基于MODIS的中国陆地植被生长及其与气候的关系

基于MODIS2000~2005(缺2004年)5a每8d的GPP资料,分析了中国陆地植被生长特征,讨论了我国气候因子(温度、降水以及辐射)对同期植被生长的影响。研究结果显示,中国陆地植被的年均GPP分布范围在0~3252.6gC.m-.2a-1之间,5a平均值为491.1gC.m-.2a-1;年均GPP最大的地方主要分布于云南南部、海南、台湾以及东南沿海部分地区,最小值主要出现在青藏高原、新疆以及内蒙古西部等高寒、干旱地区。温度是影响同期我国陆地植被生长的主要因子,其次为降水,辐射对我国陆地植被的影响比较小,主要影响区域在华南地区。陆地植被GPP与温度和降水的相关系数在我国表现为自北向南逐渐减小的分布特征,与对应气候因子的分布呈反向关系;而GPP与云量的相关系数则自北向南逐渐增大。     

盘锦湿地净初级生产力时空分布特征

主要采用中巴地球资源卫星(CBERS)、合成孔径雷达(SAR)和数字高程模型(DEM)数据,通过主成分变换融合算法、分类回归树CART算法和混合像元分解模型结合神经网络算法,进行了盘锦湿地土地覆盖类型分类。充分考虑湿地生态系统的典型特征,将盐分胁迫因子作为估算湿地耐盐植被净初级生产力(NPP)的环境影响因子之一,构建了基于光能利用率和遥感数据的湿地植被净初级生产力模型。分析了盘锦湿地植被NPP的时空分布特征,并研究了盘锦湿地植被NPP对气温和降水的响应特征。结果表明:2009年盘锦市植被净初级生产力介于0—1175 gC·m-·2a-1之间,平均值为553 gC·m-·2a-1。盘锦市植被NPP空间分布规律呈东北向西南逐渐递增的趋势。在湿地植被分类类型中,芦苇的单位面积平均NPP最高,达到1016 gC·m-·2a-1。2004—2009年盘锦植被单位面积平均NPP值在缓慢上升,湿地已呈现缓慢恢复的趋势。总体上气温对盘锦湿地主要植被类型芦苇月平均NPP的影响要强于降水。2004—2009年降水对盘锦地区植被年平均NPP的影响强于气温。     

广西植被净初级生产力(NPP)时空演变及主要影响因素分析

利用2001-2010年EOS/MODIS17A3卫星遥感资料,对广西植被净初级生产力(NPP)时空特征及其影响因素进行分析.结果表明：(1)NPP 表现出明显的年际变化,2005年植被年均 NPP 最小为625 gC??m-2??a-1,2003年最大,为714 gC??m-2??a-1,十年间广西植被年NPP平均值为662 gC??m-2??a-1;(2)不同植被类型NPP有较大差异,森林、灌木、农作物的NPP 平均值分别为834、614、517 gC??m-2??a-1;(3)十年间广西区年均NPP为显著下降趋势,且年均气温和降水对NPP时间变化作用显著,而日照时数对 NPP 时间变化的作用不显著;(4)广西区NPP空间格局形成主要影响因素为坡度,其次为经度,再次为地貌特征、纬度和降水;(5)非喀斯特区域北热带季雨林、南亚热带季雨林化/季雨化常绿阔叶林年均 NPP 大于喀斯特地区,相反,喀斯特地区中亚热带常绿阔叶林,农作物年均NPP大于非喀斯特地区.整体而言,广西非喀斯特地区植被NPP为683 gC??m-2??a-1,喀斯特地区植被NPP为620 gC??m-2??a-1.     

