Shuttleworth-Wallace Model Research Articles

Evolution of evapotranspiration in the context of land cover/climate change in the Han River catchment of China

AbstractEvapotranspiration (ET) stands as a pivotal element in the terrestrial‐atmospheric energy interchange, modulated by a complex array of factors including land use dynamics and climate change. The elucidation of regional and temporal patterns, alongside the mechanisms underpinning ET and its components, amidst environmental shifts, has emerged as a focal point in contemporary hydrological discourse. The Han River catchment, under the influence of the subtropical monsoon, presents an exemplary case study for hydrological inquiry due to its distinct catchment characteristics. This research probes the evolution and influencing mechanisms of ET within the catchment from 2000 to 2018, employing the improved Shuttleworth–Wallace model (i.e., SWH model), multivariate statistical techniques and additional methodologies. Findings reveal that (1) the annual mean ET, evaporation (E) and vegetation transpiration (T) within the Han River catchment from 2000 to 2018 were quantified at 1156.77, 784.21 and 372.56 mm, respectively. The overall spatial pattern showed a gradual decrease from the Chaoshan Plain area identified as having higher values compared to other regions, which may be attributed to the weakened vegetation cooling effect and the indirect effect of the heat island effect brought about by construction land expansion. (2) The significant decrease of E may be attributed to the optimization of vegetation growth conditions in the catchment, resulting in more solar radiation intercepted by the vegetation canopy. (3) Climatic alterations exerted a notable influence on ET, E and T than land use changes. Temperature, Normalized Difference Vegetation Index (NDVI), net radiation and wind speed were identified as the most consequential factors affecting ET. This study lays a scientific groundwork for subsequent exploration into the spatio‐temporal dynamics and mechanisms influencing evapotranspiration and its elements in the Han River catchment, contributing to a broader understanding of hydrological cycling.

Estimating crop evapotranspiration of wheat-maize rotation system using hybrid convolutional bidirectional Long Short-Term Memory network with grey wolf algorithm in Chinese Loess Plateau region

Accurate estimation of crop evapotranspiration (ET) is essential for the efficient utilization of agricultural water resources, crop production enhancement, and sustainable agricultural development. However, direct measurement of ET is highly expensive, intricate, and time-consuming, highlighting the imperative of establishing a novel model to accurately estimate ET in agricultural ecosystems. To address the above problems, this study proposed a novel model (GWA-CNN-BiLSTM), which incorporates Grey Wolf Algorithm (GWA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as a hyperparameter adjuster, feature extractor, and regression component, respectively, to estimate ET built upon various input combinations comprising net solar radiation (Rn), vapor pressure deficit (VPD), average air temperature (Ta), soil water content (SWC), and leaf area index (LAI) about winter wheat-spring maize rotation system during 2012–2020 in the Loess Plateau. Besides, following a comparative assessment within GWA-CNN-BiLSTM, Convolutional Bidirectional Long Short-Term Memory network (CNN-BiLSTM), BiLSTM, Long Short-Term Memory network (LSTM), and Shuttleworth-Wallace (SW) models, the results revealed that GWA-CNN-BiLSTM under varied inputs obtained the superior performance, ranging from 0.562 to 0.957 in determination coefficient (R2), 8.4–41.5 % in relative root mean square error (RRMSE), 0.349 mm d−1 to 1.521 mm d−1 in mean absolute error (MAE), −3.26 % to 14.11 % in percent bias (PBIAS), and 0.820–1.091 in regression coefficient (b0), respectively. Moreover, while the accuracy of BiLSTM over LSTM was evident, its performance was notably improved by the incorporation of the CNN module. Additionally, LSTM-type models under complete input combination present better precision than SW by 29.7−51.4 % in R2, 44.2−76.1 % in RRMSE, and 33.6−63.4 % in MAE, respectively. Furthermore, the accuracy of all models under varied inputs exhibited excellence in winter wheat compared to spring maize, and corresponding improvements ranged 1.4−4.3 % in R2, 5.1−20.1 % in RRMSE, and 3.1−17.9 % in MAE, respectively. Besides, the meteorological factors (Rn, VPD, Ta) proved to be the most important inputs for ET estimation in winter wheat and spring maize. Wherein the importance of SWC exceeded that of LAI in winter wheat, while the opposite trend was observed in spring maize. In brief, GWA-CNN-BiLSTM is the highly recommended model to estimate ET of winter wheat-spring maize rotation system under diverse input data scenarios in the Loess Plateau, which can facilitate to offer valuable assistance in regional agriculture water management decisions.

