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OCR for page 12
Chapter 2
Review of Modeling Methods and Results
This chapter briefly describes the major components of the hydrologic and hydrodynamic
modeling, including groundwater modeling. Some discussion of improvements that would
strengthen the analysis of the District is included, but the bulk of recommendations in this regard
are found in Chapter 3. In addition, the results of model runs for particular scenarios are
presented.
LAND USE/LAND COVER
1995 land use/land cover data, developed from interpretation of color-infrared aerial
photography, was used as the baseline for the WSIS because it is the basis for current water
supply planning conducted by the District. The 1995 land use data, together with data sets on
precipitation, evaporation, and other drivers of the models from 1995-2005, were used to
calibrate the HSPF models.
The District adopted 15 land use/land cover categories for modeling, as shown in Table
2-1. Runoff and other coefficients used in the hydrologic modeling were selected to reflect the
differing characteristics of these land use/land cover categories. For use in the HSPF modeling,
the District grouped the land-use categories according to similarities in hydrologic response—
usually corresponding to degrees of imperviousness. The model employs a lumped parameter
approach, such that all of the areas for a given land use within a subwatershed are summed and
multiplied by the discharge expected from that type of land use.
Population growth is expected to be the main driver for changes in land use through the
planning period (to the year 2030). Projected land use for the year 2030 (see Table 1-1) was
developed by expanding 1995 conditions in relation to expected population growth. County-
level population forecasts were prepared by the University of Florida Bureau of Economic and
Business Research, and a consulting firm, GIS Associates, Inc., was retained to develop detailed
spatial disaggregations based on the county-level forecasts (SJRWMD, 2009) in order to project
future populations, and thus water demand, for public water supply utility service areas. Special
algorithms were used at the parcel level to reallocate growth from high growth parcels having
spatial constraints to lower growth parcels, while still conforming to the county-level projections.
As a result of the projected population growth, residential and commercial/industrial land
use areas increased over the time period of interest, while open, range, forest, and agricultural
land areas in the drainage basin decreased. Wetlands and water areas were generally kept at
12
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Review of Modeling Methods and Results 13
TABLE 2-1 Hydrologic Model Land Use Categories
Low density residential Open land and barren land Forest
Medium density residential Pasture Water
High density residential Agriculture general Wetlands
Industrial and commercial Agriculture tree crops Forest regeneration
Mining Rangeland Non-riparian wetlands
Note that there are extra land use categories here compared to Table 1-1. The committee found that the
District was not consistent with respect to the number of land use categories presented in various
documents.
1995 values2. As shown in Figure 2-1 for the lower St. Johns River basin, projections were made
in five-year increments from 2005 to 2030.
The District has indicated that because of the recent economic downturn, the 2030
population estimates actually may be more accurate for 2033 or 2035, but this affects only the
timing of growth and not the growth itself. Land use forecasts, however, are a function not only
of population forecasts but also the nature of the population’s impact on the landscape (e.g., the
mix of housing densities and the degree and characteristics of imperviousness in developed
lands). The relationships developed for the current WSIS may not hold in the future, especially
if the actual rate of population increase or its impact on the hydrologic response of the resulting
change in land use is significantly different from the current forecast. Indeed, the land use
shown for 2004 in Figure 2-1 does not seem to reflect projected changes in population growth.
Thus, although the Committee does not have any technical issues concerning the approach used
by the District to estimate 2030 land use/land cover conditions, it recommends that the District
revisit and update the population and resulting land use projections in future periodic
reviews (e.g., the District’s water supply plans and the water supply assessments made by
the District every five years).
2
The committee cautions that this assumption of wetlands acreage remaining the same is uncertain. This is because
the predicted increases in stormwater discharge that will accompany the 2030 land use condition could result in
stream channel deterioration that would reduce the surface area of riparian wetlands (discussed in Chapter 3). These
losses would have to be offset by wetlands mitigation required under the Clean Water Act and/or restoration of the
upper basin for the assumption to hold true.
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14 Review of the St. Johns River Water Supply Impact Study: Report 3
Population/Land Use in LSJRB
FIGURE 2-1 Population and Resulting Land Use Projections in the Lower St Johns River Basin for the
Planning Period 1995–2030.
SOURCE: Cera et al. (2010).
