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By using GLOPEM-CEVSA model, the spatiotemporal pattern and its affecting factors of the vegetation net primary productivity (NPP) in Northeast China in 2000-2008 were simulated, and, taking four forest ecosystem stations (Daxing'anling, Laoyeling, Liangshui and Changbai Mountains) as the cases, the seasonal changes and their main driving force of forest NPP in Northeast China were studied. In 2000-2008, the annual averaged vegetation NPP in the region was 445 gC·m-2·a-1, being the highest in the areas from Changbai Mountains to Xiaoxing'anling Mountains and parts of Sanjiang Plain, followed by in the areas from Changbai Mountains to Liaohe River Plain, eastern Songnen Plain, Sanjiang Plain, and Daxing'anling Mountain, and the lowest in the sparse grass and desert areas in the west. Forest ecosystem had the highest annual averaged NPP, followed by shrub, cropland and grassland, and desert. In forest ecosystem, coniferous and broad-leaf mixed forests had the highest annual averaged NPP (722 g C·m-2·a-1), while deciduous needle-leaf forest had the lowest one (451 g C·m-2·a-1). During the study period, no significant inter-annual changes were observed in the forest NPP though it was higher in 2007 and 2008 probably due to the increased air temperature (1°C-2°C higher than that in other years). The beginning time of forest growth season in Northeast China advanced gradually from north to south, and the growth season became longer. [ABSTRACT FROM AUTHOR]

By using cyclic sampling method, the aboveground biomass and leaf area index (LAI) of typical grassland in tower flux footprint were measured at three growth stages, i.e., early July (July 2-7), late July (July 20-26), and late August (Aug. 25-30), with their spatial patterns analyzed by geostatistics. At the three stages, the aboveground biomass of the grassland kept rising, while the LAI decreased after an initial increase. Both the two variables had good spatial autocorrelation, with similar spatial pattern and temporal evolution trend, and changed from stripe to patch. From early July to late August, the C0/(C0+C) of the aboveground biomass and LAI all decreased significantly, indicating that the spatial autocorrelation of the two variables changed from medium to high. The change ranges of the two variables gradually decreased, presenting the decrease of spatial continuity. The fractal dimension (D) also decreased gradually, suggesting the increase of spatial dependence. Topography and field management were the main factors affecting the spatial distribution of aboveground biomass and LAI, which induced the spatial variability of water and heat, and further, affected the grass growth. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a fundamental flux in land surface hydrothermal process. Because of the differences in basic concepts, assumptions, application scales, different models have induced varying uncertainties to the estimation and simulation of evapotranspiration. With the Three-River-Source National Park as an example, we used the Bayesian model averaging (BMA) method to integrate the ET estimations from five models of PT-JPL, ARTS-GIMMS3, ARTS-MODIS, MODIS global evapotranspiration product (MOD16), and SSEBop, and tried to improve the estimating accuracy of evapotranspiration. The results showed that the five models could well capture the seasonal variations in evapotranspiration at Haibei Flux Station, with an explanation range of 64%-86% variability in the observed ET, and a root means square deviation (RMSD) ranged from 0.47 mm·(8 d) -1 to 0.76 mm·(8 d) -1 . BMA-based ET greatly improved its explanation to 89% and decreased the RMSD to 0.43 mm·(8 d) -1 . The Three-River-Source National Park experienced an overall insignificant increasing trend in its inter-annual ET from 2003 to 2015. At the regional scale, the effects of temperature and precipitation on evapotranspiration were not significant, but were significant in the Yangtze River Source Park. Temperature and precipitation had positive impacts on evapotranspiration. The evapotranspiration showed different trends due to the geographi-cal differences between parks. This study provided a method reference for other multi-source data integration analysis. The integrated evapotranspiration data could effectively reduce the uncertainty of the original models and provide a more accurate data basis for the study of regional water heat change, which is of great significance to better understand water cycle under climate changes.

By using GLOPEM-CEVSA model, the spatiotemporal pattern and its affecting factors of the vegetation net primary productivity (NPP) in Northeast China in 2000-2008 were simulated, and, taking four forest ecosystem stations (Daxing' anling, Laoyeling, Liangshui and Changbai Mountains) as the cases, the seasonal changes and their main driving force of forest NPP in Northeast China were studied. In 2000-2008, the annual averaged vegetation NPP in the region was 445 g C x m(-2) x a(-1), being the highest in the areas from Changbai Mountains to Xiaoxing' anling Mountains and parts of Sanjiang Plain, followed by in the areas from Changbai Mountains to Liaohe River Plain, eastern Songnen Plain, Sanjiang Plain, and Daxing' anling Mountain, and the lowest in the sparse grass and desert areas in the west. Forest ecosystem had the highest annual averaged NPP, followed by shrub, cropland and grassland, and desert. In forest ecosystem, coniferous and broad-leaf mixed forests had the highest annual averaged NPP (722 g C x m(-2) x a(-1)), while deciduous needle-leaf forest had the lowest one (451 g C x m(-2) x a(-1)). During the study period, no significant inter-annual changes were observed in the forest NPP though it was higher in 2007 and 2008 probably due to the increased air temperature (1 degrees C-2 degrees C higher than that in other years). The beginning time of forest growth season in Northeast China advanced gradually from north to south, and the growth season became longer.

By using cyclic sampling method, the aboveground biomass and leaf area index (LAI) of typical grassland in tower flux footprint were measured at three growth stages, i.e., early July (July 2-7), late July (July 20-26), and late August (Aug. 25-30), with their spatial patterns analyzed by geostatistics. At the three stages, the aboveground biomass of the grassland kept rising, while the LAI decreased after an initial increase. Both the two variables had good spatial autocorrelation, with similar spatial pattern and temporal evolution trend, and changed from stripe to patch. From early July to late August, the C0/(C0+C) of the aboveground biomass and LAI all decreased significantly, indicating that the spatial autocorrelation of the two variables changed from medium to high. The change ranges of the two variables gradually decreased, presenting the decrease of spatial continuity. The fractal dimension (D) also decreased gradually, suggesting the increase of spatial dependence. Topography and field management were the main factors affecting the spatial distribution of aboveground biomass and LAI, which induced the spatial variability of water and heat, and further, affected the grass growth.