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【Objective】 Transpiration is not only an important hydrological process but also a footprint of primary productivity of terrestrial systems under different environments. Its calculation at large scale is challenging. This paper proposes a method to fill this gap. 【Method】 The method is based on the modified Penman-Monteith (PM) model. The canopy resistance in the model is estimated using soil moisture based on the Ortega-Farias model, and the hydrodynamic resistance is calculated by two methods: one is based on wind speed and canopy temperature-environment relationship, and the other one is the Perrier logarithm method. The model was applied to simulate transpiration of citrus trees under water stress from June to July in 2022; its accuracy was evaluated against ground-true data. 【Result】 Water stress significantly reduced stomatal conductance and transpiration of the citrus trees. The canopy resistance estimated by the Ortega-Farias model agreed with the measured data with R2 = 0.63, accurately capturing the diurnal variation in canopy resistance under water stress. The aerodynamic resistance calculated by the leaf-based method was significantly greater than estimated by the Perrier logarithmic method. The accuracy of aerodynamic resistance calculated by the Perrier logarithmic method varied with soil water content, and the R2 between the measured and calculated transpiration from the PM model decreased from 0.66 when the plant was under slight water stress to 0.2 when the plant was under severe stress. In contrast, for the transpiration calculated using the aerodynamic resistances estimated by the leaf-simulation method, the R2 decreased from 0.71 to 0.24 with the increase in water stress. 【Conclusion】 When the soil moisture was greater than 50% of the field capacity, using the Ortega-Farias model to estimate canopy resistance and the leaf-based method to estimate aerodynamic resistance can improve the accuracy of the PM model for simulating transpiration of the citrus trees under water stress.

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Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An accurate and dynamic evaluation of the latent heat flux (LE) of greenhouse crops is imperative for advancing precision irrigation. Previous studies used the Penman-Monteith model to evaluate LE and treated the canopy as a holistic leaf, overlooking the discrepancies in structure and microclimate across canopy vertical sections. This study divided the canopy into a shaded and a sunlit layer in line with the structural characteristics of the tomato canopy and the shift in solar elevation angle β. A full-layer model (PMI) and a semi-layer model (PMT) were established based on the layering difference in the evaluation of canopy resistance (rc) and aerodynamic resistance (ra). Eight models: PMI1, PMI2, PMI3, PMI4, PMT1, PMT2, PMT3, and PMT4, were obtained using meteorological data from 2 m above the ground and 2/3 of the canopy height in 2022 and 2023. The performance was compared with the big leaf model (PMB) and verified based on the LE measured by the lysimeter (LEm). The results indicated that LE had the highest sensitivity to canopy absorbed net radiation during the flowering stage (1≤LAI≤3), while PMI2 and PMI4 overestimated LE with the fitting slopes (LE-LEm) of 1.72 and 1.67 in 2022 and 1.70 and 1.55 in 2023, respectively. PMT3 and PMB underestimated LE; the fitting slopes in two years were both 0.83 and 0.80, respectively; PMI3 with the fitting slopes were 0.99 and 0.96 in two years, respectively. In the picking period (LAI≥5), LE was the most sensitive to vapor pressure deficit (VPD); PMI3 and PMB accurately simulated LE with the fitting slops, both over 0.9 in two years. Therefore, the canopy was layered when evaluating rc while treating it as a unit in evaluating ra, PMI3 showed the best comprehensive performance when simulating LE in different seasons and growth periods using meteorological data at 2 m above the ground.

Canopy resistance is a key parameter in the Penman–Monteith (P–M) equation for calculating evapotranspiration (ET). In this study, we compared a machine learning algorithm–support vector machine (SVM) and an analytical solution (Todorovic, 1999) for estimating canopy resistances. Then, these estimated canopy resistances were applied to the P–M equation for estimating ET; as a benchmark, a constant (fixed) canopy resistance was also adopted for ET estimations. ET data were measured using the eddy-covariance method above three sites: a grassland (south Ireland), Cypress forest (north Taiwan), and Cryptomeria forest (central Taiwan) were used to test the accuracy of the above two methods. The observed canopy resistance was derived from rearranging the P–M equation. From the measurements, the average canopy resistances for the grassland, Cypress forest, and Cryptomeria forest were 163, 346, and 321 (s/m), respectively. Our results show that both methods tend to reproduce canopy resistances within a certain range of intervals. In general, the SVM model performs better, and the analytical solution systematically underestimates the canopy resistances and leads to an overestimation of evapotranspiration. It is found that the analytical solution is only suitable for low canopy resistance (less than 100 s/m) conditions. [ABSTRACT FROM AUTHOR]

Non‐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. Key Points: An approach is developed to estimate the non‐water‐limited canopy resistance (rcs) over the globeChanges in Ta ([CO2]) dominate the variabilities of rcs in regions with decreasing (increasing) rcsTraditional potential evapotranspiration (PET) overestimates (underestimates) the increasing (decreasing) PET trends in regions with increasing (decreasing) PET [ABSTRACT FROM AUTHOR]

Canopy resistance is a key parameter in the Penman–Monteith (P–M) equation for calculating evapotranspiration (ET). In this study, we compared a machine learning algorithm–support vector machine (SVM) and an analytical solution (Todorovic, 1999) for estimating canopy resistances. Then, these estimated canopy resistances were applied to the P–M equation for estimating ET; as a benchmark, a constant (fixed) canopy resistance was also adopted for ET estimations. ET data were measured using the eddy-covariance method above three sites: a grassland (south Ireland), Cypress forest (north Taiwan), and Cryptomeria forest (central Taiwan) were used to test the accuracy of the above two methods. The observed canopy resistance was derived from rearranging the P–M equation. From the measurements, the average canopy resistances for the grassland, Cypress forest, and Cryptomeria forest were 163, 346, and 321 (s/m), respectively. Our results show that both methods tend to reproduce canopy resistances within a certain range of intervals. In general, the SVM model performs better, and the analytical solution systematically underestimates the canopy resistances and leads to an overestimation of evapotranspiration. It is found that the analytical solution is only suitable for low canopy resistance (less than 100 s/m) conditions.

