Several methods are reported in the literature for determining flux direction [ 36 , 44 , 53 , 54 ]. One is based on the view that the atmosphere is typically stable at night and becomes unstable during the day [ 41 ]. To account for this pattern, assuming H stable for nighttime hours and H unstable for daytime can be used to assign H direction H sun.
Another method of determining flux direction H dT is to use the temperature gradient as determined from two co-located air temperature sensors at different heights [ 43 ]. The temperature gradient provides an indication of the stability of the atmosphere near the surface, as well as an indication as to the direction of sensible heat flux since sensible heat will flow across the gradient from high to low.
In stable atmospheric conditions, the gradient indicates air temperature increases with height. This will cause heat energy to flow down into the crop canopy and indicate negative H. In an unstable atmosphere, temperature decreases with height, so heat will move vertically and away from the canopy, resulting in positive H.
In , air temperature was measured at 1. The air temperature measurement heights provided a measurement at or below and above the SLS beam for both years. Both of the described methods were used to determine ET sls and compared to the lysimeter to determine the effects of H sls direction methodology. For H sun , the daytime and nighttime hours were determined using sunrise and sunset times for the study period a. CDT, on average, respectively.
Evaluation of techniques for estimating reference evapotranspiration for Jawa-Indonesia
The layout of the soil sensors within the lysimeter is presented in Figure 2. The instruments were connected to a datalogger CR6, Campbell Scientific with a measurement frequency of six seconds and averaged to an hourly interval. Soil sensor placement within the lysimeter. Shown are relative placement of soil heat flux SHF plates, model TDR soil water content sensors, and subsurface drip irrigation lines.
The TDR sensors give accurate soil temperature readings, eliminating the need for thermocouples where they are used. The soil water and temperature data were used to calculate G at the soil surface by the calorimetric method, and surface G was used in all energy balance calculations. The calorimetric method used in this study was described by Colaizzi et al. Using measured R n and surface G provided the available energy to accompany the H measurement and LE calculation. H was back-calculated from the lysimeter by converting the ET lys to LE , summing LE with surface soil heat flux and measured net radiation, and treating the residual of this energy balance to H.
LE was calculated from ET by:. A south-facing, nadir IRT installed 3 m above the lysimeter was used to determine the surface temperature, which was then used to calculate the latent heat of vaporization for each hourly period. Using the ET lys data, the LE was calculated by multiplying the ET lys by the latent heat of vaporization and dividing by s to convert to the hourly time period.
Evaluation of Reference Crop Evapotranspiration Equations in Various Climates | SpringerLink
During nighttime hours, LE , and subsequently ET, become very small [ 56 ]. The much smaller values can exhibit much more relative variation; even though the magnitude of the differences may be small, relative percentage differences can be large.
To determine the effects of including nighttime ET sls , evaluations were also conducted using only daytime ET sls data. This analysis provided an indication as to how much of the overall variation was influenced by the much smaller nighttime values. As most water management practices and modeling efforts use a daily time step, the 24 hourly periods were summed to provide an evaluation for daily ET sls data.
The h summation was performed for both ET lys and ET sls. Since some hourly intervals were omitted due to operations on the lysimeter, daily lysimeter data midnight—midnight mass change as determined by the processes outlined in Marek et al.
An exceptionally wet year occurred in for the study location, which may have caused greater evaporation and thus higher ET lys values as compared to for the same date range. Since the lysimeter field is irrigated using subsurface drip, the soil surface is typically dry and evaporation is minimized. When a precipitation event occurs, the soil and plant surfaces become wet and experience evaporation that does not occur with irrigation as the irrigation water typically does not reach the soil surface.
In addition, during the data period for , the sorghum crop was still in a vegetative growth stage with higher relative ET. The data for encompasses vegetative growth of the corn crop, but also, late season reproductive growth and grain filling. During the corn grain fill stage, ET lys is lower and possibly reduced the overall hourly average values. Both years exhibit the pattern of lower ET lys during the night hours, increasing from sunrise to a maximum around and then decreasing. Although ET lys is drastically reduced during the night, the measured ET lys values illustrate that a small amount of ET does occur at night, which is not negligible [ 56 ].
Crop height for a and b , LAI for c and d , and daily lysimeter ET for the e and f study periods. This similarity leads to the inference that the energy balance closure error results from error in H dt as compared to H lys. The results of the error analyses for the turbulent fluxes and ET sls are presented in Table 1. Even though the H sls had weak correlation with the H lys , and a large error, the resulting ET sls had a much smaller error, especially for daily ET sls. This indicates that errors in determining H do not have a drastic impact on ET sls. In irrigated agriculture, H is commonly small, especially compared to LE.
