Terrestrial Evapotranspiration Research Articles

Spatial-temporal variations in evapotranspiration across the continental United States: An atmospheric water balance perspective

Accurate estimation of terrestrial evapotranspiration (ET) is crucial for understanding land–atmosphere interactions, but is challenging on the regional scales. The water balance approach, including terrestrial and atmospheric water balance (TWB and AWB), provides a simple tool for estimating regional ET. This study estimated ET values based on the AWB approach (ETAWB) over the continental United States (CONUS) for the period 1979–2021. Validations using TWB-based ET estimates (ETTWB) suggest that ETAWB is accurate. ETAWB demonstrates comparable interannual variability with the other three long-term ET products over the CONUS and is consistent with ETTWB in the majority of basins. The 43-year CONUS averaged ETAWB is 548 ± 26 mm/year, with higher values in the eastern region and the coastal regions in the western CONUS, and lower values in the western arid regions. During 1979–2021, increasing trends of ETAWB are observed in the eastern CONUS, while decreasing trends occurred in the western regions. The intercomparison between ETAWB, GLEAM, Noah, and ERA5 illustrates similar spatial patterns and linear trends. In the water-limited arid basins, ETAWB anomalies over time show strong agreements with precipitation anomalies. The results of this study indicate that the AWB approach provides reasonable regional-to-continental terrestrial ET estimates over the CONUS, serving as a reference for hydrological and climatic modeling.

Evaluation of root zone soil moisture products over the Huai River basin

Abstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM is not directly observable from space, several RZSM products are available and widely used at global and continental scales. This study conducts a comprehensive and quantitative evaluation of eight RZSM products using observations from 58 in situ soil moisture stations over the Huai River basin (HRB) in China. Attention is drawn to the potential factors that contribute to the uncertainties of model-based RZSM, including the errors in atmospheric forcing, vegetation parameterizations, soil properties and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) outperforms the other RZSM products with the highest correlation coefficient (R= 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.018 m3 m−3), while SMOS Level 4 (L4) RZSM shows the worst performance among eight RZSM products. The RZSM products based on land surface models generally perform better in the wet season than in the dry season due to the enhanced ability to capture of the temporal dynamics of in situ observations in the wet season and the inertia of remaining high soil moisture values even in the dry season, while the SMOS L4 RZSM product, derived from SMOS L3 surface moisture (SSM) combined with an exponential filter method, performs better in the dry season due to the attenuated ground microwave radiation signal caused by the increased water vapour absorption and scattering in the wet season. The underestimated SMOS L3 SSM triggers the underestimation of RZSM in SMOS L4. The overestimated RZSM products based on land surface models could be associated with the overestimated precipitation amounts and frequency, the underestimated air temperature, and the underestimated ratio of transpiration to the total terrestrial evapotranspiration. In addition, the biased soil properties and flawed vegetation parameterizations affect the hydrothermal transport processes represented in different land surface models (LSMs) and lead to inaccurate soil moisture simulation. The scale mismatch between point and footprint also introduces representative errors. The comparison of frequency of normalized soil moisture between RZSM products and in situ observations indicates that the LSMs should focus on reducing the frequency of wet soil moisture, increasing the frequency of dry soil moisture and the ability to capture the frequency peak of soil moisture. The study provides some insights into how to improve the ability of land surface models to simulate the land surface states and fluxes by taking into account the issues mentioned above. Finally, these results can be extrapolated to other regions located in similar climate zones, as they share similar precipitation patterns that dominate the terrestrial water cycle.

A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020

Quantifying terrestrial evapotranspiration (ET) and soil moisture dynamics accurately is crucial for understanding the global water cycle and surface energy balance. We present a novel, long-term dataset of global ET and soil moisture derived from the newly developed Simple Terrestrial Hydrosphere model, version 2 (SiTHv2). This ecohydrological model, driven by multi-source satellite observations and hydrometeorological variables from reanalysis data, provides daily global ET-related estimates (e.g., total ET, plant transpiration, soil evaporation, intercepted evaporation) and three-layer soil moisture dynamics at a 0.1° spatial resolution. Validation with in-situ measurements and comparisons with mainstream global ET and soil moisture products demonstrate robust performance of SiTHv2 in both magnitude and temporal dynamics of ET and soil moisture at multiple scales. The comprehensive water path characterization in the SiTHv2 model makes this seamless dataset particularly valuable for studies requiring synchronized water budget and vegetation response to water constraints. With its long-term coverage and high spatiotemporal resolution, the SiTHv2-derived ET and soil moisture product will be suitable to support analyses related to the hydrologic cycle, drought assessment, and ecosystem health.

