probabilities of low nighttime temperatures during stocking and harvest seasons for inland shrimp...

Probabilities of Low Nighttime Temperatures during Stockingand Harvest Seasons for Inland Shrimp Culture

BARTHOLOMEW GREEN1

US Department of Agriculture, Agricultural Research Service, Aquaculture Systems ResearchUnit, Aquaculture/Fisheries Center, University of Arkansas at Pine Bluff, 1200 North University

Drive, Pine Bluff, Arkansas 71601 USA

THOMAS W. POPHAM

US Department of Agriculture, Agricultural Research Service, Southern Plains Area, 1301North Western Road, Stillwater, Oklahoma 74075 USA

Abstract

Litopenaeus vannamei is cultured in earthen ponds at southern US inland sites when water

temperatures permit shrimp survival and growth. The probability of a minimum air temperature

#14 C for one, three, or five consecutive days at one coastal and eight inland sites where L. vannamei

is or could be grown in ponds is high (50%) from late March through late April at all sites except

Arcadia, Florida, and Harlingen, Texas. Probabilities are 10% by early May to early June. In the

autumn, the 10 and 50% probability levels are reached in early- to mid-September and by late

September to mid-October, respectively. Arcadia, Florida, and Harlingen, Texas, have the longest

periods with low probabilities and Pecos, Texas, has the shortest periods. The maximum pond water

temperature from the previous afternoon and the minimum air temperature from that night predicted

the minimum pond water temperature the following morning for Pine Bluff, Arkansas: (R2 5 0.886):

Tmin 5 1.464 + 0.169Amin + 0.727Tmax, where Tmin 5 minimum daily pond water temperature (C),

Amin 5 minimum air temperature (C) for the same day, and Tmax 5 maximum pond water temper-

ature (C) from the previous day. Pond stock out and harvest decisions can be guided by minimum air

temperature probabilities and predicted water temperature.

Farmers who grow the Pacific white shrimp,Litopenaeus vannamei, in inland ponds in theUSA culture a tropical species in a temperate orsubtropical climate. Pond culture of L. vannameiin the southern USA generally is limited toearly May through late October, except in thesouthern most region of the country.

The lower lethal temperature for L. vannameiis not known with certainty. However, some in-sight was gained when draining of two 0.1-haearthen research ponds to approximately 50%volume for harvest of L. vannamei was initiatedin mid-afternoon on October 2, 2003 (unpub-lished data). That night an unexpected cold front(minimum air temperature was 5.0 C) moved in.The following morning pond water temperaturesranged from 13.5 to 15.3 C and shrimpwere deador moribund. Pond water temperature averaged

19.5–21.5 C for the 3 d prior to harvest. Thus,a water temperature of 14 C appears to be a rea-sonable estimate for the lower lethal temperature.

A weather front is the boundary between twoair masses of different densities, and hence, tem-peratures (Byers 1974). Displacement of warmair by cold air is called a cold front. Character-istically, the cold front has a steeper slope andmoves more quickly than a warm front, whichgives rise to sudden, intense rains and strongwinds (Byers 1974). Colder temperatures, west-erly or northerly winds, and clear skies prevailafter the front passes. Cold fronts periodicallymove into the southern USA during the springand fall and can remain in place for a numberof days before moderating.

One night with a minimum air temperature#14 C may cool water in ponds or outdoortanks sufficiently to kill L. vannamei. Factorssuch as the minimum air temperature, thevolume of water in the pond or tank, the volume1 Corresponding author.

JOURNAL OF THE

WORLD AQUACULTURE SOCIETY

Vol. 39, No. 1

February, 2008

Journal compilation � 2008 World Aquaculture SocietyNo claim to original US government works

91

of ground water inflow, mechanical aeration,wind speed, and general weather conditions,such as cloud cover, precipitation, and so on,will influence how quickly the heat content ofthe water will change. Three- to 5-d consecutiveduration of nocturnal minimum air temperature#14 C would be expected to further exacerbatethe decline in water temperature.

