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Net Irrigation Requirements for Florida Turfgrass Lawns: Part 2 - Reference Evapotranspiration Calculation1

Consuelo C. Romero and Michael D. Dukes2

Introduction

Turfgrasses are used in urban areas to provide multiple benefits to society and the environment. They cover millions of acres of home lawns, commercial properties, roadsides, parks, etc. But an important question is whether turfgrasses are properly managed. Many critics emphasize that turfgrasses demand too much urban water in a time when water resources are scarce. While indoor water use remains fairly constant throughout the year, outdoor water use increases during the spring and summer. Flattening the peak demand is an objective of water agencies (Beard and Kenna 2006); better irrigation management would result in less fertilizer and pesticide use, which would be better for the environment.

Urban landscape irrigation is one of the largest growing water use sectors in Florida. The state’s Water Management Districts have been working collectively to find ways to assist urban water users to irrigate more efficiently and to enhance planning and regulatory programs in order to conserve water. There is adequate research information to make specific recommendations, such as the specific cultural practices or systems approaches that could be applied to decrease turfgrass water use. Those recommendations could be used immediately to conserve water and maintain turfgrass quality and its functional benefits to society.

The calculation of net irrigation requirements for turfgrass is essential for determining water allocation and can help to determine irrigation scheduling. This series of publications explains the process of estimating net irrigation requirements for Florida turfgrasses. The process used here gives a long-term (30-year) historical analysis of turfgrass monthly net irrigation requirements. The first article in the series explains how the weather data was gathered and checked for quality; the second article shows the calculation of evapotranspiration for selected sites throughout the state (plus one in Alabama, to cover the west side of the Florida Panhandle); and the third and final article outlines the results of the net irrigation estimation. Since Florida’s urban landscape water demand is expected to grow considerably over the next few decades, the use of current information in terms of turfgrass irrigation needs will provide urban irrigators with information to help them reduce water volumes applied and conserve water.

Evapotranspiration is a process by which water is transferred to the atmosphere from vegetative surfaces. It consists of two components—evaporation and transpiration (Jensen et al. 1990). Evaporation accounts for the movement of water to the air from sources such as the soil canopy interception and water bodies. Transpiration accounts for the movement of water from vegetation. ETo, or reference evapotranspiration, is the evapotranspiration rate from a reference surface, well watered, and affected only by climatic parameters (Allen et al. 1998). It can be calculated using the Environmental & Water Resources Institute of the American Society of Civil Engineers (ASCE-EWRI) standardized method (Allen et al. 2005), which requires inputs from daily weather data parameters such as incoming solar radiation, air temperature, relative humidity, and wind speed (Allen et al. 1998). Evapotranspiration is greater in areas exposed to full sun than in shaded conditions. Plants transpire more when the temperature is high because high temperatures dry the air and create a larger gradient in vapor pressure between the air and the leaf (Shearman and Beard 1973). Water use increases with decreases in relative humidity, and it also increases on windy days (Huang 2006).

Weather conditions may vary throughout Florida. We evaluated this variability in terms of ETo variation in specific locations. This information could be valuable for urban water managers as managing water becomes a higher priority for policy makers and utility managers (Borisova et al. 2009).

Objective

The objective of this publication is to calculate reference ET for ten locations in Florida and one in Alabama using the corrected weather data shown in the first publication in this series, “Net Irrigation Requirements for Florida Turfgrass Lawns: Part 1—Report of Gathered Weather Data and Quality Check” (http://edis.ifas.ufl.edu/ae480).

Methodology

Daily measured meteorological data for a 30-year period (January 1, 1980–December 31, 2009) were gathered and quality checked from 11 weather stations located at airports in major cities in or near Florida (Figure 1; http://edis.ifas.ufl.edu/ae480).

Figure 1. 

Map showing locations of weather stations.


[Click thumbnail to enlarge.]

The quality-checked weather data was used to calculate ETos (short grass reference crop; equivalent to ETo at a daily time step) on a daily basis from 1980 to 2009 using the ASCE-EWRI standardized method (Allen et al. 2005). The ETos equation is expressed as follows:

(Eq. 1)

where ETos  is short grass reference crop evapotranspiration, Rn is net radiation, G is soil heat flux density at the soil surface, T is mean air temperature, u2 is wind speed, es is saturation vapor pressure, ea is actual vapor pressure, Δ is the slope of the saturation vapor pressure-temperature curve, and γ is the psychometric constant. The description and definition of each component of this equation can be found in Allen et al. (2005).

