ET Data Research Articles

Studying the Relationship between Satellite-Derived Evapotranspiration and Crop Yield: A Case Study of the Cauvery River Basin

Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management.

Modeling the Ecosystem Water Use Efficiency of Croplands Using Machine Learning and MODIS Data

The ecosystem water use efficiency (WUE), which is the ratio of carbon gain (gross primary productivity, GPP) to water consumption (evapotranspiration, ET), is widely regarded as a crucial link in coupling the carbon and water cycles of terrestrial ecosystems on a global scale. The estimation of cropland WUE is considerably important for improving agricultural management, simulating the carbon–water cycle processes of croplands, and enhancing crop yields. Currently, the estimate of WUE primary relies on the modeled GPP and ET data. However, differences in model structures and parameter uncertainties cause remarkable differences in the accuracy of the estimated WUE among different models. The eddy covariance technology enables the continuous observation and research of carbon and water fluxes at a site scale, ensuring simulation accuracy. However, the technology is constrained by its limited spatial coverage. In this study, we integrated data from 20 global cropland sites and built 7 machine learning models based on remote sensing to directly estimate cropland WUE, thereby expanding the spatial scope of WUE simulations, omitting the simulation process for obtaining GPP and ET, and ensuring a certain degree of precision. Results show that, compared with other machine learning algorithms, the ensemble learning model has the strongest ability to reproduce cropland WUE, with a determination coefficient of 0.90 and a root mean square error of 1.54–1.75 . These results indicate that a combination of machine learning methods and remote sensing can reasonably simulate the WUE of agricultural ecosystems.

Global estimates of daily evapotranspiration using SMAP surface and root-zone soil moisture

The interplay between soil moisture and evapotranspiration modulates the water available to sustain soil evaporation and influences canopy stomatal conductance controls on vegetation transpiration. Modeling this behavior remains challenging. Indeed, satellite remote sensing based Penman-Monteith (PM) ET models tend not to directly consider soil moisture constraints on evaporation and transpiration due to a lack of consistent soil moisture data. To address this issue, we modified a PM model to include satellite enhanced surface and root zone soil moisture from the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission. The resulting model was used to produce global 9-km daily ET estimates, including contributing fluxes from soil evaporation, transpiration and evaporation of canopy-intercepted precipitation. The global PM ET estimates were assessed using in situ sap flow and AmeriFlux measurements, upscaled FLUXCOM latent heat flux, as well as against other independent global ET data products (GLEAM, GLDAS, SSEBop, and LandFlux-EVAL). The modelled transpiration showed similar seasonal variation and positive correlation to in situ sap flow measurements available from several forest sites (R2 = 0.85; p < 0.01). When compared against AmeriFlux data, the PM ET estimates showed favorable agreement with annual ET measurements extracted from 34 diverse sites (R2 = 0.58; p < 0.01; RMSE = 227 mm yr−1). The PM 8-day ET results also reflected a similar hemispheric seasonality as the global FLUXCOM record (R2 = 0.94–0.98; p < 0.01), while comparisons against other global ET products showed moderate mean differences of between 49 and 107 mm yr−1 (11–25%) over the global domain. Our PM ET estimates varied up to 52% in response to SMAP surface soil moisture dynamics, displaying stronger surface (0–5 cm depth) than root zone (0–100 cm) soil moisture sensitivity. While PM ET sensitivity to soil moisture was greater in arid climate regions, it was also significant in humid climate zones, with analysis indicating that the inclusion of soil moisture predominantly acts as a sustaining influence on ET, especially in moisture limited drylands. PM ET sensitivity to temperature was stronger in humid forest regions relative to other climate and land cover regimes. Overall, the model results clarify the influence of soil moisture heterogeneity on the global ET pattern as informed by satellite-based estimates of surface and root zone soil moisture. Potential enhancements to the spatial and vertical resolution of soil moisture inputs are expected to enable further ET improvements through more realistic model representation of soil and plant available water.