Long-term records of aquaculture pond watertemperatures for predictive purposes generallyareunavailable,particularly forareaswhere inlandculture ofmarine shrimpmay be practiced. Somemechanistic models (Klemetson and Rogers1985; Cathcart and Wheaton 1987; Losordo andPiedrahita 1991; Lamoureux et al. 2006) predictaquaculture pond water temperature but requiretoo many variables to be practical for routine use.

Long-term meteorological data sets exist formany locations in the USA and can be used todevelop predictive equations for water tempera-ture. Air temperature data long have been usedto develop predictive equations for water temper-ature during ice-free periods in streams, rivers,and lakes (Webb and Nobilis 1997; Caissie et al.1998; Kettle et al. 2004; Morrill et al. 2005). Insome cases, introducing a time lag with regard towater temperature strengthens the air–water tem-perature relationship (Webb and Nobilis 1997;Erickson and Stefan 2000). Air–water tempera-ture predictive equations typically have high co-efficients of determination (Webb et al. 2003).

The first objective of this work was to calcu-late probabilities of a minimum air temperature

#14 C occurring on 1 d and on three or fiveconsecutive days during stocking and harvestseasons for inland shrimp culture at eight sitesin the USA. The second objective of this workwas to develop and validate a predictive equa-tion for minimum pond water temperature inPine Bluff, Arkansas.

Materials and Methods

Minimum Air Temperature Probabilities

The US Marine Shrimp Farming ProgramWeb site (http://www.usmsfp.org/index.html)lists inland shrimp (L. vannamei) farms, whichare concentrated in southern tier states; this listwas used as a guide in site selection. Eight siteswere selected for the present study and werelocated in the southern USA in the vicinityof where L. vannamei is or could be culturedin ponds and that had 74- to 100-yr data setsof daily minimum air temperature available(Table 1). One coastal site where shrimp cultureis practiced, Harlingen, Texas, was included forcomparison (Table 1). Air temperature data setswere obtained from the National Oceanographicand Atmospheric Administration National DataCenter (http://lwf.ncdc.noaa.gov/oa/ncdc.html)in electronic format.

Data sets were imported into SAS version 9.1(SAS Institute, Inc., Cary, NC, USA), where theDATA step was used to convert dates to Juliandays and degrees Fahrenheit to degrees Celsius.The data set for each location then was sorted

TABLE 1. Name, elevation above mean sea level, and latitude and longitude of NOAA National Data Center weather

stations from which long-term air temperature data sets were obtained.

NOAA station name Elevation (m) Latitude/longitude Data set years

Arcadia, FL 9 27°139N/81°529W 1905–2004

Gila Bend, AZ 224 32°579N/112°439W 1905–2004

Greensboro, AL 67 32°429N/87°359W 1905–2004

Harlingen, TX 12 26°129N/97°409W 1912–2004a

Hattiesburg 5SW, MS 117 31°159N/89°209W 1905–2004

Pecos, TX 796 31°259N/103°309W 1914–2004b

Pine Bluff, AR 66 34°149N/92°019W 1930–2004

Portland, AR 39 33°159N/91°309W 1909–2004

Tifton, GA 116 31°279N/83°299W 1911–2004

NOAA 5 National Oceanographic and Atmospheric Administration.a No data during the period October 1916 to May 1919.b Sporadic data from 1904 to 1913 included.

92 GREEN AND POPHAM

into 366 Julian day groups with all the years ofobservation within each Julian day group. Forthe three and five consecutive day analyses, theSASDATA step was used to create an output datafile, where each line contained the Julian day,year, minimum temperature, and the minimumtemperatures for preceding and following 1–2 d.

The SAS UNIVARIATE procedure was usedto calculate for each Julian day the probabilityof going below the threshold minimum tempera-ture for one, three, or five consecutive days. TheUNIVARIATE procedure computed the per-centiles in degrees Celsius for each Julian day,creating an output data set that has a line for eachJulian day that contained the Julian day variableand 100 variables containing the percentiles indegrees Celsius. Subjecting the output data setto the TRANSPOSE procedure yielded a newdata set that contained 100 lines for each Julianday. Each line has a Julian day variable, a namevariable carried over from the output data set,the percentiles, and a temperature variable forthat percentile. Using a SAS DATA step, the per-centiles each were divided by 100 to obtain theprobability of observing the temperature in thatline of data on that Julian day.

