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# Dense flux observations reveal the incapability of evapotranspiration products to capture the heterogeneity of evapotranspiration

Jie Wu, Yu Feng, Chunmiao Zheng, Zhenzhong Zeng

► To cite this version:

Jie Wu, Yu Feng, Chunmiao Zheng, Zhenzhong Zeng. Dense flux observations reveal the incapability of evapotranspiration products to capture the heterogeneity of evapotranspiration. Journal of Hydrology, 2023, 622, pp.129743. 10.1016/j.jhydrol.2023.129743. hal-04216702

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# Dense flux observations reveal the incapability of evapotranspiration products to capture the heterogeneity of evapotranspiration

Jie Wu¹,², Yu Feng¹,³,⁴*, Chunmiao Zheng¹,⁴ and Zhenzhong Zeng¹*

## Affiliations:

¹ School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

² Department of Geoscience and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark.

³ Laboratoire des Sciences du Climat et de l'Environnement, UMR 1572 CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France.

⁴ EIT Institute for Advanced Study, Ningbo, China.

* Correspondence: zengzz@sustech.edu.cn (Z. Zeng); yu.feng@lsce.ipsl.fr (Y. Feng)

Manuscript for *Journal of Hydrology*

April 15, 2023

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**Abstract:**

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Accurate terrestrial evapotranspiration (ET) estimation over complex surfaces with spatial heterogeneity is crucial for local, regional, and global applications. Currently, a variety of global ET products with different spatiotemporal resolutions have been developed and evaluated. However, little is known about their performance in capturing the heterogeneity of ET over complex surfaces. Focusing on the Heihe River Basin (HRB), a typical arid and semi-arid region that is hydrologically vulnerable, this study compared ET from eleven global ET products (six remotely-sensed ET, two land surface model ET, one hydrological model-based ET, one reanalysis ET, and one synthesis ET) and one regional ET dataset against dense eddy covariance observations in terms of the magnitude, seasonal cycle, and spatial pattern. In general, the remotely-sensed ET and synthesis ET outperformed other categories, with the operational Simplified Surface Energy Balance (SSEBop), Penman-Monteith-Leuning (PML), Moderate Resolution Imaging Spectroradiometer (MOD16) and Global LAnd Surface Satellite (GLASS) performing relatively better (root mean square error ranging from 1.22–1.57 mm d⁻¹). Across all the land cover types, ET products reproduced a relatively feasible ET over the desert steppe, meadow, and barren, but substantially underestimated ET over ecosystems with high ET values but a low land area fraction over HRB (cropland, wetland, and forest). This highlights the importance of accurately representing the heterogeneity and local climates in the complex land surfaces for ET quantification in regional water resource management.

**Keywords:** Eddy covariance; arid regions; land cover types; remote sensing
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# 1. Introduction

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Terrestrial evapotranspiration (ET) is the water transferred from the land surface to the atmosphere, consisting of plant transpiration, soil evaporation, and canopy interception (Wang et al., 2012). It plays a central role in the water, energy, and carbon cycles (Jung et al., 2010; Bonan et al., 2018). Accurate terrestrial evapotranspiration measurements and estimates are of great importance for local, regional and global applications, including but not limited to water resource management (Li et al., 2018; Feng et al., 2019; Goffin et al., 2022), drought monitoring (Vicente et al., 2018), and climate change studies (Zeng et al., 2017; Wu et al., 2022a). Existing observational methods, such as lysimeters, scintillometers, the Bowen ratio (BR), and the eddy covariance (EC) techniques, as well as estimates from surface water balance (WB), provide a reasonable ET at the point or local scale (Wang et al., 2012; Feng et al., 2017a). However, these direct observations or observation-based estimates are not applicable when extending to large spatial scales due to the expensive equipment installation, sparse network distribution, and uncertainties caused by scale mismatch (Baik et al., 2018; Tang et al., 2018).

To fill the observation gap, various global ET products with different spatiotemporal resolutions have been developed. These datasets can be broadly divided into five categories based on how they are generated: remotely sensed ET, land surface model (LSM) ET, machine learning-based ET, reanalysis ET, and synthesis ET. Remote sensing has long been recognized as a feasible way to estimate ET. Typical remote sensing ET datasets include MOD16 (Moderate Resolution
63 Imaging Spectroradiometer; Mu et al., 2007, 2011), PML (Penman-Monteith-Leuning model;
64 Zhang et al., 2016, 2019), SEBS (Surface Energy Balance System; Chen et al., 2018), SSEBop
65 (the Operational Simplified Surface Energy Balance; Senay et al., 2013), and GLEAM (Global
66 Land Evaporation Amsterdam Model; Miralles et al., 2011). The LSM-based ET products provide
67 a field of fluxes and land surface states via land surface modeling and data assimilation, as
68 represented by GLDAS (Global Land Data Assimilation System; Rodell et al., 2004) and FLDAS
69 (Famine Early Warning Systems Network Land Data Assimilation System; McNally et al., 2017).
70 The machine learning-based ET products, such as MTE (Jung et al., 2009) and ZEN14 (Zeng et
71 al., 2014), integrate direct observations from EC, WB, and other variables with machine learning
72 to reconstruct ET. Representative reanalysis ET products are ERA5 (European Centre for
73 Medium-Range Weather Forecasts Reanalysis-5; Hersbach et al., 2020) and MERRA2
74 (Modern-Era Retrospective Analysis for Research and Application Land version 2; Gelaro et al.,
75 2017), which are beneficial in data-scarce regions. Synthesis ET products, such as
76 LandFlux-EVAL (Mueller et al., 2013) and GLASS (Global LAnd Surface Satellite; Liang et al.,
77 2020), merged ET derived from different methods. Different categories of ET products vary in
78 magnitude and inter-annual variability due to their different algorithms, parameters, and inputs
79 (Wu et al., 2020).

