In most hydrological systems, evapotranspiration (ET) and precipitation are
the largest components of the water balance, which are difficult to estimate,
particularly over complex terrain. In recent decades, the advent of remotely
sensed data based ET algorithms and distributed hydrological models has
provided improved spatially upscaled ET estimates. However, information on
the performance of these methods at various spatial scales is limited. This
study compares the ET from the MODIS remotely sensed ET dataset (MOD16) with
the ET estimates from a SWAT hydrological model on graduated spatial scales
for the complex terrain of the Sixth Creek Catchment of the Western Mount
Lofty Ranges, South Australia. ET from both models was further compared with
the coarser-resolution AWRA-L model at catchment scale. The SWAT model
analyses are performed on daily timescales with a 6-year calibration period
(2000–2005) and 7-year validation period (2007–2013). Differences in ET
estimation between the SWAT and MOD16 methods of up to 31, 19, 15, 11 and
9 % were observed at respectively 1, 4, 9, 16 and 25 km
In most hydrological systems, evapotranspiration (ET) and precipitation are the largest components of the water balance (Nachabe et al., 2005) and yet the most difficult to estimate, particularly over complex terrain (Wilson and Guan, 2004). In arid and semi-arid environments ET is a significant sink of groundwater, with ET often exceeding precipitation (Domingo et al., 2001; Cooper et al., 2006; Scott et al., 2008; Raz-Yaseef et al., 2012). Reliable estimation of ET is integral to environmental sustainability, conservation, biodiversity and effective water resource management (Cooper et al., 2006; Boé and Terray, 2008; B. Zhang et al., 2008; Tabari et al., 2013). Moreover, ET will be one of the most severely impacted hydrological components of the water cycle alongside precipitation and runoff as a consequence of global climate change (Abtew and Melesse, 2013).
Reliable, cheap and generally accessible methods of estimating ET are essential to understand its role in catchment processes. ET is principally measured and estimated using ground based measurement tools and/or through various modelling techniques often involving remote sensing (Drexler et al., 2004; Tabari et al., 2013). Ground based measurement methods such as the Bowen Ratio Energy Balance (BREB), Eddy Covariance (EC), Large Aperture Scintillometers (LAS) and lysimeters have been regarded as the most accurate and reliable ET determination methods (Kim et al., 2012a; Rana and Katerji, 2000; Liu et al., 2013), but they are spatially and/or temporally limited (Wilson et al., 2001; Glenn et al., 2007). Despite the relative reliability of ground based measurement methods, there are inherent uncertainties associated with the different methods, which affect the accuracy of ET measurements (Baldocchi, 2003; Brotzge and Crawford, 2003; Drexler et al., 2004; B. Zhang et al., 2008). Ground based measurement methods are particularly prone to significant errors related to instrument installation (Allen et al., 2011). Mu et al. (2011) observed that multiple EC towers on a site can have uncertainties ranging between 10–30 % and Liu et al. (2013) documented uncertainty ranges of over 27 % between EC and LAS measurements over the same site on an annual scale. EC towers have also been observed to encounter energy balance closure challenges (Wilson et al., 2002), while other challenges of the EC method such as inaccuracies due to complex terrains have been documented by Feigenwinter et al. (2008). Furthermore, Kalma et al. (2008), conducted a review of 30 remote sensing ET modelling results relative to ground based measurements and contended that the ground based measurement methods were not incontrovertibly more reliable than the remote sensing ET modelling methods. Moreover, most of the ground based measurement methods are usually cost intensive thereby constraining measurements over large areas and thus making spatial extrapolation difficult (Moran and Jackson, 1991; Verstraeten et al., 2008; Melesse et al., 2009; Fernandes et al., 2012).
In more recent years, the spatial challenges associated with ET estimations are being eased by the increased availability of remotely sensed data. The use of remotely sensed input data in many surface energy balance algorithms and highly parameterised hydrological models have been extensively documented (Kalma et al., 2008; Hu et al., 2015; Zhang et al., 2016). The advances in remote sensing have seen these methods become prominent in water resource assessment studies (Sun et al., 2009; Vinukollu et al., 2011; Anderson et al., 2011; Long et al., 2014; Zhang et al., 2016).