The data were screened to eliminate all datalines whose temperatures exceeded the thresholdminimum temperature. Because more than oneline within each Julian day may have a tempera-ture less than or equal to the threshold, the SASMEANS procedure was used to select the linethat contains the maximum probability of a tem-perature less than or equal to the threshold. Thisis the probability of having a temperature belowthe threshold temperature for that Julian day.

Water Temperature Prediction

Water temperature was monitored continu-ously at 25 cm below the water surface in eachof twelve to eighteen 0.1-ha earthen ponds(1 m average depth) used for L. vannamei orchannel catfish, Ictalurus punctatus, productionresearch between April 27 to October 31, 2004,on the Aquaculture Experiment Station, Uni-versity of Arkansas at Pine Bluff (UAPB),Pine Bluff, Arkansas. A thermistor (Model 109Temperature Probe; Campbell Scientific, Inc.,Logan, UT, USA) connected to a CR-205 data

logger (Campbell Scientific, Inc.) and sampledevery 10 s was used to measure water tempera-ture in each pond. Daily mean, maximum, andminimum water temperatures were calculatedfor each 0000- to 2400-h period.

Weather data for the April 27 to October 31period were obtained in electronic format fromthe US Department of Agriculture, Natural Re-sources Conservation Service (NRCS), NationalWater and Climate Center, Soil Climate Analy-sis Network Site 2083 (http://www.wcc.nrcs.usda.gov/scan/site.pl?sitenum=2083&state5ar),located on the UAPB Agricultural ExperimentStation, Pine Bluff, Arkansas. This weather sta-tion is ,1.6 km from the Aquaculture ResearchStation. Data were formatted as hourly and dailymeans and totals.

Relationships between pond water tempera-ture and weather variables were evaluated usingthe SAS version 9.1 CORR and REG proce-dures. The rsquare method of model selectionwas used in the regression procedure. Regres-sion diagnostics were performed. The meanminimum water temperature, which occurredbetween 0000 and 0600 h, was the dependentvariable. Independent variables evaluated werethe mean maximum water temperature fromthe previous afternoon, which occurred between1200 and 1800 h, mean air temperature, mini-mum air temperature, maximum air temperaturefrom the previous day, relative humidity, baro-metric pressure (mm Hg), mean wind speed(m/s), and mean wind speed from the previousday and the previous night (1800–0600 h).The best-fit regression equation was validatedby comparing predicted minimum daily watertemperature to observed mean minimum watertemperature from eleven 0.1-ha earthen pondsused for channel catfish production researchfor the period April 2 to October 20, 2005.Water temperature was monitored in each pondand minimum air temperature data were ob-tained from NRCS as described above.

Results and Discussion

Minimum Air Temperature Probabilities

Mean long-term minimum air temperaturesranged from 1 to 11 C among the eight inland

PROBABILITIES OF LOW NIGHTTIME TEMPERATURES 93

sites in early March, and increased linearly andconverged by early June, whereas at the Harlin-gen site, the minimum air temperature duringthis period was consistently warmer (Fig. 1).Mean minimum air temperatures exceeded18 C in early September, decreased linearlythrough the autumn, and ranged from 0 to11 C in late November. Autumn temperatureswere similar at Harlingen and Arcadia. Duringboth seasons, Pecos was the coolest inland siteand Arcadia was the warmest site. Tifton, Hat-tiesburg, Greensboro, Pine Bluff, and Portlandhad similar mean minimum air temperatures.

The probability that the minimum daily airtemperature will be #14 C in Arcadia, Florida,reaches a maximum of 70–80% from mid-December through mid-February (Fig. 2). Thereis a high probability (50%) of a 1-d eventthroughout much of March into early April. Bymid-May, there is a very slight chance (10%)of a 1-d event. A 3-d period of minimum airtemperature#14 C has a 50% probability of oc-currence from December through early Marchand a 10% probability of occurrence by theend of April. There is a high probability of a5-d event in late January to early February anda very slight probability from late Marchthrough the first 3 wk of April. In the fall, a10% probability of a 1- and 3-d event occursin mid-October and the last week of October,respectively. During the third week of November,there is a high probability of a 1-d event,whereas during most of December, there is ahigh probability of a 3-d event.

