Large-scale Evapotranspiration Research Articles

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.

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.

Calibrating a remote sensing evapotranspiration model using the Budyko framework

Remote sensing (RS) evapotranspiration (ET) models are commonly used to estimate ET at a large scale. These models often include spatially varying parameters that need to be determined. Typically, these parameters are either held as constants or obtained from a pre-specified lookup table, potentially introducing bias into ET estimates. This study aimed to evaluate the feasibility of calibrating a RS ET model using Budyko-derived mean annual ET. We applied this approach to calibrate the single parameter of the Penman-Monteith-Leuning (PML) model. Instead of using the default Budyko-Fu parameter ω (ω=2.6), we developed a data-driven model that relates the basin-calibrated ω and its primary controls. Subsequently, we mapped the spatial pattern of ω across China. The calibrated PML model was evaluated using flux observations from 12 ChinaFlux sites and water balance-based ET estimates from 80 basins in China. Results indicate that the Budyko-based calibration significantly improved the performance of the PML model at both site and basin scales compared to the uncalibrated model. This study highlights the potential of leveraging the Budyko framework to constrain the parameter estimates of RS ET models, thereby enhancing the accuracy of large-scale ET estimates.

The Modified Soil Moisture Constraint Scheme Significantly Enhances the Evapotranspiration Simulation Accuracy of the MOD16 Model

Remotely sensed (RS) evapotranspiration (ET) models can make full use of the land surface information retrieved using remote sensing and are therefore widely used in large-scale ET estimates. The MODIS Global Evapotranspiration model (MOD16) is one of the most commonly used remote sensing ET models. MOD16 parameterizes the moisture constraints on soil evaporation (Es) using atmospheric vapor pressure deficit (VPD) and relative humidity (RH). This moisture constraint algorithm has been criticized by many studies due to the weak correlation between soil moisture and VPD or RH over short timescales (e.g., hourly and daily). In this study, we introduce a modified moisture constraint algorithm of ET, based on the ratio of antecedent accumulated precipitation to soil equilibrium evaporation, in order to improve the ET simulation capabilities of the MOD16 model. The original and modified MOD16 models are evaluated at 14 ChinaFlux sites and 286 basins in China, using eddy covariance measurements and water-balance-based ET estimates. The results show that the modified MOD16 model outperforms the original MOD16 model at both the site and basin scales. Compared with the original model, the modified model increases the value of KGE by an average of 0.17 at the flux site scale and by 0.01 at the basin scale. Using soil moisture measurements from flux sites as a reference, we further found that the modified MOD16 model also has a better soil moisture simulation capacity than the original model. This study highlights the importance of reliable soil moisture constraints in remotely sensed ET models.

Comparison and evaluation of evapotranspiration estimations by different approaches in the Yellow River Basin

Abstract Accurately quantifying large-scale evapotranspiration (ET) is of significant scientific importance. In this study, the Shuttleworth–Wallace (SW) model incorporates gross primary productivity (GPP) values from the near-infrared reflectance of vegetation (NIRv) GPP product (NIRvGPP). Monthly ET estimations for the Yellow River Basin (YRB) were obtained using the SW models with GPP derived from the GLASS GPP product (GLASSGPP) and NIRvGPP, as well as GLASS ET and CR ET products (SW_GLASSGPP ET, SW_NIRvGPP ET, GLASS ET, and CR ET). The study analyzes the annual spatio-temporal patterns of these products, evaluates their accuracy and uncertainty, and examines the applicability of a fused ET estimation. The results revealed: (1) differences exist in ET estimations among GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET; (2) monthly GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET align well with the monthly ETWB, with SW_GLASSGPP ET and SW_NIRvGPP ET outperforming GLASS ET and CR ET; (3) average uncertainties of GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET display different spatial variation patterns; and (4) compared to GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET, the fused ET estimation achieves the best performance within the YRB.

