Improved Global Maps of the Optimum Growth Temperature, Maximum Light Use Efficiency, and Gross Primary Production for Vegetation

Chen, Yongzhe; Feng, Xiaoming; Fu, Bojie; Wu, Xutong; Gao, Zhen

doi:10.1029/2020jg005651

Cited by 21 publications

(20 citation statements)

References 142 publications

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“…The presence of Topt possibly interfered with the behavior of the features due to different cooling directions toward Topt. Here, we concluded a Topt of 20°C for irrigation GPP effects at national-wide (Figure S16 in Supporting Information S1), which was comparable with other studies about Topt for maize yield [e.g., 20°C (Zhu et al, 2019)] and for global vegetation productivity [e.g., 20.4°C (Chen et al, 2021) and 23°C (Huang et al, 2019)]. For those regions where ambient temperatures are higher than Topt, irrigation cooling has the potential to reduce high temperatures to get closer to Topt, producing more positive SHAP values.…”

Section: Non-negligible and Diverse Irrigation Cooling Effects On Mai...supporting

confidence: 89%

Changing Climate Threatens Irrigation Benefits of Maize Gross Primary Productivity in China

Liao,

Niu,

Ciais

et al. 2024

Earth's Future

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Intensive irrigation has been proven to profoundly impact climate through the surface energy budget. However, the impacts of irrigation and climate interactions on gross primary productivity (GPP) in maize cultivated areas remain uncertain. Here we quantified the irrigation effects on maize GPP (∆GPP) across China by combining a land surface model and a light‐use efficiency model and using satellite‐based irrigation water use. We show that irrigation significantly contributed to an increase in maize GPP by an average of 430 gC · m−2 · yr−1, equivalent to 28% of the irrigated maize GPP in China. These benefits (∆GPP) were attributed to irrigation effects (water supply, surface cooling) and climate interactions based on a machine learning framework (eXtreme Gradient Boosting model‐SHapley Additive exPlanations). Irrigation water supply and surface cooling explained 54% ± 19% and 23% ± 20% of ∆GPP respectively, the rest being due to strong climate interactions with irrigation through water and energy balance. Assuming business‐as‐usual irrigation levels, changing climate both increases and decreases ΔGPP over different regions, driven primarily by temperature changes. The irrigation benefits in those areas under heat stress are greatly threatened due to changing climate. The roles of climate change on ∆GPP are reversed from beneficial to detrimental in the North China Plain, dominated by different warming levels of future scenarios. Our analysis provides new insights into assessing irrigation potential with the climate interactions and future irrigation priority regions.

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Section: Non-negligible and Diverse Irrigation Cooling Effects On Mai...supporting

confidence: 89%

Changing Climate Threatens Irrigation Benefits of Maize Gross Primary Productivity in China

Liao,

Niu,

Ciais

et al. 2024

Earth's Future

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“…In our recently published study (Chen et al, 2021), we developed an improved and unified light use efficiency (LUE) model for global gross primary production (GPP) calculations as follows:

\begin{matrix} GPP = PAR \times FPAR \times ε_{truemax} \times \frac{1.1814 \times (1 + {normale}^{0.3 \times (‐ T_{opt} ‐ 10 + T_{air})})}{1 + {normale}^{0.2 \times (T_{opt} ‐ 10 ‐ T_{air})}} \times \\ (0.25 + 0.75 \times \frac{ET}{RN}) \end{matrix}

where PAR is calculated by multiplying the CERES solar radiation and the monthly PAR/SW ratio derived from the BESS data set. We also developed global maps of

ε_{max}

and T opt (i.e., maximum LUE and optimum growth temperature, respectively) by referring to various flux measurements and sun‐induced chlorophyll fluorescence values (Chen, 2020; Chen et al, 2021). Based on the revised LUE model and the spatial maps of the key parameters, we calculated the actual and climate‐driven GPP at 0.05° resolution when either MODIS or GEOV2 LAI/FPAR data were applied.…”

Section: Methodsmentioning

confidence: 99%

“…The PML-V2 model performs well in estimating evapotranspiration (ET) at the global scale and in China (Li et al, 2020;Zhang, Kong, et al, 2019) where PAR is calculated by multiplying the CERES solar radiation and the monthly PAR/SW ratio derived from the BESS data set. We also developed global maps of max and T opt (i.e., maximum LUE and optimum growth temperature, respectively) by referring to various flux measurements and sun-induced chlorophyll fluorescence values (Chen, 2020;Chen et al, 2021). Based on the revised LUE model and the spatial maps of the key parameters, we calculated the actual and climate-driven GPP at 0.05° resolution when either MODIS or GEOV2 LAI/FPAR data were applied.…”