In Gila Bend, Arizona, a very slight chance(10%) that the minimum air temperature willbe #14 C for 1, 3, or 5 d occurs in the springduring the first week of June, the last week ofMay, and mid-May, respectively, and in the fall,during the last week of September, the begin-ning of October, and the beginning of October,respectively (Fig. 3). The 50% probability leveloccurs during the first week of May for a 1-devent, at the end of April for a 3-d event, and inmid-April for a 5-d event. In the fall, the 50%probability level occurs on about 15, 20, or 25October for a 1-, 3-, or 5-d event, respectively.There is a very high probability (90%) that theminimum air temperature will be #14 C for 1,

3, or 5 d in the spring during the first half ofApril, the third week of March, and the first weekof March, respectively, and in the fall during thefirst 10 d of November, mid-November, and mid-November, respectively.

A very slight probability (10%) that the min-imum air temperature in Greensboro, Alabama,will be#14 C occurs on about May 27, 16, or 1for a 1-, 3-, or 5-d duration, respectively, duringthe spring, and on about September 19, Septem-ber 20, or October 2, respectively, during the fall(Fig. 4). On about April 28, 13, or 6, there isa high probability of a 1-, 3-, or 5-d event. Inthe fall, the 50% probability level occurs on aboutOctober 8 for a 1-d event, October 20 for a 3-devent, and October 25 for a 5-d event. The veryhigh probability (90%) level occurs on aboutMarch 10 for a 1-d event, during most of Feb-ruary for a 3-d event, and during the first weekof February for a 5-d event. On about Novem-ber 20, throughout most of December, and inmid-December, there is a very high probability(90%) of a 1-, 3-, or 5-d event, respectively.

At the Harlingen, Texas, coastal site, a 70–80% probability that the minimum daily airtemperature will be #14 C occurs from lateNovember through mid-February (Fig. 5). Theprobability of a 1-d event is high (50%) throughthe third week of March but decreases to a 10%probability by the third week of April. A 3-dperiod of minimum air temperature #14 C hasa 50–60% probability from mid-Decemberthrough mid-February and a 10% probabilityof occurrence by the first week of April. A40–50% chance of a 5-d event occurs frommid-December through the last week of January.By mid-March, there is a 10% chance of a 5-devent. In the fall, a 10% probability of a1- and 3-d event occurs after the first and thirdweeks of October, respectively. There is a high(50%) probability of a 1-d and 3-d event bymid-November and mid-December, respectively.

In the spring in Hattiesburg, Mississippi, the10% probability level that the minimum air tem-perature will be #14 C for one, three, or fiveconsecutive days occurs on about May 28, 17,or 2, respectively, whereas in the fall, thisprobability level occurs on about September15, 24, or 30, respectively (Fig. 6). The 50%

94 GREEN AND POPHAM

0.0

5.0

10.0

15.0

20.0

25.0

1-M

ar

8-M

ar

15-M

ar

22-M

ar

29-M

ar

5-Apr

12-A

pr

19-A

pr

26-A

pr

3-M

ay

10-M

ay

17-M

ay

24-M

ay

31-M

ay7-

Jun

Tem

pera

ture

(C

)

Arcadia

Pecos

Gila Bend

Greensboro

Hattiesburg

Pine Bluff

Portland

Tifton

Harlingen

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

1-Sep

8-Sep

15-S

ep

22-S

ep

29-S

ep6-

Oct

13-O

ct

20-O

ct

27-O

ct

3-Nov

10-N

ov

17-N

ov

24-N

ov

Tem

pera

ture

(C

)