Solar Radiation (Rs) and Adjustment of the Hargreaves-Samani Coefficients (Kr)

Solar radiation (Rs) is an input required for the calculation of net radiation (Rn) in equation 1. Since Rs is infrequently measured, it can be calculated using the Hargreaves-Samani (1982) formula:

Rs = Kr (Tmax – Tmin)0.5Ra                                                          (Eq. 2)

where:

                        Kr = adjustment coefficient (0.16 for interior locations, 0.19 for coastal locations, [oC-0.5])

                        Tmax = daily maximum air temperature [°C]

                        Tmin = daily minimum air temperature [°C]

                        Ra = extraterrestrial radiation [MJ m-2 d-1]

Adjusting the Hargreaves-Samani Coefficients (Kr)

It was recognized that the Hargreaves-Samani method led to overestimation of solar radiation in Florida (Jacobs et al. 2004). The adjustment coefficient Kr is empirical and differs for ‘interior’ or ‘coastal’ regions. A value of 0.16 was recommended for interior locations, which means an area where land mass dominates, while 0.19 was the value recommended for a coastal region, which means an area where a large land mass and air masses are influenced by a nearby water body (Allen et al. 2005). However, Samani (2000) pointed out that not all coastal sites are the same, and differences in Kr values can be found among sites (Samani and Pessarakli 1986). Based on this information, a calibration procedure to adjust the Hargreaves-Samani coefficients was applied to all sites in this study.

Because Florida ground-based solar radiation data are extremely sparse from 1991 to present, a project entitled “Satellite-based Solar Radiation, Net Radiation, and Potential and Reference Evapotranspiration Estimates over Florida” (Jacobs et al. 2008) proposed an effort to provide solar radiation data from Geostationary Operational Environmental Satellites (GOES). This data was calibrated based on measured Rs data throughout Florida. The estimates have been found to be within 10% of ground-based measured values (Tarpley 1979; Jacobs et al. 2008). The resulting datasets were transferred to the U.S. Geological Survey (USGS) in Orlando, Florida, and are publicly available via the USGS web portal (http://fl.water.usgs.gov/et/publications/GOES_FinalReport.pdf [24 September 2012]). The estimated solar radiation data from 1996 through 2004 were used to calibrate the Hargreaves-Samani coefficients on a daily basis at the eleven locations under evaluation. Daily average temperature and extraterrestrial radiation data for the same locations and time span from the original weather database from the National Climatic Data Center (NCDC) were also used for the calibration process. The resulting new coefficients were applied to the entire 30-year NCDC dataset to estimate solar radiation.

Results and Discussion

Calibration of Hargreaves-Samani Coefficients (Kr) and New Solar Radiation Estimation

Table 1 shows the new adjusted Hargreaves-Samani coefficients (Kr) for 10 sites in Florida and one in Mobile, Alabama. The average was calculated for each month of the year over the 8-year USGS data record. The average monthly values across locations ranged from 0.14 to 0.24. Coastal areas like Miami and West Palm Beach tended to show high values (0.20 on average), while inland cities such as Tallahassee, Gainesville, and Orlando showed the lowest mean value (0.15). These results are similar to those recommended by Allen et al. (2005), except for Mobile and Jacksonville, which are located on coastal areas but showed low coefficient values (0.15). Key West showed an average value of 0.24, which for an island seems to be high; Allen (1995) reported a value of 0.20 for Hilo, Hawaii. The lowest mean coefficient value was found during the months of December through March, with an average value of 0.16. The highest mean coefficient values were calculated for April, May, and October with a value of 0.18. The annual average was lower in the north part of the state (0.15 in Mobile and Tallahassee) than in the south (0.22 in Key West and Miami).

The highest coefficients values calculated for Key West are related to the low temperature difference observed in the area, which on average was 5.8 for a 30-year period (8.8oC for Hilo, Hawaii (Allen 1995)). At low latitudes, the temperature difference becomes negligible and consequently equation 2 become insensitive (Jagtap 1991), overestimating the Hargreaves-Samani coefficients.

The calibrated solar radiation data for the period 1996–2004 was compared with the USGS solar radiation data. Differences were observed to be within 3.5% of USGS data (Table 2).

Table 1. 

New calibrated adjustment coefficients (Kr) of the Hargreaves-Samani equation. Adjustment coefficients were determined for each location on a monthly basis.

 

Coastal areas

Month

Mobile

Jacksonville

Daytona

Tampa

West Palm Beach

Ft. Myers

Miami

Key West

Avg.