Main influencing factors of terrestrial evapotranspiration for different land cover types over the Tibetan Plateau in 1982–2014

Introduction: Terrestrial evapotranspiration (ET) over the Tibetan Plateau (TP) has important implications for the global water cycle, climate change, and ecosystem, and its changes and driving factors have drawn increasing attention. Previous research studies have minimally quantified the effects and identified the pathways of the influencing factors on ET over different land surface types.Methods: In this study, we analyze the spatiotemporal distribution and variation of ET over the TP in 1982–2014 based on multiple datasets. Furthermore, the effects of each influencing factor on ET are quantified over different land surface types, and the major influencing factors and their affecting pathways are identified using structure equation modeling (SEM), which is a statistical method used to analyze relationships among multiple variables.Results: The results show that the climatology of ET decreases gradually from southeastern to northwestern TP, with the maximum spatial averaged value of 379.979 ± 0.417 mm a−1 for the fifth generation of European Reanalysis (ERA5) and the minimum of 249.899 ± 0.469 mm a−1 for the Global Land Data Assimilation System (GLDAS). The most significant differences among the ET datasets mainly occur in the summer. The annual ET averaged over the TP presents an increased trend from 1982 to 2014, as shown by all of the ET datasets. However, there are larger discrepancies in the spatial distribution of the increased trend for these datasets. The assessment result shows that the 0.05° land evapotranspiration dataset for the Qinghai–Tibet Plateau (LEDQTP) has the highest temporal correlation coefficient (0.80) and the smallest root-mean-square error (23.50 mm) compared to the observations. Based on LEDQTP, we find that precipitation is the main influencing factor of ET, which primarily affects ET through direct pathways in bare soil and grassland regions, with standardized estimates of 0.521 and 0.606, respectively. However, in meadow and shrub and forest regions, the primary factor influencing ET is air temperature, which is primarily affected by an indirect pathway through a vapor pressure deficit. Air temperature is also the controlling factor in sparse vegetation regions, but it affects ET through a direct pathway.Discussion: This study may provide some new useful information on the effects of climate change on ET in different land cover types over the TP.

Why does a decrease in cloud amount increase terrestrial evapotranspiration in a monsoon transition zone?

Terrestrial evapotranspiration plays a critical role in drought monitoring and water resource management. Changes in evapotranspiration are significantly influenced by cloud-related precipitation and radiation effects. However, the impact of cloud amount (CA) on evapotranspiration through its influence on precipitation remains uncertain, especially in the transition zone affected by the East Asian summer monsoon (EASM), which limits the understanding of the water cycle. Therefore, this study deeply explores the impact of CA on evapotranspiration and its potential physical mechanisms in Northwest China. The results show that the correlation between 31-year average evapotranspiration and CA is negative only in the semi-arid region and is positive in other climatic regions of Northwest China. This unique negative correlation is related to the change of precipitation pattern in the semi-arid region caused by the weak EASM. Smaller CA in weak monsoons results in more short-wave radiation reaching the surface, larger sensible heat, and weaker convective inhibition. Consequently, the proportion of convective clouds (CCs) increases and precipitation from these CCs enhances evapotranspiration. Less CA increases evapotranspiration and potentially exacerbates aridity in the semi-arid region of Northwest China. These results emphasize the role of cloud type in evapotranspiration. It is well known that global warming can change cloud type with more CCs. Therefore, this study sheds new light on evapotranspiration change under global warming.

Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method

This study presents a Bayesian-driven ensemble learning framework to minimize errors in evapotranspiration (ET) data assimilation, thereby generating a continuous-scale ET estimation with improved accuracy for the data-scarce arid Central Asia (CA). Five state-of-the-art gridded ET products were extensively tested and utilized to reconstruct new long-term ET data at grid scale, including two Global Land Data Assimilation System (GLDAS)-Land Surface Models (LSM) (Catchment and NOAH), two reanalysis products (European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) and Modern-Era Retrospective Analysis for Research and Applications – ver. 2 (MERRA2)), along with the Global Land Evaporation Amsterdam (GLEAM) satellite ET product ver. 3.6b. Taylor Skill Score was employed to evaluate the input gridded ET datasets, using in situ measurements across four arid biomes — cropland, shrubland, farmland, and grassland —as benchmarks. Bayes-Markov Chain Monte Carlo (Bayes-MCMC)-derived logarithmically transformed predictors from the posterior ensemble anomalies were standardized to resolve the mismatches in the cumulative distribution functions of ET data relating to the in situ observations. We found that the constrained residual errors were more profound between 45th and 95th percentiles for the rescaled total ET anomalies across all the input ET datasets at 95 % confidence interval. Moreover, the weighted ensemble estimates derived from the Bayes-MCMC were instrumental in correcting ET anomalies, leading to the robust reconstruction of a more reliable ensemble mean ET data for Central Asia (CAETens) at monthly temporal and 0.25° spatial resolutions, spanning from 2003 to 2020. Overall, CAETens exhibited superior performance, achieving the highest averaged Kling-Gupta Efficiency of 0.75 and registering the lowest Root Mean Square Error of 6.85 mm/m compared to the standalone gridded ET products. Comparisons of spatial distributions of fine-scale features, and evaluations using standard metrics revealed that all ET products varied significantly (p < 0.05) by biome types. Remarkably, only CAETens exhibited a reasonable low positive bias and pronounced ability to resolve the complex spatiotemporal patterns associated with the input gridded ET products. The study bridges the gap in continuous ET monitoring for sustainable water resources management in CA.

Divergent Responses of Summer Terrestrial Evapotranspiration to Cloud Increase in East Asia

AbstractCloud impact on terrestrial evapotranspiration (ET) can be significant, while a fundamental understanding of ET response to cloud change has not been addressed in regional aspects. In this study, seven ET datasets from satellite and land surface models are used to analyze the summer ET in response to low cloud cover (LCC) over East Asia, in combination with satellite microwave and optical vegetation data, as well as ERA5 climatic and surface parameters. In general, all ET datasets present a strong negative relationship with LCC over dense vegetation (e.g., forests), which leads to the ET reduction of more than −0.5 mmday−1 under the large LCC increase, compared with the least cloudy sky conditions. Such reduction is stronger under more humid and denser forests (aridity index AI &gt; 0.5 and enhanced vegetation index EVI &gt; 0.6). In contrast, a positive relationship between ET and LCC presents over arid and sparse lands (AI &lt; 0.2 and EVI &lt; 0.2), accompanied by enhanced ET (&gt;0.2 mmday−1). Analysis shows that under increased LCC conditions, substantial reduction in radiation availability results in the decline in both plant transpiration (ETveg) and soil evaporation (ETsoil) over humid dense vegetation, dominating the overall negative response. The increase of near‐surface relative humidity over arid sparse vegetation may indicate a stronger moisture availability, which promotes ETsoil and partly offsets the negative effect of radiation reduction. The change of vegetation water content, indicated by a satellite microwave vegetation index, is important for ETveg over arid mountainous lands with cloud cover. These findings contribute new insights to the understanding of vegetation‐atmosphere interactions.

Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

AbstractDue to limited direct measurements, regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Although the inhomogeneity of the observed climate data has been widely reported, its impact on the calculated ET has not been adequately quantified. This study aimed to calculate ET using the modified Penman‐Monteith (MPM) model with raw and homogenized meteorological data. Additionally, we compared the calculated ET with those estimates from variable methods (water balance, satellite‐based, and reanalysis) in China and its six major river basins from 1982 to 2020. During the overlapping period of 1997–2018, ET calculated from raw input data decreased slightly at −0.39 mm yr−2 (p = 0.64) in China, whereas homogenized ET showed a significant increasing trend of 0.93 mm yr−2 (p = 0.02), with a better agreement with water balance ET (1.93 mm yr−2, p = 0). Global Land Evaporation Amsterdam Model (GLEAM) and Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2) could reproduce the increasing trends with 2.08 mm yr−2 (p = 0) and 2.59 mm yr−2 (p = 0). The intercomparison of input variables (solar radiation, relative humidity, wind speed, precipitation, and air temperature) among ET products revealed substantial differences, which can account for the discrepancies in ET estimates. Homogenized ET, GLEAM and MERRA2 exhibited significant increasing trends in China and most river basins from 1982 to 2020. Our findings underscore the importance of utilizing homogenized input data for more accurate ET estimation.

Evaluation of seven satellite-based and two reanalysis global terrestrial evapotranspiration products

Although comprehensive evaluation of different types of global terrestrial evapotranspiration (ET) products has been conducted, the satellite remote sensing techniques have prompted the development of several available global ET products, warranting a reassessment as products continue to evolve. Recently, we produced the long-term Global LAnd Surface Satellite (GLASS) ET product, but there is a lack of comparison and evaluation with other ET products and EC observations on a global scale. In this study, we evaluated the accuracy and uncertainty of seven satellite-based (GLASS-AVHRR, GLASS-MODIS, BESS, FLUXCOM, GLEAM, MOD16, and PML_V2) and two reanalysis (ERA5 and MERRA2) global terrestrial ET products at multiple scales for selecting the most suitable ET products and developing large-scale ET models. At the point scale, their accuracy was evaluated through direct comparison with in situ observations from 230 global flux towers. The results indicate that no single ET product can provide the most accurate ET estimates for all land cover types, although GLASS-MODIS [coefficient of determination (R2) of 0.51, Kling–Gupta efficiency (KGE) of 0.68] and FLUXCOM [R2 of 0.51, KGE of 0.66] outperform the seven other products [0.54 =< KGE =< 0.66, 0.35 =< R2 =< 0.50] at all sites. At the basin scale, the accuracy of ET products was assessed through 36 large river basins. The R2 values between all ET products and the water balance-derived ET (WBET) are >0.88, while the accuracies of the nine ET products differ in some sense. The three-cornered hat (TCH) method and comparison analysis are applied to assess the uncertainty of nine ET products at the pixel level. The TCH outputs reveal that GLASS-AVHRR and GLASS-MODIS are the two products with the lowest relative uncertainty, while MERRA2 has the largest relative uncertainty. Our results imply that there is no single ET product performing best in all respects. The selection of ET products for scientific research should consider their performance differences in spatial scale as well as the influence of land cover and climate conditions.

Water-balance-based evapotranspiration for 56 large river basins: A benchmarking dataset for global terrestrial evapotranspiration modeling

A thorough validation of global terrestrial evapotranspiration (ET) models requires reliable ground-observed ET data. While the open-access eddy-covariance flux measurements are widely used for such a purpose, they have certain drawbacks, and thus, other alternative publicly available reference datasets are urgently needed. Using remote sensing and ground-based observational data, this study provides water-balance-based evapotranspiration (ETwb) estimates for 56 large (>105 km2) river basins of the world over the 1983–2016 period. For each basin, the observed runoff and four different precipitation (Prec) data sources were combined with three types of terrestrial water storage change (dS) estimates, yielding altogether 12 annual ETwb time-series. An optimally merged ETwb time-series was eventually produced using the Bayesian-based three-cornered hat method. The relative uncertainty in the ETwb estimates is less than 10 % in most basins and it stems primarily from the uncertainty in Prec. In summary, this new ETwb dataset has the following advantages: i) The gauge undercatch in Prec was corrected, thus mitigating the well-known general underestimation of ETwb in mid- and high-latitudes; ii) Multiple Prec and dS datasets were combined to account for the uncertainty in the water balance approach, thereby enabling the quantification of the uncertainty in ETwb and its sources, and; iii) The ETwb dataset stretches more than three decades, making it appropriate for evaluating long-term trends in global ET models. This ETwb dataset is publicly available (https://data.tpdc.ac.cn/en/data/e010cd0d-0881-4e7e-9d63-d36992750b04) and may serve as a benchmarking tool to calibrate/validate large-scale ET models for an improved understanding of regional- and/or global-scale ET processes.