Several hydrological models and remotely sensed based surface energy balance models are currently used in ET simulations globally (Zhao et al., 2013; Chen et al., 2014; Larsen et al., 2016; López López et al., 2016; Webster et al., 2017). However, the relative accuracy of these models relative to one another should be extensively explored to improve our understanding of the ET estimation from these algorithms. Two of the more prominent ones will be comprehensively evaluated in this study at various spatial scales – the Soil and Water Assessment Tool (SWAT) (Neitsch et al., 2011) and the MODIS ET product (Mu et al., 2013) derived from remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the National Aeronautics and Space Administration (NASA) Aqua and Terra satellites. The evapotranspiration product of a third model, the Australian Water Resource Assessment model (AWRA_L) with a coarser resolution, will also be evaluated at the catchment scale.
The MODIS ET (MOD16) is based on the Penman–Monteith equation, the AWRA-L uses the Penman equation, while the SWAT ET algorithm also has the Penman–Monteith equation as one of the three user-selectable methods of estimating ET. In this study, the Penman–Monteith method in SWAT is used for a direct comparison with the MOD16 and the AWRA-L. Moreover, the Penman–Monteith equation is regarded as one of the most reliable methods for ET estimation over various climates and regions (Allen et al., 2005, 2006). While both the MOD16 and SWAT ET use the Penman–Monteith equation, the methods for estimating the parameters of the equation are significantly different between them. For instance, the SWAT Penman–Monteith implementation requires wind speed data for the computation of the aerodynamic resistance, while the MOD16 Penman–Monteith variant does not require wind speed data, but instead uses the Biome-BGC model (Thornton, 1998) to estimate the aerodynamic resistance. This study does not seek to evaluate the individual accuracy of any method, but rather to compare the ET results from the water balance based hydrological models AWRA-L and SWAT and the energy balance based model (MOD16) over a complex terrain catchment. Two different land cover products are used in the SWAT model in this study (the Geoscience Australia and MODIS land cover products). The rationale for this is to analyse the effect of land cover on the ET modelling in SWAT, and the use of the MODIS land cover also allows for a direct comparison with the MOD16 which uses the same land cover product. The results will be compared temporally on a catchment scale and spatio-temporally on sub-catchment scales to identify the effects of input data and other drivers of ET estimation in the MOD16 and SWAT ET algorithms.
While the MODIS evapotranspiration has been widely studied and compared to other methods, this is much less the case for SWAT ET (Table 1) and the AWRA-L. Moreover, a graduated spatial-scale comparison of the SWAT and MOD16 ET products is yet to be documented over a complex terrain. The objectives of this study are therefore (1) to simulate and compare the results of the evapotranspiration of SWAT, AWRA-L and MOD16 over a complex terrain at a catchment scale in a semi-arid climate; and (2) to analyse and determine the spatial scale at which the SWAT and MOD16 ET models tend towards agreement to enhance the confidence in ET estimation in a complex terrain.
Literature studies of MODIS and SWAT evapotranspiration (see Table 2 for climate classification).
Köppen–Geiger climate classification system (Kottek et al., 2006).
e.g. Cwa – warm temperate, winter dry, hot summer.
The Soil and Water Assessment Tool (SWAT) is a physically based,
semi-distributed hydrological model designed on the water balance concept.
SWAT simulates catchment processes such as evapotranspiration, runoff, crop
growth, nutrient and sediment transport on the basis of meteorological, soil,
land cover data and operational land management practices (Neitsch et al.,
2011). The SWAT model has been used in hydrological modelling from
sub-catchment scales of under 1 km
SWAT ET flowchart (Penman–Monteith method).
The MOD16 provides evapotranspiration estimates for
109.03
Flowchart of the MOD16 ET algorithm (Mu et al., 2011).
MOD16 and SWAT ET parameterisation (
The AWRA-L is a daily 25 km
Evapotranspiration in the AWRA-L is a sum of six processes: canopy evaporation from intercepted precipitation, evaporation from the soil surface, groundwater evaporation, shallow storage transpiration, deep storage transpiration and groundwater transpiration. The evaporation in the model is constrained by the Penman equation (Penman, 1948). For a detailed structure of the AWRA-L model, see Viney et al. (2014).
The MOD16 and SWAT ET algorithms, which are both based on the Penman–Monteith equation but parameterised differently, suggests there will be similarities and differences in the results from both methods. Both algorithms are principally limited on temporal timescales by the available energy to convert liquid water to atmospheric water vapour. Their transpiration and soil evaporation algorithms are also very dependent on vegetation/biome type, VPD, and the soil moisture constraint parameterisation (Fig. 3).