The accurate determination of the latent heat flux of farmland is of great significance to the development of precise irrigation scheme and high agricultural production. The latent heat flux is often estimated by the Penman-Monteith (PM) model. In this model, the parameter, canopy resistance, is important but difficult to measure directly. The aim of this study was to find out an effective method to determine the canopy resistance for the estimation of latent heat flux in fields of winter wheat and summer maize in the southern Jiangsu, China. The experiment field of this study was located in Changzhou city, Jiangsu province (31°41 'N, 119°40 'E) . The area belongs to subtropical monsoon climate. The mean annual precipitation may reach 1 000 mm. The soil texture is loam clay with field water-holding capacity of 25% and wilting point of 9.6%. The Bowen ratio energy balance observation system was installed in the center of the field to record the water and heat flux values during the year from 2018 to 2020. The net radiation was measured at 2.5 m above ground. The air temperature and relative humidity were recorded at heights of 1.5 and 2.5 m. During the experiment, the infrared thermometer was adjusted according to the height of the crop canopy to continuously measure the temperature of the crop canopy. The wind speed and direction were observed at a height of 2.5 m. Soil heat flux was measured at 2 cm depth and rainfall was also measured. The Katerji-Perrier (KP) and Todorovic (TD) models were applied to optimize the canopy resistance in the P-M model. The performances of the two models were validated using the measured latent heat flux by the Bowen Raito and Energy Balance (BREB) method. The most suitable model for the winter wheat and summer maize was suggested based on the evaluation of the hourly time scales performance of the two models. Besides, the reasons for the error of the KP and TD models were analyzed. The results showed that during the growth period of winter wheat, the main meteorological factors showed a similar trend of change, and the daily mean value of net radiation showed a fluctuating trend of rise. The fitted slopes of linear regression model between measured and simulated latent heat flux by the two models were close to 1 in the two growing seasons of winter wheat and one growing season of maize. Both models of canopy resistance parameters had good simulation effects on latent heat flux simulation of winter wheat, with R2 and Nash coefficient not less than 0.84 and 0.86 respectively, but the accuracy of KP model was slightly higher than that of TD model. The KP model overestimated the latent heat flux of winter wheat and summer maize, while TD model overestimated the latent heat flux of summer maize. Saturated vapor pressure difference was the main factor affecting the errors of KP and TD sub-models of canopy resistance parameters, and the greater saturated vapor pressure difference would lead to larger absolute errors. The study provides scientific basis for local agricultural water management [ABSTRACT FROM AUTHOR]

The temporal up-scaling has been the most popular to estimate the regional daily evapotranspiration (ET) using remote sensing and in-situ observation data. Several estimation methods of daily ET are usually assumed that the diurnal course of ET is similar to that of solar irradiance, including the evaporative fraction, crop coefficient, canopy resistance, and daily sine function. However, such an assumption is scarce for humid paddy fields. Taking the Water-Saving Irrigation (WSI) paddy fields as the research object, this study aims to compare the differences between the four ET estimations in the daily scale and the measured values using the Eddy Covariance (EC) system in the 2015 and 2016 rice season. The results showed that the average diurnal variation in the three scale conversion factors presented the special under WSI condition, including the Evaporative Fraction (EF), crop coefficient (Kc), and canopy resistance (rc) during the growth period. It infers that the outstanding variation was responded to the changes in the crop species, soil, and meteorological conditions. The diurnal and seasonal average EF and Kc were considered as an approximately concave-down shape, with a gentle variation during the 10:00-14:00 period, and the minimum around 10:00-11:00 AM. The diurnal variation of rc increased after sunrises, further to be almost constant from 09:00 to 12:00, and then tended to increase rapidly. By contrast, the ratio of the total daily solar irradiance to the instantaneous irradiance (J) was just determined using the latitude of the study area, indicating a typical concave-down shape. Furthermore, there was a better performance on the accuracy, correlation, and consistency of the crop coefficient and canopy resistance using 10:00-11:00 hours of the estimated daily evapotranspiration, compared with the other hours. In addition, a rough ET up-scaling was comparatively stable using the sine function. More importantly, the hourly EF estimation during the 10:00-14:00 period achieved fewer errors in the daily ET, indicating the accuracy and consistency were the best from 10:00-11:00. This finding was consistent with the time of satellite overpass of Terra/Modis, although the estimated values of evaporative fraction were relatively underestimated in each period. Such a stable and considerable gap was brought forward a high demand to establish a rational relationship between the effective daily ETEC and the ETEF calculated by the best time-of-day representativeness (10:00-11:00) of EF. Therefore, a correction procedure was also conducted to incorporate the variable Vapor Pressure Deficit (VPD) with the ET value following the EF up-scaling as an independent variable. Among them, the VPD was the key factor affecting the ET at both daytime and night. As such, a linear regression equation was achieved, where a low RMSE of 0.205 mm/d, the R2 of 0.987, and IOA of 0.996. Consequently, the results demonstrated that the daily ET calculated by the best time-of-day representativeness (10:00-11:00) of EF, Kc, and rc performed better, while, the improved evaporative fraction was optimal to the temporal up-scaling of daily scale ET estimation for the WSI paddy fields in the middle and lower reaches of the Yangtze River. This finding can also provide a potential guiding for farmland irrigation and agricultural water management. [ABSTRACT FROM AUTHOR]

An accurate and dynamic evaluation of the latent heat flux (LE) of greenhouse crops is imperative for advancing precision irrigation. Previous studies used the Penman-Monteith model to evaluate LE and treated the canopy as a holistic leaf, overlooking the discrepancies in structure and microclimate across canopy vertical sections. This study divided the canopy into a shaded and a sunlit layer in line with the structural characteristics of the tomato canopy and the shift in solar elevation angle β. A full-layer model (PMI) and a semi-layer model (PMT) were established based on the layering difference in the evaluation of canopy resistance (r c) and aerodynamic resistance (r a). Eight models: PMI 1 , PMI 2 , PMI 3 , PMI 4 , PMT 1 , PMT 2 , PMT 3 , and PMT 4 , were obtained using meteorological data from 2 m above the ground and 2/3 of the canopy height in 2022 and 2023. The performance was compared with the big leaf model (PMB) and verified based on the LE measured by the lysimeter (LE m). The results indicated that LE had the highest sensitivity to canopy absorbed net radiation during the flowering stage (1≤ LAI ≤3), while PMI 2 and PMI 4 overestimated LE with the fitting slopes (LE-LE m) of 1.72 and 1.67 in 2022 and 1.70 and 1.55 in 2023, respectively. PMT 3 and PMB underestimated LE ; the fitting slopes in two years were both 0.83 and 0.80, respectively; PMI 3 with the fitting slopes were 0.99 and 0.96 in two years, respectively. In the picking period (LAI≥5), LE was the most sensitive to vapor pressure deficit (VPD); PMI 3 and PMB accurately simulated LE with the fitting slops, both over 0.9 in two years. Therefore, the canopy was layered when evaluating r c while treating it as a unit in evaluating r a , PMI 3 showed the best comprehensive performance when simulating LE in different seasons and growth periods using meteorological data at 2 m above the ground. • Suggest dividing the canopy into two layers when evaluating canopy resistance. • Suggest regard the canopy as a whole when evaluating aerodynamic resistance. • Tomato latent heat fluxes have different sensitivity to variables at different stage. • The modified model has the highest accuracy using above-canopy meteorological data. [ABSTRACT FROM AUTHOR]

Stomata is an important channel for the exchange of water and gas between crops and the external environment. Stomatal behavior can shed light on how environmental stressors affect crop growth. Traditional stomata investigation methods involve experimental laboratory measurements of specific plants using stoma-related instruments or a field plot. However, developing a new remote sensing canopy resistance model for analyzing the stomatal behavior of crops under typical environmental stress is essential for regional agricultural management. Canopy resistance is a vital microscale–macroscale (from kilometers to micrometers) bridge that represents the stomatal behavior of the entire crop. In this study, a remote sensing canopy resistance model was proposed based on the Penman-Monteith model and a remote sensing evapotranspiration model. The model was verified by field-observed winter wheat data at the Yucheng Comprehensive Experiment Station (YCES) with dry-hot wind (DHW) and brackish water irrigation treatments from 2017 to 2019. The model measured canopy resistance (regression coefficient = 1.25; R 2 = 0.98) well. Finally, remote sensing-based canopy resistance measurements were used to investigate the effects of soil salinity and dry-hot wind on the winter wheat. The results showed excellent model performance for retrieving crop stomatal behavior under two environmental stresses and proved that the proposed remote sensing canopy resistance model is a promising tool for the investigation of stomatal behavior under environmental stress; this is particularly relevant for regional precision farming. [ABSTRACT FROM AUTHOR]