Estimation of regional actual evapotranspiration based on the improved SEBAL model

The global food and water crises caused by climate warming are becoming increasingly significant. One of the important aspects of surface water transfer is evapotranspiration. The accurate estimation of evapotranspiration (ET) is of great significance in water resource management. However, accurate estimation of large-scale ET is challenging due to the adequate selection of the hot and cold pixels, the heterogeneity of the subsurface and the instability of the lower atmosphere. Therefore, in this paper, a surface energy balance model (Y-SEBAL) was proposed based on the SEBAL model and improved sensible heat flux calculation, and used to simulate large-scale actual evapotranspiration. Based on the principle of surface energy balance, this model introduced the empirical formula of sensible heat flux and simplifies the selection of limit to address the challenge of calculating the sensible heat flux of the crucial variable in the SEBAL model. The study simulated the spatial and temporal evolution of regional evapotranspiration with the model and verified the model simulation performance. In addition, the sensitivity of the model-driven data was quantified. The results showed that the simulation value of the Y-SEBAL model was highly consistent with the eddy covariance measurement data. In the verification results, the R value was 0.82, the index of agreement was 0.90, and the root mean square error was 0.81 mm·d−1. The performance verification indices were better than the SEBAL model, MOD16 model, and SSEBop model. And the model had the highest sensitivity to wind speed, reaching 0.714. The Y-SEBAL model improved the simulation performance of the original model and simulated the actual evapotranspiration. The study can provide a new solution for improving the retrieval of evapotranspiration by remote sensing at the regional scale.

Comparison of remote sensing evapotranspiration models: Consistency, merits, and pitfalls

Various models are available for large-scale evapotranspiration (ET) estimation. The performance of these models generally differs due to their differences in forcing data, model structure (mathematical representations of the ET process), and parameter estimation. Model comparison is the most straightforward way to identify the strengths, weaknesses, and uncertainty sources of a model. In this study, three widely used remote sensing ET models were considered: the air-relative-humidity-based two-source (ARTS) model, Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, and Penman-Monteith-Leuning (PML) model. The three models were evaluated based on measurements from 12 eddy-covariance (EC) towers and water-balance-based ET estimates from 286 basins. The evaluation results indicate that the PML model performs the best on both the site and basin scales. The advantage of the PML model may be due to (i) the land-cover-based parameter configuration and (ii) the consideration of differences in the responses of soil evaporation and transpiration to soil water deficit. We also investigated the consistencies and differences between the models in simulating the spatiotemporal variation and component partitioning of ET, that is, interception loss (Ei), soil evaporation (Es), and transpiration (Et). The three models show high consistency in estimating nationwide multi-year average ET (416.3–438.2 mm/yr) and its spatial pattern but large discrepancies in ET trends (0.10–0.98 mm/yr2) and component partitioning. The PT-JPL model considerably overestimates the ratio of Ei/ET, thereby underestimating the ratio of Es/ET because of the negative correlation between Ei and Es. The ARTS model presents better applicability in grasslands than in croplands or forestlands. This may be because its parameter value (constant for all biomes) and the water constraint scheme are more suitable for grasslands. Finally, we proposed specific modifications to address the potential issues of each model.

Multi-scale evaluation of global evapotranspiration products derived from remote sensing images: Accuracy and uncertainty

