aperture Radar (InSAR) Observations,
Greater Everglades Ecosystem Restoration (GEER) Conference, Abstract Volume, p. 15, 2006.
2. Kim S., S. Wdowinski, J. Wang, T. Dixon and F. Amelung, Comparison of Water Level Changes in
the Everglades as Calculated with the TIME Model and with Interferometric SAR Measurements,
Greater Everglades Ecosystem Restoration (GEER) Conference, Abstract Volume, p. 118, 2006.
3. Kim S., S. Wdowinski, T. Dixon and F. Amelung, SAR Interferometric Coherence Analysis of
Wetlands in South Florida, Greater Everglades Ecosystem Restoration (GEER) Conference, Abstract
Volume, p. 119, 2006.
4. Wdowinski S., S. Kim, T. Dixon, F. Amelung, and R. Sonenshein, High Spatial-Resolution
Space-Based Monitoring of Surface Water Level Changes in the Greater Everglades, Greater
Everglades Ecosystem Restoration (GEER) Conference, Abstract Volume, p. 242, 2006.

Project Progress

Space-based monitoring of wetland surface flow

PI: Dr. Shimon Wdowinski
Division of Marine Geology and Geophysics, Rosenstiel School of Marine and
Atmospheric Science, University of Miami

During the past six months, we have obtained progress in the following four categories:

1. Data acquisition
We continue acquiring C-band SAR data, mainly over the Everglades wetlands, but also
over other wetlands. Our main source of data for Everglades is RADARSAT-1, which
has a repeat orbit of 24 days. Using our Alaska SAR Facility (ASF) data project, we set
6 Data Acquisition Requests (DAR) that automatically acquire every repeat orbit. As a
result, we get 6 new acquisitions within every 24 days, half using fine beam (7 m pixel
resolution) and the other half with standard beam (15 m resolution). Due to a new
agreement between ASF and CSTARS (University of Miami), since October we
downlink the new acquisitions at CSTARS at no cost! So, we are getting high quality
data at no cost and in real time.
We started new data acquisition projects in other wetlands, mostly in North and Central
America, including Louisiana coast, Yucatan (Mexico), and the Bahamas. We also
started a collaborative project with a French team to monitor wetlands in Mauritania
(Africa). Our group acquires RADARSAT-1 data and the French team conducts ground
measurements and develops hydrological model for the wetlands.

2. Data processing and results
We have continued processing both archive and current data. We recently received
archived RADARSAT-1 data of both the Everglades and the Louisiana coast for the
time period of 1996-2003. These data has been processed in order to constrain detailed
flow model of both areas. We also continued processing current data that is downlinked
at CSTARS. After a long effort we managed to automate the data processing procedures,
resulting in an automated interferogram production every data acquired over the
Everglades. Data acquired over other wetlands are partially processed automatically, but
also requires human intervision.

3. Flow models

Water level is a key parameter in wetlands ecosystems, affecting flow and spatial
extent of wetlands. As part of the Everglades restoration effort, the TIME model (Tides
and Inflows in the Marshes of the Everglades) was developed by US Geological Survey
and University of Miami, enabling us to investigate interacting effects of freshwater

inflows and coastal driving forces in and along the mangrove ecotone of the Everglades
National Park. The TIME model solves for the spatial and temporal distribution of main
hydrological parameters in both surface- and ground-water, including water levels, flows,
and salinity, and is constrained by field measurements at its boundaries. The model has
been calibrated for the 1996-2002 time period, because reliable field observations are
available for that time period.
Twelve InSAR-measured water level change maps are produced using ERS-1/2
and JERS-1 SAR images during 1996-1997. In addition 2-D water level maps at the
satellite acquisition times are derived from the TIME model simulation and used to
synthesize water level change maps similar to those obtained from satellite radar
observations. We compare InSAR measurement with the synthetic water level change
map from the TIME model and field data. Our initial findings show that InSAR
measurement indicates similar patterns to those obtained using modeled water level, but
there are also some differences. Investigation of coincidence and discrepancy between
the two mapping methods will provide new scientific insight, especially regarding the
role of spatial variation of water level. Eventually, the InSAR analysis can be used to
calibrate, verify and refine the existing numerical model as well as a powerful tool to
determine water level changes in wetlands with remote sensing.
We also continued our modeling efforts of Water Conservation Area 1 (WCA-1),
as part of the MS thesis of B.M. Bieler, at the University of Miami. Using the space-
based data we obtained time series of water level changes in the entire area 1. These
maps of water level changes show very interesting patterns. Preliminary modeling
results show very good fit to some of the observations, but not all. The model needs
further improvements, which will be conducted in the next few months.

Additional project details and long-term objectives are discussed in the following
section.

Space-based monitoring of wetland surface flow
Statement of critical regional or State water problem.
Coastal wetlands provide critical habitat for a wide variety of plant and animal
species, including the larval stages of many ocean fish. Globally, most such regions are
under severe environmental stress, mainly from urban development, pollution, and rising
sea level. However, there is increasing recognition of the importance of these habitats,
and mitigation and restoration activities have begun in a few regions. A key element of
wetlands restoration involves monitoring and modeling its hydrologic system, in order to
understand the underlying flow dynamics, assess potential restoration strategies, mitigate
effects of past construction, and predict the effects of future changes to infrastructure
such as new dams or levies, or their removal.
The Everglades region in south Florida is a unique ecological environment. This
gently sloping terrain drains Lake Okeechobee in central Florida southward into the Gulf
of Mexico. The combination of abundant water and sub-tropical climate promotes a wide
diversity of flora and fauna. Anthropogenic changes in the past 50 years, mainly for water
supply, agricultural development and flood control purposes, have disrupted mtural water
flow and severely impacted the regional ecosystem. Currently, Everglades flow is
controlled by a series of hydraulic control structures to prevent flooding and regulate flow
rates, but which also suppress natural water level fluctuations, essential for supporting the
fragile wetland ecosystem. This controlled Everglades environment provides a large-scale
laboratory for monitoring and modeling wetland surface flow. Enhanced modeling
capabilities and understanding of the Everglades hydrological system are essential for the
Everglades restoration project, which is the largest and most expensive (multi-billion
dollar) wetland restoration project yet attempted.
The Everglades are currently monitored by a network of stage (water level),
meteorological, hydrogeologic, and water quality control stations, providing daily
average estimates of water level, rainfall and other key hydrologic parameters. Due to the
limited number of stations (station spacing 10 km) and their distribution, mainly along
existing structures, the current data can constrain regional scale models, such as the 2 x 2
mile2 SFWMM, but lack the spatial density for more detailed models.

Statement of results or benefits.
The proposed research will provide high resolution (-300 x 300 m2) regional
scale observations, more than an order of magnitude higher than the existing ground
network, of wetland water levels and their changes. The new observations will be used as
(1) a monitoring tool for water resources managements, and (2) constraints for high
spatial resolution of wetland surface flow. The new measurement are important for
managing and restoring wetlands damaged by human activity, because many species are
threatened by wetlands degradation depend on restoration of hydroperiod (water level as
a function of time). Flow management to achieve this depends on accurate flow models
and accurate, spatially dense elevation measurements, currently lacking. Our test area,
the Florida Everglades, is the focus of the largest wetlands restoration project yet
attempted.

Nature, scope, and objectives of the project, including a timeline of activities.

Nature The proposed research promote the usage of space-based regional-scale high
spatial resolution observations for monitoring and understanding wetland surface flow.
Scope The proposed work contains three components: InSAR analysis of wetlands,
hydrological analysis, and numerical modeling. In the first component we will us SAR
data of the Everglades (both C-band and L-band) and other wetlands (Louisiana,
Chesapeake Bay) to detect water level changes between SAR data acquisitions. The
second component hydrologic analysis will allow us to understand and utilize the
high spatial resolution InSAR observation, by evaluating the observation with respect to
terrestrial-based (e.g., stage data) and field observations. In the third component we will
use the high spatial resolution observations to constrain surface flow models. This part of
the project will be conducted by the USGS, which already developed a flow model for
the southern Everglades.
Objectives Our proposed research will provide new space-based observations, which
will be used to understand in details the complexity of wetland surface flow. Furthermore
using the new observations as constraints in 3-D flow models, we will be able to evaluate
the tempo-spatial distribution of key hydrologic parameters that govern shallow surface
flow in the Everglades and other wetlands.
Timeline During the first phase of the project, until the new Japanese L-band SAR
satellite (ALOS PALSAR) will be launched in December 2004, we will use mostly
archived SAR data (L-band JERS-1, and C-band ERS-1/2), but also current C-band data
(ENVISAT and RADARSAT-1), for further developing the technique and for gnerating
high resolution historic observations (1992-1998) for constraining flow models. After the
ALOS satellite will launched and calibrated, we will focus the research on current L-band
InSAR observations for monitoring purposes, as well as for providing better model
constraints.

Methods, procedures, and facilities.
The proposed work contains three components: InSAR analysis of wetlands, hydrological
analysis, and numerical modeling. The first two components will be conducted at the
University of Miami and the third one by the USGS.
InSAR analysis of wetlands
This component of the proposed project includes additional InSAR data
processing of the Everglades and other wetlands. We plan to process additional Lband
JERS data of southern Florida, which were acquired during the JERS mission during
1992-1998. The data are available at the Japanese Space Agency (NASDA, which
recently changed its name to JAXA). We also plan to process C-band ERS and EVISAT
data collected by the European Space Agency (ESA). In order to obtain the C-band data,
we submitted a data proposal to ESA, which was approved last August Although so far
only L-band data were successfully used to detect wetland water level changes, we plan

to test the C-band data and compare between the two data types. We also plan to
purchase and process L-band data from other wetlands, such as the Louisiana Coast and
the Chesapeake Bay.
Hydrological analysis of interferograms interpretation of the observations
The new spaced-based observations (interferograms) describe with high spatial
resolution (-30 x 30 nf) lateral phase changes between two acquisitions. Because each
phase cycle (2 pi) corresponds to 12 cm of displacement in the radar line-of-sight, which
translates into 15.1 cm of vertical displacement, we were able to translate the observed
phase changes into maps of water level changes occurring between two acquisitions.
Producing such water level change maps is only one step in understanding the
corresponding hydrological system, because the observations are relative both in time and
in space. The relative aspect in the time domain is derived from the fact that the
measurements describe water level changes from one unknown situation to another
unknown situation. In the preliminary study described below, we spent almost a year to
understand the hydrological significance of the InSAR measurements. Fortunately, we
found that during one of the SAR data acquisitions (1994/12/19) water level conditions in
the three water conservation areas (1, 2A, and 2B) were almost flat. As a result, we were
able to calculate the dynamic water level topography occurring during the two other
acquisitions (1994/6/26 and 1994/8/9). The relative aspect in the space domain arises
from the nature of the InSAR observations, which measures relative changes
continuously and not across levies or other structures. We resolved this issue by using
stage data for validation and calibration of the InSAR technique. In summary, the
translation of the interferograms into hydrological meaning observations requires a good
knowledge of the wetland environment, which we acquired by field trips, and good
integration between the high spatial resolution space-based observations with high
temporal resolution stage data.
As part of the proposed research, we plan to continue our hydrological analysis
of already processed observations (interferograms) to other regions in the Everglades,
beyond the three water conservation areas, analyzed in the preliminary study presented
below. The hydrological analysis will involve field trips to the study areas, including
airboat trips to less accessible locations. The field trip will enable us to relate space-based
phenomena to local structure, as we did in our preliminary study. The hydrological
analysis component will also include integration of stage, gate and meteorological data
with our observations. The stage and gate data collection will be conducted as a summer
job of an undergraduate student. Field work and the integration of the space- and
terrestrial-based observations will be conducted by a post-doc under the PI's supervision.

Numerical Modeling
After obtaining Hydrological understanding of surface flow in the Everglades and other
wetlands, we will use the high spatial resolution observations to constrain surface flow
models. This part of the project will be conducted by the USGS, which developed a 500 x
500 m2 resolution grid for studying surface flow in the southern section of the
Everglades. The space-based observations will allow us to (i) evaluate spatial and

temporal variation of the flow transmissivity, (ii) relate transmissivity variations to
vegetation, and (iii) estimate spatial and temporal evapo-transpiration rates.
Facilities at the University of Miami
The Geodesy Lab at the University of Miami (UMGL), located at the Rosenstiel
School of Marine and Atmospheric Sciences (RSMAS) on Virginia Key, maintains a
network of 7 Unix workstations: one SGI Octane, one Sun Ultra 60, 3 Sun Ultrasparc
10's and 2 rack-mounted Sun "pizza boxes"(Sunfire V-100). The system includes a CD
ROM reader, a CD writer for data archiving, an 8 mm tape drive, 100 Gbytes of hard disc
storage, and color and black&white laser printers. All computer equipment except the
printer is powered by a UPS (Uninterruptible Power Supply) to allow us to span power
interruptions and protect data on hard disc against voltage spikes associated with
electrical storms, a frequent problem in south Florida. The Sun workstations are
equipped with the GIPSY software (release version 2.5) for high precision GPS data
analysis, provided by the Jet Propulsion Lab. Two Linux boxes are equipped with the
"roi-pac" software from JPL for processing raw SAR data and generating interferometric
and other advanced image products. The SGI is equipped with EarthView SAR
processing software from Atlantic Scientific and VEXCEL software with similar
features. The computer facilities are adequate for all the data analysis and modeling
described in this proposal.
The Geodesy Lab performs daily analysis of more than 900 globally distributed GPS
stations, for studies of crustal deformation, volcano monitoring, coastal stability, plate
motion and plate rigidity, as well as analysis of SAR images for crustal deformation.
Selected results are available our web site: http://www.geodesy.miami.edu

CSTARS http://cstars.rsmas.miami.edu/
UMGL is connected by 2Gb/s fiber optic to CSTARS (Center for Southeastern
Advanced Remote Sensing) located at UM's Richmond campus, the center for much of
the university's space-related activities. CSTARS includes 2 11.3 m diameter X-band
antennas for downlinking data from a variety of earth-orbiting satellites. This facility
includes a 64Tbyte tape cartridge archive for raw satellite data, and numerous computers
for data analysis, with more than 2 Tbyte of hard disc storage.

Related research.
A recent study by Wdowinski et al. [2004] describes new space-based
hydrologic observations of South Florida, revealing spatially detailed, quantitative
images of water levels in the Everglades. Their observations capture dynamic water level
topography, providing the first three-dimensional regional-scale picture of wetland sheet
flow, showing localized radial sheet flow in addition to a well defined southward
unidirectional sheet flow. In this preliminary work, they used a 1-D linear diffusive flow
formulation to simulate the unidirectional flow and to determine its corresponding
hydrological parameters (vegetative friction coefficient). This proposal expands upon the
initial study of Wdowisnki et al. [2004]. The main points of this work and its relationship
to this work are described below.

Figure 1: (a) RADARSAT-1 ScanSAR image of Florida showing location of study area
(RADARSAT data C Canadian Space Agency / Agence spatiale canadienne 2002. Processed by
CSTARS and distributed by RADARSAT International). (b) Cartoon illustrating the double-
bounce radar signal return in vegetated aquatic environments. The red ray bounces twice and
returns to the satellite, whereas the black ray bounces once and scattered away. (c) JERS L-band
interferogram of the eastern south Florida area showing phase differences occurring during 44
days (1994/6/26-1994/8/9). Each color cycle represents 15.1 cm of elevation change (See color
scale in Figure 2).

InSAR Data

InSAR combines SAR images of the same area acquired at different times from
roughly the same location in space. By comparing the phase of individual pixels, cm-
level changes of the Earth's surface can be detected. Most InSAR studies use C-band
data (5.6 cm wavelength) to detect crustal deformation induced by earthquakes,
magmatic activity, or water-table fluctuations [e.g., Massonnet et al., 1994]. L-band SAR
data (24 cm wavelength), which penetrates through vegetation, were also used to study

crustal deformation in vegetated terrain [e.g., Murakami et al., 1996]. A different use of
L-band data was developed by Alsdorfet al. [2000; 2001a; 2001b] to detect water-level
variation in the Amazon wetland environment. They showed that interferometric
processing of L-band SAR data (wavelength 24 cm) acquired at different times is
suitable to detect water level variations in wetlands with emergent vegetation
(measurement accuracy 3-6 cm). The radar pulse is backscattered twice ("double-
bounce" Richards et al., 1987] Figure lb), from the water surface and vegetation
(Figure lb). A change in water level between the two acquisitions results in a change in
travel distance for the radar signal (range change), which is recorded as a phase change in
the interferogram.
The data consists of three SAR passes over South Florida acquired by the JERS
satellite in 1994 (1994/6/24, 1994/8/9, and 1994/12/19), at the beginning, middle, and
end of the local wet season (June-November). We calculated 3 interferograms, spanning
44 days (June-August), 132 days (August-December), and 176 days (June-December)
covering the rural Everglades and urban Miami-Fort Lauderdale (Figure la).
The June-August interferogram shows very high interferometric coherence, in
both rural and urban areas, and allows the following observations: (i) Significant
elevation changes occur in the controlled-flow regions (within the white box in the upper
half of Figure lb). (ii) Discontinuities occur across man-made structures (canals, levees,
and roads), and (iii) Elevation changes in Miami-Ft. Lauderdale metropolitan area are
small. The two other interferograms, spanning longer periods, have lower coherence.
Wdowinski et al. [2004] applied a spatial filter, improving the interferogram quality with
some degradation in horizontal resolution (100x100 to 300x300 nr), still significantly
better than any available terrestrial monitoring technique.

InSAR detected water level changes

The most significant elevation changes occur in the northern section of the
interferogram, across man-made structures, known as Water Conservation Areas (WCA)
1, 2A, and 2B. Figure 2 shows both the L-band backscatter amplitude and interferograms
for the three time spans. The amplitude (brightness) variations (Figure 2a) represent the
radar scatter, which depends on the surface dielectric properties and surface orientation
with respect to the satellite. The small, elongated white areas are vegetated tree islands
aligned along the long-term regional flow direction. The large white areas in areas 2A
and 2B are dense vegetated areas. The pattern of water level change is unidirectional in
the eastern section of area 2A and radial in the western part. In the northern section of
area 2B the water level change is characterized by 3 radial ("bulls eye") patterns (b and
c). The interferometric phase (Figure 2b, 2c and 2d) show water level changes in area 2A;
the change direction and amount vary. The change in Figure 2b indicates water level
decrease towards the NE by about 60 cm (4 cycles), in Figure 2c a NE decrease of about
105 cm (7 cycles), and in Figure 2d an increase by 45 cm (3 cycles with opposite color
scheme), which agrees with the difference between (2b) and (2c).

