Wind direction and footprint analysis
Figure 3 shows different aspects of the experiment site. Figure 3A is a picture of the tower obtained on 04/30/2019, showing the relative position of the instruments, including the 3D sonic anemometer and the ultra-thin thermocouple (TPUF). Figure 3B shows the compass rose made from 30-minute average data obtained with the sonic anemometer in the period from April 17 to July 25.
The compass rose in Figure 3B shows that the wind blew predominantly (63% of frequency) from the east-south sector with the following distribution: 21% from the south-southeast direction (SSE), 17.4% from the SE direction, 11, 5% from the east-southeast direction (ESE) and 13.2% from the E direction. In each direction, the average wind speeds were 1.13 m s-1 (SSE and SE), 1.23 m s-1 (ESS) and 1.5 m s-1 (E). Figure 3C shows an average footprint for the entire measurement period on an image of the experimental area obtained from Bing, regardless of the change in sonic anemometer height during the period. Overall, the compass rose and the footprint of the turbulent flows are in agreement.
The distance “seen” by the sonic anemometer in the upwind direction totals approximately 50 m on average from the micrometeorological tower. In the prevailing wind direction (SSE) the area of contribution of 90% of turbulent flows is more pronounced.
Calibration of surface air renewal method
Table 2 shows the calibration factor (α), the coefficient of determination (R2) for the regressions through the origin when H of the eddies covariance (HEC) was plotted against the H estimated by air renewal (H'). Data are presented separately for each atmospheric stability condition according to crop growth and change in heights of rapid response sensors and are also presented for the entire measurement period.
Table 2
Calibration of the air renewal method as a function of atmospheric stability conditions and height of rapid response sensors.
atmospheric condition
|
height of plants
(cm)
|
Height
of the TP
(cm)
|
calibration factor
|
r2
(HEC x H')
|
Unstable
|
60 - 150 (a)
|
180
|
1.11
|
0.918
|
150 - 200 (b)
|
200
|
0.99
|
0.917
|
200 (c)
|
250
|
0.87
|
0.931
|
Every period (d)
|
-
|
0.98
|
0.912
|
Stable
|
60 - 150
|
180
|
0.60
|
0.783
|
150 - 200
|
200
|
0.87
|
0.756
|
200
|
250
|
0.78
|
0.669
|
every period
|
-
|
0.74
|
0.713
|
Both
|
60 - 150
|
180
|
1.05
|
0.886
|
150 - 200
|
200
|
0.98
|
0.897
|
200
|
250
|
0.86
|
0.897
|
every period
|
-
|
0.96
|
0.887
|
(The) April 17th to May 27th; (b) May 28 to July 1; (c) July 2nd to July 25th; (d) April 17th to July 25th |
Under stable atmosphere conditions (H < 0), RAS overestimated the sensible heat flux H' relative to HEC obtained via eddy covariance, resulting in α values consistently lower than 1 in the three intervals (Table 2) and 0.74 for the entire measurement period. For unstable atmosphere conditions (H > 0), α was greater than 1 in the first interval, approximately 1 in the second interval, and less than 1 in the third interval with a seasonal value of 0.98. According to several authors (HU et. al., 2018; McELRONE et. al., 2013; PARRY et. al., 2019), a calibration factor α to correct H obtained via surface air renewal is necessary when there is uneven heating of the soil surface air portion up to the height of measurement of temperature fluctuations. Therefore, based on the seasonal values of α shown in Table 2, it can be said that the method of surface air renewal on the cassava crop performed better for estimating H when unstable atmosphere conditions prevailed. The tendency of RAS to perform better under such conditions was first verified by the method proponents (PAW U et al., 1995) and has been confirmed in several other studies.
Overall, the coefficient of determination (r2) was greater than 0.90 under unstable atmosphere compared to stable atmosphere. Other authors reported values even higher than those in Table 2 (MEKHMANDAROV et. al., 2012; HOLWERDA et. al., 2021).When considering both conditions (unstable + stable) and, therefore, the entire measurement period, the value of α ranged from 0.86 to 1.05 with an average of 0.96, this very close to 1 and among those found separately for each atmospheric stability condition. The same happened with the values of r2.
Figure 4 graphically illustrates the seasonal calibration of the classical RAS method on industrial cassava crop under both unstable and stable conditions. Figures 4A and 4B show that the agreement between HEC and H' was excellent even before calibration, with a coefficientα of 0.98 as previously shown in Table 2. Under stable atmosphere condition (Figures 4A and 4B), RAS consistently overestimated H (α = 0.74) with higher errors associated with more negative values of H'. But after calibration, HSR became highly correlated with HEC, as expected.
The presence of H values that differ from the other observations (outliers) (Figure 4C and 4D) was more present in the RAS method, in both atmospheric conditions, with emphasis on unstable condition, with a greater number of outliers. At the median level, the values were similar, even before and after calibration, -0.783 W m-2 and 49.347 W m-2 for stable and unstable condition with the RAS method and -0.878 W m-2 and 48.312 W m- 2 for the CT method.