Development and validation of an oil palm model for a wide range of planting densities and soil textures in Malaysian growing conditions

A semi-mechanistic oil palm growth and yield model called Sawit.jl was developed to account for a wide range of planting densities and soil textures under Malaysia's climate conditions. The model comprises components related to meteorology, photosynthesis, energy balance, soil water content, and crop growth. The model simulates instantaneous meteorological properties using daily weather data, calculates simultaneous evaporation from crop and soil with the Shuttleworth-Wallace model, determines soil water content through Darcy's law, and adapts a biochemical C3 model for photosynthesis. The model is also parameterized using updated measurements from the newer tenera oil palm, including temperature-dependent Rubisco kinetics, specific leaf area, and the partitioning of nutrients and dry matter between various tree parts. Sawit.jl was validated using historical field measurement data from seven Malaysian oil palm sites, encompassing palm ages spanning 1–23 years. These seven sites differed in soil type (Inceptisols and Ultisols), planting density (82–299 palms ha−1), soil texture (27–59 % clay and 7–67 % sand), and rainfall (1800–2800 mm yr−1). The model showed overall good accuracy in simulating oil palm parameters (except for trunk weight) across diverse conditions, with model agreement metrics ranging from 6 to 27 % for model absolute errors, −22 to +17 % for model bias, and 0.38 to 0.98 for the Kling-Gupta Efficiency index. The model also predicted the response of oil palm yield to abrupt rainfall changes, such as those during El Niño and La Niña events, while accounting for how soil texture, rainfall, and other meteorological factors influence water deficits and crop photosynthesis. However, model accuracy varied by site, planting density, and oil palm parameter. Model accuracy can be increased by more accurately representing the oil palm microclimate, incorporating fruiting activity, and refining the dry matter partitioning mechanism for the trunk.

Parameterization of Four Models to Estimate Crop Evapotranspiration in a Solar Greenhouse.

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.

A three-layer evapotranspiration model considering the vertical structure of urban green spaces

Urban green spaces with complex vertical vegetation structures typically have higher capacities for microclimate regulation. The vertical structure comprising "tree + shrub + soil" is a common feature in urban green spaces. The Penman–Monteith and Shuttleworth–Wallace models estimate the evapotranspiration (ET) from trees and soil only, but they do not account for the contribution of shrubs, thereby leading to underestimation of the ecosystem ET. We developed a three-layer model of the vertical structure comprising "tree + shrub + soil" in urban green spaces and its impacts on ecosystem ET. The three-layer model was validated based on data measurements, showing that the root mean square errors of latent heat flux for tree, shrub, and soil were 31.0 W·m–2, 21.3 W·m–2, and 19.1 W·m–2, respectively. The uncertainty of parameters can significantly affect the model’s performance when estimating ET and its components. Sensitivity analysis showed that meteorological parameters, bulk stomatal resistance, and soil surface resistance played important roles in ET estimation using the three-layer model, and these sensitivity coefficients were also influenced by the meteorological conditions and vegetation canopy structure. The three-layer model enhances our understanding of ET in urban green spaces, aiding in their management and planning.

