Estimation Of Evapotranspiration Research Articles

Advancements in Remote Sensing for Evapotranspiration Estimation: A Comprehensive Review of Temperature-Based Models

Quantifying evapotranspiration (ET) is crucial for a valid understanding of the global water cycle and for the precise management of the resource. However, accurately estimating ET, especially at large scales, has always been a challenge. Over the past five decades, remote sensing has emerged as a cost-effective solution for estimating ET at regional and global scales. Numerous models have been developed, offering valuable insights into ET dynamics, allowing for large-scale, accurate, and continuous monitoring while presenting varying degrees of complexity. They mainly belong to two categories despite the variability of their empirical or physical components: temperature and conductance-based models. This comprehensive review synthesizes the fundamental theories and development history of the most used temperature-based models. It focuses on this specific category to maintain conciseness and prevent extended work. It describes the approaches used and presents the chronology of the modifications made and suggested by researchers. Moreover, it highlights the validation studies and the models’ advantages and drawbacks. The review addresses the long-standing challenge of accurately quantifying evapotranspiration at different scales, offers a retrospective comparative analysis spanning a 15-year period, and supports practitioners in selecting the most appropriate model for a specific set of conditions. Moreover, it discusses advancements in satellite missions, such as the Copernicus Space Component and Landsat Next, and their impact on enhancing ET estimation models.

Revealing mechanisms regulating diurnal groundwater level fluctuation in a temperate fen peatland: A spatiotemporal exploration

Diurnal groundwater level fluctuations (DGLF), closely tied to the metabolic rhythm of wetland vegetation, offer insights into direct groundwater consumption. This study is centered on exploring the spatial and temporal patterns of DGLF within the riparian aquifer of the Upper Biebrza region, motivated by the need to understand those sub-daily ecohydrological dynamics at play in this ecosystem, on a process-oriented scale. Through integrated spatiotemporal multivariate analysis, we aim to identify the key potential factors driving variations in these fluctuations across the landscape gradient, as well as, at different time scales. The study employed a comprehensive approach to gather data on groundwater heads and direct groundwater fluxes, utilizing high-temporal-resolution wells within a monitoring network. Meteorological variables from a dedicated station and high-resolution remote sensing maps were also integrated into the dataset. Analysis revealed distinct seasonal patterns and correlations in diurnal groundwater fluctuations, closely correlated with air temperature, solar radiation and subsequently vegetation phenology. Soil moisture content and summer rainfall events also influenced the intensity of these diurnal fluctuations. Dry periods intensified fluctuations, indicating an elevated reliance on groundwater by phreatophytes, while fluctuations decreased after rainfall events, signaling a shift in vegetation’s water source preference to soil moisture. Based on integrated data interpretation, a couple of potential mechanisms, reasonably forming the spatial variation pattern of DGLF, were formulated. Notably, the influence of local hydraulic gradients at transitional forest landscape edges, where higher amplitude fluctuations occurred, compared to lower fluctuations at midpoints. The proximity to the peat-mineral interface influences diurnal fluctuations, with wells closer to the interface showing sustained high-amplitude fluctuations driven by higher rates of recharge. Additionally, the influence of river proximity was explored, revealing dampened fluctuations in wells closer to the river due to rapidly changing hydraulic gradients. Further systematic experimentation, including numerical and data-driven modeling, is needed to validate these hypotheses. The study findings provide perspectives into (DGLF) patterns and distributions, enabling more accurate groundwater evapotranspiration (ETg) estimation and opening new avenues for considering ecohydrological feedback regarding carbon–water interaction, in relation to the daily-scale water table position.

Sensitivity of global hydrological models to potential evapotranspiration estimation methods in the Senegal River Basin (West Africa)

Study regionSenegal River Basin in West Africa Study focusThis paper aims to evaluate the sensitivity of global hydrological models to potential evapotranspiration (PET) methods in the Senegal River Basin. Potential evapotranspiration is estimated using 21 methods and its influence on the performance of three GR models (GR4J, GR5J and GR6J) is investigated in five catchments. The data used are mean rainfall, discharge and observed and calculated PET over the period 1984–1995. PET is calculated based on observed climate data and those from NASA POWER reanalysis data. The methodology consists in: (i) comparing the consistency of reanalysis data with respect to the observed PET, (ii) assessing the robustness of GR models and their sensitivity to different PET estimation methods. The evaluation criteria used to assess the performance of the hydrological model are KGE and PBIAS. New hydrological insights for the regionGood consistency is obtained between PET calculated with observed and reanalysis data. The GR4J and GR5J are more efficient to simulate flows in the Senegal River sub-basins. The aerodynamic PET methods perform well with the three hydrological models. However, in this context where data are scarce, temperature methods such as Droogers and Allen are a good choice for hydrological modeling. The results also show that the GR models have the ability to adapt to poorly estimated PET.

