Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

Pan, Shufen; Tian, Hanqin; Dangal, Shree R. S.; Ouyang, Zhiyun; Tao, Bo; Ren, Wei; Lü, Chaoqun; Running, Steven W.

doi:10.1155/2014/965936

Cited by 65 publications

(59 citation statements)

References 110 publications

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“…(Lu et al, 2005), the difference between which may lead to the significant difference in the estimated NPP. It is necessary to estimate NPP with more accurate data and evaluate the ecosystem models against ground and satellite-based measurements and observations in order to improve the capacity of model simulation and prediction (Pan et al, 2014). The data accuracy of the explanatory variables may also influence the estimation results.…”

Section: Uncertainties and Management Implicationmentioning

confidence: 99%

Multilevel modeling of NPP change and impacts of water resources in the Lower Heihe River Basin

Yan

Zhan

Jiang

et al. 2015

Physics and Chemistry of the Earth, Parts A/B/C

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a b s t r a c tNet primary productivity (NPP) lays the foundation for provision of various ecosystem services, and understanding the impacts of potential influencing factors on NPP is of great significance to formulating appropriate management measures to guarantee the sustainable provision of essential ecosystem services. This study analyzed the impacts of potential influencing factors on NPP in the lower Heihe River Basin, a typical arid and semi-arid region in China. Results of decomposition analysis suggested that the water availability played an important role in increasing NPP, with a contribution rate of 44.17%, and it is necessary to carry out some policies that can promote the water use efficiency to increase NPP under the background of climate change and intensified human activities. There are some uncertainties in the results of this study, but these results still can provide valuable reference information for the water resource management to increase the ecosystem service supply in the lower Heihe River Basin.

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Section: Uncertainties and Management Implicationmentioning

confidence: 99%

Multilevel modeling of NPP change and impacts of water resources in the Lower Heihe River Basin

Yan

Zhan

Jiang

et al. 2015

Physics and Chemistry of the Earth, Parts A/B/C

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“…Therefore, understanding NEE responses to environmental change, to ecosystem management and to site characteristics is essential for predicting future biogeochemical cycles (Law et al 2002;Pan et al 2014;Li et al 2016). To this end, processbased vegetation models (PVMs) of varying complexity are being used, operating at varying scales (Keenan et al 2012;Fischer et al 2014;Reyer 2015).…”

Section: Introductionmentioning

confidence: 99%

Combining multiple statistical methods to evaluate the performance of process-based vegetation models across three forest stands

Horemans¹,

Henrot

Delire³

et al. 2017

Central European Forestry Journal

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Process-based vegetation models are crucial tools to better understand biosphere-atmosphere exchanges and ecophysiological responses to climate change. In this contribution the performance of two global dynamic vegetation models, i.e. CARAIB and ISBA CC , and one stand-scale forest model, i.e. 4C, was compared to long-term observed net ecosystem carbon exchange (NEE) time series from eddy covariance monitoring stations at three old-grown European beech (Fagus sylvatica L.) forest stands. Residual analysis, wavelet analysis and singular spectrum analysis were used beside conventional scalar statistical measures to assess model performance with the aim of defining future targets for model improvement. We found that the most important errors for all three models occurred at the edges of the observed NEE distribution and the model errors were correlated with environmental variables on a daily scale. These observations point to possible projection issues under more extreme future climate conditions. Recurrent patterns in the residuals over the course of the year were linked to the approach to simulate phenology and physiological evolution during leaf development and senescence. Substantial model errors occurred on the multi-annual time scale, possibly caused by the lack of inclusion of management actions and disturbances. Other crucial processes defined were the forest structure and the vertical light partitioning through the canopy. Further, model errors were shown not to be transmitted from one time scale to another. We proved that models should be evaluated across multiple sites, preferably using multiple evaluation methods, to identify processes that request reconsideration.

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“…Terrestrial NPP is also an essential component of the global carbon cycle (Melillo et al, 1993) that links terrestrial biota with the atmosphere (Beer et al, 2010). Monitoring and modeling GPP and NPP at regional and global scales have attracted much attention (Pan et al, 2014b;Running et al, 2004;Tian et al, 1998) because they not only measure the transfer of energy to the biosphere and terrestrial CO 2 assimilation, but also because they provide the basis for assessing the status of a wide range of ecological processes (Piacentini and Rosina, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Investigating factors controlling changes in terrestrial primary production could provide important clues to the underlying mechanisms and advance our understanding of the complex interactions among terrestrial ecosystems, atmosphere, and climate system (Tian et al, 2000). During the past decades, GPP and NPP estimation and investigation of their influencing factors, especially at large scales, have drawn much attention among the scientific community, policy-making agencies, and the public due to global climate change and increases in the atmospheric CO 2 concentration (Zscheischler et al, 2014;Pan et al, 2014b;Sitch et al, 2008). Since the global GPP is roughly 120 Pg C yr -1 and is the largest flux of the global carbon cycle (Beer et al, 2010), even small fluctuations in GPP and NPP (usually estimated as half of GPP) could cause large changes in the airborne fraction of anthropogenic CO 2 (Raupach et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…While there is little doubt that climate change and increasing atmospheric CO 2 are the primary drivers of terrestrial NPP during the recent decade, the mechanisms for this phenomenon are still far from clear and require further investigation. Process-based ecosystem models are an effective tool for historical assessment and future projections of terrestrial NPP in response to global change Huntzinger et al, 2012;Pan et al, 2014a;Pan et al, 2014b;Tian et al, 2000). These models integrate the understanding of complex ecological and physiological processes obtained from field measurements to study the response of terrestrial ecosystems to multiple environmental factors (Dermody, 2006;Norby and Luo, 2004;Wang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century

et al. 2015

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A wide variety of studies have estimated the magnitude of global terrestrial net primary production (NPP), but its variations, both spatially and temporally, still remain uncertain. By using an improved process-based terrestrial ecosystem model (DLEM, Dynamic Land Ecosystem Model), we provide an estimate of global terrestrial NPP induced by multiple environmental factors and examine the response of terrestrial NPP to climate variability at biome and global levels and along latitudes throughout the first decade of the 21st century. The model simulation estimates an average global terrestrial NPP of 54.6 Pg C yr -1 during 2000-2009, varying from 52.8 Pg C yr -1 in the dry year of 2002 to 56.4 Pg C yr -1 in the wet year of 2008. In wet years, a large increase in terrestrial NPP compared to the decadal mean was prevalent in Amazonia, Africa and Australia. In dry years, however, we found a 3.2% reduction in global terrestrial NPP compared to the decadal mean, primarily due to limited moisture supply in tropical regions. At a global level, precipitation explained approximately 63% of the variation in terrestrial NPP, while the rest was attributed to changes in temperature and other environmental factors. Precipitation was the major factor determining inter-annual variation in terrestrial NPP in low-latitude regions. However, in mid-and high-latitude regions, temperature variability largely controlled the magnitude of terrestrial NPP. Our results imply that projected climate warming and increasing climate extreme events would alter the magnitude and spatiotemporal patterns of global terrestrial NPP.

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Modeling and Monitoring Terrestrial Primary Production in a Changing Global Environment: Toward a Multiscale Synthesis of Observation and Simulation

Cited by 65 publications

References 110 publications

Multilevel modeling of NPP change and impacts of water resources in the Lower Heihe River Basin

Multilevel modeling of NPP change and impacts of water resources in the Lower Heihe River Basin

Combining multiple statistical methods to evaluate the performance of process-based vegetation models across three forest stands

Impacts of climate variability and extremes on global net primary production in the first decade of the 21st century

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