Search Results (15)

Working to simplify mechanistic models on the basis of reliability for estimating crop evapotranspiration (ET) in a greenhouse is still worthwhile for horticulturists. In this study, four ET models (Penman–Monteith, Priestley–Taylor, and Shuttleworth–Wallace models, and the Crop coefficient method) were parameterized after taking the restriction effect of resistance parameters in these models on ET into account, named as PA-PM, PA-PT, PA-CC, and PA-SW, respectively. The performance of these four parameterized models was compared at different growth stages, as well as the entire growing season. Tomatoes that were ET-grown in a solar greenhouse without a heating device were measured using weighting lysimeters during 2016–2017 and 2019–2021, in which data from 2016 were used to adjust the model parameters, and data from the other four study years were used to examine the model performance. The results indicated that the PA-PT and PA-CC models have a better performance in estimating tomato ET at four growth stages, while the PA-PM and PA-SW performed well only at the development and middle stages. Compared to the ET that was measured with the weighting lysimeters, the ET that was predicted using the PA-PM model was 27.0% lower at the initial stage, and 8.7% higher at the late stage; the ET that was computed using the PA-SW model was 19.5% and 13.6% higher at the initial and late stages, respectively. The PA-PT model yielded the lowest root mean square error and the highest index of agreement against the other models over the entire growing season, indicating that the PA-PT model is the best recommended model for estimating tomato ET in a solar greenhouse. Full article

It is a difficult undertaking to reliably estimate global terrestrial evapotranspiration (ET) using the Visible Infrared Imaging Radiometer Suite (VIIRS) at high spatial and temporal scales. We employ deep neural networks (DNN) to enhance the estimation of terrestrial ET on a global scale using satellite data. We accomplish this by merging five algorithms that are process-based and that make use of VIIRS data. These include the Shuttleworth–Wallace dual-source ET method (SW), the Priestley–Taylor-based ET algorithm (PT-JPL), the MOD16 ET product algorithm (MOD16), the modified satellite-based Priestley–Taylor ET algorithm (MS-PT), and the simple hybrid ET algorithm (SIM). We used 278 eddy covariance (EC) tower sites from 2012 to 2022 to validate the DNN approach, comparing it to Bayesian model averaging (BMA), gradient boosting regression tree (GBRT) and random forest (RF). The validation results demonstrate that the DNN significantly improves the accuracy of daily ET estimates when compared to three other merging methods, resulting in the highest average determination coefficients (R², 0.71), RMSE (21.9 W/m²) and Kling–Gupta efficiency (KGE, 0.83). Utilizing the DNN, we generated a VIIRS ET product with a 500 m spatial resolution for the years 2012–2020. The DNN method serves as a foundational approach in the development of a sustained and comprehensive global terrestrial ET dataset. The basis for characterizing and analyzing global hydrological dynamics and carbon cycling is provided by this dataset. Full article

Evapotranspiration (ET) determines the crop productivity in rain-fed agriculture. Global climate change alters the trade-off between soil water supply and atmospheric demand, energy partitioning, and community biophysical and structural properties; however, the interactive effects of these biotic and abiotic factors on ET and its components remain unclear. ET was measured in 2005–2020 in a rain-fed maize ecosystem in northeastern China using the eddy covariance method. By decomposing ET into transpiration (T) and evaporation (E) with the Shuttleworth–Wallace model, we investigated the abiotic and biotic interactive effects on ET and its components at annual levels. Results showed that available energy and albedo exhibited no significant time-series trends, but the Bowen ratio exhibited an increasing trend. Precipitation exhibited no significant trends; however, soil water content (SWC) decreased with time, accompanied by significantly increased air temperature (Ta) and a vapor pressure deficit (VPD). The ET decline was controlled by T, rather than E. The T decline was mainly controlled by canopy conductance and SWC. CO₂ concentrations and the VPD exhibited indirect effects on T by reducing canopy conductance, while Ta and precipitation had indirect effects on T by reducing SWC. Our results indicated that decreasing ET may be more severe with crop physiological adaptability for a decreased SWC. Aiming to enhance water resource efficiency, the practice of returning crop residues to the field to reduce soil evaporation, coupled with adjusting the sowing time to mitigate the risk of seasonal droughts during critical growth stages, represents an effective strategy in agricultural water resource management. Full article