Estimating and partitioning evapotranspiration in a film mulched cropland with shallow groundwater by the improved dual source model

Evapotranspiration (ET) is a key link in terrestrial water cycle and influenced by complex environmental factors in cropland. Accurate assessment of crop ET over heterogeneous underlying surfaces remains challenging. In this study, the dual source Shuttleworth-Wallace (SW) model was modified to estimate λET (latent heat corresponding to ET) through incorporating the effects of film mulching and shallow groundwater. λET and latent heat from transpiration (λT) were measured in a maize field in northwest China during 2017 and 2018 to assess the performance of the original (SW) and modified (SWM) models. Results indicate that the overestimation of latent heat from soil evaporation (λE) due to ignoring film mulching could be obviously mitigated by SWM. Besides, considering capillary rise from shallow groundwater in SWM improved not only λET estimates but also the accuracy of canopy resistance parameter. The SWM model outperformed the SW model that the slopes of fitting lines for λET and λT increased from 0.60 to 0.79 to 0.77–0.99 at daily scale and Nash-Sutcliffe Efficiency coefficient exceeded 0.86. Relative error and RMSE were decreased to below 0.15 and 19.88 W m−2, respectively. The estimations of λET and λT by SWM were also improved at hourly scale. Seasonal variation and partition of λET between two years were comparable and average T/ET for the entire growing season was about 78 %-84 %, which was largest at the heading stage. Path analysis on the responses of λET to the environmental factors recognized the greatest direct effect of crop height or leaf area index, and identified the significantly direct control of shallow groundwater on λT in this area. The study presented an improved dual source model to better quantify λET, which would benefit water resources management for film mulched croplands with shallow groundwater.

Improvement of evapotranspiration simulation study in the Hailar River basin under the influence of vegetation dynamics

In arid and semi-arid areas with <400 mm of precipitation, evapotranspiration (ET) accounts for about 80% of precipitation and is the main water consumer in the watershed. However, vegetation greening in recent years will increase ET and exacerbate the aridity of the area by affecting soil moisture in the root system. Vegetation changes are regional and spatially heterogeneous, therefore, in order to characterize ET changes under vegetation dynamics, it is necessary to expand the spatial scale of ET simulation. However, widely used evapotranspiration simulation models, such as the Shuttleworth-Wallace model (SW model), are deficient in reflecting the direct and indirect effects of vertical (i.e., soil depths) and horizontal (i.e., vegetation dynamics) directions. Based on field sampling and constructed structural equation model (SEM), we found that vegetation dynamics affect evapotranspiration not only directly, but also indirectly by affecting soil moisture at different depths. On this basis, we defined the weighting coefficients of 0.85 and 0.15 for grassland vegetation zones, 0.3, 0.15, 0.20, 0.25, 0.10 for forest-grass interspersed zones, and 0.20, 0.55, 0.25 for forested zones, respectively, based on the SEM results. Different soil moisture weighting coefficients were defined within different vegetation type zones and the improved SW model is called S-W-α. Comparing the simulation results with the measured data, S-W-α improved the ET simulation accuracy in this region by 33.92% and the improved ET spatial trend can respond to the dynamic changes of vegetation. Replacing the ET module in the Block-wise use of TOPMODEL and Muskingum-Cunge method mode (BTOP model) with the modified S-W-α, the results show that the simulation accuracy of the improved model is increased by 25%, and the Nash is higher than 75% for both the rate period and the validation period, which realizes the extension of the model from the point scale to the basin scale. The modified model may provide technical support for simulation of evapotranspiration and management of ecosystem health in ecologically fragile areas.

Evapotranspiration partitioning by integrating eddy covariance, micro-lysimeter and unmanned aerial vehicle observations: A case study in the North China Plain