1958-2008年太白山太白红杉林碳循环模拟

太白红杉(Larix chinensis林主要分布于我国秦岭太白山的林线位置,对气候变化的响应十分敏感.为了定量分析太白山太白红杉林在气候变化背景下的碳循环特征,基于模型(MTCLIM)模拟的温度和降水数据,应用植被动态过程模型(LPJ-GUESS)模拟了太白山南北坡1958-2008年太白红杉林的净初级生产力(NPP)、生物量和净生态系统碳交换量(NEE).结果表明:1)太白红杉和巴山冷杉(Abies fargesii)的NPP和生物量在太白红杉林占有优势,太白红杉的NPP和生物量均大于巴山冷杉.1958-2008年间太白红杉南北坡NPP的平均值为0.38 kgC·m-2·a-1,巴山冷杉为0.25 kgC·m-2,a-1,两者之和占整个太白红杉林NPP的86％;1958-2008年间太白红杉南北坡生物量的平均值为2.91 kgC/m2,巴山冷杉为2.02 kgC/m2,两者之和占太白红杉林生物量的94％.2)太白红杉和巴山冷杉的NPP均表现为北坡大于南坡,且南北坡均有逐年增加的趋势,北坡的增幅小于南坡,所以太白山南北坡太白红杉林的NPP差异有逐年减少的趋势.3)太白红杉生物量的年际波动较大,南北坡呈交替上升趋势,南坡的平均值(2.94 kgC/m2)大于北坡(2.89 kgC/m2).巴山冷杉生物量的年际波动相对较小,北坡生物量水平大于南坡.4)1958-2008年南北坡太白红杉林平均NEE均为-0.023 kgC·m-2·a-1,表现为碳汇.南北坡碳汇水平均呈逐年增加趋势,南坡的增加幅度(0.91 g·m-2·a-1)大于北坡(0.42 g·m-2·a-1).以气候和CO2为驱动因子对太白山太白红杉林的长期碳循环动态做了定量分析,从机理上揭示气候变化与生态系统碳循环的关系,还需要做进一步的野外观测和控制实验研究.     

秦岭山地植被净初级生产力及对气候变化的响应

基于1999~2009年的NDVI数据和气象数据,利用CASA模型对秦岭山地植被净初级生产力（Net primary productivity,NPP）进行模拟估算,并分析了秦岭NPP的时空变化特征及其对气候变化的响应。结果表明：1999~2009年11年间秦岭山地的平均年NPP为542.24 gC·m^-2·a^-1;研究期内秦岭NPP呈显著增长趋势（P<0.01）,2008年最高（718.77 gC·m^-2·a^-1）,2001年最低（471.78 gC·m^-2·a^-1）;四季对全年NPP的贡献率大小依次为夏季（49.90%）>春季（26.16%）>秋季（18.87%）>冬季（5.07%）;月NPP与温度和降水都显著相关,但与温度的相关性更高,月水平上温度对NPP的影响比降水大;生长季期间NPP与温度和降水的相关性在空间分布上都以正相关为主。     

山东省植被NPP时空分布特征及驱动因素分析

为了解区域生态系统植被生产力的分布规律及其变化对区域生态调控的作用,该研究基于MOD17A3的年NPP数据,采用一元线性回归分析法和Pearson相关系数法等,分析了2000—2014年山东省植被NPP的时空格局变化特征及其气候、人为等因素对其的影响。结果表明:(1) 2000—2014年,山东省植被NPP总体呈上升趋势,年均值为442 gC·m^-2·a^-1,年总量平均值为63.16 TgC。山东省植被NPP空间分布具有明显的分异特征,其中年均NPP 300～400 gC·m^-2·a^-1的面积最大,占总面积的52.28%,分布于山东省西部的绝大部分区域。(2)山东省植被NPP年增加速率平均值为3.29 gC·m-2·a^-1,增加区域占山东省植被总面积的92.63%。其中,极显著增加区域占山东省总面积的16.56%,主要分布在烟台市、青岛市、日照市、临沂市、潍坊市、东营市和滨州市;显著减少和极显著减少的区域占山东省总面积的1.33%,零星分布在山东省东南部和北部黄河三角洲地区。(3)植被NPP受气候变化和人类活动等因素的共同影响,气候因素中降雨的影响最大,城市化随着人类活动的频繁逐渐成为植被NPP变化的重要驱动力。     

Spatiotemporal evolution and attribution analysis of grassland NPP in the Yellow River source region,China