The determination of the evapotranspiration (ET) and its components in urban woodlands is crucial to mitigate the urban heat island effect and improve sustainable urban development. However, accurately estimating ET in urban areas is more difficult and challenging due to the heterogeneity of the underlying surface and the impact of human activities. In this study, we compared the performance of three types of classic two-source ET models on urban woodlands in Shenzhen, China. The three ET models include a pure physical and process-based ET model (Shuttleworth–Wallace model), a semi-empirical and physical process-based ET model (FAO dual-K_c model), and a purely statistical and process-based ET model (deep neural network). The performance of the three models was validated using an eddy correlation and stable hydrogen and oxygen isotope observations. The verification results suggested that the Shuttleworth–Wallace model achieved the best performance in the ET simulation at main urban area site (coefficient of determination (R²) of 0.75). The FAO-56 dual K_c model performed best in the ET simulation at the suburb area site (R² of 0.77). The deep neural network could better capture the nonlinear relationship between ET and various environmental variables and achieved the best simulation performance in both of the main urban and suburb sites (R² of 0.73 for the main urban and suburb sites, respectively). A correlation analysis showed that the simulation of urban ET is most sensitive to temperature and least sensitive to wind speed. This study further analyzed the causes for the varying performance of the three classic ET models from the model mechanism. The results of the study are of great significance for urban temperature cooling and sustainable urban development. Full article

Accurate prediction of orchard evapotranspiration (ET) can optimize orchard water management. Based on the jujube (Zizyphus jujuba), ET was continuously measured from 2016 to 2019 using a large weighing lysimeter; the actual jujube ET was compared with the ET simulated with the Priestley–Taylor (P–T), Dual Crop Coefficient (Dual Kc), and Shuttleworth–Wallace (S–W) models, to verify the accuracy of the three models. The results showed that, from 2016 to 2019, the whole growth period of jujube ET was 532–592 mm and the crop coefficient was 0.85–0.93. The basal crop coefficients of the calibrated Dual Kc model were 0.4, 1.0, and 0.5 at the initial, middle, and ending growth stages, respectively. The overall simulation error of the Dual Kc model was low, and simulations were stable during the four years of the study. However, because of rough estimation the water stress coefficient (K_s) simulation accuracy will be reduced in the case of serious water shortage. The simulation error of the S–W model was greater than the simulation error of the Dual K_c model, and the simulations were unstable and vulnerable to interannual changes. The simulation error of the traditional P–T model was large. When the parameter “α” solution method was improved, the simulation accuracy was significantly improved, and the P–T model’s simulation accuracy was only slightly lower than that of the Dual Kc model. However, the model was easily affected by changes in net radiation and air temperature. Therefore, the Dual Kc model is recommended for estimating the ET of young jujube trees in arid areas. Full article

Among numerous methods that have been developed to estimate potential evapotranspiration (PET), the Food and Agricultural Organization Penman–Monteith model (FAO P–M) is often recognized as a standard method to estimate PET. This study was conducted to evaluate the applicability of three other PET estimation methods, i.e., Shuttleworth–Wallace (S–W) model, Thornthwaite (TW) and pan methods, to estimate PET across Sri Lanka with respect to the FAO P–M model. The meteorological data, i.e., temperature, relative humidity, wind speed, net solar radiation, and pan evaporation, recorded at 14 meteorologic stations, representing all climate and topographic zones of Sri Lanka, were obtained from 2009 to 2019. The models’ performances were assessed based on three statistical indicators: root mean squared error (RMSE), bias, and Pearson correlation coefficient (R). In comparison with the FAO P–M model estimates, the seasonal and annual estimates of all three models show great differences. The results suggested that pan and S–W methods perform better in the dry zone of the country. Both S–W and pan methods underestimated PET over the entire county in all seasons. TW does not show consistent results over the country, thus being found as the least reliable alternative. Although S–W is highly correlated with the FAO P–M model, the application of the model in a data-scarce region is more constrained, as it requires more parameters than the FAO P–M model. Thus, the study suggests employing alternative methods based on the region of the country instead of one single method across the entire country. Full article

Tomato crops are increasingly cultivated in winter in solar greenhouses to achieve high economic benefit in the North China Plain (NCP). Accurate predictions of crop transpiration (Tr) are of great significance for formulating a scientific irrigation system and increasing water productivity in this water shortage region. In this study, tomato transpiration at daily and hourly scales were estimated using Penman-Monteith (PM), Shuttleworth-Wallace (SW), and Priestley-Taylor (PT) models, and results were compared to the measured sap flow data (SF) in three tomato growth seasons in winter from 1 November 2018 to 9 December 2020. Results showed that both PM and SW models could perfectly estimate daily tomato Tr, with a determination coefficient R² of 0.96 and 0.94 and slopes of 0.99 and 0.98, respectively, when all three seasons’ data were pooled together. The estimated daily Tr by the original PT model with a coefficient (α) of 1.26 was also linearly related to the SF with R² of 0.92; however, the Tr was underestimated by 33%. Then α was calibrated using the data in the 2018 winter season. When the calibrated α was used in the 2019 and 2020 seasons, the estimated daily Tr showed comparable results with the PM and SW models. At hourly scales, the PM model performed best with an error of 3.0%, followed by the PT model (7.8%); the SW model underestimated Tr by 18.2%. In conclusion, all three models could be used to estimate daily Tr, and the PM and calculated PT models can be used to estimate hourly Tr. Full article