 

oC-0.5

Jan

0.14

0.14

0.15

0.17

0.16

0.16

0.19

0.23

0.17

Feb

0.14

0.13

0.14

0.18

0.18

0.16

0.20

0.24

0.17

Mar

0.14

0.14

0.15

0.18

0.19

0.17

0.21

0.26

0.18

Apr

0.15

0.15

0.16

0.19

0.20

0.17

0.22

0.27

0.19

May

0.16

0.16

0.17

0.20

0.20

0.17

0.21

0.26

0.19

Jun

0.16

0.15

0.15

0.18

0.17

0.15

0.19

0.24

0.17

Jul

0.15

0.16

0.16

0.18

0.19

0.16

0.21

0.24

0.18

Aug

0.16

0.16

0.17

0.18

0.18

0.16

0.20

0.22

0.18

Sep

0.15

0.15

0.16

0.18

0.18

0.16

0.20

0.22

0.18

Oct

0.15

0.15

0.17

0.19

0.19

0.17

0.21

0.24

0.18

Nov

0.15

0.14

0.16

0.18

0.18

0.17

0.19

0.25

0.18

Dec

0.14

0.13

0.14

0.16

0.16

0.16

0.19

0.22

0.16

Avg.

0.15

0.15

0.16

0.18

0.18

0.16

0.20

0.24

0.18

 

Inland areas

 

 

 

 

 

Month

Tallahassee

Gainesville

Orlando

Avg.

         

Jan

0.13

0.14

0.15

0.14

         

Feb

0.13

0.14

0.14

0.14

         

Mar

0.14

0.14

0.15

0.14

         

Apr

0.14

0.15

0.16

0.15

         

May

0.15

0.15

0.16

0.15

         

Jun

0.14

0.14

0.14

0.14

         

Jul

0.14

0.15

0.15

0.15

         

Aug

0.15

0.15

0.15

0.15

         

Sep

0.14

0.15

0.15

0.15

         

Oct

0.14

0.15

0.16

0.15

         

Nov

0.14

0.15

0.16

0.15

         

Dec

0.13

0.14

0.14

0.13

         

Avg.

0.14

0.15

0.15

0.15

         

Table 2. 

Difference in percentage between Hargreaves-Samani solar radiation estimation to USGS solar radiation data.

 

Mobile

Tallahassee

Jacksonville

Gainesville

Daytona

Orlando

Tampa

West Palm Beach

Ft. Myers

Miami

Key West

Month

%

Jan

-0.6

1.7

-2.3

-5.4

-2.7

-2.6

0.6

0.9

0.1

-2.3

-3.2

Feb

-0.1

1.4

2.5

-6.5

3.1

3.2

-2.8

-3.1

-0.9

-1.5

-2.2

Mar

4.6

-2.6

1.7

-2.1

2.4

0.5

0.7

-4.3

0.9

0.2

-3.9

Apr

2.2

0.5

1.5

-3.0

2.1

-3.0

0.8

-4.3

1.4

-1.4

-3.8

May

1.8

-2.2

-1.7

-2.1

-2.6

-2.0

-2.1

-3.0

-0.6

0.3

-1.7

Jun

-0.4

-0.1

-1.8

-1.3

1.5

3.0

1.5

-0.8

-0.8

0.5

-3.6

Jul

2.2

2.4

-1.3

-2.9

1.9

1.8

0.8

-1.8

0.9

-2.0

-3.6

Aug

-2.9

-1.6

-1.0

-0.4

-3.4

2.4

2.0

-0.5

0.3

-2.0

-1.5

Sep

5.0

2.5

2.5

-2.1

3.0

3.3

0.3

-2.5

-1.8

-3.5

-0.4

Oct

1.5

3.3

2.3

-2.2

0.7

0.7

-0.9

-1.7

-1.2

-0.4

-0.5

Nov

-1.7

-0.7

2.2

-6.0

0.0

-1.9

0.9

-3.6

-0.1

1.9

-3.4

Dec

-3.6

-2.1

1.2

-8.2

0.9

0.6

0.9

-0.8

1.5

-2.7

-1.7

Avg

1.0

0.1

0.3

-3.1

0.5

0.5

0.2

-2.2

0.0

-1.0

-2.5

Reference Evapotranspiration Calculation

The average, minimum, and maximum ETo values over the 30-year period (1980-2009) for each location are shown in Table 3. For all locations, mean annual ETo for the 30-year period ranged from 51.0 in y-1 in Tallahassee to 65.2 in y-1 in Key West. Central areas of the state like Orlando and Tampa showed different mean ETo values, ranging from 57.9 to 59.5 in y-1, respectively. Maximum annual ETo was calculated for Key West at 72.0 in y-1. Minimum annual ETo was calculated for Mobile at 46.2 in y-1. Long-term cumulative ETo was the highest in both Miami and Key West, and the lowest was in both Tampa and West Palm Beach (Figure 2).

