, 周蕾, 王绍强An analysis of the Gross Primary Productivity simulation difference resulting from the spatial resolution
WANGMiaomiao, ZHOULei, WANGShaoqiang通讯作者:
收稿日期:2015-11-5
修回日期:2016-02-18
网络出版日期:2016-04-20
版权声明:2016《地理研究》编辑部《地理研究》编辑部
基金资助:
作者简介:
-->
展开
摘要
关键词：
Abstract
Keywords：
-->0
PDF (7921KB)元数据多维度评价相关文章收藏文章
本文引用格式导出EndNoteRisBibtex收藏本文-->
1 引言
全球气候变化尤其是气候变暖对人类生存环境的影响,越来越受到社会各界的普遍关注。IPCC第五次评估报告指出,过去的130年全球升温0.85 ℃[1]。气候变暖主要源于温室气体CO2浓度升高[2],而陆地植被通过光合作用固定大气中的CO2和能量所形成的总初级生产力（Gross Primary Productivity,GPP）是进入碳循环过程的起始水平,也是生态系统碳循环的基础[3]。准确估算全球或区域GPP,有助于定量化分析陆地生态系统生产力对全球气候变化响应。过去几十年各种生态模型的发展,为大区域尺度陆地生态系统碳循环提供了重要的研究手段[4,5]。但由于各模型本身结构、参数设定、输入数据及空间分辨率等的不同,使模型在区域及全球尺度上的模拟结果仍然存在很大的差异。目前,对模型结果差异的研究主要还集中在模型结构、参数设定及输入数据上,Zhang等[6]研究了CEVSA2模型中参数设定及气象数据的不同对2003-2005年中国长白山地区对GPP、RE及NEE模拟结果的影响,表明模型中由参数设定引起的GPP和RE差异为5%~8%,而使NEE的差异达到23%~37%;Schaefer等[7]基于39个通量站的数据,比较了26个模型模拟北美地区GPP差异,以此探讨不同模型结构及环境因素对模型模拟的响应。但即使是同一模型、同一来源输入数据,由于空间分辨率不同导致空间异质性的存在,仍有可能会使模拟结果产生差异,但目前关于这一方面的研究还较少。
空间异质性指生态学变量在空间上的不均匀性和复杂性,是尺度的函数,在不同尺度上具有不同的格局。空间异质性会导致不同的气象环境及LAI等影响生态系统固碳能力的因子。在区域尺度上,提高空间分辨率能够降低GPP模拟的不确定性[8]。但是模型在实际应用中往往需要模拟大尺度、长时间序列数据,考虑到模拟时间、数据存储容量及分析难易性等因素时,往往通过降低空间分辨率以提高模拟效率。但降低空间分辨率后的模拟结果是否可靠以及与较高空间分辨率模拟结果间的差距等问题还有待研究。
因此,本文应用生态过程模型（Boreal Ecosystem Productivity Simulator,BEPS）,结合ChinaFLUX中4个通量站点长白山站（CBS）、千烟洲站（QYZ）、海北站（HBGC）及当雄站（LSDX）2003-2005年的通量观测数据,对不同空间分辨率输入数据得到的GPP模拟结果进行差异性分析,以此定量化研究由于空间分辨率不同而导致的模型模拟结果间的差异。
2 研究方法与数据来源
2.1 研究方法
BEPS模型是Liu等在FOREST-BGC模型的基础上发展起来的生态过程模型[9-11]。该模型的主要特点是结合了生态过程和遥感数据的优势,能够反映现实条件下植被的光合作用和蒸腾作用的实际变化,模型最初用于加拿大北方森林（Boreal Forest）生产力的模拟研究,模型通过气孔导度生理调节子模型将碳、水循环耦合,将冠层分为阳叶和阴叶把叶片尺度瞬时Farquhar 光化学模型扩展到冠层尺度[12],实现遥感数据与机理生态模型的结合,模拟生态系统的碳、水和能量平衡,该模型已经在加拿大、美国和中国等地区得到广泛应用[13-17]。Liu等运用BEPS模型模拟2003-2006年HBGC站上500 m空间分辨率的GPP,并与通量观测数据进行对比,R2在0.85~0.95之间,表明BEPS模型适用于中国高寒草甸的GPP模拟。GPP的计算如式（1）~式（3）所示。式中：Asun和Ashade为单叶片的阳叶和阴叶的光合作用量;LAIsun和LAIshade表示单叶片的阳叶和阴叶的叶面积指数。
阳叶的叶面积指数为：
式中：θ为太阳高度角;
阴叶的叶面积指数：
2.2 数据来源与处理方法