METEOROLOGY
For the District’s analyses, the required meteorological data consist of rainfall and
potential evaporation (for a detailed description, see pp. 12-23 of Cera et al., 2010). Rainfall
distribution across the catchment was modeled for historical data by taking National Weather
Service (NWS) rain gage data and distributing them spatially using a network of Thiessen
polygons. Gage data for rainfall accumulations for time periods larger than the hydrologic
model time step (1 hour) were disaggregated using either nearby hourly data or (where no hourly
data were available) by assuming that rain events of less than a half inch (1.3 cm) occur within
an hour and that larger events were distributed with a triangular weighting over three to five
hours. Missing data were estimated from the nearest available measured data. Where only daily
rainfall records were available, disaggregation from daily to hourly values was accomplished
using the software package WDMUtil, which is part of the EPA BASINS package and can be
considered state-of-the-science. Although more comprehensive Doppler rainfall data are
available, the District chose to use the rain gage data due to their longer period of availability.
Potential evaporation was modeled using the Hargreaves method, which requires only
historical temperature data. Observed temperature data at meteorological stations were
distributed across the basin using Thiessen polygons.
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Review of Modeling Methods and Results 15
Model Calibration and Uncertainty
The use of NWS data for rainfall does not require (or allow) any formal calibration.
Distribution of rainfall across a watershed from point-based rain gage data introduces some
uncertainty into the rainfall model that is difficult to quantify with only the point-based gages,
but this is a problem inherent to hydrologic modeling at the watershed level and not a specific
criticism of the District’s approach.
The Hargreaves estimate of potential evaporation was calibrated based on satellite-based
estimates of potential evaporation computed for three sites using the Penman method.
Model Capabilities and Limitations
The modeling approach is limited in its ability to represent small-scale convective storms
and flashy rain events. This limitation, which is inherent in models that rely on data from point-
based gages with no better than hourly data, may not be important for present conditions because
the St. Johns is a heavily dampened system. It could become more important as the landscape
urbanizes, however, because the model will be limited in its ability to predict runoff from the
catchment during flashy events.
In the committee’s opinion, the assumptions in the models presented are reasonable. We
agree with the District that that Doppler and point rain gage data cannot be intermingled in a
single model data set to drive a watershed hydrologic model. However, the existing Doppler
radar sets that the District did not use in their hydrologic modeling efforts could be used to help
quantify the uncertainty associated with distribution of point-gage rainfall data across the
Thiessen network. A further criticism regarding the use of NWS rain gages is discussed below
relative to the calibration of the watershed hydrology model.
WATERSHED HYDROLOGY
The watershed hydrology for the entire upper basin (including river reaches) and the
middle and lower basins (excluding the main river stem) were modeled with the HSPF
hydrological model. This model is a recognized, EPA-supported “lumped parameter” model that
represents the relationship between infiltration, surface runoff, subsurface flow, and transpiration
based on the fractions of a sub-watershed with pervious and impervious cover. “Lumped
parameter” refers to models that use a set of calibrated coefficients to represent hydrologic
behavior in each subbasin based on land use or land cover. This approach can be contrasted with
other “distributed parameter” models that use small-scale grid cells to directly represent
individual regions of pervious or impervious cover, their mechanical characteristics, and their
spatially distributed connections. The District chose a reasonable approach in using the lumped
parameter model. Although this approach renders the model highly dependent on calibration, a
mechanistic distributed parameter model could not reasonably be supported for a basin the size
of the St. Johns and with the available data.
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16 Review of the St. Johns River Water Supply Impact Study: Report 3
Model Calibration and Uncertainty
Model calibration was extensively presented in Cera et al. (2010). The calibration
approach used historical rainfall and potential evaporation models (described above) for the
period 1995 to 2006 and land-use data based on a 1995 study. The accepted “Parameter
ESTimation” (PEST) software was used for calibration of eight of the 19 parameters used in the
HSPF Common Logic Table (Appendix C, p. 209, Cera et al., 2010) and the “special actions”
associated with a wetland storage-outflow model. Other parameters were fixed. The HSPF
calibration modeling reported to date has been conducted carefully and thoughtfully, and the
District is commended on this effort. The model has been demonstrated to provide reasonable
results during the calibration period.
