Estimation of Evapotranspiration of Amazon Rainforest Using the Maximum Entropy Production Method

Xu, Donghui; Agee, Elizabeth; Wang, Jingfeng; Ivanov, V. Y.

doi:10.1029/2018gl080907

Cited by 44 publications

(29 citation statements)

References 78 publications

(158 reference statements)

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“…Regarding the annual mean totals, the highest rainfall was found in the Amazon compared to the other biomes with an annual mean around 1847.9 mm year −1 . Annual rainfall totals identified in the Amazon biome are maintained by ITCZ in NNEB (Alves et al, 2017;Araújo et al, 2009;Reboita & Santos, 2015; and still associated with the process of evapotranspiration (ET) of the biome that contributes to the release of water vapor in the atmosphere and, consequently, causing the formation of rain-producing clouds (Fisch, Marengo, & Nobre, 1998;Salati, Dall'Olio, Matsui, & Gat, 1979;Xu, Agee, Wang, & Ivanov, 2019).…”

Section: Interannual Rainfall Variability In Neb Biomesmentioning

confidence: 99%

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

et al. 2019

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Brazilian biomes are home to a significant portion of the world's biodiversity, with a total of 14% of existing species and still concentrate 20% of the world's water resources. However, changes in biomes have a direct impact on rainfall patterns and water recycling. Based on this, the objective was to evaluate the variability of rainfall in the four existing biomes in the Northeast Brazil (NEB) and their interaction with the ENSO climate variability mode and regional scale meteorological systems via CHELSA product. For this, monthly rainfall data were used from 1979 to 2013, with a spatial resolution of 1 km × 1 km of the CHELSA product, and seasonal and annual rainfall patterns were extracted via boxplot. It was found that the rainy season in the Amazon, Caatinga and Cerrado biomes occurred between January and April, with varying intensities, except for the Atlantic Forest. Such seasonality patterns are associated with the NEB meteorological systems, with emphasis on ITCZ (all Biomes), UTCV (Amazon, Caatinga and Cerrado), Frontal Systems (extreme south of Caatinga, Cerrado and Atlantic Forest) and EWD/ TWD in the (Atlantic Forest). In the inter-annual scale, the remarkable influence of ENSO was verified,

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Section: Interannual Rainfall Variability In Neb Biomesmentioning

confidence: 99%

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

et al. 2019

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“…2019; Stoy et al, 2019;Sun et al, 2017). Theoretical advancements in nonequilibrium thermodynamics and Maximum Entropy Production (MEP) could be incorporated into the classical ET theories (Xu et al, 2019;Zhang et al, 2016a). In addition, quantifying the effects of CO2 fertilization on stomatal conductance is pivotal for remote sensing models to capture the longterm trend of terrestrial ET.…”

Section: Ignorance Of the Effects Of Irrigationmentioning

confidence: 99%

Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

Pan¹,

Pan²,

Tian³

et al. 2019

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Abstract. Evapotranspiration (ET) is a critical component in global water cycle and links terrestrial water, carbon and energy cycles. Accurate estimate of terrestrial ET is important for hydrological, meteorological, and agricultural research and applications, such as quantifying surface energy and water budgets, weather forecasting, and scheduling of irrigation. However, direct measurement of global terrestrial ET is not feasible. Here, we first gave a retrospective introduction to the basic theory and recent developments of state-of-the-art approaches for estimating global terrestrial ET, including remote sensing-based physical models, machine learning algorithms and land surface models (LSMs). Then, we utilized six remote sensing-based models (including four physical models and two machine learning algorithms) and fourteen LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the mean annual global terrestrial ET ranged from 50.7 × 103 km3 yr−1（454 mm yr−1）to 75.7 × 103 km3 yr−1 (6977 mm yr−1), with the average being 65.5 × 103 km3 yr−1 (588 mm yr−1), during 1982–2011. LSMs had significant uncertainty in the ET magnitude in tropical regions especially the Amazon Basin, while remote sensing-based ET products showed larger inter-model range in arid and semi-arid regions than LSMs. LSMs and remote sensing-based physical models presented much larger inter-annual variability (IAV) of ET than machine learning algorithms in southwestern U.S. and the Southern Hemisphere, particularly in Australia. LSMs suggested stronger control of precipitation on ET IAV than remote sensing-based models. The ensemble remote sensing-based physical models and machine-learning algorithm suggested significant increasing trends in global terrestrial ET at the rate of 0.62 mm yr−2 (p 0.05), even though most of the individual LSMs reproduced the increasing trend. Moreover, all models suggested a positive effect of vegetation greening on ET intensification. Spatially, all methods showed that ET significantly increased in western and southern Africa, western India and northeastern Australia, but decreased severely in southwestern U.S., southern South America and Mongolia. Discrepancies in ET trend mainly appeared in tropical regions like the Amazon Basin. The ensemble means of the three ET categories showed generally good consistency, however, considerable uncertainties still exist in both the temporal and spatial variations in global ET estimates. The uncertainties were induced by multiple factors, including parameterization of land processes, meteorological forcing, lack of in situ measurements, remote sensing acquisition and scaling effects. Improvements in the representation of water stress and canopy dynamics are essentially needed to reduce uncertainty in LSM-simulated ET. Utilization of latest satellite sensors and deep learning methods, theoretical advancements in nonequilibrium thermodynamics, and application of integrated methods that fuse different ET estimates or relevant key biophysical variables will improve the accuracy of remote sensing-based models.

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“…The MEP model holds for full range of soil moisture and snow/ice surfaces. The MEP model has been shown to outperform other existing models (Hajji et al, ; Huang & Wang, ; Shanafield et al, ; Wang et al, ; Wang et al, ; Wang & Bras, ; Xu et al, ; Yang & Wang, ). In this study, the MEP model is utilized with two objectives: (i) to evaluate the surface energy budgets across a range of vegetation covers in the Arctic permafrost areas and (ii) to confirm the MEP model as an efficient and robust model of potential evapotranspiration (PET).…”

Section: Introductionmentioning

confidence: 98%

Surface Energy Budgets of Arctic Tundra During Growing Season

et al. 2019

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This study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.

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Estimation of Evapotranspiration of Amazon Rainforest Using the Maximum Entropy Production Method

Cited by 44 publications

References 78 publications

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

Rainfall variability in the Brazilian northeast biomes and their interactions with meteorological systems and ENSO via CHELSA product

Evaluation of global terrestrial evapotranspiration by state-of-the-art approaches in remote sensing, machine learning, and land surface models

Surface Energy Budgets of Arctic Tundra During Growing Season

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