The protection of the environment and promotion of sustainable development in the Yellow River Basin (YRB) is a major national strategy in China. Madoi County (MC), at the headwaters of the YRB, plays a crucial role in ensuring the ecological security and sustainable development of the middle and lower reaches of the river. Vegetation net primary productivity (NPP) is a critical indicator for assessing the structure and function of the ecosystem. Therefore, studying the spatiotemporal evolutionary characteristics of grassland net primary productivity (GNPP) in MC and its driving mechanisms is of the utmost importance. We employed linear regression analysis, optimal parameters-based geographical detector (OPGD), multiscale geographic weighted regression (MGWR), and residual analysis to investigate the spatiotemporal change of GNPP in MC under the influence of climate change (CC) and policies in different periods based on MOD17A3 NPP data. Additionally, we explored the natural and anthropogenic factors affecting GNPP and their spatiotemporal processes on global and grid scales. Lastly, we quantified the relative contributions of CC and human activity (HA) to the observed changes in GNPP. The results were as follows: 1) GNPP in MC has demonstrated a fluctuating and increasing trend since 2000, with an average annual growth rate of 1.58 gC·m⁻²·a⁻¹ and a maximum growth rate of 6.29 gC·m⁻²·a⁻¹ in 2017–2021. 2) Spatial variation in GNPP has been evident since 2000, with a gradual increase in GNPP from the northwest to southeast. Regions with notable increases in grassland were predominantly located in areas with eco-engineering. 3) The OPGD was used to identify key driving factors (DFs), revealing that climate factors had stronger explanatory powers for the GNPP at each phase. MGWR results indicated that the effect of key DFs exhibited clear spatiotemporal heterogeneity. Annual cumulative solar radiation and annual cumulative precipitation had the most significant effect on GNPP. Regions with positive effects (PEs) of grassland area change and grassland cover change were mostly located in eco-engineering zones. The PEs of DFs on GNPP increased. 4) The contribution of CC to GNPP changes and the dominant area share were greater than those of HA. However, areas with PEs of HA increased gradually. The synergies between eco-protection policies and natural regulation are therefore critical to the eco-environmental protection in the YRB.     

基于Biome-BGC模型及树木年轮的太白红杉林生态系统对气候变化的响应研究

利用Biome-BGC模型模拟了1960—2013年太白山太白红杉林生态系统的净初级生产力(NPP),对其与太白红杉的径向生长关系进行了探讨,并分析了NPP值对气候变化的响应关系。结果表明:1960—2013年太白山太白红杉林北坡NPP年均值为305.33g C m~(-2)a~(-1),南坡为320.71g C m~(-2)a~(-1),南北坡的NPP值均呈现出一定的上升趋势,北坡的上升速率(0.47g C m~(-2)a~(-1))要小于南坡(1.29g C m~(-2)a~(-1)),但是北坡太白红杉分布下限区NPP值波动浮动较大。且北坡太白红杉NPP值随着海拔高度的上升而逐渐下降,低海拔的变化振幅要大于高海拔地区,南坡无明显变化。多数采样点的模拟NPP值与树轮宽度指数年际变化趋势趋于一致,相关关系呈显著相关。太白红杉标准年表、模型模拟NPP值与气象因子的相关分析均表明太白红杉的生长与生长季气温的相关性显著高于降水,即生长季的气温是太白红杉生长的限制因子。气候的变化作为制约太白红杉生境的重要因素,影响了太白红杉树木的生长,进而对NPP的变化产生了影响。树木年轮很好的检验了Biome-BGC模型模拟结果。     

气候变化和人类活动对中国陆地生态系统总初级生产力的影响厘定研究

总初级生产力(GPP)是生态系统植被光合作用生成有机物的能力表征,是生态系统服务功能的基础,关系到区域社会经济可持续发展及区域生态安全。基于生态系统过程模型CEVSA2,应用中分辨率成像光谱仪(MODIS)卫星遥感的叶面积指数数据产品(MCD15A2H),以强迫法构建了遥感数据驱动的模型新版本——CEVSA-RS;基于CEVSA-RS模拟分析了气候变化和人类活动对中国陆地生态系统GPP时空变化的相对影响,从气候潜在总初级生产力(GPP_CL)和现实总初级生产力(GPP_RS)的大小和趋势两方面厘定了人类活动影响。2000至2017年全国平均潜在GPP(1016.36 gC m^-2a^-1)略高于对应现实GPP(962.85 gC m^-2a^-1),但存在明显的空间分异:长江以南大部、秦岭、太行山脉以东以及大兴安岭以东和长白山地区等森林植被覆盖区,现实GPP高于潜在GPP;而西部草地及灌丛等地区现实GPP低于潜在GPP。全国GPP呈显著增加趋势(P<0.05)...     