In the SWAT ET algorithm, the VPD significantly impacts the transpiration through the constraining of the stomatal conductance. Detailed soil data on HRU scale such as layer depth, number of layers, unsaturated hydraulic conductivity and water capacity are crucial for constraining the soil moisture content, which in turn regulates the percolation and recharge into the system. Similarly, the calculated MOD16 ET is significantly impacted by the biome properties lookup table (BPLUT) and the soil moisture constraint function. The BPLUT was calibrated using the response of biomes on flux tower sites globally. The BPLUT contains information on the stomatal response of each biome to temperature, VPD and biophysical parameters. The soil moisture constraint function is applied in the estimation of the soil evaporation and is an important parameter in regions where the saturated zone is close to the ground surface such as our study area.
The study area is the Sixth Creek Catchment of South Australia, located in
the western part of the Mount Lofty Ranges, which is a range of highlands
separating the Adelaide Plains in the west from the Murray–Darling basin in
the east. The western part of the Mount Lofty Ranges runs 90 km north to
south; its summit is at 680 mAHD (metres Australian Height Datum) (Sinclair,
1980). It extends from the southernmost part at McLaren Vale on the Fleurieu
Peninsula to Freeling in the north over an area of 2189 km
Digital elevation model of the Sixth Creek Catchment study area (Gallant et al., 2011).
It covers an area of 44 km
The Sixth Creek Catchment's complex terrain plays a significant role in its hydrology, with highly localised precipitation events recorded from the two weather stations in the catchment within the study period. The weather stations are located 4.5 km apart, with an elevation difference of over 200 m (Fig. 4). Differences in annual rainfall of over 400 mm have been recorded between the two weather stations.
The annual precipitation for the period 2002 till 2016 for Station A ranges
between 500–900 and 750–1500 mm for Station B, while the temperature
ranges between 10.5 and 22.2
The GIS interfaced version of SWAT (ArcSWAT) was used in the hydrological
modelling. A 30 m Digital Elevation Model (DEM) (Dowling et al., 2011)
of the Sixth Creek Catchment was used to extract the stream network and the
catchment area. A detailed soil properties database for the catchment was
created from the soil data obtained from the Australian Soil Resource
Information System (Johnston et al., 2003). The 250 m land cover map of
Australia from Geoscience Australia's Dynamic Land Cover database (Fig. 5b)
is typically preferred to be used in the SWAT model ahead of the 500 m MOD12
land cover map (Fig. 5a) due to its finer spatial resolution and better biome
match with local field knowledge but for direct comparison with MOD16, both
maps are used to run separate SWAT models. In this study, the
0.01
The monthly MOD16 datasets for the years 2000 to 2013, at 1 km
The soil, land cover and DEM derived slope data were classified into classes
and used to create 124 and 119 unique HRUs for the Geoscience Australia and
MOD12 land covers respectively, ranging from 0.001 to 6 km
The SWAT model is calibrated by fitting simulated streamflow to observed streamflow with the SUFI-2 algorithm. This semi-automatic Latin hypercube sampling algorithm optimises SWAT model parameters while attempting to fit the simulated data as closely as possible to the observed data using the user preferred objective function from those detailed below as a measurement of simulation accuracy (Abbaspour, 2007). Although a single user objective function is used in the calibration and validation, the results of the other objective functions are also recorded for the optimal model run.
The Nash–Sutcliffe efficiency (
The ratio of the root mean squared error to the standard deviation of
measured data (
The Kling–Gupta efficiency (
After obtaining a satisfactory fit between the simulated and observed
streamflow data during calibration, the model is validated by running the
model for a different time period using the same parameters from the
calibration period. SUFI-2 further incorporates the unitless
The calibration process was conducted on daily timescales for the years 2000 to 2005, while the validation was conducted for the years 2007 to 2013. A warm-up period of 5 years between 1995 and 1999 was used in the SWAT model to equilibrate the model mass budget and internal reservoirs. The relatively long periods of streamflow calibration and validation on daily timescales were specifically used to address the potential problem of equifinality of parameters to be optimised. The principle of equifinality has been known to affect semi-distributed models such as SWAT (Qiao et al., 2013). Nevertheless, the use of many observation points has been observed to effectively constrain it (Tobin and Bennett, 2017). In this study, 21 sensitive SWAT model parameters (Table 3) are optimised with SUFI-2 to fit simulated streamflow to the observed streamflow data. In the SUFI-2 algorithm preparation for calibration, an “r_” and a “v_” prefix before a SWAT model parameter (Table 3) are indicative of a relative change (a percentage increase or decrease in the SWAT modelled value) and replacement change of the original SWAT modelled values respectively. The relative change is often used to fine-tune parameters that have been modelled within the acceptable range, while the replacement change is used when modelled parameter values are at odds with local field knowledge or established values.
