final report - blue spring...

1

Final Report

Integrated Impact Assessment of Climate Variability and Human Activities on the

Discharge of Blue Spring in Volusia County, Florida

Dingbao Wang and Jonathan Griffen

Department of Civil, Environmental, and Construction Engineering, University of Central

Florida

2

1. Introduction

Volusia Blue Spring is located Near Orange City, Volusia County (see

Figure 1). It is a first-magnitude spring with a single 30 foot-deep vent. It discharges

groundwater from the Upper Floridan Aquifer.

This report begins with an introduction to the methodologies used in the report and is

followed by an introduction and analysis of the data collected, modeling of the data to the

Budyko model, and modeling of the spring discharge.

Figure 1. Location of Volusia Blue Spring. USGS discharge measurement location is indicated with a green circle.

Blue Lines represent flowlines from National Hydrography Dataset (NHD).

2. Methodologies

2.1. Mann-Kendall-Sneyers test

In this report, the Mann-Kendall-Sneyers test is used to analyze trends in precipitation,

temperature, groundwater levels, and discharge and to identify possible change-points in these

trends. A detailed discussion of the Mann-Kendall-Sneyers test is available in Węglarczyk

(2009).

3

2.2. Budyko Model

In this report, a two-parameter Budyko-type equation is used to model the evaporation

ratio for the springshed (Wang, 2013):

Where P is rainfall; is water storage change; E is actual evaporation; Ep is potential

evaporation at annual or monthly scales. To model the evaporation ratio

to the observed

aridity index

, two parameters, ε and ϕ, need to be calibrated. Calibration is performed by

selecting values for both parameters such that the root mean square error (RMSE) is minimized

between the estimated (modeled) evaporation ratio and the observed evaporation ratio:

Where:

To check the goodness of fit of the model, the Nash–Sutcliffe model efficiency

coefficient (NSC) is calculated:

Where:

4

2.3. Discharge Modeling

In the two-stage discharge model (Chen and Wang, 2013), baseflow can be modeled as

follows:

And

Where:

P: Precipitation (mm)

E: Evaporation (mm)

Ep: Potential Evaporation (mm)

λb: Runoff constant (a fraction of potential evaporation that contributes to initial evaporation)

W: Soil wetting (mm)

: Change in groundwater storage (mm)

: Direct runoff (surface runoff) (mm)

Q: Total runoff (the combination of baseflow and direct runoff) (mm)

However, the model was developed for simulating stream discharge. For spring

discharge, the direct runoff component is negligible; in the case of Blue Spring, all direct runoff

generated from precipitation is expected to flow to the St. John’s River before it reaches the

spring. Thus, discharge is assumed to be composed of only baseflow. Consequently, the two-

stage discharge model becomes:

5

In this equation, the definition of W has been changed from to

(since Qd is assumed to be negligible in the spring discharge).

Similar to the Budyko model, the simulated discharge is modeled to the observed

discharge by calibrating the parameter using the RMSE:

Where:

To check the goodness of fit of the model, the Nash–Sutcliffe model efficiency

coefficient (NSC) is calculated:

Where:

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3. Data Collection

3.1. Springshed

Three regional groundwater flow models were previously used to delineate the

springshed for Blue Spring (Shoemaker et al., 2004). The three models include: the Lake

County/ Ocala National Forest (LCONF) model, the Peninsular Florida (PF) model, and the

Volusia County (VC) model.

Figure 2(a) through (c) illustrate each of these models.

Figure 2(d) shows the composite area of these three models.

Groundwater flow west of the St. Johns River, as simulated by the VC model, is more

likely to discharge into the river, as opposed to the LCONF and PF models, where a fraction of

groundwater flow will pass under the river and discharge at Blue Spring. Consequently, leakage

rates to the Upper Floridan Aquifer west of the river are less in the VC model than in either the

PF or LCONF models.

In this report, the composite area resulting from these three models (PF, LCONF, and

VC) is used for selecting precipitation and groundwater level monitoring stations, calculating the

area for the conversion of discharge from cubic feet per second to millimeter depth, selecting the

boundaries for land use, and converting county water use and population data to the springshed

level. This area covers 330.66 km2 (127.67 mi

2).

7

Lake County/ Ocala Peninsular Florida Volusia County Composite Area

National Forest

(a) (b) (c) (d)

Figure 2. Three MODFLOW Simulated Spring Capture Zone Boundaries (Shoemaker et al., 2004).

Figure 2(d) represents the composite area of the three models. Gray represents the area delineated by one model.

Light blue represents the area delineated by any two models. Dark blue represents the area delineated by all three

models.

3.1.1. Surface runoff

As discussed previously, the contribution of the springshed’s surface runoff to the spring

discharge is negligible. Consequently, all of the surface runoff generated within the springshed

flows outside of the boundaries. The eastern watershed, which makes up about 75% of the

springshed area, primarily drains its surface runoff into Lake Monroe.