Partitioning of evapotranspiration (ET) into soil evaporation (E) and plant transpiration (T) poses a significant challenge. This study proposed a novel approach that combines eddy covariance, micro-lysimeter, and high-resolution unmanned aerial vehicle (UAV) images within the flux footprint to optimize the canopy and soil resistances of the Shuttleworth-Wallace model (S-W) using a multi-objective optimization scheme. A two-year experiment was conducted at the Yucheng and Luancheng sites to investigate the effectiveness of the proposed method for the summer maize-wheat winter system in the North China Plain. Our results showed that the S-W model with multi-objective optimization by using high-resolution UAV images within the flux footprint significantly improved the estimation accuracy of ET components compared with the traditional one- and two-objective optimization schemes. The estimation of ET, T, and E of our method had an average root mean square error of 0.67 mm day−1, 0.66 mm day−1, and 0.28 mm day−1, respectively. The optimized S-W model using high-resolution UAV data within the flux footprint produced better ET partitioning than that optimized using Moderate Resolution Imaging Spectroradiometer (MODIS) data. We then applied the proposed method to estimate ET and ET components in the North China Plain. This study highlighted the importance of utilizing multi-source data (especially high-resolution UAV images) and multi-objective optimization for accurate ET partitioning.

Evapotranspiration and potential water saving effect evaluation of mulched maize fields in China

To alleviate the contradiction between crop evapotranspiration (ET) and available water resources, and to develop agricultural production, mulched irrigation, as a water saving technology, is popularized in arid areas of China. However, the observations of ET in long-term mulched farmlands are scarce, so the inter-annual variation and controlling factors of ET after mulching remain uncertain, and the overall water saving effect of mulching in arid areas has not been quantified. Therefore, we conducted the observation experiments (2007–2021) on a mulched maize field, applied the modified Shuttleworth-Wallace model and obtained multi-source data statistics to fill in the gaps in the previous research. The results showed that the mean ET in the past 15 years was 521.68 mm, and the arid areas faced more severe water demand pressure. The inter-annual ET trend of the mulched maize field increased significantly, mainly caused by the reference evapotranspiration (ET0), while the inter-annual fluctuation was mainly attributed to the crop coefficient (Kc). The contribution of plant transpiration (Tr) to ET was always greater than that of soil evaporation (Es), and the contribution of solar radiation (Rs) to ET was the largest among environmental factors. In addition, based on observation data and model simulation, it was revealed that the water saving amount and ratio of the long-term mulched maize field were estimated to be 83.8 mm and 13.3 %, respectively. Furthermore, the statistical results from 49 sites in five provinces (regions) in Northwest China showed that compared with the agricultural water use in the recent 10 years, the mean annual total potential water saving amount and ratio of the mulched maize field were 2.2 billion m3 and 2.5 %, respectively. Our study will provide important guidance for understanding the variation of crop ET and water resources allocation.

The Decreased Availability of Soil Moisture and Canopy Conductance Dominate Evapotranspiration in a Rain-Fed Maize Ecosystem in Northeastern China

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.

A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model

Abstract. As the theoretical upper bound of evapotranspiration (ET) or water use by ecosystems, potential ET (PET) has always been widely used as a variable linking a variety of disciplines, such as climatology, ecology, hydrology, and agronomy. However, substantial uncertainties exist in the current PET methods (e.g., empiric models and single-layer models) and datasets because of unrealistic configurations of land surface and unreasonable parameterizations. Therefore, this study comprehensively considered interspecific differences in various vegetation-related parameters (e.g., plant stomatal resistance and CO2 effects on stomatal resistance) to calibrate and parametrize the Shuttleworth–Wallace (SW) model for forests, shrubland, grassland, and cropland. We derived the parameters using identified daily ET observations with no water stress (i.e., PET) at 96 eddy covariance (EC) sites across the globe. Model validations suggest that the calibrated model could be transferable from known observations to any location. Based on four popular meteorological datasets, relatively realistic canopy height, time-varying land use or land cover, and the leaf area index, we generated a global 5 km ensemble mean monthly PET dataset that includes two components of potential transpiration (PT) and soil evaporation (PE) for the 1982–2015 time period. Using this new dataset, the climatological characteristics of PET partitioning and the spatiotemporal changes in PET, PE, and PT were investigated. The global mean annual PET was 1198.96 mm with PT/PET of 41 % and PE/PET of 59 %, controlled moreover by PT and PE of over 41 % and 59 % of the globe, respectively. Globally, the annual PET and PT significantly (p&lt;0.05) increase by 1.26 and 1.27 mm yr−1 over the last 34 years, followed by a slight decrease in the annual PE. Overall, the annual PET changes over 53 % of the globe could be attributed to PT, and the rest to PE. The new PET dataset may be used by academic communities and various agencies to conduct climatological analyses, hydrological modeling, drought studies, agricultural water management, and biodiversity conservation. The dataset is available at https://doi.org/10.11888/Terre.tpdc.300193 (Sun et al., 2023).