Estimating Dynamic Non‐Water‐Limited Canopy Resistance Over the Globe: Changes, Contributors, and Implications

AbstractNon‐water‐limited canopy resistance (rcs, also known as the bulk stomatal resistance or surface resistance) is a critical variable in estimating potential evapotranspiration (PET), which is widely used in ecohydrology related fields. However, quantifying rcs is a challenging work. Here we develop an approach for estimating rcs over the globe. Comparing results over the globe and across 10 ET data sets (used as inputs), which are based on diverse mechanisms and algorithms, we find that the approach can capture canopy resistance well (mean correlation of 0.84 ± 0.04, mean relative Root Mean Squared Error of 4.4% ± 1.0%, and mean relative Mean Absolute Error of 5.8% ± 1.4%), and the estimated rcs are very close to those estimated using another method (R2 = 0.92), which is based on a quite different hypothesis that is only suitable for saturated regions. Based on these, we find that the rcs shows an overall increasing trend (0.43 ± 0.13 s m−1 year−1) over the globe (at 77.6% ± 3.9% of the land grid cells) during 1982–2014, and the air temperature dominates the variabilities of rcs in regions with decreasing rcs (mean relative contribution of 57.9% ± 11.4%), while air CO2 concentration controls the changes in rcs in regions with increasing rcs (mean relative contribution of 47.3% ± 8.0%). Moreover, we also find that the traditional PET estimator explicitly overestimates the increasing trends in PET, and tends to overestimate (underestimate) the increasing (decreasing) trends in regions with increasing (decreasing) PET. These findings can improve our knowledge on the complex water‐vegetation‐environment interactions and would be helpful for developing more accurate models for quantifying the impacts of global change on water resources.

P-544 Biochemical markers as a predictor of clinical pregnancy on day 6 after embryo transfer

Abstract Study question Is it possible to predict clinical pregnancy using Machine Learning models on day 6 after embryo transfer to an acceptable degree of certainty? Summary answer Using selected biochemical markers it is possible to build a machine learning model to predict clinical pregnancy. What is known already Human chorionic gonadotropin (β-hCG) is considered the main early predictor of pregnancy. Due to the fact that the highest percentage of spontaneous abortions occurs in the first trimester predicting clinical pregnancy outcomes at the early stages of pregnancy is particularly difficult. The level of B-hcg measured at the early stages of implantation and the dynamics of its increase can help predict clinical pregnancy and predict its further development. However, the specific cut-off points at which pregnancy can be determined vary from study to study, and a different factor is often suggested to increase the certainty of β-hCG-based predictions. Study design, size, duration 1474 single frozen embryo transfers (1205 patients between 23 and 47 years of age), January 2019 -December 2022 were used for data modelling and statistical testing. The dataset consisted of endocrinal markers:β-hCG, PRG (progesterone), E (estradiol) measured on the 6th day after ET and other practitioner-chosen data: age, PGD results, embryo quality status, RIF or RPL history, embryo maturation days as well as pregnancy outcomes. Exclusion criteria were: PRG &amp;gt; 50ng/ml, β-hCG &amp;gt;70 mIU/ml. Participants/materials, setting, methods A machine learning model for predicting clinical pregnancy was trained using the gradient boosting technique. The model used 100 decision trees with 8 leaves, maximal depth of 2 nodes and a learning rate of 0.1. The performance of the model was measured using the accuracy score metric and area under ROC (AUC). Feature importance was based on Shapley values. Additional analysis was performed using statistical testing using a two-sided Student’s t-test. Main results and the role of chance A Machine Learning naive model employed chosen variables to predict clinical pregnancy. The most important variables turned out to be the serum level of β-hCG and PRG on day 6 after embryo-transfer. The final model, limited to those variables, performs with an accuracy of around 83 % (accuracy score = 0.834), the area under ROC (AUC) was 0.927. Based on Shapley values, β-hCG followed by PRG are the most relevant for the model’s predictions. Low PRG concentrations increased the chances of clinical pregnancy. The findings were confirmed by additional statistical analysis. Observations were divided into two groups for which the cut-off point was the level of PRG of 24 ng/ml – the median for the whole group. The probability of clinical pregnancy is higher in the group where the serum PRG is below 24 ng/ml even if β-hCG levels are the same. A statistically significant difference between these groups regarding β-hCG (P value &amp;lt; 0.001) was noticed. In order for serum β-hCG to be used as a more accurate clinical pregnancy predictor its level should be interpreted in the context of PRG serum levels. Limitations, reasons for caution Since in the dataset, only data regarding frozen embryo transfers were included, fresh ETs need to be explored further. The model was based on endocrinal markers as measured on day six after ET hence it cannot be used to interpret those markers as measured on any other date. Wider implications of the findings In order to most accurately inform patients regarding the possible outcomes of embryo-transfers, practitioners should not limit themselves to monitoring β-hCG serum levels but also include PRG as an important prognostic of clinical pregnancy. Trial registration number Not Applicable