The watershed area contributing to the spring run at USGS station 02235500 is 0.35 km2

(Figure 3). Only 0.1 km2 of this area is outside of the springshed area (330.67 km

2); that is,

surface runoff contributing to the station measurements from outside of the springshed only has a

0.03% impact on the measurements and, consequently, is considered negligible.

8

Figure 3. Watershed Contributing to Spring Run. On the left, the streamlines generated from a National Elevation

Dataset (NED) Digital Elevation Model (DEM) (Gesch, 2007; Gesch et al., 2002). On the right, the watershed

contributing to the spring run at USGS station 02235500.

3.2. Discharge

3.2.1. Description

Discharge was measured from the spring run at USGS station 02235500 (Survey, 2012b).

The station is 850 ft upstream from the St. Johns River and 1300 ft downstream from the spring

head. Daily records collected begin 12/8/2001 and end 12/31/2012. Field measurement records

collected begin 3/7/1932 and end 12/18/2012.

Both field measurement data and daily data have missing records (Figure 4). Records for

field data are very sparse; on average, there is one record every 41.3 days, with a standard

deviation of 23.5 days. For daily records, there are four gaps in the dataset; the following is a list

of those missing records:

5/19/2003 to 6/13/2003 (26 days)

9/8/2004 to 3/20/2005 (194 days)

3/17/2006 to 3/23/2006 (7 days)

8/27/2008 to 11/5/2008 (71 days)

9

Figure 4. Daily Data and Field Measurement Data for Blue Spring at USGS 02235500. Daily records span from

12/8/2001 to 12/31/2012. Field measurements span from 3/7/1932 to 12/18/2012.

3.2.2. Conversion of discharge from volume to depth

For the discharge simulation and the Budyko model that follows, discharge will need to

be converted from cubic feet to millimeters. This is done by dividing the discharge (cfs) by the

springshed area (and other conversion factors).

3.2.3. Discharge Trend Analysis and MKS Analysis

Based on the annual average, the discharge decreased from 152 cfs in 1976 to 127 cfs in

2012 (1.09 mm/day to 0.93 mm/day, based on the springshed area). Based on the 10-year

moving average, the discharge declined from 158 cfs in 1976 to 146 cfs in 2012 (1.16 mm/day to

1.08 mm/day). Please see Figure 5.

0.44

0.64

0.84

1.04

1.24

1.44

1.64

60

80

100

120

140

160

180

200

220

240

Dai

ly D

isch

arge

(Q

) (m

m/d

ay)

Dai

ly D

isch

arge

(Q

) (c

fs)

Blue Spring Near Orange City, FL (USGS 02235500)

Daily Discharge (cfs) Field Measurement Discharge (cfs)

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Figure 5. Annual Average and 10-Year Moving Average Trend Analysis for Discharge.

The Mann-Kendall-Sneyers Test was performed on the discharge dataset. In Figure 6, it

can be seen that the spring discharge had been increasing since at least 1932 until 1955. During

late 1976, however, the discharge began to decline.

120

130

140

150

160

170

180

Mean Annual Discharge 10-year Moving Average

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Figure 6. Mann-Kendall-Sneyers Test for Discharge.

3.3. Precipitation and Temperature

Precipitation data was collected from 6 NOAA NCDC gages (Center, 2013), as illustrated

in Figure 7 (please see the appendix for station names). Temperature data was also collected

from these stations, except for station 6, which did not have any records. Available precipitation

and temperature records spanned 1892 to 2013. However, some records were highly

discontinuous (e.g. see Figure 42 in the appendix to see individual station temperature records).

These data (excluding 1892 to 1899) were aggregated to the annual scale and are illustrated in

Figure 8 and Figure 9. Figure 10 shows a comparison between precipitation and temperature

records.

-6

-5

-4

-3

-2

-1

0

1

2

3 N

orm

aliz

ed

MK

S te

st s

tati

stic

Forward Backward significance level (5% or ±1.96)

10/26/1976

12

Figure 7. Locations of Precipitation Stations Used In This Report.

Records for temperature provided the maximum and minimum temperatures for each

day; the average temperature for each day was calculated based on the average of these two

values. Overlapping records of temperature and precipitation between stations were averaged at

the daily time scale before being aggregated to longer time scales.

13

Figure 8. Annual Precipitation Records from 1900 to 2012. Records from 1892 through 1899 were not analyzed.

Figure 9. Annual Temperature Records from 1900 to 2012. Records from 1892 through 1899 were not analyzed.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Pre

cip

itat

ion

(m

m)

20

20.5

21

21.5

22

22.5

23

Tem

per

atu

re (

°C)

14

Figure 10. Comparison between Precipitation and Temperature Records.