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81 Considering the massive availability of various datasets, it is important to understand the
82 strengths and weaknesses of these ET products and choose the appropriate ones for practical
83 purposes. For example, some satellite ET datasets are unsuitable for dry areas (Jung et al., 2019).
Till now, one or more ET products have been evaluated at different scales (Long et al., 2014; Chao et al., 2021; Nguyen et al., 2022). For example, Xu et al (2019) compared 12 ET products from remote sensing, LSMs and machine learning with AmeriFlux observations and ET derived from WB over the conterminous United States. Salazar-Martínez et al (2022) assessed remote sensing-based ET products at low-latitude EC sites. However, limited by the sparse EC observation, whether these global models could accurately estimate ET over heterogeneous surfaces is unknown. The heterogeneity makes the magnitude, seasonal cycle, and spatial patterns of ET dissimilar.

The Heihe River Basin (HRB) is a typical region with spatial heterogeneity. It is China's second-largest inland endorheic river basin and is representative of arid and semi-arid regions. HRB has unique environmental conditions and diverse landscapes, including meadows, bare lands/desert/gobi desert, cropland, wetlands, and residential areas. ET differed significantly over different land cover types. HRB also has a multiscale, multi-element integrated observatory network that was built in 2007 (Liu et al., 2018; Xu et al., 2020), making the comprehensive observations of ET over HRB possible. This observatory provides a unique opportunity to evaluate the performances of different ET products in capturing the heterogeneity of ET.

This study integrated dense flux observations over different ecosystems to assess eleven global terrestrial ET datasets and one regional ET dataset. The main question we seek to answer is whether the currently available ET products can explicitly identify the heterogeneity of ET over
HRB with complex land types. Specific research questions include: (1) Can the models correctly estimate ET in terms of the magnitude, seasonality, and spatial pattern? (2) Are the models' performances affected by land cover types? (3) Is there any difference between the performance of ET products from different categories?

## 2. Materials and Methods

### 2.1. Heihe River Basin

The study was carried out in the HRB located in the arid and semi-arid region of northwestern China (97.1–102.0°E, 37.2–42.7°N). HRB is China's second-largest endorheic river basin, with a land area of ~0.14 million km². HRB has various land cover types; the primary land cover types are barren, grasslands, and croplands. The upstream and middle streams of the basin are vegetated areas (Fig. 1a). Midstream is mostly covered by croplands (mainly maize and wheat (Zhang et al., 2021)) and barren lands. Downstream is mostly covered by barren, with some natural oases.

The water resource in the HRB is unevenly distributed. The annual precipitation decreased from 400-550 mm in the upstream area to 100-160 mm in the midstream area and 30-40 mm in the downstream area (Xu et al., 2020). Upstream, characterized as a mountainous and cold region, is the primary water production area, with massive runoff generated from precipitation, glacial and snow melt (Shang et al., 2021); midstream and downstream are the consumers of upstream runoff. In recent years, increasing agricultural water use for irrigation in the midstream has resulted in a
decline in ecological water use and deterioration of the ecological environment in downstream
(Cheng et al., 2014).

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## 2.2 Eddy covariance measurements and ET dataset for reference

We used both EC measurements and an ET product in HRB (i.e., ETMap, Liu and Xu, 2019) as the reference at different scales. At the site scale, we collected latent heat flux (LE) data from 2012–2020 at 15 eddy covariance sites, totaling 81 site-year data (Table 1). Specifically, 4 sites (AR, DSL, JYL, and YK) are in the upstream, 5 sites in the midstream (BJT, DM, HZZ, SSW, and ZYSD), and 6 sites in the downstream (HHL, HYL, LD, NT, SDQ, and HM). In terms of ecosystem types, these sites could represent the typical land covers in the HRB, with 4 sites in the meadow ecosystem, 5 sites in bare lands/desert/gobi ecosystems, 2 sites in the agroecosystem, 1 site in the wetland ecosystem, and 3 sites in the forest ecosystem. The duration of the measurements in each site varied, with the longest measurements greater than 8 years and the shortest measurements less than 3 years.