The backward‐averaged iterative two‐source surface temperature and energy balance solution (BAITSSS) model was developed to calculate evapotranspiration (ET) at point to regional scales. The BAITSSS model is driven by micrometeorological data and vegetation indices and simulates the water and energy balance of the soil and canopy sources separately, using the Jarvis model to calculate canopy resistance. The BAITSSS model has undergone limited testing in Idaho, United States. We conducted a blind test of the BAITSSS model without prior calibration for ET against weighing lysimeter measurements, net radiation, and surface temperature of drought‐tolerant corn (Zea mays L. cv. PIO 1151) in a semiarid, advective climate (Bushland, Texas, United States) in 2016. Later in the season (20 days), BAITSSS consistently overestimated ET by up to 3 mm d−1. For the entire growing season (127 days), simulated versus measured ET resulted in a 7% error in cumulative ET, RMSE = 0.13 mm h−1, and 1.70 mm d−1; r2 = 0.66 (daily) and r2 = 0.84 (hourly); MAE = 0.08 mm h−1 and 1.24 mm d−1; and MBE = 0.02 mm h−1 and 0.58 mm d−1. The results were comparable with thermally driven instantaneous ET models that required some calibration. Next, the initial soil water boundary condition was reduced, and model revisions were made to resistance terms related to incomplete cover and assumption of canopy senescence. The revisions reduced discrepancies between measured and modelled ET resulting in <1% error in cumulative ET, RMSE = 0.1 mm h−1, and 1.09 mm d−1; r2 = 0.86 (daily) and r2 = 0.90 (hourly); MAE = 0.06 mm h−1 and 0.79 mm d−1; and MBE = 0.0 mm h−1 and 0.17 mm d−1 and generally mitigated the previous overestimation. The advancement in ET modelling with BAITSSS assists to minimize uncertainties in crop ET modelling in a time series. [ABSTRACT FROM AUTHOR]

Highlights • A method to partition latent (LET) and sensible (H) heat fluxes of greenhouse was presented. • Canopy resistance was modelled with stomatal conductance. • Cumulative heat fluxes of greenhouse cucumber were estimated with Bulk transfer and Penman-Monteith models. • The latent heat flux calculated by Bulk transfer and Penman-Monteith models were compared with the lysimeter-measured values. Abstract Partitioning between latent (LET) and sensible (H) heat fluxes is critical in improving the greenhouse crops irrigation scheduling and microclimate. By applying the Bulk Transfer (BT) and Penman-Monteith (PM) models, the energy fluxes in different growing stages of the cucumber crop were determined in a Venlo-type greenhouse. The application of the BT and PM models is constrained by accurate parameterizations of canopy resistance (r c) and aerodynamic resistance (r a). In this paper, we measured micrometeorological data, crop growth index and LET during cucumber growing seasons in spring and autumn of 2016. The r c was modelled with stomatal conductance of cucumber leaves and validated with actual measurement of LET by lysimeters. The results showed that r c varied from 35 s m−1 during the day to 500 s m−1 at night in spring season, whilst it ranged from 40 s m−1 during the day to 1000 s m−1 at night in autumn season. Comparison of r c estimated by the PM and the BT models demonstrated that the r c estimated by the two methods were similar and highly correlated for both seasons. During the spring season, the PM and the BT models gave the determination coefficients (R 2) of predicted hourly LET equal to 0.94 and 0.83, whereas during the autumn season, the values were 0.94 and 0.76, respectively. The average root mean square errors (RMSE) of measured and predicted hourly LET were 96.97 and 74.74 W m-2 for the spring and autumn seasons for PM model, respectively. In contrast, the BT model gave RMSE of measured and predicted hourly LET of 82.47 and 69.14 W m-2, for spring and autumn, respectively. The results also depicted that the simplified energy balance approach was a feasible alternative to partition the energy fluxes in the greenhouse. The predictions made in this study would be an easy and relatively accurate way to partition greenhouse cucumber energy fluxes and thus, scientifically plan the irrigation schedule. In conclusion, this study provides scientific basis for optimizing efficient water-saving irrigation, development of a suitable irrigation scheduling and improving crop water use efficiency in the greenhouse, and consequently, more energy savings by avoiding excessive water application and thereby the objective of having improved and satisfactory yield and higher economic returns can be achieved. [ABSTRACT FROM AUTHOR]

Accurate determination of crop transpiration in greenhouses is very important in making exact irrigation scheduling and climate control. The most common method for calculating crop transpiration is the Penman-Monteith (PM) model. In this study, we analyzed the estimating methods of cucumber transpiration by PM method in greenhouse of South China. We measured meteorological data, transpiration and plant growth indicators of cucumber during 2 planting seasons (autumn-winter season in 2017 and spring-summer in 2018) in a Venlo-type greenhouse in south China. The PM model was used to predict the transpiration based on the data averaged over 30-min intervals using different approaches in the calculation of aerodynamic resistance (Perrier logarithm approach and heat transfer coefficient approach). The results showed that transpiration of the cucumber plants was mainly affected by meteorological factors when there was sufficient water supply. The fluctuations of transpiration were consistent with solar radiation, air temperature and canopy temperature, and reached the maximum at noon, while relative humidity of air had the opposite trends. Canopy temperature of the cucumber plants were lower than the air temperature during daytime. The maximum differences between air temperature and canopy temperature were 4.4 ℃ (spring-summer season) and 2.0 ℃ (autumn-winter season) due to the evaporative cooling during high transpiration rates at noon time. By analyzing the response relationships between the stomatal resistance and the meteorological factors, a sub-model was constructed by using the effective leaf area index and stomatal resistance to simulate the canopy resistance of the PM model. A significant exponential relationship between stomatal resistance and solar radiation was observed in the study (R2 = 0.89). The simulated canopy resistance determined by stomatal resistance and leaf area index remained constant during the night, with 533 and 574 s/m for spring-summer and autumn-winter season, respectively, and 96 and 112 s/m during the daytime when transpiration was high. The transpiration simulated by PM model were compared with the values measured by lysimeters. Results showed that the convection types of the greenhouse were conducted through mixed convection in about 80% and 94% of the study periods, respectively, for the spring-summer and autumn-winter seasons, while free and forced convection occurred at the rest periods at different times in the 2 planting seasons. In spring-summer season, free convection occurred in 5% of the study period during the day, forced convection occurred in 15% of the study period at night; In autumn-winter season, 2% of the study period was free convection, which all occurred during the day, 4% of the study period was forced convection, which occurred day and night. The variation ranges of aerodynamic resistance calculated by the Perrier logarithm approach and the heat transfer coefficient approach were small, and the average values was 388 and 383 s/m for the spring-summer and the autumn-winter season by the Perrier logarithm approach and 141 and 158 s/m for the spring-summer and the autumn-winter season by the heat transfer coefficient approach, respectively. The study showed that based on the PM model to simulate the cucumber transpiration had a good consistency with the measured values. However, the PM model underestimated transpiration when the Perrier logarithm approach was applied, the determination coefficients were 0.87 and 0.91 for spring-summer and autumn-winter seasons, respectively, and the PM model can accurately simulate the greenhouse cucumber transpiration using the heat transfer coefficient approach, the determination coefficients were 0.91 and 0.95, respectively, for spring-summer and autumn-winter seasons. [ABSTRACT FROM AUTHOR]