• Five global remote sensing-based evapotranspiration (ET) products are compared. • Their accuracy at point scale is evaluated based on 94 worldwide flux stations. • Accuracy at basin scale is assessed by water balance analysis across 414 basins. • ET uncertainty at pixel scale is quantified with three-cornered hat method. • ET quantity and trend at the global scale is assessed through comparison analysis. Advances in satellite remote sensing (RS) techniques have greatly prompted the development of large-scale evapotranspiration (ET) models, yielding several freely available global ET products. A comprehensive evaluation on these products is necessary for selecting the most suitable ET products and developing large-scale ET models step forward. In this study, the accuracy and uncertainty of five RS-based global ET products (MOD16, SSEBop, GLEAM, AVHRR, and BESS) were evaluated at multi scales. At point scale their accuracy was evaluated through a direct comparison with in-situ observations from 94 worldwide flux towers. Results indicate that accuracy differences of these five products vary with statistical metrics and land cover types. The combination of all statistics illustrates that GLEAM and AVHRR outperform three other products, which is consistent with the findings of most previous continental-scale studies. The accuracy at basin scale was assessed through water balance analysis across 19 big river basins. BESS is found to be the product that is superior to four other ET products at both monthly and multi-annual scale, which may be related with its low random error at point scale. The uncertainty of these five products at pixel scale was assessed by the three-cornered hat (TCH) method and comparison analysis. The TCH outputs reveal that BESS and SSEBop are the two products with the lowest and largest relative uncertainty, respectively. That explains the good performance of BESS at basin scale from another perspective. As for the two MODIS-based ET products (MOD16 and SSEBop), their accuracy at FLUXNET site scale is comparable in monthly ET estimation. However, SSEBop has special advantage in the capture of multiyear ET patterns. The comparison analysis indicates that SSEBop is the product that agrees best with the ensemble mean not only on a global scale but also in four of the six continents. Our results imply that it is difficult or even impossible to figure out the best ET product in all respects. The selection of ET products for scientific research should consider their performance difference in spatial scale as well as the influence of land cover and climate conditions.

Estimating evapotranspiration using remotely sensed solar-induced fluorescence measurements

Terrestrial evapotranspiration (ET) is a critical component of the land surface and its accurate estimation is crucial in understanding the global water and energy budgets. Current large-scale ET estimation models depend on remotely sensed data products of meteorological conditions and vegetation phenology, but high uncertainty remains to correctly represent ecological factors, which are regulated by biochemistry. In recent years, solar-induced fluorescence (SIF), a proxy for photosynthesis, has been extensively used in different ecological research and is conceived to be of great potential for ET estimation to constrain the transpiration flux, which is the significant component of ET. So far, most SIF-based ET estimation methods are achieved via empirical methods, which lack a solid physical foundation. In this work, we estimated ET by combining SIF and meteorological variables with Fick's law and an optimal stomatal behavior model and validated the model across different eddy-covariance flux stations around the globe. The model showed overall good performance across different ecosystems and when compared with other remote sensing-based ET models, such as the Priestley Taylor-JPL (PT-JPL) model, the Moderate Resolution Imaging Spectroradiometer (MODIS)-ET products, and a simple empirical linear SIF-ET model, the RMSE of estimated ET of the model proposed in this paper demonstrated better performance. The model we proposed here can be potentially extended for a reliable ET estimation at a global scale.

An operational parameterization scheme of surface temperature-vegetation index contextual model for large-scale temporally continuous evapotranspiration estimation: The case study of contiguous United States

A recent review has suggested relatively simple contextual models would remain attractive in the estimation of evapotranspiration (ET). The surface temperature-vegetation index feature space model is a typical representative since it provides direct monitoring of ET using simply and effective contextual information. However, the temporally continuous estimation and large-scale application issues are not well identified at previous studies. Under this background, the main motivation for this study is to develop an operational new parameterization scheme within the feature space framework for large-scale temporally continuous ET estimation. Specifically, the spatial domain division strategy was first designed to obtain the optimal grid division size for large-scale ET estimation. The model boundaries of these sub-regions were parameterized using spatial domain based temporal domain information at seasonal scale. The applicability of new method was demonstrated over the entire contiguous United States (CONUS) in the year 2008 using Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The comparison between present method and traditional method indicates that the new parameterization scheme produced at different reference spatial domains has a slight accuracy improvement over the 14 FLUXNET sites. In addition, the new method holds unique advantages in its simplicity and continuity, especially superior in the temporally continuous estimation of ET over a large extended region. The validation results of 14 FLUXNET sites indicate the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE) the mean bias (B) values for continuous estimation of daily ET were 0.62, 28.430 W/m2, 36.133 W/m2 and −0.059, respectively. The comparison results with existing ET product also demonstrated the comparable of ET distribution and estimates. Meanwhile, the boundary parameters obtained at seasonal scale also enhanced the physical meanings of feature space model. Consequently, the new parameterization scheme proposed in this study can be regarded as a suitable tool to provide large-scale temporally continuous estimation of ET.