Ensemble Estimation of Historical Evapotranspiration for the Conterminous U.S.

AbstractEvapotranspiration (ET) is the largest component of the water budget, accounting for the majority of the water available from precipitation. ET is challenging to quantify because of the uncertainties associated with the many ET equations currently in use, and because observations of ET are uncertain and sparse. In this study, we combine information provided by available ET data and equations to produce a new monthly data set for ET for the conterminous U.S. (CONUS). These maps are produced from 1895 to 2018 at an 800 m spatial scale, marking a finer resolution than currently available products over this time period. In our approach, the relative performance of a suite of ET equations is assessed using water balance, flux tower, and remotely sensed ET estimates. At the observation locations, we use error distributions to quantify relative weights for the equations and use these in a modified Bayesian model averaging weighted ensemble approach. The relative weights are spatially generalized using a random forest regression, which is applied to wall‐to‐wall explanatory variable maps to generate CONUS‐wide relative weight maps and ensemble estimates. We assess the performance of the ensemble using a reserved subset of the observations and compare this performance against other national‐scale map products for historical to modern ET. The ensemble ET maps are shown to provide an improved accuracy over the alternative comparison products. These ET maps could be useful for a variety of hydrologic modeling and assessment applications that benefit from a long record, such as the study of periods of water scarcity through time.

Evapotranspiration in a subtropical wetland savanna using low‐cost lysimeter, eddy covariance and modelling approaches

AbstractEvapotranspiration (ET) constitutes the largest loss of water from subtropical grassland and wetland ecosystems, yet data in much of the world have high uncertainty at the landscape scale as there is little information on plant water use. Additionally, anthropogenic alterations to grasslands are a major threat globally and alter ecosystem water use, but the impact of these changes is often unquantified. A major reason for this is the complexity and expense of field‐based ET quantification methods such as agricultural lysimeters and eddy covariance systems. Accurate measurements of ET are critical for sustainable water management. This study developed two different low‐cost lysimeters—weighing‐type and water level‐based, to measure ET under partly controlled conditions for single species as well as mixed grassland and wetland communities. Lysimeters were placed in an open‐sided, transparent‐roofed shadehouse and watered periodically. ET values were then compared with (i) actual ET from an eddy covariance tower onsite, (ii) vapour transport‐based reference ET models (FAO Penman–Monteith, modified Turc and Abtew simple radiation models), and (iii) reference ET data customized for vegetation types from the Florida Automated Weather Network (FAWN). Both weighing‐type and water level lysimeters yielded seasonal patterns and annual magnitudes similar to other ET methods. Annual ET measurements from single‐species weighing‐type lysimeters (ranging from 881 to 1278 mm for four plant species, n = 5 per species, 20 in total) and water level lysimeters (1085 ± 306 mm, plant community average, n = 31) were about 10–20% lower than reference ET models (~1300 mm) and similar to FAWN data (1050 mm). Actual ET from eddy covariance was 722 mm for 10 months (missing data for February and March), while weighing lysimeter measurements for the dominant grass Paspalum notatum was 885 mm for the same 10 months. While rainfall exclusion and watering can result in ET differing from actual field conditions, low‐cost lysimeters can provide an independent measure of ET for comparison with regional ET models/remote sensing data lacking field validation. Thereby, low‐cost lysimeters are potentially useful for water resources and ecosystem management in data‐poor regions of the world.