Highlights • The N-Jarvis model was constructed considering the effects of soil mineral nitrogen content on canopy resistance. • The N-Jarvis model is more accurate than the Jarvis model in predicting canopy resistance. • The transpiration estimated by the P–M model with the N-Jarvis model is more accurate than with the Jarvis model. Abstract Nitrogen partially regulates stomatal conductance and crop transpiration. We conducted greenhouse experiments into the effects of different nitrogen applications on the transpiration (T) of greenhouse pot-grown tomato plants with plastic film mulch in 2016–2017. We modeled the relationship between soil mineral nitrogen content and canopy resistance (r c) and incorporated our model into the Jarvis canopy resistance model to create the nitrogen modified Jarvis (N-Jarvis) model. We calculated transpiration under three nitrogen application levels, each with four irrigation programs using the Penman–Monteith (P–M) model with the Jarvis model and with the N-Jarvis model. Canopy resistance estimated by the Jarvis model differed greatly from observed canopy resistance, which was calculated by reformulating the P–M model and using the measured transpiration by sap flow system (T SF). The N-Jarvis model is more accurate than the Jarvis model in predicting r c. Similarly, the transpiration estimated by the P–M model with N-Jarvis model is more accurate than with the Jarvis model when compared to T SF , with higher values for the coefficient of determination and the modified efficiency coefficient, and lower values for the root mean square error and the Akaike information criterion. Including predictions from the N-Jarvis model improves the accuracy of the P–M model in estimating tomato transpiration under different nitrogen application levels. [ABSTRACT FROM AUTHOR]

Τhis study evaluates the hourly actual evapotranspiration (AΕT), predicted either by the two modified Penman-Monteith models (PM) which take into account the canopy resistance (rc) from the Katerji-Perrier (KP) or Todorovic (TD) models, or the simplified PM model with zero rc, as proposed by Priestley and Taylor (PT). The evaluation is based on comparisons with experimental measurements of AΕT applying the 'Bowen ratio' method. Hourly experimental data, of air temperature, humidity, wind speed and radiation balance measurements, taken at a 0.5 ha olive orchard in the rural area of Sparta (37° 04΄ N, 22°05΄ E), during the period from June 2010 up to July 2014, are used. The rc estimated by KP model is parameterized by a semi-empirical approach which requires a simple calibration procedure, while rc from TD model is parameterized using a theoretical approach. For estimating AET from minimum data (air temperature, humidity and radiation balance components) the PT model is also employed, since rc is not required and the aerodynamic term of PM is taken into account in the empirical parameter of the model. The results show that PT and KP models are the most appropriate [Refined Index of Agreement (RIA) equal to 0.89 or 0.88, respectively] followed by the TD model (RIA = 0.78). PT or KP models underestimate AET by 9.3% or 9.8%, respectively, while TD model overestimates AET by 15.0%, increased up to 25.8%, during warm period. [ABSTRACT FROM AUTHOR]

Introduction: Subdaily estimates of reference evapotranspiration (ET o) are needed in many applications such as dynamic agro-hydrological modeling. However, in many regions, the lack of subdaily weather data availability has hampered the efforts to quantify the subdaily ET o. In the first presented paper, a physically based framework was developed to desegregate daily weather data needed for estimation of subdaily reference ET o, including air temperature, wind speed, dew point, actual vapour pressure, relative humidity, and solar radiation. The main purpose of this study was to estimate the subdaily ETo using disaggregated daily data derived from developed disaggregation framework in the first presented paper. Materials and Methods: Subdaily ET o estimates were made, using ASCE and FAO-56 Penman–Monteith models (ASCE-PM and FAO56-PM, respectively) and subdaily weather data derived from the developed daily-to-subdaily weather data disaggregation framework. To this end, long-term daily weather data got from Abadan (59 years) and Ahvaz (50 years) synoptic weather stations were collected. Sensitivity analysis of Penman–Monteith model to the different meteorological variables (including, daily air temperature, wind speed at 2 m height, actual vapor pressure, and solar radiation) was carried out, using partial derivatives of Penman–Monteith equation. The capability of the two models for retrieving the daily ETo was evaluated, using root mean square error RMSE (mm), the mean error ME (mm), the mean absolute error ME (mm), Pearson correlation coefficient r (-), and Nash–Sutcliffe model efficiency coefficient EF (-). Different contributions to the overall error were decomposed using a regression-based method. Results and Discussion: The results of the sensitivity analysis showed that the daily air temperature and the actual vapor pressure are the most significant meteorological variables, which affect the ETo estimates. In contrast, low sensitivity coefficients got for wind speed and the solar radiation. The similar patterns of ETo sensitivity coefficient to the air temperature ( ) and the air temperature (TA) showed that the extent of the seasonal variation of was mainly determined by the TA. Results showed a good agreement between daily and 24h sum of subdaily ETo derived from ASCE-PM (with an EF of 0.990 to 0.994) and FAO56-PM (with an EF of 0.992 to 0.995) models. The results showed a good generalization capability of the disaggregation models to estimate the subdaily ETo for the validation data set (Ahvaz). The 24h sum of subdaily ETo derived from both models underestimated and overestimated the daily ETo in calibration (Abadan) and validation (Ahvaz) data sets, respectively. In case of both models, the daily values of aerodynamic component of ETo were reproduced more efficiently, compared to radiation part. In case of the FAO56-PM model, the goodness of agreement between 24h sum of subdaily and daily values of aerodynamic part of the ETo showed a low sensitivity to variation of the time scale of weather data. With the increase of the time scale of the subdaily weather data, the ability of both models in retrieving the radiation component of the daily ETo was improved. Generally, there was no apparent relationship between the efficiency of the ASCE-PM and FAO56-PM models for retrieving the daily ETo and the time scale of weather data. Results showed that adoption of a smaller time step does not always leads to an improvement in the agreement between 24h sum of subdaily and daily values of ETo. For most of the studied subdaily time scales (1 to 360 min), the FAO56-PM model had better performance in retrieving the daily ETo, compared to the ASCE-PM model. Conclusion: The results of this study showed that the developed disagregation framework was able to estimate the subdaily ET o. In this study, the promising results got in retrieving the daily ETo can be attributed mainly to the high sensitivity of ETo to the air temperature and actual vapor pressure (which were desegregated with a reasonable accuracy) and low sensitivity to the wind speed (which were desegregated with a low accuracy) and the solar radiation (which were disaggregated with a reasonable accuracy). The main reason for the absence of an apparent relationship apparent relationship between the efficiency of the ASCE-PM and FAO56-PM models for retrieving the daily ETo and the time scale of weather data can be attributed to adopted nighttime and daytime criteria in both models which is highly affected by time-scale of weather data and the estimated net long wave radiation.