The District chose to use data from the NWS rainfall gages rather than NEXRAD
Doppler radar data for their calibration study because they are using a 32-year NWS rainfall gage
record for scenario analysis. However, to limit the influence of land use changes, model
calibration was based only on the 1995-2006 period rather than the 32-year period. This
approach is reasonable with some limitations. It should be recognized that the model response to
rainfall outside the calibrated period is uncertain, and so statistics gathered from the model based
on the larger 32-year data set will be reliable only if the weather patterns are similar to those in
the 11-year data set. There is little reason to assume that a lumped parameter model will respond
correctly to rainfall events that are significantly different than those in the calibration data set.
The calibration data set thus should contain a reasonable approximation of all expected weather
over the scenario testing data set. If there are significantly different weather patterns in the 32-
year set than in the 11-year set, then the calibration approach may need to be reconsidered.
The District has not yet presented or proposed any methods to estimate the uncertainty
associated with the HSPF model.
Model Capabilities and Limitations
The hydrologic model appears to provide reasonable approximations of the major fluxes
through the landscape. The runoff values predicted to the river main stem are reasonable
approximations of the observed flows. The principal limitation appears to be whether or not the
approach to modeling wetlands (FTABLES) provides enough accuracy for evaluation of
ecological impacts. Lumped-parameter hydrological models inherently perform better under
high-flow conditions, where the modeled physical processes are strongly affected by the
calibration coefficients. Under low-flow conditions that are often important to ecology, a
lumped-parameter model may have significant hydrological flux uncertainty because of the
difficulty of obtaining data and calibrating for such conditions. Thus, the model’s ability to
represent water retention times for short events (e.g., convective thunderstorms), flux rates
through wetlands, and saturated soil fluxes in areas undergoing periodic immersion/drying cycles
is more problematic.
The District may not be taking advantage of the best available rainfall data by relying on
the NWS gage data rather than using NEXRAD Doppler data for scenario testing. The principal
reason for the modeling work is to predict likely future catchment characteristics. The District
likely will want to use this model for adaptive management and to monitor how well its decisions
hold up under rainfall and land-use conditions that actually occur in the future. Thus, using the
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Review of Modeling Methods and Results 17
best available rainfall data (NEXRAD Doppler) is appropriate. As noted in the outside peer-
review of modeling commissioned by the District (INTERA, 2009, page 16):
The District should consider calibrating the sub-watershed models with the shorter 1995
to present NEXRAD data set using the same approach and objective functions... [This]
would provide the District an indication of how much the model underperformance is due
to issues with the major forcing variable (rainfall) versus uncertainty in the model
parameters such as vegetative cover and evaporation.
The committee agrees with the above comment and recommends that the District consider
conducting a secondary set of calibration runs.
The issue of how accurately wetlands are modeled under drying conditions is a principal
concern and is discussed below in the section “Applicability of HSPF to Wetlands Hydrology.
The report section on “Scenario Simulations for Water Quantity” (Cera et al., 2010) is
difficult to follow and understand. Different naming and numbering conventions were used to
describe the scenarios and to present them in figures and tables. The withdrawal scenario for
Taylor Creek Reservoir (p. 177) is particularly difficult to follow, and is presented without any
reasoning for its development.
RIVER HYDRODYNAMICS
The District provided an extensive PowerPoint presentation on the river hydrodynamic
model development and calibration. It is clear that they are proceeding carefully and analyzing
how model implementation choices affect results. The Committee appreciates the level of detail
provided and the development of graphs that clearly illustrate what the model can and cannot
achieve.
For the lower SJR, where tidal oscillations are a dominant forcing, the District has
conducted grid resolution tests to determine whether their 1X (coarse) model grid is consistent
with results produced by a 4X (medium) and 16X (fine) model grid. This effort is a standard
modeling exercise to establish that the coarser, more practical grid provides results that are
sufficiently similar to the fine grid. The results are reasonable, showing very small percentage
differences in the tidal amplitude in the lower reaches (where the amplitudes are larger). The
only points that seem questionable are those near Lake George, where the absolute difference in
the tidal amplitude between the models is small, but the percentage difference is large because
the Lake George tidal amplitude is quite small. This problem may not be resolvable through
calibration at the 1X grid scale, and so the District should analyze the differences to understand
their effects on model predictions. It is likely that the differences are irrelevant at the upstream
locations. Tidal phase differences were also examined by the District. Phase differences
between results on the different grids are expected for this type of model, and the results shown
appear well within the acceptable bounds. Overall, the grid resolution tests show that the coarse
resolution model is adequate for this stage of the project and the available computer power.