Table 3. 

Average, minimum, and maximum yearly calculated reference evapotranspiration (ETo) for the 30-year period (1980-2009) of weather station data records.

Site

Location

 ETo (in y-1)

Average

Minimum

Maximum

Mobile

52.7

46.2

58.3

Tallahassee

51.0

47.1

55.6

Jacksonville

52.2

46.8

59.6

Gainesville

51.7

46.4

55.4

Daytona

53.9

47.6

59.6

Orlando

57.9

51.7

63.9

Tampa

59.5

53.6

67.0

Fort Myers

58.1

51.5

66.1

West Palm Beach

62.3

56.2

69.5

Miami

65.3

58.9

71.6

Key West

65.2

57.4

72.0

Figure 2. 

Long-term (1980-2009) cumulative average monthly ETo at 10 weather stations in Florida and one in Mobile, Alabama.


[Click thumbnail to enlarge.]

The 30-year average ETo values were higher than the 9-year USGS ETo values for most of the locations, except Tampa, which had much lower values. However, when selecting the same period of our calculated ETo values, we found that the differences decreased and approached the USGS data (Table 4). Solar radiation for the 30-year period at each location was analyzed to see any inconsistencies in the data to explain the difference in ETo. The values were constant and showed the same trend year by year. However, when temperature data was reviewed, a decrease in the cumulative maximum temperatures was observed from 1991 through 1998, compared to the rest of the years. Data is shown in Table 5, where annual cumulative degrees were compared for the study period. This cool period justified the decrease in annual ETo values from 1996 through 2004 and coincided with part of the USGS available ETo data.

Table 4. 

Difference between maximum and minimum cumulative degrees on an annual basis for two locations in Florida and one in Alabama.

 

Mobile

Gainesville

Key West

Year

Cumulative degrees (oC y-1)

1980

4,700

4,655

2,237

1981

4,897

5,009

2,334

1982

4,479

4,457

2,184

1983

4,609

4,418

2,179

1984

4,746

4,596

2,048

1985

4,631

4,498

2,352

1986

4,653

4,362

2,425

1987

4,836

4,615

2,500

1988

4,837

4,667

2,534

1989

4,636

4,748

2,547

1990

4,890

4,822

2,464

1991

3,533

3,882

1,767

1992

3,696

3,864

1,769

1993

3,732

4,179

1,833

1994

4,531

4,382

2,372

1995

3,887

4,069

1,907

1996

3,810

4,326

1,875

1997

3,610

4,107

1,793

1998

3,629

4,616

1,906

1999

4,240

5,348

2,126

2000

4,960

5,682

2,408

2001

4,698

5,209

2,289

2002

4,438

5,176

2,313

2003

4,569

5,081

2,311

2004

4,450

5,057

2,138

2005

4,745

4,917

2,279

2006

4,713

4,802

2,157

2007

4,796

4,547

2,133

2008

4,700

4,610

2,159

2009

4,361

4,412

2,238

Table 5. 

Comparison of ETo values for all locations, according to period of time and source.

 

Mobile

Tallahassee

Jacksonville

Gainesville

Daytona

Orlando

Tampa

West Palm Beach

Ft. Myers

Miami

Key West

 

in y-1

H-S 30-y*

52.7

51.0

52.2

51.7

53.9

57.9

51.0

51.0

58.1

65.3

65.2

H-S 9-y§ 

49.8

50.2

50.5

52.0

52.5

56.2

57.2

60.1

54.7

62.4

62.3

USGS 9-y¥

50.2

50.4

51.8

48.7

52.5

48.0

55.1

55.6

56.0

57.5

61.4

* Hargreaves-Samani equation – 30-year period average

§ Hargreaves-Samani equation – 9-year period average

¥ USGS ETo data – 9-year period average

The highest ETo values calculated for both Miami and Key West may be overestimated due to the insensitivity of the Hargreaves-Samani equation for areas where differences in maximum and minimum temperature are small, as reported previously (5.8 and 8.1oC for Key West and Miami, respectively). ETo values for locations nearby North Florida were close to some data previously published. Jacobs and Satti (2001) estimated annual ETo at 55.3, 53.5, and 58.3 in y-1 for Jacksonville, Gainesville, and Daytona using the reference ET equation for the period 1985–1990. Our results were 54.5, 52.8, and 56.6 in y-1 for the same period of time. In another study in Central Florida, Jia et al. (2009) estimated ETo for Gainesville using the same equation, finding a value of 47.6 in y-1 (in this case, 5.4 in less than what we calculated).