Improving the spatiotemporal resolution of remotely sensed ET information for water management through Landsat, Sentinel-2, ECOSTRESS and VIIRS data fusion

Robust information on consumptive water use (evapotranspiration, ET) derived from remote sensing can significantly benefit water decision-making in agriculture, informing irrigation schedules and water management plans over extended regions. To be of optimal utility for operational usage, these remote sensing ET data should be generated at the sub-field spatial resolution and daily-to-weekly timesteps commensurate with the scales of water management activities. However, current methods for field-scale ET retrieval based on thermal infrared (TIR) imaging, a valuable diagnostic of canopy stress and surface moisture status, are limited by the temporal revisit of available medium-resolution (100 m or finer) thermal satellite sensors. This study investigates the efficacy of a data fusion method for combining information from multiple medium-resolution sensors toward generating high spatiotemporal resolution ET products for water management. TIR data from Landsat and ECOSTRESS (both at ~ 100-m native resolution), and VIIRS (375-m native) are sharpened to a common 30-m grid using surface reflectance data from the Harmonized Landsat-Sentinel dataset. Periodic 30-m ET retrievals from these combined thermal data sources are fused with daily retrievals from unsharpened VIIRS to generate daily, 30-m ET image timeseries. The accuracy of this mapping method is tested over several irrigated cropping systems in the Central Valley of California in comparison with flux tower observations, including measurements over irrigated vineyards collected in the GRAPEX campaign. Results demonstrate the operational value added by the augmented TIR sensor suite compared to Landsat alone, in terms of capturing daily ET variability and reduced latency for real-time applications. The method also provides means for incorporating new sources of imaging from future planned thermal missions, further improving our ability to map rapid changes in crop water use at field scales.

Management of vegetative land for more water yield under future climate conditions in the over-utilized water resources regions: A case study in the Xiong’an New area

Seeking more water yield in the over-utilized water resources regions that are home to nearly 35% of the population all over the world is a great challenge under future climate change. Impacts of land conversion (i.e., land conversion from cropland to grassland and forest) and climate change during near-future 2030s (2021–2040), mid-future 2050s (2041–2060), and far-future 2080s (2071–2090) periods on vegetation dynamics (LAI) and water budgets (evapotranspiration and runoff) were investigated in a typical over-utilized water resources area of China (Xiong’an New Area, XNA) using the WAVES ecohydrological model. Future climate change impacts were assessed with statistically downscaled forcing of 18 global circulation models (GCMs) under three representative concentration pathways (RCPs) using secondary bias-correction technique. Projected future climate indicated that XNA would become warmer and wetter in future. The WAVES model was calibrated against remotely sensed LAI and ET data. Modeling results during the historical period of 1982–2012 showed that forest evaporated more water (32.0 mm yr−1) than cropland, while grassland used almost the same amount of water as cropland. Under future climate conditions, both water use and water yield would increase in cropland and grassland due to increased precipitation. Forest was predicted to use 9.5–14.6% (36.4–55.8 mm yr−1) more water and to have 2.6-52.4% (2.6–51.1 mm yr−1) more water yield in the 2050s and 2080s under RCP4.5 and in all three future periods (2030s, 2050s, and 2080s) under RCP8.5 due to greater increases in precipitation. Afforestation is not recommended as a land management practice for more water yield in near-future, but could be implemented to achieve a win-win situation for more water yield and more forest coverage in mid- and far-future periods in XNA due to increased precipitation. This study highlights that uncertainties in projected future climate pose formidable challenges in management of vegetative land for more water yield in the over-utilized water resources regions.