The Penman-Monteith (PM) equation is a state-of-the-art modelling approach to simulate evapotranspiration (ET) at site and local scale. However, its practical application is often restricted by the availability and quality of required parameters. One of these parameters is the canopy conductance. Long term measurements of evapotranspiration by the eddy-covariance method provide an improved data basis to determine this parameter by inverse modelling. Because this approach may also include evaporation from the soil, not only the ‘actual’ canopy conductance but the whole surface conductance (gc$g_{c}$) is addressed. Two full cycles of crop rotation with five different crop types (winter barley, winter rape seed, winter wheat, silage maize, and spring barley) have been continuously monitored for 10 years. These data form the basis for this study. As estimates of gc$g_{c}$ are obtained on basis of measurements, we investigated the impact of measurements uncertainties on obtained values of gc$g_{c }$. Here, two different foci were inspected more in detail. Firstly, the effect of the energy balance closure gap (EBCG) on obtained values of gc$g_{c}$ was analysed. Secondly, the common hydrological practice to use vegetation height (hc$h_{c}$) to determine the period of highest plant activity (i.e., times with maximum gc$g_{c}$ concerning CO2-exchange and transpiration) was critically reviewed. The results showed that hc$h_{c}$ and gc$g_{c}$ do only agree at the beginning of the growing season but increasingly differ during the rest of the growing season. Thus, the utilisation of hc$h_{c}$ as a proxy to assess maximum gc$g_{c}$ (gc,max$g_{c,\text{max}}$) can lead to inaccurate estimates of gc,max$g_{c,\text{max}}$ which in turn can cause serious shortcomings in simulated ET. The light use efficiency (LUE) is superior to hc$h_{c}$ as a proxy to determine periods with maximum gc$g_{c}$. Based on this proxy, crop specific estimates of gc,max$g_{c,\text{max}}$ could be determined for the first (and the second) cycle of crop rotation: winter barley, 19.2 mm s−1 (16.0 mm s−1); winter rape seed, 12.3 mm s−1 (13.1 mm s−1); winter wheat, 16.5 mm s−1 (11.2 mm s−1); silage maize, 7.4 mm s−1 (8.5 mm s−1); and spring barley, 7.0 mm s−1 (6.2 mm s−1).

Accurate estimation of transpiration (Tr) is important in the development of precise irrigation scheduling and to enhance water-use efficiency in agricultural production. In this study, the air temperature (Ta) and relative humidity (RH) were measured at three different heights (0.5, 1.0, and 1.8 m above the ground near the plant canopy) parameterize aerodynamic resistance (ra) based on the heat transfer coefficient method and to estimate Tr using the Stanghellini model (SM) during two growing seasons of cucumber in a greenhouse. The canopy resistance (rc) was parameterized by an exponential relationship of stomata resistance and solar radiation, and the estimated Tr was compared to the values measured with lysimeters. After parameterization of ra and rc, the efficiency (EF) and the Root Mean Square Error (RMSE) of the estimated Tr by the SM based on micrometeorological data at a height of 0.5 m were 95% and 18 W m−2, respectively, while the corresponding values were 86% and 29 W m−2 at a height of 1.8 m for the autumn planting season. For the spring planting season, the EF and RMSE were 92% and 34 W m−2 at a height of 0.5 m, while the corresponding values were 81% and 56 W m−2 at a height of 1.8 m, respectively. This work demonstrated that when micrometeorological data within the canopy was applied alongside the data measured above the canopy, the SM led to better agreement with the lysimeter measurements.

Summary: Previous evapotranspiration (ET) partitioning studies have usually neglected competitions and interactions between antagonistic plant functional types. This study investigated whether shrubs and grasses have divergent ET partition dynamics impacted by different water‐use patterns, canopy structures, and physiological properties in a shrub‐encroached steppe ecosystem in Inner Mongolia, China. The soil water‐use patterns of shrubs and grasses have been quantified by an isotopic tracing approach and coupled into an improved multisource energy balance model to partition ET fluxes into soil evaporation, grass transpiration, and shrub transpiration. The mean fractional contributions to total ET were 24 ± 13%, 20 ± 4%, and 56 ± 16% for shrub transpiration, grass transpiration, and soil evaporation respectively during the growing season. Difference in ecohydrological connectivity and leaf development both contributed to divergent transpiration partitioning between shrubs and grasses. Shrub‐encroachment processes result in larger changes in the ET components than in total ET flux, which could be well explained by changes in canopy resistance, an ecosystem function dominated by the interaction of soil water‐use patterns and ecosystem structure. The analyses presented here highlight the crucial effects of vegetation structural changes on the processes of land–atmosphere interaction and climate feedback. [ABSTRACT FROM AUTHOR]

The single layer Penman-Monteith (PM) method is widely used method for the estimation of crop evapotranspiration (ET c ). The accuracy of ET c estimate relies upon the quality of input weather data and capacity to approach adequately canopy (r c ) and aerodynamic resistance (ra). In this study, the PM method was used to estimate daily crop evapotranspiration of irrigated maize for the years 2013 and 2014 in a sub-humid region. Four different approaches ( Monteith, Katerji-Perrier, Todorovic , and Jarvis ) were used to estimate canopy resistance and, then after, crop evapotranspiration by PM equation were evaluated. The comparison was made to daily crop evapotranspiration obtained from the soil water balance (SWB) and soil water content variation measured by time domain reflectometry (TDR) . The cumulative crop evapotranspiration of SWB , Monteith, Katerji-Perrier, Todorovic and Jarvis approach was respectively, 260.4, 266.8, 252.8, 263.4, 256 mm for the year 2013, and 250.5, 257.7, 240.6, 251.8, 247.6 mm for the year 2014. The comparison of results and the statistical analysis confirmed that Todorovic and Jarvis approach gave reliable values, while the Katerji-Perrier approach could be used as an alternative method. [ABSTRACT FROM AUTHOR]

Models for predicting hourly canopy resistance (rc) and latent heat flux (LET) based on the Penman-Monteith (PM) and bulk transfer methods are presented. The micrometeorological data and LET were observed during paddy rice-growing seasons in 2010 in Japan. One approach to model rc was using an aerodynamic resistance (ra) and climatic resistance (r*), while another one was based on a relationship with solar radiation (SR). Nonlinear relationships between rc and r*, and between rc and SR were found for different growing stages of the rice crop. The constructed rc models were integrated to the PM and bulk transfer methods and compared with measured LET using a Bowen ratio-energy balance method. The root mean square errors (RMSEs) were 155.2 and 170.5 W m−2 for the bulk transfer method with rc estimated using r* and with a function of SR, respectively, while the RMSEs were 87.4 and 85.7 W m−2 for the PM method with rc estimated using r* and SR, respectively. The rc integrated PM equation provided better performance than the bulk transfer equation. The results also revealed that neglecting the effect of ra on rc did not yield a significant difference in predicting LET. [ABSTRACT FROM AUTHOR]