The average, minimum, and maximum rainfall values for each location are shown in Table 6. Mean yearly rainfall for the 30-year period ranged from 69.7 in y-1 in Mobile to 41.7 in y-1 in Key West. Central locations like Orlando and Daytona showed mean annual rainfall values of 52.1 and 47.4 in y-1, respectively. The long-term cumulative mean monthly rainfall for all locations is shown in Figure 3. Figure 4 shows bar graphs comparing both the 30-year mean monthly rainfall and ETo amounts.

Table 6. 

Average, minimum, and maximum yearly rainfall for the 30-year period (1980-2009) of weather station data records.

Site

Location

Rainfall (in y-1)

Average

Minimum

Maximum

Mobile

69.7

45.7

94.7

Tallahassee

61.5

44.1

93.2

Jacksonville

53.4

32.2

79.5

Gainesville

46.2

33.3

63.3

Daytona

47.4

28.7

70.6

Orlando

52.1

30.8

70.1

Tampa

48.4

31.3

68.9

Fort Myers

56.7

33.2

84.6

West Palm Beach

62.2

39.0

90.0

Miami

62.5

43.6

85.0

Key West 

41.7

24.9

63.6

Figure 3. 

Long-term (1980-2009) cumulative average monthly rainfall at 10 weather stations in Florida and one in Mobile, Alabama.


[Click thumbnail to enlarge.]

Figure 4. 

Long-term (1980-2009) average monthly ETo and rainfall for five weather stations in Florida and one in Alabama.


[Click thumbnail to enlarge.]

Summary

In the previous EDIS publication in this series (“Net Irrigation Requirements for Florida Turfgrass Lawns: Part 1 – Report of Gathered Weather Data and Quality Check”; http://edis.ifas.ufl.edu/ae480), we used the Hargreaves-Samani equation to estimate solar radiation, although this method led to an overestimation of the value under Florida conditions. In this publication (the second in the series), the Hargreaves-Samani coefficients (Kr) were adjusted for 10 sites in Florida and one in Alabama, and new solar radiation values were estimated. The equation has been demonstrated as insensitive for areas where temperature difference becomes negligible, such as South Florida, overestimating its coefficients. This overestimation in solar radiation leads to overestimated ETo. Overestimation was within 10–15% of other weather data sets and within the range of variation observed in weather data sets in Florida.

In the final publication in this series (“Net Irrigation Requirements for Florida Turfgrass Lawns: Part 3 – Theoretical Irrigation Requirements”; http://edis.ifas.ufl.edu/ae482), the daily ETo values are used as inputs to run a daily soil water balance to determine net irrigation requirements for turfgrass in Florida.

Acknowledgements

The authors wish to thank the Southwest Florida Water Management District (SWFWMD) for funding this research. We are also grateful to our four reviewers (K. Migliaccio, C. Martinez, M. McCready, and J. Tichenor) for their valuable comments to the manuscript.

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Footnotes

1.

This document is AE481, one of a series of the Agricultural and Biological Engineering Department, UF/IFAS Extension. Original publication date August 2011. Reviewed August 2014. Visit the EDIS website at http://edis.ifas.ufl.edu.

2.

Consuelo C. Romero, assistant-in research scientist; and Michael D. Dukes, professor, Agricultural and Biological Engineering Department; UF/IFAS Extension, Gainesville, FL 32611.


The Institute of Food and Agricultural Sciences (IFAS) is an Equal Opportunity Institution authorized to provide research, educational information and other services only to individuals and institutions that function with non-discrimination with respect to race, creed, color, religion, age, disability, sex, sexual orientation, marital status, national origin, political opinions or affiliations. For more information on obtaining other UF/IFAS Extension publications, contact your county's UF/IFAS Extension office.

U.S. Department of Agriculture, UF/IFAS Extension Service, University of Florida, IFAS, Florida A & M University Cooperative Extension Program, and Boards of County Commissioners Cooperating. Nick T. Place, dean for UF/IFAS Extension.