Global Terrestrial Evapotranspiration Estimation from Visible Infrared Imaging Radiometer Suite (VIIRS) Data

It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth–Wallace dual-source ET method (SW), the Priestley–Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley–Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R2, 0.71), RMSE (21.9 W/m2) and Kling–Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012–2020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset.

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

Constraining the power-function complementary relationship with a steady-state Budyko equation for predicting terrestrial evapotranspiration globally

The power-function complementary relationship (CR) represents the latest advancement in definitive CR models, offering enhanced predictions of terrestrial evapotranspiration (E), all while elucidating the physical interpretations of its associated parameters. In this study, we introduced practical approaches to integrate the traditional Budyko framework into the novel CR model for global-scale E prediction. Under a steady-state assumption, the power-function CR was equated with the Turc-Mezentsev equation, thereby introducing an additional constraint for determining key parameters. We presented two illustrative cases where the aridity index and land-specific parameter constrain the exponent and the Priestley-Taylor coefficient of the CR, respectively. Compared to its calibration-free counterpart, the CR model constrained by the Budyko framework demonstrated improved performance in capturing spatial and temporal variations of E in point locations, mesoscale catchments, and large river basins. This improvement was not only from considering long-term precipitation partitioning, but also from the ability of the combined framework to rectify biases in input data. While the calibration-free methods exhibited poorer performance, they hold potential for superior results with higher-quality forcing inputs. By merging the Turc-Mezentsev equation with the power-function CR, this study provides insights into refining E predictions, accounting for climatic conditions and input data quality.

Recycling and transport of evapotranspiration over the Tibetan Plateau: Detected by a water vapour tracer method embedded in regional climate model

AbstractWith an average elevation of 4000 m, the Tibetan Plateau (TP) exerts a global influence on weather and climate. To investigate and quantify the impact of terrestrial evapotranspiration (ET) over the TP on both local and downstream regions, we employed the water vapour tracer (WVT) method integrated into the Weather Research and Forecasting (WRF) model to track the propagation of evaporative moisture across the TP. Based on precipitation landing location, termination of evaporative moisture over the TP was categorized into recycled ET (ETin, which precipitates inside the TP) and moving‐out ET (ETout, which precipitates outside the TP). Our findings reveal that ETin dominates 70% of ET moisture termination over the TP, exhibiting a decrease gradient from eastern to western regions. The majority of ETout spreads eastward with some reaching southward. Seasonal variations indicate that more than 75% of ET recycles during summer, while more ET moves out from TP in spring and autumn. The interplay between convection and advection leads to different reach‐outs for moving‐out ET in summer compared to other seasons: strong convection and diabatic heating plus a relative weak large‐scale advection result in shorter reach‐outs during summer, whereas weaker convection combined with strong advection leads to longer ‐reach‐outs in other seasons. This study enhances our understanding of terrestrial ET moisture transport over the TP as well as its mechanism influencing both local water cycle dynamics and downstream processes.

High uncertainty of evapotranspiration products under extreme climatic conditions

