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Land evapotranspiration consists of transpiration from vegetation and evaporation from water day, wet leaf, and soil.
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Evapotranspiration (ET) is a pivotal process for ecosystem water budgets and accounts for a substantial portion of the global energy balance. In this paper, the exited actual ET main datasets in global scale, and the global ET modeling and estimates were focused on discussion. The Source energy balance (SEB) models, empirical models and other proce...
Contexts in source publication
Context 1
... over the land, the evaporation (E) process includes water evaporating from water body, wet soil and wet vegetation layer above the soil surface, while transpiration (T) is a process of water up-taking via the leaf stoma coupled with carbon emission during photosynthesis process (Bai, Zhang, Zhang, Yao, & Magliulo, 2018;Fisher et al., 2011;Wang & Dickinson, 2012), and is the dominating water fluxes (Jasechko et al., 2013) ( Figure 2). Evapotranspiration (ET) is a biophysical process surface energy cycle involved ( Yan et al., 2012Yan et al., , 2013, it accounts for about half of the land surface energy consuming and returning about 60% of the land precipitation into the atmosphere on global scale ( Jung et al., 2010), which help cooling the land surface. ...
Context 2
... soil moisture (SM) is an important source of atmospheric water vapor through the ET process, including plant transpiration (T) and bare soil evaporation (E) (see Figure 2). A rich theoretical literature has predicted that SM feedbacks should be more prominent in soil-moisture-limited (Anderegg, Trugman, Bowling, Salvucci, & Tuttle, 2019). ...
Citations
... In recent years, ET products have undergone rapid development and significant improvement in accuracy. However, each product still has its limitations, and enhancing ET accuracy remains a hot and frontier research topic in hydrological and meteorological studies [3,4]. ...
Due to limited observational data, there remains considerable uncertainty in the estimation and spatiotemporal variations of land surface evapotranspiration (ET). Reanalysis products, with their advantages of high spatiotemporal resolution, global coverage, and long-term data availability, have emerged as powerful tools for studying ET. Nevertheless, the accuracy of reanalysis ET products varies among different products and the reasons for these accuracy differences have not been thoroughly investigated. This study evaluates the ability of different reanalysis ET products to reproduce the spatiotemporal patterns and long-term trends of ET in China, using remote sensing and water-balance-derived ET as reference. We investigate the possible reasons for their disparity by analyzing the three major climatic factors influencing ET (precipitation, solar radiation, and temperature). The findings reveal that compared to the water balance ET, the Global Land Evaporation Amsterdam Model (GLEAM) product is capable of reproducing the mean, interannual variability, and trends of ET, making it suitable for validating reanalysis ET products. In comparison to GLEAM ET, all reanalysis ET products exhibit consistent climatology and spatial distribution but show a clear overestimation, with multi-year averages being overestimated by 16–40%. There are significant differences among the reanalysis products in terms of interannual variability, long-term trends, and attribution. Within the common period of 2003–2015, GLEAM and water balance ET products demonstrate consistent increasing trends. The second-generation Modern-Era Retrospective analysis for Research and Applications (MERRA2) and the offline (land-only) replay of MERRA (MERRA-Land) could produce similar increasing trends because of the consistent precipitation trends with observed precipitation. The European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and ERA5-Land cannot capture the consistent increasing trends as they obtain decreasing precipitation. These findings have significant implications for the development of reanalysis products.
... Drought relies not only on water supply but also on water demand, for which ET can be the proxy (Speich 2019). ET forces around 60% of the land P to return to the atmosphere (Zhang et al. 2020) and creates two-thirds of the planet's annual P. It also consumes more than half of the solar energy absorbed by the land surface as latent heat. Accordingly, ET, which contributes to mass and energy exchange between land and atmosphere (Zhang et al. 2020;Hobeichi et al. 2021), is crucial in improving our vision of land-atmosphere interactions and the terrestrial water cycle (Zheng et al. 2019;Xiao et al. 2020). ...
... ET forces around 60% of the land P to return to the atmosphere (Zhang et al. 2020) and creates two-thirds of the planet's annual P. It also consumes more than half of the solar energy absorbed by the land surface as latent heat. Accordingly, ET, which contributes to mass and energy exchange between land and atmosphere (Zhang et al. 2020;Hobeichi et al. 2021), is crucial in improving our vision of land-atmosphere interactions and the terrestrial water cycle (Zheng et al. 2019;Xiao et al. 2020). These explain ET's important role in releasing droughts (Mukherjee et al. 2018) and drought severity at both the local and global scales (Dhungel & Barber 2018;Zhang et al. 2020). ...