AN APPROACH TO PROCESSING ELECTROTOMOGRAPHY DATA FOR ESTIMATING THE VOLUMES OF SUBSTANCES IN TAILINGS

Methodical recommendations for processing the data of electrotomography are given. Taking into account a priori information, in particular the results of the study of pits, can be carried out by carrying out a limited inversion of the ET data, while it is possible to limit either the resistance of some layers of the model or the position of the boundaries of some layers [1]. In the software used for processing electrical tomography data Res2dinv [2], it is possible to set the boundary of the assumed layer or to limit the resistance of the model section (to introduce local inhomogeneities with a given resistance). But it is difficult to fix the resistance of a particular layer with the available software tools. The proposed approach makes it possible to take into account the parameters of the model built from the pitting data using a preliminary one-dimensional inversion of the electrical tomography data in the Ip2win [1] program, followed by the export of the obtained assumed layer boundaries to the Res2dinv software for carrying out a limited two-dimensional inversion.

Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

Temporal dynamics of evapotranspiration in semiarid native forests in Brazil and Spain using remote sensing

AbstractEvapotranspiration (ET) plays an important role in integrated water resource planning, development and management. This process is particularly relevant in semiarid regions. The aim of this study is, hence, to compare spatial and temporal patterns of actual ET, as well as the temporal trends in two different semiarid forests, Caatinga (Brazil) and Tierra de Pinares (Spain). We used the surface energy balance algorithm for land (SEBAL) to assess actual evapotranspiration (ETa) in both areas. In the Brazilian semiarid forest, Caatinga is the main vegetation, while it is Pinares in Spain. For this purpose, 69 Landsat‐5 and 42 Landsat‐8 images (1995–2019) were used. The Mann–Kendall test was applied to assess the occurrence of trends in precipitation, temperature and potential ET data; and the Temporal Stability Index (TSI) to know which areas have greater seasonal ETa. The annual amplitude of the potential evapotranspiration (ET0) is the same in both areas, however, the Caatinga values are higher. In the Caatinga forest, when ET0 presents its highest values throughout the year, ETa presents the lowest, and vice versa. In the Pinares forest, ETa follows the ET0 dynamics during the year, and the difference between ET0 and ETa is maximum during the summer. The Caatinga forest showed a greater spatial variation of ETa than the Pinares forest as well as a greater extension with lower temporal stability of ETa than the Pinares forest. Both the Caatinga forest and the Pinares forest showed significant positive trends in annual ET0 and ETa. We estimate that the value of ETa increases more rapidly in Pinares than in the Brazilian Caatinga. Taking Caatinga as a hydrological mirror, some consequences are expected to Pinares, such as significant changes in the water balance, increase of biodiversity vulnerability, and reduction of water availability in soil and reservoirs.

Simulation of dryland maize growth and evapotranspiration using DSSAT‐CERES‐Maize model

AbstractEvapotranspiration (ET) is a crucial component of plant water use and ecosystem energy balance. This study aims to simulate maize (Zea mays L.) ET using a crop modeling system and compare that to ET measured using an eddy‐covariance (EC) system. An EC system was used to collect growing season ET data from a rainfed maize field in 2017, 2018, and 2019. Supporting soil and plant growth data were also collected. The Decision Support System for Agrotechnology Transfer (DSSAT)‐Crop Environment Resource Synthesis (CERES)‐Maize model was used in this study. Results from DSSAT's ET simulation methods, FAO‐56 and Priestley–Taylor, were compared to measured ET. Results indicated a low coefficient of determination (R2) between simulated and measured ET, due to overestimation of simulated ET in all 3 yr. The average percent root mean square error for the daily ET simulation across all 3 yr was 34.6% for the Priestley–Taylor method and 30.4% for the FAO‐56 method. For the FAO‐56 simulation, the average seasonal overestimation compared to measured ET was 59 mm and the average R2 was .59. For the Priestley–Taylor method, the average seasonal overestimation was 72 mm and the average R2 was .57. Possible sources of error contributing to this overestimation include DSSAT's potential ET estimations, crop coefficient estimation, and the simulation of soil water balance in vertisols with high swell‐shrink clay minerals. The mixed simulation results, particularly compared to those seen in studies of irrigated crops, indicate a greater need to improve ET estimation capabilities in rainfed systems.

Robust and ultrafast fiducial marker correspondence in electron tomography by a two-stage algorithm considering local constraints.

