4.3 The performance comparison of all the models
By comparing the three type models, we found that the radiation-based
models display better performance(small RMSE) when compared with the
Penman family models and temperature-based models, average RMSE is 1.09
mm d−1, 1.26 mm d−1 and 1.29 mm
d−1 for radiation-based models, combination models and
temperature-based models, respectively (Table 2). The better performance
of the radiation-based models compared with other two type models might
be attributed to the incorporation of important input parameter such as
net radiation. Although the combination models also include net
radiation parameter, the combination models incorporate much other
meteorological factors that not mainly controlling ET except for net
radiation, which lead to a poor performance for combination models. In
addition, the radiation-based models require less meteorological input
compared with combination models, and the net radiation was the
dominated factor that controlling the ET for this humid region. Overall,
most radiation-based models generally underestimated the measured ET
during the whole study period, whereas the temperature-based models
tended to overestimate ET. This is consistent with previous studies
where the Makkink and Priestley-Taylor models generally underestimated
ET (Fooladmand & Haghighat, 2010; Priestley & Taylor, 1972; Xu &
Singh, 2002), while the Hargreaves equations often overestimate ET in
cold-humid conditions and requires a local calibration (Berti, Tardivo,
Chiaudani, Rech, & Borin, 2014). Given that the study region in our
study belongs to humid alpine meadow, thus ET tended to be
overestimated. An alternative explanation for the poor performance of
the Hargreaves model in humid regions may also relate to the
Ra parameter used in the Hargreaves model (Fontenot,
2004), which is based on the maximum possible radiation value and does
not take the atmospheric transmissivity into account. However, the
atmosphere transmissivity in humid regions is affected by many factors,
such as atmospheric moisture; thus, the solar radiation reaching the
surface is significantly reduced due to the high atmospheric moisture
content (Temesgen, Allen, & Jensen, 1999), resulting in the
overestimation of solar radiation, ultimately leading to an
overestimation of ET by the Hargreaves method.
Furthermore, there were also common features of all three groups of
models. All the models tended to underestimate the measured ET during
the growing season (with larger evaporative demand), and overestimated
ET during the non-growing season (with reduced evaporative demand),
which was consistent with a previous study conducted in a semi-arid
region (Liu et al., 2017b). Furthermore, we found that the measured ET
and calculated ET0 were less correlated during
non-growing season than during growing season. These discrepancies may
relate to the dominant component between transpiration and evaporation.
The transpiration was the dominant during growing season, almost account
for 75% of evapotranspiration, whereas the evaporation was the dominant
component during non-growing season in the same study site (Zhang et
al., 2018). Considering the evaporation process was much complex and
affected by many environmental factors compared with transpiration
process, ultimately lead to a poor correlation between measured ET and
calculated ET0 during non-growing season. Therefore,
both Hargreave’s equations and other models need further local or region
calibration before being applied to a given region (Xu & Singh, 2002).
Besides, it should be noted that the data used in this study just
obtained from a single lysimeter and a single weather station, which may
insufficient to represent the whole humid climate or the alpine
ecosystem but represent a specific site. Thus, more lysimeter systems
should be used in the alpine ecosystem in the future to obtain more
accurate estimates of evapotranspiration over the northeastern
Qinghai-Tibetan Plateau.