1_ __ 1 (d^j
I cycle (2 i 12 Cm raNge change 15.1 Om waler-lvei change
0t 22 4 64
Range increase Water-level increase
Figure 2: L-band backscatter amplitude and interferograms of the Water Conservation Areas
(WCA) 1, 2A, and 2B (location in Figure Ic). (a) Amplitude (brightness) variations represent
radar backscatter, which depends on the surface dielectric properties and surface orientation with
respect to the satellite. The small elongated white areas in the WCAs are vegetated tree islands
(10), aligned along regional flow direction. Large white areas in 2A and 2B are dense vegetated
areas. (b) 176-day (June-December) interferogram, (c) 132-day (August-December) interferogam,
and (d) 44-day (June-August) interferogram. The interferograms show the largest water level
changes occurred in area 2A (up to 1 m 7 cycles in (c)) and smaller scale ones in areas 1 and
2B. (ii) The pattern of water level change is unidirectional in the eastern section of area 2A and
radial in the western part. In the northern section of area 2B the water level change is
characterized by 3 bulls-eye radial patterns (b and c).
Figure 3 shows the June-December water level changes in areas 1, 2A, 2B and
their surroundings. Because InSAR measures relative changes within each area, but not
between the areas, we assigned in each area the lowest change level to zero. The most
significant water level changes occur in the eastern section of area 2A, where the level
changes can be described by a series of NWN-ESE almost parallel contours.

cm -A-1 W'C.2,i *TC-29
90 -- 7 0 -am ~g. I

In ro m A I1sC 109 a se kw
'A ~s-abage-nred wber level changes (cm)
0 j W'-l WC'A-2A 'J'A-2e

level prole location. The characters (A, C and D) and (digits 4, 5 and 6) mark gate locations,
presented in Table 1. (b, c, and d) Comparison between the zero-offset InSAR and the stage data
10

40 '10 0 0 20

Dltrcalcu d WaaaCan nIn A Level chage

Figure 3: (a) InSAR-based water level change map for the June-December time interval of areas
1, 2A and 2B. Red triangles mark the location of stage stations and the white line marks the water
level profile location. The characters (A, C and D) and (digits 4, 5 and 6) mark gate locations,
presented in Table 1. (b, c, and d) Comparison between the zero-offset InSAR and the stage data
calculated separately for each area. (e) Comparison between InSAR and stage water level changes
along the profile. Stage data observed in the center of areas 1 and 2A are projected onto the
profile. The vertical dashed lines mark the location of levees separating between the conservation
areas. The corrected InSAR curve (dashed line) is calculated from a least-squares adjustment.
Table 1: Flow observation (CFS cubic feet per second) collected by the SFWMD during the
JERS data acquisition at the gates feeding and draining area 1A. (Data source: DBHYDRO -
http://www.sfwmd.gov/org/ema/dbhydro/index.html). The symbol indicates the gate
location in Figure 3a.

Station Levee Symbol 1994/06/26 1994/08/09 1994/12/19
S10D 1-2A D 0 1170 0
S10C 1-2A C 489 1447 88
S10A 1-2A A 510 1485 0
S144 2A-2B 4 90 83 0
S145 2A-2B 5 117 95 0
S146 2A-2B 6 82 53 0

In order to validate and calibrate our InSAR technique, we compared the InSAR
observations with stage data (red triangles in Figure 3a) collected by the South Florida
Water Management District (SFWMD). The stage data consist of daily average level
above the NGVD29 datum. We use these data to calculate water level differences

between the two acquisition dates. Comparison between the InSAR and stage data shows
excellent agreement for each of the three water conservation areas (Figure 3b, 3c and 3d).
It also allows us to compute and correct the datum offset between stage and InSAR data,
which were set arbitrarily to zero value at the lowest level in each area (Figure 3e).

20 15 r^---. ------ fQ-j--_- - 2 0_ 10 ---------1--- -- 1-I
I June -Deiimn" ; I A Iu Dwi '
7S

0 I
*-4, -" --E.-----.-_ --'
I__ o .
a : * fl Jbbd5A *

igue 4 W1atr level s along te pro1 6 i i 5 0 3 1 2cor 2In
C sse atig t WWCAs. ( WA2A Level changes fr the A t-ecebe r tie interval. () Je

Jun Dee after level a t Augonst eleer lvel

^ B ^-

Figure 4: (a) Water level changes along the profile in Figure 3c, showing corrected InSAR curve
and stage data for June-December time interval. Vertical dashed lines mark location of levees
separating the WCAs. (b) Water level changes for the August-December time interval. (c) June
and December water levels along the profile. Based on the December stage data (red squares) and
gate information (Table 1), we assume flat water level in each area (red lines). The June water
level (blue line) is obtained by subtracting the corrected InSAR curve from the assumed flat
December level. (d) August and December water levels following the same procedure as in (c). (e)
Three-dimensional illustration of the June water levels, calculated by subtracting the corrected
InSAR data from the assumed flat December levels, for the entire studied area. (f) Three-
dimensional illustration of the August water levels.

From water level change to absolute water level

The new space-based observations provide, with very high spatial resolution,
water level changes in the Everglades occurring over 44, 136 and 176 day time intervals
(Figures 3, 4a and 4b). Because these time intervals are long compared to the duration of
natural and anthropogenic water level changes in the Everglades (days to several weeks),
the observed water level changes represents the differences between two states and not a
continuous process. Figures 4c and 4d present stage elevations during the three
observation periods. The December InSAR observation occurred during a period of

negligible water flow across the conservation areas (Table 1), resulting in almost flat
water levels in the three areas (red lines in Figures 4c and 4d).
Hydrologic Model
Wdowinski et al. [2004] used a diffusion flow model to explain the observed
dynamically supported water topography and derive quantitative estimates of
transmissivity and Manning's friction parameter. Their model follows the Akan and Yen's
[1981] diffusion flow formulations, which are derived from conservation of mass and
momentum principles, neglecting inertial terms. The model is appropriate to low-
Reynolds hydrologic flows, where the flow is predominantly laminar. It follows the same
formulation as the SFWMM [Lal, 1998, 2000] which has been used extensively to model
surface flow in South Florida. For overland flow, the water level (H) can be describe in
terms of its derivatives in time (IH/dt) and space (lH dx), water transmissivity )r
diffusivity) (D), and sink terms representing rainfall, evapotranspiration, and infiltration
[Lal, 1998, 2000]. The transmissivity diffusivityy) represents a Fickian form for the
relation between volumetric flow rate and surface water elevation gradient and is a
function of flow frictional characteristics. For instance, if Manning's friction relationship
is applied, then D = h5 3 / (n 1dH/d'l/2), where n is Manning's dimensionless friction
coefficient, a measure of the resistance to flow [Lal, 1998, 2000].
In order to calculate dynamically supported water levels, we need to specify initial
and boundary conditions, which are poorly constrained. Rather then modelling water
levels in all three water conservation areas and accounting for complex gate operation
history, we focus on the process that governs dynamically supported water level
topography and model the region where this phenomenon is most pronounced the
eastern section of area 2A (Figure 3a). In this region the hydrologic flow lines are
orthogonal to InSAR water level contours, indicating a southward flow during June and
August 1994. The unidirectional flow in this region allows a simple one-dimensional
analytical solution. As a first approximation, we: (1) assume a spatially uniform
transmissivity (D); and (2) neglect sink terms, deriving the familiar one-dimensional
diffusion equation:
dH d2 H
? D d(1)
dt dx2
The boundary conditions are derived from the stage and gate operation time series. We
apply an instantaneous gate opening model, which assumes (1) a flat water level in area
2A prior to the opening of the gates (supported by the stage data), (2) area 2A is infinitely
long, and (3) water level in area 1 remains constant (supported by stage data). The above
assumptions allow us to determine initial and boundary conditions and to solve equation
(1) analytically [Carslaw and Jaeger, 1959] using a three term series expansion. We use
a best-fit adjustment to estimate the polynomial coefficients and calculate two flow
parameters: the initial water elevation difference across the gate (Qo) and the flow
characteristic length (Dt)1'2
Figure 5 and Table 2 show that the InSAR data constrain the model parameters to
-5% uncertainty. However, the full coupling of time and transmissivity as a single
parameter the diffusion characteristic length (Dt)1/2 does not allow us to uniquely
determine the transmissivity coefficient (D). Nevertheless, we use the gate operation

history to estimate the time since opening, as 162 days for June and 82 days for
August, in order to estimate transmissivity.

Wdowinski et al. [2004] also calculated the corresponding Manning friction factor
n for diffusive flow. Reported values of n for sheet flow through vegetation are in the
range 0.10 < n < 1.0 [Overton and Meadows, 1976; Akan and Yen, 1993; Nepf, 1999;
Lal, 2000; Lee et al., 1999; Wu et al., 1999; Kouwen and Irrig, 1992]. We find our
InSAR based determined n to be in the same range. For the June period, n is somewhat
higher (0.7-1.0) compared to the August period (0.3-0.7). The difference represents the
influence of vegetation on flow, with higher friction at lower water levels (June)
compared to higher water levels (August). This decrease of resistance to flow with
increasing water depths has been reported in the literature [Lee et al., 1999; Wu et al.,
1999; Kouwen and Irrig, 1992]. The obtained range of friction values of the flow
transmissivity for vegetated flow is, as expected, higher than that of unvegetated earthen
beds, in the range of 0.01-0.04 [Akan and Yen, 1993]. A higher Manning friction
coefficient implies a lower flow transmissivity and vice versa. This finding is also
consistent with estimated decreases of 1-2 orders of magnitude in scalar transport
dispersion between unvegetated and vegetated surface flows [Nepf, 1999].

5.0
Jung
4.5
4.0
3.0
3,a

4.0

in June and August 1994 across area 2A (along the N-S profile shown in Figure 3).
Model parameters are described in Table 2.
0nstance tinn WCA-IrMCA-2A Lovo (km)

Figure 5: Comparison between observed stage, InSAR, and best-fit modeled water levels
in June and August 1994 across area 2A (along the NS profile shown in Figure 3).
Model parameters are described in Table 2.

Table 2: Summary of Hydrologic Modeling (see in Figure 5).
Parameter Description June 1994 August 1994
Ho (m) Elevation difference 0.850.04 1.41+0.05
Dt (m2) Characteristic length 73.23.3 x 10b 91.9 3.8 x 10"
D (m2/s) Transmissivity 5211 133+46
n1 Manning's coefficient 0.7- 1.0 0.3-0.7
': n is estimated using reference values of h=1 m and -dH/dx=7 10- m/km.
Training potential.

The proposed research will support the training of one graduate student and one
undergraduate student. The graduate student is Dawn James, who is a USGS employee
and is in the process of applying to UM for her Ph.D. program. She is hydrologist by

training and is interested to learn and apply InSAR technique and observations to
hydrological problems. Her research focus will be on the usage of InSAR-measured
surface elevation changes and aquifer-system deformation in southern
Florida. Although her thesis research has a different focus than our proposed research to
NIWR, she will greatly benefit from the data, resources and personnel working on a
related problem.
An undergraduate student will work for the project during two summers. He will be
trained in remote sensing, InSAR data processing and hydrology, as well as enriching his
computer skills. The planned undergraduate training will prepare him/her to any good
graduate programs in Earth Science and/or to a real job.
Stateme nt of Government Involvement.
The co-PI Roy Sonenshein (USGS) will be responsible to transfer and use the space-
based water-level observations for constraining the USGS's TIME (Tides and Inflows in
the Mangroves of the Everglade http://time.er.usgs.gov/) flow model of the Everglades.
The high-spatial resolution InSAR measurements (300 x 300 m2) are excellent
observations for constraining the 500 x 500 m2 spatial resolution of the TIME model.
Information Transfer Plan.
The project's disseminating information plan includes the following three components:
1. Local (southern Florida) We plan to present the project results at local academic,
research, environmental, and water managements organizations. So far, we presented
our preliminary study results and received very positive feedback at following local
institutes: University of Miami, Florida International University, Everglades National
Park and the Loxahatchee Wildlife refuge. We plan to further inform these institutes
about our results, as well as contacting other local organizations, such as the South
Florida Water Management District, the local office of the Army Core of Engineers,
the Miami and other regional offices of the USGS. We also plan to present the project
results at the up coming GEER (Greater Everglades Ecosystem Restoration)
Conferences. By presenting our results locally we hope to promote the usage of the
space-based measurements as a monitoring tool, as well as important constraints for
detailed surface flow models of the Everglades.
2. National We plan to present the project results at least two national meetings, e.g.,
AGU, the NASA Surface Water Working Group (SWWG), etc. When we'll achieve
significant results for the Louisiana Coast and Chesapeake Bay wetlands, we'll
contact local organization in these areas, in order to expose them to the project and its
results.
3. International As part of project's information transfer plan, we also plan to attend
the upcoming JAXA's (Japan Aerospace Exploration Agency) ALOS meeting (9
month after the launch of the ALOS satellite, which is scheduled for 12/04) to report
on our progress and further promote the usage of SAR data for hydrological
applications. If possible, we will also attend other international meetings with
SAR/InSAR focus.

Literature Citations/References
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Alsdorf D, Birkett C, Dunne T, Melack J, Hess L, Water level changes in a large Amazon
lake measured with spaceborne radar interferometry and altimetry,
GEOPHYSICAL RESEARCH LETTERS, 28 (14): 2671-2674 JUL 15 2001a
Alsdorf DE, Smith LC, Melack JM, Amazon floodplain water level changes measured
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GEOSCIENCE AND REMOTE SENSING, 39 (2): 423-431 FEB 2001b.
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Amelung, F., S. J6nsson, et al. (2000). "Widespread uplift and 'trapdoor' faulting on
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812, 2001
Bhallamudi, S.M., S. Panday and P.S. Huyakorn, Sub-timing in fluid flow and transport
simulations, Adv. Wat. Resour. 26, 477-489, 2003.
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2000.
Carslaw H. S., and J. C. Jaeger, Conduction of heat in solids (second edition), Oxford
University Press, New York, 1959.
Kouwen N., and J. Irrig. Drain. E.-ASCE 118, 733, 1992.
Lal, W.A.M, J. Hydraul. Eng., 124, 342 1998; Water Resources Res., 36, 1237, 2000.
Lee, J.K, V. Carter, and N.B. Rybicki, Proceedings of the Third International Symposium
on Ecohydraulics, July 1999.
Massonnet, D., M. Rossi, C. Carmona, F. Adragna, G. Peltzer, K. Feigl, and T. Rabaute,
The displacement field of the Landers earthquake mapped by radar
interferometry, Nature, 364, 138-142, 1993.
Massonnet, D., K. Feigl, M. Rossi, F. Adragna, Radar interferometric mapping of
deformation in the year after the Landers earthquake. Nature, 369, 227-230,
1994.
Massonnet, D., and K. L. Feigl, Discriminating geophysical phenomena in satellite radar
interferograms, Geophys. Res. Lett., inpress, 1995a.
Massonnet, D., P. Briole, et al. (1995). "Deflation of Mount Etna monitored by
spaceborne radar interferometry." Nature 375: 567-570.
Massonnet, D. and K. L. Feigl, Rev. Geophys., 36, 441, 1998.

Mumby, P.J., Edwards, A.J., 2002, Mapping marine environments with IKONOS
imagery: enhanced spatial resolution can deliver greater thematic accuracy,
Remote Sensing of Environment, 82, 248-257.
Murakami, M., M. Tobita, et al. (1996). "Coseismic crustal deformations of the 1994
Northridge, California earthquake detected by interferometric analysis of SAR
images acquired by the JERS-1 satellite." J. Geophys. Res. 101: 8605-8614.
Overton, D.E., and M.E. Meadows, Stormwater Modeling (Academic Press, New York,
1976).
Maidment D.R., Handbook ofHydrology, McGraw-Hill, New York, 1993.
McLaughlin, D.B. and L.R. Townley, A reassessment of the groundwater inverse
problem, Water Resour. Res., 32(5), 1131-1161, 1996.
Nepf H. M., Water Resour. Res. 35, 479, 1999.
Richards, L. A., P. W. Woodgate, and A. K. Skidmore., Inter. J. Remote Sensing, 8,
1093, 1987.
Storm, B. and A. Rafsgaard, Distributed physically-based modeling of the entire land
phase of the hydrologic cycle, Kluwer Academic Publishers, 1996.
Wdowinski, S., F. Amelung, F. Miralles-Wilhelm, T. Dixon, and R. Carande, Space-
based measurements of sheet-flow characteristics in the Everglades wetland,
Florida, Submitted to Geophys. Res. Lett., 2004.
Wu F.-C., H. W. Shen, and Y.-J. Chou, J. Hydraul. Eng.-ASCE, 125, 934, 1999.
Yeh, G. T., 1999. Computational Subsurface Hydrology Fluid Flows. Kluwer Academic
Publishers
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Geosci. Rem. Sensing, 30, 950-959, 1992.
Zebker, H., P. Rosen, R. Goldstein, A. Gabriel, and C. Werner, On the derivation of
coseismic displacement fields using differential radar interferometry: the
Landers earthquake, J Geophys. Res., 99, 19617-19634, 1994a.
Zebker, H.A., T.G. Farr, R.P. Salazar, and T. Dixon, Mapping the world's topography
using radar interferometry: the TOPSAT mission, Proc. Inst. Electrical
Electonic Eng., 82., 1774-1786, 1994b.
Zebker, H.A., C. Werner, P.A. Rosen, S. Hensley, Accuracy of topographic maps derived
from ERS-1 interferometric radar, IEEE Trans. Geosci. Rem. Sens., 32, 823-
836, 1994c.
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A comparison of FSU/NWS and OneRain precipitation data and
their insertion into the WAM hydrologic model

Basic Information

Title: A comparison of FSU/NWS and OneRain precipitation data and their insertion
into the WAM hydrologic model

Project Number: 2006FL140B
Start Date: 3/1/2006

End Date: 2/28/2008

Funding Source: 104B

Congressional
District:

Research Category: Climate and Hydrologic Processes

Focus Category: Climatological Processes, Hydrology, None

Descriptors:

Principal
Principal Henry Fuelberg
Investigators:

Publication

1. Fuelberg, H.E., S.M. Martinaitis, J.L. Sullivan, Jr., and C.S. Pathak, 2007: An intercomparison of
precipitation values from the OneRain Corp. algorithm and the National Weather Service Procedures.
World Environmental and Water Resources Congress, Tampa, May 2007, in press.
2. Fuelberg, H.E., D.D VanCleve, Jr., and T.S. Wu 2007: An intercomparison of mean areal
precipitation from gauges and a multisensor procedure, World Environmental and Water Resources
Congress, Tampa, May 2007, in press.
3. D.D. VanCleve, and H.E. Fuelberg, 2007: An intercomparison between mean areal precipitation from
gauges and a multisensor procedure, 21st Conf. on Hydrology, Amer. Meteor. Soc., San Antonio,
January 2007, in press.
4. Martinaitis, S.M., H.E. Fuelberg, J.L. Sullivan, Jr., and C. Pathak, 2007: An intercomparison of
precipitation values from the OneRain Corp. algorithm and the National Weather Service procedure.
21st Conf. on Hydrology, Amer. Meteor. Soc., San Antonio, January 2007, in press.

