Investigation of Uncertainties of Establishment Schemes in Dynamic Global Vegetation Models

In Dynamic Global Vegetation Models （DGVMs）, the establishment of woody vegetation refers to flowering, fertiliza- tion, seed production, germination, and the growth of tree seedlings. It determines not only the population densities but also other important ecosystem structural variables. In current DGVMs, establishments of woody plant functional types （PFTs） are assumed to be either the same in the same grid cell, or largely stochastic. We investigated the uncertainties in the competition of establishment among coexisting woody PFTs from three aspects： the dependence of PFT establishments on vegetation states; background establishment; and relative establishment potentials of different PFTs. Sensitivity experi- ments showed that the dependence of establishment rate on the fractional coverage of a PFT favored the dominant PFT by increasing its share in establishment. While a small background establishment rate had little impact on equilibrium states of the ecosystem, it did change the timescale required for the establishment of alien species in pre-existing forest due to their disadvantage in seed competition during the early stage of invasion. Meanwhile, establishment purely fiom background （the scheme commonly used in current DGVMs） led to inconsistent behavior in response to the change in PFT specification （e.g., number of PFTs and their specification）. Furthermore, the results also indicated that trade-off between irtdividual growth and reproduction/colonization has significant influences on the competition of establishment. Hence, further development of es- tablishment parameterization in DGVMs is essential in reducing the uncertainties in simulations of both ecosystem structures and successions.

Combining European Earth Observation products with Dynamic Global Vegetation Models for estimating Essential Biodiversity Variables

Global,fast and accessible monitoring of biodiversity is one of the main pillars of the efforts undertaken in order to revert it loss.The Group on Earth Observations Biodiversity Observation Network(GEO-BON)provided an expert-based definition of the biological properties that should be monitored,the Essential Biodiversity Variables(EBVs).Initiatives to provide indicators for EBVs rely on global,freely available remote sensing(RS)products in combination with empirical models and field data,and are invaluable for decision making.In this study,we provide alternatives for the expansion and improvement of the EBV indicators,by suggesting current and future data from the European Space Agencýs COPERNICUS and explore the potential of RS-integrated Dynamic Global Vegetation Models(DGVMs)for the estimation of EBVs.Our review found that mainly due to the inclusion of the Sentinel constellation,Copernicus products have similar or superior potential for EBV indicator estimation in relation to their NASA counterparts.DGVMs simulate the ecosystem level EBVs(ecosystem function and structure),and when integrated with remote sensing data have great potential to not only offer improved estimation of current states but to provide projection of ecosystem impacts.We suggest that focus on producing EBV relevant outputs should be a priority within the research community,to support biodiversity preservation efforts.

Vegetation scattering attenuation characteristics of terahertz wave

A terahertz(THz)wave transmitted through vegetation experiences both absorption and scattering caused by the air molecules and leaves.This paper presents the scattering attenuation characteristics of vegetation in a THz range.The theoretical path loss model near the vegetation yields the average attenuation of THz waves in a mixed channel composed of air and vegetation leaves.Furthermore,a simplified model of the vegetation structure is obtained for generic vegetation types based on a variety of parameters,such as leaf size,distribution,and moisture content.Finally,based on specific vegetation species and different levels of air humidity,the attenuation characteristics under different conditions are calculated,and the influence of different model parameters on the attenuation characteristics is obtained.