Arcadia

Gila Bend

Pecos

Greensboro

Hattiesburg

Pine Bluff

Portland

Tifton

Harlingen

FIGURE 1. Long-term (74–100 yr) mean minimum air temperature at selected sites during spring (March 1 to June 10;

upper graph) and fall (September 1 to November 30; lower graph). Degree of overlap prevents individual identification of

lines for Greensboro, Hattiesburg, Pine Bluff, Portland, and Tifton.


probability level occurs April 25, April 15, orMarch 25 for a 1-, 3-, or 5-d event, respectively.In the fall, a 1-, 3-, or 5-d event has a 50%chance of occurring on about October 8, 15, or21, respectively. There is a 90% probability ofa 1-d event occurring on about March 9 andNovember 11 and a 3-d event occurring onabout February 12 and December 10. The max-

imum probability of a 5-d event ranges from 82to 84% in December to January.

Pecos, Texas, is the site with the fewest dayswith a low probability that the minimum airtemperature will be #14 C. The 10% probabil-ity level occurs on about June 2, May 23, orMay 17 for a 1-, 3-, or 5-d event, respectively,in the spring and on about September 7, 15, or

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

31-Jan 20-Feb 12-Mar 1-Apr 21-Apr 11-May

Prob

abili

ty

1-Day 3-Day 5-Day 1-Day 3-Day 5-Day

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

30-Sep 20-Oct 9-Nov 29-Nov 19-Dec 8-Jan 28-Jan

Prob

abili

ty

FIGURE 2. Probabilities of a minimum air temperature of 14 C or less being observed for one, three, or five consecutive

days during the spring and fall in Arcadia, Florida.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

30-Mar 13-Apr 27-Apr 11-May 25-May 8-Jun

Prob

abili

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

30-Aug 13-Sep 27-Sep 11-Oct 25-Oct 8-Nov

Prob

abili

ty



days during the spring and fall in Gila Bend, Arizona.

96 GREEN AND POPHAM

21 for a 1-, 3-, or 5-d event, respectively, in thefall (Fig. 7). There is a high probability (50%) ofa 1-d event on about May 13 and September 24,a 3-d event on about May 4 and October 7, and a5-d event on about May 2 and October 8. The veryhigh probability (90%) level occurs on about April26 and October 16 for a 1-d event, on about April10 and October 23 for a 3-d event, and on aboutApril 3 and October 25 for a 5-d event.

A 10% probability that the minimum air tem-perature will be #14 C for one, three, or fiveconsecutive days in Pine Bluff, Arkansas, oc-curs on about May 28, 15, or 3, respectively,and on about September 10, 21, or 24, respec-tively (Fig. 8). In the spring, a 1-, 3-, or 5-d eventhas a high probability of occurring on aboutMay 4, April 15, or March 31, respectively; inthe fall, the corresponding dates are September28, October 17, or October 24, respectively.There is a very high probability (90%) that a1-d event occurs on about March 27 andNovember 4, a 3-d event on about March 6and November 23, and a 5-d event on about Feb-ruary 24 and November 30.

In Portland, Arkansas, a very slight chance(10%) that the minimum air temperature will be#14 C for one, three, or five consecutive daysoccurs on about May 31, 20, or 2, respectively,in the spring and on about September 8, 19, or

26, respectively, in the fall (Fig. 9). On aboutMay 4, April 16, or April 12, there is a high prob-ability of a 1-, 3-, or 5-d event, respectively. In thefall, a 1-, 3-, or 5-d event has a 50% chance ofoccurring on about September 30, October 8, orOctober 21, respectively. There is a very highprobability of one, three, or five consecutive dayswith the minimum air temperature#14 C occur-ring on about March 28, March 7, or February21, respectively, and on about November 4, 26,or 28, respectively.

A minimum air temperature #14 C for one,three, or five consecutive days in Tifton, Geor-gia, has a 10% probability of occurring onabout May 27, 15, or 1, respectively and onabout September 20, September 27, or October2, respectively (Fig. 10). The 50% probabilitylevel occurs April 28, April 16, or March 26for a 1-, 3-, or 5-d event, respectively. In thefall, a 1-, 3-, or 5-d event has a 50% chance ofoccurring on about October 9, October 21, orNovember 5, respectively. There is a 90% proba-bility of a 1-d event occurring on about March 10and November 11, a 3-d event occurring on aboutFebruary 15 and December 1, and a 5-d eventoccurring on about January 29 and December 25.

