2.0 Data and Methods
2.1 The dataset
The analysis dataset is the ETIa-WPR V2.0 products available on the
WaPOR portal. The ETIa-WPR is based on a modified version of the ETLook
model described in Bastiaanssen, Cheema, Immerzeel, Miltenburg &
Pelgrum (2012). The ETLook model uses Penman-Monteith (PM) to estimate
ETa adapted to remote sensing input data (FAO, 2018, 2020a). The PM
approach uses the combined approaches of the energy balance equation and
the aerodynamic equation and is described in the FAO-56 drainage paper
(Allen, Pereira, Raes & Smith, 1998). The ETIa-WPR defines soil
evaporation and transpiration separately using Equation 1 and Equation
2. The interception is a function of the vegetation cover, leaf area
index (LAI) and precipitation (PCP). The ETI-WaPOR is then calculated as
the sum of evaporation, transpiration and interception.
- \(\lambda E=\ \frac{\Delta\left(\ Rn,soil\ \ -G\ \right)+\ \frac{\rho_{\text{air}}\text{\ C}_{P}(\ e_{\text{sat}}\ -e_{a}\ )}{r_{a,soil}}\ }{\Delta\ \ +\ \gamma\ (1+\frac{r_{s,soil}}{r_{a},soil})}\)
- \(\lambda T=\ \frac{\Delta\left(\ Rn,canopy\ \ \right)+\frac{\rho_{\text{air}}\text{\ C}_{P}\left(\ e_{\text{sat}}\ -e_{a}\ \right)}{r_{a,canopy}}\ }{\Delta\ \ +\ \gamma\ (1+\frac{r_{s,canopy}}{r_{a,canopy}})}\)
Where E and T (mm/day) are the evaporation and transpiration
respectively and λ is the latent heat of vaporisation. Rn
(MJ/m2/day) of the soil (Rn,soil) and canopy (Rm,
canopy) is the net radiation and G (MJ/m2/day) is the
ground heat flux. \(\rho_{\text{air}}\) (kg/m3) is the
density of air,\(\text{\ C}_{P}\) (MJ/kg/°C) is the specific heat of
air,( \(e_{\text{sat}}\ -e_{a})\) (kPa) is the vapour pressure deficit
(VPD), \(r_{a}\) (s/m) is the aerodynamic resistance, \(r_{s}\) (s/m) is
the soil resistance, or canopy resistance when using the PM-model to
estimate evaporation or transpiration respectively. Δ =d (\(e_{\text{sat}}\))/d T (kPa/°C) is the slope of the
curve relating saturated water vapour pressure to the air temperature,
and γ is the psychometric constant (kPa/°C). This approach partitions
the ETIa-WPR to evaporation and transpiration using the modified
versions of PM, which differentiate the net available radiation and
resistance formulas based on the vegetation cover according to the
ETLook model (Bastiaanssen et al., 2012). A major difference between the
WAPOR model and the ETLook model is the source of remote sensing data
for the soil moisture. In the original ETLook soil moisture is derived
from passive microwave, and in the WAPOR approach soil moisture is
derived from Land Surface Temperature (LST). The WaPOR database provides
ETIa-WPR in three spatial resolutions dependent on the location and
extent. The products available specifically for Africa are shown in
Table 1.
Datasets (including intermediate datasets) available for the validation
include soil moisture content (SMC), normalised difference vegetation
index (NDVI), solar radiation (SR), NDVI quality layer, land surface
temperature (LST) quality layer, PCP and reference evapotranspiration
(RET) (Table 2). The producer provided the SMC and NDVI layers for the
validation. All other layers are available on the WaPOR portal. The NDVI
quality layer and the LST quality layer are indicators of the quality of
the input satellite data. The NDVI quality layer provides the gap, in
days, to the nearest valid observation for that variable. The LST
quality layer provides the number of the days between the date of the
data file and the earlier remote sensing observation on which the data
is based.
WaPOR further relies on input from weather data, air temperature,
relative humidity wind speed, which are obtained from MERRA up to the
start of 21-02-2014 and GEOS-5 after 21-02-2014 (Rienecker et
al. , 2011). The weather data is resampled using a bilinear
interpolation method to the 250m resolution. The temperature is also
resampled based on elevation data (FAO, 2018).
2.2 Validation approach and workflow
The validation approach comprises three components, physical validation,
direct validation and level consistency (Figure 1). The physical
validation and direct validation were undertaken on the L1 product for
the period 2009-2018. The physical validation (section 2.3) includes an
assessment of the water balance and water availability (2.3.1) and a
spatial and temporal consistency check (2.3.2) for the extent of Africa.
The water balance utilises other existing continental datasets to
complete the water balance and is therefore also considered
cross-validation. The spatial and temporal consistency checks if spatial
and temporal patterns were being captured. The direct validation
(section 2.4) involves a comparison to ETa estimations from EC stations.
The level consistency (section 2.5) checks for the consistency between
levels and therefore indicates if the quality of the L1 product is
representative of the L2 and L3 products.
2.3 Physical consistency