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At each site, latent heat flux was measured by an eddy covariance system at 10 Hz intervals. The 10 Hz data were processed by Eddypro software to 30-min data. Missing data were gap-filled using the REddyProc online tool (https://www.bc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb). To reduce the energy balance closure problem of eddy covariance systems (Feng et al., 2017b), we applied an energy

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145 balance closure correction factor to correct LE data (Jiang et al., 2021):

$$
146 \qquad \delta = (R_h - G) - (LE + H) \qquad (1)
$$

$$
147 \qquad LE_{corr} = LE + \frac{LE}{LE + H} \delta \qquad (2)
$$

148 where δ is the residual energy (W m⁻²), Rn, G, and H are the net radiation, soil heat flux, and
149 sensible heat flux, respectively, (W m⁻²). LEcorr is the corrected latent heat flux (W m⁻²).

150 To ensure the data quality for the evaluation, we only used the gap-filled half-hourly data with
151 quality flags of 0 (original) and 1 (most reliable), as indicated by the REddyProc online tool. We
152 then averaged half-hourly data to the daily interval. For the average, only days with 24 valid
153 half-hourly values and 12 valid half-hourly values during daytime (07:00–18:00) were included.

154 Daily data were further averaged to 8d, 10d, and monthly intervals to match the temporal
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157 To eliminate the scale mismatch issue when comparing site obversions with ET products, we
158 compared the performances of different ET products at the regional scale. We did not use the
159 widely-used WB method due to the uncertainty in the two inputs of the WB model: runoff is
160 negligible in the arid HRB, and total water storage change (TWSC) is hard to estimate. Although
161 the Gravity Recovery and Climate Experiment (GRACE) satellite provides TWSC estimates
162 (Tapley et al., 2019), its coarse spatial resolution enables a limited number of pixels in HRB,
163 whose land area is ~0.14 million km² and makes it hard to detect the effects of small-scale
164 human-exploring groundwater on ET process. As an alternative, we used the ETMap, a
165 high-quality surface ET product in the HRB provided by National Tibetan Plateau Data Center
(http://data.tpdc.ac.cn). This dataset provides growing-season (May to September) ET of HRB between 2012 and 2016. The ET was upscaled from 36 eddy covariance flux tower sites with a random forest algorithm. Large aperture scintillation measurements showed that the upscaled ET could capture the spatial and temporal patterns of ET at the regional scale (Xu et al., 2018). ETMap can also reveal the heterogeneity of ET in HRB, with all the land types with high ET values captured (Fig. 2).

## 2.3 Evapotranspiration products

We selected 12 gridded ET products that match the duration of EC observation for evaluation (Table 2). Six are remote sensing-based models, two are LSM-based products, one is from the hydrological model, one is the reanalysis product, one is the synthesis ET, and one is the regional ET product.

The six remote sensing-based ET can be categorized into (1) models based on the Penman-Monteith (PM; Penman, 1948) equation, e.g., MOD16 and SSEBop, or the revised PML model, (2) models based on Priestley-Taylor (PT; Priestley and Taylor, 1972) equations, e.g., GLEAMa/GLEAMb, and (3) those based on surface energy balance (SEB), e.g., SEBS model. MOD16 ET is estimated via the improved PM algorithm (Mu et al., 2011). The inputs include daily meteorological reanalysis data and MODIS remotely sensed data such as land cover, albedo and vegetation property dynamics. We used the 500m, 8-day MOD16A2 Version 6 covering 2001-2020. PML is a coupled diagnostic biophysical model; it provides ET and its three
components, as well as gross primary product (GPP) at a 500m and 8-day resolution. By coupling
vegetation transpiration and GPP through canopy conductance, this model makes GPP and
vegetation transpiration mutually restrictive, thereby improving the simulation accuracy (Gan et
al., 2018; Zhang et al., 2019). SEBS provides daily and monthly terrestrial ET obtained through
satellite data and the widely used single-source SEB model, a method that estimates ET as the
residual of the energy balance (Chen et al. 2019). The SSEBop estimates actual ET from 2003 to
the present using the SSEBop mode, a setup based on the SSEB method but with some unique
parameterization for operational applications. The potential ET is calculated with the standard
PM equation. GLEAM estimates the components of ET separately: the actual ET is converted
from PET which is estimated based on observational surface net radiation and air temperature;
the rainfall interception module is the Gsah analytical algorithm. Version v3.5 we used includes
two datasets, i.e., GLEAM v3.5a and GLEAM v3.5b. These two datasets differ only in their
forcing and temporal coverage: the former is based on satellite and reanalysis data and spans from
1980, while the latter is based on satellite data and covers from 2003.

FLDAS and GLDAS are two LSM-based ET products. FLDAS provides a series of
climate-related variables simulated from the Noah 3.6.1 model. The monthly data spanning from
1982 is at 0.10-degree resolution. The GLDAS we used is version 2.2, the simulation of which
was forced with model and observation data from 2003 to the present. The observational
meteorological analysis fields are from the operational European Centre for Medium-Range
Weather Forecasts Integrated Forecasting System. The dataset is at a 0.25x0.25 spatial resolution
and daily temporal resolution. TerraClimate is a hydrological model, and ET is estimated based
on the one-dimensional modified Thornthwaite-Mather climatic water-balance model (WBM;
Abatzoglou et al., 2018). We also used the ERA5 reanalysis ET and GLASS synthesis ET.
GLASS emerged five process-based ET estimates, including PT-JPL (Priestley-Taylor based ET
algorithm; Fisher et al. 2008), RRS-PM (revised remote sensing-based Penman-Monteith ET
algorithm; Yuan et al. 2010), UMD-SEMI (semi-empirical Penman ET algorithm of the
University of Maryland; Wang et al. 2010), MS-PT (modified satellite-based Priestley-Taylor ET
algorithm; Yao et al. 2013), and the MOD16 product mentioned above.

