Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015

Sun, Zhongyi; Wang, Xiufeng; Yamamoto, Haruhiko; Tani, Hiroshi; Zhong, Guosheng; Yin, Shuai; Guo, Enliang

doi:10.1016/j.ecoinf.2018.06.006

Cited by 56 publications

(44 citation statements)

References 61 publications

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“…Our analyses estimate increases in both LAI and GPP for much of Brazil, but declines in water-limited regions (e.g. Caatinga) (Figure A8, A9), consistent with other studies using alternate methodologies (He et al, 2017;Jung et al, 2020;Sun et al, 2018;Zhang et al, 2019).…”

Section: Does Increasing Model Complexity Improve Agreement With Evaluation Information?supporting

confidence: 90%

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

Smallman¹,

Milodowski²,

Sousa‐Neto³

et al. 2021

Preprint

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Abstract. Identification of terrestrial carbon (C) sources and sinks is critical for understanding the earth system and to mitigate and adapt to climate change results from greenhouse gas emissions. Predicting whether a given location will act as a C source or sink using terrestrial ecosystem models (TEMs) is challenging due to net flux being the difference between far larger, spatially and temporally variable fluxes with large uncertainties. Uncertainty in projections of future dynamics, critical for policy evaluation, has been determined using multi-TEM intercomparisons, for various emissions scenarios. This approach quantifies structural and forcing errors. However, the role of parameter error within models has not been determined. TEMs typically have defined parameters for specific plant functional types generated from the literature. To ascertain the importance of parameter error in forecasts we present a Bayesian analysis that uses data on historical and current C cycling for Brazil to parameterise five TEMs of varied complexity with a retrieval of model error covariance at 1 degree spatial resolution. After evaluation against data from 2001–2017, the parameterised models are simulated to 2100 under four climate change scenarios spanning the likely range of climate projections. Using multiple models, each with per pixel parameter ensembles, we partition forecast uncertainties. Parameter uncertainty dominates across most of Brazil when simulating future stock changes in biomass C and dead organic matter (DOM). Uncertainty of simulated biomass change is most strongly correlated with net primary productivity allocation to wood (NPPwood) and wood mean residence times (MRTwood). Uncertainty of simulated DOM change is most strongly correlated with MRTsoil and NPPwood. Due to the coupling between these variables and C stock dynamics being bi-directional we argue that using repeat estimates of woody biomass will provide a valuable constraint needed to refine predictions of the future carbon cycle. Finally, evaluation of our multi-model analysis shows that wood litter contributes substantially to fire emissions necessitating a greater understanding of wood litter C-cycling than is typically considered in large-scale TEMs.

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Section: Does Increasing Model Complexity Improve Agreement With Evaluation Information?supporting

confidence: 90%

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

Smallman¹,

Milodowski²,

Sousa‐Neto³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…As for the influencing factors of the terrestrial ecosystem pattern, they are mainly analyzed from the aspects of climate change [19], urban expansion [20], CO 2 concentration [21], biodiversity [22], land use change [23] and land cover change [16], transfer of animals and plants [24], vegetation vulnerability [12], and so on. Based on these studies, several conclusions have been drawn.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Precipitation and Temperature on Changes in the Terrestrial Ecosystem Pattern in the Yangtze River Economic Belt, China

Xiang

Zhang

Song

et al. 2019

IJERPH

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The terrestrial ecosystem plays an important role in maintaining an ecological balance, protecting the ecological environment, and promoting the sustainable development of human beings. The impacts of precipitation, temperature, and other natural factors on terrestrial ecosystem pattern change (TEPC) are the basis for promoting the healthy development of the terrestrial ecosystem. This paper took the Yangtze River Economic Belt (YREB) as the study area, analyzed the temporal and spatial characteristics of TEPC from 1995 to 2015, and used spatial transfer matrix and terrestrial ecosystem pattern dynamic degree models to analyze the area transformation between different terrestrial ecosystem types. A bivariate spatial autocorrelation model and a panel data regression model were used to study the impacts of precipitation and temperature on TEPC. The results show that: (1) The basic pattern of the terrestrial ecosystem developed in a relatively stable manner from 1995 to 2005 in the YREB, and transformations between the farmland ecosystem, forest ecosystem, and grassland ecosystem were more frequent. The temporal and spatial evolution of precipitation and temperature in the YREB showed significant regional differences. (2) There was a significant negative bivariate global spatial autocorrelation effect of precipitation and temperature on the area change of the forest ecosystem, and a positive effect on the area change of the settlement ecosystem. The local spatial correlation between precipitation or temperature and the terrestrial ecosystem showed significant scattered distribution characteristics. (3) The impacts of precipitation and temperature on TEPC showed significant regional characteristics on the provincial scale. The impact utility in the tail region is basically negative, while both positive and negative effects exist in the central and head regions of the YREB. Moreover, the impact showed significant spatial heterogeneity on the city scale. (4) The Chinese government has promulgated policies and measures for strategic planning, ecological environment protection, and economic support, which could effectively promote ecological and sustainable development of the YREB and promote the coordinated development of the ecology, economy, and society in China.

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“…The produced 1-km resolution global LULC data over 2015-2100 will be particularly useful for a wide range of land-related sustainability studies 23,24 . Also, the methodology we employed for disentangling the individual impacts of climate and LULC changes on GPP dynamics can be readily applied or easily adapted to study regional or historical GPP dynamics (e.g., You et al 25 ), and for decomposing the relative contributions of more GPP-drivers with appropriate process-based GPP simulation models (e.g., Cai & Prentice 26 ; Sun et al 9 ). On top of these research opportunities, our results shed timely insights on land monitoring and management imperatives for navigating our human-Earth system toward a more sustainable future.…”

Section: Relative Contributions Of Climate and Lulc Changes To Global Gpp Dynamicsmentioning

confidence: 99%

Land use cover change has non-trivial and potentially dominant impact on global gross primary productivity (2000-2100)

Hou¹,

Zhou²,

Pei³

et al. 2021

Preprint

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Anthropogenic land use/cover (LULC) change alters terrestrial gross primary productivity (GPP), which is a major atmospheric carbon sink. Identifying the impacts of future LULC changes on terrestrial GPP has been challenging due to the complexity of incorporating future LULC into ecosystem models. Here, we present eight-scenario-based projections of global spatially explicit LULC at 1km resolution over the period 2015-2100. We further conducted Fourteen experiments to quantify the contribution of LULC change to GPP dynamics relative to that of climate change under different scenarios. We find that global GPP change would be underestimated by 10.92%‒16.16% during 2000-2050 and 1.41%‒14.57% during 2050-2100 when modeled without LULC dynamics—as in most existing GPP modeling efforts. Particularly, LULC-change-dominated areas would account globally for 1.65‒2.20 the size of the Amazon rainforest. Our findings underline the necessity of incorporating future LULC dynamics into process-based models and highlight the non-trivial role of LULC in transitioning toward sustainability.

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Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015

Cited by 56 publications

References 61 publications

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

Impacts of Precipitation and Temperature on Changes in the Terrestrial Ecosystem Pattern in the Yangtze River Economic Belt, China

Land use cover change has non-trivial and potentially dominant impact on global gross primary productivity (2000-2100)

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