长江流域陆地植被总初级生产力时空变化特征及其气候驱动因子

长江流域是我国重要的工农业生产区和生态安全屏障。深入开展长江流域陆地植被总初级生产力（GPP）时空变化特征和驱动因子研究,对了解变化环境下区域植被生长状况和生物固碳能力、掌握生态环境质量具有重要意义。基于MODIS GPP遥感数据产品、土地利用和气象观测数据,采用趋势分析和偏相关分析法,系统研究了2000-2015年间长江流域陆地植被GPP时空变化特征,探讨了不同二级水资源区内气候因子对GPP变化影响的空间差异,揭示了不同土地利用类型GPP变化特征以及气候因子作用。结果表明：1）长江流域陆地植被覆盖区GPP在0.3-2765 gC m^-2 a^-1之间,均值约990.46 gC m^-2 a^-1,多年平均GPP总量为1.735 P gC;2）近年来,长江流域GPP呈不显著上升趋势,趋势率为2.39 gC m^-2 a^-1。空间上,GPP上升区和下降区分别占总流域面积的68%和32%。各二级水资源区内,除了洞庭湖流域和太湖流域GPP呈下降趋势外,其他区GPP均呈上升趋势;3）不同土地利用类型GPP均值在198.50-1276.90 gC m^-2 a^-1之间。各土地利用类型中除水田GPP呈微弱下降外,其他均呈上升趋势,尤其是高、中、低覆盖度草地GPP上升趋势较为显著;4）不同气候因子对植被GPP变化的影响程度在不同二级水资源区、不同土地利用类型间均存在一定差异,但就长江流域整体而言,GPP年际变化受温度影响显著,其次是蒸发,而降水等其他气候因子的影响不大。     

南方丘陵山地带植被净第一性生产力时空动态特征

基于MODIS数据并结合气象资料和植被参数,利用修正过最大光能利用率的CASA(Carnegie-Ames-Stanford Approach)模型,对国家生态安全屏障区的"两屏三带"之一南方丘陵山地带2000—2010年的植被净第一性生产力(NPP)进行模拟,并对其时空分布格局进行了分析。研究结果表明:(1)研究区2000—2010年期间年NPP的变化范围为406.0—485.6 g C m-2a-1,年平均NPP为445.7 g C m-2a-1,高于全国平均水平;NPP年际上升趋势不显著(P=0.39),平均增加值为2.28 g C m-2a-1;(2)NPP空间分布特征与植被类型具有较好的一致性,单位面积NPP以混交林覆盖区最高(501.0 g C m-2a-1),草地覆盖区NPP最低(390.7 g C m-2a-1);(3)植被NPP的时空变化与气温、降雨和太阳辐射等自然因素的变化有直接关系,而社会、经济、政策等人为因素通过改变土地利用方式来间接影响。     

近40a甘肃省气候生产潜力时空变化特征

根据甘肃省69个气象站1971-2007年温度和降水资料,采用Miami模型、Thornthwaite Memorial模型计算了全省温度生产潜力、降水生产潜力和气候生产潜力,分析了影响气候生产潜力的气候驱动力,用经验正交函数(EOF)分析其时空变化特征。结果表明:温度生产潜力显著增加,降水生产潜力略有减少,平均气候生产潜力为733.86 kg · hm^-2 · a^-1,呈逐渐减少趋势。区域差异性明显,呈东南-西北递减,陇南山区＞陇东高原＞陇中高原＞甘南草原＞河西走廊,气候生产潜力以1997年为转型年。增湿和增温均有利于气候生产潜力的增加,但增湿增益更为显著,另气候的暖干化趋势是研究区气候生产潜力减少的重要原因。     

