Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate

Valayamkunnath, Prasanth; Sridhar, Venkataramana; Zhao, Wei; Allen, Richard G.

doi:10.1016/j.agrformet.2017.08.025

Cited by 39 publications

(23 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the measured data sets, net radiation generally have less uncertainties and are more accurate than turbulent fluxes, especially under the conditions of rain, strong turbulence, and heterogeneous topography Journal of Geophysical Research: Atmospheres (Barr et al, 2012;Cava et al, 2008;Foken, 2008;Foken et al, 2006;Foken et al, 2010;Gao et al, 2010;Huang & Wang, 2016;Oren et al, 2006;Twine et al, 2000;Valayamkunnath et al, 2018). LE EC often has large measurement errors compared to SH EC (Berbigier et al, 2001;Nordbo et al, 2012).…”

Section: The Assessment Of Surface Energy Balance For the Tipex-iii Dmentioning

confidence: 99%

Estimation of Surface Heat Fluxes Over the Central Tibetan Plateau using the Maximum Entropy Production Model

Zhao

Wang

et al. 2019

JGR Atmospheres

View full text Add to dashboard Cite

Surface heat fluxes over the central Tibetan Plateau have been estimated using the maximum entropy production (MEP) model with the surface energy balance and the observation data from the Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX‐III). The MEP surface heat fluxes are highly correlated with those of the TIPEX‐III observations. The agreement between the MEP and TIPEX‐III heat fluxes is higher when the observed surface energy balance closure is better. The errors of the MEP heat fluxes are smaller compared to those of the fluxes derived from the bulk transfer method, the Land Data Assimilation Systems, and the Simple Biosphere Model version 2 reported in the previous studies. The values of MEP sensible and latent heat fluxes tend to be less than those of the previous bulk heat fluxes. Thus, the MEP model can reasonably estimate surface heat fluxes over the central Tibetan Plateau.

show abstract

Section: The Assessment Of Surface Energy Balance For the Tipex-iii Dmentioning

confidence: 99%

Estimation of Surface Heat Fluxes Over the Central Tibetan Plateau using the Maximum Entropy Production Model

Zhao

Wang

et al. 2019

JGR Atmospheres

View full text Add to dashboard Cite

show abstract

“…Quantifying ET a from agricultural lands is vital to the management of water resources. Measurement methods of ET a are available through water vapor transfer methods (e.g., Bowen ratio, eddy covariance) [4][5][6], water budget measurements (e.g., soil water balance, weighting lysimeters) [7][8][9], or remote sensing techniques [10][11][12]. However, the application of field scale methods is limited due to cost, complexity, and maintenance.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches

2021

View full text Add to dashboard Cite

Evapotranspiration (ET) is widely employed to measure amounts of total water loss between land and atmosphere due to its major contribution to water balance on both regional and global scales. Considering challenges to quantifying nonlinear ET processes, machine learning (ML) techniques have been increasingly utilized to estimate ET due to their powerful advantage of capturing complex nonlinear structures and characteristics. However, limited studies have been conducted in subhumid climates to simulate local and spatial ETo using common ML methods. The current study aims to present a methodology that exempts local data in ETo simulation. The present study, therefore, seeks to estimate and compare reference ET (ETo) using four common ML methods with local and spatial approaches based on continuous 17-year daily climate data from six weather stations across the Red River Valley with subhumid climate. The four ML models have included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF) with three input combinations of maximum and minimum air temperature-based (Tmax, Tmin), mass transfer-based (Tmax, Tmin, U: wind speed), and radiation-based (Rs: solar radiation, Tmax, Tmin) measurements. The estimates yielded by the four ML models were compared against each other by considering spatial and local approaches and four statistical indicators; namely, the root means square error (RMSE), the mean absolute error (MAE), correlation coefficient (r2), and scatter index (SI), which were used to assess the ML model’s performance. The comparison between combinations showed the lowest SI and RMSE values for the RF model with the radiation-based combination. Furthermore, the RF model showed the best performance for all combinations among the four defined models either spatially or locally. In general, the LR, GEP, and SVM models were improved when a local approach was used. The results showed the best performance for the radiation-based combination and the RF model with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among the models and approaches.

show abstract

“…Valayamkunnath et al . (2018) noted that the spatial heterogeneities in the available energy, topography, soil moisture conditions, soil type, surface emissivity and vegetation are the major factors that influence surface energy partitioning. A comparison of these values with the elevation map indicates that the elevation corresponds somewhat to the land surface energy fluxes, with the exception of glacial and snowy areas.…”

Section: Resultsmentioning

confidence: 99%

Estimation of surface energy fluxes in the permafrost region of the Tibetan Plateau based on in situ measurements and the surface energy balance system model

Yao

Cheng

et al. 2020

Intl Journal of Climatology

View full text Add to dashboard Cite

The surface energy balance is a key issue in land surface process research and important for studies of climate and hydrology. In this paper, the surface energy fluxes (net radiation, ground heat flux, sensible heat flux and latent heat flux) at the Tanggula (TGL) and Xidatan (XDT) sites were measured and the distributions of the regional surface energy fluxes on the Tibetan Plateau were obtained using a revised surface energy balance system (SEBS) model. The results show that the surface energy fluxes have obvious seasonal variations. At both sites, the sensible heat flux is highest in spring and lowest in summer, and the latent heat flux is highest in summer and lowest in winter. The high elevation, snow cover, freeze–thaw process, precipitation, vegetation and soil texture are important influencing factors for land surface energy fluxes. The time‐phase difference between the net radiation and ground heat flux for bare soils is estimated to be 2–3 hr. The ratio of ground heat flux and net radiation ranged from approximately 0.18 to 0.33, and a parameterization scheme for the remote sensing of ground heat flux over the Tibetan Plateau bare soil in summer is developed. The simulation results of the regional surface energy fluxes show that the distributions of surface parameters, such as vegetation, soil texture and soil moisture content, are important for understanding regional changes in the surface energy fluxes.

show abstract

Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate

Cited by 39 publications

References 58 publications

Estimation of Surface Heat Fluxes Over the Central Tibetan Plateau using the Maximum Entropy Production Model

Estimation of Surface Heat Fluxes Over the Central Tibetan Plateau using the Maximum Entropy Production Model

Estimation of Reference Evapotranspiration Using Spatial and Temporal Machine Learning Approaches

Estimation of surface energy fluxes in the permafrost region of the Tibetan Plateau based on in situ measurements and the surface energy balance system model

Contact Info

Product

Resources

About