... Accordingly, ET, which contributes to mass and energy exchange between land and atmosphere (Zhang et al. 2020;Hobeichi et al. 2021), is crucial in improving our vision of land-atmosphere interactions and the terrestrial water cycle (Zheng et al. 2019;Xiao et al. 2020). These explain ET's important role in releasing droughts (Mukherjee et al. 2018) and drought severity at both the local and global scales (Dhungel & Barber 2018;Zhang et al. 2020). Therefore, using ET together with P in the structure of drought indices allows for a more comprehensive drought assessment (Zargar et al. 2011;Lu et al. 2019). ...
Drought assessment and monitoring are essential for its proper management. Drought indices play a fundamental role in this. This research introduces the Wet-environment Evapotranspiration and Precipitation Standardized Index (WEPSI) for drought assessment and monitoring. WEPSI incorporates water supply and demand into the drought index calculation. WEPSI considers precipitation (P) for water supply and wet-environment evapotranspiration (ETw) for water demand. We use an asymmetric complementary relationship to calculate ETw with actual (ETa) and potential evapotranspiration (ETp). WEPSI is tested in the transboundary Lempa River basin in the Central American dry corridor. ETw is estimated based on evapotranspiration data calculated using the Water Evaluation And Planning (WEAP) system hydrological model. To investigate the performance of WEPSI, we compare it with two well-known meteorological indices (Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index), together with a hydrological index (Standardized Runoff Index), in terms of statistical metrics and mutual information (MI). We compare WEPSI-derived droughts and historical information, including crop production, cereal yield, and the Oceanic Nino Index (ONI). Results show WEPSI has the highest correlation and MI, and the lowest deviation. It is consistent with the records of the crop production index, cereal yield, and the ONI. Findings show that WEPSI can be used for agricultural drought assessments.
HIGHLIGHTS
WEPSI for drought assessment and monitoring is introduced.;
A step-by-step methodology for calculating WEPSI, including the computation of wet-environment evapotranspiration is presented.;
Spatiotemporal analysis of drought with hydrological modeling data and WEPSI is illustrated.;
WEPSI is suitable for running on remote sensing data.;
Results indicate WEPSI for agricultural and hydrological drought applications.;
... Evapotranspiration (ET) is the water vapor flux evaporated from the land surface to the atmosphere (Fisher et al., 2017;Zhang et al., 2020a). Accurate estimates of ET are required to understand agricultural water management and utilization, surface biophysical processes, and hydrological cycles (Fisher et al., 2008;Zhao et al., 2021). ...
... SM directly affects soil evaporation and controls stomatal resistance to regulate plant transpiration and photosynthesis (Yuan et al., 2007). Therefore, SM variations can affect land surface energy and water transport and strongly influence the partitioning of the available energy (Gentine et al., 2011(Gentine et al., , 2012Mladenova et al., 2017;Zhang et al., 2020a). Effective LAI and SM information are essential in oasis irrigated agriculture to avoid waste of water, energy, and human inputs . ...
Accurate estimation of evapotranspiration (ET) is essential for understanding terrestrial energy, water, andcarbon cycles. This study proposes a hybrid model integrating in-situ and remote sensing-derived soil moisture(SM) observations and remote sensing leaf area index (LAI) with the Noah-MP model. The ensemble Kalman filter
(EnKF) approach updates the leaf biomass and specific leaf area (SLA) by assimilating the remotely sensed LAI. Amachine learning (ML) surrogate model is used to integrate multi-site SM profile observations and remotesensing SM products to estimate the three-layer SM. An iterative coupling of two parts implements the hybrid
model: optimization of leaf biomass and SLA by assimilation of LAI in the Noah-MP model and simulation of three-layer SM in the ML surrogate model. The performance of the hybrid model is evaluated in the Heihe River Basin (HRB) in northwest China. The estimated ET from the hybrid model is compared with observations from
the large aperture scintillometer (LAS) at the Arou, Daman, and Sidaoqiao sites and up-scaled watershed ET over the HRB. The findings indicate that the hybrid model performs well in vegetated areas but underestimates ET in extreme arid deserts. The three-site unbiased root mean squared errors (ubRMSEs) of ET estimates from thehybrid model are 29.06%, 42.76%, and 50%，respectively. The coupling of data assimilation (DA) and ML methods can improve vegetation dynamics and SM transport estimation in the Noah-MP model. The hybrid model can take advantage of DA and ML methods and integrate multi-source observations to improve the accuracy of ET estimation. The results also indicate that the
ET predictions are more sensitive to root zone SM (0–40 cm) over croplands, grasslands, and shrublands, while
the ET simulations are more affected by deeper rooting depths SM (0–100 cm) and groundwater over forests.
