Identification of the Meteorological Variables Influencing Evapotranspiration Variability Over Florida

Abstract

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.

Annex

The MK test is defined as follows:

$$\mathrm{S}={\sum }_{\mathrm{i}=1}^{\mathrm{i}=\mathrm{N}-1}{\sum }_{\mathrm{j}=1+1}^{\mathrm{j}=\mathrm{N}}\mathrm{sign}\left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right),$$

(6)

$$\mathrm{sign}\left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)=\left\{\begin{array}{c}1 if \left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)>0\\ 0 if \left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)=0\\ -1 if \left({\mathrm{x}}_{\mathrm{j}}-{\mathrm{x}}_{\mathrm{i}}\right)<0,\end{array}\right.$$

(7)

$$\mathrm{VAR}\left(\mathrm{S}\right)=\frac{1}{18}\left[\mathrm{N}\left(\mathrm{N}-1\right)\left(2\mathrm{N}+5\right)-{\sum }_{\mathrm{i}=1}^{\mathrm{i}=\mathrm{g}}{\mathrm{t}}_{\mathrm{i}}\left({\mathrm{t}}_{\mathrm{i}}-1\right)\left(2{\mathrm{t}}_{\mathrm{i}}+5\right)\right],$$

(8)

$$Z=\left\{\begin{array}{c}\frac{\mathrm{S}-1}{\sqrt{\mathrm{VAR}\left(\mathrm{S}\right)}} if S>0\\ 0 if S=0\\ \frac{\mathrm{S}+1}{\sqrt{\mathrm{VAR}\left(\mathrm{S}\right)}} if S<0,\end{array}\right.$$

(9)

where VAR is the variance, S is the summation of signs, Z is Z-statistics, x_j and x_i are the data values in years j and i, respectively, with j > i, t_i 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.