A review on models, products and techniques for evapotranspiration measurement, estimation, and validation

AbstractIn this review study, the major available methods for measurement and estimation of evapotranspiration (ET) are discussed briefly while explaining the latest developments. The best available validation methods are also reviewed and explained. It highlights the importance of accurate ET quantification in managing water resources, evaluating climate change impacts, and supporting crop water requirement management. Measurement methods such as scintillometry, lysimetry, and the eddy covariance (EC) flux method are presented. Additionally, hydrological models are discussed as estimation approaches for actual and potential ET. The paper explores various ET estimation products, particularly those based on remote sensing techniques. Specifically, methods like Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Operational (SSEBop ET), Moderate Resolution Imaging Spectroradiometer (MOD16), Surface Energy Balance Algorithm for Land (SEBAL), Global Land Surface Evaporation: Amsterdam Methodology (GLEAM), Satellite Application Facility on Land Surface Analysis (LSA‐SAF), and Global Land Data Assimilation System (GLDAS) are described. The integration of machine learning (ML) with EC and remote sensing is investigated, with a comprehensive discussion of different ML approaches. Validation methods including the EC method, water balance method‐derived ET (WBET), and statistical techniques are explained. Overall, this review paper provides a comprehensive overview of ET quantification, covering measurement techniques, estimation approaches, remote sensing methods, and the integration of ML. The insights gained from this review contribute to a profound knowledge of ET dynamics and helps those sectors dealing with drought monitoring, water resource management and climate change assessments.

Evapotranspiration evaluation using three different protocols on a large green roof in the greater Paris area

Abstract. Nature-based solutions have appeared as relevant solutions to mitigate urban heat islands. To improve our knowledge of the assessment of this ecosystem service and the related physical processes (evapotranspiration), monitoring campaigns are required. This was the objective of several experiments carried out on the Blue Green Wave, a large green roof located in Champs-sur-Marne (France). Three different protocols were implemented and tested to assess the evapotranspiration flux at different scales: the first one was based on the surface energy balance (large scale); the second one was carried out using an evapotranspiration chamber (small scale); and the third one was based on the water balance evaluated during dry periods (point scale). In addition to these evapotranspiration estimates, several hydrometeorological variables (especially temperature) were measured. Related data and Python programs providing preliminary elements of the analysis and graphical representation have been made available. They illustrate the space–time variability in the studied processes regarding their observation scale. The dataset is available at https://doi.org/10.5281/zenodo.8064053 (Versini et al., 2023).

Differences in sum-of-hourly and daily reference evapotranspiration for a rice–wheat cropping system in Ganga Basin, India

ABSTRACT Estimating reference evapotranspiration (ETo) at 24 h timesteps has been considered sufficiently accurate for a long time. However, recent advances in weather data acquisition have made it feasible to apply hourly procedures in ETo computation. Hourly timesteps can improve the accuracy of ETo estimates, as data averaged daily may misrepresent evaporative power during parts of the day. This study investigates the differences between daily ETo computations performed at 24 h (ETo,d) and sum of hourly (ETo,h) for rice–wheat cropping systems in the Ganga Basin, India. The meteorological data were collected from an automatic weather station located in an experimental plot at IIT Kanpur, India. Daily and sum-of-hourly ETo computations were performed according to the FAO-PM equation for rice and wheat cropping seasons. Diurnal variations of meteorological variables resulted in an underestimation of ETo when the daily timestep was considered. No significant difference was observed during wet periods. The sum-of-hourly estimates of ETo were able to capture the abrupt changes in climate variables, while the daily ETo failed to represent it as it considered the average values only. As a result, the sums of hourly ETo estimates are more reliable in the Ganga Plains.