Apart from the eleven global ET products, we used a regional ET dataset for comparison, i.e.,
complementary relationship (CR) ET (Ma et al., 2019). This dataset provides surface ET over
mainland China based on CR. The input data include downward short-wave radiation, downward
long-wave radiation, air temperature, air pressure, surface emissivity, albedo, surface temperature,
air humidity, and diffuse radiation rate.

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222 3. Results

223 3.1. Overall performance

We first made an overall evaluation by comparing the ET estimates from different ET products
against flux tower measurements over HRB (Fig. 3). The comparison was made at the native
temporal resolution of each product. Hence, the number of observations for comparison varied a
lot (see numbers in Fig. 3). Generally, all ET products tend to underestimate ET at higher ET values. Among all the products, GLASS, PML and SSEBop performed best regarding coefficient of correlation (R) and root mean square error (RMSE). Specifically, GLASS has the largest R-value (0.763), followed by SSEBop (0.746) and PML (0.703). As for RMSE, SSEBop (1.22 mm d⁻¹) and PML (1.31 mm d⁻¹) outperformed GLASS (1.41 mm d⁻¹). The different performances could be explained by the divergent ways these ET products are produced: SSEBop and PML are based on physical processes, thus having smaller RMSE values. In contrast, GLASS ET merged five process-based ET estimates using the Bayesian model averaging (BMA) method, a multi-model ensemble approach (Yao et al. 2014), achieving the highest R-value. As for other ET products, SEBS had the worst performance with the lowest R-value (0.101) and highest RMSE (2.25 mm d⁻¹), followed by FLDAS in terms of R-value (0.289) and GLDAS for RMSE (2.03 mm d⁻¹). Both FLDAS and GLDAS are datasets based on land surface models.

It should be noted that some products did not stand out despite their high spatial or temporal resolution. For example, MOD16 featured the highest spatial resolution (500m) but had a low R-value of 0.463. Temporally, the hourly ERA5 and daily GLDAS, GLEAMa and GLEAMb also performed poorly, with R-value ranging from 0.389 to 0.502 and RMSE from 1.76 to 2.03 mm d⁻¹. These four datasets also fail to reproduce high ET values greater than 4 mm d⁻¹ whereas the observed ET could reach 12 mm d⁻¹.
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## 3.2. Evaluation of the spatial pattern of ET

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Fig. 4 shows the spatial patterns of ET from EC measurements and models over HRB during 2013–2020. Spatially, an obvious gradient is seen: ET over the upstream area of HRB is much higher than that in the downstream desert area. The upstream area has abundant precipitation and is covered by meadows; the downstream area is mostly desert, where ET is constrained by precipitation. This general pattern is captured by most ET products except for GLDAS, although nearly all the ET products underestimate the ET values over most of the whole basin compared with ETMap (Fig. 5).

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Despite the ability to reflect the general pattern, most of these products fail to capture the complex heterogeneity of ET. One typical example is the two croplands surrounded by barren land in the center of HRB (Fig. 1a). Croplands, especially those irrigated lands, receive additional water from irrigation, so they usually hold more soil moisture than the surrounding barren lands whose water only comes from precipitation. With the higher soil moisture, not only did soil evaporation increase but also plant photosynthesis and transpiration rate boosted, both of which led to higher ET (Fig. 2). Among the 12 ET products, only GLASS, MOD16, PML and SSEBop outlined the shape of croplands with higher ET. In addition, there is a small natural oasis in the northeast of HRB, the ET of which is higher than the surrounding desert based on the EC measurement (red dots in Fig.4 ) and ETMap (Fig. 2). Among all the models, only SSEBop of the 1-km resolution shows the spatial difference of ET. In contrast, other products, even though those
with finer resolution, such as the MOD16 and PML of 500-m resolution, failed to reflect the heterogeneity of ET. It should be noted that MOD16 does not have ET estimates over desert surfaces.

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## 3.3. Evaluation of the magnitude at the site level

To better investigate the effects of land cover types, we compared ET from models against EC measurements at the site scale with land types varying greatly (Fig. 6). Among all the land types, ET products performed best over desert steppe (site HZZ) and desert (site HM), two land types that featured the lowest observed ET, with only 100 mm yr⁻¹ for desert and 250 mm yr⁻¹ for desert steppe. Almost all the ET estimates are in the uncertainty range of ET observed from EC, except for SEBS and GLDAS, whose estimates are lowest at all 9 sites regardless of the ecosystems. In the meadow ecosystems (sites AR, DSL and YK) where the observed ET ranges from 450 to 600 mm yr⁻¹, ET products also perform relatively well with ET estimates close to EC observation. Over these sites, MOD16, FLDAS and PML performed better than the other models. When the observed ET increased to more than 600 mm yr⁻¹, all the ET products tended to underestimate the ET values. For example, over sites of agroecosystem (DM site) and wetland (ZYSD site), only SSEBop, PML, MOD16, and GLASS produced a relatively higher value, but the magnitudes are still much lower than the EC observation. The underestimation of ET is most severe over sites of forest ecosystems (sites HHL and SDQ). Most models produce a mean magnitude of less than 100 mm yr⁻¹, only one-sixth of the observation.