Quantification of roughness effects on free surface flows is unquestionably necessary when describing water and material transport within ecosystems. The conventional hydrodynamic resistance formula empirically shows that the Darcy⁻Weisbach friction factor f~(r/hw)1/3 describes the energy loss of flowing water caused by small-scale roughness elements characterized by size r (<

A canopy-resistance-based Penman-Monteith (PM) model’s performance in estimating rice evapotranspiration (ET) under water-saving irrigation (WSI) condition at hourly and daily intervals was evaluated. To improve the performance of Jarvis-type canopy-resistance model in calculating rice ET under water-saving irrigation condition with Penman-Monteith (PM) model, a term of effective leaf area index was used to reflect the influence of canopy coverage condition, and field capacity was replace by saturated soil moisture (θ s ) in soil water response functions due to the specific field moisture condition in WSI rice fields. Two years of hourly rice ET measured using eddy covariance ( ET h EC ) with energy balance closure adjustment served to calibrate (2014 data) and validate (2015 data) a canopy-resistance-based PM model. Results indicate that the PM model’s output ( ET h sim ) with calibrated canopy resistance model parameters closely matched measured ET h EC , with a regression slope, intercept, coefficient of determination (R 2 ), root mean squared error (RMSE) and index of agreement (IOA) of 1.001, 0.010 mm h −1 , 0.942, 0.057 mm h −1 and 0.985, respectively for validation data set in 2015, better than the results calculated by model with parameters calibrated for general paddy field. The improved PM model performed well not only in a dense canopy condition ( LAI > 3) but also for a sparse canopy ( LAI < 3). The model also performed well both in sunny and cloudy days of each rice growth stages. A more detailed analysis indicated that ET h sim tended to overestimate or underestimate ET h EC in varied degrees at noon time or during the transition period from day to night. These differences between ET h sim and ET h EC associated with the variation of net radiation ( Rn ), soil heat flux ( G 0 ), and soil moisture ( θ ). Additionally, the PM model calibrated on hourly dataset, performed well in calculating the daily ET directly under WSI for 2014 ( R 2 = 0.973, RMSE = 0.237 mm d −1 , IOA = 0.993) and 2015 ( R 2 = 0.961, RMSE = 0.302 mm d −1 , IOA = 0.990). Therefore, the PM model calibrated using the hourly data was capable of accurately estimating both hourly and daily ET for rice under WSI condition. [ABSTRACT FROM AUTHOR]

Gravestone decay and atmospheric concentrations of SO 2 are used to determine deposition velocities in two adjacent cemeteries in the Birmingham, UK, Jewellery Quarter. Warstone Lane cemetery is essentially open to the environment with only a limited number of trees. Key Hill Cemetery, located within 100 m, has a continuous canopy of 100 + year-old London plane; gravestone decay at Key Hill is 50% less than at Lane for the period after 1960. This difference is used to calculate canopy resistance as a residual term assuming that aerodynamic and quasilaminar resistances are generally similar at both sites. Calculated resistances range from approximately 300 to 900 sm − 1 and are consistent with estimated and calculated values from a wide variety of studies. [ABSTRACT FROM AUTHOR]

Evaporative cooling due to transpiration of urban trees in two contrasting climates is the subject of this study. Transpiration was studied experimentally on local tree species at 'tree lab' sites in Munich, Germany (temperate climate) and in Beer Sheva, Israel (hot arid climate), within various settings (park, street, square) with natural and sealed surface conditions. The comparison was based on linear relationship of midday canopy resistance to atmospheric vapor pressure deficit (VPD), where the slope is proportional to tree hydraulic conductance and midday stem water potential. Sap flux densities in all trees were similar but crown projected area (CPA) was larger in Beer Sheva, resulting in higher hydraulic resistance that limits transpirational cooling (regression slopes of 0.25–0.44 in Beer Sheva vs. 0.10–0.18 in Munich). The contribution of the transpirational cooling, studied as the proportion of latent heat to total available energy was about 40% less significant at noon hours in summer in Beer Sheva than in Munich. Results suggested that shading should be the main consideration in planting local trees in hot dry climates. The linear relationship of midday canopy resistance to VPD is proposed as an operative tool for comparing evaporative cooling between local trees in different climatic regions. • The canopy resistance to VPD relationship quantified tree hydraulic resistance • Due larger crown projected area, water use per m2 area was lower in the arid city • Latent heat dissipation was ∼40% less in hot arid climate than in temperate climate • Transpirational cooling from trees is prominent in temperate climates • Shading should be the main consideration in planting trees in hot dry climates [ABSTRACT FROM AUTHOR]

Crop evapotranspiration ( ET ) is an important basis for irrigation management, and is often estimated using the Penman-Monteith model (P-M model). In the P-M model, the calculation of canopy resistance directly affects the accuracy of estimated ET . The field experiments about maize for seed production with film-mulching were conducted in 2013 and 2014 in the arid region of northwest China, and the measured canopy resistance ( r c PM ) was obtained by the re-arranged P-M model using the observed evapotranspiration by eddy covariance ( ET EC ) and meteorological data in 2013. The BP neural network method was used to analyze the sensitivity of r c PM to different affecting factors ( R n net radiation, T air temperature, VPD vapor pressure deficit, θ soil moisture content, LAI leaf area index) to determine the input factors in Jarvis model of canopy resistance (Jarvis model). Thus the estimated canopy resistance ( r cx ) in 2014 using the Jarvis model with the parameters fitted by the segmented method according to different LAI thresholds was applied into P-M model to estimate ET in 2014, and then the estimated ET was compared with ET EC to obtain the best segmented method based on LAI threshold. Results showed that the sensitivity of r c PM to different affecting factors was in the order of R n , LAI , θ , VPD and T , which were taken as the input factors of Jarvis model. Fitting the parameters in Jarvis model according to LAI thresholds can improve the accuracies of r cx and estimated ET , and the parameters in Jarvis model fitted by the segmented method using the LAI threshold of 0.5 m 2 m −2 can effectively improve the accuracy of ET estimation by P-M model in the whole growing period, with the determination coefficient ( R 2 ), root mean square error ( RMSE ), Akaike information criterion ( AIC ) and modified affinity index ( d ) between the estimated and observed ET of 0.83, 0.77 mm d −1 , −26.97 and 0.83, respectively. [ABSTRACT FROM AUTHOR]