Section: Calculations Of Net Radiation Evapotranspiration Gross Prima...mentioning

confidence: 99%

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Accelerated increase in vegetation carbon sequestration in China after 2010: A turning point resulting from climate and human interaction

Chen

Feng

Tian

et al. 2021

Global Change Biology

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183

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China has increased its vegetation coverage and enhanced its terrestrial carbon sink through ecological restoration since the end of the 20th century. However, the temporal variation in vegetation carbon sequestration remains unclear, and the relative effects of climate change and ecological restoration efforts are under debate. By integrating remote sensing and machine learning with a modelling approach, we explored the biological and physical pathways by which both climate change and human activities (e.g., ecological restoration, cropland expansion, and urbanization) have altered Chinese terrestrial ecosystem structures and functions, including vegetation cover, surface heat fluxes, water flux, and vegetation carbon sequestration (defined by gross and net primary production, GPP and NPP). Our study indicated that during 2001–2018, GPP in China increased significantly at a rate of 49.1–53.1 TgC/yr2, and the climatic and anthropogenic contributions to GPP gains were comparable (48%–56% and 44%–52%, respectively). Spatially, afforestation was the dominant mechanism behind forest cover expansions in the farming‐pastoral ecotone in northern China, on the Loess Plateau and in the southwest karst region, whereas climate change promoted vegetation cover in most parts of southeastern China. At the same time, the increasing trend in NPP (22.4–24.9 TgC/yr2) during 2001–2018 was highly attributed to human activities (71%–81%), particularly in southern, eastern, and northeastern China. Both GPP and NPP showed accelerated increases after 2010 because the anthropogenic NPP gains during 2001–2010 were generally offset by the climate‐induced NPP losses in southern China. However, after 2010, the climatic influence reversed, thus highlighting the vegetation carbon sequestration that occurs with ecological restoration.

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“…According to previous studies on the sensitivity of Noah-MP parameters (e.g., Arsenault et al, 2018), we selected two most sensitive parameters related to the vegetation dynamics, i.e., max carboxylation rate and optimal growth temperature of vegetation. We followed an error and trial procedure to calibrate the parameters so that the simulated gross primary product (GPP) data best fit the observed GPP data at the two sites by referring to existing studies (Chen et al, 2021;Yang et al, 2021). At the Damxung site, simulation results were validated against the observed time series (2004)(2005)(2006) of soil temperature and soil moisture content, and of GPP (2004GPP ( -2005.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Spatiotemporal Characteristics of NPP Changes in Frozen Ground Areas of the Three-River Headwaters Region, China: A Regional Modeling Perspective

Zhang

2022

Front. Earth Sci.

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Permafrost degradation triggered by climate warming can disturb alpine ecosystem stability and further influence net primary productivity (NPP). Known as the “water tower of China”, the Three-River Headwaters Region (TRHR) on the eastern Qinghai-Tibet plateau (QTP), is characterized by a fragile alpine meadow ecosystem underlain by large areas of unstable permafrost and has been subject to rapid climate change in recent decades. Despite some site-specific studies, the spatial and temporal changes in NPP in the different frozen ground zones across the TRHR associated with climate change remain poorly understood. In this study, a physically explicit Noah land surface model with multi-parameterization options (Noah-MP) was employed to simulate NPP changes on the TRHR during 1989–2018. The simulation was performed with a spatial resolution of 0.1° and a temporal resolution of 3h, and validated at two sites with meteorological and flux observations. The results show that the average NPP was estimated to be 299.7 g C m−2 yr−1 in the seasonally frozen ground (SFG) zone and 198.5 g C m−2 yr−1 in the permafrost zone. NPP in the TRHR increased at a rate of 1.09 g C m−2 yr−2 during 1989–2018, increasing in 1989–2003 and then decreasing in subsequent years. The NPP in permafrost area increased at a rate of 1.43 g C m−2 yr−2 during 1989–2018, which is much higher than the rate of change in NPP in the SFG area (0.67 g C m−2 yr−2). Permafrost degradation has complicated ecosystem implications. In areas where permafrost degradation has occurred, both increasing and decreasing changes in NPP have been observed.

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Improved Global Maps of the Optimum Growth Temperature, Maximum Light Use Efficiency, and Gross Primary Production for Vegetation

Cited by 21 publications

References 142 publications

Changing Climate Threatens Irrigation Benefits of Maize Gross Primary Productivity in China

Changing Climate Threatens Irrigation Benefits of Maize Gross Primary Productivity in China

Accelerated increase in vegetation carbon sequestration in China after 2010: A turning point resulting from climate and human interaction

Spatiotemporal Characteristics of NPP Changes in Frozen Ground Areas of the Three-River Headwaters Region, China: A Regional Modeling Perspective

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