MotivationElectron tomography (ET) has become an indispensable tool for structural biology studies. In ET, the tilt series alignment and the projection parameter calibration are the key steps toward high-resolution ultrastructure analysis. Usually, fiducial markers are embedded in the sample to aid the alignment. Despite the advances in developing algorithms to find correspondence of fiducial markers from different tilted micrographs, the error rate of the existing methods is still high such that manual correction has to be conducted. In addition, existing algorithms do not work well when the number of fiducial markers is high.ResultsIn this article, we try to completely solve the fiducial marker correspondence problem. We propose to divide the workflow of fiducial marker correspondence into two stages: (i) initial transformation determination, and (ii) local correspondence refinement. In the first stage, we model the transform estimation as a correspondence pair inquiry and verification problem. The local geometric constraints and invariant features are used to reduce the complexity of the problem. In the second stage, we encode the geometric distribution of the fiducial markers by a weighted Gaussian mixture model and introduce drift parameters to correct the effects of beam-induced motion and sample deformation. Comprehensive experiments on real-world datasets demonstrate the robustness, efficiency and effectiveness of the proposed algorithm. Especially, the proposed two-stage algorithm is able to produce an accurate tracking within an average of ms per image, even for micrographs with hundreds of fiducial markers, which makes the real-time ET data processing possible.Availability and implementationThe code is available at https://github.com/icthrm/auto-tilt-pair. Additionally, the detailed original figures demonstrated in the experiments can be accessed at https://rb.gy/6adtk4.Supplementary information Supplementary data are available at Bioinformatics online.

Hydrological Model Calibration with Streamflow and Remote Sensing Based Evapotranspiration Data in a Data Poor Basin

Conventional calibration methods adopted in hydrological modelling are based on streamflow data measured at certain river sections. However, streamflow measurements are usually sparse and, in such instances, remote-sensing-based products may be used as an additional dataset(s) in hydrological model calibration. This study compares two main calibration approaches: (a) single variable calibration with streamflow and evapotranspiration separately, and (b) multi-variable calibration with both variables together. Here, we used remote sensing-based evapotranspiration data from Global Land Evaporation: the Amsterdam Model (GLEAM ET), and measured streamflow at four stations to calibrate a Soil and Water Assessment Tool (SWAT) and evaluate the performances for Chindwin Basin, Myanmar. Our results showed that when one variable (either streamflow or evapotranspiration) is used for calibration, it led to good performance with respect to the calibration variable but resulted in reduced performance in the other variable. In the multi-variable calibration using both streamflow and evapotranspiration, reasonable results were obtained for both variables. For example, at the basin outlet, the best NSEs (Nash-Sutcliffe Efficiencies) of streamflow and evapotranspiration on monthly time series are, respectively, 0.98 and 0.59 in the calibration with streamflow alone, and 0.69 and 0.73 in the calibration with evapotranspiration alone. Whereas, in the multi-variable calibration, the NSEs at the basin outlet are 0.97 and 0.64 for streamflow and evapotranspiration, respectively. The results suggest that the GLEAM ET data, together with streamflow data, can be used for model calibration in the study region as the simulation results show reasonable performance for streamflow with an NSE &gt; 0.85. Results also show that many different sets of parameter values (‘good parameter sets’) can produce results comparable to the best parameter set.

Spatiotemporal variation of actual evapotranspiration and its response to changes of major meteorological factors over China using multi-source data

Abstract Evapotranspiration (ET) is an important component of the water and energy cycle. To obtain more accurate actual ET estimation, three commonly used actual ET products (i.e., GLEAM, GLDAS, and SSEBop) are merged by the simple Taylor skill score fusion method. Validation results show that the merged actual ET data exhibits better correlation with eddy-covariance ET observation than single-source ET data. Based on the merged ET, the spatiotemporal variations of ET over China and their links to changes of three meteorological factors, i.e., precipitation, air temperature (Ta), and shortwave radiation (Rs), are analyzed. During 2003–2016, there is a general upward trend in ET with an increase rate of 1.6 mm/year over China. ET increases very significantly in northeastern China, the southern coastal region, and the northwestern corner of China, whereas it decreases significantly in Huang-Huai-Hai Plain. There is a clear spatial pattern of the relationship between ET and its driving factors, i.e., the variation of ET in the humid southeast is dominated by Ta and Rs; in the semi-humid and semi-arid zone, the variation of ET is controlled mainly by precipitation, followed by Ta; and in arid northwestern China, the variation of ET is controlled mainly by precipitation.