Comparison of surface resistance‐based models for estimating maize evapotranspiration in a humid region of China

AbstractAccurate simulation of evapotranspiration (ET) is essential to enhance efficient irrigation management in the maize field. Here, we evaluated the performance of four mathematical models for estimating the ET of maize. The four models based on surface resistance calculate ET from different vapor sources, which are Penman‐Monteith (PM) through the “big leaf” model, the Shuttleworth‐Wallace (SW) model for distinguishing between soil and canopy, the clumping (C) model for distinguishing between canopy, soils under the canopy and bare soil, and the seasonal clumping (Cj) model for dividing ET into transpiration of sunlit leaves and shaded leaves, evaporation of bare soil surface, sunlit soil surface of canopy gap fraction, and canopy shaded soil surfaces. The models were calibrated by ET measured from a weighing lysimeter, transpiration by the sap flux method, and soil evaporation by micro‐lysimeters in 2014, 2015, and 2017. Results showed that the measured daily transpiration was 3.32 mm/day during the full‐grown stage of maize, and the mean measured daily soil evaporation was 1.46 mm/day. The performance of the sap flow for transpiration plus micro‐lysimeter for soil evaporation method was consistent with the large‐weighted lysimeter method in measuring daily ET. For simulating versus measuring hourly transpiration, the Cj model performed better than the C model with a slope of 0.94, determination coefficient (R2) of 0.85, mean absolute error (MAE) of 0.08 mm/h, and modified agreement index (d) of 0.81. In simulating daily soil evaporation, the Cj model also had a higher slope and less MAE than the C and SW models. Nevertheless, the Cj model yielded increased slope and d and decreased MAE between simulated and measured daily ET. The most sensitive environmental factor in the Cj model is temperature. With a 50% increase in temperature, ET, transpiration, and evaporation increase by 45%, 36%, and 69%, respectively. In summary, the Cj model improved the accuracy for hourly and daily ET of maize and helped separate plant transpiration and soil evaporation, thus giving an available approach for precision irrigation in water management of maize planting systems.

Comparison and evaluation of evapotranspiration estimations by different approaches in the Yellow River Basin

Abstract Accurately quantifying large-scale evapotranspiration (ET) is of significant scientific importance. In this study, the Shuttleworth–Wallace (SW) model incorporates gross primary productivity (GPP) values from the near-infrared reflectance of vegetation (NIRv) GPP product (NIRvGPP). Monthly ET estimations for the Yellow River Basin (YRB) were obtained using the SW models with GPP derived from the GLASS GPP product (GLASSGPP) and NIRvGPP, as well as GLASS ET and CR ET products (SW_GLASSGPP ET, SW_NIRvGPP ET, GLASS ET, and CR ET). The study analyzes the annual spatio-temporal patterns of these products, evaluates their accuracy and uncertainty, and examines the applicability of a fused ET estimation. The results revealed: (1) differences exist in ET estimations among GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET; (2) monthly GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET align well with the monthly ETWB, with SW_GLASSGPP ET and SW_NIRvGPP ET outperforming GLASS ET and CR ET; (3) average uncertainties of GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET display different spatial variation patterns; and (4) compared to GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET, the fused ET estimation achieves the best performance within the YRB.