基于Biome-BGC模型的北方杨树人工林碳水通量对气候变化的响应研究

研究中国北方杨树人工林碳水通量对气候变化的响应,对于制定合理的经营管理措施以应对区域的气候变化具有重要意义。基于对杨树人工林碳水通量的连续监测数据和对Biome-BGC模型参数的校准,模拟分析杨树人工林碳水通量及水分利用效率(WUE)对气候变化(气温上升、降水变化和大气CO_2浓度上升)的响应规律。结果表明,Biome-BGC模型校准后显著提升了其对杨树人工林碳水通量的模拟精度,对GPP、ET模拟结果的Nash-Sutcliffe效率系数(NS)分别为0.69和0.63,各自提高了64.3%和80%,均方根误差(RMSE)则分别降低至1.94 g C m~(-2) d~(-1)和0.88 mm/d,分别下降了26.5%和25.4%。在未来气候变化情景中,单独的气温上升、降水增加和大气CO_2浓度上升分别造成GPP的降低、升高和升高,其中GPP对大气CO_2浓度上升的响应程度(28%—44%)远高于对气温上升(1%—5%)和降水变化(3%—10%)的,ET则主要受降水的影响,响应程度在5%—14%之间。GPP和ET对气候变化的响应则受不同水平的气温上升、降水变化和大气CO_2浓度上升三者综合作用的影响。基于GPP和ET对气候变化的响应,WUE随气温上升、降水增加表现为降低趋势,随降水减少和大气CO_2浓度升高则呈升高趋势;其对未来气候中大气CO_2浓度升高的响应程度为27.7%—43.6%,远高于对气温上升(1.2%—5.8%)和降水变化(1.2%—3.5%)的,说明未来气候变化中大气CO_2浓度上升是促进杨树生长的主要因素;其中相对于当前WUE(2.8 g C/kg H_2O),C2T2P1和C0T3P0情景下WUE的升高和降低幅度最大,分别为45.4%和5.8%。     

Interannual variations in GPP in forest ecosystems in Southwest China and regional differences in the climatic contributions

Forest ecosystem plays an important role as carbon sinks in Southwest China. Currently, remote sensing technology has been widely used to substantially model the high temporal and spatial variation in gross primary production (GPP) at a site or regional scale. However, during the growing season, the regional uncertainty of GPP in the forest ecosystem and the relative contributions of climate variations to interannual variation (IAV) of GPP are not well understood across Southwest China. Our research focuses on the joint analysis of the three-cornered hat (TCH) algorithm and uses the contribution index to analyse the model's uncertainties varying with plant functional types (PFTs), climate zones, and the contribution of climate variabilities to GPP IAV. Here, three GPP datasets are used to investigate how climate variabilities contribute to the GPP IAV during the growing season. The uncertainties in GPP vary from 829.33 g C m⁻² year⁻¹ to 2031.86 g C m⁻² year⁻¹ for different models in different climate zones and different PFTs. Additionally, the results highlight that precipitation dominates the interannual variation in GPP in forest ecosystem during the growing season in Southwest China. It makes the largest contribution (34.46%) to the IAV of GPP in the climate zone of E (cold subtropical highland area) and the largest contribution (80.83%) to PFTs of the MF (mixed forest). Our study suggests the availability and applicability of GPP products can be used to assess GPP uncertainties and analyse the contributions of climate factors to GPP IAV in forest ecosystem or other ecosystems.     

Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate

Aim To examine the global pattern of the net primary production (NPP)/gross primary production (GPP) ratio of the Earth's land area along geographical and climatic gradients. Location The global planetary ecosystem. Methods The 4‐year average annual NPP/GPP ratio of the Earth's land area was calculated using 2000–03 Moderate Resolution Imaging Spectroradiometer (MODIS) data. The global pattern of the NPP/GPP ratio was investigated by comparing it among each typical terrestrial ecosystem and plotting it along a geographical and climatic gradient, including latitude, altitude, temperature and precipitation. Results The global terrestrial ecosystem had an average NPP/GPP ratio value of 0.52 with minor variation from 2000 to 2003. However, the NPP/GPP ratio showed considerable spatial variation associated with ecosystem type, geographical location and climate. Densely vegetated ecosystems had a lower NPP/GPP ratio than sparsely vegetated ecosystems. Forest ecosystems had a lower NPP/GPP ratio than shrub and herbaceous ecosystems. Geographically, the NPP/GPP ratio increased with altitude. In the Southern Hemisphere, the NPP/GPP ratio decreased along latitude from 30° to 10° and it exhibited high fluctuation in the Northern Hemisphere. Climatically, the NPP/GPP ratio exhibited a decreasing trend along enhanced precipitation when it was less than 2300 mm year^?1 and a static trend when the annual precipitation was over 2300 mm. The NPP/GPP ratio showed a decreasing trend along temperature when it was between –20 °C and 10 °C, and showed an increasing trend along rising temperature when it was between –10 °C and 20 °C. Within each ecosystem, the NPP/GPP ratio revealed a similar trend to the global trend along temperature and precipitation. Conclusions The NPP/GPP ratio exhibited a pattern depending on the main climatic characteristics such as temperature and precipitation and geographical factors such as latitude and altitude. The findings of this research challenge the widely held assumption that the NPP/GPP ratio is consistent regardless of ecosystem type.     

基于遥感-过程耦合模型的1988～2004年青海三江源区净初级生产力模拟

 三江源区不仅是地处青藏高原的全球气候变化的敏感区, 也是我国甚至亚洲最重要河流的上游关键源区。作为提供物质基础的植被净初级生产力(Net primary production, NPP), 是评价生态系统状况的重要指标。该文应用已在碳通量观测塔验证, 扩展到区域水平的遥感-过程耦合模型GLOPEM-CEVSA, 以空间插值的气象数据和1 km分辨率的AVHRR遥感反演的FPAR数据为模型主要输入, 模拟并分析了1988～2004年该区NPP时空格局及其控制机制。结果表明, 该区植被平均NPP为143.17 gC·m^–2·a^–1, 呈自东南向西北逐渐降低的空间格局, 其中, 以森林NPP最高(267.90 gC·m^–2·a^–1), 其次为农田(222.94 gC·m^–2·a^–1)、草地(160.90 gC·m^–2·a^–1)和湿地(161.36 gC·m^–2·a^–1), 荒漠最低(36.13 gC·m^–2·a^–1)。其年际变化趋势在空间上呈现出明显的差异, 西部地区NPP表现为增加趋势, 每10 a增加7.8~28.8 gC·m^–2; 而中、东部表现为降低趋势, 每10 a降低13.1~42.8 gC·m^–2。根据显著性检验, NPP呈增加趋势(趋势斜率b>0), 显著性水平高于99%和95%的区域占研究区总面积的13.43%和20.34%, 主要分布在西部地区; NPP呈降低趋势(趋势斜率b<0), 显著性水平高于99%和95%的区域占研究区面积的0.75%和3.77%, 主要分布在中、东部地区, 尤以该区长江和黄河等沿线区分布更为集中, 变化显著性也更高。三江源NPP的年际变化趋势的气候驱动力分析表明, 整个区域水平上该地区植被生产力受气候变化的主导, 西部地区暖湿化趋势, 造成了该地区生产力较为明显的、大范围的增加趋势; 但东、中部地区则主要受人类活动的影响, 特别是长江、黄河等河流沿线, 是人类居住活动密集的地区, 造成这些地区放牧压力较大、草地退化严重, 而该地区暖干化趋势加剧了这一过程。     

Decrease in winter respiration explains 25% of the annual northern forest carbon sink enhancement over the last 30 years

Aim Winter snow has been suggested to regulate terrestrial carbon (C) cycling by modifying microclimate, but the impacts of change in snow cover on the annual C budget at a large scale are poorly understood. Our aim is to quantify the C balance under changing snow depth. Location Non‐permafrost region of the northern forest area. Methods Here, we used site‐based eddy covariance flux data to investigate the relationship between depth of snow cover and ecosystem respiration (R_eco) during winter. We then used the Biome‐BGC model to estimate the effect of reductions in winter snow cover on the C balance of northern forests in the non‐permafrost region. Results According to site observations, winter net ecosystem C exchange (NEE) ranged from 0.028 to 1.53 gC·m^?2·day^?1, accounting for 44 ± 123% of the annual C budget. Model simulation showed that over the past 30 years, snow‐driven change in winter C fluxes reduced non‐growing season CO₂ emissions, enhancing the annual C sink of northern forests. Over the entire study area, simulated winter R_eco significantly decreased by 0.33 gC·m^?2·day^?1·year^?1 in response to decreasing depth of snow cover, which accounts for approximately 25% of the simulated annual C sink trend from 1982 to 2009. Main conclusion Soil temperature is primarily controlled by snow cover rather than by air temperature as snow serves as an insulator to prevent chilling impacts. A shallow snow cover has less insulation potential, causing colder soil temperatures and potentially lower respiration rates. Both eddy covariance analysis and model‐simulated results show that both R_eco and NEE are significantly and positively correlated with variation in soil temperature controlled by variation in snow depth. Overall, our results highlight that a decrease in winter snow cover restrains global warming as less C is emitted to the atmosphere.     