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## 3.4 Evaluation of the seasonality at the site level

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Figure 7 shows the monthly average ET from EC measurements and 12 ET products at the site scale. An obvious seasonality of EC ET exists over all land types except for the desert (site HM), with the maximums during the summer (June, July, and August in the Northern Hemisphere) and a gradual decline until reaching the bottom in the winter. The seasonality is well captured by most of the ET products over the desert steppe (site HZZ) and meadow ecosystem (sites AR, DSL, and YK). The relatively good performance is also seen over the wetland (site ZYSD) where SSEBop, PML, MOD16 and GLASS reproduced a similar seasonal pattern despite the lower ET peak than the EC observation. In contrast, most ET products fail to capture the seasonality of ET over sites of forest ecosystems (sites HHL and SDQ): even though SSEBop, GLASS and PML presented a relatively higher ET in the summer season, the magnitude is far lower than the EC observation, let alone the rest models with no seasonality. Among all the ET products, SEBS fails to reproduce the seasonality regardless of the land cover types.

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It should be noted that there are two ET peaks in cropland (site DM), which could be explained by the irrigation events. The principal crop of the irrigated fields over the middle reaches of HRB is maize, whose growing season is from April to October when more water is consumed. Generally, to maintain crop growth and recharge groundwater, people irrigated the croplands twice a year, i.e., during spring (March or April) and autumn (September). With the additional
water during spring, the soil moisture could maintain a high value and supports ET (Xu et al., 2020). However, none of these products could capture the higher ET caused by irrigation in spring.

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## 4. Discussion

### 4.1. Divergent performance of ET products over various land types

This study compared the ET estimates from twelve ET products over HRB with complex land surfaces. Some research has investigated ET datasets' performance over different land cover types (Vinukollu et al., 2011). For example, Baik et al. (2018) demonstrated large uncertainties in the performances of four widely available ET (GLDAS, GLEAM, MOD16, and MERRA) across seven different land types in Australia between 2005 and 2014; Melo et al. (2021) found that skills of multiple remote sensing ET presented little dependency on land use but instead related to biome and climate in South America. However, these studies either lack direct measurements or the research scope is too large, while flux observations are insufficient. Our research has the unique advantage of dense EC observations in a small river basin, which could exclude the effects of climate to some extent and better focus on the land cover types.

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Our assessment revealed that across all the land cover types, desert steppe, meadow and barren are land types where ET products could reproduce a relatively feasible ET both in magnitude and seasonality, followed by cropland and wetland. The worst performance is seen over forest
ecosystems, where the estimated ET is substantially lower than the observations. The different performances could be explained by the spatiotemporal heterogeneity of ET under highly heterogeneous land surfaces. Temporally, the intra-annual variability of the desert steppe, meadow and barren lands is small. In contrast, agroecosystems/forest ET is regulated by the growth of crops/forests, showing strong seasonal patterns. Cropland ET is also strongly affected by human irrigation events. Fig 7 shows that the maximum ET could reach 140 mm month⁻¹ in July for cropland and 180 mm month⁻¹ for forest ecosystems, much higher than the around 60 mm month⁻¹ for desert steppe and 100 mm month⁻¹ for meadow. The drastic variability in roughness and the evaporative fraction over different stages affect land-atmosphere interactions and hinder ET estimation.

Spatially, desert steppe, meadow, and barren lands are the dominant land types in HRB and are relatively homogeneous. In contrast, wetlands and forests are small patches surrounded by deserts (Fig. 1a). The dissimilar local characteristics make ET hard to quantify. Using the high-resolution land cover map of the HRB (Zhong et al., 2016), we estimated that the crop, forest, and wetland occupy an area of 6,115, 789, and 460 km², respectively, much lower than the 14,910 km² of grass and 120,122 km² of bare land (gobi and desert) (Fig. 1a). The low land area fraction makes these land cover types usually ignored by the global ET products, which would bring about large uncertainties concerning the basin-average ET estimation, as these land types have substantially high ET values. More efforts are needed to identify the heterogeneity and diurnal cycle of ET at the field scale. The next-generation ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer
Experiment on Space Station) satellite onboard the International Space Station makes this possible owing to its high spatial and temporal resolutions. ECOSTRESS has provided valuable measurements of temperature and ET since its launch in 2018. Previous studies have successfully applied the ECOSTRESS products to analyze the diurnal cycle of plant water use (Liang et al., 2022) and to delineate fine-scale irrigation that cannot be captured by coarse resolution land surface models (Pascolini-Campbell et al., 2021). Thus, constraining the high-spatiotemporal-resolution satellite measurements from ECOSTRESS would be a good way to improve these ET products, particularly in HRB where vegetation is sparse and irrigation is intensive.

