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Estimation of evapotranspiration (ET) for alpine meadow areas in the Tibetan Plateau (TP) is essential for water resource management. However, observation data has been limited due to the extreme climates and complex terrain of this region. To address these issues, four representative methods, Penman-Monteith (PM), Priestley-Taylor (PT), Hargreaves-Samani (HS), and Mahringer (MG) methods, were adopted to estimate ET, which were then compared with ET measured using Eddy Covariance (EC) for five alpine meadow sites during the growing seasons from 2010 to 2014. And each site was measured for one growing season during this period. The results demonstrate that the PT method outperformed at all sites with a coefficient of determination (R2) ranging from 0.76 to 0.94 and root mean square error (RMSE) ranging from 0.41 to 0.62 mm d-1. The PM method showed better performance than HS and MG methods, and the HS method produced relatively acceptable results with higher R2 (0.46) and lower RMSE (0.89 mm d-1) compared to MG method with R2 of 0.16 and RMSE of 1.62 mm d-1, while MG underestimated ET at all alpine meadow sites. Therefore, the PT method, being the simpler approach and less data dependent, is recommended to estimate ET for alpine meadow areas in the Tibetan Plateau. The PM method produced reliable results when available data were sufficient, and the HS method proved to be a complementary method when variables were insufficient. On the contrary, the MG method always underestimated ET and is, thus, not suitable for alpine meadows. These results provide a basis for estimating ET on the Tibetan Plateau for annual data collection, analysis, and future studies. [ABSTRACT FROM AUTHOR]

Diurnal variation of land surface temperature (LST) is essential for land surface energy and water balance at regional or global scale. Diurnal temperature cycle (DTC) model with least parameters and high accuracy is the key issue in estimating the spatial–temporal variation of DTC. The alpine meadow is the main land cover in the Tibetan Plateau (TP). However, few studies have been reported on the performance of different DTC models over alpine meadows in the TP. Four semi-empirical types of DTC models were used to generate nine 4-parameter (4-para) models by fixing some of free parameters. The performance of the nine 4-para DTC models were evaluated with four in situ and MODIS observations. All models except GOT09-dT-ts (dT means the temperature residual between T0 and T (t→∞); ts means the time when free attenuation begins) had higher correlation with in situ data (R2 > 0.9), while the INA08-ts model performed best with NSE of 0.99 and RMSE of 2.04 K at all sites. The GOT09-ts-τ (τ is the total optical thickness), VAN06-ts-ω1 (ω1 means the half-width of the cosine term in the morning), and GOT01-ts models had better performance, followed by GOT09-dT-τ, GOT01-dT, and VAN06-ts-ω2 (ω2 means the half-width of the cosine term in the afternoon) models. All models had higher accuracy in summer than in other seasons, while poorer performance was produced in winter. The INA08-ts model showed best performance among all seasons. Models with fixing ts could produce higher accuracy results than that with fixing dT. The comparison of INA08-ts model driven by in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) data indicated that the simulation accuracy mainly depended on the accuracy of MODIS LST. The daily maximum temperature generated by the nine models had high accuracy when compared with in situ data. The sensitivity analysis indicated that the INA08-dT and GOT09-dT-ts models were more sensitive to parameter dT, while all models were insensitive to parameter ts, and all models had weak relationship with parameters ω and τ. This study provides a reference for exploring suitable DTC model in the TP. [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 long-term change of evapotranspiration (ET) is crucial for managing water resources in areas with extreme climates, such as the Tibetan Plateau (TP). This study proposed a modified algorithm for estimating ET based on the MOD16 algorithm on a global scale over alpine meadow on the TP in China. Wind speed and vegetation height were integrated to estimate aerodynamic resistance, while the temperature and moisture constraints for stomatal conductance were revised based on the technique proposed by Fisher et al. (2008). Moreover, Fisher’s method for soil evaporation was adopted to reduce the uncertainty in soil evaporation estimation. Five representative alpine meadow sites on the TP were selected to investigate the performance of the modified algorithm. Comparisons were made between the ET observed using the Eddy Covariance (EC) and estimated using both the original and modified algorithms. The results revealed that the modified algorithm performed better than the original MOD16 algorithm with the coefficient of determination (R 2 ) increasing from 0.26 to 0.68, and root mean square error (RMSE) decreasing from 1.56 to 0.78 mm d −1 . The modified algorithm performed slightly better with a higher R 2 (0.70) and lower RMSE (0.61 mm d −1 ) for after-precipitation days than for non-precipitation days at Suli site. Contrarily, better results were obtained for non-precipitation days than for after-precipitation days at Arou, Tanggula, and Hulugou sites, indicating that the modified algorithm may be more suitable for estimating ET for non-precipitation days with higher accuracy than for after-precipitation days, which had large observation errors. The comparisons between the modified algorithm and two mainstream methods suggested that the modified algorithm could produce high accuracy ET over the alpine meadow sites on the TP. [ABSTRACT FROM AUTHOR]

• A consecutive 6 year in-situ evapotranspiration dataset was constructed using eddy covariance. • The seasonal variation of ET was analyzed for the typical alpine meadow from 2014 to 2019. • ET was primarily controlled by the available energy in the typical alpine meadow. • Soil water content affected ET via bulk surface conductance within a certain range. To understand the water, energy, and carbon cycles in the Tibetan Plateau (TP), it is essential to estimate seasonal and inter-annual variations in energy fluxes and evapotranspiration (ET) for alpine meadow ecosystems. The multiyear (2014–2019) energy fluxes and ET, for a typical alpine meadow at Arou station (northeastern TP), and their environmental and biophysical controls were evaluated using the eddy covariance method in this study. Latent heat flux (LE) was the dominant component of energy consumption during the growing season, whereas sensible heat flux (H) dominated energy partitioning during the non-growing season. H showed the opposite trend to LE, while the seasonal variation of soil heat flux (G) was small. The daily ET was primarily controlled by the available energy on the seasonal scale. Soil water content (SWC) and normalized difference vegetation index (NDVI) displayed secondary effects on ET during the non-growing and growing seasons, respectively. The inter-annual ET was relatively stable, ranging from 562.6 to 661.9 mm (coefficient of variation; CV = 7.4 %); this was slightly higher than the annual precipitation despite large variations in inter-annual precipitation (CV = 19.9 %) and was most likely due to snow and frozen ground melting. The cumulative ET in the growing season was about 77 % of the annual ET. There was a nonlinear increase in the daily Priestley–Taylor coefficient (α = ET/ET eq , where ET eq is the equilibrium evaporation) with an increase in bulk surface conductance (g c), which was insensitive to increases in g c that exceeded 15 mm s−1. There was a good relationship between g c and NDVI. This study provides insights into the driving mechanisms of long-term variations in the energy partitioning and biophysical controls on ET in alpine meadow ecosystems. [ABSTRACT FROM AUTHOR]