Accurate estimates of plant transpiration (T sf) are essential to maximizing the efficient use of water. When only considering the surface canopy resistance (r c), the Penman-Monteith (PM) model is commonly used in T sf modeling. However, the r c is difficult to measure but it can be accurately estimated using the Jarvis canopy resistance model (JA). Our objectives were to evaluate the r c of apple trees calculated with an inverted PM model integrated with different versions of the JA model for different growth stages and to compare their accuracy using four optimization algorithms, the least squares method (LSM), genetic algorithm (GA), quantum particle swarm optimization (QPSO), and the mind evolutionary algorithm (MEA). We explored the effect of environmental constraint functions and parameter optimization of three environmental variables, vapor pressure deficit (VPD), net irradiance (R n), and air temperature (T a), and of soil water content (θ a) on the accuracy of JA sub-models to calculate apple tree r c and T sf. In our analysis, we used rainfed data from experiments on an apple orchard conducted during 2008–2010 at Wuwei City on the Loess Plateau of China. We compared 81 segmented JA sub-models (by canopy growth stage), comprising of the combination of the environmental constraint functions that were used to calculate r c. Moreover, the JA sub-models were optimized and results were compared to improve the accuracy of T sf estimates with five empirical r c models, combined with the PM model. The results showed that sub-model JA 3322 , i.e., the third constraint for VPD, the third constraint for R n , the second constraint for T a , and the second constraint for θ a attained the best estimate of r c with a coefficient of determination (R2 = 0.71), a Nash-Sutcliffe efficiency coefficient (NSE = 0.65), root mean squared error (RMSE = 1257.4 s m-1), and global performance indicator (GPI = 1.0) for the whole growth stage. The equivalent values for the partial canopy stage were 0.78, 0.72, 1203.3 s m-1 and 0.99, and the values for the dense canopy stage were 0.78, 0.77, 445.6 s m-1 and 0.97, respectively. Segmented JA models based on the leaf area index threshold significantly improved the accuracy of r c estimation, where the median R2 and NSE were improved by 7.1 % and 12.4 % in the partial canopy stage and by 12.2 % and 13.4 % in the dense canopy stage. Despite pointing out the best environmental constraint functions of the JA model in the different growth stages, results indicated that the MEA yielded the most accurate estimates of r c , followed by QPSO, GA, and LSM. Moreover, the JA model with environmental constraints was the most accurate method to estimate the apple tree T sf , and MEA was the most suitable parameter optimization algorithm. Overall, the findings of this study provide accurate actual water consumption information of apple trees using easily accessible meteorological data for the effective day-to-day water management decision-making of rain-fed apple tree orchards on the Loess Plateau of China previously. • Jarvis was superior to other empirical models for canopy resistance estimation. • Jarvis segmented by canopy growth stage gave a more accurate estimation. • Finding the most appropriate Jarvis model for apple tree canopy resistance. • Intelligence optimization algorithms improved the accuracy of the Jarvis models. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three types of r c models were embedded to single-source and dual-source ET models. • The r c coupled models optimized by GA performed better than original ET models. • The r c and ET were most sensitive to soil moisture and net radiation. • The SW-JA model performed best in modeling maize ET over the whole growing season. The reliability and accuracy of canopy resistance (r c) are crucial for the evapotranspiration (ET) estimation in sparsely vegetated ecosystems, but most studies are still limited to the single-source Penman-Monteith (PM) model. In this study, we coupled three types of r c models (three-way model: Jarvis, JA; two-way model: Kelliher-Leuning, KL; Farias, FA; one-way model: Katerji-Perrier, KP; Massman, MA) to the PM and Shuttleworth-Wallace (SW) models to assess their applicability and stability under single-source and dual-source hypotheses in a maize field in northwest China. The models were calibrated and validated against the observed data from a three-year (2011–2013) field experiment based on genetic algorithms (GA). The results indicated that the r c coupled models optimized by GA greatly alleviated the overestimation of the original PM and SW models with mean absolute error (MAE) decreased by 13.85%-40.77% and determination coefficient (R2) increased by 0.40%-7.37%. The SW-type models achieved overall high performance to simulate ET at a diurnal timescale over the entire crop season compared to the equivalent PM-type models. The three-way JA model including crop, soil and meteorological factors tended to perform more accurately than the other r c models, and the coupled SW-JA model was optimal for predicting maize ET over the entire growing season. The two-way KL model produced lower errors than the FA model, whereas the FA model had the potential to be improved due to the highest R2 with the field-based ET for both PM- and SW- hypotheses. In addition, the sensitivity analysis to environmental variables showed the effect of leaf area index (LAI) and vapor pressure deficit (VPD) was less on ET compared with net radiation (R n) and soil moisture (θ). This study contributes to the knowledge in the area of actual ET modeling over sparse vegetation, which provides a reference for agricultural and environmental applications. [ABSTRACT FROM AUTHOR]

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How to improve the reliability and accuracy of the single-layer Penman–Monteith (PM) model for estimating crop evapotranspiration (ET) over the entire growing season in arid regions, is still of great challenge for hydrologists. In the study, we developed a coupled surface resistance model (CO) after taking the combined restriction effect of vegetation and soil layers on ET into account. The CO model was compared with the modified Shuttleworth–Wallace model (MSW), and the traditional Jarvis, Katerji and Perrier, Irmak and Mutiibwa, Stannard, Leuning, Shuttleworth and Gurney, Massman, Garcia-Santos, Ortega-Farias, and Todorovic canopy resistance models over the partial and dense canopy stages. Maize and vineyard ET measured by the eddy covariance method during 2007–2013 were used to examine the model performance. Results indicate that the PM equation combined with the coupled surface resistance model yielded the lowest root mean square error against the other methods during all the years under either partial or dense canopy stages. Especially, the PM–CO method also performed superiorly against the dual-layer MSW model during the partial maize canopy period. After considering the meteorological, physiological and soil controls on surface resistance simultaneously, the coupled surface resistance model improved the accuracy significantly against the traditional canopy resistance models, and enhanced the reliability of the PM model for estimating partial canopy ET. Thus the coupled surface resistance equation integrated with PM model is recommended to estimate crop ET for the entire growth stages in arid regions. [ABSTRACT FROM AUTHOR]

Nowadays, eddy covariance is the state-of-the-art method to quantify turbulent exchange fluxes in the surface boundary layer. In the absence of instruments suitable for high-frequency measurements, fluxes can also be determined using e.g., chamber techniques. However, up to date fluxes of depositing compounds were rarely determined using chamber techniques, mainly due to a modification of the aerodynamic conditions for the trace gas transport within the chamber. In this study, we present ozone (O 3 ) deposition fluxes measured by the dynamic chamber technique and validate them against the eddy covariance (EC) method for a natural grassland site in Germany. The chamber system presented in Pape et al. (2009) was used and optimized to (i) reduce the likelihood of non-stationarities, (ii) yield 30 min averages of flux measurements, and (iii) supply simultaneous profile measurements. The raw O 3 fluxes of the dynamic chamber were corrected for gas-phase chemistry in the chamber volume and for the modification of the aerodynamic and boundary layer resistances. Simultaneously measured carbon dioxide and water vapor fluxes by both methods compared well during daytime documenting an equal vegetation activity inside and outside the chambers. The final corrected O 3 deposition fluxes of both methods deviated on average by only 11% during daytime. The findings demonstrate the capability of the dynamic chamber method to capture representative O 3 deposition fluxes for grassland ecosystems, even when the canopy height is similar to the chamber height. The canopy resistance to O 3 was assessed by both methods and showed a characteristic diurnal cycle with minimum hourly median values of 180 s m −1 (chambers) and 150 s m −1 (EC) before noon. During nighttime the fluxes and resistances showed a higher uncertainty for both methods due to frequent low wind associated with non-stationary conditions at the experimental site. Canopy resistances for nitrogen dioxide (NO 2 ) deposition were determined analogously with the chambers and were on average 86% higher than for O 3 . [ABSTRACT FROM AUTHOR]