The resultant SWAT simulated ET was compared with the MOD16 ET using the
root mean square error (
Optimised SWAT parameters and their final range.
The streamflow was calibrated and validated on daily timescales according to
the guidelines set out in Moriasi et al. (2007) and Abbaspour (2007)
(Table 4, Fig. 6). The result indicates an observed data bracketing of
between 87 and 89 % for both calibration and validation with
Streamflow calibration and validation results.
Table 4 shows better results for the validation than calibration for the
Streamflow calibration (2000–2005) and validation (2007–2013).
The SWAT ET model is calculated at the HRU scale (Fig. 7a, b), however
for direct comparison with the MOD16 ET (Fig. 7c), the HRU ET results were
reprocessed into 1 km
Further analyses were carried out to determine the effect of spatial
aggregation on the correspondence between the ET methods. For the spatial
aggregation analysis, the SWATGEO model was used due to its improved land
cover accuracy based on field knowledge. The box and whisker plot in Fig. 8
shows the spread of the difference between the SWAT ET and the MOD16, with
the bottom, middle and top of the box indicating the 25th, 50th and
75th quartiles of the distribution. The lowest and highest bars in the
plot indicate the minimum and maximum differences between the ET products at
the different spatial scales. Figure 8 show that with increasing cell
aggregation the difference in the ET between SWAT and MOD16 decreases. At 1,
4, 9, 16 and 25 km
Differences between SWATGEO ET and MOD16 for spatial aggregations
between 1 and 25 km
The grand variances for the monthly data of the three models were calculated
and partitioned into the spatial and temporal components at the 1, 4, 9, 16
and 25 km
Variance partitioning into space and time components at various spatial resolutions.
At catchment scale, the mean annual ET of the SWATGEO, SWATMOD12 and MOD16 models are 873, 864 and 865 mm respectively. The means show better agreement between the SWATMOD12 and MOD16 models, which is attributed to the use of the same land cover in both models.
To compare the temporal dynamics of the MOD16, the SWAT ET and the AWRA-L ET, the data were aggregated to catchment scale. As both SWAT models tend towards unity at the catchment scale with less than 1 % difference in their annual mean ET, only the SWATGEO model is evaluated at catchment scale as the more accurate model to keep with the philosophy of the study.
Monthly MOD16 ET and AWRA-L ET values at 1 and 25 km
The mean annual graduated spatial-scale analysis across the SWAT models and
the MOD16 for 2007–2013 exhibits a wide spread at the 1 km
The differences between regions in the catchment are more significant at finer spatial resolutions due to the diverse input data and their associated errors: these impacts become less significant as the outputs are up-scaled (Fig. 8). This trend was also observed by Hong et al. (2009). The simple averaging method was preferred in this study over the bilinear, cubic and other methods as the simple averaging method has been observed to be the best in flux aggregation after a study of various methods (Ershadi et al., 2013).
The recognised principal sources of differences between the three ET methods are associated with land cover, the Revap component in SWAT and the HRU parameterisation in the AWRA-L; they are discussed in the following sections.
The land cover is an important parameter in the MOD16 and SWAT ET algorithms
as it determines the values allocated to biophysical properties such as leaf
conductance and boundary layer resistance, which significantly impact ET
calculations. The impact of the land cover on the SWAT models is evident from
the spatially divergent high-resolution SWAT models (Fig. 9a and b), at the
HRU scale, though the streamflow calibration and validation parameters and
results were similar. With the spatial aggregation of the SWAT models to
1 km
The Revap component of the AET in SWAT is mostly significant in forested
catchments with deep rooted trees that can access the saturated zone and as
such are governed by land use parameters (Neitsch et al., 2011). However,
the relative accuracy of the Revap component of the ET on HRU scales has been
questioned (Liu et al., 2015) due to the linear relationship between the
Revap coefficient and potential evapotranspiration in SWAT (see Eq. A23).