BEPS模型输入数据包括气象数据、遥感数据及土壤数据等。为了比较不同空间分辨率引起模型模拟结果的差异,并排除由于输入数据来源及处理方法的不一致而使模拟结果产生系统性偏差,因此本文采用的两套输入数据（除了分辨率不同外的数据来源及处理方法均一致）。按模型输入数据要求,同一套输入参数需要具有相同的行列号及投影坐标,以保证像元一一对应,本文统一采用Albers投影（第一标准纬线、第二标准纬线以及中央经线分别为25°N、47°N、110°E）。2.2.1 气象数据 在中国气象科学数据共享服务网上下载气象站点数据,包括逐日最高气温、最低气温、日降水量、日相对湿度及辐射等,利用AUSPLINE算法[18,19]插值成1 km及8 km空间分辨率的栅格数据。ANUSPLIN是由澳大利亚国立大学利用FORTRAN语言开发的空间插值模型,利用薄板平滑样条函数来拟合气象数据[19],目前已在国际上广泛应用。AUSPLINE插值的优点是输入参数灵活,且考虑了高程对气象因子的影响,插值精度高。
2.2.2 叶面积指数数据 叶面积指数（leaf area index,LAI）通常定义为单位地表面积上绿叶总面积的一半[20],作为表征冠层结构的关键参数,它影响植被光合、呼吸、蒸腾、降水截留、能量交换等诸多生态过程[21,22],是BEPS模型的重要输入参数[23]。本文使用的LAI数据采用MOD09 A1及MCD43 A1数据和基于4尺度几何光学模型的反演算法生成的每8天的1 km空间分辨率数据产品[23]。8 km空间分辨率的LAI数据由对应1 km空间分辨率内的64个像元的LAI取平均得到。
2.2.3 土壤质地数据 土壤质地数据来源于北京师范大学全球变化与地球系统科学研究院陆面过程和资源生态实验室的数据网站（http://globalchange.bnu.edu.cn/research/）,空间分辨率为1 km。8 km空间分辨率数据由1 km数据重采样得到。
2.2.4 土地覆盖数据 通量站点一般都选择具有典型生态系统类型的区域内,其周围的土地覆盖类型不会轻发生变化。本文选用中国科学院遥感与数字地球研究所提供的2010年最新的中国土地覆盖数据ChinaCover2010。ChinaCover2010分类系统的一级类与IPCC系统保持一致共6类,二级类采用了由FAO的 LCCS生成的具有全球统一代码的38个类型。ChinaCover2010数据精度全国平均一级为94%、二级为86%,空间分辨率为250 m[24]。
由于土地覆盖数据本身的特殊性,不能使用常规的最邻近等方式重采样,故本文采用众数重采样法：将对应250 m空间分辨率的16个像元内出现频数最多的类型作为1 km空间分辨率所对应像元的植被类型。首先在GRID环境下使用Block-majority工具将4×4像元内的共16个像元都赋值成该范围出现频率最多的值,然后再使用最邻近方法重采样成1 km空间分辨率的输入数据,以此保证1 km空间分辨率像元所对应的植被类型为对应范围内的主要土地覆盖类型。并用同样的方法重采样得到8 km空间分辨率的土地覆盖数据。
2.2.5 通量观测数据 采用2003-2005年中国通量观测网提供的4个通量站点月尺度通量观测数据来验证BEPS模型模拟结果,其中包括2个森林站、2个草地站,通量站点的具体信息如表1所示。
Tab. 1
表1
表14个通量站点信息
Tab. 1The information of four ChinaFLUX sites
| 站点名 | 简写 | 植被类型 | 经度（ | 纬度（ |
|---|---|---|---|---|
| 长白山 | CBS | 针阔混交林 | 128.09 | 42.40 |
| 千烟洲 | QYZ | 常绿针叶林 | 115.05 | 26.73 |
| 海北 | HBGC | 高寒草甸 | 101.31 | 37.61 |
| 当雄 | LSDX | 高寒草甸 | 91.06 | 30.50 |
新窗口打开
CBS站位于吉林省东南部,长白山北坡,属于温带季风气候,根据中国科学院长白山森林生态系统定位研究站1982-2003年的气候观测资料显示,该地区22年的平均气温为3.6℃,1月和7月的平均气温分别为-15.6℃和19.7℃,年均降水量为695.3 mm,最大积雪深度为27 cm,平均相对湿度为72%。林木株数为560株/hm2,郁闭度为0.8,林分总蓄积量超过380 m3/hm2。
QYZ站地处江西省泰和县,属亚热带季风气候,年平均气温为17.9℃,年平均降水量为1542.4 mm。通量塔周围大约1 km2范围内森林覆盖度高达90%,近100 km2范围内森林覆盖近70%,林分为1985年前后营造的人工林,主要植被类型为马尾松、湿地松和杉木。
HBGC站地处青藏高原东北隅,属温带大陆性气候,年均气温为-2.0 ℃,年均降水量为580 mm,以草地为主,冠层高度0.3~0.6 m,主要的类型为金露梅、异针茅、藏异燕麦等。
LSDX站地处西藏当雄草原站内,属高原山地气候,具有太阳辐射强、气温低、日差较大、年差较小等特点。年均气温为1.3 ℃,年均降水量为477 mm,以草地为主,冠层高度小于0.2 m,主要植被类型为丝颖针茅、窄叶苔草和小嵩草等。
3 结果分析
3.1 输入数据的差异