Modeling the evapotranspiration of Lycium barbarum with drip irrigation based on the shuttleworth-wallace model in Ningxia, Northwest China

Modeling the evapotranspiration of Lycium barbarum with drip irrigation based on the shuttleworth-wallace model in Ningxia, Northwest China

Evaluation the Performance of Three Types of Two-Source Evapotranspiration Models in Urban Woodland Areas

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-Kc 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 Kc 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.

Dependence of evapotranspiration validity on shallow groundwater in arid area-a three years field observation experiment

Groundwater resources strongly support crop water requirement and economic yield improvement in semi-arid and arid areas. Water resources management require more investigation in the response of evapotranspiration (ET) partitioning and water use efficiency to groundwater change. Observational data collected over a three-year period, including water and carbon flux, vegetable sap flow, meteorological factors, soil water content, and groundwater depth, were employed in this study. Additionally, an improved Shuttleworth-Wallace model was used for partitioning plant transpiration and bare soil evaporation (E) in a sunflower cropland with shallow groundwater. The ET of the sunflower was founded to be 353.5 ± 15.0 mm, and approximately 48.3 ± 7.4% derived from the groundwater evaporation. E amounted approximately 145.6 ± 6.1 mm over the growth season, and around 47.2% of which was lost during the seeding stage. Furthermore, its dynamics depended on crop growth and groundwater table depth (WTD). These large amounts of non-productive soil E had a negative impact on the water-carbon coupling. The presence of E had resulted in a lower ecosystem water use efficiency (WUEe), measured at 1.02 ± 0.33 g C kg−1 H2O, compared to canopy water use efficiency (WUEc) of 1.68 ± 0.41 g C kg−1 H2O. These results demonstrated that regulating WTD could improve WUEe by increasing WUEc and reducing soil E. To achieve the higher WUEe, an optimum WTD was range of 1.5–2.0 m for border-irrigated sunflower field grown on sandy loam, identified by an analysis of normalized evaporation difference at the varying WTD. This finding highlights the significance of maintaining specified WTD, providing valuable insights into the complex interplay between crop evapotranspiration components and shallow groundwater. Moreover, it offers guidance for precision agriculture irrigation practices.

SimET: An open-source tool for estimating crop evapotranspiration and soil water balance for plants with multiple growth cycles

Accurate estimation of crop evapotranspiration (ETc) and soil water balance, which is vital for optimizing water management strategy in crop production, can be performed by simulation. But existing software has many deficiencies, including complex operation, limited scalability, lack of batch processing, and a single ETc model. Here we present simET, an open-source software package written in the R programming language. Many concepts involved in crop ETc simulation are condensed into functions in the package. It includes three widely used crop ETc models built on these functions: the single-crop coefficient, double-crop coefficient, and Shuttleworth–Wallace models, along with tools for preparing model data and comparing estimates. SimET supports ETc simulation in crops with repeated growth cycles such as alfalfa, a perennial forage crop that is cut multiple times annually.

Estimating the maximum vegetation coverage and productivity capacity supported by rainwater resources on the Loess Plateau

Large-scale anthropogenic revegetation brings new challenges by enhancing water consumptions in non-humid regions, even if it improves ecosystems. Water resources in water-stressed areas are primarily provided by precipitation, and excessive afforestation can potentially lead to severe imbalance between water supply and demand due to limited rainwater resources. Therefore, it is essential to evaluate the maximum vegetation coverage and productivity capacity supported by rainwater resources. In this study, the sustainability of revegetation was characterized by calculating the upper limit of vegetation leaf area index (LAI) supported by rainwater resources. The carbon sequestration capacity and efficiency of the maximum and the actual vegetation scenario were compared over the Loess Plateau (LP), which is well known as a region experiencing both large-scale vegetation restoration and severe water shortages. The upper limit on LAI was calculated based on evapotranspiration (ET) supported by rainwater resources, using the Shuttleworth-Wallace (S-W) model optimized in this study by adding dynamic vegetation and carbon dioxide components. The carbon sequestration capacity and efficiency of vegetation under different restoration scenarios were estimated in an analytical water use efficiency (WUE) model. Both the models perform well and are consistent with observations. The results show that the LAI of the maximum vegetation scenario is 11.5% higher than the actual scenario if vegetation on the LP is restored to its maximum level. The average gross primary productivity under the maximum vegetation scenario is 25.0% higher than the actual scenario, while the ecosystem WUE will increase by 17.9%. It should be noticed that the maximum scenario is a theoretical upper limit based on ideal assumptions. Our findings reveal that improving rainwater utilization efficiency can further develop the potential of sustainable vegetation restoration while improve its efficiency, and will provide guidance and theoretical support for vegetation restoration planning on water-scarce regions.