Dynamic Scaling of the Generalized Complementary Relationship Improves Long-term Tendency Estimates in Land Evaporation

Most large-scale evapotranspiration (ET) estimation methods require detailed information of land use, land cover, and/or soil type on top of various atmospheric measurements. The complementary relationship of evaporation (CR) takes advantage of the inherent dynamic feedback mechanisms found in the soil-vegetation-atmosphere interface for its estimation of ET rates without the need of such biogeophysical data. ET estimates over the conterminous United States by a new, globally calibrated, static scaling (GCR-stat) of the generalized complementary relationship (GCR) of evaporation were compared to similar estimates of an existing, calibration-free version (GCR-dyn) of the GCR that employs a temporally varying dynamic scaling. Simplified annual water balances of 327 medium and 18 large watersheds served as ground-truth ET values. With long-term monthly mean forcing, GCR-stat (also utilizing precipitation measurements) outperforms GCR-dyn as the latter cannot fully take advantage of its dynamic scaling with such data of reduced temporal variability. However, in a continuous monthly simulation, GCR-dyn is on a par with GCR-stat, and especially excels in reproducing long-term tendencies in annual catchment ET rates even though it does not require precipitation information. The same GCR-dyn estimates were also compared to similar estimates of eight other popular ET products and they generally outperform all of them. For this reason, a dynamic scaling of the GCR is recommended over a static one for modeling long-term behavior of terrestrial ET.

Adoption of IoT technology to estimate evapotranspiration from satellite imagery

Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of reference evapotranspiration and thus measuring air temperature, humidity and wind speed. Calculation of evapotranspiration is usually based on obtained information from the nearest weather stations to the study area, which can be error-prone. Therefore, in this study, IoT sensors were used to accurately measure air temperature at 2 m above the ground, as well as air humidity and wind speed in the study area. The study area is the farms of Moghan Agricultural Company in Ardabil province. In this study, 23 nodes were installed in a number of farms. The ground-based energy balance algorithm (SEBAL) was used to calculate the evapotranspiration using Landsat 8 images in 2015.

Benchmarking large-scale evapotranspiration estimates: A perspective from a calibration-free complementary relationship approach and FLUXCOM

Accurately quantifying large-scale terrestrial evapotranspiration (ET) remains hampered by poor parameterization of the physical processes that relate to ET. Previous studies suggested that the calibration-free complementary relationship (CR) method that requires only routine meteorological data performed better than main-stream atmospheric reanalyses, land surface or remote sensing models in estimating large-scale ET. Here we simultaneously evaluate the latest machine learning-based upscaling of eddy-covariance measurements (FLUXCOM) and the CR estimates against the water-balance derived ET rates of 18 large Hydrologic Unit Code-2 (HUC2) and 327 medium HUC6 basins across the conterminous United States. Overall, CR and FLUXCOM perform comparably in representing the multiyear mean and temporal variations in annual ET at both, HUC2 and HUC6, scales for the 1979–2013 period. Such equally good skills also hold true for the 2003–2015 period, during which FLUXCOM was driven solely by remote sensing data. However, the CR generally captures the long-term linear tendencies in annual ET rates somewhat better than FLUXCOM. Because of its minimal data requirement, the calibration-free version of the CR may continue to serve as a benchmarking tool for large-scale ET simulations.

