基于CMIP6模式优化集合平均预估21世纪全球陆地生态系统总初级生产力变化

利用国际耦合模式比较计划第六阶段(CMIP6)中18个地球系统模式总初级生产力(GPP)模拟数据,基于传统的多模式集合平均(MME)和可靠集合平均方法(REA),在4个未来情景(SSP1-2.6,SSP2-4.5,SSP3-7.0和SSP5-8.5)下预估了21世纪全球陆地生态系统GPP的变化量,并分析了GPP变化的驱动因子。研究结果表明：在4个未来情景下,基于REA方法预估的全球陆地生态系统年GPP在未来时期(2068—2100年)比历史时期(1982—2014年)分别增长了(14.85±3.32)、(28.43±4.97)、(37.66±7.61)和(45.89±9.21)Pg C,其增量大小和不确定性都明显低于MME方法。在4个情景下,大气CO₂浓度增长对GPP变化的贡献最大,基于REA方法计算的贡献占比分别为140%、137%、115%和75%;除SSP5-8.5(24%)外,其他情景下升温均导致全球陆地生态系统GPP降低(-42%、-37%、-16%),部分抵消了CO₂施肥效应的正面贡献。温度的影响存在纬度差异：升温在低纬度地区对GPP有负向贡献,在中高纬度地区为正向贡献。降水和辐射变化对GPP变化的贡献相对较小。     

Spatiotemporal variations of water use efficiency and its driving factors in Inner Mongolia from 2001 to 2020

Water use efficiency (WUE) is an important variable to explore coupled relationships in carbon and water cycles. In this study, we first compared the spatial variations of annual gross primary productivity (GPP) and evapotranspiration (ET) using four GPP and ET products. Second, we selected the products closest to the flux towers data to estimate WUE. Finally, we quantitatively analyzed the impact of climate change and soil water content on WUE. The results showed that: (1) Four GPP and ET products provided good performance, with GOSIF-GPP and FLDAS-ET exhibiting a higher correlation and the smallest errors with the flux tower data. (2) The spatial pattern of WUE is consistent with that of GPP and ET, gradually decreasing from the northeast to the southwest. Higher WUE values appeared in the northeast forest ecosystem, and lower WUE values occurred in the western Gobi Desert, with a value of 0.28 gC m^?2 mm^?1. The GPP and ET products showed an increasing trend, while WUE showed a decreasing trend (55.15%) from 2001 to 2020. (3) The spatial relationship between WUE and driving factors reveal the variations in WUE of Inner Mongolia are mainly affected by soil moisture between 0 and 10 cm (SM0-10cm), vapor pressure deficit (VPD), and precipitation, respectively. (4) In arid regions, VPD and precipitation exhibit a major influence on WUE. An increase in VPD and precipitation has a negative and positive effect on WUE, with threshold values of approximately 0.36 kPa and 426 mm, respectively. (5) In humid regions, SM0-10cm, VPD, SM10-40cm, and SM40-100cm exert a significant impact on WUE, especially SM0-10cm, and weakens with increasing soil depths, these differences may be related to physiological structure and living characteristics of vegetation types in different climate regimes. Our results emphasize the importance of VPD and soil moisture in regional variability in WUE.     

Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information

Global warming associated with climate change is one of the greatest challenges of today’s world. Increasing emissions of the greenhouse gas CO₂ are considered as a major contributing factor to global warming. One regulating factor of CO₂ exchange between atmosphere and land surface is vegetation. Measurements of land cover changes in combination with modelling the Gross Primary Productivity (GPP) can contribute to determine important sources and sinks of CO₂.The aim of this study is to accurately model the GPP for a region in West Africa with a spatial resolution of 250 m, and the differentiation of GPP based on woody and herbaceous vegetation. For this purpose, the Regional Biomass Model (RBM) was applied, which is based on a Light Use Efficiency (LUE) approach. The focus was on the spatial enhancement of the RBM from the original 1000–250 m spatial resolution (RBM+). The adaptation to the 250 m scale included the modification of two main input parameters: (1) the fraction of absorbed Photosynthetically Active Radiation (FPAR) based on the 1000 m MODIS MOD15A2 FPAR product which was downscaled to 250 m using MODIS NDVI time series; (2) the fractional cover of woody and herbaceous vegetation, which was improved by using a multi-scale approach. For validation and regional adjustments of GPP and the input parameters, in situ data from a climate station and eddy covariance measurements were integrated.The results of this approach show that the input parameters could be improved significantly: downscaling considerably reduces data gaps of the original FPAR product and the improved dataset differed less than 5.0% from the original data for cloud free regions. The RMSE of the fractional vegetation cover varied between 5.1 and 12.7%. Modelled GPP showed a slight overestimation in comparison to eddy covariance measurements. The in situ data was exceeded by 8.8% for 2005 and by 2.0% for 2006. The model results were converted to NPP and also agreed well with previous NPP measurements reported from different studies. Altogether a high accuracy and suitability of the regionally adjusted and downscaled model RBM+ can be concluded. The differentiation between vegetation growth forms allows a separation of long-term and short-term carbon storage based on woody and herbaceous vegetation, respectively.     

The Effect of Higher Warming on Vegetation Indices and Biomass Production is Dampened by Greater Drying in an Alpine Meadow on the Northern Tibetan Plateau

In order to understand whether or not the response of vegetation indices and biomass production to warming varies with warming magnitude, an experiment of field warming at two magnitudes was conducted in an alpine meadow on the northern Tibetan Plateau beginning in late June, 2013. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI) and soil adjusted vegetation index (SAVI) data were obtained using a Tetracam Agricultural Digital Camera in 2013-2014. The gross primary production (GPP) and aboveground plant biomass (AGB) were modeled using the surface measured NDVI and climatic data during the growing seasons (i.e. June-September) in 2013-2014. Both low and high warming significantly increased air temperature by 1.54 and 4.00°C, respectively, and significantly increased vapor pressure deficit by 0.13 and 0.31 kPa, respectively, in 2013-2014. There were no significant differences of GNDVI, AGB and ANPP among the three warming treatments. The high warming significantly reduced average NDVI by 23.3% (-0.06), while the low warming did not affect average NDVI. The low and high warming significantly decreased average SAVI by 19.0% (-0.04) and 27.4% (-0.05), respectively, and average GPP by 24.2% (i.e. 0.21 g C m^-2 d^-1) and 44.0% (i.e. 0.39 g C m^-2 d^-1), respectively. However, the differences of the average NDVI, SAVI, and GPP between low and high warming were negligible. Our findings suggest that a greater drying may dampen the effect of a higher warming on vegetation indices and biomass production in alpine meadow on the northern Tibetan Plateau.     

空间分辨率对总初级生产力模拟结果差异的影响

利用模型分析气候变化对陆地生态系统功能的影响,是当前全球变化生态学的研究热点,然而模型模拟不确定性来源之一就是空间异质性的问题。空间异质性是尺度的函数,基于气象和遥感数据驱动的生态系统过程模型（BEPS模型）,分别模拟2003-2005年中国生态系统通量观测与研究网络（ChinaFLUX）长白山站、千烟洲站、海北站及当雄站在1 km和8 km空间分辨率下的总初级生产力（GPP）的时间动态变化,并结合土地覆盖类型及叶面积指数(LAI)的差异,探讨两种空间分辨率输入数据对GPP模拟结果的影响。结果表明：① 差异性主要是由于8 km范围内混合像元导致LAI的不同,4个站点月均差异值分别为0.85、1.60、0.13及0.04;② 两种空间分辨率均能较好地反映各站点GPP的季节动态变化,与GPP观测值的相关性R²为0.79~0.97 (1 km)、0.69~0.97(8 km),月均差异值为11.46~29.65 gC/m²/month (1 km)、11.87~24.81 gC/m²/month (8 km);③ 4个通量站点在两种空间分辨率下的GPP月均差异值分别为14.43,12.05,4.79,3.22 gC/m²/month,不同空间分辨率的模拟结果在森林站的差异大于草地站,且生长季的差异大于非生长季。因此,模型在模拟大尺度、长时间序列GPP时,为了提高模型模拟效率,适度降低空间分辨率是可行的,但应尽量减小低空间分辨率对于森林生态系统以及生长季GPP模拟上的误差。     