3.3.1. Precipitation MKS Analysis

From the Mann-Kendall-Sneyers Test (1900-2012), the only observed change-point

detected occurs in 1909. This change-point marks the beginning of an increasing trend which

appears to end about 1962. After this, there is no apparent trend in precipitation (Figure 11).

20

21

22

23

24

25

26

27

28

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Tem

pe

ratu

re (°

C)

Pre

cip

itat

ion

(m

m)

Blue Spring Precipitation (mm) Blue Spring Air Temperature (°C)

15

Figure 11. Mann-Kendall-Sneyers Test for Precipitation.

3.3.2. Temperature Trend Analysis and MKS Analysis

In Figure 12, a linear trend line shows that the temperature from 1976 to 2012 is

increasing at a rate of about 0.34°C every ten years.

However, analyzing a larger time period (1900 to 2012) using the MKS test (Figure 13)

indicates that there are multiple change-points in the temperature record. The most significant

one occurs in 1952, where the temperature is observed to decline until 1989. In 2002, an

increasing trend begins until the end of the data record.

-3

-2

-1

0

1

2

3 N

orm

aliz

ed M

KS

test

sta

tist

ic


16

Figure 12. Linear Trend Line of Annual Temperature Data from 1976 to 2012.

Figure 13. Mann-Kendall-Sneyers Test for Temperature.

Trendline: T(t) = 0.0338t - 45.694

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18

19

20

21

22

23

24

Tem

per

atu

re, T

(°C

)

Blue Spring Air Temperature (°C)

-3

-2

-1

0

1

2

3

4

No

rmal

ize

d M

KS

test

sta

tist

ic


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3.4. Land Use and Land Cover

Land use/land cover maps from 1973, 1995, 2000, 2004, and 2009 were provided by the

St. Johns River Water Management District (SJRWMD) (District, 1995). Please see Figure 15

through Figure 19. Unlike land use data, land cover data is not available before 2000. Thus, only

land use data was used to compare the land use/land cover maps for all years.

To simplify the analysis, land use categories were aggregated into 9 categories, based on

the SJRWMD’s PhotoInterpretation Key for 1973, 1995, and 2000-2009. These include the

following: Urban, Agricultural, Upland Forests, Upland Non-Forested, Water, Wetlands, and

Barren Lands. The category for “Transportation, Communication and Utilities” was included in

the definition of urban area.

An additional land use map was generated from 45 aerial photographs taken between

1941 and 1943 (Figure 14). Please see the appendix for an explanation of how the land use map

was delineated.

The percentage of land cover area for each category was calculated (Table 1). The

change in land cover for the six different time periods is displayed in Figure 20 and Figure 21.

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Figure 14. 1941-1943 Land Use Map for Blue Spring. The western half of the springshed is based on aerial

photography from 1941 while the eastern half is based on aerial photography from 1943.

Figure 15. 1973 Land Use Map for Blue Spring.

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20



21

Figure 20. Time Series Graph of Blue Spring Land Use Area by Percentage and Square Kilometer (1940s - 2009).

Figure 21. Bar Graph of Blue Spring Land Use Area by Percentage (1940s - 2009).

0

20

40

60

80

100

120

140

160

180

200

0%

10%

20%

30%

40%

50%

60%

1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Area (km

2) %

Are

a

% Upland Forests % Upland Non-Forested % Agriculture % Wetlands

% Water % Urban % Barren

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1940 1973 1995 2000 2004 2009

% Barren

% Urban

% Agriculture

% Upland Non-Forested

% Upland Forests

% Wetlands

% Water

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From the 1940’s to 1973, the water table declined, causing a decrease in the number of

wetlands (which have been reclassified as “Upland Forests” and “Upland Non-Forested”). This

was also accompanied by a 10.5% increase in urban area.

In the next 22 years (1973-1995), urban development accelerated from 0.35% per year

between 1943 and 1973 to 1.41% per year between 1973 and 1995, reducing forest land by

12.5% and increasing urban area by 31.1%. Upland Non-Forested area also decreased

significantly during this time period (13%).

From 1995 to 2009, the increase in urban area was followed by a similar decrease in

forest area. The change in the other five land use categories has been relatively insignificant

during this time period.

Year

Percentage Area

Water Wetlands Upland Forests

Agriculture Upland Non-Forested Urban Barren

1941-1943

5.64% 32.81% 32.07% 9.50% 16.59% 3.40% 0.00%

1973 5.01% 11.19% 42.18% 8.78% 18.90% 13.92% 0.01%

1995 4.80% 9.24% 29.69% 5.21% 5.90% 44.97% 0.19%

2000 4.94% 9.72% 27.85% 5.36% 6.34% 45.49% 0.31%

2004 4.95% 9.85% 25.93% 5.68% 3.78% 49.51% 0.31%

2009 5.62% 9.82% 22.63% 5.14% 3.66% 52.77% 0.36%

Table 1. Table of Blue Spring Land Use Area by Percentage (1940s - 2009).