Progress Report

"A Comparison of FSU/NWS and OneRain Precipitation
Data and Their Insertion Into the WAM Hydrologic Model"

Submitted by

Henry E. Fuelberg
Florida State University

This project compares precipitation values from two procedures (National Weather
Service (NWS) and the OneRain Corp. (OR)) that combine radar- and gauge-derived
precipitation estimates into a single high resolution dataset over areas of the South Florida Water
Management District. The NWS scheme is used operationally by the NWS to issue flood
watches and warnings. The OR scheme is used by various private and government agencies to
monitor potential flood situations and as data for making decisions about water quality
regulations. This project intercompares the two procedures, noting their strengths and
weaknesses, and using the two procedures as input to the WAM hydrologic model. The project
research constitutes the M.S. thesis research for Mr. Steve Martinaitis.

The statistical intercomparison of precipitation from the two procedures is well
underway. Initial results have been obtained for calendar years 2004 and 2005. A detailed study
of rainfall differences during Hurricane Wilma also is well underway. The OR data are on a
2x2 km Cartesian grid at 15 min intervals, while the NWS hourly data are on a 4x4 km grid that
is oriented approximately northeast-southwest. The OR data were summed to hourly values and
placed onto the coarser NWS grid using procedures within GIS. Results show that this
transformation was achieved with a very high degree of accuracy-differences between original
and transformed data were < 1%. Our various kinds of intercomparisons are based on these data
sets now on a common grid. Standard statistical products have been computed to quantify spatial
and area-wide differences over days, months, and years. This is being done for individual basins
within the SFWMD as well as for their entire area of jurisdiction.

Insertion of the two data types into the WAM Hydrologic Model is just beginning. The
source code has been obtained from its inventors (SWET Corp. of Gainesville, FL), and the
graduate student has been trained by SWET personnel. Some modifications currently are being
made to the WAM model so it can accept the high resolution radar-derived data. These
modifications will insure that differences in streamflow will be due to differences in the input
rainfall data, and not to other factors.

We have made excellent progress so far. The results to date have been presented at
seminars at the South Florida Water Management District in West Palm Beach and at the Florida
Department of Environmental Protection in Tallahassee. The results will be presented as two
accepted papers at the 2007 World Environmental and Water Resources Congress and two

papers at the 21st Conference on Hydrology (sponsored by the American Meteorological
Society). As soon as the research is completed, results will be submitted to a refereed journal for
publication. The results may be split into two manuscripts An additional one year of funding will
be required for the graduate student to complete all of the tasks of the project.

Additional project details and long-term objectives are discussed in the following sections.

A Comparison of FSUINWS and OneRain Precipitation

Data and Their Insertion Into the WAM Hydrologic Model-Phase II

Key Words--Radar-deri- ed Precipitation, Hydrologic Modeling

1. Statement of the Florida Water Problem

Two widely used procedures by which radar- and gauge-derived rainfall can be optimally
combined are those by the OneRain Corporation and the National Weather Sernice (NWS) The
several Florida Water Management Districts use rainfall data from the OneRain algorithm.
Conversely, Florida State Uniersity (FSU) has employed the National Weather Service scheme
to create an hisiurical precipitation database for the Florida Department of Environmental
Protection (FDEP). Although the methodologies to produce each dataset as well as the spatial
and temporal resolution of each differ, each is being used by their respective agencies to make
water management and regulatory decisions. Thus, it is important to know how rainfall values
from the two schemes compare to each other This research statistically compares results from
the two schemes, develops procedures so that both versions of data can be inserted into the
WAM hydrologic model, and performs WAM model runs over various watersheds within the
South Florida Water Management District (SFWMD) using both datasets.

2. Statement of Benefits

The Florida Water Management DisIricts and the FDEP will base important decisions on
their respective rainfall datasets. Thus, there is the possibility that the two groups will reach
different conclusions-each of which is supported by their own data. This research will quantify
differences between the two rainfall datasets to determine how similar'dissimilar they are. The
research also will expand our understanding of how high resolution rainfall data can be best used
effectively in hydrologic modeling.

3. Nature, Scope, and Objectives of the Research

a. Florida State Universitr's High Resolution Hisiorical Database

The FSU precipitation database was prepared for the FDEP using software developed by
the NWS for real time use at their regional River Forecast Centers (RFCs) and local forecast:
offices. Called the RFC-wide Multi-sensor Precipitation Estimator (denoted MPE), the
procedure blends radar-derived hourly digital precipitation data at 4 km resolution with hourly
gage data. Details of MPE are provided by Fulton et al. (1998), Seo et al. (1999), and Marzen et
al. 12005 .

Radar Input-The continental United States is scanned continuously by approximately
125 Doppler (NE XRAD) radars operated by the NWS. Each radar produces an hourly estimate
of rainfall on a 4 x 4 km grid. Since most grid points within the U.S. are viewed by more than
one radar, MPE each hour determines the radar providing the best coverage of each individual 4

km grid point. A Ithtugh radars provide excellent spatial resolution of rainfall, there are various
limitations, many of which are descnbed in Baeck and Smith (1998) and similar publications.
These limitations include improper beam filling and the overshooting of low cloud tops at farther
ranges, hail contamination, radar mis-calibration, and the unknown relation between radar
reflectivity and rainfall (Z-R relations) for a particular storm. Because ofthese limitations, the
MPE procedure also incorporates rain gauge data into its algorithm.

Gauge lnput-FSU obtained hourly rain gauge data from each of the State's Water
Manugernent Districts and from gauges whose data are archived by the National Climatic Data
Center. These gauge data were rigorously quality controlled-a very time consuming but very
neceisur) task. The MPE scheme objectively analyzes the hourly gauge data onto the 4 x 4 km
grid employed for the radar data.

Blendingthe Radar and Gagec Estimates-Using pairs of ain gauges and raw radar
precipitation estimates, the MPE software calculates bias correction factors each hour for every
radar to improve the remotely sensed precipitation values. When the hourly radar-derived
precipitation values are multiplied by this correction, radar wide biases due to factors such as
radar mis-calibiration are removed. Then as a second step, the bias corrected radar-derived
precipitation data are merged with the hourly rain gage observations using optimal interpolation.
There are a number of "adaptable parameters" within the MPE software that allow users to
optimize the procedure for the specific area for which calculations are made.

An example of the final MPE product for the Black Creek Basin of the St. Johns River is
given in Fig. 1. The figure shows the summation of hourly values for February 2001.

MPE hadhr *3.uge Prg.du.
Black G(ek Basitn. Flotida
S- February 2001

's'0, inches (equal interval)
*'. 0.396400 -3 537160
S 0.537161 -0.677920
,.0 677921 -0 818680
S. ; -. I 0818681 -0 959440

-.. 0.959441 -1.,10200
Rain Gauge
SRiver Gauge
--- Interstate
*. B3lck Creek Perimeter

Fig. 1. The Black Creek Basin wilh superimposed total rainraa (inches) for February 2001. Note
that only two rain gauges lie within the Basin.

Recent studies and validations that have utilized radar schemes to estimate precipitation
include Smith et al. (1996), Steiner et al. (I 994), Klazura et al. (1999), Wang et at. (2000), and
Marzen and Fuelberg (2005). These studies hae noted the improvements provided by optimally
combining radar and gauge information.

b. The OneRain Precipiiation Algorithm

The Florida Water Management Districts have contracted with the OneRain Corpora ion
to provide real time and historical precipitation data. The historical database consists of years
2002-2005 (4 years). The OneRain product is on a 2x2 km grid at 15 min. intervals. Although
the procedure that OneRain uses is proprietary, the cursory description below is believed to be
correct based on information at their web site (lii t Il.l iL,... "') and in Nelson et al. (2003).

Radar input for the algorithm is the composite radar rellectivil. maps produced by the
Weather Services Inemrational (WSI) Corporation based on data from the national network of
WSR-88D radars operated by the NWS and other federal agencies. The Level III radar data from
each site (Fulton et al. 1998) are collected by WSI and used to produce a national mosaic of
radar-deried precipitation at 15 min inlernas on a 2x2 km grid. When one or more radars
overlap a grid point, the greatest rainfall value is used Rainfall estimates are based on the
lowest available antenna angle at each radar. The web site states, "WST's rainfall estimation
procedure uses a dynamic weather condiiion-based algorithm to convert reflectivity values to
rainfall estimates The WS1 procedure uses a variety of weather parameters to sense what type .of
weather condition exists, then chooses the most appropriate conversion from rclcectivity to
rainfall rate."

Rain gauge-derived precipitation also is input to the OneRain algorithm. FSI assumes
that data from most or all of the Florida Water Management Districts are cmpklo ed. We do not
know whether NWS or other gauge data are input. FSU also does not know the nature of the
quality control that is used on the gauge data. The gauge data are used to calibrate the radar-
den ed dataset The OneRain site states that if there is an insufficient number of gauges with
15-min data, "'daily and hourly data were disiaggrgated to 15 min time steps using the
normalized radar data at each gauge location as the distribution afnction. Calibrations were
performed to adjust the radar estimates to match the rain gauge estimaues, on average, at the
monthly level."

Rainfall values from the OneRain and F SU.IN WS procedures have never been compared
by any group. Thus, this research will demonstrate the characier-iltics of these data within
Florida.

c. Objectives

Available information about the OneRain and FSU.T.NWS precipitation algorithms clearly
indicates that different methodologies and input data are used. The resolutions of the final
products also differ, i.e., 2x2 vs. 4x4 km grid, and 15 min. vs. hourly intervals. This suggests
that aJues from the two algorithms also differ. The objectives of this research are to 1) quantify
the amount of that difference and 2) develop procedures to insert both types of rainfall data into

the WAM hydrologic model, and make separate runs using each type ,fFdata for selected
watersheds 'within the SFWMD,

d. Estimated Ti meline-Task 1 (described below) will be completed by the end of the initial
one year period (Spring 2007). Tasks 2 and 3 (described below) will be completed by the end of
the second year of funding.

4. Methods and Procedures

Task I -FSU will quantify differences between the FSU/NWS historical dataset and the
OneRain datasel using standard statistical procedures (scatter diagrams, mean differences,
standard deviation of differences, etc.). This will be done for the four year period of record
2002-2005 when both datasets are available. This step is virtually complete, and a summary of
results is provided in the following major section

Task 2-FSU will develop procedures so that both the OneRain and FSU datasets can be
input to the Watershed Assessment Model (WAM) hydrologic model such that optimal results
are obtained. WAM is a GIS-based model developed by Soil and Water Engineering
Technology, Inc. (SWET) of Gainesville. FL. It is described in detail in a number of
publications by SWET personnel. WAM simulates the hydrology of a watershed using various
imbedded models. Although WAM has been coded to accept the FSU 4x4 km MPE rainfall,
initial streamflow results for the Black Creek Basin were mixed (SWET report to FDEP, 2003).
These findings are counter to most of the literature which indicates superior results from radar-
derived precipitation. This suggests that WAM must be modified to properly utilize the high
resolution data. That is one of the goals of the current proposed effort.

Successful use of either OneRain or FSUiNW"S data requires more than just changing
format input statements from accepting rain gauge data to accepting the gridded gauge plus radar
combination. For example, a number of run off and percolation schemes have been developed
for use in hydrologic models. GLEAMS is designed for daily precipitation input and works best
in well drained soil. The EAAMod scheme can use any interval of rainfall data (hourly, daily ,
etc ) and was designed for regions with a high water table. A number of other models are
descr bed in the literature, and the SWET team currently is preparing alternative schemes for use
in WAM. With the assistance of SWET personnel, several schemes will be tested within WAM
to determine their impacts on runoff and percolation. Based on the result. FSU and SWET will
select the most appropriate equation to use for each dataset and develop coefficients for use with
the various spatial and temporal scales of the rainfall data. Thus, the goal is to optimize WAM
for using high resolution rainfall data.

It should be noted that the FDEP c currently is supporting the Task 2 efforts for FSU/NWS
input. One FSU student has received WAM training by SWET personnel, and SWET has been
collaborating with us. The funds requested here will support similar efforts for OneRain data

Task 3--This task will perform WAM-based hydrologic modeling using data from both
the FSU/NWS and OneRain procedures.

a. Several basins will be selected for study. These basins will be of special interest
to the Water MIanagement Districts and to FDEP. SWET either already will have configured
WAM for the-e basins or will currently be configuring them. FSU is not expected to perform the
detailed model configuration for a basin.

b. FSU will make WAM runs over the selected basins over various periods of time
and for various rainfall scenarios. These scenarios will include widespread heavy or light
precipitation as well as scattered convective rain, different seasons, different basin sizes, etc. One
set of runs will utilize the OneRain daasel A second set of runs will unlize the FSU'NWS
dataset and hopefully will be sponsored by FDEP.

c. The various computed streamflows, together vith observed streamflows, will be
compared using hydrographs and various statistical tools. As a result, we will understand the role
of differences in the OneRain and FSU.'NWS datasets in producing differences in streamflow.

5. Accomplishments from Last Year

The statistical intercomparison of precipitation from the two algorithms is almost
complete. Results have been obtained for calendar years 2004 and 2005. A detailed stud) of
rainfall differences during Hurricanes Wilma and Katrina also is nearly complete. The OneRain
data on a 2x2 km Cartesian grid at 15 min intervals were summed to hourly values and placed
onto the coarser hourly, 4x4 km NWS grid using procedures within GIS. This transformation
was achieved with a very high degree of accuracy-differences between original and
transformed data were < 1%. Our various inter-comparisons are based on these data sets now on
a common grid. Standard statistical products have been computed to quantify spatial and area-
wide differences over days, months, and years. As an example, Figure 2 shows spatial fields of
the OneRain and NWS/FSU annual rainfall totals for 2004. The right panel of Fig. 2 shows that
OneRain annual rainfall is greater than the FSU amounts over much of the area. Figure 3 shows
scatter diagram comparing the two versions of rainfall for two individual months during 2004.
Statistics also are being compiled for individual basins within the SFWMND. Table 1 compares
mean area precipitation for the Tamiami East Walershed.

Insertion of the two data types into the WAM Hydrologic Model is just beginning The
source code has been obtained from its inventors (SWET Corp. of Gainesville, FL), and a
graduate student has been trained by SWET personnel. Some modifications currently are being
made to the WAM model so it can accept the high resolution radar-derived data. These
modifications will insure that differences in streamflow will be due to differences in the input
rainfall data, and not to other factors.

r -*0 _a F.SLMWS MPE RAP 2W IAmurainl F t .MPE'vO r ran i .rmu|Arel

I I . .

^ .. ... *. mn 1 S...

Fig. 2. Annual precipitation for 2004 from OneRain and MPE, and differences between the two prioduci.
Warm colors show greater estimates from OneRain. Coal colors show greater estimates from FSUINWS.

FSUNWS ve Oneorin February 2004

FSUINWS vs OneRaln September 2004

14DM000000

9G00000
6000000

2 BUD=
inoonoa
51H10{K~

S 0-8148 .+ ,124

y= 0.814S(+ 0.7124

30JO3000

200000DD

S1100000

s /o!!t

I -o -m i -0-000 ------ r --- -I- .. I
0U 210 440 &00 00W W io z0 000am 5 10W 1 It 00 20 a0 2s5D0 3B. 0s0 4MOO
FShiNS WRaEtW l {(l FSUMWa Rihafll (h)

Fig. 3. Scatter diagrams comparing values of FSUINWS pixel values with those from OneRain for Februar3
and September 2004.

Table 1. Comparison of Mean Areal Prripilalin (.MAP) for the Tamiami East Watershed,
Mnnth. rar MAP FSLi'NI S MAP OneRain MIAP DaiTrenrte Perent
]in.L in.) in.) Diference

an 2004 .371031 .151367 .226664 9.32 %
Feb 2(il4 3.-42546 3.45 05 ).024741 U.710 %.
Mar 2004 1.27959 0.902159 .76430 19.441 %
Apr2004 3.9J3CI57 2.967373 .96769* .592 %
May 2004 2,582166 1.435037 1.147129 .425%

Jun 2004 3.78446 1.631247 1.1461"9 3.335 %
Jul2004 7.677876 -69'3 .790983 1i1302 %
Aug 2MI4 14.103016 12.41011)5 I.w07Wi 0 -1.158

Sep 2004 13.585865 .677520 3.90345 8.768

ct2004 .930458 -.1619-53 1.7650 25.518 %
n v 2004 009164 .91"9537 0 894i .11 %
Dec 2004 3855655 .4 1541t6 .434149 0.739%

4nnuaI 2001 5.996,'17 9.023'13 .973165 15.472 %'

Publications During Last Year

Fuellhrg. H.E., S.M. Martinaitis, .L Sullivan, Jr., and C.S. Pathak. 2007: An intercomparison of
pre:ipiiation values from the OneRain Corp. algorithm and the National Weather Service
Procedutres. World En, ironmennli and Water Resources Congress, Tampa, May 2007, in press,

Fuelberg, H.E. D.D VanCleve, Jr., and T.S. Wu 2007: An intercomparison of mean area precipitation
from gauges and a multiensor procedure, World Environmental and Water Resources Congress,
Tampa, NMa, 2007, in press.

D,D. VanCleve, and H.E. Fuelberg, 2007: An intercoiparison between mean real precipitation from
gau;ges and a multisensor procedure, 21 Conf. on H) drolog). Amer. Meteor. Soc., San Antonio,
January 2007, in press.