<figure><img src="image_3.png" /></figure>

## 4.2. Comparison of the performances of different ET products

Our result revealed better performances of global remotely-sensed ET and synthesis ET than other categories at the site scale over the complex land surface over HRB. Remote sensing has long been recognized as a feasible way to estimate ET as it can incorporate the spatially distributed local meteorological and land surface information. Synthesis ET merged ET derived from different methods, thus achieving good performance.

<figure><img src="image_4.png" /></figure>

Among the remote-sensed ET products, our result showed better performance of SSEBop, MOD16, and PML than GLEAMa/ GLEAMb and SEBS. Apart from the finer spatial resolution of the former three ET products, the different ET algorithms used could explain the performance difference. The former three models are based on the standard PM equation or the revised version,
i.e., PML, while GLEAMa/ GLEAMb is based on the PT equation. The PM equation estimates
ET via surface radiation, temperature, and humidity data. The PT equation is similar to the PM
method but simplifies the vapor deficit and convection terms that vary with climate and forest
type (Komatsu, 2005) to a single empirical constant α. In GLEAM, α is the typical value of 1.26
for bare soil and short vegetation and 0.97 for tall vegetation (Martens et al., 2017). Even though
the simplification can reduce the computational complexity, the semi-empirical parameterization
of some physical processes may ignore the heterogeneous climatic and land surface properties,
thus lowering the accuracy. This further highlights the importance of accurately representing local
conditions, especially in the complex land cover types where the micrometeorological variables
and land surface properties vary (Liu et al., 2021; Wu et al., 2022b).

Our result revealed the worst performance of SEBS in nearly all aspects. SEBS is a widely used
energy balance model for estimating daily evapotranspiration. However, previous research
inferred that SEBS needs to be modified when implemented at a local to regional scale (Gokmen
et al., 2012; Chen et al., 2013; Ma et al., 2015) due to its incapability in estimating surface soil
heat flux (G₀) accurately at the regional scales, as well as the uncertainties caused by the
parameterization schemes of surface roughness length for heat transfer (z₀h) and momentum
transfer (z₀m) under a complex surface. For example, Chen et al. (2013) improved the roughness
height parameterization of SEBS over the Tibetan Plateau; Gokmen et al. (2012) integrated soil
moisture in SEBS to improve ET estimation under water stress conditions; Ma et al. (2015)
modified some key parameters of the SEBS model through ground measurements over HRB.
GLDAS and FLDAS also have relatively poor performance, which could be attributed to the uncertainty in the forcing data. Unlike MOD16, PML and SSEBop use satellite observational data as direct input, GLDAS and FLDAS use reanalysis datasets. Reanalysis datasets suffer from the lack of actual observation, which weakens their ability to represent local conditions. It could also explain the mediocre performance of ERA5 at the site scale. Besides, LSMs usually lack irrigation information, leading to the underestimation of ET over cropland (Fig. 6d). As for TC, the poor performance could be explained by the simple model: the heterogeneity in vegetation types is not considered in the one-dimensional modified Thornthwaite-Mather climatic water-balance model, nor is the physical response to changing environmental conditions (Abatzoglou et al., 2018).

## 4.3. Caveats and limitations

Focusing on the magnitude, seasonality, and spatial pattern, our study investigated the performance of 12 ET products over heterogeneous land surfaces in the HRB. It should be noted that the evaluation result may be affected by the spatial scale mismatch between the footprint of the EC observations and the pixels of the ET products. The footprint of EC is typically limited to hundreds of meters to several kilometers (Liu et al., 2016), while the pixel sizes of ET products compared in this study vary from 500 hundred to 0.25 degree. To eliminate the effects of scale mismatch, we compared the growing-season ET from 12 ET products with that from ETMap. At the basin scale, all the ET products underestimated ET greatly, underscoring the importance of land cover types with small areas but high ET values, such as croplands, wetlands, and forests
over HRB. Among the models, ERA5 and PML performed best, with an underestimation of
around 40 mm/year during the growing season. In contrast, SEBS and MOD16 are the two
products with the highest underestimation of growing-season ET, up to 140 mm/year. The
underestimation of MOD16 could be explained by the missing estimates over the vase desert.
Besides, the performances of ET products at the site scale do not match those at the basin scale.
For example, the well-performed GLASS and SSEBop did not stand out at the basin scale (Fig. 8).
At the same time, ERA5 and TC with poor performance have relatively lower underestimation of
basin-averaged ET.

