All test locations show a similar pattern in the comparison of PET estimation on cloudy days. A detailed analysis of the test locations revealed that Licola, Mt. Tamboritha, Mt. Howitt and Barkley River Point have 5.1%, 2.8%, 1.9% and 3.2% highly underestimated days respectively, whilst the percentages of highly overestimated days were 4.6%, 11.2%, 13.3% and 7.6%. These values show that highly overestimated days are greater than the highly underestimated ones during cloudy day PET estimation.
4.2.2.3Mean annual PET
Mean annual PET values (calculated by considering both non-cloudy and cloudy days together) at each test location are shown in Table 4.5. Considering the percentage difference, this table shows that the estimated PET on the mean annual basis at Mt. Howitt and Mt. Tamboritha are slightly higher compared to the PM based estimates. In contrast, the estimated PET is slightly lower than the PM based PET at Barkley River Point. The difference at Licola is considerably high. This large difference at Licola may be due to the existing mix class of LULC, (i.e. bushes and farm houses) which may have led to overestimated surface albedo values in estimated PET, which in turn underestimated the estimated PET compared to the PM based PET.
Table 4.5 Estimated and PM based mean annual PET-Macalister catchment
Test Location Mean Annual PET (mm)
Difference (mm) % difference RS PM Barkley River 1001.3 1051.8 -50.5 - 4.8 Mt. Tamboritha 1070.5 1036.4 34.1 3.3 Licola 940.1 1098.2 -158.1 - 14.4 Mt. Howitt 978.9 928.6 50.2 5.4
4.2.2.4Comparison of PET estimates for total period and seasons
The potential evapotranspiration is mostly affected by physical factors such as net radiation (i.e. net available energy for PET), air temperature, pressure deficit and wind speed in the environment. RS data and air temperature were used as input data to compute net radiation in estimated PET estimation. However, wind speed and pressure deficit were not considered, and this may have caused differences in the estimated PET for both non- cloudy and cloudy days compared to the PM based estimates, which specifically accounted for wind speed and pressure deficit. Similarly, the net short wave radiation is the most
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significant component in estimating net available energy, which was calculated using the surface albedo in the estimated PET estimation, and hence the surface albedo is the most significant single variable in determining PET (Liang, 2001). The surface albedo was calculated from RS data which accounted for actual LULC on the surface. These calculated surface albedo values showed a difference from those values available in the literature corresponding to different LULC classes (Table 4.4). This difference may also have contributed to differences in the estimated PET compared to the PM based estimates which used literature values for surface albedo uniformly across the catchment. Allen et al. (2011) also noted that each PET estimation procedure has deficiencies because of the way these procedures model the complexity of the ET process, but the difference between estimated PET and real PET should be minimized for applications. They also reported that the range of this difference depends on the estimation procedure, and that the typical range for RS based estimates varies from 10 to 40 %. The mean annual PET comparison in Table 4.5 for the Macalister catchment showed that the differences of estimated PET were within this range.
The estimated PET and PM based PET estimates were considered as base values in comparing the estimated PET; however the accuracy of the PM based PET has been questioned especially on days with extreme PET values (Barton and Meyer, 2008). The PM method uses a uniform surface albedo value to estimate net available energy spatially and temporally. However, the RS based surface albedo considered the actual spatio- temporal changes in the catchment. Furthermore, the PM method uses uniform values for soil heat flux and slope of saturation pressure curve for the entire catchment, which were not considered at all in RS based estimates. Therefore, the handling of surface albedo, soil heat flux and slope of the saturation curve in the two methods were different, and consequently produced different results.
The RS based and PM based estimates were compared in statistical terms at the selected four test locations of the Macalister catchment (in Figure 4.10), and are shown in Table 4.6. The comparison is shown for both the total period and the seasons. Winter, spring, summer and autumn are defined in this table by their monthly blocks of June-August, September-November, December-February and March-May respectively. Winter is the wettest period during the year followed by spring, while summer and autumn are drier.
4-23 *RMSE is expressed in mm day-1
The performance measures used in this table – Root Mean Square Error (RMSE) and Nash- Sutcliffe efficiency (Ef) – were explained in Section 3.9.