Determination of evaporation (E) and transpiration (T) in tea fields separately is important in developing precise irrigation scheduling and enhancing water use efficiency. In this study, the Shuttleworth–Wallace (S-W) model was applied to simulate the variations of E and T based on the data from 2015 to 2018 in a tea field in southeast China. The dual crop coefficient (D-K) method recommended by FAO-56 was also applied to calculate E and T, using the same data set to compare with the S-W model. The measured crop coefficient (K_c) ranged from 0.43 to 1.44 with the average value was 0.90 during 1–150 DOY (days of year), and the measured K_c tended to be stable with the average value of 0.83 during 151–365 DOY in 2015. The S-W model estimated ET_c with root mean square error (RMSE) and R² of 0.45 mm d⁻¹ and 0.97, while for the D-K method the values were 0.61 mm d⁻¹ and 0.95. Therefore, both approaches could estimate the E and T separately in tea fields in southeast China, however, the D-K method had a slightly poorer accuracy compared to the S-W model in the estimation of ET_c. Full article

The two-layer Shuttleworth–Wallace (SW) evapotranspiration (ET) model has been widely used for predicting ET with good results. Since the SW model has a large number of specific parameters, these parameters have been estimated using a simple non-hierarchical Bayesian (SB) approach. To further improve the performance of the SW model, we aimed to assess parameter estimation using a two-level hierarchical Bayesian (HB) approach that takes into account the variation in observed conditions through the comparison with a traditional one-layer Penman–Monteith (PM) model. The difference between the SB and HB approaches were evaluated using a field-based ET dataset collected from five agricultural fields over three seasons in Myanmar. For a calibration period with large variation in environmental factors, the models with parameters calibrated by the HB approach showed better fitting to observed ET than that with parameters estimated using the SB approach, indicating the potential importance of accounting for seasonal fluctuations and variation in crop growth stages. The validation of parameter estimation showed that the ET estimation of the SW model with calibrated parameters was superior to that of the PM model, and the SW model provided acceptable estimations of ET, with little difference between the SB and HB approaches. Full article

A new approach is proposed to derive evapotranspiration (E) and irrigation requirements by implementing the combination equation models of Penman–Monteith and Shuttleworth and Wallace with surface parameters and resistances derived from Sentinel-2 data. Surface parameters are derived from Sentinel-2 and used as an input in these models; namely: the hemispherical shortwave albedo, leaf area index and water status of the soil and canopy ensemble evaluated by using a shortwave infrared-based index. The proposed approach has been validated with data acquired during the GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) in California irrigated vineyards. The E products obtained with the combination equation models are evaluated by using eddy covariance flux tower measurements and are additionally compared with surface energy balance models with Landsat-7 and -8 thermal infrared data. The Shuttleworth and Wallace (S-W S-2) model provides an accuracy comparable to thermal-based methods when using local meteorological data, with daily E errors < 1 mm/day, which increased from 1 to 1.5 mm/day using meteorological forcing data from atmospheric models. The advantage of using the S-W S-2 modeling approach for monitoring ET is the high temporal revisit time of the Sentinel-2 satellites and the finer pixel resolution. These results suggest that, by integrating the thermal-based data fusion approach with the S-W S-2 modeling scheme, there is the potential to increase the frequency and reliability of satellite-based daily evapotranspiration products. Full article

The Qinghai-Tibetan Plateau (QTP) is generally considered to be the water source region for its surrounding lowlands. However, there have only been a few studies that have focused on quantifying alpine meadow evapotranspiration (ET) and its partitioning, which are important components of water balance. This paper used the Shuttleworth–Wallace (S–W) model to quantify soil evaporation (E) and plant transpiration (T) in a degraded alpine meadow (34°24′ N, 100°24′ E, 3963 m a.s.l) located at the QTP from September 2006 to December 2008. The results showed that the annual ET estimated by the S–W model (ET_SW) was 511.5 mm (2007) and 499.8 mm (2008), while E estimated by the model (E_SW) was 306.0 mm and 281.7 mm for 2007 and 2008, respectively, which was 49% and 29% higher than plant transpiration (T_SW). Model analysis showed that ET, E, and T were mainly dominated by net radiation (R_n), while leaf area index (LAI) and soil water content at a 5 cm depth (SWC_5cm) were the most important factors influencing ET partitioning. The study results suggest that meadow degradation may increase water loss through increasing E, and reduce the water conservation capability of the alpine meadow ecosystem. Full article