However, as computational power increases over the next decade, continued modeling for
adaptive management should plan on using the 4X or perhaps even the 16X grid. It can be
expected that as more data become available, the ability of the 1X model to distinguish finer
detail will limit the model’s utility.
The District analyzed oscillation frequencies in Crescent Lake using the hydrodynamic
model and a field-deployed drifter, which provided confidence that the large-scale surface
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18 Review of the St. Johns River Water Supply Impact Study: Report 3
seiching of this lake is reasonably modeled, both in velocity magnitude and decay. The District
is congratulated for this effort; numerical error in many models artificially damps the surface
seiche and prevents representation of this behavior.
The District used the PEST software to calibrate the model by adjusting the bottom
roughness height. In the main stem of the tidal river, the calibrated bottom roughness heights are
sub-millimeter, which is an order of magnitude or more smaller than expected physical
roughness heights. This result is not entirely surprising for the model using the 1X grid. It is
well known that coarser model grids induce a damping error – “numerical dissipation” – that has
the same effect on the model as the turbulent dissipation produced by the bottom boundary. The
small bottom roughness height produced by calibration indicates that numerical dissipation is of
the same order of magnitude or larger than physical dissipation produced by boundary-driven
turbulence. From an academic modeling standpoint, this is not an ideal model formulation. It
clearly would be better to have the energy dissipation developed through bottom boundary
dissipation rather than the numerical dissipation. However, engineering efforts must always
balance the ideal against the practical. In this case, the additional computational expense of
moving to a 4X grid simply to decrease the numerical dissipation is not justified. The grid
resolution study shows that the 1X model captures the bulk physical transport fairly well. Thus,
it is unlikely that using a 4X grid to obtain better resolution of bottom turbulence will
significantly alter the overall model results. However, the District should keep in mind that
physical processes associated with the bottom boundary layer are poorly represented; it follows
that any conclusions that depend on the vertical distributions in the water column driven by
turbulent mixing may be suspect. In particular, these effects may limit the ability of the model to
represent the physical/ecological dynamics driven by salinity gradients and resulting
stratification in the Lower St. Johns River. The model predictions of salinity provided to the
committee have reasonable comparison to field data; however, the amount of data available on
vertical stratification is fairly sparse.
The hydrodynamic model results for the middle St. Johns River are quite satisfactory.
The calibrated model captured daily discharge at various stations along the river. The model was
calibrated based on a one-year subset of a 10-year data record, and performed equally well in the
nine years outside of the calibration period. Not only did the model perform well over the longer
time scale, but shorter time-scale (daily and hourly) discharge features were also reasonably
represented. At the hourly scale, the model output has errors in phase and amplitude of smaller-
scale oscillations that are not unexpected, but the overall characteristics are very similar to the
observations. Modeled salinity in the Middle St. Johns River also compared well with both
observed data and model results from HSPF.
The District provided some preliminary PowerPoint slides (Sucsy, 2010) and a draft
report (Sucsy et al., 2010)3 on sensitivity and uncertainty analyses. The combination of First-
Order Error Analysis and Monte Carlo simulations were used to understand the major
contributors to model sensitivity and uncertainty. The approach appears to be reasonable and
well-considered.
3
This document was received too late in the report preparation process to be thoroughly reviewed by the committee,
but it appears to support the conclusions drawn from the District’s PowerPoint presentation in March 2010.
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Review of Modeling Methods and Results 19
GROUNDWATER
Multiple interactions in the hydrologic cycle within the St. Johns basin involve
groundwater. Along the main stem, the St. Johns River collects runoff, and shallow groundwater
contributes to the base flow. At the same time, a multi-layered groundwater flow system has
been grouped into three distinct hydrogeologic systems: the surficial aquifer (SAS), the
Intermediate Confining Unit (ICU), and the Floridan Aquifer System. Recharge to the SAS is
primarily from rainfall, although infiltration due to irrigation, reclaimed water, seepage from
surface waterbodies, and septic tanks also make contributions. Discharge from the SAS occurs
through evapotranspiration, from seepage into surface waterbodies, and from well withdrawals.
In addition, the Floridan Aquifer System recharges to the SAS in areas where the potentiometric
surface of the Upper Floridan Aquifer (UFA) is higher than the water table (otherwise,
downward leakage from the SAS to the underlying UFA occurs). Springs discharging from the
UFA exist throughout the basin and exert a significant influence upon the base flow in the river.