Table 4.6 Performance indices of estimated PET and PM based PET - Macalister catchment
Location Day Condition Total Period
Seasons
Winter Spring Summer Autumn
RMSE* Ef RMSE Ef RMSE Ef RMSE Ef RMSE Ef
Barkley River Non-cloudy days 0.99 0.68 0.89 -0.85 0.73 0.42 1.19 -0.66 0.79 0.52 Cloudy days 1.02 0.61 0.63 -1.61 1.14 0.04 1.40 -0.30 0.98 0.28 Both 1.02 0.64 0.66 -1.40 1.09 0.12 1.36 -0.23 0.82 0.50 Mt. Tamboritha Non-cloudy days 0.93 0.72 0.74 -0.19 0.70 0.51 1.08 -0.49 0.90 0.42 Cloudy days 1.10 0.56 0.48 -0.63 1.31 -0.22 1.49 -0.51 1.04 0.26 Both 1.08 0.60 0.51 -0.47 1.24 -0.08 1.43 -0.37 0.92 0.42 Licola Non-cloudy days 1.15 0.56 1.04 -1.94 0.81 0.24 1.41 -1.28 1.02 0.16 Cloudy days 1.08 0.57 0.77 -2.82 1.01 0.23 1.39 -0.31 1.15 -0.13 Both 1.09 0.58 0.80 -2.57 0.98 0.25 1.39 -0.33 1.04 0.13 Mt. Howitt Non-cloudy days 0.86 0.73 0.87 -0.59 0.71 0.48 0.98 -0.22 0.76 0.53 Cloudy days 1.11 0.48 0.50 -0.95 1.33 -0.38 1.53 -0.63 0.89 0.38 Both 1.09 0.53 0.52 -0.76 1.26 -0.20 1.44 -0.45 0.78 0.52
Table 4.6 shows that the calculated RMSE for the total period varies from 0.86 mm day-1 to 1.15 mm day-1 for all day conditions (i.e. non-cloudy, cloudy, and both cloudy and non- cloudy). All locations except Licola showed slightly lower RMSE during non-cloudy days compared to the cloudy days. Table 4.6 also shows that the magnitude of RMSE changes with season, with the highest RMSE occurring during summer. Spring shows the highest variation in seasonal RMSE (0.70 to 1.33) and autumn shows the lowest (0.76 to 1.15), irrespective of the day conditions. The RMSE of non-cloudy days is less than in other day conditions in all seasons except winter.
Table 4.6 shows that Ef over the Macalister catchment during the total period represents
higher values irrespective of the day conditions, but is reduced with seasons. The Nash- Sutcliffe efficiency values vary from 0.48 to 0.72 during the total period over all test locations. Generally, Ef of non-cloudy days at all test locations show higher values
compared to cloudy days and both non-cloudy and cloudy day conditions during the total period, due to more accurate estimates of surface albedo and net available energy on non-
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cloudy days than on cloudy days. The highest Ef value was observed for non-cloudy days
at Mt. Tamboritha and the lowest at Licola for the same day condition. The findings in terms of RMSE and Ef values are consistent at each location, showing the expected inverse
correlation of RMSE and Ef.
The seasonal analysis shows a mixed result in Ef values. Autumn shows highest Ef value in
all test locations irrespective of the day condition. Spring has comparatively higher Ef than
winter and summer, and both winter and summer seasons show negative Ef under all day
conditions. Table 4.6 shows that non-cloudy day Ef values perform better than those of the
other day conditions during seasons. When Ef values for the total period are compared with
those of the seasons, it was found that the total period Ef values were not within the range
of seasonal Ef values. This has also been observed by Wang et al. (2006) and Sachindra et
al. (2013). This is because of the significant difference between the total period mean (which was used to calculate Ef for the total period) and the seasonal mean of the particular
seasons (which was used to calculate for the seasonal Ef).
Mt. Tamboritha and Mt. Howitt show better results than the other locations for both total period and seasons. Both these locations have relatively homogenous LULC class, while the other two locations have mixed LULC classes. This show that the estimated PET are relatively closer to the PM based PET over homogenous LULC classes during the total period as well as the seasons. This is mainly due to the similarity and consistency of surface albedo values computed from RS data for the catchment (used in estimated PET) and obtained from literature (used in PM based PET estimates). Furthermore, Licola shows the poorest performance with both RMSE and Ef compared to the other locations during the
total period as well as the seasons. This is because Licola has mixed LULC, which has been considered in the RS based method in estimating surface albedo. Mixed LULC information is, however, not considered in the PM method in estimating surface albedo.
4.2.3
Landuse/landcover classification
The spatial distribution and the extent of the landuse/landcover (such as forests, meadows, agricultural land, urban, bare and water bodies) in a catchment influence the spatio- temporal dynamics of evapotranspiration, surface runoff, soil moisture and ground water recharge. Therefore, related landuse/landcover (LULC) information in a catchment is