Martinaitis, S.M., H.E. Fuelberg. JIL. Sullian, Jr., and C. Pathak, 2007: An intercomparison of
precipitation values from the OneRain Corp. algorithm and the National Weather Service
procedure. 21" Conf. on Hydrology, Amer. Meteor. Soc., San Antonio, January 2007, in press.

Presentations During Last Year

Seminar at the South Florida Water Management District Headquarters on October 4, 2006.

Seminar at the Florida Department of Environmental Protection on December 5, 2006

6. Related Research Cited Above

Baeck, M.L., and JA. Smith, 1998: Rainfall estimation by the WSR-88D for heavy
rainfall events. Weather and Forecasrmn, 13,416-436

Be-dienr. P.B., B.D. Hoblin, D.C. Gladwell, and .E. Vieux, 2000: NEXRAD radar for
flood prediction in Houston. J Hydrolog Engrg., Vol. 5, Issue 3, 269-277.

Fulton, R.A., J.P. Breidenbach, D.J. Sea. DA. Miller, and T. O'Bannon, 1998: The
WSR-88D rainfall alonrfiihm. Weather and Forecasting, 13,377-395.

Gourley, J.., and B.E. Vieux, 2005: A method for evahluaing the accuracy of
quantitative precipitation estimates from a hydrologic nmoieling perspective. Joi-nal of
Hydromeeorology, 6, 115-113

Hoblit, B.C., P. Bedient, B. View%. and A. Holder, 2000: Urbdn hydrologic forecasting application issues usilig the
WSR-88D radar. Joint Crinf on Water Resource Engineering and Water Resources Planrnmg and
NM.uageirern 2000, ASCE Conference Prucdedings.
Klazura G.E., J.M. Thomale, D.S. Kelly, and P. Jtndro 1i;. 1999: A comparison of NEXRAD WSR-BgD radar
estimates of rain accumulation with gaugr measurements for high- and lew -releciivit3 horizontal gradient
precipitation events. Journal qfAtmo& and Oceanic Tech., 16, 184 2-1850.
Marzen J., and H.E. Fuelberg, 2005: Developing a high resolution precipiracion dataset for Florida hydrologic
studies, 19* Conf. Hydrology, Amer. Meteor. Soc., San Diego, paper 19.2, available on CD.

Nelorn. B.R., W.F. Krajewski, A. Kruger, J.A. Smith. and M.L. Baeck, 200x: Archival precipitation data set Fur the
Mississippi Rvier Basin Algorithm development. J. G-..pin s Re', 108 (D22), 8857,
doi 10 1029/2002JD003158.
Ogden, F.L., and H,0. Sharif, 2000: Rainfall input for distributed hydrologic modeling: The case for radar.
Watershed Management and Operations Management, ASCE Conference Proceedings.
Quina, G,, H. Fuelberg, B. Mroczka, R. Garza, J. Bradberry, and J. Lanier, 2003: The effects of rainfall network
density on river forecasts-A case study in the St. Johns basin. Proceedings of the 2003 Georgia Water
Resources Conference, University of Georgia, Athens, paper available on CD.
Seo, D.., J.P. Breidenbah, and E.R. Johnson, 1999 Real-time estimation of mean field bias in radar rainfall data.
Journal of lhJdrikgi, 223, 131-147.
Smith J.A., D.J. Seo, M.L. Back, and M.D. I udIlow, 1996: An intercomparison study of NEXRAD precipitation
estimates. Water Re', iu, C: Research, 32, 2035-2045,
Steiner, M., LA. Smith, SJ, Burges, C.V. Alonso, and R.W. Darden, 1999: Etflct otf bias adjislmrni and rain gauge
data quality control on radar rainfall estimation. Water Resources Research, 35, 2487-2503.
Stellman, K., H. Fuelberg, R. Garza, and M. Mullusky, 1999: Utilizing radar data to improve streamflow forecasts.
Preprinis. Twenty-ninth Intl. Conf. on Radar Meteor., Amer. Meteor. Soc., Montreal, in press.

Sicllrnan, K., H. Fuelberg. IR Garza, and M. Mullusky, 2000 Investigating forecasts ofstreamflow utilizing radar
data. Preprints. Fifteenth Conf, on Hydrology. Amer. Meteor. Soc., Long Beach, 115-118.

Stellman, K.M., H.E. Fuelberg, IL Garza, and M. Mullusky, 2001: An examination of radar- and rain gage-derived
mean area precipianiion over Georgia watersheds. Wea and_Forecasting, 16, 133-144.

Walch, M.P., and P. Jelonek, 2002: Managing urban watersheds with the use ofNEXRAD radar virtual rain gauges:
The Miami-Dade county experience. 9' Intl. Conf. on Urban Drainage. ASCE Con ference Proceedings.

ui ang. D., M.B. Smith, Z. Zhang. S, Reed, and V.I Koren, 2000: Statistical comparison of mean real precipitation
estimates from WSR-88D, operational and historical gage networks. 15* Conf. on Hydrology, Paper 2.17,
American Meteorological Society.

Watkins, D.W., Jr., H. Li, K.A. Thiemann, and T.E. Adams iII, 2003: Radar rainfall estimates for Great Lakes
hydrologic models, World Water and En% irornmcnial Resources Congress, ASCE Conference Proceedings.

Wride, D., M. Chen and R. Johnstone, 2004: Characterizing the spatial variability of rainfall across a large
metropolitan area. World Water and Environmental Resources Congress 2004, ASCE Conference
Prt. clcdings

7. Training Potential

One graduate student, Steven Maniinailis, is supported by this project and thereby
receives Iraining from it. Mr. Martinaitis collaborates with another graduate student, John
Sullivan, who is supported by the FDEP and whose graduate research utilizes the FSU/NWS
dataset. Thus, two graduate students will benefit from the project.

Measurement of evapotranspiration, recharge, and runoff in a
transitional water table environment

Basic Information

Title: Measurement of evapotranspiration, recharge, and runoff in a transitional water
table environment

Project Number: 2006FL142B
Start Date: 3/1/2006

End Date: 2/29/2008

Funding Source: 104B

Congressional
District:

Research Category: Climate and Hydrologic Processes

Focus Category: Climatological Processes, Hydrology, None

Descriptors:

Principal Mark Ross
Mark RosInvestigators:
Investigators:

Publication

1. Trout, Ken and Mark Ross, Estimating Evapotranspiration in Urban Environments, Urban
Groundwater Management and Sustainability, J.H. Tellam, et al., editors, pgs 157-168, Springer
2006.
2. Shah, N., M. Nachabe, and M.Ross. 2007. Extinction Depth and Evapotranspiration from Ground
Water under Selected Land Covers. Ground Water, Paper # GW20060417-0057R, doi:
10.1111/j.1745-6584.2007.00302., published online March 12, 2007, awaiting paper publication,
submitted 4/17/2006 Accepted December 2006, In Press.
3. Shah, N., M.Ross. 2006. Variability in Specific Yield for Different Drying and Wetting Conditions.
Vadose Zone Journal, In Review.
4. Shah, N., J.Zhang, and M.Ross. 2006. Long Term Air Entrapment Affecting Runoff and Water Table
Observations. Water Resources Research, In Review.
5. Zhang, Jing and Mark A. Ross, 2007. Conceptualization of a 2-layer Vadose Zone Model for Surface
and Groundwater Interactions, J. Hydrologic Engrg., HE/2005/022952, in press.
6. Nilsson, Kenneth A., Ken Trout and Mark A. Ross, 2006. Analytic Method to Derive Wetland
Stage-Storage Relationships Using GIS Areas, J. Hydrologic Engrg., manuscript number
HEENG-07-55, In Review.
7. Shah, N., J.Zhang, and M. Ross, 2006. Long Term Air Entrapment Affecting Runoff and Water Table
Observations, Water Resources Research, AGU Paper # 2006WR005602, In Review.
8. Rahgozar, Mandana, Nirjhar Shah, and Mark Allen Ross, 2006. Estimation of Evapotranspiration and
Water Budget Components Using Concurrent Soil Moisture and Water Table Monitoring, Journal of
Hydrology, paper no. HYDROL5813, In Review.

Measurement of Evapotranspiration, Recharge, and Runoff
in
Transitional Water Table Environments

Year One Progress Report

Prepared by
Center for Modeling Hydrologic and Aquatic Systems
Department of Civil and Environmental Engineering
University of South Florida

Funding Agency

Southwest Florida Water Management District
Brooksville, Florida

March, 2007

Table of Contents

List of Fig u res.......................................... ........................................................... 3
1. Summary of First-Year Progress ............................................................................ 4
2. Study A rea ........................................................... ....................... ............... 6
3 Eq uipm ent ......................................................... ........ ..................... . ........ 7
3.1 Water-level Monitoring Wells ........................................................................ 7
3.2 Soil Moisture Monitoring ............................. .. ............ ........... ...................... .. 8
3.3 Weather Monitoring ............................................... .............. .......... .............. 8
3 .3 .1 W weather Station ................................................................................ ... 8
3.3.2 Evaporation Pan .................................................... .......................... 9
4. Stratigraphic Logs ............................................................................. 10
5. D ata Collection ............................................................. ............................... 21
5.1 Soil Moisture Data.................................................................................... 21
5.2 Water Table Elevations ................................................................ ....... 25
Appendix ....... ................................... ....................................... 31

List of Figures

Figure 1. USF Eco Area flanked by Fletcher Ave (CR-582A) on the south side................... 6
Figure 2. Data collection sites with contour lines showing the land elevation feet above
National Geodetic Vertical Datum (NGVD)........... .......................................... 7
Figure 3. Solinst Levelogger transducers with built-in data logger.............................. 7
Figure 4. EnviroSMART soil m oisture probe ................................................................. 8
Figure 5. Campbell Scientific weather station installed in the study area ......................... 9
Figure 6. Class A ET pan with GeoKon water level monitoring device installed next to the
w weather station n .................................................... ....................... ........... 9
Figure 7. ECO -1 Core, 0-16 ft. ..................... ............................... .......................... ... 10
Figure 8. ECO -2 Core, 0-14 ft. ..................... ............................... .......................... ... 11
Figure 9. ECO -2 Core, 14-22 ft. .......................................... ... ............ ........ ............... 12
Figure 10. ECO -3 Core, 0-22 ft. .......................................... ... ............ ........ ............... 13
Figure 11. ECO -5 Core, 0-4 ft. ........................................................................ 14
Figure 12. ECO -5 Core, 4-19 ft. .......................................... ... ............ ........ ............... 15
Figure 13. ECO -6 Core, 0-10 ft. ............................................ ............................ ... 16
Figure 14. FL-1 Core, 0-40+ ft................ ................................... ............................... 17
Figure 15. FL-2 Core, 0-28 ft. .................................................. ............................ 19
Figure 16. FL-2 Core, 28-43.7 ft ................................... .... .......... .. .................. ......... 20
Figure 17. Soil moisture content and water table fluctuations at ECO-05........................21
Figure 18. Soil m oisture data at ECO-1. ....................................................................22
Figure 19. Soil m oisture data at ECO-2. ....................................................................22
Figure 20. Soil m oisture data at ECO-3. ....................................................................23
Figure 21. Soil m oisture data at ECO-4. ..................................... ...............................23
Figure 22. Soil m oisture data at ECO-5. ....................................................................24
Figure 23. Soil m oisture data at ECO-6. ....................................................................24
Figure 24. Continuous water-table measurements at ECO-3 with weekly manual
m easurem ents........................................................................................ ... 2 5
Figure 25. Continuous water-table measurements at ECO-4 with weekly manual ..............
m easurem ents.................................. ............... .................................... 26
Figure 26. Continuous water-table measurements at ECO-5 with weekly manual
m easurem ents........................................................................................ ... 26
Figure 27. Continuous water-table measurements at ECO-6 with weekly manual
m easurem ents........................................................................................ ... 27
Figure 28. Continuous approximate Floridan Aquifer water levels at FL-01 with weekly
m annual m easurem ents............................. ................................ .................... 27
Figure 29. Continuous water-table measurements at FL-02 with weekly manual
m easurem ents.................................. ............... .................................... 28
Figure 30. Water elevation comparison between FL-2 and ECO-4. .................................29
Figure 31. Water levels above the transducers at the ECO-6 wells illustrating a possible air
pressurization event ..................................................................................... 30

1. Summary of First-Year Progress

The first year of the USF eco-site hydrology study has been completed. The primary
objectives of the first year were to: 1) obtain permission to install wells at the USF eco-site;
2) identify potential sites for data collection; 3) install both surficial aquifer and Floridan
aquifer monitor wells at the chosen sites; 4) install soil moisture probes at each well site; 5)
install pressure transducers in each well and data loggers to record high-resolution measure
(at 10-minute intervals) water levels and soil moisture; 6) install an evaporation pan to
measure real-time open-pan evaporation rates; 7) install a weather station to continuously
monitor atmospheric conditions; and 8) begin collecting all data above plus background
topologic and hydro-geologic data to characterize the site. All of these tasks have been
completed. The wells were installed by the Southwest Florida Water Management District
(SWFWMD) and cores were recovered at each location. All of the data collection equipment
was installed by USF personnel and all instrumentation is operational and recording data.
Also, a database (Microsoft Access) has been created to organize and facilitate further
assessment of the data.
The sites selected for aquifer water level and soil moisture data were chosen by
topography and accessibility and so that they would lie on a general down-slope flow path.
The sites range from the top of a ridge, approximately at 55 feet in elevation, to a low-lying
area near the Hillsborough River at approximately 28 feet elevation. The vegetative cover
transitions from a pine forest at the top of the ridge to a predominately palmetto scrub with
scattered slash pine trees.
The upper site is characteristic of a deep water table. It is covered by dry very-fine
(Dso ~ 0.5 mm) dune sand. The predominant vegetative cover is pine and scrub oak forest.
The two upper-most shallow wells have not contained water since they were installed. Both
of those wells are in a relatively thin unit of very-fine dune sand overlying a thick clay lens.
Precipitation has been unusually light this year and the sand unit has remained unsaturated.
All other shallow wells have contained water since installation.
A Florida aquifer monitor well was installed next to the upper-most dry surficial well.
The purpose of this Floridan well was to evaluate the geologic structure of the ridge,
determine if any actual or potential aquifer units exist above the Floridan aquifer and below
the surficial, and to obtain measurements of Floridan aquifer water elevations from a second
location. No additional aquifer units were located in the unconsolidated sediments above the
Floridan limestone. Below the top 14 feet of dune sand were primarily clay and sandy-clay
lenses. If a water table forms on the upper portion of the ridge, it will probably be an
ephemeral appearance, present only during the wet season and perched above the
underlying clay.
The well at the lowest elevation is approximately 1/4 mile from the Hillsborough River
and is in a high (shallow) water-table environment. A second well, screened from the
bottom of the well to the ground surface, was installed approximately 20 feet away. The
purpose of the second well is to compare the water levels in a well fully screened to water
levels in a monitor well of standard construction where the well screen is present only at the
bottom portion of the well. If the water level in a well is influenced by air pressurization due
to an infiltrating wetting front, the water level in a cased well should be more responsive
than the water level in a fully-screened well where the air pressure inside the well can
equilibrate to the air pressure outside of the well.
A Floridan aquifer monitor well was installed next to the ECO-4 surficial aquifer well
to measure the head gradient between the surficial and Floridan aquifers. The ECO-4 well
was drilled to a depth of 27 feet, where limestone was encountered. No significant clay
(confinement) was detected. For the Floridan well installed approximately 18 feet from ECO-
4, limestone was encountered at 44 feet with a total depth of 58 feet. Significant clay units
were found at 22 and 37 feet bls. Despite the difference in depths to the limestone (and the

difference in clay content) between the two wells, the water elevations in the wells are
almost identical. It is believed that both wells reflect the Floridan aquifer water elevations.
Active data collection is now in progress. USF personnel visit the site weekly to
download data and maintain the equipment. Water levels in the wells and in the evaporation
pan are measured manually and compared to the transducer measurements for validation.
Also the total rainfall recorded by the tipping-bucket gauge is compared to a manual gauge.
Data collection will continue this year and slight modifications to the network may be
made to utilize new insights gained from the project.

2. Study Area

The study area, shown in Figure 1, was inspected by the faculty, staff and the
graduate students involved in the project. A formal request for permission to use the USF
Ecological Research Area was sent to the designated authority, Dr. Gordon Fox. The
permission and access was granted subject to specific terms and conditions. (Ref. Appendix
A: Use of USF Eco-site to Establish and Monitor Hydrologic Processes)
A reconnaissance survey was conducted and the instrument installation sites were
identified. The sites were identified based on topographic elevation, soil type, and existing
vegetation coverage. The selected sites were flagged. These sites were approved by the USF
Eco Area committee and were later instrumented (Figure 2).

0 0.25 0.5 1 Miles

Figure 1. The Orange oval identifies the study area with white line showing the
boundary of the USF Eco Area flanked by Fletcher Ave (CR-582A) on the south
side. See detailed view in Figure 2.

I US Eoloica Reeac Are Dat Colcto Site

Well ID

CarMOr k,.Wdwg vlvdroloqic and Aju&L.C SySIOM6
Jprrpruv ol Sron FArfva

Figure 2. Data collection sites with contour lines showing the land elevation feet
above National Geodetic Vertical Datum (NGVD). Floridan wells have an FL prefix.

3. Equipment

3.1 Water-level Monitoring Wells

SWFWMD installed six surficial wells and two Floridan wells at identified sites in the
USF Ecological Research Study Area (Figure 2). At the time of well installation, a core was
taken and stratigraphic well logs were compiled. Well logs are shown in Tables 1-8 and
Cores are shown in Figures 4-16. All wells, with the exception of the most recently installed
Floridan well, were then surveyed. Water-level data collection began immediately using
Solinst Leveloggers (Solinst Canada Ltd., Figure 3).

Figure 3. Solinst Levelogger transducers with built-in data logger.

3.2 Soil Moisture Monitoring

Along with the monitoring wells, EnviroSMART soil moisture probe (Sentek Pty. Ltd
Australia) was installed at the data collection sites to measure water content of the soil
profile at high vertical resolution. Figure 4 (a) shows the soil moisture probe with multiple
sensors mounted on the rail. Figure 4 (b) shows the soil moisture probe as connected to the
Starlogger PRO (Unidata Ltd., Australia) data logger used to log the water content
readings.

(a) (b)

Figure 4. (a) EnviroSMART soil moisture probe with multiple soil moisture sensors
and (b) The probe as installed a with the data logger.