Some of the above interactions are understood well and amenable to quantitative modeling (e.g.,
water movement in the Floridan Aquifer) or measurement (spring discharges), but others are
poorly understood and difficult to model at the current state of understanding (e.g., exchange of
water between the unconfined shallow aquifer and wetlands).
Using a single model to represent these hydrologic interactions would be a complicated
task, to say the least, and consequently the District is using several independent models to
represent the system: a GIS tool to identify wetlands, HSPF for surface water runoff from the
watershed, the mainstem river hydrodynamic model EFDC, and the Eastern Central Florida
(ECF) groundwater model, which is based on MODFLOW, a widely used groundwater flow
model of the U.S. Geological Survey. One of the main criticisms of this approach is the limited
extent to which HSPF can model wetland hydrology, including groundwater–surface water
interactions in the riparian wetlands. This issue is discussed in Chapter 3. This section focuses
on resolving comments made previously by the Committee about the assumption of constant
chloride concentrations in groundwater input to the river channel over time and about the use of
a steady state, non-density-dependent groundwater model.
Groundwater Salinity
An earlier criticism of the District’s analysis of groundwater was their assumption of a
constant average chloride concentration value for a given location in their groundwater
modeling. As discussed in the Phase 1 report (SJRWMD, 2009), the District calculated the
chloride load into the river by obtaining chloride concentration data from monitoring wells and
from a chloride concentration map produced by the District. Then they used a GIS feature to
distribute those concentration values to each ECF model grid. Those values were then multiplied
by the groundwater flux from MODFLOW to get the chloride load contributed by the
groundwater.
Upon being asked to show data that demonstrate the stability of the chloride
concentration at more locations, District scientists presented chloride concentration data from
eight wells from 1990 to 2009. The chloride concentrations across the eight wells ranged from a
mean of about 5,300 mg/L over a 22-year period to a mean of only 9 mg/L. For any individual
well, the highest temporal variability about the mean was 2.2 percent. Wet vs. dry years
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20 Review of the St. Johns River Water Supply Impact Study: Report 3
appeared to make little difference. Based on these findings, the committee no longer is
concerned with the use of a temporally constant chloride concentration.
Density and Temporal Dependence of Models
In NRC (2009a), the District was criticized for employing a steady-state groundwater
model (ECF) that was not density-dependent, which limits its ability to predict changes in
salinity that might result from water withdrawals. District scientists considered both
assumptions (steady state and density independence) to be valid, and they were asked to provide
evidence for their opinions. To prove density independence, the District analyzed salt
concentrations at three wells (S0025, V0083, and BR1526) in the Lake Harney area with high
chloride concentrations and showed that converting real head data from these wells to their
freshwater equivalent did not lead to a significant change in the potentiometric surface. When
asked to address what criteria were used to determine whether changes were “significant,” the
District presented data suggesting that a change in head of 0.3 m or a change in chloride flux of
0.02 m3/s was not significant. In Lake Harney, which has one of the highest chloride
concentrations, the salinity change due to density stratification is only 0.012 practical salinity
units or PSU (or 6.6 mg/L of chloride ion).
To determine that transience in groundwater flux is not important enough to forgo the use
of steady state values in the modeling, the District analyzed how sensitive the river
hydrodynamics model (EFDC) is to using transient vs. steady state groundwater input. Using
well level and river stage data from four locations in the middle basin, the EFDC model was run
with both the steady state and transient groundwater flux (using 10 years of transient
groundwater discharge data). The change in water level or salinity predicted by the model when
using steady state groundwater flux vs. transient flux was small (i.e., 95 percent of the time there
was less than half a centimeter change in water level and less than 0.035 PSU change in salinity).
As a second line of evidence, the District argued that in absolute value terms river water
storage and surface water discharge are much larger than groundwater discharge, such that the
impact of groundwater on overall flow is not large. The committee accepts this statement as
being generally correct, but notes that there are likely to be some occasions when groundwater is
significant because there are times of zero surface water discharge. Of course, if such occasions
(where groundwater is a major contributor to flow) happen once every 10 years, they are less
important than if they occur several times a year.
SIMULATIONS FOR VARIOUS SCENARIOS AND FUTURE CONDITIONS
Results of the hydrologic/hydrodynamic simulations, expressed in terms of changes in
flow and water stage, were generated for the 12 scenarios described earlier for two upper basin
locations—Christmas and Cocoa. As shown in Table 2-2, several major results became evident.