4个站点在1 km及8 km空间分辨率上的日降水量、日相对湿度及辐射的差异为0,日最高温差异的最大值为0.21 ℃,日最低温差异最大值为0.17 ℃,即气象数据的差异较小,忽略不计。土壤质地在4个站点的差异分别为4.89%、9.72%、5.83%以及5.44%。BEPS模型中LAI的敏感性最大,土壤质地的敏感性较小[3],因此本文不讨论土壤质地对模拟结果的影响,主要讨论不同空间分辨率下的土地覆盖数据与叶面积指数的差异。本文统计了4个通量站点在8 km空间分辨率范围内对应的1 km空间分辨率的64个像元内各土地覆被类型所占比例,以此研究不同空间分辨率下空间异质性对模拟结果的影响。统计结果如图1所示（占比小于5%的未标出具体类型）。
显示原图|下载原图ZIP|生成PPT图1通量站点8 km × 8 km范围内1 km分辨率64个像元内各植被类型所占比例
-->Fig. 1The proportion of each vegetation type in the 1 km pixels around the flux towers in the range of 8 km × 8 km
-->
由图1可知,8 km范围内的4个通量站点均有不同程度的混合情况,森林站的混合程度大于草原站。CBS以落叶阔叶和针阔混交林为主,占81.25%,另外含有6.25%的农田和12.5%的人工表面,人工表面的存在可能会使LAI值变小,而农田由于作物收割等因素,会使LAI出现“双峰”现象;QYZ站主要以常绿针叶林为主,并含7.81%的农田,由于农田的LAI小于森林的LAI,所以农田的存在会降低LAI值,并使其出现一定程度“双峰”现象。各站点在1 km及8 km下LAI值及两者间差距的绝对值如图2所示。
显示原图|下载原图ZIP|生成PPT图21 km及8 km内LAI及两者间的差异
-->Fig. 2The LAI differences in the 1 km and 8 km spatial resolution
-->
图2表明,4个通量站点的LAI在两种空间分辨率下的变化趋势一致,但大小不同。CBS站、QYZ站、HBGC站以及LSDX站在1 km空间分辨率像元内LAI 平均值依次为：3.65、6.23、0.72和0.06;在8 km空间分辨率下的平均值依次为：2.82、4.63、0.65、0.08;由于8 km空间分辨率像元范围内混合像元的存在,使得CBS站,QYZ站和HBGC站的1 km空间分辨率像元内的LAI均大于8 km对于像元的LAI值。
两种空间分辨率间LAI的差异的平均值分别为：0.85、1.60、0.13、0.04。LAI差异的相对大小为：CBS>QYZ>HBGC>LSDX,即在森林站的LAI差异大于草地站。CBS站在4月份的差异达到峰值,最大值为1.58,最小差异值出现在每年的11月,最小值为0.002;QYZ站在4月份及9月份出现两个峰值,最大值达5.03,最小值出现在每年的1月份,为0.30;HBGC站差异的峰值出现在每年的7月份,最大差为0.78,最小值为0;而LSDX站峰值出现在每年的6月份,最大差值为0.34,最小差值为0。表明1 km和8 km空间分辨率的LAI在生长季上的差异大于非生长季。
3.2 模拟结果差异分析
3.2.1 与通量数据间差异 BEPS模型模拟结果为日尺度的,为了比较模拟结果与实际观测值差异,将每日模拟的GPP结果合成每月的进行比较,分析其季节性变化趋势与实际观测值的一致性及其相关性。各站点模拟GPP结果与实测数据对比如图3所示。
显示原图|下载原图ZIP|生成PPT图3GPP模拟结果与实测数据对比图
-->Fig. 3Comparison of the simulated GPP and observation data
-->
从图3可知,无论在1 km空间分辨率还是8 km空间分辨率下,4个通量站点的GPP模拟结果的季节动态变化趋势与通量观测值一致,但在大小上略微有所差异;两种空间分辨率的模拟结果与通量观测数据间的P值均小于0.001,且两者间的相关性R2分别为0.79~0.97(1 km),0.69~0.97(8 km),表明两种空间分辨率下BEPS模型模拟的结果与通量观测值呈显著相关,能较好地反映各站点GPP的季节动态变化;另外,结合图2中LAI的变化趋势,可知GPP与LAI的变化趋势及相对大小均一致,表明不同空间分辨率GPP模拟的差异性主要是由于8 km范围内混合像元导致LAI的不同,因此,LAI的精度是BEPS模型模拟GPP的关键。
为了更直观比较两种空间分辨率下GPP的模拟值与观测值之间差异的大小,本文将两种分辨率下的模拟值与观测值作了差值比较。各站点在两种分辨率下的模拟值与观测值差异的绝对值如图4所示。
显示原图|下载原图ZIP|生成PPT图4两种分辨率模拟结果与实测数据间差异的绝对值
-->Fig. 4The absolute difference values simulations and observations between the two spatial resolutions
-->
图4表明,两种空间分辨率模拟结果与通量观测值间差异的季节性动态变化趋势大致相同。除了CBS站及HBGC站个别月份的模拟值与通量观测值差距较大外,其余模拟结果与通量观测值间的差异均小于60 gC/m2/month。森林站的差异趋势较草地站复杂,CBS及QYZ两个森林站在每年的春季和秋季的差异大于夏季和冬季;HBGC和LSDX草地站在每年的4-8月份的差异较大。表明各通量站点的模拟结果与通量观测数据在生长季上的差异大于非生长季,且森林站的差异大于草地站（表2）。
Tab. 2
表2
表2GPP模拟结果的月均值与通量观测值差异（gC/m2/month）
Tab. 2Sumulated GPP in monthly and its difference with observation data (gC/m2/month)
| 站点名 | 1 km | 8 km | Observed | Abs(1 km-Observed) | Abs(8 km-Observed) |
|---|---|---|---|---|---|
| CBS | 142.64 | 128.84 | 132.79 | 18.86 | 18.67 |
| QYZ | 167.90 | 155.96 | 145.94 | 29.65 | 24.81 |
| HBGC | 29.77 | 27.70 | 41.20 | 11.46 | 13.74 |
| LSDX | 4.07 | 6.26 | 16.36 | 13.42 | 11.87 |
新窗口打开
3.2.2 两种分辨率模拟结果差异 由于通量观测数据本身存在这一定的不确定性,为了更直观的比较两种空间分辨率模拟结果间的差异,本文对两种空间分辨率下的GPP模拟结果做了差异性分析,两者间的差异如图5所示。
显示原图|下载原图ZIP|生成PPT图51 km与8 km空间分辨率GPP模拟结果差异绝对值
-->Fig. 5The absolute difference of simulated GPP between the 1 km and 8 km spatial resolution
-->
由图5可知,4个站点的月均差异值分别为14.43 gC/m2/month、12.05 gC/m2/month、4.79 gC/m2/month及3.22 gC/m2/month。整体上4个通量站点在两种空间分辨率上的差异大小为：CBS>QYZ>HBGC>LSDX,结合图1可知,CBS及QYZ站8×8 km范围内的土地覆盖类型较HBGC及LSDX站复杂,使森林站下垫面的均一性低于草地站,也即森林站的空间异质性高于草地站,所以使两种空间分辨率下森林站的差异大于草地站。另外,CBS站在每年的3-6月间的差异较大,其余月份差异较小;QYZ站在每年的6月及12月份差异小,其余月份差异较大;HBGC及LSDX站,在5-9月份间的差异较大,其余月份差异接近0。根据通量站点的植被类型以及植被生长季时间分析表明：整体上,两种空间分辨率在生长季上的差异大于非生长季。
4 讨论
（1）本文使用的土地覆盖数据与反演LAI使用的土地覆盖数据并非同一套数据,所以在研究混合像元对LAI差异的影响上可能有所偏差。（2）通量贡献区的大小会与位置会随当时的风向、仪器观测高度、下垫面粗糙度和边界层特征（如大气稳定度等）的变化而发生瞬时的改变。因此,本文用于模型结果验证的通量数据本身也存在一定的不确定性。
（3）由于条件限制,本文仅仅选择了BEPS模型进行试验,没有采用更多的模型模拟结果进行对比分析,空间分辨率对于模型模拟结果的影响还需要更多的实证研究。
5 结论
本文通过BEPS模型在1 km与8 km空间分辨率上的GPP模拟结果,结合8 km范围内1 km分辨率的64个像元内的土地覆盖类型的混合情况,分析两种分辨率下LAI的差异、GPP模拟值与观测值间的差异以及两种GPP模拟结果间的差异,以此定量研究1 km与8 km空间分辨率下GPP模拟结果的差异,结果表明：（1）1 km与8 km分辨率模拟结果的变化趋势及相对大小均与叶面积指数的变化一致,表明实际应用中混合像元的存在导致的叶面积指数不同是模拟差异的主要来源,叶面积指数的精度是遥感数据驱动模型模拟GPP的关键。
（2）1 km及8 km空间分辨率的模拟值与观测值的季节动态变化一致,P值小于0.001,R2分别为0.79~0.97(1 km)、0.69~0.97(8 km),表明两种模拟结果均能较好的反映对应站点植被类型的GPP季节性动态变化趋势。
（3）1 km与8 km空间分辨率下4个通量站点的模拟结果在森林生态系统的差异大于草地生态系统,且生长季的差异大于非生长季。因此,BEPS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,但是应研究如何减小模型在森林生态系统及生长季上的差异。
The authors have declared that no competing interests exist.
参考文献 原文顺序
文献年度倒序
文中引用次数倒序
被引期刊影响因子
| [1] | Page 1. CLIMATE CHANGE 2013 The Physical Science Basis WORKING GROUP ICONTRIBUTION TO THE FIFTH ASSESSMENT REPORT OF THE INTERGOVERNMENTALPANEL ON CLIMATE CHANGE WG I INTERGOVERNMENTAL PANEL ON climate change Page |
| [2] | Nutrient cycling affects carbon uptake by the terrestrial biosphere and imposes controls on carbon cycle response to variation in temperature and precipitation, but nutrient cycling is ignored in most global coupled models of the carbon cycle and climate system. We demonstrate here that the inclusion of nutrient cycle dynamics, specifically the close coupling between carbon and nitrogen cycles, in a terrestrial biogeochemistry component of a global coupled climate system model leads to fundamentally altered behavior for several of the most critical feedback mechanisms operating between the land biosphere and the global climate system. Carbon-nitrogen cycle coupling reduces the simulated global terrestrial carbon uptake response to increasing atmospheric COconcentration by 74%, relative to a carbon-only counterpart model. Global integrated responses of net land carbon exchange to variation in temperature and precipitation are significantly damped by carbon-nitrogen cycle coupling. The carbon cycle responses to temperature and precipitation variation are reduced in magnitude as atmospheric COconcentration rises for the coupled carbon-nitrogen model, but increase in magnitude for the carbon-only counterpart. Our results suggest that previous carbon-only treatments of climate-carbon cycle coupling likely overestimate the terrestrial biosphere's capacity to ameliorate atmospheric COincreases through direct fertilization. The next generation of coupled climate-biogeochemistry model projections for future atmospheric COconcentration and climate change should include explicit, prognostic treatment of terrestrial carbon-nitrogen cycle coupling. |
| [3] | . . |
| [4] | abstract Future climate change induced by atmospheric emissions of greenhouse gases is believed to have a large impact on the global carbon cycle. Several offline studies focusing either on the marine or on the terrestrial carbon cycle highlighted such potential effects. Two recent online studies, using ocean–atmosphere general circulation models coupled to land and ocean carbon cycle models, investigated in a consistent way the feedback between the climate change and the carbon cycle. These two studies used observed anthropogenic COemissions for the 1860–1995 period and IPCC scenarios for the 1995–2100 period to force the climate – carbon cycle models. The study from the Hadley Centre group showed a very large positive feedback, atmospheric COreaching 980 ppmv by 2100 if future climate impacts on the carbon cycle, but only about 700 ppmv if the carbon cycle is included but assumed to be insensitive to the climate change. The IPSL coupled climate – carbon cycle model simulated a much smaller positive feedback: climate impact on the carbon cycle leads by 2100 to an addition of less than 100 ppmv in the atmosphere. Here we perform a detailed feedback analysis to show that such differences are due to two key processes that are still poorly constrained in these coupled models: first Southern Ocean circulation, which primarily controls the geochemical uptake of CO, and second vegetation and soil carbon response to global warming. Our analytical analysis reproduces remarkably the results obtained by the fully coupled models. Also it allows us to identify that, amongst the two processes mentioned above, the latter (the land response to global warming) is the one that essentially explains the differences between the IPSL and the Hadley results. |
| [5] | 姝To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately. During the past decade multi-scale ecological experiment and observation networks have been established using various new technologies (e.g. controlled environmental facilities, eddy covariance techniques and quantitative remote sensing), and have obtained a large amount of data about terrestrial ecosystem carbon cycle. However, uncertainties in the magnitude and spatio-temporal variations of the terrestrial carbon sink and in understanding the underlying mechanisms have not been reduced significantly. One of the major reasons is that the observations and experiments were conducted at individual scales independently, but it is the interactions of factors and processes at different scales that determine the dynamics of the terrestrial carbon sink. Since experiments and observations are alwa |
| [6] | Abstract<br/>The uncertainty in the predicted values of a process-based terrestrial ecosystem model is as important as the predicted values themselves. However, few studies integrate uncertainty analysis into their modeling of carbon dynamics. In this paper, we conducted a local sensitivity analysis of the model parameters of a process-based ecosystem model at the Chaibaishan broad-leaved Korean pine mixed forest site in 2003–2005. Sixteen parameters were found to affect the annual net ecosystem exchange of CO<sub class="a-plus-plus">2</sub> (NEE) in each of the three?years. We combined a Monte Carlo uncertainty analysis with a standardized multiple regression method to distinguish the contributions of the parameters and the initial variables to the output variance. Our results showed that the uncertainties in the modeled annual gross primary production and ecosystem respiration were 5–8% of their mean values, while the uncertainty in the annual NEE was up to 23–37% of the mean value in 2003–2005. Five parameters yielded about 92% of the uncertainty in the modeled annual net ecosystem exchange. Finally, we analyzed the sensitivity of the meteorological data and compared two types of meteorological data and their effects on the estimation of carbon fluxes. Overestimating the relative humidity at a spatial resolution of 10?km?×?10?km had a larger effect on the annual gross primary production, ecosystem respiration, and net ecosystem exchange than underestimating precipitation. More attention should be paid to the accurate estimation of sensitive model parameters, driving meteorological data, and the responses of ecosystem processes to environmental variables in the context of global change.<br/> |
| [7] | Abstract Top of page Abstract 1.Introduction 2.Methods 3.Results 4.Conclusions Acknowledgments References Supporting Information [1] Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0 C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0 C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as max (LUE), V cmax (unstressed Rubisco catalytic capacity) or J max (the maximum electron transport rate). |
| [8] | Terrestrial vegetation gross primary production (GPP) is an important variable in determining the global carbon cycle as well as the interannual variability of the atmospheric COconcentration. The accuracy of GPP simulation is substantially affected by several critical model drivers, one of the most important of which is photosynthetically active radiation (PAR) which directly determines the photosynthesis processes of plants. In this study, we examined the impacts of uncertainties in radiation products on GPP estimates in China. Two satellite‐based radiation products (GLASS and ISCCP), three reanalysis products (MERRA, ECMWF, and NCEP), and a blended product of reanalysis and observations (Princeton) were evaluated based on observations at hundreds of sites. The results revealed the highest accuracy for two satellite‐based products over various temporal and spatial scales. The three reanalysis products and the Princeton product tended to overestimate radiation. The GPP simulation driven by the GLASS product exhibited the highest consistency with those derived from site observations. Model validation at 11 eddy covariance sites suggested the highest model performance when utilizing the GLASS product. Annual GPP in China driven by GLASS was 5.55 Pg C yr, which was 68.85%–94.87% of those derived from the other products. The results implied that the high spatial resolution, satellite‐derived GLASS PAR significantly decreased the uncertainty of the GPP estimates at the regional scale. |
| [9] | This paper describes a boreal ecosystems productivity simulator (BEPS) recently developed at the Canada Centre for Remote Sensing to assist in natural resources management and to estimate the carbon budget over Canadian landmass (10-10km). BEPS uses principles of FOREST biogeochemical cycles (FOREST-BGC) (Running and Coughlan, 1988) for quantifying the biophysical processes governing ecosystems productivity, but the original model is modified to better represent canopy radiation processes. A numerical scheme is developed to integrate different data types: remote sensing data at 1-km resolution in lambert conformal conic projection, daily meteorological data in Gaussian or longitude-latitude gridded systems, and soil data grouped in polygons. The processed remote sensing data required in the model are leaf area index (LAI) and land-cover type. The daily meteorological data include air temperature, incoming shortwave radiation, precipitation, and humidity. The soil-data input is the available water-holding capacity. The major outputs of BEPS include spatial fields of net primary productivity (NPP) and evapotranspiration. The NPP calculated by BEPS has been tested against biomass data obtained in Quebec, Canada. A time series of LAI over the growing season of 1993 in Quebec was derived by using 10-day composite normalized difference vegetation index images acquired by the advanced very high resolution radiometer at 1-km resolution (resampled). Soil polygon data were mosaicked, georeferenced, and rasterized in a geographic information system (ARC/INFO). With the use of the process-based model incorporating all major environmental variables affecting plant growth and development, detailed spatial distributions of NPP (annual and four seasons) in Quebec are shown in this paper. The accuracy of NPP calculation is estimated to be 60% for single pixels and 75% for 3x3 pixel areas (9 km). The modeled NPP ranges from 0.6 kg C/m/year at the southern border to 0.01 kg C/m/year at the northern limit of the province. The total annual NPP in Quebec is estimated to be 0.24 Gt C in 1993, which is about 0.3-0.4% of the global NPP. |
| [10] | The purpose of this paper is to upscale tower measurements of net primary productivity (NPP) to the Boreal Ecosystem-Atmosphere Study (BOREAS) study region by means of remote sensing and modeling. The Boreal Ecosystem Productivity Simulator (BEPS) with a new daily canopy photosynthesis model was first tested in one coniferous and one deciduous site. The simultaneous CO 2 flux measurements above and below the tree canopy made it possible to isolate daily net primary productivity of the tree canopy for model validation. Soil water holding capacity and gridded daily meteorological data for the region were used as inputs to BEPS, in addition to 1 km resolution land cover and leaf area index (LAI) maps derived from the advanced very high resolution radiometer (AVHRR) data. NPP statistics for the various cover types in the BOREAS region and in the southern study area (SSA) and the northern study area (NSA) are presented. Strong dependence of NPP on LAI was found for the three major cover types: coniferous forest, deciduous forest and cropland. Since BEPS can compute total photosynthetically active radiation absorbed by the canopy in each pixel, light use efficiencies for NPP and gross primary productivity could also be analyzed. From the model results, the following area-averaged statistics were obtained for 1994: (1) mean NPP for the BOREAS region of 217 g C m 612 yr 611 ; (2) mean NPP of forests (excluding burnt areas in the region) equal to 234 g C m 612 yr 611 ; (3) mean NPP for the SSA and the NSA of 297 and 238 g C m 612 yr 611 , respectively; and (4) mean light use efficiency for NPP equal to 0.40, 0.20, and 0.33 g C (MJ APAR) 611 for deciduous forest, coniferous forest, and crops, respectively. |
| [11] | CiteSeerX - Scientific documents that cite the following paper: A General Model of Forest Ecosystem |
| [12] | Because Farquhar’s photosynthesis model is only directly applicable to individual leaves instantaneously, considerable skill is needed to use this model for regional plant growth and carbon budget estimations. In many published models, Farquhar’s equations were applied directly to plant canopies by assuming a plant canopy to function like a big-leaf. This big-leaf approximation is found to be acceptable for estimating seasonal trends of canopy photosynthesis but inadequate for simulating its day-to-day variations, when compared with eddy-covariance and gas-exchange chamber measurements from two boreal forests. The daily variation is greatly dampened in big-leaf simulations because the original leaf-level model is partially modified through replacing stomatal conductance with canopy conductance. Alternative approaches such as separating the canopy into sunlit and shaded leaf groups or stratifying the canopy into multiple layers can avoid the problem. Because of non-linear response of leaf photosynthesis to meteorological variables (radiation, temperature and humidity), considerable errors exist in photosynthesis calculation at daily steps without considering the diurnal variability of the variables. To avoid these non-linear effects, we have developed an analytical solution to a simplified daily integral of Farquhar’s model by considering the general diurnal patterns of meteorological variables. This daily model not only captures the main effects of diurnal variations on photosynthesis but is also computationally efficient for large area applications. Its application is then not restricted by availability of sub-daily meteorological data. This scheme has been tested using measured CO 2 data from the Boreal Ecosystem–Atmosphere Study (BOREAS), which took place in Manitoba and Saskatchewan in 1994 and 1996. |
| [13] | <h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">We modeled net primary productivity (NPP) at high spatial resolution using an advanced spaceborne thermal emission and reflection radiometer (ASTER) image of a Qilian Mountain study area using the boreal ecosystem productivity simulator (BEPS). Two key driving variables of the model, leaf area index (LAI) and land cover type, were derived from ASTER and moderate resolution imaging spectroradiometer (MODIS) data. Other spatially explicit inputs included daily meteorological data (radiation, precipitation, temperature, humidity), available soil water holding capacity (AWC), and forest biomass. NPP was estimated for coniferous forests and other land cover types in the study area. The result showed that NPP of coniferous forests in the study area was about 4.4 tC ha<sup>−1</sup> y<sup>−1</sup>. The correlation coefficient between the modeled NPP and ground measurements was 0.84, with a mean relative error of about 13.9%.</p> |