Global assessment of how averaging over spatial heterogeneity in precipitation and potential evapotranspiration affects modeled evapotranspiration rates

Abstract. Accurately estimating large-scale evapotranspiration (ET) rates is essential to understanding and predicting global change. Evapotranspiration models that are applied at a continental scale typically operate on relatively large spatial grids, with the result that the heterogeneity in land surface properties and processes at smaller spatial scales cannot be explicitly represented. Averaging over this spatial heterogeneity may lead to biased estimates of energy and water fluxes. Here we estimate how averaging over spatial heterogeneity in precipitation (P) and potential evapotranspiration (PET) may affect grid-cell-averaged evapotranspiration rates, as seen from the atmosphere over heterogeneous landscapes across the globe. Our goal is to identify where, under what conditions, and at what scales this “heterogeneity bias” could be most important but not to quantify its absolute magnitude. We use Budyko curves as simple functions that relate ET to precipitation and potential evapotranspiration. Because the relationships driving ET are nonlinear, averaging over subgrid heterogeneity in P and PET will lead to biased estimates of average ET. We examine the global distribution of this bias, its scale dependence, and its sensitivity to variations in P vs. PET. Our analysis shows that this heterogeneity bias is more pronounced in mountainous terrain, in landscapes where spatial variations in P and PET are inversely correlated, and in regions with temperate climates and dry summers. We also show that this heterogeneity bias increases on average, and expands over larger areas, as the grid cell size increases.

Intercomparison and evaluation of three global high-resolution evapotranspiration products across China

Accurate quantification of large-scale evapotranspiration (ET) has an important scientific and practical significance. In this study, three global high-resolution ET products were intercompared and evaluated across China; the evaluated ET products were the Global Land Evaporation Amsterdam Model (GLEAM) version 3.2, the Global Land Data Assimilation System (GLDAS) version 2.0 Catchment Land Surface Model (CLSM) dataset, and the Numerical Terradynamic Simulation Group (NTSG) dataset. The evaluations were performed at the site scale with eddy covariance (EC) based observations from eight flux stations, and at the basin scale with water balance-based ET estimates from 22 river basins in China. The intercomparison results indicated that the three products consistently presented an increasing trend over a large proportion of China but large differences in the trend of the annual ET and the mean annual ET estimates. The trends of the annual ET estimates derived from the GLEAM, GLDAS, and NTSG products are 0.449, 0.904 and 1.261 mm/yr2, respectively; the countrywide mean annual ET derived from the three products are 390.2, 443.4 and 419.9 mm/yr, respectively. The site-scale evaluation results indicated that the GLEAM and NTSG products achieved comparable consistencies with the monthly gauge observations and outperformed the GLDAS product; GLEAM was more consistent with the daily gauge observations than GLDAS. At the basin scale, all three products were unable to reasonably reproduce the water balance-based annual ET time series in most basins; the three products systematically overestimated ET in the wet basins compared with the water balance-based ET estimates. The differences in the ET estimates among the ET products may be largely attributed to the discrepancies in the forcing data and model algorithms.

Dominant effect of increasing forest biomass on evapotranspiration: interpretations of movement in Budyko space

Abstract. During the last 6 decades, forest biomass has increased in Sweden mainly due to forest management, with a possible increasing effect on evapotranspiration. However, increasing global CO2 concentrations may also trigger physiological water-saving responses in broadleaf tree species, and to a lesser degree in some needleleaf conifer species, inducing an opposite effect. Additionally, changes in other forest attributes may also affect evapotranspiration. In this study, we aimed to detect the dominating effect(s) of forest change on evapotranspiration by studying changes in the ratio of actual evapotranspiration to precipitation, known as the evaporative ratio, during the period 1961–2012. We first used the Budyko framework of water and energy availability at the basin scale to study the hydroclimatic movements in Budyko space of 65 temperate and boreal basins during this period. We found that movements in Budyko space could not be explained by climatic changes in precipitation and potential evapotranspiration in 60 % of these basins, suggesting the existence of other dominant drivers of hydroclimatic change. In both the temperate and boreal basin groups studied, a negative climatic effect on the evaporative ratio was counteracted by a positive residual effect. The positive residual effect occurred along with increasing standing forest biomass in the temperate and boreal basin groups, increasing forest cover in the temperate basin group and no apparent changes in forest species composition in any group. From the three forest attributes, standing forest biomass was the one that could explain most of the variance of the residual effect in both basin groups. These results further suggest that the water-saving response to increasing CO2 in these forests is either negligible or overridden by the opposite effect of the increasing forest biomass. Thus, we conclude that increasing standing forest biomass is the dominant driver of long-term and large-scale evapotranspiration changes in Swedish forests.