3.5. Population

Population by county was acquired from the USGS (Survey, 2012a) and the US Census

Bureau (Bureau, 2013a). 2010 Population data by block group was obtained from the U.S.

Census TIGER database (Bureau, 2013b).

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Census block group population data is more detailed than county data, providing a much

better approximation of the population distribution around the springshed (Figure 22). However,

this data is only available for several years. If it can be assumed that the ratio of the population

living in the springshed to the population living in each county remains the same throughout all

years, then block group data for 2010, used in conjunction with county data, can be used to

approximate the population distribution in the springshed from 1900 to 2012. This was done

using the following procedure:

1. Block groups in Lake County (within the springshed) were aggregated into one

group while block groups in Volusia County were aggregated into another.

2. The aggregated populations for the two groups were then divided by their

respective county populations for 2010.

This resulted in a fraction of 0.010 for Lake County and 0.273 for Volusia County (a

larger fraction of Volusia County’s population resides in the springshed compared with Lake

County). Multiplying the county data records (for all years) by these fractions and adding both

components (Lake County component and Volusia County component) together gives an

approximation of the population trend for the springshed.

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Figure 22. U.S. Census Population Block Groups. On the left, dark gray lines represent block group boundaries. On

the right, these individual block groups were aggregated for the springshed to more accurately capture the

population distribution within the springshed.

From Figure 23, it can be seen that the population seems to show an increasing trend

beginning in 1950, then especially after 1970.

Figure 23. Projected Population of Blue Spring Springshed.

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

Po

pu

lati

on

Projected Population of Blue Spring Springshed

Total West Springshed East Springshed

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3.6. Water Use

Water use data for Volusia and Lake Counties (Figure 24 and Figure 25) were collected

from the Florida USGS (Survey, 2012a). Power Generation was excluded for Volusia County

because the only power plant found within the vicinity of the springshed was for a gas turbine

power plant (powerplantjobs.com, 2013), which uses little, if any, water. As discussed earlier,

about 1% of the population of Lake County and 27.3% of the population of Volusia County

resides in the springshed. Multiplying the water use data by these percentages, the water use, in

units of MGD and mm (based on the springshed area), can be calculated for the springshed

(Figure 26). This calculation assumes that the distribution of the water use is the same as the

population.

Figure 24. Water Use for Volusia County.

0

20

40

60

80

100

120

140

160

180

200

1965 1970 1975 1980 1985 1990 1995 2000

Wat

er U

se (

MG

D)

Groundwater Surface Water Combined

26

Figure 25. Water Use for Lake County.

Figure 26. Water Use for Blue Spring Springshed.

0

20

40

60

80

100

120

140

160

180

200

1965 1970 1975 1980 1985 1990 1995 2000

Wat

er U

se (

MG

D)


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0

5

10

15

20

25

30

35

1965 1970 1975 1980 1985 1990 1995 2000

Wat

er

Use

(m

m/d

ay)

Wat

er

Use

(M

GD

)


27

3.7. Groundwater Levels

As mentioned earlier, the observed change-point for the discharge data occurred in late

1976. To measure the relationship that this declining trend may have with groundwater levels,

groundwater level records prior to 1976 were needed. Using groundwater level records from the

USGS and SJRWMD (District, 2013; Survey, 2013), six stations were found near the springshed

that have records before 1976 (Figure 27); no stations within the springshed boundaries met this

criteria. Please see the appendix for station names.

Figure 27. Location of USGS and SJRWMD Groundwater Level Monitoring Stations.

28

For all stations, observation data was aggregated from the daily time scale to the annual

time scale and analyzed using the Mann-Kendall-Sneyers Test. In addition, Data Series 584

(Bellino, 2011) was used in conjunction with ArcGIS’s Topo to Raster tool to help determine the

position of each well relative to the Floridan Aquifer System; Table 2 summarizes the results of

this analysis.

For the remainder of this section, each groundwater well is discussed, followed by a

conclusion regarding groundwater levels for Blue Spring.

29

Figure 28. Station 1 Water Levels and MKS Test. Figure 28a shows water levels for Station 1, Figure 28b shows the corresponding Mann-Kendall-Sneyers Test.