3.3 Weather Monitoring

3.3.1 Weather Station

Campbell ET-106 (Campbell Scientific Inc., Logan, Utah) weather station was
installed in the study area. The weather station measures rainfall, wind velocity, solar
radiation, temperature and relative humidity (Figure 5).

K

Figure 5. Campbell Scientific weather station installed in the study area.

3.3.2 Evaporation Pan
A standard USGS Class A evaporation pan was also installed to give a direct measure
of the open water evaporation rate. A Geokon Model 4675LV water level monitor (Geokon
Inc., Lebanon, New Hampshire) along with Geokon 8001 LC-1 single channel data logger
was used to record the fluctuation in the water level in the evaporation pan. The installation
of the evaporation pan and water-level monitor beside the weather station is shown in
Figure 6.

Figure 6. Class A ET pan with GeoKon water level monitoring device installed next
to the weather station.

4. Stratigraphic Logs

Table 1. Stratigraphic well log for ECO-1
Eco-1
Well Log 6/1/2006

Depth (ft) Soil Description
0-1 Brown Fine Sand
1-4 Light Brown Fine Sand
4-6 Light Brown-Red Fine Sand
6-10 Very Light Brown Fine Sand
10-12 Very Light Brown Fine Sand
12-12.5 Light Brown Fine Sand
12.5-13.5 Tan Clayey Sand
13.5-16 Gray Clay

Notes:
Total Depth: 16 ft
Screen Length: 5 ft
Screened Interval: 11-16 ft

Figure 7. ECO-1 Core, 0-16 ft.

Table 2. Stratigraphic well log for ECO-2
Eco-2
Well Log 6/1/2006

Depth (ft) Soil Description
0-1.5 Light Brown Fine Sand
1.5-6.5 Very Light Brown Very Fine Sand
6.5-10 Very Light Brown Very Fine Sand-almost white
10-10.7 Light Brown Fine Sand
10.7-11.3 Brown Fine Sand (maybe fall)
11.3-13.5 Very Light Brown Very Fine Sand
13.5-14.5 Red-Tan Very Fine Sand
14.5-18 Red Clayey Sand
18-22 Light Brown Sandy Clay

Notes:
Total Depth: 21 ft
Screen Length: 10 ft
Screened Interval: 11-21 ft
Top of screen in Very Light Brown Very Fine Sand

Figure 8. ECO-2 Core, 0-14 ft.

Figure 9. ECO-2 Core, 14-22 ft.

Table 3. Stratigraphic well log for ECO-3
Eco-3
Well Log 6/1/2006

Depth (ft) Soil Description
0-4 Brown Fine Sand
4-10 Light Brown Fine Sand
10-19 Light Brown Fine Sand
19-24 Light-Red Clayey Sand, with Red Lenses

Notes:
Total Depth: 22 ft
Screen Length: 10 ft
Screened Interval: 12-22 ft
wet at 14 ft; water table possible at 17 ft

z161111,
'62 -d I ~ LC ~-LI

Figure 10. ECO-3 Core, 0-22 ft.

Table 4. Stratigraphic well log for Eco-4
ECO-4
Well Log 6/2/2006

Depth (ft) Soil Description
No Core taken

Notes:
Total Depth: 27 ft
Screen Length: 10 ft
Screened Interval: 17-27 ft
No obvious confining layer observed when well installed
Rock (may be Limestone or Chert) at 27 ft

Table 5. Stratigraphic well log for ECO-5
ECO-5
Well Log 6/2/2006

Depth (ft) Soil Description
0-1 Gray Fine-Medium Sand
1-2 Brown Fine-Medium Sand with Organics
2-4 Light Brown Fine Sand
4-5.5 Brown Fine Sand with darker brown Organics
5.5-13 Light Gray Fine Sand
13-13.5 Light Gray to Orange Grading Fine Sand
13.5-14 Orange Clayey Sand
14-19 Light Gray Clayey Sand Grading to More Clay Content

Notes:
Total Depth: 19 ft
Screen Length: 10 ft
Screened Interval: 9-19 ft

Figure 11. ECO-5 Core, 0-4 ft.

0rI
-~~~ -- F

Figure 12. ECO-5 Core, 4-19 ft.

Table 6. Stratigraphic well log for ECO-6
ECO-6
Well Log 6/5/2006

Depth (ft) Soil Description
0-2 Dark Brown Medium-Fine Sand
2-9 Light Brown Fine Sand
9-10 Very Light Fine Sand-Clean Quartz, Well Rounded and Sorted

Notes:
Wet at 5 ft
Standing Water inhole at 6 ft below land surface

-"" -- ----- ..........'~r I P
ilk Og

Figure 13. ECO-6 Core, 0-10 ft.

Table 7. Stratigraphic well log for FL-1 (ECO-8)
Well Log 9/11/2006

Depth (ft) Soil Description
0-6 Light Red-Brown Fine Sand Hollow Stem
6-14 Very Light Brown Fine Sand
14-19 Brown Clayey Sand
19-28 Gray Clay Tight
28-31 Clayey Sand
31-32 Very Light Brown Dry with Small Limestone Nodules
32-33 Red-Brown Clayey Sand Wet
33-36 Very Light Brown Clayey Sand with Limestone Pieces
36-37 Gray-Brown Sandy Clay
37-38 Blue-Gray Clay with Limestone Pieces
38 Stopped core sampling, began mud drilling; Lost circulation at 40 ft

Notes:
Total Depth: 60 ft
Screen Length: 15 ft
Screened Interval: 45-60 feet

r~s~""CI ~Ii-~

- 6 -k. aj 4b l As
-~~ I1^ -^ Y'r .t- i~

/-

L z .
L'.

T ---

a-

W4LIY i"~

Figure 14. FL-1 Core, 0-40+ ft

L .

rr

Table 8. Stratigraphic well log for FL-2 (ECO-7)
Well Log 6/2/2006

Depth (ft) Soil Description
0-8 Light Brown Fine Sand-loose
8-12 Very Light Brown Fine Sand-damp
12-13 Very Light Brown Fine Sand-damp
13-21 Light Gray Fine Sand-water table near 16 ft
21-21.5 Reddish Fine Sand
21.5-22 Orange Silty Fine Sand, some clay
22-29.5 Gray Clay with Orange Staining
29.5-30 Orange Clay with weathered Limestone
30-30.5 Gray Clay with Orange Staining
30.5-32 Red-Gray Clay with Limestone nodules
32-33 Orange Wet Sandy Clay with Limestone
33-34 Gray Silty Medium Sand
34-35 Orange-Gray Sandy Clay with Small Chert Fragments
35-36 Gray Sandy Clay
37-37 Wet (sat) Sandy Clay with Limestone Pieces
37-37.8 Orange-Gray Clay with Limestone fragments
37.8-38 Light Gray Limestone Chips
38-40 Tan-Gray Sandy Clay with Limestone
40-42.5 Light Brown Silty Clay with Limestone Pieces
42.5-43.7 Light Tan Silty Clay with Limestone pieces (up to 2.5 inch diameter)
43.7+ Rock at 44 feet; Stopped core sampling, began mud drilling

Notes:
Total Depth: 58 ft
Screen Length: 15 ft
Screened Interval: 43-58 feet
Well drilled into limestone to 64 feet with button bit.
When augers removed, 6 feet of casing pulled out of well.
When pumped, yield from well was good as was water clarity.

.' I

@3

Figure 15. FL-2 Core, 0-28 ft.

A -

: ~?
at~ tJZ,~ I;

-;p. -,.

I& ^&

qk^ A

Figure 16. FL-2 Core, 28-43.7 ft.

5. Data Collection

The data from all the equipment are collected at a 10 minute intervals and stored in
a Microsoft-Access database. Figure 17 illustrates data collected at site ECO-5 during a
nineteen-day period in December 2006. Soil moisture sensors near the land surface respond
rapidly to rainfall event. Deeper sensors respond more slowly, and the deepest sensors
show little change during this period. The water table responds to major rainfall events but
more slowly.

50

40

30

20-

10

0
11/1/06

-10 cm
110 cm

21.6

21.4 "

21.2 Z

21

20.8
LJ
20.6 .

20.4

20.2

20

11/21/06 12/11/06 12/31/06 1/20/07

-20 cm -30 cm 50 cm -80 cm

-150 cm

--190 cm

-Water Table

Figure 17. Soil moisture content and water table fluctuations at ECO-05.

5.1 Soil Moisture Data

Soil moisture probes were installed at sites ECO-1 through ECO-6. Each probe has
eight moisture sensors at depths below the land surface of 10 cm to 190 cm, except at
ECO-6. Site ECO-6 is in a high water-table environment and the deepest moisture sensor at
that site is 140 cm. Figures 18-23 show the moisture content at each of the sites through
1/31/2007. In general, shallower sensors respond more quickly to rainfall and subsequent
evaporation.

40

4 30
4 0------------------------------------------

-

120

60 0--------------------------------------------------
10

9/15/06 10/5/06 10/25/06 11/14/06 12/4/06 12/24/06 1/13/07

-10cm -20cm -30cm -50cm -80cm -110cm -150cm -190cm

Figure 18. Soil moisture data at ECO-1.

50

40

( 30
0

L ------ ---------------------
120

10

9/19/06 10/9/06 10/29/06 11/18/06 12/8/06 12/28/06 1/17/07

--10cm -20cm -30cm 50cm -80cm -110cm -150cm -190cm

Figure 19. Soil moisture data at ECO-2.

0 -,-'I y ,1 ,')'" I I
9/20/06 10/10/06 10/30/06 11/19/06 12/9/06 12/29/06 1/18/07

10 cm 20cm 30cm 50cm 80 cm 110 cm 150 cm 190 cm

Figure 20. Soil moisture data at ECO-3.

U)u

40

S30
..2 0 - . .
UU ~ ~ ~ ~ ~ ~ ~ ~ ~ .. --------------------------------------- ......---....-----------------

120
40 ~ ~ ~ ~ ~ ~ ~ ..... -------------------4----- ---

1 0 -- --- j^^-- --------

0
10/4/06 10/24/06 11/13/06 12/3/06 12/23/06 1/12/07 2/1/07

10 cm 20cm 30cm 50cm 80cm 110 cm 150 cm 190 cm

Figure 21. Soil moisture data at ECO-4.

40

30

I 20

10

o----------------------------------------------------
0
10/4/06 10/24/06 11/13/06 12/3/06 12/23/06 1/12/07 2/1/07

10 cm 20cm 30cm 50cm 80cm 110 cm 150cm 190cm

Figure 22. Soil moisture data at ECO-5.

50

40 __-------------------------------------

40

S30

-------- -- ---- -- -----

20

10

0
10/4/06 10/24/06 11/13/06 12/3/06 12/23/06 1/12/07 2/1/07

-- 10 cm 20 cm 30 cm 50 cm 70 cm 90 cm 110 cm 140 cm

Figure 23. Soil moisture data at ECO-6.

5.2 Water Table Elevations

Pressure transducers were installed in the monitor wells to record ground water
levels. ECO-1 and ECO-2 have been dry since they were installed. The wells with the ECO
prefix were intended as surficial aquifer monitor wells; they were installed to the first
competent clay unit or, in the case of ECO-4, to rock as no clay was encountered. The wells
with the FL prefix were installed into the first competent limestone unit which is the Upper
Floridan Aquifer. Initially, one Floridan well (FL-2) was installed near ECO-4 to provide head
gradient information between the surficial and Floridan aquifers. A second Floridan well (FL-
1) was then installed near ECO-1. All the wells except FL-1 have been surveyed and their
water levels corrected to NGVD. Figures 24-29 display the continuously recorded water-level
elevations (blue line) and the manual measurements (red box) for each of the wells. The
water-levels in ECO-3 are the deepest of any of the surficial wells and that well shows very
little response to rainfall. The water levels in the Floridan wells exhibit pronounced diurnal
fluctuations while the diurnal fluctuations in the surficial wells are less obvious.

22.6

22.4

22.2

Z 22
.o
21.8

> 21.6
iJ

I 21.4

21.2

21
10/30/06 11/9/06 11/19/06 11/29/06 12/9/06 12/19/06 12/29/06 1/8/07 1/18/07 1/28/07
-Continuous U Manual

Figure 24. Continuous water-table measurements at ECO-3
measurements.

with weekly manual

19

18.8

18.6

18.4

c 18.2

S18
LU
17.8

2 17.6

17.4

17.2

17
11/1/06 11/11/06 11/21/06 12/1/06 12/11/06 12/21/06 12/31/06 1/10/07 1/20/07 1/30/07

-Continuous Manual

Figure 25. Continuous water-table measurements at ECO-4 with weekly manual
measurements.

22

21.8

S21.6

21.4

Z 21.2

I 21

LU
20.8-

S20.6

20.4

20.2

20
10/29/06 11/8/06 11/18/06 11/28/06 12/8/06 12/18/06 12/28/06 1/7/07 1/17/07 1/27/07

Continuous Manual

Figure 26. Continuous water-table measurements at ECO-5 with weekly manual
measurements.

23

22.5

22

Z 21.5

0 21

S20.5

S20

19.5

19
10/29/06 11/8/06 11/18/06 11/28/06 12/8/06 12/18/06 12/28/06 1/7/07 1/17/07 1/27/07

Continuous Manual

Figure 27. Continuous water-table measurements at ECO-6 with weekly manual
measurements.

16

15.8

C 15.6
C
o' 15.4-

o
15.2

15

14.8

S14.6
E
x 14.4

0- 14.2

14
11/10/06 11/20/06 11/30/06 12/10/06 12/20/06 12/30/06 1/9/07 1/19/07 1/29/07 2/8/07
-- Continuous m Manual

Figure 28. Continuous approximate Floridan Aquifer water levels at FL-01 with
weekly manual measurements.

18.8

18.6

18.4 N A .

C 18.2

S18

17.8

0 17.6

17.4

17.2

17 ,,
10/29/06 11/8/06 11/18/06 11/28/06 12/8/06 12/18/06 12/28/06 1/7/07 1/17/07 1/27/07

-Continuous Manual

Figure 29. Continuous water-table measurements at FL-02 with weekly manual
measurements.

ECO-4 was installed as a water-table monitor well. However, no significant clay unit
was penetrated. The well was ended at 27 feet below land surface when rock was
encountered. The well was screened from 17-27 feet below land surface (bls).
Approximately 18 feet from ECO-4, a Floridan Aquifer well was installed, FL-2. FL-2
passed through two significant clay units, one between 22 and 32 feet bls and the other
between 37 and 44 feet bls. Several smaller clay layers or lenses were encountered
between the two thickest clay units. Rock was encountered at 44 feet bls. The well was
continued for an additional 20 feet through the limestone to a total depth of 64 feet. A 15-
foot well screen was installed in the well, but the bottom six feet of the well was lost when
the auger flight was extracted and the well casing pulled up. The final depth of the screen is
from 43 to 58 feet bls.
Although ECO-4 is only 27 feet deep and FL-2 is 58 feet deep and finished in
limestone, the water elevations in both wells match. Figure 30 illustrates the
correspondence between the water elevations in the two wells. Both wells reflect water
elevations in the Floridan Aquifer.

18.8

18.6

~ 18.4

j 18.2

j 17.8

17.6

17.4

17.2

17
10/30/06 11/9/06 11/19/06 11/29/06 12/9/06 12/19/06 12/29/06 1/8/07 1/18/07 1/28/07

FL-2 ECO-4

Figure 30. Water elevation comparison between FL-2 and ECO-4.

A second well was manually installed at the ECO-6 location to a depth of
approximately four feet. This well is screened for its entire length below the ground surface.
Because air entrapment or compression is believed to play a role in the rapid water-table
response to rainfall events, this second well will provide a water-table comparison to the
partially screened initial well. A water-table response in the cased well that is not present in
the fully-screened well may indicate a water-table change due to air pressurization. Figure
31 illustrates the water levels recorded in the two wells and a possible air pressurization
response.

6.5 2.5
Possible air pressurization
\ response
=6 -2.0 "

S5.5 1.5

5 1.0

4.5 0.5

4 ... 0.0
10/30/06 11/9/06 11/19/06 11/29/06 12/9/06 12/19/06 12/29/06 1/8/07 1/18/07 1/28/07 2/7/07

ECO-6 ECO-6 Screened

Figure 31. Water levels above the transducers at the ECO-6 wells illustrating a
possible air pressurization event.

Appendix

Use of USF Eco-site to Establish and Monitor Hydroloaic Processes

Brief explanations are included here. Please refer to the proposal for details on
methodology and instruments used.

1. What is the general purpose of your research?
The objective of the study is to measure evapotranspiration, recharge, and
groundwater elevations in a transitional water-table environment. These
measurements will be used to understand major hydrologic processes and their
interdependence. The Findings from the study should be of immediate importance
and use to water management entities. It will provide useful information for
parameterization and conceptualization of processes for emerging integrated surface
and groundwater computer models of the region.

2. Describe the methods you intend to use number of plots, types of markers, etc.
Rainfall: Tipping-bucket rainfall gauge
Evapotranspiration:
o Central Weather Station
o Evaporation Pan
Soil Moisture: EnviroSMART soil moisture Probes
o A 2-meter rail that slides vertically into a 2" PVC-cased dry well to a
desired depth, accompanied by a data logger enclosed in a small box
Runoff: Doppler flow velocity meter
o Runoff from a small basin will be routed to a channel where this
velocity meter will be installed
Water Table: Ground water monitoring wells

Survey Flags will be used to mark the location of instruments. There will be six soil
moisture probes and seven wells installed.

3. Describe how you will minimize damage to soils and plants.
Once the wells are installed, trips made to the site will only be to collect data from
the loggers and to repair or replace equipment.

The instruments described above (in section 2) are designed to have minimum
damage to soils and plants. The study and the instruments require us to collect all
the data in an undisturbed and natural state, which itself means we will try to
minimize the damage. Also, we are aware of the importance of maintaining the
health of the eco-area.

4. Identify (on a map) where you intend to conduct this work. Explain why these spots
are desirable; this information may be used to suggest other locations in the EcoArea
if there are problems of heavy or concurrent use.

(Ref. Fig. 1)
The Orange oval identifies the study area. The instruments will be installed along hill
slope transect (towards the river).

5. How will your markers, plots, etc., be labeled so that we can tell they are yours?
Describe your plan to repair damage, remove markers, etc., at the end of the study.
All instrumentation sets will have labels saying "USF-CMHAS-SWFWMD Eco Area
Project" and the Instrument Identification Number or the Location Number. Survey
flags will be used to mark the instrument locations.
The markers and instruments will be removed at the end of the study.