基于IDL语言的植被指数UNVI软件插件

植被指数是地球陆表植被覆盖度和植被活力的指示因子,对环境监测、植被理化参量估算等应用研究有重要的意义。基于植被的反射光谱特征,通过遥感数据波段的组合,可以计算得到遥感植被指数。传统的植被指数如NDVI、EVI等仅利用有限波段信息的线性或非线性组合构建而成,没有充分利用遥感传感器所提供的多波段遥感信息,通用归一化植被指数UNVI(Universal Normalized Vegetation Index)充分利用了遥感传感器提供的多波段植被光谱信息,因此在反演植被叶绿素、生物量等植被理化参量上较其他传统植被指数更具优势。为方便UNVI指数的计算,本文基于IDL语言开发了UNVI软件插件,可直接作为ENVI商业遥感软件进行调用,并可满足多个传感器的UNVI计算需求。为了验证UNVI的应用效果,以植被总初级生产力GPP(Gross Primary Productivity)估算为例,比较了不同植被指数估算GPP的效果,结果表明:基于UNVI估算的GPP与通量站点获得的GPP具有较高的相关性(相关系数R2为0.79),验证了UNVI在GPP估算方面的优势。本文提供的UNVI软件插件可为遥感研究和应用人员提供便捷的计算工具。     

Impact of aerosols on terrestrial gross primary productivity in North China using an improved boreal ecosystem productivity simulator with satellite-based aerosol optical depth

ABSTRACT

Atmospheric aerosols can alter the direct and diffuse components of global solar radiation, which further influences terrestrial gross primary productivity (GPP) via photosynthesis. To investigate the impact of aerosols on GPP, GPP is modeled using the Boreal Ecosystem Productivity Simulator (BEPS) under two aerosol scenarios (S1& S2) over cropland and grassland ecosystems in the highly polluted North China. In S1, the aerosol-effect is not considered and an original empirical method is used when estimating direct and diffuse solar radiation in BEPS. In S2, BEPS is improved by a new empirical method which incorporates the impact of aerosols using the remote sensing-based aerosol optical depth (AOD). Results suggest that aerosols can reduce GPP of the sunlit leaves by decreasing direct solar radiation, but increase GPP of the shaded leaves by increasing diffuse solar radiation. The impact of aerosols on GPP is more significant over the cropland ecosystem (p < 0.05) with a more complex canopy structure during the peak period of the growing season. Furthermore, an AOD value of 0.3–0.6 with a diffuse fraction (the fraction of diffuse solar radiation in global solar radiation) around 30-40% can largely increase total GPP over the cropland ecosystem. The study improves the accuracy of GPP modeling using BEPS by highlighting the aerosol-effect on GPP via solar radiation over highly polluted regions.

Abbreviations: gross primary productivity (GPP); aerosol optical depth (AOD); boreal ecosystem productivity simulator (BEPS)     

Simulation of olive grove gross primary production by the combination of ground and multi-sensor satellite data

We developed and tested a methodology to estimate olive (Olea europaea L.) gross primary production (GPP) combining ground and multi-sensor satellite data. An eddy-covariance station placed in an olive grove in central Italy provided carbon and water fluxes over two years (2010–2011), which were used as reference to evaluate the performance of a GPP estimation methodology based on a Monteith type model (modified C-Fix) and driven by meteorological and satellite (NDVI) data. A major issue was related to the consideration of the two main olive grove components, i.e. olive trees and inter-tree ground vegetation: this issue was addressed by the separate simulation of carbon fluxes within the two ecosystem layers, followed by their recombination. In this way the eddy covariance GPP measurements were successfully reproduced, with the exception of two periods that followed tillage operations. For these periods measured GPP could be approximated by considering synthetic NDVI values which simulated the expected response of inter-tree ground vegetation to tillages.