Operational Simplified Surface Energy Balance Research Articles

On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?

Abstract. The miombo woodland is the largest dry woodland formation in sub-Saharan Africa, covering an estimated area of 2.7–3.6 million km2. Compared to other global ecosystems, the miombo woodland demonstrates unique interactions between plant phenology and climate. For instance, it experiences an increase in the leaf area index (LAI) during the dry season. However, due to limited surface exchange observations in the miombo region, there is a lack of information regarding the effect of these properties on miombo woodland evaporation. It is crucial to have a better understanding of miombo evaporation for accurate hydrological and climate modelling in this region. Currently, the only available regional evaporation estimates are based on satellite data. However, the accuracy of these estimates is questionable due to the scarcity of field estimates with which to compare. Therefore, this study aims to compare the temporal dynamics and magnitudes of six satellite-based evaporation estimates – the Topography-driven Flux Exchange (FLEX-Topo) model, Global Land Evaporation Amsterdam Model (GLEAM), Moderate-Resolution Imaging Spectrometer (MODIS) MOD16 product, operational Simplified Surface Energy Balance (SSEBop) model, Thornthwaite–Mather climatic Water Balance (TerraClimate) dataset, and Water Productivity through Open access of Remotely sensed derived data (WaPOR) – during different phenophases in the miombo woodland of the Luangwa Basin, a representative river basin in southern Africa. The goal of this comparison is to determine if the temporal dynamics and magnitudes of the satellite-based evaporation estimates align with the documented feedback between miombo woodland and climate. In the absence of basin-scale field observations, actual evaporation estimates based on the multi-annual water balance (Ewb) are used for comparison. The results show significant discrepancies among the satellite-based evaporation estimates during the dormant and green-up and mid-green-up phenophases. These phenophases involve substantial changes in miombo species' canopy phenology, including the co-occurrence of leaf fall and leaf flush, as well as access to deeper moisture stocks to support leaf flush in preparation for the rainy season. The satellite-based evaporation estimates show the highest agreement during the senescence phenophase, which corresponds to the period of high temperature, high soil moisture, high leaf chlorophyll content, and highest LAI (i.e. late rainy season into the cool-dry season). In comparison to basin-scale actual evaporation, all six satellite-based evaporation estimates appear to underestimate evaporation. Satellite-based evaporation estimates do not accurately represent evaporation in this data-sparse region, which has a phenology and seasonality that significantly differ from the typical case in data-rich ground-truth locations. This may also be true for other locations with limited data coverage. Based on this study, it is crucial to conduct field-based observations of evaporation during different miombo species phenophases to improve satellite-based evaporation estimates in miombo woodlands.

Assessing Satellite-Derived OpenET Platform Evapotranspiration of Mature Pecan Orchard in the Mesilla Valley, New Mexico

Pecan is a major crop in the Mesilla Valley, New Mexico. Due to prolonged droughts, growers face challenges related to water shortages. Therefore, irrigation management is crucial for farmers. Advancements in satellite-derived evapotranspiration (ET) models and accessibility to data from web-based platforms like OpenET provide farmers with new tools to improve crop irrigation management. This study evaluates the evapotranspiration (ET) of a mature pecan orchard using OpenET platform data generated by six satellite-based models and their ensemble. The ET values obtained from the platform were compared with the ET values obtained from the eddy covariance (ETec) method from 2017 to 2021. The six models assessed included Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL), Google Earth Engine implemonthsmentation of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) model, Operational Simplified Surface Energy Balance (SSEBop), Satellite Irrigation Management Support (SIMS), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and Atmosphere–Land Exchange Inverse and associated flux disaggregation technique (ALEXI/DisALEXI). The average growing season ET of mature pecan estimated from April to October of 2017 to 2021 by geeSEBAL, eeMETRIC, SSEBop, SIMS, PT-JPL, ALEXI/DisALEXI, and the ensemble were 1061, 1230, 1232, 1176, 1040, 1016, and 1130 mm, respectively, and 1108 mm by ETec. Overall, the ensemble model-based monthly ET of mature pecan during the growing season was relatively close to the ETec (R2 of 0.9477) with a 2% mean relative difference (MRD) and standard error of estimate (SEE) of 15 mm/month for the five years (N = 60 months). The high agreement of the OpenET ensemble of the six satellite-derived models’ estimates of mature pecan ET with the ETec demonstrates the utility of this promising approach to enhance the reliability of remote sensing-based ET data for agricultural and water resource management.

SSEBop Evapotranspiration Estimates Using Synthetically Derived Landsat Data from the Continuous Change Detection and Classification Algorithm

The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by the availability of Landsat data. Here, in this proof-of-concept paper, we utilize the Continuous Change Detection and Classification (CCDC) algorithm to generate synthetic Landsat data, which are then used as input for SSEBop to generate evapotranspiration estimates for six target areas in the continental United States, representing forests, shrublands, and irrigated agriculture. These synthetic land cover data are then used to generate the LST data required for SSEBop evapotranspiration estimates. The synthetic LST, evaporative fractions, and evapotranspiration data from CCDC closely mirror the phenological cycles in the observed Landsat data. Across the six sites, the median correlation in seasonal LST was 0.79, and the median correlation in seasonal evapotranspiration was 0.8. The median root mean squared error (RMSE) values were 2.82 °C for LST and 0.50 mm/day for actual evapotranspiration. CCDC predictions typically underestimate the average evapotranspiration by less than 1 mm/day. The average performance of the CCDC evaporative fractions, and corresponding evapotranspiration estimates, were much better than the initial LST estimates and, therefore, promising. Future work could include bias correction to improve CCDC’s ability to accurately reproduce synthetic Landsat data during the summer, allowing for more accurate evapotranspiration estimates, and determining the ability of SSEBop to predict regional evapotranspiration at seasonal timescales based on projected land cover change from CCDC.

Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models

Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.

Assessment of Precipitation and Evapotranspiration in an Urban Area Using Remote Sensing Products (CHIRP, CMORPH, and SSEBop): The Case of the Metropolitan Region of Belem, Amazon

The aim of this study was to assess precipitation (P) and actual evapotranspiration (ET) by analyzing data from in situ stations compared with remote sensing products. Climate Hazards Center InfraRed Precipitation (CHIRP) and Climate Prediction Center morphing technique (CMORPH) were used for P and Operational Simplified Surface Energy Balance (SSEBop) was used for ET. The P in situ data for six stations were also compared to a reference station in the city. ET was analyzed for a single in situ station. The region chosen for this study was the Metropolitan Area of Belem (MAB), close to the estuary of the Amazon River and the mouth of the Tocantins River. Belem is the rainiest state capital in Brazil, which causes a myriad of challenges for the local population. The assessment was performed using the statistical metrics root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), mean bias error (MBE), coefficient of determination (R2), regression slope, and Nash–Sutcliffe coefficient (NS). For the reference station, the automatic and conventional CHIRP and CMORPH results, in mm/month, were as follows: automatic CHIRP: RMSE = 93.3, NRMSE = 0.32, MBE = −33.54, R2 = 0.7048, Slope = 0.945, NS = 0.5668; CMORPH: RMSE = 195.93, NRMSE = 0.37, MBE = −52.86, R2 = 0.6731, Slope = 0.93, NS = 0.4344; conventional station CHIRP: RMSE = 94.87, NRMSE = 0.32, MBE = −33.54, R2 = 0.7048, Slope = 0.945, NS = 0.5668; CMORPH: RMSE = 105.58, NRMSE = 0.38, MBE = −59.46 R2 = 0.7728, Slope = 1.007, NS = 0.4308. In the MAB region, ET ranges on average between 83 mm/month in the Amazonian summer and 112 mm/month in the Amazonian winter. This work concludes that, although CMORPH has a coarser resolution than CHIRP for the MAB at a monthly resolution, both remote sensing products were reliable. SSEBop also showed acceptable performance. For analyses of the consistency of precipitation time series, these products could provide more accurate information. The present study validates P and ET from remote sensing products with station data in the rain-dominated urban MAB.

Using a crop water stress index to determine water use efficiency in a raspberry crop in the Mediterranean Central Chile

AbstractWater availability is projected to decrease in Mediterranean Central Chile, necessitating sustainable production strategies based on improved irrigation management. This study focuses on estimating water use efficiency (WUE) in raspberry crops at two validation sites using remote sensing and soil irrigation data. By employing the crop water stress index (CWSI), we demonstrate the potential of this tool in enhancing irrigation management and establishing sustainable production practices. The results demonstrate the successful estimation of actual evapotranspiration (ETa) using the Operational Simplified Surface Energy Balance (SSEBop) model (coefficient of determination [R2] = 0.92; root mean square error [RMSE] = 0.97 mm day−1), while the CWSI indicated high stress levels after 5 days of irrigation. Moreover, validation at two sites reveals significant differences in applied irrigation, with sites A and B receiving 17,097 and 3760 m3 ha−1, respectively, while the average water demand is close to 5300 m3 ha−1. These variations result in discrepancies in WUE, with values of 0.79 and 3.64 kg m−3. By integrating remote sensing indices and soil data, this study proposes that maintaining an 85% ETa rate during noncritical periods can enhance WUE. This work demonstrates the potential use of a water stress index to monitor crops in the Chilean central zone for efficient water resource use under future scarcity scenarios.

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

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 to 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.

Phenophase-based comparison of field observations to satellite-based actual evaporation estimates of a natural woodland: miombo woodland, southern Africa

Abstract. The trend and magnitude of actual evaporation across the phenophases of miombo woodlands are unknown. This is because estimating evaporation in African woodland ecosystems continues to be a challenge, as flux observation towers are scant if not completely lacking in most ecosystems. Furthermore, significant phenophase-based discrepancies in both trend and magnitude exist among the satellite-based evaporation estimates (i.e. Global Land Evaporation Amsterdam Model (GLEAM), moderate resolution imaging spectroradiometer (MODIS), operational simplified surface energy balance (SSEBop), and water productivity through open-access remotely sensed derived data (WaPOR)), making it difficult to ascertain which of the estimates are close to field conditions. Despite the many limitations with estimation of evaporation in woodlands, the development and application of the distributed temperature system (DTS) is providing deepened insights and improved accuracy in woodland energy partitioning for evaporation assessment. In this study, the Bowen ratio distributed temperature sensing (BR-DTS) approach is used to partition available energy and estimate actual evaporation across three canopy phenophases of the miombo woodland, covering the entire 2021 dry season (May–October) and early rain season (November–December) at a representative site in Mpika in Zambia, southern Africa. To complement the field experiment, four satellite-based evaporation estimates are compared to the field observations. Our results show that actual evaporation of the miombo woodland appears to follow the trend of the net radiation, with the lowest values observed during the phenophase with the lowest net radiation in the cool dry season and the highest values during the phenophase with peak net radiation in the early rainy season. It appears the continued transpiration during the driest period in the dormant phenophase (with lowest canopy cover and photosynthetic activities) may be influenced by the species-dependent adapted physiological attributes such as access to moisture in deep soils (i.e. due to deep rooting), plant water storage, and the simultaneous leaf fall and leaf flush among miombo plants. Of the four satellite-based evaporation estimates, only the WaPOR has a similar trend to the field observations across the three phenophases. However, all four satellite-based estimates underestimate the actual evaporation during the dormant and green-up phenophases. Large coefficients of variation in actual evaporation estimates among the satellite-based estimates exist in the dormant and green-up phenophases and are indicative of the difficulty in estimating actual evaporation in these phenophases. The differences between field observations and satellite-based evaporation estimates can be attributed to the model structure, processes, and inputs.

Developing a dynamic semi-distributed hydrological model considering interactions between soil moisture and evapotranspiration: application of bulk transfer method

ABSTRACT Empirically-derived relationships are mainly used to estimate evapotranspiration (ET) in water balance models. Due to a lack of effective factors and correct conceptual relationships, the resulting ET may be inaccurate. Therefore, a hydrological model was developed by combining water balance and bulk transfer theory, called semi-distributed coupled water balance and bulk transfer (SD-CWBT). According to it, a dynamic interrelationship between ET and soil moisture was correctly established to estimate ET via physical concepts. Global gridded ET retrievals, which provide the spatiotemporal distribution of ET at a global scale, including ECMWF Reanalysis v5-Land (ERA5-Land), TERRA, Global Land Evaporation Amsterdam Model (GLEAM), and Operational Simplified Surface Energy Balance (SSEBop), were employed to evaluate the model. The results indicated the SD-CWBT, with Nash–Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Squared Error (RMSE) of [0.6, 0.95], [0.68, 0.96], and [12.61, 0.99] for the streamflow and groundwater level simulation, respectively, had better performance compared to the alternatives. Furthermore, the estimated spatiotemporal distributions of ET agreed well with the climatic-vegetation conditions.

Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (ETa) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration (ETr) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications.