Besides, the temporal inconsistency between daily ET observations (Beijing time) and global ET products (UTC) would also affect the assessment. To eliminate this effect, we converted UTC to Beijing time for the hourly ERA5-Land ET product. As for the daily ET datasets, since ET mainly occurs during the day (~7:00-18:00 at HRB local time (with a longitude of ~100°E) and 8:30-19:30 at Beijing time), even if the -8:00 offset is added, the EC observations are still covered within one day when the daily ET estimated are provided by the ET products, so the effect is negligible. In addition, limited by the short length of the observational data time series, we did not evaluate the interannual variation of ET from different ET products in detail. Currently, large uncertainty exists in the detection of ET trends. At the basin scale, previous research reported inconsistent or even divergent estimates of the multi-year (1982–2011) ET trends by five remote sensing ET products over the Amazon basin, despite the similar magnitudes (Wu et al., 2020). Globally, the long-term ET trend is also under debate. Pan et al. (2020) found that the
ensembles of remotely-sensed ET and machine-learning ET reported increasing ET globally between 1982 and 2011, while LSMs showed no statistically significant trend. The potential ET between 2003 and 2012 estimated from eight different global model datasets showed little agreement, with the trends ranging from -0.201 to 0.056 mm month⁻¹ yr⁻¹ (Anabalón & Sharma., 2017). With a five-year observation, we found that only PML could present a similar trend, but the magnitude is lower than the EC observation. Long observation data are needed to investigate the ET trend over HRB further.

## 5. Conclusions

Focusing on the ability to capture the heterogeneity of ET, this study compared the magnitude, seasonal cycle, and spatial pattern of ET from eleven global ET products and one regional ET dataset against dense eddy covariance observations over the Heihe River Basin with complex land surfaces. In general, the remotely-sensed ET and synthesis ET outperformed other categories. Among the remotely-sensed ET products, our result showed better performance of operational Simplified Surface Energy Balance (SSEBop), Moderate Resolution Imaging Spectroradiometer (MOD16), and Penman-Monteith-Leuning (PML), which could be explained by the ET estimation equations used. Across all the land cover types, ET products reproduce a more feasible ET over the relatively homogeneous desert steppe, meadow, and barren than the spatiotemporally heterogeneous cropland, wetland, and forest ecosystems. The comparison underscores the importance of land cover types with small areas but high ET values, such as croplands, wetlands,
and forests over HRB, which are not captured well by existing ET products. More efforts are
needed to represent heterogeneity and local climates in the complex land surfaces for ET
quantification, which is of great significance in regional water resource management.

**Funding:** This study was supported by the National Natural Science Foundation of China (42071022 and 41861124003) and the startup fund provided by the Southern University of Science and Technology(29/Y01296122).

**Conflicts of Interest:** The authors declare no conflict of interest.

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664
665 Table Legends.

666 Table 1. Information of eddy covariance sites in the Heihe River Basin.

667 Table 2. Information on the 12 evapotranspiration (ET) products used in this study.

668
<table><caption>Table 1. General information of eddy covariance sites in the Heihe River Basin.</caption><thead><tr><th rowspan="2">Site name</th><th rowspan="2">Ecosystem</th><th rowspan="2">Longitude (°E)</th><th rowspan="2">Latitude (°N)</th><th rowspan="2">Altitude (m)</th><th colspan="2">Data duration</th></tr><tr><th>Begin</th><th>End</th></tr></thead><tbody><tr><td>AR</td><td>Meadow</td><td>100.46</td><td>38.05</td><td>3033</td><td>Jan. 2013</td><td>Dec. 2020</td></tr><tr><td>DSL</td><td>Meadow</td><td>98.94</td><td>38.84</td><td>3739</td><td>Aug. 2013</td><td>Dec. 2020</td></tr><tr><td>JYL</td><td>Meadow</td><td>101.12</td><td>37.84</td><td>3750</td><td>Aug. 2018</td><td>Dec. 2020</td></tr><tr><td>YK</td><td>Meadow</td><td>100.24</td><td>38.01</td><td>4148</td><td>Jan. 2015</td><td>Dec. 2020</td></tr><tr><td>BJT</td><td>Gobi</td><td>100.30</td><td>38.91</td><td>1562</td><td>Sep. 2012</td><td>Dec. 2014</td></tr><tr><td>DM</td><td>Cropland</td><td>100.37</td><td>38.86</td><td>1556</td><td>Sep. 2012</td><td>Dec. 2020</td></tr><tr><td>HZZ</td><td>Desert steppe</td><td>100.32</td><td>38.77</td><td>1731</td><td>Sep. 2012</td><td>Dec. 2020</td></tr><tr><td>SSW</td><td>Desert</td><td>100.49</td><td>38.79</td><td>1594</td><td>Jun. 2012</td><td>Apr. 2015</td></tr><tr><td>ZYSD</td><td>Wetland</td><td>100.45</td><td>38.98</td><td>1460</td><td>Sep. 2012</td><td>Dec. 2020</td></tr><tr><td>HHL</td><td>Mixed forest</td><td>101.13</td><td>41.99</td><td>874</td><td>Jul. 2013</td><td>Dec. 2020</td></tr><tr><td>HYL</td><td>Populus forest</td><td>101.12</td><td>41.99</td><td>876</td><td>Jul. 2013</td><td>Dec. 2015</td></tr><tr><td>LD</td><td>Bare land</td><td>101.13</td><td>42.00</td><td>878</td><td>Jul. 2013</td><td>Dec. 2015</td></tr><tr><td>NT</td><td>Cropland</td><td>101.13</td><td>42.00</td><td>875</td><td>Jul. 2013</td><td>Oct. 2015</td></tr><tr><td>SDQ</td><td>Tamarix</td><td>101.14</td><td>42.00</td><td>873</td><td>Jul. 2013</td><td>Dec. 2020</td></tr><tr><td>HM</td><td>Desert</td><td>100.99</td><td>42.11</td><td>1054</td><td>May. 2015</td><td>Dec. 2020</td></tr></tbody></table>
<table><caption>Table 2. General information on the 12 evapotranspiration (ET) products used in this study.</caption><thead><tr><th>Product</th><th>Spatial and temporal resolution</th><th>Duration</th><th>ET method</th></tr></thead><tbody><tr><td>MOD16A2 V6</td><td>500 m, 8 d</td><td>2001–2020</td><td>PM</td></tr><tr><td>PML V2</td><td>500 m, 8 d</td><td>2001–2020</td><td>PML</td></tr><tr><td>SEBS</td><td>5 km, 8 d</td><td>2000–2017</td><td>RS-SEB</td></tr><tr><td>SSEBop</td><td>1 km, 10 d</td><td>2003–2020</td><td>PM</td></tr><tr><td>GLEAMa</td><td>0.25°, daily</td><td>1980–2020</td><td>PT</td></tr><tr><td>GLEAMb</td><td>0.25°, daily</td><td>2003–2020</td><td>PT</td></tr><tr><td>FLDAS</td><td>0.1°, monthly</td><td>1982–2020</td><td>LSM</td></tr><tr><td>GLDAS</td><td>0.25°, daily</td><td>2003–2020</td><td>LSM</td></tr><tr><td>TerraClimate</td><td>4km, monthly</td><td>1958–2020</td><td>SWB</td></tr><tr><td>ERA5-Land</td><td>0.1°, hourly</td><td>1981–2020</td><td>LSM</td></tr><tr><td>GLASS</td><td>1 km, 8 d</td><td>2000–2020</td><td>Bayesian multimodel estimation</td></tr><tr><td>CR</td><td>0.1°, monthly</td><td>1981–2017</td><td>Complementary relationship</td></tr></tbody></table>
675 **Figure Legends.**