Influences of error distributions of net ecosystem exchange on parameter estimation of a process-based terrestrial model: A case of broad-leaved Korean pine mixed forest in Changbaishan, China

Model predictions can be improved by parameter estimation from measurements. It was assumed that measurement errors of net ecosystem exchange (NEE) of CO₂ follow a normal distribution. However, recent studies have shown that errors in eddy covariance measurements closely follow a double exponential distribution. In this paper, we compared effects of different distributions of measurement errors of NEE data on parameter estimation. NEE measurements in the Changbaishan forest were assimilated into a process-based terrestrial ecosystem model. We used the Markov chain Monte Carlo method to derive probability density functions of estimated parameters. Our results showed that modeled annual total gross primary production (GPP) and ecosystem respiration (Re) using the normal error distribution were higher than those using the double exponential distribution by 61–86 gC m^?2 a^?1 and 107–116 gC m^?2 a^?1, respectively. As a result, modeled annual sum of NEE using the normal error distribution was lower by 29–47 gC m^?2 a^?1 than that using the double exponential error distribution. Especially, modeled daily NEE based on the normal distribution underestimated the strong carbon sink in the Changbaishan forest in the growing season. We concluded that types of measurement error distributions and corresponding cost functions can substantially influence the estimation of parameters and carbon fluxes.     

甘南高寒草甸碳收支时空格局及动态模拟

草甸生态系统具有强大的碳汇功能,在全球碳循环过程中发挥着重要作用。区域尺度草甸生态系统碳通量的精准模拟,可以为揭示草地碳循环对全球变化的反馈机制提供理论依据。生态过程模型则是分析和预测区域碳平衡的重要途径。以甘南州高寒草甸生态系统为研究对象,利用参数优化后的 Biome-BGC 模型,模拟1979-2018年高寒草甸总初级生产力 (Gross Primary Productivity, GPP)和净生态系统生产力 (Net Ecosystem Productivity, NEP),以表征该区域碳收支的时空分布特征。以上述40年实测气象数据为基准,并结合第六次国际耦合模式比较计划 (Coupled Model Intercomparison Project phase 6,CMIP6)中的3种共享社会经济路径 (Shared Socio-economic Pathways, SSPs)情景,对甘南州2019-2100年高寒草甸碳收支进行情景模拟。结果表明:(1) 参数优化后的Biome-BGC模型能较好的模拟甘南州高寒草甸GPP和NEP,且GPP模拟对比NEP的模拟效果更好;(2) 甘南州高寒草甸在整个研究阶段表现为碳汇,过去40年GPP、NEP波动范围为600-1100 g C m^-2 a^-1、150-300 g C m^-2 a^-1,GPP显著上升,NEP呈波动性上升趋势。未来暖湿化情景下,高寒草甸碳收支年际波动较大,NEP呈先上升再下降趋势,2060年前后出现极小值,年均增幅约为2.02 g C m^-2 a^-1,气温、降水和大气CO₂浓度升高共同影响该地碳收支格局;(3) 季节尺度上表现为冬春季节为碳源、夏秋季节为碳汇,植被生长季固碳作用增强。年内GPP、NEP呈倒"U"型变化趋势,峰值均出现在7、8月,低温以及持续增温对碳汇具有抑制作用,生长季降水量与植被生产力呈正相关;(4) 碳汇/碳源的空间分布随时间而变化,具有明显的地域差异性,总体上碳汇增长率由西南向东北递减。