6. If you are working with vertebrates, provide information on your IACUC permit. We
will not approve any use that violates IACUC rules.
Not Applicable. We are not working with vertebrates or other animals.

7. Over what time period will your work be conducted? Any time extensions must be
approved.
The study will be conducted for a period of three years.

8. Permission to use the EcoArea is conditional on your providing the EAAC with reprints
of all relevant publications and links to all web sites referring to this work. Seminars
and publications must acknowledge the use of the EcoArea.
Agreed.

Investigating arsenic mobilization during aquifer storage
recovery (ASR)

Basic Information

Title: Investigating arsenic mobilization during aquifer storage recovery (ASR)
Project Number: 2006FL143B
Start Date: 3/1/2006
End Date: 2/29/2008
Funding Source: 104B
Congressional District: 6
Research Category: Ground-water Flow and Transport
Focus Category: Hydrogeochemistry, Hydrology, Water Supply
Descriptors:
Principal Investigators: Mike Annable

Publication

1. Norton, S. Quantifying the Near-Borehole Geochemical Response During ASR. Masters Thesis.
University of Florida. May, 2007.

Status Update

Investigating Arsenic Mobilization During Aquifer Storage Recovery (ASR)

Project Background

Due the growing demand on water resources within the State of Florida, alternative water supply
and water storage technologies are becoming increasingly attractive to municipalities. Aquifer
Storage Recovery (ASR) has the potential to provide much of the seasonal storage need for
many municipalities within areas of increased water demand. However, as with any engineered
water supply process, ASR must meet stringent Federal and State regulations to insure the
protection of human health and the health of the environment.

Recently, facilities in southwest Florida utilizing the Suwannee Limestone of the Upper Floridan
Aquifer for ASR have reported arsenic concentrations in recovered water at levels greater than
112 pg/L (Arthur et al., 2002). On January 23, 2006 the Maximum Contaminant Level for arsenic
was lowered from 50 pg/L to 10 pg/L (FDEP: Chapter 62-550 F.A.C., Table 1).

Research has been conducted to determine the abundance and mineralogical association of
arsenic within the Suwannee Limestone (Pichler, et al., 2006). This research suggests that the
bulk matrix of the Suwannee Limestone generally contains low concentrations of arsenic.
However, according to this research, arsenic is concentrated within the Suwannee Limestone in
arsenic bearing minerals such as pyrite.

The potential mechanisms by which arsenic may be mobilized during ASR have been
investigated (Arthur, et al., 2002) and suggested by others (Pichler, et al., 2006). The
conclusions of this research suggest that the introduction of the injectate containing oxidants,
such as oxygen and chlorine, into a highly reduced groundwater environment produces a
geochemical response that releases arsenic from the aquifer matrix.

Several ASR projects are under testing in southwest Florida. Of these, the recently constructed
Bradenton Potable ASR facility presents several benefits for further research including the
following:
Only a few small volume recharge and recovery cycles have been performed at the
facility. Therefore, the aquifer matrix has not been repeatedly exposed to water with
high levels of oxidizers.
One large volume cycle was recently completed (Cycle 6) with recharge being initiated
immediately at the end of the recharge event. Because no storage occurred during this
cycle it may be possible to determine the rate at which the oxidizers are consumed in the
matrix.
The data sets collected to date at this facility are fairly extensive.
The City of Bradenton has authorized the use of the data set in this study.
Site access has been granted by the City of Bradenton.

Work Scope

Based on the research completed to date, it appears that one of the primary mechanisms by
which arsenic is mobilized during ASR is by the introduction of oxidizers into the aquifer.
Therefore, the following work scope was developed to further evaluate the role of oxygen and
other oxidizers in the mobilization of arsenic during ASR:
Compile and evaluate in-situ measurements collected at the Bradenton ASR site during
recovery for Cycle 6 to include field measurements (pH, temperature, dissolved oxygen,

conductivity, and ORP) and laboratory measurements (sulfate, sulfide, hydrogen sulfide,
carbonate, bicarbonate, total chlorine, total phosphorous, and ortho-phosphate).
Review the data being collected per the FDEP temporary operations (cycle testing)
permit for this facility and additional data being collected by FGS.
Employ Istok's approach to data analysis and compare Istok's push pull test method to
the current method of Cycle Testing regulated by FDEP.
Utilize the existing Bradenton ASR data to:
o Attempt to quantify the consumption rates (reaction rates) of oxygen and other
oxidizers (i.e. chlorine) during ASR.
o Investigate the applicability of solute transport models to predict the behavior of
arsenic during ASR to suggest future studies.
Make suggestions for further studies.

Schedule and Deliverables

The timeline to complete this research and submit a paper for publication will be as follows:
In-Situ data collection occurs January 2006.
FGS grant awarded by end of February 2006.
WRC funding awarded by end of March 2006.
Funds dispersed over three semesters; Summer 2006, Fall 2006, and Spring 2007.
Thesis defense Spring 2007.
Thesis submitted for publication Spring 2007.

Project Status

Funding was awarded from the FGS and WRC, through the State Water Resources Research
Institute (WRRI) Program and the following research components are underway.

In-Situ data collection and review was extended through March 2006 to include Cycle 6a
conducted at the Bradenton ASR facility. The available field data and laboratory analytical data
have been reviewed. Istok's push-pull analytical model has been employed to quantify DO
consumption rates. Results are similar for three of the four cycle tests completed to date. The
results indicate that DO undergoes first order decay during ASR. Variability in the measured
decay rates appears to be due to a reaction rate dependence on temperature (Prommer, 2005).
While recharge water temperatures were similar for three of the four cycle test, one of the test
was conducted during the summer with recharge water temperatures exceeding 30C.
Therefore, additional computations are underway to correlate the decay rate at varying
temperatures.

A review of potential reactive transport geochemicall transport) models is nearly complete. The
reactive transport model PHT3D appears best suited for modeling arsenic mobilization during
ASR. PHT3D couples the geochemical model PHREEQC-2 with the multi-component transport
model MT3DMS. The model is being maintained by Henning Prommer at the University of
Western Australia. Future studies may include the application of PHT3D to the Bradenton
dataset, or others.

A project status update was presented, in power-point format, to the graduate committee (Dr.
Mike Annable and Dr. Kirk Hatfield) and Dr. Jon Arthur of FGS on November 9, 2006. Two
committee members, Dr. Mark Newman and Dr. Jean-Claude Bonzongo could not attend the
presentation. Therefore, separate review meetings will be held with these members in the near
future. Comments received by the committee members and FGS will be incorporated into the
draft thesis due April 2007.

Dr. Arthur has expressed his interest in providing funding support for continuing the project during
the following year. A prospectus will be drafted for review by the graduate committee and
subsequent submittal to FGS for approval.

References

Arthur, Jonathan D., Dabous, Adel A., and Cowart, James B., 2002, Mobilization of
arsenic and other trace elements during aquifer storage and recovery, southwest Florida:
USGS Open-File Report 02-89

Price, Roy E., and Pichler, Thomas, 2006, Abundance and mineralogical association of
arsenic in the Suwannee Limestone (Florida): Implications for arsenic release during
water-rock interaction: Chemical Geology, Vol. 228, pp. 44-56

Prommer, Henning, and Stuyfzand, Pieter J., 2005, Identification of temperature-
dependent water quality changes during a deep well injection experiment in a pyritic
aquifer: Environmental Science and Technology, Vol. 39, pp. 2200-2209

Cooperative Graduate Research Assistantships Between the
Florida Water Resources Research Center-South Florida Water
Management District UF/ABE in Critical Water Resources
Areas for South Florida

Basic Information

Cooperative Graduate Research Assistantships Between the Florida Water
Title: Resources Research Center-South Florida Water Management District UF/ABE in
Critical Water Resources Areas for South Florida

Project Number: 2006FL144B
Start Date: 3/1/2006
End Date: 2/29/2008
Funding Source: 104B
Congressional
District:

Research
R h Climate and Hydrologic Processes
Category:
Focus Category: Hydrology, Ecology, Solute Transport
Descriptors:

Principal Rafael Munoz-Carpena, Wendy D Graham, Gregory Alan Kiker
Investigators:

Publication

1. Jawitz, J.W., R. Mufoz-Carpena, K.A. Grace, S. Muller, A.I. James. 2007. Spatially Distributed
Modeling of Phosphorus Reactions and Transformations in Wetlands. Scientific Investigations
Report 2006-XXXX. U.S. Department of the Interior. U.S. Geological Survey (under review).
2. Muller. S. and R. Mufoz-Carpena. 2007. Effect of variable model structure in modelling wetlands
phosphorus water quality in South Florida. (in review, Adv. Water Resources).
3. Mufoz-Carpena,R. Z. Zajac, Y.-M. Kuo. 2007. Evaluation of water quality models through global
sensitivity and uncertainty analyses techniques: application to the vegetative filter strip model
VFSMOD-W. (in review, Trans. of ASABE)
4. Lagerwall, Kiker, Mufoz-Carpena. 2007. TaRSE-ECO: Ecological algorithms for the RSM model.
FL-Section of ASABE, MAy 31, 2007, St. Pete Beach, FL.
5. Zajac, Z., R. Mufoz-Carpena, Y-M. Kuo. 2007. Application of Global Sensitivity and Uncertainty
Analysis Techniques to the Vegetative Filter Strip Model (VFSMOD). University of Florida,
Department of Agricultural and Biological Engineering. FL-Section of ASABE, MAy 31, 2007, St.
Pete Beach, FL.
6. Muller. S., R. Mufoz-Carpena and G. Kiker. 2007. Global Sensitivity and Uncertainty of a Wetland
Phosphorus Water-Quality Model, Accounting for Variable Model Structure. FL-Section of ASABE,
May 31, 2007, St. Pete Beach, FL.
7. Muller. S. and R. Mufoz-Carpena. 2007. Towards an objective framework for evaluation of
hydrologic models: state-of-the-art methods. ASABE Paper No. 072212. St. Joseph, Mich.: ASABE.

Cooperative Graduate Research Assistantships Between the Florida Water
Resources Research Center-South Florida Water Management District UF/ABE
in Critical Water Resources Areas for South Florida

Project Description

Two specific research projects have been agreed and contracted with South Florida Water
Management District (SFWMD). The status of each research project is presented
followed by more specific program details and long-term objectives.

Topic 1: Sensitivity Analysis Of South Florida Regional Modeling
Topic 2: Addition of Ecological Algorithms into the RSM Model

Progress report: Topic 1

Sensitivity Analysis for the SFWMM

PI: Dr. Rafael Mufioz-Carpena1, Co-PI: Dr. Wendy Graham2

Department of Agricultural and Biological Engineering, University of Florida
2 Water Institute, University of Florida

1. Review of Previous Sensitivityy Analysis of the SFWMM
We conducted a detailed review of the sensitivity analysis performed on the South
Florida Water Management Model (SFWWM), as presented in the Model Documentation
of SFWMM Version 5.5). The traditional approach of varying one parameter at a time
was used for this analysis. The results indicated that most geographical regions, of the
model's domain were most sensitive to WPET (Wetland Potential ET) and that, coastal
areas were strongly influenced by CPET (Coastal Potential ET). However, this review
found that the methods applied for the different inputs and modeling subdomains are
often inconsistent and at times subjective. Examples of this are the different and
insufficiently explained variational ranges applied to the parameters (which changed for
the different regions where the model is applied), or the varying criteria selected to
identify a parameters as sensitive or not. As a result, the sensitivity analysis performed
appears too simplistic and not appropriate for the level of complexity and importance of
the SFWMM as a regional management tool. Our findings are in agreement with those of
the "SFWMM Peer Review Panel", which recommended a more thorough approach
towards sensitivity analysis of SFWMM, including global sensitivity techniques.

2. Review of Alternative Global Sensitivity and Uncertainty Methods
We performed a review of modem global sensitivity analysis techniques suitable for
application to SFWMD models. Hydrological and water quality models are often

complex and require a large number of parameters and other inputs. Mathematical
models like these are built in the presence of uncertainties of various types (input
variability, model algorithms, model calibration data, and scale). The role of uncertainty
analysis is to propagate all these uncertainties, using the model, onto the model output of
interest. Complementarily, sensitivity analysis is used to determine the strength of the
relation between a given uncertain input and the output. As a result of these analyses, the
model user can learn what model input factors affect the output of interest the most, and
possibly quantify the uncertainty of the model due to the most sensitive inputs. This
knowledge is critical to efficiently guide the model calibration as well as to document the
validity of the model outputs for management or decision tasks. In spite of their
importance, these analyses are not usually performed in many model development and
application efforts today. Even if they are performed, the procedures used are often
arbitrary and lack robustness. Usually derivation techniques (variation of the model
output over the variation of the model input) are employed. These methods are applied
just over a prescribed (and usually small) parametric range, only can handle one-
parameter-at-a-time (OAT techniques), and can consider efficiently but a few parameters.
When the model output response is non-linear and non-additive, as with most complex
model outputs, the derivative techniques are not appropriate. As an alternative, new
global sensitivity and uncertainty techniques are available that evaluate the input factors
of the model concurrently over the whole parametric space (described by probability
distribution functions). So far, two modern global techniques, a screening method (Morris
method) and an analysis of variance one (Fourier Analysis Sensitivity Test-FAST) were
identified as potentially suitable for performing the sensitivity analysis of the SFWMM.
An initial application of these techniques to the new water quality component of the
SFWMD RSM model has been recently performed (Jawitz et. al. 2007), as well as to the
water quality model VFSMOD (Mufioz-Carpena et. al. 2007).

3. Publications

Jawitz, J.W., R. Mufioz-Carpena, K.A. Grace, S. Muller, A.I. James. 2007. Spatially
Distributed Modeling of Phosphorus Reactions and Transformations in
Wetlands. Scientific Investigations Report 2006-XXXX. U.S. Department of
the Interior. U.S. Geological Survey (under review)

Mufioz-Carpena, R. S. Muller, and Z. Zajac. 2007. Application of Global Sensitivity and
Uncertainty Analyses Techniques to a the vegetative filter strip model
VFSMOD (Invited paper for Soil and Water Centennial Collection, Trans. of
ASABE, in preparation)

Progress Report: Topic 2
Addition of Ecological Algorithms into the RSM Model

CoPIs: Gregory Kiker, Rafael Mufioz-Carpena, Wendy D. Graham,
SWFMD Coordinator: Naiming Wang
Ph.D. Student: Gareth Lagerwall
Collaborator: Andrew James

Progress to Date:
This research project aims to systematically review, design and develop selected
ecological algorithms for the RSM model using a similar methodology to the
development of recent water quality algorithms (RSM-WQ).

Activities for this project have included the usual startup related activities including
formation of Mr. Lagerwall's supervisory committee and the design/submission of a
coursework plan. The graduate committee consists of the following persons:
Dr G A Kiker (Dept of Agr. & Bio. Engineering) Chair
Dr R Munoz-Carpena (Dept of Agr. & Bio. Engineering) Co-Chair
Dr K Hatfield (Civil and Coastal Engineering)
Dr A James (Soil and Water Sciences)
Dr N Wang (SFWMD) (to be added shortly)

Research activities have been primarily focused on a review of the RSM and RSM-WQ
models (including their fundamental designs, code layout/design and input/output
structures). Weekly meetings were conducted with Dr Andy James, Prof Munoz-Carpena
and Prof Kiker to understand and explore potential design challenges in adding ecological
components to the RSM structure. In addition, other integrated regional models (FT-
LOADS/SICS/TIME) were included in review discussions to provide a variety of design
viewpoints for upcoming object/code design discussions. It is expected that increased
communications/discussions with SFWMD modelers will be required to establish
upcoming design and implementation strategies for code expansion.

Program Details and long-term objectives:

Cooperative Graduate Research Assistantships Between the Florida Water
Resources Research Center-South Florida Water Management District UF/ABE
in Critical Water Resources Areas for South Florida

As mentioned previously, two specific research projects have been agreed and contracted
with South Florida Water Management District (SFWMD): Topic 1: Sensitivity Analysis
Of South Florida Regional Modeling and Topic 2: Addition of Ecological Algorithms
into the RSM Model.