0

2

4

6

8

10

12

14

16

18

1965 1975 1985 1995 2005

Wat

er

Leve

l, ft

-4

-3

-2

-1

0

1

2

3

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

No

rmal

ize

d M

KS

test

sta

tist

ic


0

5

10

15

20

25

30

1975 1980 1985 1990 1995 2000 2005 2010

Wat

er

Leve

l, ft

-2

-1

0

1

2

3

4

1975 1980 1985 1990 1995 2000 2005 2010

No

rmal

ize

d M

KS

test

sta

tist

ic


30



0

5

10

15

20

25

30

35

40

1965 1975 1985 1995 2005

Wat

er

Leve

l, ft

-3

-2

-1

0

1

2

3

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

No

rmal

ize

d M

KS

test

sta

tist

ic


33

34

35

36

37

38

39

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Wat

er

Leve

l, ft

-5

-4

-3

-2

-1

0

1

2

3

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

No

rmal

ize

d M

KS

test

sta

tist

ic


31



0

5

10

15

20

25

30

35

1935 1945 1955 1965 1975 1985 1995 2005

Wat

er

Leve

l, ft

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

1935 1945 1955 1965 1975 1985 1995 2005

No

rmal

ize

d M

KS

test

sta

tist

ic


32


0

5

10

15

20

25

30

1970 1975 1980 1985

Wat

er

Leve

l, ft

-4

-3

-2

-1

0

1

2

3

1970 1975 1980 1985

No

rmal

ize

d M

KS

test

sta

tist

ic


33

Station Number

Distance from

Springshed (miles)

Elevation of the top of the Upper-

Floridan Aquifer (feet)

Elevation of the bottom of the

Upper-Floridan Aquifer (feet)

Elevation of the top of the

Lower-Floridan Aquifer (feet)

Elevation of the bottom of the

Lower-Floridan Aquifer (feet)

Land Surface

Elevation (feet)

Well Depth (feet)

Elevation at bottom

of well (feet)

Location of the

bottom of the well

1 0.18 0.45 -355.47 -618.95 -2060.65 41.65 500 -458.35 Middle

Confining Unit

2 1.46 -26.85 -363.34 -748.86 -2202.59 42.44 92 -49.56 Upper

Floridan Aquifer

3 2.25 -7.27 -376.25 -678.22 -2129.52 37.03 241 -203.97 Upper

Floridan Aquifer

4 3.65 -4.61 -380.10 -699.97 -2117.17 38.00 575 -537.00 Middle

Confining Unit

5 5.11 -49.17 -384.78 -699.95 -2215.85 35.90 121 -85.10 Upper

Floridan Aquifer

6 3.46 0.00 -317.67 -720.29 -2103.16 38.99 364 -325.01 Middle

Confining Unit

Table 2. Aquifer Information for Six Groundwater Level Stations.

34

3.7.1. Station 1

Station 1 is about 947.8 feet from the springshed boundary. Data records span from 1967

to 2010. From Table 2, Station 1 penetrates about 39% of the Middle Confining Unit (between

the Upper and Lower Floridan Aquifers). In Figure 28a, a slight downward trend in the

groundwater levels can be observed. To quantify the change-point, a Mann-Kendall-Sneyers test

was performed. Figure 28b shows a decreasing trend, which begins in 1973.

3.7.2. Station 2

Station 2 is about 1.46 miles (7686 ft) from the springshed boundary. Data records span

from 1965 to 2010. However, continuous records span from 1976 to 2010. From Table 2,

Station 2 is a shallow well, penetrating about 6.7% of the Upper Floridan Aquifer. This leaves it

fairly influenced by the unconfined Surficial Aquifer above. Additionally, it is in close

proximity with a group of lakes (specifically the Lake Butler Chain).

From Figure 29b, a change-point is observed for late 1977. However, discontinuous data

near the beginning of the time series prevents the record from preceding 1976. Thus, though the

change-point is approximated at 1977, it may have occurred earlier and, thus, an accurate

change-point cannot be determined.

3.7.3. Station 3

Station 3 is about 2.25 miles from the springshed boundary. Data records span from

1969 to 2010. From Table 2, Station 3 penetrates about halfway (53.3%) through the Upper

Floridan Aquifer. From Figure 30b, a large drop in the water levels occurred in 1977. However,

this decline lasted for only one year since water levels in the following years were about the

same as the years preceding 1977.

35

3.7.4. Station 4


1969 to 2010. From Table 2, Station 4 penetrates about halfway (49.1%) through the Middle

Confining Unit. From Figure 31b, a decreasing trend is observed beginning in 1986. The results

are similar to Station 1, where the change-point occurred in 1973. This result suggests that the

factor(s) causing the decline in groundwater levels may be closer to the spring (or the St. John’s

River).

3.7.5. Station 5


1936 to 2010. From Table 2, Station 5 penetrates 10.7% through the Upper Floridan Aquifer.

From Figure 32b, a decreasing trend is observed starting in 1978. Of the six stations, Station 5 is

the furthest from the springshed. However, it also has the longest records.

3.7.6. Station 6


1974 to 2001. However, continuous records span from 1974 to 1986 and 1996 to 1999.

Consequently, only records from 1974 to 1986 were analyzed.

From Table 2, Station 6 penetrates 1.8% through the Middle Confining Unit. From

Figure 33b, a decreasing trend is observed in 1975.