• Twelve combination models of the Jarvis-type model were compared for canopy resistance estimation of alpine meadows. • Six-year eddy covariance data were used to evaluate the model performance. • The model 10 (M10) performed best for the alpine meadow. • Selection of appropriate stress functions is essential for canopy resistance modeling. Canopy resistance (r c) is a critical parameter for estimating vegetation transpiration. The site-specific r c can be calculated using the inversed Penman-Monteith (PM) equation with the effective leaf area index (LAI), which requires meteorological and turbulent flux data. The spatial distribution of r c is difficult to characterize due to the harsh environment of the Tibetan Plateau. The Jarvis-type model for modeling r c , described as a multiplicative function of environmental variables, has been widely used. However, the differences and optimization of different Jarvis-type models for alpine meadows have not been fully addressed. Consequently, our overall objective was to determine the appropriate functions for r c estimation and improve its accuracy for the alpine meadow ecosystem. Twelve Jarvis-type models composed of different stress functions were examined and compared with the observed r c calculated using PM equation at the Arou site in the northeastern Tibetan Plateau. The results suggest that the proper air temperature function and vapor pressure deficit function could improve model performance obviously. There was no obvious difference between the two different stress functions of downward shortwave radiation. The best model (M10), which was composed of an asymptotical function of downward shortwave radiation, a linear function of air temperature, an exponential function of vapor pressure deficit and a piecewise function of soil water content, had best performance with coefficient of determination of 0.93, root mean square error of 60.2 s m−1 and Nash-Sutcliffe efficiency coefficient of 0.92. The selection of proper stress functions is important for r c modeling. Models that considered the air temperature for r c calculations produced better results than those without temperature. The sensitivity analysis of r c to environmental variables indicated that r c was most sensitive to vapor pressure deficit, followed by LAI and downward shortwave radiation, whereas r c was less sensitive to soil water content. For all optimized parameters, r c was the most sensitive to k T (a fitting parameter for temperature), followed by k D (a fitting parameter for vapor pressure deficit) and r cmin (minimum r c under the optimal physiological condition). This study addresses the selection of proper stress functions in modeling r c for the alpine meadow site, which can also provide a reference for other ecosystems. [ABSTRACT FROM AUTHOR]

The aim of this work is to study the actual evapotranspiration and surface resistance of the savannah using the Bowen-ratio method for two contrasted periods, dry and rainy season. The reliability of this method has been assessed by comparison with the Monteith equation and the soil-water balance method in a 90% Loudetia arundinacea dominated savannah (Pointe Noire, Congo). Our results relate to the period from 18 September to 11 October 1998 (24 days): (a) from 18 to 29 September ('dry season'), the soil-water content was less than 70% of the soil-water content at field capacity (63-70% of R; large soil-water stress; T/ E from 0.2 to 0.4); (b) from 30 September to 11 October ('rainy season') soil-water content close to 90-92% of RFC; no soil-water stress; T/ E from 0.73 to 0.77). The mean daily surface resistance reulting from the Bowen-ratio method was 317 s m, 355 s m during the 'dry season' and 279 s m during the 'rainy season'. The total actual evapotranspiration ( E) resulting from the Bowen-ratio method, Penman-Monteith equation and soil-water balance method were, respectively of 58.6-57.8 and 56.2 mm, with the mean daily E of 2.4-2.4 and 2.3 mm day (2.4-1.5 and 2.2 mm day in 'dry season' and of 2.5-3.4 and 2.5 mm day in 'rainy season'). The Bowen-ratio method was used for the assessment of the actual evapotranspiration from the temperature and specific humidity differences, net radiation and the soil heat flux measurement: its advantages are a rapidity of installation, a temporal resolution of measurement in less than one hour and a good integration of the heterogeneousness of the savannah's latent flux of vaporization. This method is adapted to eco-physiological studies in tropical conditions with reduced teams. [ABSTRACT FROM AUTHOR]

Canopy resistance substantially affects the partitioning of available energy over vegetated surfaces. This study analyzed the variability of canopy resistance and associated driving environmental factors over a desert steppe site in Inner Mongolia, China, through the use of eddy-flux and meteorological data collected from 2008 to 2010. Distinct seasonal and interannual variabilities in canopy resistance were identified within those three years, and these variabilities were controlled primarily by precipitation. Strong interannual variability was found in vapor pressure deficit (VPD), similar to that of air temperature. Based on the principal component regression method, the analysis of the relative contribution of five major environmental factors [soil-water content (SWC), leaf-area index (LAI), photosynthetically active radiation ( K), VPD, and air temperature] to canopy resistance showed that the canopy-resistance variation was most responsive to SWC (with > 35% contribution), followed by LAI, especially for water-stressed soil conditions (> 20% influence), and VPD (consistently with an influence of approximately 20%). Canopy-resistance variations did not respond to K due to the small interannual variability in K during the three years. These analyses were used to develop a new exponential function of water stress for the widely used Jarvis scheme, which substantially improved the calculation of canopy resistance and latent heat fluxes, especially for moist and wet soils, and effectively reduced the high bias in evaporation estimated by the original Jarvis scheme. This study highlighted the important control of canopy resistance on plant evaporation and growth for the investigated desert steppe site with a relatively low LAI. [ABSTRACT FROM AUTHOR]

Highlights: [•] We investigated the effect of wind velocity on transpiration of mixed-species broadleaved canopy. [•] As wind velocity increased (∼1–5ms−1), so did vapor pressure deficit (∼1–2.5kPa). [•] Sap flux density and stand transpiration responded weakly to none to wind velocity. [•] Lowered aerodynamic resistance (<15% of total) was offset by higher canopy resistance. [•] Canopy resistance followed hydraulically predicted changes of stomatal resistance. [Copyright &y& Elsevier]

Thus far, measurements and estimations of actual evapotranspiration (ET) from high-altitude grassland ecosystems in remote areas like the Qinghai-Tibetan plateau are still insufficient. To address these issues, a comparison between the results of the eddy covariance (EC) measurements and the estimates, considering the Katerji and Perrier (KP), the Todorovic (TD) and the Priestley-Taylor (PT) models, was carried out over an alpine grassland (38o03'1.7'' N, 100o 27' 26'' E; 3032 m a.s.l.) during the growing seasons in 2008 and 2009. The results indicated that the KP model after a particularly simple calibration gave the most effective ET values in different time scales, the PT model slightly underestimate ET at night and the TD model significantly overestimated ET at noon. In addition, the canopy resistance calculated by the TD model was completely different from that calculated using the inverted EC-measured data and the KP model, which may be due to some unrealistic assumptions made by the TD model. The KP parameters were a = 0.17 and b = 1.50 for the alpine grassland and appeared to be interannually stable. However, the PT parameter showed some interannual variations ( α = 0.83 and 0.74 for 2008 and 2009, respectively). Therefore, the KP model was preferred to estimate the actual ET at both hourly and daily time scales. The PT model, being the simplest approach and field condition dependent, was recommended when available weather data were rare. On the contrary, the TD model always overestimated the actual ET and should be avoided in case of the alpine grassland ecosystems. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]