The estimation and partitioning of evapotranspiration in a coniferous plantation in subtropical China.

Accurate estimations of forest evapotranspiration (ET) and its components, transpiration (T) and evaporation (E), are important for deep understanding and predicting the responses of forest water cycles to climate change. In this study, the improved Shuttleworth-Wallace model (SWH) was applied to estimate ET, T, and E during 2003-2014 in a subtropical planation, and the modeled results were verified using in situ measurements by the eddy covariance technique, sap flow, and micro-lysimeter method. The study aimed to clarify whether it is feasible and reliable to use the SWH model to estimate and partition ET in forests. In addition, depending on the long-term data, the specific performances in modeling ET under different climatic backgrounds were investigated, and the underlying mechanisms were explored. The results verified that the SWH performed relatively well in the subtropical forest, and the modeled ET, T and E could track the seasonal variations, although overestimations were found in the peak seasons. However, the model was relatively weaker in estimating the interannual variabilities. It performed well in modeling ET in normal years but showed larger model residuals in years with obvious climatic anomalies. In the severe summer-drought (2003) and cold-spring (2005) years, the model greatly overestimated ET. It also overestimated ET in summer since 2010, which may be ascribed to the less dependency of ET on VPD induced by the more humid microclimate in forest accompanied with forest development. For the ET partitioning results, the modeled and measured E and T values were all in reasonable ranges. The possible reasons for underestimations (overestimations) of E and T by measurements (SWH model) were discussed. In this study, the data obtained using different methods and from different scales matched each other and could be cross validated, and the discussion on discrepancies would be beneficial for understanding the advantages and flaws of different methods and could be the basis for optimizing the measurement and model methods. In sum, this study verified that it is feasible to use the SWH model in forests and provided a basis for further improving and optimizing the modeled results under different climate backgrounds.

Two Alternatives to the Two‐Source Energy Balance Evapotranspiration Model

AbstractEnvironmental models are sensitive to inaccuracies in their approximation algorithms, which can bias model simulations and even lead to incorrect concepts. We present two alternatives to the two‐source evapotranspiration (ET) model, which utilize a more accurate approximation of the Clausius‐Clapeyron relation. Model performance was evaluated through a comparison with observed half‐hourly eddy covariance fluxes. Modeled representations of sensible heat dynamics and the limiting behaviors were also evaluated to identify the causes of model inaccuracies. Our analysis shows that the new exponential approximation used here significantly reduces errors that stem from solutions to the energy balance equations. The proposed parallel and series models are both more accurate than the Shuttleworth‐Wallace (SW) model under the conditions evaluated here, but we cannot conclude that the new models are consistently more accurate over a broader range of conditions. The new models can correctly reproduce several theoretical limiting behaviors, whereas the SW model generates conceptually incorrect results for several important limiting cases in which some key conductances approach both zero and infinity. Importantly, these models seriously underestimate the observed latent heat fluxes at night; the major causes of the nighttime model inadequacies are thought to be uncertainties of the estimated conductances and forcing data errors. The central concepts and processes related to new models are described via plots of temperature versus specific humidity to explore the theory behind an ET model. Through these efforts, we anticipate making more reliable ET predictions that will lead to a complete understanding of vegetation–atmosphere interactions.