Correction to: Estimation of evapotranspiration through an improved daily global solar radiation in SEBAL model: a case study of the middle Heihe River Basin

Correction to: Estimation of evapotranspiration through an improved daily global solar radiation in SEBAL model: a case study of the middle Heihe River Basin

Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

Improving evapotranspiration estimation models through quantitative watershed parameter analysis and remote sensing applications

AbstractNumerous models had been developed to predict the annual evapotranspiration (ET) in vegetated lands across various spatial scales. Fu's (Scientia Atmospherica Sinica, 5, 23–31) and Zhang's (Water Resources Research, 37, 701–708) ET simulation models have emerged as highly effective and have been widely used. However, both formulas have the non‐quantitative parameters (m in Fu's model and w in Zhang's model). Based on the collected 1789 samples from global long‐term hydrological studies, this study discovered significant relations between m (or w) and vegetation coverage or greenness in collected catchments. Then, we used these relations to qualify the parameters in both Zhang's and Fu's models. Results show that the ET estimation accuracies of Fu's (or Zhang's) model are significantly improved by about 13.49 mm (or 6.74 mm) for grassland and cropland, 38.52 mm (or 29.84 mm) for forest and shrub land (coverage<40%), 19.74 mm (or 16.17 mm) for mixed land (coverage<40%), respectively. However, Zhang's model shows higher errors compared with Fu's model, especially in regions with high m (or w) values, such as those with dense vegetations or P/E0 (annual precipitation to annual potential ET) smaller than 1.0. Additionally, this study also reveals that for regions with vegetation cover less than 40%, the annual ET is not only determined by vegetation types, but also relates to the sizes of vegetation‐covered areas. Conversely, for regions with vegetation cover more than 40%, the annual ET is mainly determined by the vegetation density rather than vegetation types or vegetation coverage. Thus, linking m (or w) parameters with vegetation greenness allows leveraging remote sensing for forest management in data‐scarce areas, safeguarding regional water resources. This study pioneers integrating vegetation‐related indices with basin parameters, advocating for their crucial role in more effective hydrological modelling.

Weather-based Scheduling and Pulse Drip Irrigation Increase Growth and Production of Northern Highbush Blueberry

Northern highbush blueberry (Vaccinium corymbosum L.) often requires frequent irrigation for commercial production, but irrigation is becoming increasingly challenging for many growers because of warmer and drier weather conditions, increased water regulations, and other water-use limitations. The purpose of this study was to develop improved methods of irrigation to prepare the industry more effectively against future water uncertainties. Treatments were applied for 2 years (2021 and 2022) and included a combination of weather-based or fixed irrigation schedules using continuous or pulse irrigation in a commercial field of ‘Draper’ blueberry in eastern Washington, USA. The soil at the site was a silt loam, and irrigation was applied using two laterals of drip tubing per row. Plants on a fixed schedule were irrigated for 12 to 13 hours per application (set by the grower), whereas those on a weather-based schedule were irrigated according to daily estimates of crop evapotranspiration (downloaded from an automated weather station). In both cases, irrigation was applied every 2 to 4 days as a single, continuous application or in 30- to 50-minute pulses every 2 hours (up to nine times per day) with the same amount of water as the continuous treatment. During the first year of the study, weather-based scheduling maintained greater stem water potentials in the plants and, on average, increased yield by 3.4 t⋅ha–1, berry weight by 0.14 g/berry, berry diameter by 0.4 mm, and fruit bud set by 4.3% when compared with fixed scheduling. Likewise, pulse irrigation maintained greater stem water potentials and, on average, increased berry weight and diameter by 0.10 g and 0.4 mm, respectively, fruit bud set by 3.3%, and canopy cover by 2.4% relative to continuous irrigation. Yield and canopy cover were unaffected by any treatment in the second year, which was likely a result of uncharacteristically cool, wet weather in the spring. However, weather-based scheduling continued to maintain greater stem water potentials and, when combined with pulse irrigation, increased berry weight and diameter by 3.7 g and 1.0 mm, respectively, relative to continuous irrigation on a fixed schedule. Pulse drip irrigation also increased fruit bud set by 5.1% during the second year. These results demonstrate the potential benefits of using weather-based scheduling and pulse drip in northern highbush blueberry, especially when the plants are grown on light-textured soils in hot, dry climates.