This station is about as far from the springshed as Station 4. Station 4, however,

penetrates half-way through the Middle Confining Unit while Station 6 measures water levels

just below the Upper Floridan Aquifer. The difference between their change-points is

approximately ten years, highlighting the role that aquifer depth has on increasing response time

in groundwater levels.

36

3.7.7. Summary of results

Blue Spring, which receives its water from the Upper Floridan Aquifer, would be more

accurately evaluated if groundwater levels within the Upper Floridan Aquifer were used to

evaluate the state of its groundwater levels. Additionally, comparing results from the Middle

Confining Unit may be misleading due to the lower hydraulic conductivity in the region.

Though Stations 2 and 3 did not show a significant change-point in groundwater levels,

the longer record of Station 5 did. Consequently, the change-point for groundwater levels within

Blue Spring can be approximated to have occurred in 1978 and even possibly a year or two

earlier than this.

The results from the six stations, excluding Station 4, suggest a strong correlation

between the declining discharge from Blue Spring and the decline in groundwater levels around

the spring.

3.8. Evapotranspiration and Potential Evapotranspiration

Daily evapotranspiration (ET) and monthly potential evapotranspiration (PET) data for

the Continental United States were collected from the University of Montana (Zhang, 2010).

The data is provided at the 8-km pixel level (stored in binary data files) and ranges from January

1, 1983 to December 31, 2006.

However, there is one pixel with missing data that covers the corresponding area over the

eastern half of the springshed (see Figure 34). The records for the selected pixels were averaged

together, with each pixel being given equal weight.

37

Figure 34. The yellow outline represents the springshed boundary, the red square represents a missing pixel in the

dataset, and the green squares represent the pixels that were used to extract ET/PET data from.

4. Budyko Model and Discharge Modeling

In the following section, discharge modeling and the Budyko model will be applied using

discharge data at the monthly and annual time scales. Daily Discharge in this report is based on

the combined records of daily data and field observational data from USGS station 02235500

(Survey, 2012b).

While continuous data records are required for discharge modeling, the Budyko model

does not share this requirement. Furthermore, evaporation data is critical to this analysis and,

consequently, the Montana ET/PET dataset restricts the analysis from January 1, 1983 to

December 31, 2006.

The longest period of continuous discharge data in either dataset (daily data or field data)

between January 1, 1983 and December 31, 2006 is December 8, 2001 to May 18, 2003 (527

days, or less than 1.5 years of continuous discharge data). This does not change even after both

datasets are combined.

38

After combining both datasets, the number of missing dates between 11/27/2001 and

12/31/2012 is decreased by 13 days and the maximum gap between records is reduced from 194

days to 33 days. Furthermore, all months from November 2001 to December 2012 now have at

least one record. Please see Table 4 in the appendix for a list of missing days in each month

from January 1983 to December 2006.

Since a continuous monthly record will be needed for modeling discharge at the monthly

scale, daily records will be averaged for each month, then multiplied by the number of days in

each month:

Where:

As discussed previously, discharge modeling requires a continuous record and there is at

least one record per month between November 2001 and December 2012. Using the equation

above, a continuous monthly record is obtained between these months.

To apply the Budyko model at the annual scale, monthly records for each year are

averaged for each year and then multiplied by 12:

39

Where:

For years between 1983 to 2001, only 5 to 7 months are available (see Table 4 in the appendix).

Thus, while using discharge data aggregated to the annual scale using this method is sufficient

for the annual Budyko model, it would not be for discharge modeling, especially when

considering the effects of the seasonal variance of streamflow.

4.1. Budyko Model

4.1.1. Inter-Annual Scale, from 1983 to 2006

Using the methodologies discussed earlier and in the previous section, the Budyko model

is calibrated for the annual scale, from 1983 to 2006. In this analysis, all available records

between 1983 and 2006 are used for calibration (no validation is performed). From Figure 35, it

can be seen that the curve fits the data fairly well, with a Nash-Sutcliffe coefficient of 0.75.

However, the change-point, as indicated by the discharge records and groundwater level records,

occurs in the 1970s. Consequently, the Budyko model cannot be used to identify the impact of

human-induced and climate-induced change on the springshed. Please see the appendix for a

more detailed version of the graph, showing years and precipitation values for each point.

40

Figure 35. Graph of the Equity Equation at the Inter-Annual Time Scale.

Years where the aridity index exceeds 1 indicate dry years. Conversely, years where the

aridity index is less than 1 indicate wet years. Areas under the diagonal 1:1 line are part of the

energy-limited region because at this point the available energy (evaporation) is limited by the

potential energy (potential evaporation). Areas under the E/(P-ΔS)=1 line are part of the water-

limited region because the evaporation is limited by the available water supply (precipitation).