A Comparative Study of GRACE with Continental Evapotranspiration Estimates in Australian Semi-Arid and Arid Basins: Sensitivity to Climate Variability and Extremes

This study examines the dynamics and robustness of large-scale evapotranspiration products in water-limited environments. Four types of ET products are tested against rainfall in two large semi-arid to arid Australian basins from 2003 to 2010: two energy balance ET methods which are forced by optical satellite retrievals from MODIS; a newly developed land surface model (AWRA); and one approach based on observations from the Gravity Recovery and Climate Experiment (GRACE) and rainfall data. The two basins are quasi (Murray-Darling Basin: 1.06 million km2) and completely (Lake Eyre Basin: 1.14 million km2) endorheic. During the study period, two extreme climatic events—the Millennium drought and the strongest La Niña event—were recorded in the basins and are used in our assessment. The two remotely-sensed ET products constrained by the energy balance tended to overestimate ET flux over water-stressed regions. They had low sensitivity to climatic extremes and poor capability to close the water balance. However, these two remotely-sensed and energy balance products demonstrated their superiority in capturing spatial features including over small-scale and complicated landscapes. AWRA and GRACE formulated in the water balance framework were more sensitive to rainfall variability and yielded more realistic ET estimates during climate extremes. GRACE demonstrated its ability to account for seasonal and inter-annual change in water storage for ET evaluation.

Multi-scale validation of GLEAM evapotranspiration products over China via ChinaFLUX ET measurements

ABSTRACTLand evapotranspiration (ET) is a key component of terrestrial ecosystems, as it is the nexus of hydrological, energy, and carbon cycles. Satellite-based observations are commonly utilized to provide high-resolution, large-scale ET estimates. The ground-based validation of such large-scale estimates is necessary to ensure that remotely sensed ET characteristics are accurate, and to extend their various applications. The Global Land-surface Evaporation Amsterdam Methodology (GLEAM) combines a wide range of multi-satellite observations to estimate daily actual evaporation through a process-based methodology. In this study, we focused on evaluating a daily GLEAM 0.25° ET product using in-situ eddy covariance (EC) ET data (2003–2005) as a benchmark at eight sites from the Chinese Flux Observation and Research Network (ChinaFLUX), which contains several biome types (croplands, grasslands, shrublands, savannas, and forests) across China at a range of temporal scales (from daily, to monthly, to annual). The results indicated that the ET products of the Global Land-surface Evaporation Amsterdam Methodology (GLEAM ET) over different time scales can estimate actual ET with reasonable accuracy. GLEAM showed high skill scores for most of the land-cover types except at the Xishuangbanna forest site (XSBN), where significantly systematic bias was detected at each individual temporal scale. Overall, GLEAM ET products were closer to the EC observations at the three grassland sites than at the four forest sites or the cropland site. GLEAM significantly overestimated the EC measurements at the four forest sites and one cropland site, while a slight underestimation occurred at the three grassland sites; there was a year-long systematic overestimation for GLEAM at the four forest sites. The daily GLEAM ET aggregated by monthly and annual data agreed more closely with EC measurements than those taken at the daily timescale. The results also showed a high average correlation coefficient (r) with in-situ EC observations at all sites, at daily (r = 0.71), monthly (r = 0.86), and annual (r = 0.79) time scales in addition to ET season-dependent characteristics for satellite estimation errors. The results presented here contribute to further assessment of the quality and uncertainty of GLEAM ET products, which may benefit future advancements in the ET algorithm and its product quality.

Remote Sensing Based Estimation of Evapo-Transpiration Using Selected Algorithms: The Case of Wonji Shoa Sugar Cane Estate, Ethiopia

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.