Quantifying Scaling Effect on Gross Primary Productivity Estimation in the Upscaling Process of Surface Heterogeneity

Xie, Xinyao; Li, Ainong; Chen, Jing M.; Guan, Xiaobin; Leng, Jiye

doi:10.1029/2021jg006775

Cited by 8 publications

(7 citation statements)

References 81 publications

(120 reference statements)

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“…Different from these ESMs, BEPS‐TerrainLab assigns various biophysical parameters at the pixel level, which is an effective tool to obtain fine spatial resolution estimates (e.g., 30 m). The BEPS‐TerrainLab model has been used by many investigators as an effective tool in analyzing vegetation productivity in mountain ecosystems (Chen et al., 2013; Govind et al., 2011; Sonnentag et al., 2008; Xie et al., 2021, 2022, 2023). Over the past decades, BEPS has also been successfully employed to obtain carbon and water fluxes at the national‐scale (Y. Liu et al., 2014; Zhou et al., 2007), continental‐scale (Matsushita & Tamura, 2002; Sprintsin et al., 2012), and the global scale (Chen et al., 2012, 2019; L. M. He et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Xie et al. (2022) suggested that different climatic characteristics might lead to different levels of water and radiation redistributions. To capture the combined effect of climatic factors and topography on GPP estimation, TCI clim is modeled as:

\mathrm{T}\mathrm{C}{\mathrm{I}}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}={f}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}\left(\mathrm{Pr}{\mathrm{e}}_{\mathrm{g}\mathrm{s}},\mathrm{R}\mathrm{a}{\mathrm{d}}_{\mathrm{g}\mathrm{s}}\right)

where f clim (*) is a function of the total precipitation (Pre gs ) and average daily radiation (Rad gs ) during growing season.…”

Section: Methodsmentioning

confidence: 99%

“…Then, to capture the different topographical effects on GPP estimation across climatic factors, the total precipitation and average daily radiation during the growing season were also used to model TCI clim . Xie et al (2022) suggested that different climatic characteristics might lead to different levels of water and radiation redistributions. For example, a higher precipitation during the growing season would cause a larger topography-induced redistribution of water, which finally lead to different topographical effects on vegetation photosynthesis.…”

Section: Reasonability Of the Proposed Tci Algorithmmentioning

confidence: 99%

“…In this study, the R LSZ was first calculated at the daily scale, and then averaged to the annual value. Xie et al (2022) suggested that different climatic characteristics might lead to different levels of water and radiation redistributions. To capture the combined effect of climatic factors and topography on GPP estimation, TCI clim is modeled as:…”

Section: 1029/2023jg007553mentioning

confidence: 99%

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A Practical Algorithm for Correcting Topographical Effects on Global GPP Products

et al. 2023

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Vegetation in mountainous areas contributes about 36% to the global gross primary productivity (GPP). However, the influences of topography on radiation and water redistributions in mountain ecosystems are so far ignored in existing global GPP data sets. Here, an eco‐hydrological model was adopted to simulate 30 m resolution mountain and flat GPP over 16 watersheds. Then, a topographical correction index (TCI) was developed based on simulated soil water redistribution (TCIwater), radiation redistribution (TCIrad), and redistribution of climate factors (TCIclim). Finally, the proposed TCI was applied to four GPP data sets. The mean‐bias‐error (MBE), determination coefficient (R2), and Root‐Mean‐Square‐Error (RMSE) between mountain GPP and flat GPP (or GPP data sets) were used for evaluation. Results showed that the MBE of flat GPP before correction (194 g C m−2 yr−1) was reduced to 126, 94, and 2 g C m−2 yr−1 after the corrections of TCIwater, TCIrad, and TCIclim, highlighting the effectiveness of integrated redistribution information in correcting the topographical effect on GPP estimation. The relationship between mountain and flat GPP after the TCI correction was improved at the 30 m resolution (increasing R2 by 0.09 and reducing RMSE by 90 g C m−2 yr−1) and 480 m resolution (increasing R2 by 0.13 and reducing RMSE by 178 g C m−2 yr−1). Regarding the four GPP data sets after the TCI correction, the MBE of 183 g C m−2 yr−1 was averagely reduced to 17 g C m−2 yr−1, and RMSE was reduced by 118 g C m−2 yr−1 at 480 m resolution. This study suggests that integrating topography‐induced interactions into current GPP data sets is a feasible way to understand the carbon budget in mountain ecosystems.

