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.