Stocking date decisions in the spring canbe guided by the probabilities of a minimumair temperature #14 C. Generally, postlarval

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


Prob

abili

ty


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

31-Aug 20-Sep 10-Oct 30-Oct 19-NovPr

obab

ility


days during the spring and fall in Greensboro, Alabama.


shrimp transported to an inland farm from thehatchery must be acclimated in tanks to pondsalinity over a several day period prior to stock-ing. For example, if ponds in Greensboro, Ala-bama, were to be stocked during the first weekof May, postlarval shrimp would be transportedto the farm for acclimation from the last days ofApril to the first days of May. There is a highprobability (50%) that the minimum air temper-

ature will be #14 C for 1 d from late Aprilthrough May 5. During this same period, thereis a moderate probability (about 20–30%) ofa 3-d event. Water temperature in acclimationtanks outdoors or lacking temperature controlmay drop to levels lethal to L. vannamei. How-ever, the 1- and 3-d probabilities each decreaseby about 25% /wk during the second and thirdweeks of May. Thus, postponing initiation of

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

31-Jan 20-Feb 12-Mar 1-Apr 21-Apr 11-May

Prob

abili

ty


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

30-Sep 20-Oct 9-Nov 29-Nov 19-Dec 8-Jan 28-Jan

Prob

abili

ty


days during the spring and fall in Harlingen, Texas.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

7-Mar 22-Mar 6-Apr 21-Apr 6-May 21-May 5-Jun

Prob

abili

ty

Prob

abili

ty

30-Aug 14-Sep 29-Sep 14-Oct 29-Oct 13-Nov 28-Nov



days during the spring and fall in Hattiesburg, Mississippi.

98 GREEN AND POPHAM

acclimation until the second or third week ofMay can decrease the likelihood of stock lossto hypothermia.

Shrimp pond harvest generally requires.1 dbecause most shrimp are captured as the lastapproximately 10% of the pond volume isdrained. Ponds likely will be aerated at nightto ensure adequate dissolved oxygen concentra-tions while ponds are draining. Incursion ofa cold front with a minimum air temperature#14 C could result in substantial cooling ofpond waters, particularly at night in partiallyfull, aerated ponds. In planning a harvest inPecos, Texas, review of the probabilities thatthe minimum air temperature will be #14 Cfor one, three, or five consecutive days can assistin making the harvest decision. There is a highprobability (50%) that the minimum air temper-ature will be #14 C for 1, 3, or 5 d starting onSeptember 24, October 1, or October 9, respec-tively. Moderate 1-, 3-, or 5-d probabilities(20–30%) begin on September 11, 22, or 25,respectively. Thus, pond harvest could be initi-ated during the period September 11–22 de-pending upon individual risk tolerance.

The probabilities of a minimum air tempera-ture #14 C also can be used to guide stock outand harvest decisions for other pond-culturedtropical species. Tilapia, Oreochromis spp., and

freshwater prawn, Macrobrachium rosenbergii,are two such species with temperature require-ments similar to L. vannamei. Additionally, min-imum air temperature probabilities may be usefulin planning for fish spawning, for example,hybrid striped bass.

Water Temperature Prediction

Knowing the probabilities that the minimumair temperature will be #14 C for one to fiveconsecutive days is useful for planning purpo-ses but does not estimate the resultant pondwater temperature. A predictive equation thatrelates one or more variables to water tempera-ture can be developed for a particular site orregion. Multiple regression analysis showedthat the minimum air temperature from thesame day and the maximum water temperaturefrom the previous day predicted the minimumpond water temperature at Pine Bluff best.Input data were analyzed for autocorrelationand collinearity, and the regression model wasfound to be acceptable. The regression equa-tion (R2 5 0.886) was as follows:

Tmin ¼ 1:4641 0:169Amin 1 0:727Tmax;

where Tmin 5 minimum pond water tempera-ture (C) for the current day, Amin 5 minimum

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

13-Apr 23-Apr 3-May 13-May 23-May 2-Jun 12-Jun

Prob

abili

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

30-Aug 9-Sep 19-Sep 29-Sep 9-Oct 19-Oct 29-OctPr

obab

ility



days during the spring and fall in Pecos, Texas.


air temperature (C) for the current day, Tmax 5

maximum pond water temperature (C) fromthe previous day.