Topic 1: Sensitivity Analysis Of South Florida Regional Modeling

CoPIs: Rafael Mufioz-Carpena, Wendy D. Graham, Gregory Kiker
SWFMD Coordinator: Jayantha Obeysekera
Ph.D. Student: Zuzanna Zajac

Introduction
Mathematical models are built in the presence of uncertainties of various types (input
variability, model algorithms, model calibration data, and scale) (Haan, 1989; Beven,
1989; Luis and McLaughlin, 1992). Propagating via the model all these uncertainties onto
the model output of interest is the job of uncertainty analysis. Determining the strength of
the relation between a given uncertain input and the output is the job of sensitivity
analysis (Saltelli et al., 2004). The evaluation of model sensitivity and uncertainty must
be an essential part of the model development and application process (Reckhow, 1994;
Beven, 2006). Although sensitivity analysis is useful in selecting proper parameters and
models, and model uncertainty provides much needed assessment of results, they are
rarely used in most water quality modeling efforts today (Mufioz-Carpena et al., 2006). If
uncertainty is not evaluated formally, the science and value of the model will be
undermined (Beven, 2006). The consideration of model uncertainty should be linked to
the availability or efficient collection of data. This combination will allow: a) to improve
the representation of the inputs and boundary conditions; b) refine the evaluation of the
model complexity structure; c) indicate what models are adequate for specific
applications; and d) constrain feasible sets of effective parameter values at particular
applications (Beven, 2006).
"Input factor" in a broad sense refers to anything that changes the model prior to
execution. This not only includes the model parameters, but entirely different
conceptualizations of the system. Input parameters of interest in the sensitivity analysis
are those that are uncertain; that is, their value lies within a finite interval of non-zero
width. Traditionally, model sensitivity has been expressed mathematically as model
output derivatives; these are normalized by either the central values where the derivative
is calculated or by the standard deviations of the input parameter and output values.
These sensitivity measurements are "local" because they are fixed to a point or narrow
range where the derivative is taken. Local sensitivities are used widely and are the basis

of many applications, such as the solution of inverse problems. These local sensitivity
indexes, used in "one parameter at a time" methods, quantify the effect of a single
parameter by assuming all others are fixed (Saltelli et al., 2005). Sometimes a crude
variational approach is selected in which incremental ratios are taken by moving factors
one at a time from the base line a fixed amount (for example, 5 percent). This is often
done without prior knowledge of the factor uncertainty range or the linearity of the output
response.
Techniques that vary one parameter at a time relative to a chosen initial value have
some inherent drawbacks. Using such local sensitivity indexes for the purpose of
assessing relative importance of input factors can only be effective if the effects of model
parameters are all linear, unless some kind of average over the parametric space can be
made (Saltelli 2004). Often, the models are non-additive (non-linear) and an alternative
"global" sensitivity approach is more appropriate. Exploring the entire parametric space
of the model may answer questions such as (1) which of the uncertain input parameters
largely determine uncertainty of a specific output, or (2) eliminating uncertainty in which
input parameter would reduce output uncertainty by the greatest amount (Saltelli et al.,
2005).
Different types of global sensitivity methods can be selected based of the objective of
the analysis. For computationally expensive models or if a large number of parameters
need to be evaluated simultaneously, it is usually more efficient to apply a screening
method. This type of method provides a parameter ranking in terms of relative effect over
output variation. Screening tools yield a qualitative parameter ranking that allows the
user to focus the calibration or development effort on the most sensitive parameters. If
quantitative information is desired, an analysis of variance technique is usually required.
The South Florida Water Management Model (SFWMM) is a regional-scale
computer model that simulates the hydrology and the management of the southern
Florida water resources system from Lake Okeechobee to Florida Bay. The model
simulates all the major components of the hydrologic cycle in southern Florida on a daily
basis using climatic data for the 1965-1995 period. The SFWMM is widely accepted as
the best available tool for analyzing regional-scale structural and/or operational changes
to the complex water management system in southern Florida
(http://www.sfwmd.gov/org/pld/hsm/models/sfwmm/index.html). A derivative based
local sensitivity analysis was performed by the South Florida Water Management District
(SFWMD) for 8 parameters of the SFWMM for a number of sites to which the model
was differentially calibrated based on land use characteristics and availability of
historical hydrological data (SFWMD, 2005). As parameters ranges characteristic for
South Florida conditions were not found in the literature, they were assessed in an
unconventional way: "for each parameter, a series of model runs were completed to
determine the range of acceptable values such that each parameter within the range can
be used without significantly affecting the calibration". A recommended permissible
variation of the parameters was thus determined to be 10% for WPET (Wetland PET),
20% for CPET (Coastal PET) and 50% for other parameters.
Such derivative-based techniques as were applied in this work were found inadequate
by the "SFWMM Peer Review Panel" and in a recent report (Bras et al., 2005). The panel
recommended that the District adopts effective and quantitative measures of sensitivity.

Objectives
In the proposed research, global sensitivity approaches and alternative sensitivity
techniques will be undertaken. Global sensitivity measures will provide a measure of the
overall model sensitivity to a parameter across the entire distribution of its possible
values. Choice of sensitivity analysis techniques will depend on the following criteria: 1)
the computational cost of running the model, 2) the number of input parameters, 3) the
degree of complexity of model coding, 4) the amount of time available to perform the
analysis and 5) the ultimate the objectives of the analysis. For computationally expensive
models, or when large numbers of parameters need to be evaluated simultaneously, a
screening technique can prove more efficient. These kind of methods provides a
qualitative measure of the relative importance of each of the parameters. For quantitative
comparisons of parameter sensitivity variance-based methods will be used

Scope of Work:

Year 1
Literature review on sensitivity analysis methods and theory
Understanding the fundamental principles, inputs and parameter requirements of
the model
Selection of an application case (domain/subregion) for sensitivity analyses

Year 2
Identification parameters and the distribution of each parameter from existing data
at the application site and literature (for the intended area of application i.e. South
Florida).
Selection of sensitivity analysis methods) to be applied, tools and training.
*
Years 3
Carry out the sensitivity analysis.
Interpretation of results.

Deliverables:

These are proposed to permit the evaluation of the project by the three partners of this
project as included in the UF/SFWMD Cooperative Agreement:

1) One-page quarterly reports summarizing the progress in recruiting, enrolling,
developing supervisory committees, developing plans of study, developing research
proposals, courses taken, and research conducted by the Ph.D. student.
2) Annual summary report.
3) Regular progress meetings at UF and/or SFWMD.
4) A final report at the completion of each students degree program (the content of this
report will be close to that of the students dissertation).

5) One or more papers submitted to a peer-reviewed journal, co-authored with the
student's adviser and the South Florida Water Management District Staff that actively
works with the student in his research study. The papers) should cite the financial,
in-kind, and technical support received from the South Florida Water Management
District and the Water Resources Research Center.

References:

Beven, K. (1989), Changing Ideas in Hydrology -- The Case of Physically Based
Models. Journal of Hydrology, 105: 157.
Beven, K (2006), On undermining the science? Hydrological Processes, 20:1-6.
Bras, R.L., A. Donigian, W.D. Graham, V. Singh, J. Stedinger (2005), The South Florida
Water Management Model, version 5.5: Review of the SFWMM Adequacy as a Tool
for Addressing Water Resources Issues. Final Panel Report, Oct. 28, 2005. SFWMD :
West Palm Beach.
Haan, C. T. (1989), Parametric Uncertainty in Hydrologic Modeling. Transactions of the
ASAE, 32(1): 137-.
Luis, S.J., and D. McLaughlin (1992), A Stochastic Approach to Model Validation.
Advances in Water Resources, 15:15.
Mufioz-Carpena, R., G. Vellidis, A. Shirmohammadi and W.W. Wallender (2006),
Evaluation of modeling tools for TMDL development and implementation.
Transactions of ASABE 49(4):961 -965.
Reckhow, K. H. (1994), Water-Quality Simulation Modeling And Uncertainty Analysis
For Risk Assessment And Decision-Making, Ecological Modelling, 72, 1-20.
Saltelli A., Ratto M., Tarantola S. Campolongo F. (2004), Sensitivity analysis in
chemical models, Chemical Reviews, 105, 2811-2827.
Saltelli, A., M. Ratto, S. Tarantola, and F. Campolongo (2005), Sensitivity analysis for
chemical models, Chemical Reviews, 105, 2811-2827.
SFWMD (2005), Final Documentation for the South Florida Water Management Model
(v5.5). South Florida Water Management District and the Interagency Modeling Center
West Palm Beach, Florida, November, 2005.

Topic 2: Addition of Ecological Algorithms into the RSM Model

CoPIs: Gregory Kiker, Rafael Mufioz-Carpena, Wendy D. Graham,
SWFMD Coordinator: Naiming Wang
Ph.D. Student: Gareth Lagerwall
Collaborator: Andrew James

Introduction
Alterations to the natural delivery of water and nutrients into the Everglades of the
southern Florida peninsula have been occurring for nearly a century. Major regional
drainage projects, large-scale agricultural and urban development, and changes to the
hydrology of the Kissimmee River-Lake Okeechobee-Everglades watershed have
resulted in substantial changes in ecological components of all these systems. The highly
connected nature of groundwater and surface water systems over large spatial areas has
necessitated the development of integrated regional modeling approaches to adequately
represent the unique hydrological and ecological conditions of southern Florida.

The Regional Simulation Model (RSM) was developed to provide an integrated surface
and subsurface hydrological model for the development and exploration of water
management and habitat restoration objectives (SFWMD, 2005). One of the primary
challenges in developing RSM and its concomitant implementation within southern
Florida (SFRSM) is to provide a flexible and adaptable framework for new code
additions and expanded functionality while maintaining stable simulations for
management analysis. Recent RSM code addition projects have successfully added
generic water quality components into the model structure (RSM-WQ: Jawitz et al.,
2006). This approach used an innovative mixture of conceptual model design, XML
implementation and global sensitivity analysis to provide water quality algorithm designs
for multiple modeling platforms while being implemented and tested within RSM. Given
the initial success of the RSM-WQ module additions, interest has been growing to utilize
elements of this approach to add ecological algorithms with a similar methodology.
Ecological components and their representation within modeling platforms present a
significant challenge as a variety of algorithm designs exist ranging from simplified
Habitat Suitability Indices (USFWS, 1981; Tarboton et al., 2004) to Spatially Explicit
Species Index (SESI) and individual models (DeAngelis et al., 1998; Curnutt et al., 2000)
to more complex, high resolution, individual-based models (Goodwin et al., 2006).

Objectives
This research project aims to systematically review, design and develop selected
ecological algorithms for the RSM model (RSM-ECO) using a similar methodology to
the development of water quality algorithms (RSM-WQ). To this end, the objectives of
this research are the following:
* Review of relevant ecological models, design concepts and code
implementation tools for development of RSM-ECO ecological algorithms.

* Selection of ecological species (habitat, plant and/or animal) to be included
in the initial development and testing of RSM-ECO.
* Development of the conceptual model of RSM-ECO organisms
* Prototype model development and testing on the "10x4" mesh (Jawitz et al.,
2006)
* Selection of a test site for model calibration and testing
* Model implementation and testing on selected test site
* Systematic global sensitivity analysis

Scope of Work:

Evolution of the RSM-ECO model and its associated simulation results will be posted on-
line with reports and deployed software (Years 1-3). A basic schedule is listed as
follows:

Year 1: Review of relevant models and concepts
Review current RSM and RSM-WQ design and code structure
Current ecological model designs/algorithms (i.e. HSI models, ATLSS,
ELM, ELAMS)
Object-oriented design and code implementation (Java and C++)

Year 2: Development of conceptual models for selected organisms
Selection of organisms for initial RSM-ECO inclusion and testing.
Development of the "10x4" site with selected organisms for prototype testing
Selection and development of parameters for a South Florida test site for RSM-
ECO

Year 3: Model implementation on selected test site
Sensitivity Analysis
Calibration/Validation with SFWMD ecological data
Development of technical documentation

Deliverables:

These are proposed to permit the evaluation of the project by the three partners of this
project as included in the UF/SFWMD Cooperative Agreement:

6) One-page quarterly reports summarizing the progress in recruiting, enrolling,
developing supervisory committees, developing plans of study, developing research
proposals, courses taken, and research conducted by the Ph.D. student.
7) Annual summary report.
8) Regular progress meetings at UF and/or SFWMD.
9) A final report at the completion of each students degree program (the content of this
report will be close to that of the students dissertation).

10)One or more papers submitted to a peer-reviewed journal, co-authored with the
student's adviser and the South Florida Water Management District Staff that actively
works with the student in his research study. The papers) should cite the financial,
in-kind, and technical support received from the South Florida Water Management
District and the Water Resources Research Center.

References:

Curnutt, J.L., J. Comiskey, M.P. Nott, and L.J. Gross. 2000. Landscape-based spatially
explicit species index models for Everglades restoration. Ecological Applications
10:1849-1860.

DeAngelis, D.L., L.J. Gross, M.A. Huston, W.F. Wolff, D.M. Fleming, E.J. Comiskey,
and S.M. Sylvester. 1998. Landscape modeling for Everglades ecosystem restoration.
Ecosystems 1:64-75.

Goodwin, R.A., Nestler, J.M., Anderson, J.J., Weber, L.J. and Loucks, D.P. 2006.
Forecasting 3-D fish movement behavior using a Eulerian-Lagrangian-agent method
(ELAM). Ecological Modelling 192: 197-223

Jawitz, J., Mufioz-Carpena, R. Grace, K., Muller, S. and James, A.I. (2006).
Spatially Distributed Modeling of Phosphorus Reactions and Transformations in
Wetlands. USGS Draft Report.

South Florida Water Management District (SFWMD), 2005. Regional Simulation
Model (RSM): Theory Manual. South Florida Water Management District,
Office of Modeling (OoM), 3301 Gun Club Road, West Palm Beach, FL 33406.
Web:
http://www.sfwmd.gov/site/sub/mrsm/pdfs/rsmtheoryman.pdf#RSMTheory

Tarboton, K. C., Irizarry-Ortiz, M. M., Loucks, D. P., Davis, S. M., and
Obeysekera, J. T. (2004). Habitat Suitability Indices for Evaluating Water
Management Alternatives. Office of Modeling Technical Report. South Florida
Water Management District, West Palm Beach, Florida. December, 2004..

USFWS. (1981). Standards for the Development of Habitat Suitability Index Models
forUse in the Habitat Evaluation Procedures. Report ESM 103, Interagency,
Interdisciplinary Development Group, Division of Ecological Services, U.S. Fish and
Wildlife Service, U.S. Department of the Interior, Washington, DC.

Related research.
As described above, these training efforts will complement existing research conducted
by the PIs. Currently this includes work with NSF, FDEP, FDACS, USGS and SFWMD
that will provide a foundation for the new assistantships. Within the first year the PIs,
with the close cooperation of SFWMD staff, have recruited the listed students and have
developed supervisory committees, plans of study and Ph. D. research proposals that
match the interests formulated by the SFWMD. These research areas encompass, but are
not limited to, global sensitivity analysis and uncertainty of hydrologic/water quality
models, ecological modeling in South Florida. These research proposals are co-funded
with matching funds from SFWMD.

Training potential.

Two Ph.D. graduate students will be trained through this effort.

Investigator's qualifications.

Resumes from the project PIs and collaborator are included in the next pages.

Measurement of erosion around hydraulic structures

Basic Information

Title: Measurement of erosion around hydraulic structures

Project Number: 2006FL145B

Start Date: 3/1/2006

End Date: 2/29/2008

Funding Source: 104B

Congressional District: 6

Research Category: Engineering

Focus Category: Sediments, Models, None

Descriptors:

Principal Investigators: Tian-Jian Hsu

Publication

Erosion at Hydraulic Structures

Tian-Jian Hsu
Civil and Coastal Engineering
University of Florida

1 Introduction

1.1 The problem addressed in this report
As a result of recent active hurricane seasons, many District waterways
experienced bank and bed erosion. The erosion was more severe downstream of flow
control structures, particularly spillways and weirs. These erosions cause several
undesired problems, for example, erosion on the discharge canal potentially endangers
the structural stability of the flow control structure. In addition, bank erosion may also
result in damages to the levees. The eroded sediments may also be carried by the flow to
the lakes and reservoirs and causing undesired sedimentation, and resulting in reduction
of storage capability for water supply and deterioration of the water quality.

The primary objective of this report is to summarize an effort on literature survey
for existing experimental studies of erosion problems, specifically at hydraulic structures
and river banks and to recommend a process-based experimental approach to further
investigate erosion problem at selected District field sites. A process-based approach
based on physical principles allows effective field experimental design and data analysis
so that eventually a general formulation for evaluating erosion problem can be proposed
for District's management purposes.

In the past several decades, there have been extensive studies on bridge pier and
abutment scour for both cohesionless and cohesive sediments. Many of these studies
adopted process-based approach and had greatly advanced our physical understanding on
local scour and common sediment erosion processes. Therefore in this report, after a
general discussion on scour types, we begin our investigation by summarizing some of
the major finding from bridge pier scour studies (section 2). Some of the lessons learned
from these studies, such as the concept of equilibrium, timescale to equilibrium and
differences between noncohesive and cohesive sediments, are very important guidelines
to our major objective regarding erosion at hydraulic structures and bank erosion. In
section 3, erosion below spillway and culvert outlets are discussed, respectively. Section
4 focuses on bank erosion. In each of the sections 3 and 4, we begin with a general
description of the problem, and a literature survey on existing approaches. By the end of
each section, recommendations for new field experiments to improve our current
predictive capability are described and planned. Finally, in section 5 a brief literature
survey on recent advances in sediment transport modeling and three-dimensional
numerical approach based on computational fluid dynamics (CFD) for erosion at
structures is discussed. Major conclusions from this investigation are remarked in Section
6.

1.2 Scour types
Scour is the loss of soil by erosion due to the flow. Scour is generally divided into several
types (e.g., Mueller & Wagner 2005; Briaud et al. 1999) and each scour type does not
necessarily have a precise definition in all physical aspects when compared to other types.
Therefore, for the purpose of clarity and relevance to this report, they are first defined
here.

In terms of the mechanism, scour is a result of acceleration of the flow (and
possibly enhancement of flow turbulence) and it is generally a time-dependent process.
Considering the stream flow and the sediment bed as one system in equilibrium at a
specific time, then the scour is a process that represents how the streambed morphology
is in respond to the local flow acceleration through sediment erosion/accretion and
eventually arrives at another equilibrium state. The acceleration of the local flow can be
resulted from increase of stream flow velocity due to flooding or due to local obstructions
(e.g., contraction) to the water flow, or both. The type of flow disturbance can be due to
the enhanced shear flow and bottom/wall stress near the streambed/bank (e.g., bridge
scour, bank erosion) or the direct impact to the soil through a jet-like flow (scour below
spillway, culvert outlets).

The long-term scour is the general aggradation or degradation of streambed
elevation due to natural and human causes. In this study, we focus more on the short-term
scour in which the streambed respond to short-term stream-flow runoff cycles, e.g., a
stream's storm hydrograph. Within the context of short-term scour, we can further
distinguish between the contraction scour and the local scour. The contraction scour is
resulted from the increase of normal stream flow due to natural or manmade contractions.
It includes removal of soil from a river's bed and banks and is a concern of the overall
channel stability. The local scour refers to removal of soil from around piers, abutments
or of more concerns here, the hydraulic control structures.

The local scour can be further classified based on the mode of sediment transport
due to the approaching flow (e.g., Melville and Chiew 1999; Barbhuiya and Dey 2004).
The clear-water scour occurs when the approaching flow intensity is not sufficient to
initiate ambient sediment transport (except around the structure). Hence, there is no
upstream supply of sediment relative to the local scour. On the other hand, live-bed scour
occurs when the approaching flow is energetic enough to entrain bed sediment from the
upstream and hence the local scour is continuously fed with upstream supply of sediment.
The time-dependent behavior of the scour processes is rather different for clear-water
scour and live-bed scour (Fig 1). The equilibrium scour depth is attained more rapidly
during live-bed scour and strictly speaking, it is a quasi-equilibrium state due to for
example, the migration of bedforms. The clear-water scour reaches its equilibrium more
slowly. However, the resulting magnitude of the maximum equilibrium scour depth is
greater (about 10%) than that for live-bed condition (Graf 1998).