Terrestrial Evapotranspiration (ET) is an important process for understanding regional or global water, energy, and carbon cycles, and with the development of satellite observations and increased research investment, grid ET products covering a broad spatial extent are becoming more readily available. However, as global warming becomes a reality and extreme climate events occur worldwide, existing studies do not go far enough to verify that these grid products can still be used under extreme climate conditions. This study evaluates nine global ET products, including land surface reanalysis products (GLDAS_CLSM, GLDAS_NOAH, ERA5, and FLDAS), remote sensing (RS) products (GLEAM_v3.6b, MOD16A2, and PMLv2), and multi-source data fusion products (REA and Synthesized), using observations from 153 flux towers worldwide. The objective is to evaluate their performance in estimating ET under extreme climatic conditions (high temperature, high vapor pressure deficit (VPD), and drought). The results indicate that the estimation accuracy of all ET products is significantly reduced under extreme climatic conditions, showing high uncertainty, and this impact is most severe in the Americas (AM) region. Overall, multi-source data fusion products showed the best estimation performance and were less affected by extreme climatic conditions. Among the remote sensing modeling products, GLEAM_v3.6b showed the best performance, while MOD16A2 has the lowest estimation accuracy. Land surface reanalysis products were most affected by extreme conditions, with CLSM and NOAH showing similar performance and ERA5 having the largest errors (RMSE_ERA5 = 1.699 mm/d, MAE_ERA5 = 1.294 mm/d). The ET products show significant error fluctuations and overestimation (PBias > 0.5) in most of North America, and there is a decline in simulation accuracy in arid and semi-arid regions near 30°N, with most ET products showing overestimation. The most significant errors were observed in cropland areas (CRO) and deciduous broadleaf forest areas (DBF), with significant overestimation in mixed forest areas (MF). The results of this study provide valuable insights for researchers in selecting ET products under extreme climatic conditions and encourage product developers to consider uncertainty under such conditions, thereby improving product accuracy.

Comprehensive evaluation of terrestrial evapotranspiration from different models under extreme condition over conterminous United States

Terrestrial evapotranspiration (ET) is a key process in the water, energy, and carbon cycles. ET products are becoming more widely accessible with the development of remotely sensed inversions and availability of land surface assimilation information. However, the response of these products to extreme weather is not yet clear and challenging to evaluate. This study evaluated and compared the consistency and uncertainty of nine evaporation products (including GLEAM3.6b, reliability ensemble averaging (REA), CLSM, ERA5-Land (ERA5), MOD16A2, PML-V2 (PML), NOAH, FLDAS, and Synthesized (Syn)) for the period 2003–2015. Eddy covariance (EC) flux observations and the three-cornered hat (TCH) method were used to validate and assess the uncertainty of these products at both the point-scale and grid-scale under normal climatic conditions. More importantly, we conducted uncertainty assessments of ET products at daily and monthly scales under extreme climatic conditions (including high temperature (Temp), high vapor pressure deficit (VPD), and drought), referencing 50 EC measurements over the conterminous United States (COUNS). The results showed that the accuracy of different ET products varied with cover type. Compared to other products, GLEAM3.6b, REA, and Syn showed superior results for most land cover types. Specifically, GLEAM3.6b showed higher performance for the grasslands; REA showed improved performance in mixed forests, open shrubs, and wetlands, and Syn fared better in forests and croplands. The point-scale indicated that the lowest root mean square deviations (RMSD) come from Syn grid products (mean 16.13 mm/month), followed by GLEAM3.6b (mean 18.53 mm/month) and REA (mean 19.42 mm/month). The highest RMSD (mean 49.49 mm/month) originated from ERA5, while the lowest R was found in MOD16A2. The uncertainty ranking calculated using the TCH method is similar to the findings of the point-scale evaluation based on EC measurements, with Syn recording the lowest uncertainty of 5.13 mm/month, after GLEAM3.6b (5.72 mm/month), followed by REA (6.24 mm/month), and ERA5 (12.26 mm/month) with the largest uncertainty. When extreme weather conditions were encountered, the uncertainty of the nine ET products showed an increasing trend, and their consistency varied. Under high-temperature and high-VPD conditions, the consistency decreased, while during drought, the consistency showed a slight increase. Among the ET products, GLEAM3.6b performed best under high-temperature and high-VPD conditions, closely followed by REA. However, ERA5 exhibited the largest error in its estimations. On the other hand, when drought conditions occurred, GLEAM3.6b, REA, and Syn showed better performance, while CLSM, NOAH, and FLDAS ranked second, ERA5 had the largest uncertainty, and MOD16A2 and PML exhibited the worst consistency. The findings of this study offer a foundation for selecting appropriate ET products for hydrologic analysis and agricultural water resource management in the CONUS and give developers suggestions for lowering the uncertainty of ET products in the face of extreme climatic conditions.