Improvements of a Dynamic Global Vegetation Model and Simulations of Carbon and Water at an Upland-Oak Forest

The interest in the development and improvement of dynamic global vegetation models （DGVMs）, which have the potential to simulate fluxes of carbon, water and nitrogen, along with changes in the vegetation dynamics, within an integrated system, has been increasing. In this paper, some numerical schemes and a higher resolution soil texture dataset were employed to improve the Sheffield Dynamic Global Vegetation Model （SDGVM）. Using eddy covariance-based measurements, we then tested the standard version of the SDGVM and the modified version of the SDGVM. Detailed observations of daily carbon and water fluxes made at the upland oak forest on the Walker Branch Watershed in Tennessee, USA offered a unique opportunity for these comparisons. The results revealed that the modified version of the SDGVM did a reasonable job of simulating the carbon and water flux and the variation of soil water content （SWC）. However, at the end of the growing season, it failed to simulate the effect of the limitations on the soil respiration dynamics and as a result underestimated this respiration. It was also noted that the modified version overestimated the increase in the SWC following summer rainfall, which was attributed to an inadequate representation of the ground water and thermal cycle.

Sensitivity of the Carbon Storage of Potential Vegetation to Historical Climate Variability and CO_2 in Continental China

The interest in the national levels of the terrestrial carbon sink and its spatial and temporal variability with the climate and CO2 concentrations has been increasing. How the climate and the increasing atmospheric CO2 concentrations in the last century affect the carbon storage in continental China was investigated in this study by using the Modified Sheffield Dynamic Global Vegetation Model （M-SDGVM）. The estimates of the M-SDGVM indicated that during the past 100 years a combination of increasing CO2 with historical temperature and precipitation variability in continental China have caused the total vegetation carbon storage to increase by 2.04 Pg C, with 2.07 Pg C gained in the vegetation biomass but 0.03 Pg C lost from the organic soil carbon matter. The increasing CO2 concentration in the 20th century is primarily responsible for the increase of the total potential vegetation carbon. These factorial experiments show that temperature variability alone decreases the total carbon storage by 1.36 Pg C and precipitation variability alone causes a loss of 1.99 Pg C. The effect of the increasing CO2 concentration alone increased the total carbon storage in the potential vegetation of China by 3.22 Pg C over the past 100 years. With the changing of the climate, the CO2 fertilization on China＇s ecosystems is the result of the enhanced net biome production （NBP）, which is caused by a greater stimulation of the gross primary production （GPP） than the total soil-vegetation respiration. Our study also shows notable interannual and decadal variations in the net carbon exchange between the atmosphere and terrestrial ecosystems in China due to the historical climate variability.

Evaluating the Dependence of Vegetation on Climate in an Improved Dynamic Global Vegetation Model

The capability of an improved Dynamic Global Vegetation Model （DGVM） in reproducing the impact of climate on the terrestrial ecosystem is evaluated. The new model incorporates the Community Land Model- DGVM （CLM3.0-DGVM） with a submodel for temperate and boreal shrubs, as well as other revisions such as the ＂two-leaf＂ scheme for photosynthesis and the definition of fractional coverage of plant functional types （PFTs）. Results show that the revised model may correctly reproduce the global distribution of temperate and boreal shrubs, and improves the model performance with more realistic distribution of di？erent vege- tation types. The revised model also correctly reproduces the zonal distributions of vegetation types. In reproducing the dependence of the vegetation distribution on climate conditions, the model shows that the dominant regions for trees, grasses, shrubs, and bare soil are clearly separated by a climate index derived from mean annual precipitation and temperature, in good agreement with the CLM4 surface data. The dominant plant functional type mapping to a two dimensional parameter space of mean annual temperature and precipitation also qualitatively agrees with the results from observations and theoretical ecology studies.

Development of the IAP Dynamic Global Vegetation Model

ABSTRACT The lAP Dynamic Global Vegetation Model （IAP-DGVM） has been developed to simulate the distribution and structure of global vegetation within the framework of Earth System Models. It incorporates our group＇s recent developments of major model components such as the shrub sub-model, establishment and competition parameterization schemes, and a process-based fire parameterization of intermediate complexity. The model has 12 plant functional types, including seven tree, two shrub, and three grass types, plus bare soil. Different PFTs are allowed to coexist within a grid cell, and their state variables are updated by various governing equations describing vegetation processes from fine-scale biogeophysics and biogeochemistry, to individual and population dynamics, to large-scale biogeography. Environmental disturbance due to fire not only affects regional vegetation competition, but also influences atmospheric chemistry and aerosol emissions. Simulations under observed atmospheric conditions showed that the model can correctly reproduce the global distribution of trees, shrubs, grasses, and bare soil. The simulated global dominant vegetation types reproduce the transition from forest to grassland （savanna） in the tropical region, and from forest to shrubland in the boreal region, but overestimate the region of temperate forest.