ВЛИЯНИЕ ВНЕШНИХ ПАРАМЕТРОВ НА ВЕЛИЧИНУ ЭВАПОТРАНСПИРАЦИИ В МОДЕЛИ ДЕЯТЕЛЬНОГО СЛОЯ СУШИ ИВМ РАН - МГУ

The paper investigates the sensitivity of evapotranspiration in the INM RAS-MSU land surface model to the changes in the weights of land surface classes in the cells of the latitude-longitude grid and the leaf area index LAI. It is demonstrated that the refinement of the values of the mentioned parameters based on modern observational data significantly reduces an error in evapotranspiration. The annual sum of evapotranspiration averaged over 10 years (2002-2012) from the surface of medium-sized (2-50 × 103 km2) watersheds of northern European Russia is used for the analysis. The model error has been calculated against the empirical estimates of evapotranspiration obtained from the watershed water balance equation. As an intermediate task, the accuracy of satellite data on terrestrial water storage used in the calculations is assessed by the comparison with the data of snow route surveys.

Evaluating the precise grapevine water stress detection using unmanned aerial vehicles and evapotranspiration-based metrics

AbstractPrecise irrigation management requires accurate knowledge of crop water demand to adequately optimize water use efficiency, especially relevant in arid and semi-arid regions. While unoccupied aerial vehicles (UAV) have shown great promise to improve the water management for crops such as vineyards, there still remains large uncertainties to accurately quantify vegetation water requirements, especially through physically-based methods. Notably, thermal remote sensing has been shown to be a promising tool to evaluate water stress at different scales, most commonly through the Crop Water Stress Index (CWSI). This work aimed to evaluate the potential of a UAV payload to estimate evapotranspiration (ET) and alternative ET-based crop water stress indices to better monitor and detect irrigation requirements in vineyards. As a case study, three irrigation treatments within a vineyard were implemented to impose weekly crop coefficient (Kc) of 0.2 (extreme deficit irrigation), 0.4 (typical deficit irrigation) and 0.8 (over-irrigated) of reference ET. Both the original Priestley-Taylor initialized two-source energy balance model (TSEB-PT) and the dual temperature TSEB (TSEB-2T), which takes advantage of high-resolution imagery to discriminate canopy and soil temperatures, were implemented to estimate ET. In a first step, both ET models were evaluated at the footprint level using an eddy covariance (EC) tower, with modelled fluxes comparing well against the EC measurements. Secondly, in-situ physiological measurements at vine level, such as stomatal conductance (gst), leaf (Ψleaf) and stem (Ψstem) water potential, were collected simultaneously to UAV overpasses as plant proxies of water stress. Different variants of the CWSI and alternative metrics that take advantage of the partitioned ET from TSEB, such as Crop Transpiration Stress Index (CTSI) and the Crop Stomatal Stress Index (CSSI), were also evaluated to test their statistical relationship against these in-situ physiological indicators using the Spearman correlation coefficient (ρ). Both TSEB-PT and TSEB-2T CWSI related similarly to in-situ measurements (Ψleaf: ρ ~ 0.4; Ψstem: ρ ~ 0.55). On the other hand, stress indicators using canopy fluxes (i.e. CTSI and CSSI) were much more effective when using TSEB-2 T (Ψleaf: ρ = 0.45; Ψstem: ρ = 0.62) compared to TSEB-PT (Ψleaf: ρ = 0.18; Ψstem: ρ = 0.49), revealing important differences in the ET partitioning between model variants. These results demonstrate the utility of physically-based models to estimate ET and partitioned canopy fluxes, which can enhance the detection of vine water stress and quantitatively assess vine water demand to better manage irrigation practices.

Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture.

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Unraveling phenological and stomatal responses to flash drought and implications for water and carbon budgets

Abstract. In recent years, extreme droughts in the United States have increased in frequency and severity, underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by the rapid dry down of soils due to lack of precipitation, high temperatures, and dry air. These events are also associated with reduced preparation, response, and management time windows before and during drought, exacerbating their detrimental impacts on people and food systems. Improvements in actionable information for flash drought management are informed by atmospheric and land surface processes, including responses and feedbacks from vegetation. Phenologic state, or growth stage, is an important metric for modeling how vegetation modulates land–atmosphere interactions. Reduced stomatal conductance during drought leads to cascading effects on carbon and water fluxes. We investigate how uncertainty in vegetation phenology and stomatal regulation propagates through vegetation responses during drought and non-drought periods by coupling a land surface hydrology model to a predictive phenology model. We assess the role of vegetation in the partitioning of carbon, water, and energy fluxes during flash drought and carry out a comparison against drought and non-drought periods. We selected study sites in Kansas, USA, that were impacted by the flash drought of 2012 and that have AmeriFlux eddy covariance towers which provide ground observations to compare against model estimates. Results show that the compounding effects of reduced precipitation and high vapor pressure deficit (VPD) on vegetation distinguish flash drought from other drought and non-drought periods. High VPD during flash drought shuts down modeled stomatal conductance, resulting in rates of evapotranspiration (ET), gross primary productivity (GPP), and water use efficiency (WUE) that fall below those of average drought conditions. Model estimates of GPP and ET during flash drought decrease to rates similar to what is observed during the winter, indicating that plant function during drought periods is similar to that of dormant months. These results have implications for improving predictions of drought impacts on vegetation.

