Why is WaPOR overestimating when ETIa is low?
ETIa-WPR is overestimating ETa in dry, hot, water-stressed conditions
(e.g., water-limited). The ETIa-WPR estimates for prolonged dry weather
and the dry seasons of WaPOR are usually higher than the observed values
(flux towers, field). These overestimations are small in terms of
absolute values (mm/day) but can lead to overestimation of results in
higher annual ETIa-WPR when compared to water mass balance checks of
river basins. The overestimation in dry regions is likely to be
primarily due to the functioning of the SMC constraint or the too high
SMC in dry regions.
The WaPOR SMC is considered, on average, high in arid regions (e.g.,
Figure 6) and therefore, ETIa-WPR is likely not effectively accounting
for soil moisture limitations. The high SMC is resulting in an
overestimation of the evaporation component in particular, as NDVI is
low and therefore the region is dominated by the evaporation component
of ETIa-WPR. Arid regions should be largely regulated by water
availability rather than energy. Conversely, under well-water
conditions, the PM method is primarily driven by Rn (e.g. energy
limited) (Rana & Katerji, 1998). As PM is a linearised approximate
solution, problems may occur in extreme conditions and errors in the
soil evaporative term (Leca, Parisi, Lacointe, & Saudreu 2011). Majozi
et al., (2017b) noted that PM methods need to include a SMC constraint.
Though the ETIa-WPR methodology does include a SMC constraint,
overestimations in SMC are reducing its functionality. The SMC is
estimated using the trapezoidal method (function of NDVI and LST) (FAO,
2018). Where the NDVI is low, the LST component could be the primary
contributing factor to SMC errors.
For water-stressed crops, crop resistance errors can attribute to the
large error in ETa estimations, while for tall crops, the VPD can have a
large influence on the error (Rana & Katerji, 1998). Extreme conditions
include when aerodynamic resistance is high, >50m/s (Paw,
1992). High aerodynamic resistance can occur in sparse vegetation, when
surface temperature is much greater than air temperature (e.g.
water-stressed conditions) and when wind speed is very low (Paw, 1992;
Dhungel, Allen, Trezza, & Robison, 2014). Cleverly et al., (2013) and
Steduto, Todorovic, Caliandro, & Rubino, (2003) found when the standard
aerodynamic resistance values were used the PM method over- and
underestimated RET when RET is low and high respectively and suggested
the aerodynamic resistance should vary with climatic variables as it is
responsive to relative humidity gradients.
It is recommended to further verify the behaviour of the SMC (soil
moisture content index). The SMC relative moisture index is derived from
land surface temperature and vegetation cover (NDVI) data. Therefore,
verification against highest available physically-based satellite soil
moisture data (e.g., active microwave sensors onboard Sentinel-1A,
Metop, etc.) is advised. It may be helpful to use SMC for transpiration
and passive microwave sensors for evaporation.
The main source of error in the ET-WB method is the uncertainty in PCP.
Studies on the CHIRPS PCP product shows high correlations, at monthly
and regional scales, in Eastern Africa (r = 0.7-0.93) (Dinku et
al. , 2018; Gebrechorkos, Hülsmann, & Bernhofer, 2018) and Burkino Faso
(r = 0.95) (Dembélé and Zwart, 2016) with little to no bias. Muthoni et
al., (2018) reported that CHIRPS v2 slightly over-estimated
low-intensity rainfall below 100 mm and slightly under-estimated
high-intensity rainfall above 100 mm compared in Eastern and Southern
Africa. On an annual, basin-scale, the CHIRPS PCP product does not show
significant bias, except for in largely ungauged tropical basins (e.g.
Congo) (Liu et al. , 2016). Weergeshi et al., (2019) compared
terrestrial water storage by Rodell et al. , (2018) and found they
represented a maximum of 2.3% of long term basin ETa for basins in
Africa. Therefore the large overestimations of ETIa-WPR should not be
attributed to the simplified water balance approach.