If the interest here is erosion due to storm, live-bed scour may be more likely to
occur. However, the duration of the storm becomes another critical factor to be
incorporated. In this case, the timescale to reach equilibrium must be a competing factor
with the storm duration. It is well-known that the erodibility for non-cohesive (sand) and
cohesive (clay, fully consolidated) sediments are rather different and hence the timescale
to reach equilibrium must also depend on the cohesion property of the sediment. In the
field condition the size of sediment is often non-uniform and hence the armoring effects
due different sizes of sediments become another concern. Also because of the non-
uniform sediment, most upstream approaching flow may consist of the fines (or at least
some washloads) which is recently shown to be sensitive to the local scour (Sheppard et

Clear-water scouL

I Live-bed scour

Time
Fig. 1: Schematic descriptions for clear water scour (black curve) and live-bed scour
(blue curve). See Graf (1998) for a similar plot.

al. 2004). Therefore, the definition of clear-bed and live-bed scour can not be definite.
These are the critical issues that are relevant to both the fundamental sediment transport
and various kinds of erosion problems that we will address in this report through a
comprehensive literature survey.

2 Lessons Learned from Bridge Scour
In the Unite States, there are about 500-thousand bridges that are over water (National
Bridge Inventory 1997). In the past 30 years, about 60% of the bridges failed were due to
scour (Shirole & Holt 1991; Briaud et al. 1999). Therefore, there has been an extensive
research on bridge scour ranging from theoretical analyses, laboratory/field experiments,
and numerical modeling. Research findings resulted from these studies, especially those
related to the physical processes of erosion, can certainly provide useful guidelines for
other type of erosion problems relevant to District's interests. Hence, this section
concentrate on summarizing important lessons learned from extensive bridges scour
studies that will be useful for our major objective regarding erosion at hydraulic
structures and bank erosion

2.1 Dimensional analysis
Bridges scour is a rather complex problem form the fluid mechanics point of view. It
involved interactions among turbulent fluid flow, sediment and the geometry of the
structure. Dimensional analysis is a very useful tool as the first step toward a more
comprehensive study. Here we utilize a framework for analysis following Melville &
Chiew (1999). This framework is concise but provides considerable insights into the
dynamical processes and is also used by other researchers recently for interpreting
measured scour data (Sheppard et al. 2004; Sheppard & Miller 2006).

The local scour is caused by the presence of structure that alters the original flow
field from an equilibrium state. Therefore, it is reasonable to expect that after the
installation of structure, flow and sediment bed may evolve to another equilibrium state
through the removal of soil and the adjustment of bed morphology. The maximum
equilibrium scour depth ds, is perhaps the most important quantity in the scour
prediction. The maximum scour depth at a bridge pier generally depends on flow
parameters, bed sediment properties, pier geometry and time.

Assuming uniform sediment properties, fully turbulent flow and simple pier
geometry, the maximum scour depth at a cylindrical pier of diameter D can be written as
(Melville & Chiew 1999):

D f hU, DD (1)

where U is the averaged stream velocity at a significant distance upstream of the structure
(stream velocity without the obstruction of structure), Uc is the critical velocity for
sediment entrainment, h is the mean flow depth, and d is the mean sediment particle
diameter, usually calculate from do0.

The 1st parameter on the right-hand-side of (1) represents the nondimensional
flow intensity. This parameter not only characterizes the intensity of the stream flow but
also differentiates between the clear-water scour ( U/U, <1 ) and live-bed scour

(U/U, > 1, see their definitions in section 1). The 2nd parameter represents the effect of
flow shallowness. The 3rd parameter represents the sediment coarseness. The dependence
of maximum scour depth with respect to these parameters reveals important mechanisms
controlling scour processes and is discussed in more details in section 2.2.

The timescale to reach the equilibrium scour depth and the time-dependent
behavior of scour are not incorporated in equation (1). However, this is another important
aspect of the scour processes and has been studied in details by several studies (e.g.,
Melville & Chiew 1999; Briaud et al. 1999) for both non-cohesive and cohesive
sediments. The importance of timescale in scour processes is discussed in section 2.3 and
2.4.

2.2 Prediction for the maximum equilibrium scour depth
The maximum equilibrium scour depth is the most important quantity for a scour
prediction and has received the most investigations in the literature. It represents the
maximum scour damage that can occur for a given flow condition, sediment properties
and structure dimension if the duration of the flow forcing (i.e., a storm) is long enough
to attain the equilibrium. Therefore, the maximum equilibrium scour depth is also the
most conservative engineering design guideline.

Using the dimensional analysis described in section 2.1. Melville (1997) and
Melville and Chiew (1999) proposed an empirical relation using several laboratory data
sets conducted in 4 different flumes (totally 70 cases). The data used in this study is for
relatively small structure due to the constraint of the laboratory facility and hence the
largest ratio D/d is about 200. This value is smaller than what typically encounter in the
field condition. Following Melville and Chiew (1999), Sheppard et al. (2004) and
Sheppard and Miller (2006) further utilize a proto-type scale flume facility (at USGS and
University of Auckland), extend the database for D/d as high as 4155, and propose a
new empirical formulation to estimate the maximum equilibrium scour depth. On the
other hand, one of the most commonly used scour prediction equation is the HEC-18
equation (Richardson and Davis 2001) recommended by Federal Highway
Administration, U.S. Department of Transportation. Since the original HEC-18 was
proposed, it has been revised few times by calibrating with new field data (Mueller &
Wagner 2005). Other empirical equations for scour prediction can be found in a recent
review paper by Barbhuiya and Dey (2004).

Before presenting several widely-used formulae for predicting maximum
equilibrium scour depth, it is useful to exam the general dependence of d,m on each of
the nondimensional parameters on the right-hand-side of (1). Summarizing the results and
analyses presented by Melville and Chiew (1999) and Sheppard et al. (2004), their most
important conclusions are shown here graphically in Fig 2-4.

As the nondimensional flow intensity increases, the scour depth has two peak
values (Fig. 2). The maximum clear-water scour occurs when U/U =1. Subsequent
increment of flow intensity initiates sediment movement over the entire streambed (not

just the scour hole area). In such live-bed condition, the upstream flow (before approach
the scour hole) already consists of suspended sediment and the suspension capacity of the
overall flow is reduced (e.g., suspended sediment reduces flow turbulence, Hsu et al.
2003). The maximum equilibrium scour depth thus reduces according (usually about 10-
20%). Even at the live-bed scour maximum (the second peak in Fig. 2), its magnitude is
still smaller than that at clear-bed scour maximum (i.e., at U/Uc 1 in Fig 2).

threshold live-bed
peak peak

D ,

1.0 U/UC

Fig2: Influence of flow intensity U/U, on nondimensional
differentiates clear-water scour and live-bed scour.

scour depth. U/U,

As the flow shallowness increases, the nondimensional scour depth increases until
it reaches an asymptotic value (Fig. 3). Approximately, when the water depth is about
several times larger than the pier diameter, further increment of water depth has no effect
on the scour depth. The flow turbulence around the pier, which more or less determines
the amount of sediment transport, can be approximately characterized by the largest size
of the turbulent eddy. When the water depth is sufficiently deep, it has no effect on the
local flow and the largest turbulent eddy size is determined by pier diameter and the so is
the scour depth. On the other hand, when the water depth is relatively small compared to
the pier diameter (or relatively wide pier), the largest turbulent eddy size must be
confined by the water depth and the scour depth must scale with the water depth.

Based on most of the small-scale laboratory results, it is generally believe that
then grain size has no effect on scour depth except for relatively coarse grain (D/d<50,
Fig 4, black curve), the scour depth decreases because coarse grains provide significant
bed roughness and porous effect that dissipate the flow energy (e.g., Ettema 1980).
However, this conclusion is made from small-scale laboratory results with limited size of

d
D /narrow
S pier

wide h/D
pier

Fig. 3: Influence of flow shallowness on nondimensional scour depth.

pier and D/d value is no more than about -100. Recently, new evidences based on
prototype experiments, with D/d as large as 1000-4000 suggest nondimensional scour
depth clearly decreases as D/d>>50 (Sheppard et al. 2004). There is no definite
explanation at this point for the reason why nondimensional scour decreases for fine
sediment (Sheppard, personal communication). One possible explanation could be due to
the effect of suspended sediment on damping the flow turbulence (Ross and Mehta 1988;
Hsu et al. 2003; 2006), which has been proved to be important in controlling the lutocline
dynamic of soft fluid mud at estuary or continental shelf (Trowbridge and Kineke 1994)
when mud concentration is greater than about 10g/1. This important finding in scour
process by Sheppard et al. (2004) also demonstrates the importance and the justification
for pursuing field experiments on scour processes.

The scour prediction equation proposed in Melville and Chiew (1999) is a
function flow-pier width, flow intensity and particle size. However, the equation is
dimensional (even though they propose equation (1) that is nondimensional). Sheppard et
al (2004) and Sheppard and Miller (2006) later followed equation (1) and propose scour
formulae for bridge pier that is more complete. The Sheppard's equations are given as,
for clear water scour,

S= 2.5 tanh[i 0fd 1-1.75 In U (3.1)
D D \u I

and for scour above live-bed peak

d,
D

coarser sand or finer sand or
lab. scale pier protot. I'c pier

-50 D/d

Fig.4: Influence of sediment coarseness on nondimensional scour depth. The
red curve represents new findings based on prototype scale experiments.

dm 2.2 tanh (3.2)
D

A linear interpolation can be used in between live-bed scour range up to live-bed peak. In
(3.1), a complete functional dependence of D/d is obtained through large-scale flume
experiment as:

fd -. D/d (3.3)
Jd 0.4(D/d)12 +10.6(D/d)0.13

The HEC-18 equation, which is used and calibrated in the field, is
nondimensionalized in a rather different way as compared to Melville's and Sheppard's
formula. HEC-18 formula is based on the Froude number (Richardson and Davies 2001):

d =2.0K3 F0.43 (4.1)
D \D

where K3 is a numerical coefficient that account for bedforms (1.1 for plane bed and
small dunes and up to 1.3 for large dunes). The Froude number Fr is defined as

U
F, = (4.2)
Jgh

2.3 Time-dependent scour behavior
The timescale to attain the equilibrium maximum scour depth has received less
investigation than the equilibrium scour depth itself. This is partly because for sandy
environment, the timescale to attain equilibrium is relatively short (or on the same order
of magnitude) when compared to typical duration of an extreme event (e.g., storm). In
addition, the maximum equilibrium scour depth already provided the most conservative
design criterion (but costly). However, as our capability for predicting the scour depth
advances, the time-dependent behavior received more and more interests in the past
several years (Melville and Chiew 1999; Briaud et al. 1999; Sheppard et al. 2004).
Predicting the time-dependent scour depth is essential when considering storm of
relatively short duration or even more importantly when considering fully consolidated
cohesive soil erosion (see section 2.4 for details).

Following the nondimensional form in (1), the time t for scour depth evolution
can be normalized by T,, the timescale to reach the equilibrium maximum scour depth.
Hence, we can add the 4th nondimensional quantity t/T, into equation (1) for predicting
the general time-dependent scour depth d,:

d -fU h d t (2)
c= f hd (2)
D U, D 'D' Te

According to Melville & Chiew (1999), the time evolution of scour depth d,
approaching the final equilibrium maximum scour depth d,m can be well represented by
the following equation:

d t 1.6
sm U 1T ()

This equation requires an estimate of Te. Existing data suggest (Melville & Chiew, 1999)
T, itself when normalized by D/U, depends on flow intensity, flow shallowness and
sediment coarseness. An empirical formula is suggested to predict T, as:

T (days) = 48.26 D 0.4, h>6
UU D
0.25 (4)
T (days)= 30.89 -0.4 ) -< 6
u\u, D D

Notice here that when the water depth is large enough (6 times the structure diameter),
the effect of water depth on scour vanishes, consistent with that observed for maximum
equilibrium depth.

An estimate of the typical time evolution for scour to reach equilibrium is
insightful at this point. Considering a peak flood velocity of U=0.8m/s, sand diameter
d=0.22mm, structure diameter D=1.0m, and water depth h=1.2m. The threshold velocity
in this case can be confidently estimated as Uc = 0.32 m/s (Melville 1997). The time
scale for the scour to reach equilibrium, according to (4) is calculated as 84.87 days,
which is seemingly a very long time. However, the time-dependent behavior described in
(3) is rather nonlinear (see Fig 5, for an example). In fact, in simply 1 day, the scour
depth is as deep as 93% of the final equilibrium depth. Therefore, when considering the
uncertainties in estimating maximum equilibrium scour depth itself, the scour processes
reach its maximum scour depth in a rather short period of time (-iday) when compare to
typical flood duration.

On the other hand, a fully consolidated cohesive soil (clay) has a rather low
erodibility and the threshold velocity can be several times higher than that for sand. Let's
now assuming equation (3) and (4) are equally applicable to cohesive sediment. Using a
typical threshold velocity for clay of U, =1.0 m/s (Briaud et al. 2004) but with other
parameters unchanged, it will take 4 day to reach 93% of the final equilibrium scour
depth. This is about 4 times slower as compared to sandy condition. Therefore, for
cohesive sediment the scour process is much slower and the duration of a storm is often
not long enough for the scour to attain its maximum equilibrium depth. Notice that in
reality, equation (3) and (4) may only qualitatively applicable to cohesive sediment and
one would expect the empirical coefficient involved in (3) and (4) different from that
used in sandy condition. As we will discuss in the next section (section 2.4), the scour
processes for cohesive sediment is even much slower than our crude estimate here using
(3) and (4) (see Fig. 5).

2.4 Scour for cohesive soils
Previous sections focus on bridge scour for non-cohesive, sandy environments (coarse-
grained). The major difference between a non-cohesive and a cohesive sediment scour is
that the erodibility for a fully consolidated, cohesive clay material is much less (sometime
1000 times less, Briaud et al. 2004; Ansari et al. 1999) than that of sand. Therefore, the
scour depth for cohesive soil develops much slower than that for non-cohesive sandy
material. An example for comparing sand scour and clay scour demonstrated by
Brandimarte et al (2006) is reproduced in Fig 5.

For typical peak flow duration due to storm of say 1 day, it is sufficient for sandy
scour to develop to its maximum equilibrium scour depth. Hence, simply estimating the
maximum equilibrium scour depth at sandy environment is sufficient for engineering
purposes. However, 1-days of storm duration are too short for cohesive soil to develop to
the maximum scour depth. Hence, using an estimated maximum scour depth in a
cohesive sediment condition usually over-predicts the scour and hence provides a design
criterion that is too conservative. For scour in cohesive sediment, it is important to study
the time-dependent behavior. Accurate descriptions on the time-dependent scour process
for cohesive soil can save lots of money in building a reliable structure.

Briaud et al. (1999, 2004) developed a useful approach to predict the time-
dependent behavior of scour depth for cohesive soil. This method is called SRICOS
(Scour Rate In Cohesive Soils). In this approach, the maximum scour depth in clay is in
fact considered to be similar to that in sand (same formulae presented in section 2.3 can
be use). SRICOS is more complicated in predicting the time-dependent behavior. Briefly,
SRICOS method can be described in several steps:

1. Estimate maximum initial bottom shear stress around the structure (i.e., structure
with an initial flat bed). This can be estimated by measurements, or CFD
simulations.
2. Obtain the initial scour rate. Again, if it were non-cohesive sediment, the initial
scour rate can be estimated with good confidence using the maximum bottom
stress obtained in (1) and a power law (Graf 1998). However, for cohesive clay
material, such a simple relation does not exist. The erodibility of cohesive
sediment is too complicated to allow for developing effective mathematical
formulae to relate the bottom stress and erosion rate. In SRICOS, samples of
cohesive material is taken from the field and tested in a laboratory facility, called
EFA (Erosion Function Apparatus) to estimate the initial scour rate.
3. Estimate maximum equilibrium scour depth using well-developed method for
non-cohesive sediment (e.g., formulae presented in section 2.3).
4. Using the initial scour rate (obtained from step 1 and step 2) and maximum
equilibrium scour depth (obtained in step 3), the time-dependent behavior of scour
can be calculated by a hyperbolic model. It is basically a nonlinear interpolation
scheme to get "scour depth versus time". This method has been validated by
extensive experimental data.

%sAND
40 ......

CLAY

20 5O 100 16o 20

TIE.1 h
Fig 5. Scour development in clay is much slower than that in sand. (adopted from
Brandimarte et al. 2006, see also Briaud et al. 2002)

The basic concept of this method appears to be rather general and hence may be
applied to other type of erosion problems involving cohesive soil. For other type of
erosion problem, different empirical formulae or experimental setups in getting the initial
scour rate, the maximum erosion depth and the hyperbolic interpolation relation are
required.

2.5 Summary
Several important experiences learned from extensive bridge scour studies that may be
useful for other type of erosion problems for the District are summarize here:

(1) An equilibrium state exists for bridge scour and possibly other type of scour
problems. The state of equilibrium provides the most important step toward
simplifying the erosion problem from a engineering point of view because the
maximum equilibrium scour can be estimated as the most conservative design
criterion. Predicting the maximum equilibrium scour is the most fundamental step
to study a scour problem.

(2) The time scale to attain the equilibrium state is another important parameter that
needs to be estimated. The relative magnitudes between the equilibrium time scale
for a specific scour problem and the duration of the episodic forcing (e.g.,
flooding) determine whether the time-dependent behavior of the scour needs to be
further explored; or simply estimating the maximum equilibrium scour is
sufficient. In general, the time scale for non-cohesive sediment (e.g., sand) scour
is much shorter than that of cohesive sediment scour. If the driving force for scour
is short-term stream-flow runoff, then predicting the maximum equilibrium scour
depth is sufficient for non-cohesive sediment. However, for cohesive sediment the
problem is more complex and time-dependent behavior of scour need to be further
estimated or parameterized. The SRICOS method developed by Briaud et al.
(1999, 2004) appears to be effective for predict bridge scour in cohesive soil. The
concept of this method may also be applicable to other type of scour for cohesive
soil.

(3) The general believe based on laboratory-scale experiment that fine sediment has
no effect on scour is disproved by new prototype scale experimental finding
(Sheppard et al. 2004). New finding suggests fine sediment scour is smaller than
previously predicted and old design principle may be too conservative and the
criterion may be too costly. This provides an important lesson for sediment
transport: It is easy to match the similitude principles for pure hydrodynamics
experiments but it is impossible to also match the sediment parameters
concurrently. Hence for sediment transport study, it is extremely important to
consider field or proto-type scale experiments.

(4) From a fluid mechanics point of view, scour formulae developed based on
laboratory experiments are more complex and perhaps more complete. On the
other hand, formulae developed from field studies are usually simpler. This is
partly because in an idealized laboratory environment, some of the parameters are

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