Evaluation of the New Dynamic Global Vegetation Model in CAS-ESM

In the past several decades, dynamic global vegetation models（DGVMs） have been the most widely used and appropriate tool at the global scale to investigate vegetation-climate interactions. At the Institute of Atmospheric Physics, a new version of DGVM（IAP-DGVM） has been developed and coupled to the Common Land Model（CoLM） within the framework of the Chinese Academy of Sciences＇ Earth System Model（CAS-ESM）. This work reports the performance of IAP-DGVM through comparisons with that of the default DGVM of CoLM（CoLM-DGVM） and observations. With respect to CoLMDGVM, IAP-DGVM simulated fewer tropical trees, more ＂needleleaf evergreen boreal tree＂ and ＂broadleaf deciduous boreal shrub＂, and a better representation of grasses. These contributed to a more realistic vegetation distribution in IAP-DGVM,including spatial patterns, total areas, and compositions. Moreover, IAP-DGVM also produced more accurate carbon fluxes than CoLM-DGVM when compared with observational estimates. Gross primary productivity and net primary production in IAP-DGVM were in better agreement with observations than those of CoLM-DGVM, and the tropical pattern of fire carbon emissions in IAP-DGVM was much more consistent with the observation than that in CoLM-DGVM. The leaf area index simulated by IAP-DGVM was closer to the observation than that of CoLM-DGVM; however, both simulated values about twice as large as in the observation. This evaluation provides valuable information for the application of CAS-ESM, as well as for other model communities in terms of a comparative benchmark.

Impact of Spin-up Forcing on Vegetation States Simulated by a Dynamic Global Vegetation Model Coupled with a Land Surface Model

A dynamic global vegetation model （DGVM） coupled with a land surface model （LSM） is generally initialized using a spin-up process to derive a physically-consistent initial condition. Spin-up forcing, which is the atmospheric forcing used to drive the coupled model to equilibrium solutions in the spin-up process, varies across earlier studies. In the present study, the impact of the spin-up forcing in the initialization stage on the fractional coverages （FCs） of plant functional type （PFT） in the subsequent simulation stage are assessed in seven classic climate regions by a modified Community Land Model’s Dynamic Global Vegetation Model （CLM-DGVM）. Results show that the impact of spin-up forcing is considerable in all regions except the tropical rainforest climate region （TR） and the wet temperate climate region （WM）. In the tropical monsoon climate region （TM）, the TR and TM transition region （TR-TM）, the dry temperate climate region （DM）, the highland climate region （H）, and the boreal forest climate region （BF）, where FCs are affected by climate non-negligibly, the discrepancies in initial FCs, which represent long-term cumulative response of vegetation to different climate anomalies, are large. Moreover, the large discrepancies in initial FCs usually decay slowly because there are trees or shrubs in the five regions. The intrinsic growth timescales of FCs for tree PFTs and shrub PFTs are long, and the variation of FCs of tree PFTs or shrub PFTs can affect that of grass PFTs.

Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity

Background: Global warming has brought many negative impacts on terrestrial ecosystems, which makes the vulnerability of ecosystems one of the hot issues in current ecological research. Here, we proposed an assessment method based on the IPCC definition of vulnerability. The exposure to future climate was characterized using a moisture index(MI) that integrates the effects of temperature and precipitation. Vegetation stability, defined as the proportion of intact natural vegetation that remains unchanged under changing climate, was used together with vegetation productivity trend to represent the sensitivity and adaptability of ecosystems. Using this method, we evaluated the vulnerability of ecosystems in Southwestern China under two future representative concentration pathways(RCP 4.5 and RCP 8.5) with MC2 dynamic global vegetation model.Results:(1) Future(2017–2100) climate change will leave 7.4%(under RCP 4.5) and 57.4% of(under RCP 8.5) of areas under high or very high vulnerable climate exposure;(2) in terms of vegetation stability, nearly 45% of the study area will show high or very high vulnerability under both RCPs. Beside the impacts of human disturbance on natural vegetation coverage(vegetation intactness), climate change will cause obvious latitudinal movements in vegetation distribution, but the direction of movements under two RCPs were opposite due to the difference in water availability;(3) vegetation productivity in most areas will generally increase and remain a low vulnerability in the future;(4) an assessment based on the above three aspects together indicated that future climate change will generally have an adverse impact on all ecosystems in Southwestern China, with non-vulnerable areas account for only about 3% of the study area under both RCPs. However, compared with RCP 4.5, the areas with mid-and highvulnerability under RCP 8.5 scenario increased by 13% and 16%, respectively.Conclusion: Analyses of future climate exposure and projected vegetation distribution indicate widespread vulnerability of ecosystems in Southwestern China, while vegetation productivity in most areas will show an increasing trend to the end of twenty-first century. Based on new climate indicators and improved vulnerability assessment rules, our method provides an extra option for a more comprehensive evaluation of ecosystem vulnerability, and should be further tested at larger spatial scales in order to provide references for regional, or even global, ecosystem conservation works.

Earth System Model FGOALS-s2: Coupling a Dynamic Global Vegetation and Terrestrial Carbon Model with the Physical Climate System Model

Earth System Models （ESMs） are fundamental tools for understanding climate-carbon feedback. An ESM version of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS） was recently developed within the IPCC AR5 Coupled Model Intercomparison Project Phase 5 （CMIP5） modeling framework, and we describe the development of this model through the coupling of a dynamic global vegetation and terrestrial carbon model with FGOALS-s2. The performance of the coupled model is evaluated as follows. The simulated global total terrestrial gross primary production （GPP） is 124.4 PgC yr-I and net pri- mary production （NPP） is 50.9 PgC yr-1. The entire terrestrial carbon pools contain about 2009.9 PgC, comprising 628.2 PgC and 1381.6 PgC in vegetation and soil pools, respectively. Spatially, in the tropics, the seasonal cycle of NPP and net ecosystem production （NEP） exhibits a dipole mode across the equator due to migration of the monsoon rainbelt, while the seasonal cycle is not so significant in Leaf Area Index （LAI）. In the subtropics, especially in the East Asian monsoon region, the seasonal cycle is obvious due to changes in temperature and precipitation from boreal winter to summer. Vegetation productivity in the northern mid-high latitudes is too low, possibly due to low soil moisture there. On the interannual timescale, the terrestrial ecosystem shows a strong response to ENSO. The model- simulated Nifio3.4 index and total terrestrial NEP are both characterized by a broad spectral peak in the range of 2-7 years. Further analysis indicates their correlation coefficient reaches -0.7 when NEP lags the Nifio3.4 index for about 1-2 months.

Simulation and Evaluation of Terrestrial Ecosystem NPP with M-SDGVM over Continental China