... Available evapotranspiration models are either analytical where they are fully based on physical laws, mechanistic where they use physical laws to predict estimates based on causality relationships such as the original Penman-Monteith model, or empirical (statistical) where they are based on correlations developed from experimental observations such as the Hargreaves model [36][37][38]. Empirical models are favored for their simplicity but lack physical significance and regional accuracy [39]. When there are enough accurate available input data, the use of mechanistic approaches is more suitable than empirical models [14]. ...
Detailed knowledge of energy and mass fluxes between land and the atmosphere are necessary to monitor the climate of the land and effectively exploit it in growing agricultural commodities. One of the important surface land fluxes is evapotranspiration, which combines the process of evaporation from the soil and that of transpiration from plants, describing the movement of water vapour from the land to the atmosphere. Accurately estimating evapotranspiration in agricultural systems is of high importance for efficient use of water resources and precise irrigation scheduling operations that will lead to improved water use efficiency. This paper reviews the major mechanistic and empirical models for estimating evapotranspiration including the Penman–Monteith, Stanghellini, Priestly–Taylor, and Hargreaves and Samani models. Moreover, the major differences between the models and their underlined assumptions are discussed. The application of these models is also reviewed for both open and closed field mediums and limitations of each model are highlighted. The main parameters affecting evapotranspiration rates in greenhouse settings including aerodynamic resistance, stomatal resistance and intercepted radiation are thoroughly discussed for accurate measurement and consideration in evapotranspiration models. Moreover, this review discusses direct evapotranspiration measurements systems such as eddy covariance and gas exchange systems. Other direct measurements appertaining to specific parameters such as leaf area index and surface leaf temperature and indirect measurements such as remote sensing are also presented, which can be integrated into evapotranspiration models for adaptation depending on climate and physiological characteristics of the growing medium. This review offers important directions for the estimation of evapotranspiration rates depending on the agricultural setting and the available climatological and physiological data, in addition to experimentally based adaptation processes for ET models. It also discusses how accurate evapotranspiration measurements can optimise the energy, water and food nexus.
... ET forces around 60% of the land P to 101 return to the atmosphere (Zhang et al., 2020) and creates two-thirds of the planet's annual P. It 102 also consumes more than half of the solar energy absorbed by the land surface as latent heat. 103 Accordingly, ET, which contributes to mass and energy exchange between land and atmosphere 104 (Zhang et al., 2020), is crucial in improving our vision of land-atmosphere interactions and the ET has several types, and selecting its type is highly critical in defining the drought index. 111 For instance, the so-called Standardized Precipitation Actual Evapotranspiration Index uses actual 112 evapotranspiration (ETa) in its structure (Homdee et al., 2016). ...
As recommended by Water Resources Research (WRR) journal, the preprint of our paper, is now available on the Earth and Space Science Open Archive (ESSOAr).
In collaboration with Dr. Gerald Corzo Perez, Vitali Díaz Mercado, and Dr. Milad Aminzadeh, we have introduced a new drought index "Wet-environment Evapotranspiration and Precipitation Standardized Index (WEPSI)", which has shown a very good performance in #agricultural drought monitoring.
The paper is availabe in the following link; We will be happy to have your invaluable feedback.
#Drought
https://doi.org/10.1002/essoar.10507882.1
... Huntington, 2006;Ukkola et al., 2013;Fisher et al., 2017, and references therein), as well as in the energy and carbon cycles among others (e.g. Zhang et al., 2020;Halladay and Good, 2017;Mystakidis et al., 2016, and references therein). Its importance has triggered great efforts to quantify and predict ET at different spatial and temporal scales. ...
In this work we have developed a random forest regressor to predict daily evapotranspiration in three eddy-covariance sites in Northern Australia from in-situ meteorological data and fluxes, and satellite leaf area index and land surface temperature data. The variable analysis for the random forest regressor suggests that leaf area index is the most important one at this temporal scale. A development sample corresponding to the period 2010–2013 was used, while the year 2014 has been separated for testing. Using this approach, we have obtained satisfactory performances in the three sites, with RMSE errors around 1 mm/day (and relative RMSEs ~0.3) in comparison to the measured values. With the final aim of testing the predictive capability of a model that uses remote sensing data as input, regressors that predict evapotranspiration exclusively from leaf area index and land surface temperature, have been trained resulting in reasonable performances. The RMSEs over the test set are ~1.1−1.2 mm/day. In all cases, the errors are comparable to those obtained in previous works that use similar locations and different methods. When compared to the measured values, the random forest predictions of evapotranspiration are more accurate than the global MODIS ET 8-day 1 km product, which was used as benchmark for the performance evaluation of our models, in the three selected locations.