Estimation of Water Use in Center Pivot Irrigation Using Evapotranspiration Time Series Derived by Landsat: A Study Case in a Southeastern Region of the Brazilian Savanna

In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and farming production. In this work, we propose a methodology to estimate water used in agriculture irrigated by center pivots in the municipality of Itobi, São Paulo, in the Brazilian Savanna (known as Cerrado), which has strong potential for agricultural and livestock production. The methodology proposed for the water use estimate is based on mapping crops irrigated by center pivots for the 2015/2016 crop year and actual evapotranspiration (ETa). ETa is derived from the Operational Simplified Surface Energy Balance model (SSEBop) and parameterized for edaphoclimatic conditions in Brazil (SSEBop-Br). Three meteorological data sources (INMET, GLDAS, CFSv2) were tested for estimating ETa. The water use was estimated for each meteorological data source, relating the average irrigation balance and the total area for each crop identified in the map. We evaluated the models for each crop present in the center pivots through global accuracy and f1-score metrics, and f1-score was more significant than 0.9 for all crops. The potato was the crop that consumed the most water in irrigation, followed by soy crops, beans, carrots, and onions, considering the three meteorological data sources. The total water volume consumed by center pivots in the municipality of Itobi in the 2015/2016 agricultural year for each meteorological data source was 3.2 million m3 (INMET), 2.5 million m3; (GLDAS), and 1.8 million m3 (CFSv2).

Linking fire-induced evapotranspiration shifts to streamflow magnitude and timing in the western United States

• Climate influence can dampen or cause misattribution of fire effects on streamflow. • Evaluation at annual scale masks wildfire effects on monthly ETa and streamflow. • ETa reductions only distinguishable in basin-scale monthly data when at least 73% of basin burned. • Seasonal streamflow shifts detected in basins with as little as 6% burned area. • Surplus water from ETa reduction sufficient to account for increased discharge. The impact of wildfire on water availability is a critical issue in the western United States. Because actual evapotranspiration (ETa) constitutes the largest loss in the terrestrial water budget, it has been suggested that fire-induced ETa reduction is a primary driver of elevated post-fire discharge. Ten gaged watersheds with burns exceeding 5% of their total contributing drainage area were selected from California, Oregon, Montana, Utah, New Mexico, and Colorado. Continuous daily stream gage data were compiled, and 30-meter ETa estimates were calculated with the Operational Simplified Surface Energy Balance (SSEBop) model. Fire-induced ETa shifts were quantified with statistical tests that compared pre and post-fire monthly ETa in burned and unburned pixels; the dampening effect of scale was also evaluated by repeating tests on all pixels from the entire basin. As streamflow data are point measurements that aggregate a large spatial area, additional statistical methods were required to isolate the effect of fire from climate on baseflow and runoff. Key findings include a) significant fire-induced ETa reductions were only distinguishable in basin-scale monthly datasets when at least 73% of the basin burned, b) the effect of wildfire disturbance on streamflow magnitude was seasonably variable, c) streamflow was modified in basins with as little as 6% burned drainage area; however, shifts only persisted beyond the fifth post-fire year where more than three-quarters of the basin was fire-impacted, and d) surplus water from ETa reduction was sufficient to account for boosted fire-induced streamflow. Where fire-induced streamflow increases were not significantly correlated with ETa anomaly, other fire-impacted landscape processes may have contributed to modified runoff generation and routing. Where fire reduced ETa but streamflow shifts were not detected, compensatory ETa pathways may have consumed the excess water before it reached the gage. Findings suggest that water providers with small source-water collection areas have higher relative risk for fire-induced hydromodification than providers with larger or more diversified supply portfolios. Results also illustrate the tendency of overarching climate signals to mask or artificially boost the apparent effect of landscape disturbance on streamflow at the basin outlet.

Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region

For agricultural areas facing water scarcity, sustainable water use policy relies on irrigation information that is timely and at a high resolution, but existing publicly available water use data are often insufficient for monitoring compliance or understanding the influence of policy on individual farmer decisions. This study attempts to fill this data gap by using remote sensing to map annual irrigation quantity at the field-scale within the central Ogallala aquifer region of the United States. We compiled in situ annual irrigation volume data at the field scale in the Republican River Basin of Colorado for 2015–2018 and at the Public Land Survey System (PLSS) section scale in western Kansas for 2000–2016, which served as reference data in random forest models that relied on Landsat-based actual evapotranspiration from the Operational Simplified Surface Energy Balance model (SSEBop) along with maps of irrigated area, Landsat spectral indices, climate, soils, and derived hydrologic variables. The models explained 87% of the variability in irrigation volume in Colorado and 75% in Kansas, but accuracy declined when transferring the models in spatial cross-validation (Colorado R2 =0.81; Kansas R2 =0.51) and temporal cross-validation (Colorado R2 =0.82; Kansas R2 =0.68). Predicted annual totals of irrigation volume in western Kansas had a mean absolute error of 11.9%, which was slightly higher than the average annual change of 11%. Use of predicted irrigation maps also lead to an underestimated effect size for a water use restriction policy in Kansas. These results indicate that field- and section-scale irrigation can be mapped with reasonable accuracy within a region and time period that has adequate sample data, but that methods may need to be improved for applying the models more broadly in areas that lack extensive in situ irrigation data to support further research on water use and aid in structuring policy.

Estimating evapotranspiration within a dryland watershed: Comparison of eddy‐covariance observations, water budget estimates, and satellite‐based products

AbstractAccurate and continuous estimates of evapotranspiration (ET) are crucial for comprehending associated hydrological processes, especially in dryland watersheds. However, there remains a scarcity of comprehensive evaluations of multi‐source ET estimates' performance and uncertainties. To further assist ET estimation improvement and water resources management, we incorporated diffuse radiation partitioning and water‐carbon coupling processes into the satellite‐driven Vegetation Interface Processes (VIP) model for estimating ET in the Heihe River Basin, Northwest China, and compared it with eddy covariance (EC) observations, water budget estimates, and five global satellite‐based products from Breathing Earth System Simulator (BESS), MODerate‐resolution Imaging Spectroradiometer (MODIS), Penman‐Monteith‐Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop), and Global LAnd Surface Satellite (GLASS). Sobol' sensitivity analysis was applied to the VIP model. Uncertainties in ET estimates were identified by the three‐cornered hat method. Results indicate that stomatal conductance coefficient, quantum efficiency, and clear sky index were dominant influencing parameters for VIP ET. Moreover, VIP and GLASS ET were in strong agreement with the EC‐based ET, although SSEBop ET was not, and MODIS ET could not capture seasonal variations. VIP could best represent ET dynamics from water budgets on the basin scale, followed by PML. During the period 2003–2015, GLASS, PML, and VIP provided reasonable ET estimates in this region, with an annual ET of 159.3, 153.2, and 173.5 mm, respectively, across the basin. Furthermore, ET algorithm benefits were presented: BESS and VIP, including diffuse radiation partitioning, performed better in cropland; PML and VIP that address water‐carbon coupling were stable and independent of the aridity index, and GLASS could considerably reduce uncertainty. The findings contribute to the collective knowledge of ET estimates in similar dryland areas and are relevant worldwide.

Global Evapotranspiration Datasets Assessment Using Water Balance in South America

Evapotranspiration (ET) connects the land to the atmosphere, linking water, energy, and carbon cycles. ET is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in ET are needed from regional to continental scales. This study compared eight global actual ET datasets (ETgl) and the average actual ET ensemble (ETens) based on remote sensing, climate reanalysis, land-surface, and biophysical models to ET computed from basin-scale water balance (ETwb) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of ET, comparing ETgl and ETens with ETwb. Global ET datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using ETwb as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (ETens), and RMSE ranged from 35.6 (ETens) to 40.5 mm·month−1 (MOD16). Overall, ETgl estimates ranged from 0 to 150 mm·month−1 in most basins in South America, while ETwb estimates showed maximum rates up to 250 mm·month−1. ETgl varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly ETgl and ETwb, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between ETgl and ETwb in the humid tropics (in the Amazon), with ETgl showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between ETgl and ETwb for interannual variability. Assessing ET datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of ET estimation and monitoring for water management.

Incorporating ECOSTRESS evapotranspiration in a paired catchment water balance analysis after the 2018 Holy Fire in California

Ecohydrological processes such as evapotranspiration (ET) and streamflow are highly variable after fire in Mediterranean systems and require accurate assessments to improve long-term risk mitigation of erosion and peak flows and revegetation strategies, especially at the small catchment scale. Using the case of the 2018 Holy Fire in southern California, we characterized 1) pre-fire rainfall and evapotranspiration conditions and 2) recovery of ecohydrological processes using a paired analysis between an unburned (Santiago) and burned (Coldwater) catchment. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), Operational Simplified Surface Energy Balance Model (SSEBop), vegetation indices, and local rainfall-runoff data were used to characterize the sites and investigate spatial and temporal patterns of post-fire ET. Consistent with the drought conditions in California, we observed low precipitation and ET prior to the fire. Additionally, compared to other vegetation types, montane hardwood species were more likely to be classified as high soil burn severity. We also found that the high spatial and temporal resolution of ECOSTRESS provided more information about the general ET patterns. After the fire, ECOSTRESS ET was sensitive to parameters such as slope aspect, soil burn severity, and vegetation species, which has implications for post-fire vegetation recovery and water storage. This work demonstrates opportunities to apply ECOSTRESS ET across globally diverse ecoregions and small catchment scales to identify potentially high-risk areas and improve fire risk and vegetation recovery assessments.

Mapping actual evapotranspiration using Landsat for the conterminous United States: Google Earth Engine implementation and assessment of the SSEBop model

The estimation and mapping of actual evapotranspiration (ETa) is an active area of applied research in the fields of agriculture and water resources. Thermal remote sensing-based methods, using coarse resolution satellites, have been successful at estimating ETa over the conterminous United States (CONUS) and other regions of the world. In this study, we present CONUS-wide ETa from Landsat thermal imagery-using the Operational Simplified Surface Energy Balance (SSEBop) model in the Google Earth Engine (GEE) cloud computing platform. Over 150,000 Landsat satellite images were used to produce 10 years of annual ETa (2010–2019) at unprecedented scale. The accuracy assessment of the SSEBop results included point-based evaluation using monthly Eddy Covariance (EC) data from 25 AmeriFlux stations as well as basin-scale comparison with annual Water Balance ETa (WBET) for more than 1000 sub-basins. Evaluations using EC data showed generally mixed performance with weaker (R2 < 0.6) correlation on sparsely vegetated surfaces such as grasslands or woody savanna and stronger correlation (R2 > 0.7) over well-vegetated surfaces such as croplands and forests, but location-specific conditions rather than cover type were attributed to the variability in accuracy. Croplands performed best with R2 of 0.82, root mean square error of 29 mm/month, and average bias of 12%. The WBET evaluation indicated that the SSEBop model is strong in explaining the spatial variability (up to R2 > 0.90) of ETa across large basins, but it also identified broad hydro-climatic regions where the SSEBop ETa showed directional biases, requiring region-specific model parameter improvement and/or bias correction with an overall 7% bias nationwide. Annual ETa anomalies over the 10-year period captured widely reported drought-affected regions, for the most part, in different parts of the CONUS, indicating their potential applications for mapping regional- and field-scale drought and fire effects. Due to the coverage of the Landsat Path/Row system, the availability of cloud-free image pixels ranged from less than 12 (mountainous cloud-prone regions and U.S. Northeast) to more than 60 (U.S. Southwest) per year. However, this study reinforces a promising application of Landsat satellite data with cloud-computing for quick and efficient mapping of ETa for agricultural and water resources assessments at the field scale.

Operational daily evapotranspiration mapping at field scale based on SSEBop model and spatiotemporal fusion of multi-source remote sensing data.

Accurate understanding of daily evapotranspiration (ET) at field scale is of great significance for agricultural water resources management. The operational simplified surface energy balance (SSEBop) model has been applied to estimate field scale ET with Landsat satellite imagery. However, there is still uncertainty in the ET time reconstruction for cloudy days based on limited clear days’ Landsat ET fraction (ETf) computed by SSEBop. The Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data can provide daily surface observation over clear-sky areas. This paper presented an enhanced gap-filling scheme for the SSEBop ET model, which improved the temporal resolution of Landsat ETf through the spatio-temporal fusion with SSEBop MODIS ETf on clear days and increased the time reconstruction accuracy of field-scale ET. The results were validated with the eddy covariance (EC) measurements over cropland in northwestern China. It indicated that the improved scheme performed better than the original SSEBop Landsat approach in daily ET estimation, with higher Nash-Sutcliffe efficiency (NSE, 0.75 vs. 0.70), lower root mean square error (RMSE, 0.95 mm·d-1 vs. 1.05 mm·d-1), and percent bias (PBias, 16.5% vs. 25.0%). This fusion method reduced the proportion of deviation (13.3% vs. 25.5%) in the total errors and made the random error the main proportion, which can be reduced over time and space in regional ET estimation. It also evidently improved the underestimation of crop ET by the SSEBop Landsat scheme during irrigation before sowing and could more accurately describe the synergistic changes of soil moisture and cropland ET. The proposed MODIS and Landsat ETf fusion can significantly improve the accuracy of SSEBop in estimating field-scale ET.

Estimation of actual evapotranspiration and water stress in the Lijiang River Basin, China using a modified Operational Simplified Surface Energy Balance (SSEBop) model

Due to the simultaneous impacts of economic development and climate change, the Lijiang River Basin in China—the largest karst tourist attraction in the world—has experienced dramatic water shortages during the dry season. As actual evapotranspiration (ETa) plays a critical role in the water cycle, accurate estimation of ETa and water stress are important for sustainable water resources management. In this paper, we mapped the distribution of daily ETa using a modified Operational Simplified Surface Energy Balance (SSEBop) model in combination with Landsat 8 images and assessed water stress using the Crop Water Stress Index (CWSI) during the dry season in the Lijiang River Basin. In general, the daily ETa simulated by the SSEBop model with aerodynamic resistance value of 110 s m−1 was higher than that of satellite-based actual evapotranspiration products (i.e., MOD16A2 and Penman-Monteith-Leuning (PML)_V2 actual evapotranspiration products in this study). Aerodynamic resistance plays a critical role in the estimation of energy fluxes in the SSEBop model and should be readjusted and calibrated with available datasets to improve the model’s performance in estimating actual evapotranspiration for particular regions. Readjusted values between 20 and 35 s m−1 of aerodynamic resistance produced reasonable agreement with satellite-based actual evapotranspiration products in the Lijiang River Basin. In addition, insufficient ground-level measurements of actual evapotranspiration might have increased the uncertainty of the SSEBop model’s performance. The achievement of higher accuracy in the estimation of actual evapotranspiration and water availability will require establishing local flux towers, particularly in forested areas, to collect evapotranspiration, temperature and other in situ data. For different land-cover classes, forest areas exhibited the highest actual evapotranspiration, whereas farmland and built-up areas had the lowest actual evapotranspiration values compared to the other land-cover classes. All land-cover classes, especially farmland areas, experienced severe water stress. Inadequate precipitation as a result of climate change, combined with high actual evapotranspiration will result in less water being available for the Lijiang River Basin. Additional water is required to compensate for evapotranspiration and support plant growth in the Lijiang River Basin during the growing season.

Evaluations of Remote Sensing-Based Global Evapotranspiration Datasets at Catchment Scale in Mountain Regions

Evapotranspiration (ET) is essential for connecting ecosystems and directly affects the water consumption of forests, grasslands, and farmlands. Eight global remote sensing-based ET (RS_ET) datasets generated using satellite imagery and ground-based observations were comprehensively assessed using monthly ET time series simulated by the water balance (WB) method at the catchment scale in the Hengduan Mountain (HDM) region, including the Nu River, Lancang River, and Jinsha River basins. The complementary relationship (CR) model, which derives ET from meteorological data, was also evaluated against WB-based ET (WB_ET). In addition, WB_ET, RS_ET, and CR-based ET (CR_ET) data were used to investigate ET spatial and temporal variations at the catchment, grid, and site scale, respectively. Most RS_ET datasets accurately simulated monthly ET with an average index of agreement ranging from 0.71–0.91. The Operational Simplified Surface Energy Balance dataset outperformed other RS_ET datasets, with Nash–Sutcliffe efficiency coefficient (NSE) and Kling–Gupta efficiency values of 0.80 and 0.90, respectively. RS_ET datasets generally performed better in northern semiarid areas than in humid southern areas. The monthly ET simulation by the CR model was consistent with that of the WB_ET in the HDM region, with mean values of correlation coefficient (cc) and NSE at each site of 0.89 and 0.68, respectively. The model showed better performance in simulating monthly ET in the Lancang River Basin than in the Nu River and Lancang River basins, with mean cc and NSE of 0.92 and 0.83, respectively. Generally, annual ET trends were consistent at the catchment, grid, and site scale, as estimated by the WB method, RS_ET datasets, and CR model. It showed a significant decreasing trend in the northern semiarid region of the HDM while exhibiting an increasing trend in the humid southern region.