[Integration of evapotranspiration remote sensing products based on Bayesian model averaging: An example from Three-River-Source National Park]. / 基于贝叶斯模型平均的蒸散遥感产品集成­­ä»¥ä¸‰æ±Ÿæºå›½å®¶å ¬å›­ä¸ºä¾‹.

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.

[Vegetation net primary productivity in Northeast China in 2000-2008: simulation and seasonal change].

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.

[Spatiotemporal variations of aboveground biomass and leaf area index of typical grassland in tower flux footprint].

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.

Assessment of effects of climate change and grazing activity on grassland yield in the Three Rivers Headwaters Region of Qinghai-Tibet Plateau, China.

Inter-annual dynamics of grassland yield of the Three Rivers Headwaters Region of Qinghai-Tibet Plateau of China in 1988-2005 was analyzed using the GLO-PEM model, and the herbage supply function was evaluated. The results indicate that while grassland yield in the region showed marked inter-annual fluctuation there was a trend of increased yield over the 18 years of the study. This increase was especially marked for Alpine Desert and Alpine Steppe and in the west of the region. The inter-annual coefficient of variation of productivity increased from the east to the west of the region and from Marsh, Alpine Meadow, Alpine Steppe, Temperate Steppe to Alpine Desert grasslands. Climate change, particularly increased temperatures in the region during the study period, is suggested to be the main cause of increased grassland yield. However, reduced grazing pressure and changes to the seasonal pattern of grazing could also have influenced the grassland yield trend. These findings indicate the importance of understanding the function of the grassland ecosystems in the region and the effect of climate change on them especially in regard to their use to supply forage for animal production. Reduction of grazing pressure, especially during winter, is indicated to be critical for the restoration and sustainable use of grassland ecosystems in the region.