676 **Fig. 1. Geographic information of the Heihe River Basin.** a, Land cover and eddy covariance (EC) sites (red pentagrams). Land cover information is obtained from MODIS land cover data (MCD12Q1) in 2015, with International Geosphere-Biosphere Programme classification system. b and c, EC sites in the downstream (b) and middle stream (c). The background images in (b) and (c) were obtained from Google Earth.

681 **Fig. 2. Spatial pattern of the basin-averaged growing season (May-September) ET of ETMap.** The data spans from 2012-2016.

682 **Fig. 3. Overall statistical performance of ET products over Heihe River Basin.**

684 **Fig. 4. Spatial patterns of ET from measurements and models over the Heihe River Basin during 2013–2020.** Dots show measured ET values from EC sites that have complete measurements during 2013–2020.

687 **Fig. 5. Comparison of the magnitude of ET values from EC measurements and 12 ET products at the site scale.**

689 **Fig. 6. Multi-year averaged evapotranspiration seasonal cycle from flux EC measurements and 12 ET products at the site scale.**

691 **Fig. 7. Spatial pattern of the growing season ET difference between the ET products and ETMap (ET from ET products minus that from ETMap) during 2012-2016.**

693 **Fig. 8. The comparison of basin-averaged ET estimated by ETMap and 12 ET products (b).**

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<figure><img src="image_5.png" /><figcaption>**Fig. 1. Geographic information of the Heihe River Basin.** a, Land cover and eddy covariance (EC) sites (red pentagrams). Land cover information is obtained from MODIS land cover data (MCD12Q1) in 2015, with the International Geosphere-Biosphere Programme classification system. b and c, EC sites in the downstream (b) and middle stream (c). The background images in (b) and (c) were obtained from Google Earth®.</figcaption></figure>
703 Fig. 2. Spatial pattern of the basin-averaged growing season (May-September) ET of
704 ETMap. The data spans from 2012-2016.

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 3. Overall statistical performance of ET products over Heihe River Basin.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 4. Spatial patterns of ET from measurements and models over Heihe River Basin during 2013–2020. Dots show measured ET values from EC sites that have complete measurements during 2013–2020.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 5. Spatial pattern of the growing season ET difference between the ET products and ETMap (ET from ET products minus that from ETMap) during 2012-2016. The ETmap data (at a spatial resolution of 1km) have been resampled to the spatial resolution of the compared dataset for a better comparison at the same spatial scale.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 6. Comparison of the magnitude of ET values from EC measurements and 12 ET products at the site scale.</figcaption></figure>
Fig. 7. Multi-year averaged evapotranspiration seasonal cycle from flux EC measurements and 12 ET products at the site scale.

<figure><img src="image_11.png" /></figure>

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<figure><img src="image_12.png" /><figcaption>728 Fig. 8. The comparison of basin-averaged ET estimated by ETMap and 12 ET products.</figcaption></figure>

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