The Revap component in this study appears consistent with the studies by
Benyon et al. (2006) in south-eastern Australia with similar climatic
condition as the Sixth Creek Catchment. Benyon et al. (2006) observed
that under the combined conditions of highly permeable soils, available
groundwater resources of low salinity (< 2000 mg L
Monthly comparison of SWAT, AWRA-L and MOD16 ET at catchment scale.
Monthly comparison of the Revap component of the ET and total ET in SWAT.
On a catchment scale, the results show that MOD16 simulates higher ET in the winter periods, while SWAT simulates higher ET during the summer periods (Fig. 9). Generally, the agreement between the products is more consistent during the winter seasons when ET is lower. The lesser correlation during higher ET seasons may be related to the linearly determined Revap component of the ET, which is a more dominant process in the summer months when the demand for soil evaporation, plant transpiration and groundwater ET is significantly higher.
The HRU parameterisation method in AWRA-L significantly impacts the
evapotranspiration modelling process. While the AWRA-L does not use a robust
land cover product that distinguishes between vegetation including trees, it
uses a fraction of the tree cover product to parameterise the HRU. AWRA-L
discretises each 5 km
The SWAT ET and MOD16 methods both have challenges associated with input data, which are subsequently propagated through the algorithm. In semi-arid environments such as the Sixth Creek Catchment, high-intensity rainfall events are common occurrences, which impacts hydrologic processes such as infiltration and evapotranspiration differently than if the precipitation were evenly distributed through the day (Syed et al., 2003). Yang et al. (2016) observed that the use of hourly rainfall in SWAT significantly improved the modelling of streamflow and hydrological processes. In this study, due to the unavailability of hourly precipitation data, daily precipitation data were used, thus neglecting the impact of high-intensity precipitation events in the catchment.
Another challenge encountered with the SWAT model is associated with the semi-distributed model methodology. The use of a single value for wind speed, relative humidity and solar radiation for a sub-catchment with spatial scale, which could be in the order of tens of square kilometres, affects the accuracy of hydrological processes at the HRU scale. The “elevation band” method of temperature and precipitation distribution with respect to elevation changes across a catchment was introduced into the SWAT algorithm to attenuate orographic effects in complex terrain catchments (Neitsch et al., 2011). The elevation band algorithm in SWAT has performed well in predominantly snowy, complex terrain catchments, which are significantly larger than the Sixth Creek Catchment with elevation changes in the order of kilometres (Abbaspour et al., 2007; X. Zhang et al., 2008; Pradhanang et al., 2011). However, the application of the elevation band algorithm in the non-snowy Odiel River basin (Spain) with Mediterranean climate similar to the Sixth Creek Catchment yielded less than satisfactory results (Galván et al., 2014). In the non-snowy Sixth Creek Catchment, the orographic effects are a dominant atmospheric process when winds are moving from the lower elevations in the north of the catchment to the higher elevations in the South particularly during the winter months. The orographic lift leads to significantly higher precipitation in the south-westerly direction in the Sixth Creek Catchment, which the elevation band algorithm in SWAT would not represent accurately in non-snowy catchments.
The various meteorological and remote sensing input data used in the processing of the MOD16 all have their inherent uncertainties, with cloud cover challenges and coarse-resolution resampling (Mu et al., 2011), while errors have been associated with the land cover product used (Ruhoff et al., 2013). The land cover map (MOD12) used in MOD16 (Fig. 5a), in conjunction with the calibrated Biome Properties Lookup Table (BPLUT), significantly influences the ET output from the various land covers under different climatic conditions. A more detailed map and local knowledge of the Sixth Creek Catchment indicates that the MOD12 land cover spatially mismatches some biomes (Fig. 5a and b). Besides the obvious land cover mismatches that were observed between the input data of the two models, the variety of accepted national, regional and global land cover classification systems contributes to the challenges of hydrological modelling. In this MOD12, the “mixed forest” category covered over 50 % of the catchment, while the category does not exist in the local field map land cover classification. The global standardisation and harmonisation of land cover maps and biome classification at high resolution may improve model performance.
The main objectives of this paper are to compare three ET products (SWAT, MOD16 and AWRA-L) on a catchment scale, while also evaluating the two finer-resolution products (SWAT and MOD16) on a graduated spatial scale. We also attempted to determine the spatial scale at which the models tend towards agreement, while also seeking to understand the sources of disagreements between the models.