Using the regional terrestrial Net Primary Production （NPP） from different observations and models over China, we validated the NPP simulations and explored the relationship between NPP and climate variation at interannual and decadal scales in the Modified Sheffield Dynamic Global Vegetation Model （M-SDGVM） during 1981–2000. M-SDGVM shows agreement with the NPP data from 743 sites under the Global Primary Production Data Initiative （GPPDI）. The spatial and the zonal averaged NPP of M-SDGVM agree well with different historic datasets and are closest to the IGBP NPP. Compared to the 1980s, NPP in the 1990s increases in most of China with a high degree of spatial heterogeneity. The multi-year mean NPP of forest types is reasonably modeled （above 500 g C m-2 yr-1 ） while that of C 3 path of photosynthesis （C 3 ） grasslands is underestimated. The NPP of 7 M-SDGVM main plant functional types （PFTs） increases and the increment of the broad-leaved deciduous forest is the most obvious （5.05 g C m-2 yr-1 ）. During the studied period, the annual NPP of M-SDGVM over China increases, with significant fluctuations, at an average rate of 0.0164 Gt C yr-1 . Regulated by annual temperature and precipitation, the interannual variation of the total NPP shows more significant correlation with temperature （relativity and probability are R= 0.61, P = 0.00403） than precipitation （R = 0.40, P = 0.08352）. CO 2 fertilization may play a key role in the increase of terrestrial ecosystem NPP over continental China, and CO 2 stimulation increases with CO 2 concentrations, and also with the climate variability of the 1980s and 1990s.

Evaluation of CMIP5 Earth System Models in Reproducing Leaf Area Index and Vegetation Cover over the Tibetan Plateau

The abilities of 12 earth system models（ESMs） from the Coupled Model Intercomparison Project Phase5（CMIP5） to reproduce satellite-derived vegetation biological variables over the Tibetan Plateau（TP） were examined.The results show that most of the models tend to overestimate the observed leaf area index（LAI）and vegetation carbon above the ground,with the possible reasons being overestimation of photosynthesis and precipitation.The model simulations show a consistent increasing trend with observed LAI over most of the TP during the reference period of 1986-2005,while they fail to reproduce the downward trend around the headstream of the Yellow River shown in the observation due to their coarse resolutions.Three of the models：CCSM4,CESM1-BGC,and NorESM1-ME,which share the same vegetation model,show some common strengths and weaknesses in their simulations according to our analysis.The model ensemble indicates a reasonable spatial distribution but overestimated land coverage,with a significant decreasing trend（-1.48%per decade） for tree coverage and a slight increasing trend（0.58%per decade） for bare ground during the period 1950-2005.No significant sign of variation is found for grass.To quantify the relative performance of the models in representing the observed mean state,seasonal cycle,and interannual variability,a model ranking method was performed with respect to simulated LAI.INMCM4,bcc-csm-1.1m,MPI-ESM-LR,IPSL CM5A-LR,HadGEM2-ES,and CCSM4 were ranked as the best six models in reproducing vegetation dynamics among the 12 models.

Evaluating the Tree Population Density and Its Impacts in CLM-DGVM

Vegetation population dynamics play an essential role in shaping the structure and function of terrestrial ecosystems. However, large uncertainties remain in the parameterizations of population dynamics in current Dynamic Global Vegetation Models （DGVMs）. In this study, the global distribution and probability density functions of tree population densities in the revised Community Land Model-Dynamic Global Vegetation Model （CLM-DGVM） were evaluated, and the impacts of population densities on ecosystem characteristics were investigated. The results showed that the model predicted unrealistically high population density with small individual size of tree PFTs （Plant Punetional Types） in boreal forests, as well as peripheral areas of tropical and temperate forests. Such biases then led to the underestimation of forest carbon storage and incorrect carbon allocation among plant leaves, stems and root pools, and hence predicted shorter time scales for the building/recovering of mature forests. These results imply that further improvements in the parameterizations of population dynamics in the model are needed in order for the model to correctly represent the response of ecosystems to climate change.