First, substantially more flow (51 MGD) was generated with the 2030 land use baseline scenario
than with the 1995 land use baseline scenario because of the increase in impervious area over
time. Second, the surface water restoration projects in the upper basin also increased the mean
flow (by 11 MGD for case with no withdrawal) and stage over the 1995 baseline condition.
Third, the effects of full and half withdrawal were more pronounced upstream at Cocoa than
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Review of Modeling Methods and Results 21
TABLE 2-2 Stage/Flow Modeling Results at Cocoa and Christmas for 9 Withdrawal Scenarios
Cocoa Cocoa % Change Christmas Christmas %Change in
Scenarios
Mean stage Mean flow in Flow Mean stage Mean flow Flow from
(ft NGVD) (mgd) from 1995 (ft NGVD) (mgd) 1995 Base
Base Case Case
1995 Base 11.88 568 NA 6.73 721 NA
1995 + Half 11.77 545 -4.0% 6.65 693 -3.9%
SJR
Withdrawal
1995 + Full 11.7 524 -7.7% 6.57 669 -7.2%
SJR
Withdrawal
1995 + USJB 12.09 579 2.0% 6.85 732 1.5%
Projects
1995 + USJB 11.97 556 -2.0% 6.76 705 -2.2%
Projects + Half
SJR
Withdrawal
1995 + USJB 11.9 537 -5.5% 6.69 683 -5.4%
Projects + Full
SJR
Withdrawal
2030 Base + 12.22 619 9.1% 7.02 793 9.9%
USJB
2030 + USJB 12.12 596 5.0% 6.93 765 6.1%
+ Half SJR
Withdrawal
2030 + USJB 12.04 576 1.5% 6.87 741 2.8%
+ Full SJR
Withdrawal
downstream at Christmas. Thus, despite the initial (and intuitive) assumption that water
withdrawals would reduce river flows and stage, the modeling revealed that both variables
in fact would increase under the full withdrawal condition, assuming management of the
upper basin to bring water back into the system and the 2030 land use condition.
These results should not be interpreted to mean that stage and flow would never fall
below the 1995 baseline condition, but rather that on average both variables would increase.
Temporal changes in stage and flow were represented as the difference in stage at Cocoa
between the 1995 land use baseline case for zero withdrawal vs. half or full withdrawal (see
Figure 2-2). Each dot in the figure represents a day, such that lines of dots indicate “events.”
Figure 2-2 indicates that a full withdrawal could, at times, lead to as much as 0.7 ft drop in stage
around the 10-11 ft water level. It should be noted that there are smaller stage and flow
differences on either side of the 10-11 ft mark because (1) when there is less water in the river,
the full withdrawal is not permitted, and (2) when there is more water in the river, the effect of a
withdrawal becomes less important on a relative scale.
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22 Review of the St. Johns River Water Supply Impact Study: Report 3
FIGURE 2-2 Changes in Stage Difference at Cocoa Under Various Water Withdrawal Scenarios
assuming 1995 Land Use.
SOURCE: Jobes (2010).
The analysis shown in Figure 2-2 was repeated with the 2030 land use scenario, sea level
rise, and upper basin project management included (Figure 2-3). This scenario brought so much
water into the river that the effects on stage were reversed compared to Figure 2-2. For example,
at Cocoa there was a 1.5-ft rise in stage at the 11-12 ft mark because the upper basin project and
the 2030 land use more than compensated for any loss in stage from water withdrawal. The
modeled results indicate that the effect of the upper basin projects would taper out as stage rises,
because the projects will store water in the upper basin at high flows. As seen in Figure 2-3,
there still would be some events where the water level drops below the baseline condition, but
most of the curves are around zero. The same results were observed at Christmas, although the
graphs are “noisier” at this location.
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Review of Modeling Methods and Results 23
FIGURE 2-3 Changes in Stage Difference at Cocoa Under Various Water Withdrawal Scenarios
assuming 2030 Land Use and Upper Basin projects.
SOURCE: Jobes (2010).
It should be noted that the modeling studies predicted that full withdrawal plus the
Ocklawaha withdrawal would lead to a 9.3 percent decrease in flow in the Ocklawaha River
basin compared to the 1995 baseline condition. However, the impacts of the reduction in flow to
the Ocklawaha River at the Rodman Reservoir are not part of the WSIS and consequently are not
analyzed further in this report.
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