From the graph of the inter-annual data, data pairs of aridity index and evaporation ratio

are concentrated closely together, with evaporation ratios ranging from 0.67 to 0.76 and aridity

indices ranging from 1.06 to 1.23. That is, the springshed for Blue Spring has consistently

experienced slightly dry years from 1983 to 2006, indicating that the springshed has a slightly

water-limited climate, at least during this time period.

41

4.1.2. Inter-Monthly scale, from 1983 to 2006

The analysis in the previous section is repeated again, this time on the monthly dataset.

As before, all records between 1983 and 2006 were used for calibration. Compared with the

annual scale, the monthly observation data follows the Budyko curve much better, with a Nash-

Sutcliffe coefficient of 0.92 (see Figure 36). Excluding the record for December 1994 (bottom-

left-most point), observation data is between the ranges of 0.67 and 1.46 for the aridity index and

0.53 and 0.84 for the evaporation ratio. From Figure 36, the monthly climate varies between

water-limited and energy-limited conditions. This is expected, due to the effect of seasonality at

the monthly scale. In

Figure 37 and

Table 3, all months, excluding March and October, have NSC values between 0.81 and

0.98. March, in particular, has a very low NSC value (0.23); the cause of this deviation,

however, is not clear, but may be the result of three years of missing records between 1984 and

1988.

42

Figure 36. Graph of the Equity Equation at the Inter-Monthly Time Scale, from 1983 to 2006.

43

Figure 37. Graph of the Equity Equation at the Inter-Monthly Time Scale, from 1983 to 2006, Separated by Month.

The same line based on the monthly model in Figure 36 is used (ε=0.58 and ϕ=0.08).

44

Month NSC No. of

Records

January 0.98 15

February 0.81 12

March 0.23 13

April 0.93 16

May 0.96 16

June 0.87 16

July 0.91 13

August 0.89 18

September 0.84 16

October 0.61 16

November 0.91 12

December 0.97 16

Table 3. Individual NSC Values and Number of Records from 1983 to 2006.

4.1.3. Inter-Monthly scale, from 2002 to 2006

The analysis from the previous section is repeated, except the months analyzed are

confined to continuous monthly data (excluding November and December 2001).

As expected, the calibrated parameters did not change significantly; ε decreased by 0.01

and ϕ increased by 0.01 (Figure 38). Additionally, the NSC improved slightly (0.93).

45

Figure 38. Graph of the Equity Equation at the Inter-Monthly Time Scale, from 2002 to 2006.

4.2. Discharge Simulation

As discussed in the beginning of Section 4, November 2001 to December 2006 is the

longest continuous monthly record for discharge. Furthermore, discharge simulation can only be

performed from 2002 to 2006 (November and December 2001 data are not included since they

represent a minor fraction of 2001). As was done with the Budyko model, all available records

between 2002 and 2006 were used for calibration (no validation was performed).

Figure 39 shows the simulated discharge from 2002 to 2006. The resulting discharge

model fits the data moderately well (Figure 40). The NSC for the simulated discharge is 0.69. It

should be noted that the time period of September 2004 to March 2005 had a large number of

missing daily data —a total of 184 missing days (Table 4).

46

Figure 39. Monthly Discharge Simulation from 2002 to 2006.

Figure 40. 1:1 line showing the goodness of fit of the simulated (estimated) and observed discharge values.

47

5. Summary of Results and Conclusion

Precipitation trends were analyzed for a possible explanation of the declining discharge at

Blue Spring, but no significant decreasing trend was observed.

From the evaporation and potential evaporation data from 1983 to 2006, it could be

assumed that the increasing evaporation and potential evaporation were primary drivers in Blue

Spring's declining discharge. These records do not extend far back enough to make solid

conclusions. Thus, temperature, which correlates closely with potential evaporation and actual

evaporation (see Figure 41), was used to extend the analysis before 1983. From this record, it

was observed that the temperature trend did not correlate closely with the discharge trend.

Starting about 1952, the temperature had been on a declining trend, which ended around 1989

and began to increase in 2002. Discharge, on the other hand, had been on a declining trend since

1976. That is, though increasing temperatures may have had some impact on the declining

discharge trend, it is likely not the primary cause of the trend. Therefore, land use change and

water uses significantly contribute to the declining trend of spring discharge.

48

Figure 41. The Relationship between Temperature, Evaporation, and Potential Evaporation from 1983 to 2006.

From the population and water use data it is evident that human activities have had a

significant impact on the springshed, especially around the 1970's, as evidenced by the change in

land use which resulted in a loss of wetlands and an increase in urban area. This is further

expressed by the decreasing groundwater levels and spring discharge around this same time.