Figure 5 shows the calibration process of the surface air renewal method in relation to the eddies covariance. As mentioned earlier, Figure 5A highlights a calibration coefficient α close to 1 before calibration, which indicates a strong trend of uniform heating and cooling of the air mass from the ground surface to the air temperature measurement heights with the thermocouple ultra-thin, as highlighted byShapland et. al. (2012a, 2012b); Shapland et. al. (2013). Figure 5B shows the calibration result with excellent agreement between HEC and HSR.
The correlation between LEEC and LESR is high (R2 > 0.95) (Figure 5C) as expected as the agreement between HEC and HSR was also high and both LEEC and LESR are calculated from the same set of values of Rn and G. The data suggest that a single calibration coefficient equal to 0.96 can be used for cultivation conditions and climate similar to those presented in this work, with the aim of estimating the sensible heat flux (HSR) in other years of planting provided that the conditions are approximately the same, that is, the same variety is cultivated in the same spacing conducted under rainfed conditions.
Furthermore, the value found for the calibration coefficient α being very close to 1, there is the possibility of using the surface air renewal method with this crop and under the conditions mentioned above without the need for calibration, which in principle would be An ultra-thin thermocouple installed around 50 cm above the crop is sufficient to colLEEC air temperature data and direct application of the air renewal method to determine H.
Energy balance components
The diurnal variations of these components for the months of May and June are shown in Figure 7. For the months under study, the maximum LE values were observed on 05/03/2019 and 06/16/2019, respectively, in the order of 460 .56 and 332.45 W m-2. From the data on global solar radiation, days (13/05 and 17/06) different from those mentioned above were identified, these have high cloudiness, whose LE values represent the lowest for the period, 180.34 and 65.23 W m-2. For 05/13/2019 and 06/17/2019, the sensible heat fluxes are practically equivalent to the heat flux in the soil, which indicates that there was little energy available to heat the air and the soil, and that 90% de Rn was used for the processes of loss of water to the atmosphere, which is in agreement with Jensen and Allen, (2016) and Gao et. al. (2020).
For both dates 05/03/2019 and 06/16/2019 observed in Figure 7, daily average of G was negative, therefore, all the heat was released to the ground. The opposite situation occurred on the dates of 05/13 and 05/17 when the largest portion of Rs was not converted into LE, which represented about 17.45% and 14.34%, corresponding to an (EF) Evaporative Fraction, EF = LE/(RG) around 56.72% and 51.95%, respectively. For the dates 05/03/2019 and 06/16/2019 the highest available energy resulted in evaporative fraction 79.64% and 69.22% respectively.
Figure 8 shows the relationship between components of the radiation/energy balance in the cultivation of industrial cassava from hourly averages of the data collection period in the experimental area.
Figure 8B shows the relationship between soil heat flux (G) and net radiation (Rn). During the entire period of data collection, which coincided with the vegetative phase of the cassava crop, the degree of ground cover was visually significant, especially because measurements started when the plants had an average height of 60 cm and a predominance of cloudy days. The average value found for the G/Rn ratio was only 6%, which is explained not only by the soil cover by the crop, but also by the proliferation of weeds, considering that the crop was conducted under rainy conditions. In addition to the change in soil water content, the type of cover is a factor responsible for variations in soil heat flux.
Figure 8C shows the relationship between the sensible heat flux obtained via air renewal on the HSR surface and the net radiation Rn. The average H/Rn ratio with this method was around 22%, indicating that most of the available energy must have been used for water evaporation, whose LE/Rn ratio was around 72%, considering that the culture was conducted under rainfall conditions. The period from March to September is the wettest in the region with over 70% of annual precipitation concentrated in these months. Rain data were not collected during the experimental period.
From the point of view of partitioning the energy available for sensible heat fluxes, the values presented here are consistent with those found by Lima et al. (2011) in work carried out with beans in dry conditions. The H/Rn ratio found by the authors ranged between 0.23 and 0.34. In a work involving different types of coverage and associated with the cultivation of cassava, Attarod et al. (2005), using the Bowen Ratio, verified that at the closing of the energy balance the LE/Rn ratio was 0.72 in periods with high water availability and 0.54 in periods of low water availability.
According to Zhou et al. (2012) this high LE/Rn ratio is expected because without water restriction and with a high LAI (current crop phase, 150 DAP) there is an increase in transpiration, thus contributing to higher LE/Rn values and vice versa.
In an area of irrigated cotton Bezzera et. al. (2015), working in a period of high-water availability, found that the LE/Rn ratio was 0.70, with the highest values occurring when the soil was wetter. In this same work, the authors verified that for two consecutive years (2008 and 2009) the G/Rn ratios were 10% and for H/Rn 17% in 2008 and 16% in 2009. Gao et. al. (2020) performed a comparison of evapotranspiration and energy partition related to the main biotic and abiotic controllers in vineyards using different irrigation methods. The authors found that the LE/Rn ratio was 0.75, H/Rn 0.13 and G/Rn equal to 0.12.
Similar values for another crop, that of beans, were reported by Lima et al. (2005), whose LE/Rn ratio was 0.71. Whereas Neves et al. (2008), in the opposite condition of water availability, when quantifying the components of the energy balance in cowpea beans, they found mean values of LE/Rn equal to 0.21, mainly due to the low water availability throughout the crop cycle.