The 95% confidence intervals for a (inter-cept), b1 (Amin coefficient), and b2 (Tmax co-efficient) were 61.326, 60.055, and 60.064,respectively. Maximum pond water temperatureon the previous afternoon and the minimum air

temperature that night accounted for 89% of thevariability in minimum pond water temperaturethe following morning. In catfish ponds inMississippi, the maximum air and pond watertemperatures from the previous day and the min-imum air temperature that night predicted(R2 5 0.88) the minimum pond water tempera-ture the following morning (Wax and Pote

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Prob

abili

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

30-Aug 15-Sep 1-Oct 17-Oct 2-Nov 18-Nov

Prob

abili

ty



days during the spring and fall in Pine Bluff, Arkansas.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0


Prob

abili

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

30-Aug 19-Sep 9-Oct 29-Oct 18-Nov

Prob

abil

ity



days during the spring and fall in Portland, Arkansas.

100 GREEN AND POPHAM

1990). In the Wax and Pote (1990) study, pondwater temperature was measured discretely atdifferent times and water depths at the differentsites, which may have excluded the true maxi-mum and minimum water temperatures andincreased the variability, and thereby requiredthree variables to predict minimum water tem-perature best.

The statistical model developed for Pine Bluffprovides, within the range of data evaluated,good prediction of minimum pond water tem-perature based on two variables that are mea-sured easily. Validation of the model using the2005 data set showed a close correspondenceof predicted and observed minimum pond watertemperatures (Fig. 11). While the model workswell for Pine Bluff, its application at other siteslikely will require refinement for local condi-tions of the parameters associated with eachterm in the model.

As farmers increasingly adopt data loggersto monitor pond dissolved oxygen concentra-tions and control aerator operation, pond watertemperature records will increase in magnitudeand scope. Long-term air temperature recordsoften are available from local weather stations.The low number of variables required for a pre-dictive statistical model for minimum pond

water temperature should facilitate its refine-ment and use. Once refined, the predictiveequation can be used in conjunction with thehistoric air temperature records to synthesizea long-term daily pond water temperaturerecord from which pond water temperatureprobabilities can be calculated. Predictive equa-tions were derived and used to develop a 36-yr record of morning and afternoon pond watertemperatures for channel catfish ponds inMississippi, and probabilities were calculatedfor minimum and maximum daily pond watertemperatures (Wax and Pote 1990). The avail-ability of specific water temperature probabili-ties should result in more informed decisionsregarding stocking and harvesting dates ofinland shrimp ponds.

Summary

Probabilities of the occurrence of minimumdaily air temperatures #14 C were calculatedfor one coastal and eight inland sites in thesouthern USA where L. vannamei is or couldbe grown in inland ponds. These probabilitiescan be used to guide pond management deci-sions for L. vannamei, as well as for otherpond-cultured tropical species such as tilapiaand freshwater prawn, at the beginning and

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

27-Feb

14-Mar

29-Mar

13-Apr

28-Apr

13-May

28-May

Prob

abili

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

30-Aug

12-Sep

25-Sep

8-Oct

21-Oct

3-Nov

16-Nov

29-Nov

Prob

abili

ty



days during the spring and fall in Tifton, Georgia.


end of the growing season when intrusion ofcold fronts may cause pond water tempera-tures to drop to critical levels. The probabilityof 1 d, three consecutive days, or five consec-utive days with a minimum air temperature#14 C is high (50%) from late March throughlate April at all inland sites except Arcadia,Florida. Probabilities drop to 10% for thethree scenarios by early May to early June.In the autumn, the 10% probability level isreached in early- to mid-September and the50% probability level is reached by late Sep-tember to mid-October. Arcadia, Florida, hasthe longest period with low probabilities oflow minimum air temperatures and Pecos,Texas, has the shortest period. The coastalsite, Harlingen, Texas, is warmer in the springthan the inland sites and in the fall, it is sim-ilar to Arcadia, Florida.