Geographical variations in gross primary production and evapotranspiration of paddy rice in the Korean Peninsula

The quantification of canopy photosynthesis and evapotranspiration of crops (ETc) is essential to appreciate the effects of environmental changes on CO2 flux and water availability in agricultural ecosystems and crop productivity. This study simulated the canopy photosynthesis and ET processes of paddy rice (Oryza sativa) based on the development of physiological modules (i.e., gross primary production [GPP] and ETc) and their incorporation into the GRAMI-rice model that uses remote sensing data. We also projected spatiotemporal variations in the GPP, ET, yield, and biomass of paddy rice at maturity using the updated GRAMI-rice model combined with geostationary satellite images to identify the relationships of canopy photosynthesis and ETc with crop productivity. GPP and ET data for paddy rice were obtained from three KoFlux sites in South Korea in 2015 and 2016. Vegetation indices were acquired from the Geostationary Ocean Color Imager (GOCI) of the Communication Ocean and Meteorological Satellite (COMS) from 2012 to 2017 and integrated into GRAMI-rice. GPP and ETc estimates using GRAMI-rice were in close agreement with flux tower estimates with Nash-Sutcliffe efficiency ranges of 0.40–0.79 for GPP and 0.49–0.62 for ETc. Also, GRAMI-rice was reasonably well incorporated with the COMS GOCI imagery and reproduced spatiotemporal variations in the GPP and ET of rice in the Korean peninsula. The current study results demonstrate that the updated GRAMI–rice model with the canopy photosynthesis and ETc modules is capable of reproducing spatiotemporal variations in CO2 assimilation and ET of paddy rice at various geographical scales and for regions of interest that are observable by satellite sensors (e.g., inaccessible North Korea).

A national-scale drought assessment in Uganda based on evapotranspiration deficits from the Bouchet hypothesis

For effective management of drought risks, a process-specific indicator needs to be employed rather than depending only on precipitation anomalies. In this study, we performed an evapotranspiration-based (ET-based) assessment of agricultural droughts in Uganda where drought has usually been assessed with precipitation deficiency. The land-surface ET and evaporative demand were estimated with a state-of-the-art complementary principle across the country under poor ET data availability. Using the ET estimates, the Standardized Evapotranspiration Deficit Index (SEDI) was quantified to assess agricultural droughts for 1984–2017. The ET-based drought assessment was compared with conventional precipitation-based analyses using the Standardized Precipitation Index (SPI) in respect of severity, areal extent, and temporal trends. Results showed that the complementary principle provided acceptable performance in reproducing interpolated latent flux observations. SPI and SEDI were strongly correlated, and thus yielded identical temporal variations of mean drought severity and areal extent. The trend maps of SPI and SEDI also tended to agree, and indicate that central and northwestern Uganda would face increasing drought risks in the future. The declining precipitation in Uganda has been attributed to rising surface temperatures of the Indian Ocean, and is expected to persist up to the next decade. This study suggests that droughts in major croplands could be often accompanied by high heat-wave temperatures in Uganda due to strong land-atmosphere coupling, and thus mortality risks in ecosystems could increase together with water stress.

Insight from AIP modelling of VTEM ET data from Colorado

Insight from AIP modelling of VTEM ET data from Colorado

Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 &gt; 0.70, root mean square error (RMSE) &lt; 0.86) and CCC (R2 &gt; 0.75, RMSE &lt; 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 &gt; 0.71, RMSE &lt; 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 &gt; 0.67, RMSE &lt; 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 &gt; 0.63 and a RMSE &lt; 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 &gt; 0.90, RMSE &lt; 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas.