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Identification of the Meteorological Variables Influencing Evapotranspiration Variability Over Florida

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Evapotranspiration (ET) plays an important role in agricultural water management and crop modeling. The highest mean annual ET values (889–1016 mm) in the United States (US) occur in Florida where there is a combination of ample rainfall (R) and warm air temperatures. Therefore, it is crucial to know the synergistic influence of meteorological variables (MV) on ET variability. This study aims to evaluate how reference evapotranspiration (ET0) over Florida from 2008 to 2018 was influenced by MV using simultaneous changes in all variables. These changes were evaluated interannually, seasonally, monthly, and daily using trend analysis. We used weather information including R, relative humidity (RH), solar radiation (SR), wind, and temperature parameters as well as ET0 from 33 synoptic stations over Florida recorded by the Florida Automated Weather Network (FAWN). Results of this study showed that SR had the strongest positive annual correlation with ET0 for all climate regions over Florida. However, temporal analysis showed that during December and January, temperature was the dominant factor to control variations of ET0 which was highly consistent with anomalies of ET0 and temperature parameters in December. The correlation coefficients between ET0 and RH were negatively higher than −0.6 from May to September, compared to the entire year, where RH was negligible (between −0.1 and −0.2). The significant trend of air and soil temperature, SR, and RH might be considered as an early alarm system for climate variability over Florida. Finally, sensitivity analysis revealed that ET0 changed at least 1% for 16–18% variations of MV in 67% of the weather stations (22 stations); this range (16–18%) can be assigned as an average range to force ET0 to change at least 1% across Florida. The results of this study can be used as a guideline to assess the annual, seasonal, and monthly relationships between the most influential MV and ET0 across Florida, as a source to identify the most sensitive MV in the modeling of ET related studies, and as the base to develop climate-based management plans for agricultural water management.

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Availability of Data and Material

The availability of data and materials for this study came from public or university owned platforms. Data is publicly available at



Atlantic Multi-decadal Oscillation


Barometric pressure

CO2 :

Carbon dioxide


Dew point temperature

ea :

Actual vapor pressure


El Niño-Southern Oscillation

ERRp :

Error range

es :

Saturation vapor pressure



ET0 :

Reference evapotranspiration


Reference evapotranspiration obtained for maximum value of each variable


Reference evapotranspiration obtained for minimum value of each variable

\({\mathrm{ET}}_{0\mathrm{i}}\) :

ith reference evapotranspiration

\(\overline{{\mathrm{ET} }_{0}}\) :

Average of daily reference evapotranspiration


Food and Agriculture Organization of the United Nations, Version 56-Penman Monteith


Florida Automated Weather Network


Number of ties


Soil heat flux

H0 :

Null hypothesis


Alternative hypothesis


Humid Subtropical


A counter


Intergovernmental Panel on Climate Change

Meti :

ith value of meteorological variables

\(\overline{\mathrm{Met} }\) :

Average of daily values of meteorological variables




Meteorological variables


Number of observation data


North Atlantic Oscillation


Pearson’s correlation coefficient




Relative humidity

Rn :

Net radiation


Summation of signs


Solar radiation


Average daily air temperature


Average air temperature


Minimum air temperature

ti :



Tropical rainforest


Tropical savanna


Soil temperature


Maximum air temperature


Mean daily wind speed


United States




Wet bulb temperature

\(\mathrm{WB}\_\mathrm{i}\) :

Wet bulb temperature at time i

\({\mathrm{WB}}_{\mathrm{i}-1}\) :

Wet bulb temperature at time i-1


Wind direction


Wind speed

xi :

Data values in years i

xj :

Data values in years j




Psychrometric constant


Slope of the saturation vapor pressure-temperature curve


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This research is based upon work supported by the National Institute of Food and Agriculture, US Department of Agriculture, Hatch project under accession number #1021250.

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Conceptualization, Mohammad Valipour and Sandra Guzmán; data retrieval and data analysis, Mohammad Valipour. Original draft preparation, Mohammad Valipour. Manuscript review and editing Sandra Guzmán.

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Correspondence to Sandra M. Guzmán.

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The MK test is defined as follows:

$$\mathrm{S}={\sum }_{\mathrm{i}=1}^{\mathrm{i}=\mathrm{N}-1}{\sum }_{\mathrm{j}=1+1}^{\mathrm{j}=\mathrm{N}}\mathrm{sign}\left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right),$$
$$\mathrm{sign}\left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)=\left\{\begin{array}{c}1 if \left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)>0\\ 0 if \left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)=0\\ -1 if \left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)<0,\end{array}\right.$$
$$\mathrm{VAR}\left(\mathrm{S}\right)=\frac{1}{18}\left[\mathrm{N}\left(\mathrm{N}-1\right)\left(2\mathrm{N}+5\right)-{\sum }_{\mathrm{i}=1}^{\mathrm{i}=\mathrm{g}}{\mathrm{t}}_{\mathrm{i}}\left({\mathrm{t}}_{\mathrm{i}}-1\right)\left(2{\mathrm{t}}_{\mathrm{i}}+5\right)\right],$$
$$Z=\left\{\begin{array}{c}\frac{\mathrm{S}-1}{\sqrt{\mathrm{VAR}\left(\mathrm{S}\right)}} if S>0\\ 0 if S=0\\ \frac{\mathrm{S}+1}{\sqrt{\mathrm{VAR}\left(\mathrm{S}\right)}} if S<0,\end{array}\right.$$

where VAR is the variance, S is the summation of signs, Z is Z-statistics, xj and xi are the data values in years j and i, respectively, with j > i, ti indicates ties, and g indicates number of ties. The MK test tests whether to reject the null hypothesis (H0) and accept the alternative hypothesis (Ha), where H0, no monotonic trend, and Ha, monotonic trend is present.

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Valipour, M., Guzmán, S.M. Identification of the Meteorological Variables Influencing Evapotranspiration Variability Over Florida. Environ Model Assess 27, 645–663 (2022).

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