The Budyko analysis and discharge modeling could not be performed for this time

period, but as indicated in the land use data, water use data, and population data, a significant

increase in the impact of human development on the springshed was observed. First, there was a

61% increase in total water use for the springshed from 1965 to 1975, which accelerated to a

58% increase from 1975 to 1980 (which is nearly the same rate in half the time). Also, there was

a significant increase in the population growth rate beginning in 1950 and then especially after

1970. Finally, there was a significant increase in the urban area, particularly from 1973 to 1995

(31.1%).

19

19.5

20

20.5

21

21.5

22

22.5

23

23.5

24

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1983 1988 1993 1998 2003

Tem

pe

ratu

re (

°C)

Evap

ora

tio

n a

nd

Po

ten

tial

Eva

po

rati

on

(m

m)

E (mm) Ep (mm) Temperature (°C)

49

The Budyko models, especially the monthly model, fit the data very well; the annual

model has a Nash-Sutcliffe coefficient of 0.75, while the corresponding value for the monthly

model is 0.92.

The discharge simulation models performed fairly well, with a Nash-Sutcliffe coefficient

of 0.69. However, it is believed that the large number of missing daily data between September

2004 and March 2005 diminishes the performance of the model.

50

6. Appendix

6.1. Precipitation and Temperature Data

Precipitation data were collected from the following stations:

Station 1 (GHCND:USC00086584)




Station 5 (GHCND:US1FLVL0014)


Temperature data were collected from these stations as well, excluding Station 6

(GHCND:US1FLVL0014), which did not have temperature data. Please see Figure 42.

Figure 42. Temperature data for individual NOAA NCDC stations. Station 6 (GHCND:USC00087982) was

excluded because no temperature data was available.

15

16

17

18

19

20

21

22

23

24

25

Tem

pe

ratu

re (

°C)

Blue Spring

USC00082229 USC00087977 USC00086584

USC00080070 USC00087982 Average Blue Spring

51

6.2. Land Use for the 1940’s

In this report, the land use map for the 1940’s is based on 45 aerial photographs taken

between 1941 and 1943, as illustrated in Figure 43. The images were obtained from the

University of Florida Map & Digital Imagery Library (Collections, 2013) and were

georeferenced using ArcGIS 10.1. Three methods were used to delineate the same land use

categories used for the 1973-2009 land use/land cover maps (excluding the “Barren” category):

(1) the SJRWMD’s 2009 Land Cover and Land Use Classification System

PhotoInterpretation Key, (2) the 1973 land use shapefile provided by the SJRWMD, and (3) the

DEM provided by the National Elevation Dataset (Gesch, 2007; Gesch et al., 2002). Land cover

was primarily determined based on the photointerpretation key while the DEM and the 1973 land

use map were used to check for consistency.

52

Figure 43. Aerial Photography of Blue Spring. The western half is based on aerial photography from 1941 while the

eastern half is based on aerial photography from 1943.

6.3. Groundwater Level Monitoring Stations

Station 1 represents both USGS Station 290138081203202 and SJRWMD Station

05831097. Stations 2, 3, 4, 5, and 6 represent USGS stations 285221081095002,

290230081123401, 290541081132902, 285745081054001, and 285028081253301, respectively.

53

6.4. Missing daily records

1 2 3 4 5 6 7 8 9 10 11 12

1983 30 All All All 30 29 All 30 All 30 All All

1984 All All 30 29 All 29 All 30 All 30 All 30

1985 All 27 All 29 All 29 All 30 29 All 29 All

1986 30 All All All 30 All 30 All 29 30 All 30

1987 All 27 All 29 All 29 All 30 29 All 29 All

1988 30 All 30 All 30 All 30 All 29 30 All 30

1989 All 27 All 29 All 29 All 30 All 30 29 All

1990 30 All 30 All 30 All 30 30 All 30 All 30

1991 All 27 All 29 30 All All 30 All 30 All 30

1992 30 All 30 All 30 All 30 30 All 30 All 30

1993 All 27 All 29 All 29 30 All 29 All 29 All

1994 30 All 30 All 30 29 All 30 29 All All 30

1995 All 27 All 29 All 29 All 30 All 28 All 30

1996 All 28 All 28 30 29 All 30 All 30 All 30

1997 30 All All 29 30 All 30 All 29 30 All All

1998 30 All 30 29 All 29 All 30 29 All All 30

1999 30 All 30 All 30 All 30 All 29 All 29 All

2000 All All All 29 All 29 All 30 29 All 29 All

2001 30 All 30 All 30 All 30 All 29 All 29 7

2002

2003

13 13

2004

22 29 28 30

2005 28 27 20

2006

7

Table 4. The number of missing daily records for each month from 1983 to 2006. Columns represent months (1-12)

and rows represent years. Cells with “All” indicate that there were no available records (all days were missing).

Blank cells represent months with no missing data.

54

6.5. Zoomed-In Version of the Equity Equation at the Inter-Annual Scale.

Figure 44. Zoomed-In Version of Figure 35. Year and annual precipitation is displayed for each point.

55

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02






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