The calibrated SWAT model using the SUFI-2 algorithm and various objective
functions could simulate ET to within 6 % of the MOD16 on catchment
scale, annually. The
SWAT and MOD16 show good correlation on a catchment scale, while the AWRA-L and SWAT models without the inclusion of the groundwater ET component of the SWAT model showed good agreement. Biome differences and the input spatial scale contribute to poor agreement at finer spatial scales. The challenge of the lack of a globally accepted and harmonised land cover classification system at high resolution was encountered in the study, with two products derived from the MODIS satellite data classifying land cover differently and thus impacting the results from the SWAT models. The use of different land covers with different classification systems and parameters is observed to have limited impact on evapotranspiration modelling at coarse spatial resolutions due to spatial averaging. Nevertheless, the tree cover fraction used in place of a land cover product in the AWRA-L is also observed to impact the ET modelling, particularly in a groundwater-dependent catchment like our study area. The inherent differences and uncertainties associated with these land cover products will continue to be propagated through the models, thereby promoting divergence in the drive towards more accurate and finer-resolution evapotranspiration data products. While many concerted research efforts have been made in the past (Latham, 2009; Friedl et al., 2010), a globally accepted harmonised world land cover database at high resolution can significantly improve correlation and confidence in high-resolution ET products.
The result of the spatial-resolution analysis corroborates the view that prevailing ET algorithms and measurement methods will have a certain degree of variability due to the complexity of ET estimation and various drivers of the contributory processes. The study shows that correlation at catchment scale does not necessarily translate to correlation at finer spatial scales. The study also highlights the possible challenges of the semi-distributed SWAT ET algorithm in a complex terrain as the input climate data can be a challenge due to spatial resolution and climate variability.
The datasets for this research can be accessed through the Flinders University repository in the future. This is part of a current PhD research; hence, until the completion of the research, the datasets belong to Flinders University of South Australia.
SWAT provides the user with three options of modelling ET at the HRU scale
and at daily temporal resolution (Penman–Monteith, Hargreaves or
Priestly–Taylor methods). In this study, the Penman–Monteith method is
used. SWAT initially calculates the potential evapotranspiration (PET) for a
reference crop (alfalfa) using the Penman–Monteith equation for well-watered
plants (Jensen et al., 1990):
Total ET (AET) in SWAT is made up of four components: canopy evaporation,
transpiration, soil evaporation and groundwater ET (Revap). Revap is the
movement of water from the saturated zone into the overlying unsaturated
zone to supplement the water need for evapotranspiration. The Revap process
may be insignificant in regions where the saturated zone is much deeper than
the root zone and as such the result is separately reported from the ET
result in the SWAT result database. As SWAT calculates Revap separately, for
a calculation of AET in regions where the saturated zone is within the root
zone, the user should add the Revap result column to the ET calculations.
The AET components are calculated from the PET starting with the canopy
evaporation. For this first component the following storage equations are
used in determining the volume of water available for evaporation from the
wet canopy in SWAT
The SWAT ET algorithm initially evaporates as much water as can be
accommodated in the PET from the wet canopy. If the total volume of water in
canopy storage equals or exceeds PET for the day, then ET is calculated as
The third AET SWAT component, the soil evaporation on a given day, is a
function of the transpiration, degree of shading and potential
evapotranspiration adjusted for canopy evaporation. The maximum soil
evaporation on a given day
The fourth component of the ET calculations in SWAT is referred to as
“Revap”. Revap in SWAT is the amount of water transferred from the
hydraulically connected shallow aquifer to the unsaturated zone in response
to water demand for evapotranspiration. The Revap component in SWAT is akin
to ET from groundwater. Revap is often a dominant catchment process in a
groundwater dependent ecosystem and it is calculated at the HRU scale. Revap
is estimated as a fraction of the potential evapotranspiration (PET) and it
is dependent on a threshold depth of water in the shallow aquifer which is
set by the user.
ET in the MOD16 is a summation of three components: wet canopy evaporation,
plant transpiration and soil evaporation. Wet canopy evaporation
The plant transpiration
Total evapotranspiration
The authors declare that they have no conflict of interest.
We would like to acknowledge John Hutson and Tomasz Berezowski for sharing their insight on SWAT modelling in support of this research work. Edited by: Pierre Gentine Reviewed by: two anonymous referees