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Section: Methodsmentioning

confidence: 99%

\mathrm{T}\mathrm{C}{\mathrm{I}}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}={f}_{\mathrm{c}\mathrm{l}\mathrm{i}\mathrm{m}}\left(\mathrm{Pr}{\mathrm{e}}_{\mathrm{g}\mathrm{s}},\mathrm{R}\mathrm{a}{\mathrm{d}}_{\mathrm{g}\mathrm{s}}\right)

where f clim (*) is a function of the total precipitation (Pre gs ) and average daily radiation (Rad gs ) during growing season.…”

Section: Methodsmentioning

confidence: 99%

Section: Reasonability Of the Proposed Tci Algorithmmentioning

confidence: 99%

Section: 1029/2023jg007553mentioning

confidence: 99%

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A Practical Algorithm for Correcting Topographical Effects on Global GPP Products

et al. 2023

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“…Wu et al (2022) evaluated bottom-up scaling across multiple products for global soil nitrous acid (HONO) emissions. Levy et al (2022) reviewed the key challenges for upscaling when it comes to the UK greenhouse gas program and finds an essential role for uncertainty propagation, a factor also evaluated for gross primary productivity upscaling by Xie et al (2022). For comparing to top-down measurements, such as flux towers to satellites, source areas and (non-)linearities in downscaling need to be taken into account, whether that is for carbon emissions in a salt marsh (Hill & Vargas, 2022), hotspots of methane in eddy covariance flux tower footprints (Rey-Sanchez et al, 2022), or land surface temperature over heterogeneous landscapes (Desai, Khan, et al, 2021).…”

Section: A Scale For All Silosmentioning

confidence: 99%

Scaling Land‐Atmosphere Interactions: Special or Fundamental?

et al. 2022

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Viewed from billions of kilometers away in space, Earth appears as a single "Pale Blue Dot," in the immortalized phrase of Carl Sagan bestowed upon the image taken by the Voyager 1 space probe. Coming closer, though, a sharper image emerges (Figure 1).One finds structure to that dot, shades of green and brown continents, a dark ocean, a bright cryosphere, and a hazy, thin blue atmosphere. Zooming further in, those components break into patterns of mountains and rivers, seas and bays, forests and grasslands, layers, and cloud decks. And getting closer, one finds each component has oscillations and variations of branches and rivulets, canyons and plateaus, currents and coastlines. These objects keep revealing more structure in finer, often self-similar form, like Mandelbrot's fractals, down to eddies and organisms, and further into leaves, cells, enzymes, molecules, and atoms.And then, if you wait seconds, days, decades, or eons, landscape patterns change. Bigger things typically take longer than smaller ones. As a result, the pattern changes-sometimes occurring slowly and subtly, ebbing and flowing in an oscillatory manner, or they can occur quickly and abruptly, morphing into a new state of order.Each element and its dynamics come with variations in space and time that can be encompassed by the concept of scale. Earth systems science is preoccupied with the interactions of these elements, which cannot be understood without a stipulation of the scales of interest (Ge et al., 2019). The most straightforward of these interactions are ones where common processes at all scales can be defined by a single relationship, often a power-law, leading to the concept of scale invariance (Paleri et al., 2022). The most interesting interactions are the ones that break those rules and lead to "upscale" and "downscale" behavior, whereby processes at one scale determine the shape and function of another scale. These are most common at the intersections of biology, hydrology, geology, and meteorology, often within what is termed the "critical zone." This interlocking also harkens to the origins of

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Widespread mismatch between satellite observed vegetation greenness and temperature isolines during 2000–2020 in China

Zhang

et al. 2023

Ecological Indicators

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Quantifying Scaling Effect on Gross Primary Productivity Estimation in the Upscaling Process of Surface Heterogeneity

Cited by 8 publications

References 81 publications

A Practical Algorithm for Correcting Topographical Effects on Global GPP Products

A Practical Algorithm for Correcting Topographical Effects on Global GPP Products

Scaling Land‐Atmosphere Interactions: Special or Fundamental?

Widespread mismatch between satellite observed vegetation greenness and temperature isolines during 2000–2020 in China

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