Large errors in a small component may not result in large error in the final product. Results for the AE and incoming solar irradiance regressed against ET lys are also included in Table 1. This analysis showed the extent of error associated with estimating ET without measuring H. The slopes for the regression equations from AE and irradiance are provided in Table 1 ; however, the slope values are much larger than for ET and H since AE and irradiance values are considerably larger than ET. Errors for ET were larger using AE and irradiance as predictors of ET, which indicates that although large errors exist for H sls , ET sls errors are still lower when accounting for the H component.
The error rates and small error suggest that potential exists for additional research and potential improvements in SLS measurements for H, and possibly the resulting ET sls. The average daily energy balance components for the lysimeter are presented in Figure 4. Since R n is the main energy input, when one component LE increases, another component H should decrease.
This is not always the case as the environment in the Texas High Plains commonly generates advection where heat energy is transferred from a warmer, typically drier adjacent field to cooler irrigated fields.
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Evidence of advection is also seen in cases where LE exceeds R n [ 7 , 57 ]. The influx of heat energy from nearby warmer fields increases the energy available for evaporation of water during transpiration, thus increasing the latent heat flux. Average daily energy balance components on the lysimeter for a ; b Figure 5 presents average daily data for all four components of the energy balance equation as determined by the SLS.
For the SLS, the temperature difference was used to determine flux direction for H shown in Figure 5. In some instances, LE is greater than R n during the daytime, which in this case, corresponds to instances where LE was greater than R n with the lysimeter. This usually indicates the occurrence of advection rather than an LE error since the same higher LE was measured on the lysimeter for the same dates. Average daily energy balance components for the SLS for a ; b The results from the statistical analyses of H showed that using the temperature gradient produced a lower error than using H sun.
The assumption that the atmosphere was stable at night and unstable during the day proved to not always be true. Comparisons of H dT and H sun are presented in Figure 6. The SLS overestimated H as compared to the lysimeter, which is illustrated by Figure 6 as well as the slope of the regression equation 0. The lysimeter showed a broader range of H values compared to the SLS, although mostly with regard to negative flux values.
The measurement footprints of the lysimeter and SLS are not exactly the same.
The lysimeter measures a 3 m by 3 m square whereas the SLS measures an average across the m path, which is 25 m north of the lysimeter. Although care is taken to ensure the lysimeter is representative of the surrounding field, the difference in measurement footprint may contribute to the error in H. Average hourly H directions, as indicated by the temperature gradient, for the study period are presented in Figure 7. A clear diurnal pattern is found with the flux direction, as it is common to find a stable atmosphere during nighttime hours and an unstable atmosphere during the daytime.
Having a greater occurrence of stable atmospheric conditions was not expected as the atmosphere is typically unstable during the day, and the day length was longer than the length of nighttime period. Seeing more stable conditions than unstable indicates that stable atmospheric conditions must have occurred during some of the daytime hours, as also indicated by Figure 7.
With being one of the wettest years on record for the study area, the relative humidity was greater for than In addition, the average wind speed was lower for compared to 3.
The lower wind speeds and greater humidity in may have allowed the atmosphere to stay more stable than in The greater humidity could have allowed the atmosphere to hold more heat and the lower wind speed could have reduced turbulence and atmospheric mixing. Temperature difference calculated as the difference between values from two air temperature sensors at different heights—the lower sensor minus the higher sensor and used as the indicator for flux direction. Coefficients of determination ranged from 0. Although the slopes are close to 1, hourly ET sls is slightly overestimated as compared to the lysimeter.
RMSE values ranged from 0. This is possibly due to the timing of the data. The same period of 29 July—22 August for had a similar error to 0. The period before 29 July, from 23 June to 28 July had an error of 0.
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- 1. Introduction.
The sorghum crop in reached maximum plant height around 21 August, so the data were collected over at least a partially growing crop. The corn crop in reached maximum height around 15 July, so both later periods included the same crop height; however, the latter period was during grain fill when the corn ET values were less. For the period of 29 July—22 August , average daily ET lys was 6. In addition to the crop growth, the weather may have contributed the differences in accuracy of ET sls for and Comparing ET ref which is calculated based on weather measurements for the same date range of 29 July—22 August in and , had larger values than see Figure 9.
Differences in ET ref between the years were significant with a p -value of 0.
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