Evapotranspiration (ET) is an important part of the water, carbon, and energy cycles in ecosystems, especially in the drylands. However, due to the particularity of sparse vegetation, the estimation accuracy of ET has been relatively low in the drylands. Therefore, based on the dry climate and sparse vegetation distribution characteristics of the drylands, this study optimized the core algorithms (canopy boundary resistance, aerodynamic resistance, and sparse vegetation coverage) and explored an ET estimation method in the Shuttleworth–Wallace two-layer model (SW model). Then, the Beijing–Tianjin sandstorm source region (BTSSR) was used as the study area to evaluate the applicability of the improved model in the drylands. Results show that: (1) The R² value of the improved model results was increased by 1.4 and the RMSE was reduced by 1.9 mm, especially in extreme value regions of ET (maximum or minimum). (2) Regardless of the spatial distribution and seasonal changes of the ET (63–790 mm), the improved ET estimation model could accurately capture the differences. Furtherly, the different vegetation regions could stand for the different climate regions to a certain extent. The accuracy of the optimized model was higher in the semi-arid region (R² = 0.92 and 0.93), while the improved model had the best improvement effect in the arid region, with R² increasing by 0.12. (3) Precipitation was the decisive factor affecting vegetation transpiration and ET, with R² value for both exceeding 0.9. The effect of vegetation coverage (VC) was less. This method is expected to provide a more accurate and adaptable model for the estimation of ET in the drylands. Full article

A study was carried out to evaluate the potential use of the two-source Shuttleworth and Wallace (SW) model to compute the intra-orchard spatial variability of actual evapotranspiration (ET) of olive trees using satellite images and ground-based climate data. The study was conducted in a drip-irrigated olive orchard using satellite images (Landsat 7 ETM+), which were acquired on clear sky days during the main phenological stages (2009/10 growing season). The performance of the SW model was evaluated using instantaneous latent heat flux (LE) measurements that were obtained from an eddy correlation system. At the time of satellite overpass, the estimated values of net radiation ( ${\mathrm{Rn}}_{\mathrm{i}}$ ) and soil heat flux ( ${\mathrm{G}}_{\mathrm{i}}$ ) were compared with ground measurements from a four-way net radiometer and soil heat flux plates, respectively. The results indicated that the SW model subestimated instantaneous LE (W m⁻²) and daily ET (mm d⁻¹), with errors of 12% and 10% of observed values, respectively. The root mean square error (RMSE) and mean absolute error (MAE) values for instantaneous LE were 26 and 20 W m⁻², while those for daily values of ET were 0.31 and 0.28 mm d⁻¹, respectively. Finally, the submodels computed ${\mathrm{Rn}}_{\mathrm{i}}$ and ${\mathrm{G}}_{\mathrm{i}}$ with errors of between 4.0% and 8.0% of measured values and with RMSE and MAE between 25 and 39 W m⁻². Full article

The modeling and partitioning of regional evapotranspiration (ET) are key issues in global hydrological and ecological research. We incorporated a stomatal conductance model and a light-use efficiency-based gross primary productivity (GPP) model into the Shuttleworth–Wallace model to develop a simplified carbon-water coupling model, SWH, for estimating ET using meteorological and remote sensing data. To enable regional application of the SWH model, we optimized key parameters with measurements from global eddy covariance (EC) tower sites. In addition, we estimated soil water content with the principle of the bucket system. The model prediction of ET agreed well with the estimates obtained with the EC measurements, with an average R² of 0.77 and a root mean square error of 0.72 mm·day⁻¹. The model performance was generally better for woody ecosystems than herbaceous ecosystems. Finally, the spatial patterns of ET and relevant model outputs (i.e., GPP, water-use efficiency and the ratio of soil water evaporation to ET) in China with the model simulations were assessed. Full article

Accurate estimation of evapotranspiration (ET) and its components is critical to developing a better understanding of climate, hydrology, and vegetation coverage conditions for areas of interest. A hybrid dual-source (H-D) model incorporating the strengths of the two-layer and two-patch schemes was proposed to estimate actual ET processes by considering varying vegetation coverage patterns and soil moisture conditions. The proposed model was tested in four different ecosystems, including deciduous broadleaf forest, woody savannas, grassland, and cropland. Performance of the H-D model was compared with that of the Penman-Monteith (P-M) model, the Shuttleworth-Wallace (S-W) model, as well as the Two-Patch (T-P) model, with ET and/or its components (i.e., transpiration and evaporation) being evaluated against eddy covariance measurements. Overall, ET estimates from the developed H-D model agreed reasonably well with the ground-based measurements at all sites, with mean absolute errors ranging from 16.3 W/m² to 38.6 W/m², indicating good performance of the H-D model in all ecosystems being tested. In addition, the H-D model provides a more reasonable partitioning of evaporation and transpiration than other models in the ecosystems tested. Full article