Assimilation of NDVI data in a land surface-Vegetation model for leaf area index predictions in a tree-grass ecosystem

Periodic observations of vegetation index,such as the normalized difference vegetation index(NDVI),can be used for data assimilation in heterogenous ecosystems.Indeed,the new Sentinel 2 Multispectral instrument and Landsat 8 Operational Land Imager sensor data are available at such high temporal and spatial resolutions that can be used to detect the patches of the main vegetation components(grass and trees)of heterogenous ecosystems,and capture their dynamics.We demonstrate the possibility to merge grass and tree NDVI observations and models,to optimally provide robust predictions of grass and tree leaf area index.The proposed assimilation approach assimilates NDVI data through the Ensemble Kalman filter(EnKF)and dynamically calibrates a key vegetation dynamic model parameter,the maintenance respiration coeffcient(ma).In the presence of large bias of the grass and tree ma base values,only the use of the proposed assimilation approach removes the large bias in the biomass balance,dynamically calibrating maintenance respiration coefficients,and corrects the model.The performance of a land surface-vegetation model was improved by assimilating observations of NDVl.The effective impact of the proposed assimilation approach on the evapotranspiration and CO_(2) uptake predictions in the heterogenous ecosystem is also demonstrated.

Analyzing and modeling the coverage of vegetation in the Qaidam Basin of China： The role of spatial autocorrelation

Relationship between vegetation and environmental factors has always been a major topic in ecology, but it has also been an important way to reveal vegetation＇s dynamic response to and feedback effects on climate change. For the special geographical location and climatic characteristics of the Qaidam Basin, with the support of traditional and remote sensing data, in this paper a vegetation coverage model was established. The quantitative prediction of vegetation coverage by five environmental factors was initially realized through multiple stepwise regression （MSR） models. However, there is significant multicollinearity among these five environmental factors, which reduces the performance of the MSR model. Then through the introduction of the Moran Index, an indicator that reflects the spatial autocorrelation of vegetation distribution, only two variables of average annual rainfall and local Moran Index were used in the final establishment of the vegetation coverage model. The results show that there is significant spatial autocorrelation in the distribution of vegetation. The role of spatial autocorrelation in the establishment of vegetation coverage model has not only improved the model fitting R2 from 0.608 to 0.656, but also removed the multicollinearity among independents.

Waveform LiDAR concepts and applications for potential vegetation phenology monitoring and modeling:a comprehensive review

Researchers continually demonstrated through published literature how LiDAR could create unparalleled measurements of ecosystem structure and forest height.There are a number of studies conducted utilizing waveform LiDAR products for terrestrial monitoring,but those that deal specifically with the assessment of space-borne waveform LiDAR for monitoring and modeling of phenology is very limited.This review highlights the waveform LiDAR system and looks into satellite sensors that could link waveform LiDAR and vegetation phenology,such as the proposed NASA’s Global Ecosystem Dynamics Investigation(GEDI)and the Japanese Experimental Module(JEM)-borne LiDAR sensor named MOLI(Multi-footprint Observation LIDAR and Imager).Further,this work examines the richness and utility of the waveform returns and proposes a spline-function-derived model that could be exploited for estimating the leaf-shooting date.The new approach may be utilized for ecosystem-level phenological studies.

Fractional vegetation cover estimation in heterogeneous areas by combining a radiative transfer model and a dynamic vegetation model

A fractional vegetation cover(FVC)estimation method incorporating a vegetation growth model and a radiative transfer model was previously developed,which was suitable for FVC estimation in homogeneous areas because the finer-resolution pixels corresponding to one coarseresolution FVC pixel were all assumed to have the same vegetation growth model.However,this assumption does not hold over heterogeneous areas,meaning that the method cannot be applied to large regions.Therefore,this study proposes a finer spatial resolution FVC estimation method applicable to heterogeneous areas using Landsat 8 Operational Land Imager reflectance data and Global LAnd Surface Satellite(GLASS)FVC product.The FVC product was first decomposed according to the normalized difference vegetation index from the Landsat 8 OLI data.Then,independent dynamic vegetation models were built for each finer-resolution pixel.Finally,the dynamic vegetation model and a radiative transfer model were combined to estimate FVC at the Landsat 8 scale.Validation results indicated that the proposed method(R^(2)=0.7757,RMSE=0.0881)performed better than either the previous method(R^(2)=0.7038,RMSE=0.1125)or a commonly used method involving look-up table inversions of the PROSAIL model(R^(2)=0.7457,RMSE=0.1249).