Significant Disparity in Spatiotemporal Changes of Terrestrial Evapotranspiration across Reanalysis Datasets in China from 1982 to 2020

Significant Disparity in Spatiotemporal Changes of Terrestrial Evapotranspiration across Reanalysis Datasets in China from 1982 to 2020

Weakening amplification of grassland greening to transpiration fraction of evapotranspiration over the Tibetan Plateau during 2001-2020

Evapotranspiration plays a key role in regulating the water cycle in terrestrial ecosystems. Transpiration fraction to evapotranspiration (TF) characterizes the role of vegetation function in evapotranspiration and is a pivot feature for the interactions between soil, ecosystem, and the atmosphere. Generally, the amount of vegetation has the most direct impact to TF, but the sensitivity of TF to vegetation change is not known, which could be a metric to evaluate the role of greening in land-atmosphere water transportation. To address this key knowledge gap, we used remote sensing observations of leaf area index (LAI) as a proxy for vegetation amount and derived the sensitivity of TF to LAI (θ =∂TF∂LAI) for the grasslands over the Tibetan Plateau (TP). Results showed there is a substantial decline of θ over the TP during 2001–2020, decreasing from over 4% m−2 m2 to below 1.5% m−2 m2. To analyze the drivers of θ, we introduced a diagnosis framework for soil-atmosphere hydraulic stress shifts based on trends in soil moisture and atmospheric vapor pressure deficit. Results implicated the θ decline over the TP is likely not induced by the dry-wet balance shifts by soil and atmosphere, nor by the vegetation's stomal conductance. Instead, we speculate that the θ decline is induced by a marginal damping effect in case the TF rise is approaching to grassland's limit over the TP. Our finding appeals future studies on terrestrial evapotranspiration to consider the role of vegetation in controlling transpiration and emphasize that greening may not lead to higher TF.

Evaluating European ECOSTRESS Hub Evapotranspiration Products Across a Range of Soil‐Atmospheric Aridity and Biomes Over Europe

AbstractThe ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio‐temporal resolution (∼70 m, 1–5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio‐temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability and atmospheric aridity. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature (LST) retrievals, we generated ECOSTRESS ET products over Europe and Africa using three models with different structures and parameterization schemes, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non‐parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites in Europe over six different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ∼70 W m−2). However, the relatively complex TSEB model produced a higher RMSE of ∼90 W m−2. Comparison between STIC ET estimates and the operational ECOSTRESS ET product from NASA PT‐JPL model showed a larger RMSE (around 50 W m−2 higher) for the PT‐JPL ET estimates. Substantial overestimation (&gt;80 W m−2) in PT‐JPL ET estimates was evident over shrublands and savannas, presumably due to weak constraint of LST in the model. Overall, the EEH supports ET retrieval for the future high‐resolution thermal missions.

Tracking Moisture Sources of Precipitation Over China

AbstractMoisture of precipitation originates from terrestrial and oceanic evaporation through atmospheric transport. Understanding the contributions from different moisture sources to precipitation is essential for the hydrologic study. China is the world's third largest country in terms of land area and has contributed 25% of the global net increase in vegetation leaf area since 2000. Thus, it provides a good platform to understand the contributions from different moisture sources to precipitation and how increasing vegetation greenness affects precipitation trends. In this study, we investigated the moisture sources of precipitation and their changes in China during 1980–2018 based on the UTrack moisture recycling data set. We found that 47.3% of the moisture was from oceanic evaporation, and 52.7% was from terrestrial evapotranspiration (ET). For the moisture from terrestrial ET, 33.0% was from vegetation transpiration. The observed annual precipitation showed a significant increasing trend with 2.07 mm/year in China from 1980 to 2018. Increasing moisture from oceanic evaporation contributed the most to the increasing precipitation trend (0.98 mm/year), followed by the increasing moisture from vegetation transpiration (0.72 mm/year) and other terrestrial evaporation (0.37 mm/year). The increasing contribution of vegetation transpiration from China was comparable with that from neighboring countries (0.37 vs. 0.35 mm/year). The increasing moisture from vegetation transpiration could be due to the nationwide afforestation projects in China since 1999. The results provide a reference for understanding changes in precipitation and hydrologic processes.