... Remote Sens. 2020, 12, 3503 2 of 22 kilometers. While this product is critical for many large-scale climate studies, a higher spatio-temporal resolution SM product is needed to advance applications in hydrometeorology, atmospheric research, and water resource management at regional and local scales [7][8][9]. ...
This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)'s high spatio-temporal resolution observations over the tropics (within ±38 • latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm 3 cm −3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm 3 cm −3 and 0.054 cm 3 cm −3 , respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.
... As a consequence of ongoing global warming, the mainly varied characteristic of northwest China is ever-increasing rainfall and temperature, and the latter has a significant increased since 1960s, which were confirmed by a drought case study of Xinjiang by An et al. (2020). Evapotranspiration, a key process for the surface hydrological cycle, is a better indicator for characterize on local water resource reallocation, and also is sensitive to temperature, vegetation and soil water content (Zhang et al., 2020). The increasing temperature has led to intense evapotranspiration, and water loss in shallow soils of 0-10, 10-40 cm in the entire northwest region was obvious since 2000 , especially in Xinjiang over the past three decades (Yao et al., 2018). ...
Tarim River Basin is experiencing heavy soil degeneration in a long term because of the extreme natural conditions, added with improper human activities such as reclamation and rejected field repeatedly, which hindered the soil health. One of the mainly form is soil salinization. Spatial distribution and variation of soil salinity is essential both for agricultural resource management and local economic development. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since 1980s while land use and climate have undergone major changed. Electromagnetic induction (EMI) has been successfully used to directly measurement the spatial distribution of targeting soil property at field- scale, and apparent electrical conductivity (ECa, mS m⁻¹) has become a surrogate of soil salinity (EC, dS m⁻¹) studied by many researchers at local scale. However, the effectiveness of this equipment has not been verified in the typical soil salinization areas in southern Xinjiang, especially on a large scale. This study was aimed to test the performance of ECa jointed with Random Forest (RF) for soil salinity regional–scale mapping at a typical arid area, taking Tarim River Basin as an example. The result showed that ECa together with environmental derivative variables and with RF were suited for regional–scale soil salinity mapping. Predicted accuracy of EC was higher at surface (0–20 cm, R² = 0.65, RMSE = 5.59) and deeper soil depth (60–80 cm, R² = 0.63, RMSE = 2.00, and 80–100 cm, R² = 0.61, RMSE = 1.73), lower at transitional zone (20–40 cm, R² = 0.55, RMSE = 2.66, and 40–60 cm, R² = 0.51, RMSE = 2.49). When ECa is involved in modeling, the prediction accuracy of multiple depths of EC is improved by 13.33%–61.54%, of which the most obvious depths are 60–80 cm and 0–20 cm. The results of variable importance show that SoilGrids were also favored the power EC model. Hence, we strongly recommended to joint EMI reads with remote sensing imagery for soil salinity monitoring at large scale in southern Xinjiang. These EC and ECa map can provide a data source for environmental modeling, a benchmark against which to evaluate and monitor water and salt dynamics, and a guide for the design of future soil surveys.
... For example, the quantification of soil moisture memory is limited to the time scale of observations [10]. Several ongoing efforts are also using soil moisture as a proxy for other quantities, such as rainfall [14,15] or evaporation [16,17], and having daily or sub-daily data would increase the success of these techniques. ...
Currently, the ability to use remotely sensed soil moisture to investigate linkages between the water and energy cycles and for use in data assimilation studies is limited to passive microwave data whose temporal revisit time is 2–3 days or active microwave products with a much longer (>10 days) revisit time. This paper describes a dataset that provides soil moisture retrievals, which are gridded to 36 km, for the upper 5 cm of the soil surface at sparsely sampled 6-hour intervals for +/− 38 degrees latitude for 2017–present. Retrievals are derived from the Cyclone Global Navigation Satellite System (CYGNSS) constellation, which uses GNSS-Reflectometry to obtain L-band reflectivity observations over the Earth’s surface. The product was developed by calibrating CYGNSS reflectivity observations to soil moisture retrievals from NASA’s Soil Moisture Active Passive (SMAP) mission. Retrievals were validated against observations from 171 in-situ soil moisture probes, with a median unbiased root-mean-square error (ubRMSE) of 0.049 cm3 cm−3 (standard deviation = 0.026 cm3 cm−3) and median correlation coefficient of 0.4 (standard deviation = 0.27). For the same stations, the median ubRMSE between SMAP and in-situ observations was 0.045 cm3 cm−3 (standard deviation = 0.025 cm3 cm−3) and median correlation coefficient was 0.69 (standard deviation = 0.27). The UCAR/CU Soil Moisture Product is thus complementary to SMAP, albeit with a larger random noise component, providing soil moisture retrievals for applications that require faster revisit times than passive microwave remote sensing currently provides.