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.

Recurrent neural networks and sensitivity analysis for accurate monthly evapotranspiration estimation in the region of Fez, Morocco

AbstractGood water management is essential, including addressing water scarcity, which is exacerbated by climate change and increasing food demand due to population growth. In modern era of high technology, artificial intelligence and the Internet of Things have an important role to play in decision‐making approaches. According to hydro‐agrological studies, an accurate assessment of evapotranspiration is necessary for irrigation control, and to have an in‐depth understanding of the environmental factor interactions, a sensitivity analysis should be carried out. One of the tools, alongside empirical equations, is artificial intelligence for time‐series prediction. Thus, we compared several models for Penman–Monteith ET0 estimation to see which one performs better in the arid area of Morocco using meteorological data from the region of Fes. They were evaluated according to the MSE, RMSE, MAE, MAPE, and R2 and the variance of error distribution to show the performances of linear regression, K‐Nearest Neighbor, decision tree, random forest, support vector regression, long short‐term memory, and artificial neural network. According to the findings, LSTM outperformed the models with satisfactory results and an accuracy rate of 99.82%. An underlying mechanism of sensitivity analysis was also introduced to find the contribution of each element and estimate the target with a limited dataset. The findings show satisfactory results of R2 = 98.97%. The distribution and reliability of the prediction were proven using the Taylor diagram and Kruskal–Wallis test for the effectiveness of the study. This research demonstrates the potential of employing data‐driven techniques for evapotranspiration estimation to improve the efficacy of water management strategies. This will help to address present issues and establish sustainable water practices in the face of changing environmental conditions.

Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm

Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm

Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and function

Abstract. Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the “rock moisture” reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM; LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soil databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a temperate mixed broadleaf–needleleaf evergreen forest, whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models.

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.

The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation

Remotely sensed land surface temperature (LST) is critical for retrieving evapotranspiration (ET). However, due to cloud contamination, LST is often limited to clear-sky conditions and the differences between clear-sky and all-sky LST will lead to clear-sky biases of LST. Consequently, the accuracy of ET varies drastically under different weather conditions. To evaluate the impact of clear-sky biases on ET estimation, the monthly ET under clear-sky and all-sky conditions was estimated using a nonparametric method at several sites with different humid conditions and vegetation coverage in 2014. 35.7 % of the clear-sky LST values were acquired in the arid region (Heihe River Basin) with different vegetation coverage. As the climate became more humid, only 14.2 % of the clear-sky LST data were available (Poyang Lake Basin). The clear-sky biases of LST variation at the sites resulted in a significant reduction in the accuracy of monthly ET, with an increase in the relative error (RE) of approximately 16.6 %. The impact of clouds can be reduced by at most half by the introduction of all-sky LST products, with a significant decline (6.3 % ∼ 34.2 % of ET) in the error contribution of LST to monthly ET. The variation in vegetation coverage of land cover and the change in humidity of the climate significantly affected the influence of the clear-sky biases of LST on the ET estimation and the error contribution to ET. Replacing all-sky LST with clear-sky LST in areas with less vegetation coverage exacerbates the underestimation of ET estimation and strengthens the error contribution of LST with the decline in RE (error contribution of LST) from −7.7 % (−6.3 %) in densely vegetated areas to −29.7 % (−34.2 %) in non-vegetated areas, especially in the summer. Similarly, clear-sky biases of LST tended to have a relatively significant impact on the error contributions of LST to monthly ET estimation when the climate became humid, with a relatively significant enlarging from −6.3 % in the arid area to −10.0 % in the humid area, especially in the rainy season in the humid area (−61.3 % of ET). This study contributes to the understanding of the relationship between clear-sky biases and vegetation coverage/humidity. Quantitative analysis of the impact of clear-sky biases of LST on monthly ET estimation will be helpful for improving the accuracy of ET retrieval in the future.