Mapping Spatial Distribution Characteristics of Lineaments Extracted from Remote Sensing Image Using Fractal and Multifractal Models

Mapping mineral prospectivity in vegetated areas is a challenge. For this reason, we aimed to map spatial distribution characteristics of linear structures detected in remote sensing images using fractal and multifractal models. The selected study area was the Pinghe District of the Fujian Province（China）, located in the Shanghang-Yunxiao polymetallic and alunite ore belt（within the Wuyishan polymetallic belt）, where mineral resources such as copper, molybdenum, gold, silver, iron, lead, zinc, alunite and pyrophyllite have been discovered. The results of our study showed that：（1） the values of fractal dimension for all lineaments, NW-trending lineaments, and NE-trending lineaments, are 1.36, 1.32, and 1.23, respectively, indicating that these lineaments are statistically self-similar;（2） the fractal dimensions of the spatial distribution of the linear structures in the four selected hydrothermal-type ore deposits of the Pinghe District, named Zhongteng, Panchi, Xiaofanshan and Fanshan, are 1.43, 1.52, 1.37 and 1.37, respectively, which are higher than the mean value in South China;（3） the spatial distribution of the linear structures extracted from the remote sensing image and displayed by the contour map of fractal dimensions, correlates well with the known hydrothermal ore deposits; and（4） the results of the anomaly map decomposed by the spectrum-area（S-A） multifractal model is much better than the original fractal dimension contour map, which showed most of the known hydrothermal-type deposits occur in the high anomalous area. It is suggested that a high step tendency possibly matches with the boundary of the volcanic edifice and the deep fault controlling the development of the rock mass and the volcanic edifice. The complexity of the spatial distribution of mapped lineations（faults） in the Pinghe District, characterized by high values in the anomaly map, may be associated with the hydrothermal polymetallic ore mineralization in the study area.

在显著变绿的地区叶面积指数控制总初级生产力的变化

The leaf area index(LAI)shows a significant increasing trend from global to regional scales,which is known as greening.Greening will further enhance photosynthesis,but it is unclear whether the contribution of greening has exceeded the CO_(2)fertilization effect and become the dominant factor in the gross primary productivity(GPP)variation.We took the Yangtze River Delta(YRD)of China,where cropland and natural vegetation are significantly greening,as an example.Based on the boreal ecosystem productivity simulator(BEPS)and Revised-EC-LUE models,the GPP in the YRD from 2001 to 2020 was simulated,and attribution analysis of the interannual variation in GPP was performed.In addition,the reliability of the GPP simulated by the dynamic global vegetation model(DGVM)in the area was further investigated.The research results showed that GPP in the YRD had three significant characteristics consistent with LAI:(1)GPP showed a significant increasing trend;(2)the multiyear mean and trend of natural vegetation GPP were higher than those of cropland GPP;and(3)cropland GPP showed double-high peak characteristics.The BEPS and Revised-EC-LUE models agreed that the effect of LAI variation(4.29 Tg C yr^(-1)for BEPS and 2.73 Tg C yr^(-1)for the Revised-EC-LUE model)determined the interannual variation in GPP,which was much higher than the CO_(2)fertilization effect(2.29 Tg C yr^(-1)for BEPS and 0.67 Tg C yr^(-1)for the Revised-EC-LUE model).The GPP simulated by the 7 DGVMs showed a huge inconsistency with the GPP estimated by remote sensing models.The deviation of LAI simulated by DGVM might be a potential cause for this phenomenon.Our study highlights that in significant greening areas,LAI has dominated GPP variation,both spatially and temporally,and DGVM can correctly simulate GPP only if it accurately simulates LAI variation.