The maximum pond water temperature fromthe previous afternoon and the minimum airtemperature from that night predicted minimumpond water temperature the following morningfor Pine Bluff, Arkansas. This predictive equa-

tion was validated using data sets from a differ-ent year. Development of farm- or regional-levelpredictive equations should permit pond watertemperature probabilities to be calculated.

Acknowledgments

We thank Dr. S. Duke, Dr. P. Pearson, Dr. K.Schrader, and Dr. J. Westbrook for their com-ments and suggestions for improving this manu-script. Mention of trade names or commercialproducts is solely for the purpose of providingspecific information and does not imply recom-mendation or endorsement by the US Depart-ment of Agriculture.

Literature Cited

Byers, H. R. 1974. General meteorology, 4th Edition.

McGraw-Hill, Inc., New York, New York, USA.

Caissie, D., N. El-Jabi, and A. St-Hilaire. 1998. Stochas-

tic modelling of water temperatures in a small stream

using air to water relations. Canadian Journal of Civil

Engineering 25:250–260.

Cathcart, T. P. and F. W. Wheaton. 1987. Modeling

temperature distribution in freshwater ponds. Aqua-

cultural Engineering 6:237–257.

14.0

16.0

18.0

20.0

22.0

24.0

26.0

28.0

30.0

32.0

01-A

pr-0

5

15-A

pr-0

5

29-A

pr-0

5

13-M

ay-0

5

27-M

ay-0

5

10-Ju

n-05

24-Ju

n-05

08-Ju

l-05

22-Ju

l-05

05-A

ug-0

5

19-A

ug-0

5

02-S

ep-0

5

16-S

ep-0

5

30-S

ep-0

5

14-O

ct-05

Tem

pera

ture

(C

)

Predicted Observed

FIGURE 11. Predicted and observed minimum pond water temperatures in Pine Bluff, Arkansas, during 2005.

102 GREEN AND POPHAM

Erickson, T. R. and H. G. Stefan. 2000. Linear air/water

temperature correlations for streams during open

water periods. Journal of Hydrologic Engineering 5:

317–322.

Kettle, H., R. Thompson, N. J. Anderson, and D. M.

Livingstone. 2004. Empirical modeling of summer

lake surface temperature in southwest Greenland.

Limnology and Oceanography 49(1):271–282.

Klemetson, S. L. and G. L. Rogers. 1985. Aquaculture

pond temperature modeling. Aquacultural Engineering

4:191–208.

Lamoureux, J., T.R.Tiersch, andS.G.Hall. 2006. Pond heat

and temperature regulation (PHATR): Modeling tem-

perature and energy balances in earthen outdoor

aquaculture ponds. Aquacultural Engineering 34:

103–116.

Losordo, T. M. and R. H. Piedrahita. 1991. Modelling

temperature variation and thermal stratification in

shallow aquaculture ponds. Ecological Modelling 54:

189–226.

Morrill, J. C., R. C. Bales, and M. H. Conklin. 2005.

Estimating stream temperature from air temperature:

Implications for future water quality. Journal of Envi-

ronmental Engineering 131:139–146.

Wax, C. L. and J. W. Pote. 1990. A derived climatology

of water temperature for the Mississippi catfish

industry. Journal of the World Aquaculture Society 21:

25–34.

Webb, B. W. and F. Nobilis. 1997. Long-term

perspective on the nature of air-water temperature

relationship: a case study. Hydrological Processes 11:

137–147.

Webb, B. W., P. D. Clack, and D. E. Walling. 2003.

Water-air temperature relationships in a Devon river

system and the role of flow. Hydrological Processes

17:3069–3084.


probabilities of low nighttime temperatures during stocking and harvest seasons for inland shrimp...

Documents