| [14] | <h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Large scale process-based modeling is a useful approach to estimate distributions of global net primary productivity (NPP). In this paper, in order to validate an existing NPP model with observed data at site level, field experiments were conducted at three sites in northern China. One site is located in Qilian Mountain in Gansu Province, and the other two sites are in Changbaishan Natural Reserve and Dunhua County in Jilin Province. Detailed field experiments are discussed and field data are used to validate the simulated NPP. Remotely sensed images including Landsat Enhanced Thematic Mapper plus (ETM+, 30 m spatial resolution in visible and near infrared bands) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, 15 m spatial resolution in visible and near infrared bands) are used to derive maps of land cover, leaf area index, and biomass. Based on these maps, field measured data, soil texture and daily meteorological data, NPP of these sites are simulated for year 2001 with the boreal ecosystem productivity simulator (BEPS). The NPP in these sites ranges from 80 to 800 g C m<sup>−2</sup> a<sup>−1</sup>. The observed NPP agrees well with the modeled NPP. This study suggests that BEPS can be used to estimate NPP in northern China if remotely sensed images of high spatial resolution are available.</p> |
| [15] | <h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Local topography significantly affects spatial variations of climatic variables and soil water movement in complex terrain. Therefore, the distribution and productivity of ecosystems are closely linked to topography. Using a coupled terrestrial carbon and hydrological model (BEPS-TerrainLab model), the topographic effects on the net primary productivity (NPP) are analyzed through four modelling experiments for a 5700 km<sup>2</sup> area in Baohe River basin, Shaanxi Province, northwest of China. The model was able to capture 81% of the variability in NPP estimated from tree rings, with a mean relative error of 3.1%. The average NPP in 2003 for the study area was 741 g C m<sup>−2</sup> yr<sup>−1</sup> from a model run including topographic effects on the distributions of climate variables and lateral flow of ground water. Topography has considerable effect on NPP, which peaks near 1350 m above the sea level. An elevation increase of 100 m above this level reduces the average annual NPP by about 25 g C m<sup>−2</sup>. The terrain aspect gives rise to a NPP change of 5% for forests located below 1900 m as a result of its influence on incident solar radiation. For the whole study area, a simulation totally excluding topographic effects on the distributions of climatic variables and ground water movement overestimated the average NPP by 5%.</p> |
| [16] | Net primary productivity (NPP) is an important component of the terrestrial carbon cycle. Accurately mapping the spatial-temporal variations of NPP in China is crucial for global carbon cycling study. In this study the process-based Boreal Ecosystem Productivity Simulator (BEPS) was employed to study the changes of NPP in China's ecosystems for the period from 2000 to 2010. The BEPS model was first validated using gross primary productivity (GPP) measured at typical flux sites and forest NPP measured at different regions. Then it was driven with leaf area index (LAI) inversed from the Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products and meteorological data interpolated from observations at 753 national basic meteorological stations to simulate NPP at daily time steps and a spatial resolution of 500 m from January 1, 2000 to December 31, 2010. Validations show that BEPS is able to capture the seasonal variations of tower-based GPP and the spatial variability of forest NPP in different regions of China. Estimated national total of annual NPP varied from 2.63 to 2.84Pg C center dot yr(-1), averaging 2.74 Pg C center dot yr(-1) during the study period. Simulated terrestrial NPP shows spatial patterns decreasing from the east to the west and from the south to the north, in association with land cover types and climate. South-west China makes the largest contribution to the national total of NPP while NPP in the North-west account for only 3.97% of the national total. During the recent 11 years, the temporal changes of NPP were heterogamous. NPP increased in 63.8% of China's landmass, mainly in areas north of the Yangtze River and decreased in most areas of southern China, owing to the low temperature freezing in early 2008 and the severe drought in late 2009. |
| [17] | . 生态系统蒸散（ET）的时空动态是研究气候变化和陆地生态系统碳水循环的重要因子，与生态系统的净初级生产力（NPP）密切关联。基于1991年～2000年NOAA-AVHRR遥感数据、气象数据以及土壤数据和其他辅助数据，利用改进的生态系统过程模型（Boreal Ecosystem Productivity Simulator， BEPS），模拟了中国陆地生态系统不同时间尺度ET的空间分布格局，分析了10年来中国陆地生态系统ET的时空变化特征及其对气候变化的响应。结果表明：20世纪90年代中国陆地生态系统ET呈上升趋势，10年ET平均值为442.55mm/a；最高值为475.91mm/a，出现在温度和降水都达到峰值的1998年；最低值为425.59mm/a，出现在年降水量最少的1992年。中国陆地生态系统年ET与年均温和年总降水量显著正相关，而年ET与年总降水量的相关性（R<sup>2</sup> = 0.950， P<0.05，n= 10）优于与年均温的相关性（R2 = 0.399， P<0.05，n=10），说明降水可能是中国陆地生态系统年ET变化的主要决定因子。中国陆地生态系统年ET表现出明显的空间分布格局：从西北地区到东南地区呈三级阶梯逐渐增加，与相应的降水分布格局类似；而基于不同植被类型和气候带的年ET表现出一定的地带性规律。从时间格局上看，ET的年内变化主要表现为单峰形式，而年际变化和相应植被类型及气候条件有关。全国年ET变化趋势在空间上有很大的异质性。 生态系统蒸散（ET）的时空动态是研究气候变化和陆地生态系统碳水循环的重要因子，与生态系统的净初级生产力（NPP）密切关联。基于1991年～2000年NOAA-AVHRR遥感数据、气象数据以及土壤数据和其他辅助数据，利用改进的生态系统过程模型（Boreal Ecosystem Productivity Simulator， BEPS），模拟了中国陆地生态系统不同时间尺度ET的空间分布格局，分析了10年来中国陆地生态系统ET的时空变化特征及其对气候变化的响应。结果表明：20世纪90年代中国陆地生态系统ET呈上升趋势，10年ET平均值为442.55mm/a；最高值为475.91mm/a，出现在温度和降水都达到峰值的1998年；最低值为425.59mm/a，出现在年降水量最少的1992年。中国陆地生态系统年ET与年均温和年总降水量显著正相关，而年ET与年总降水量的相关性（R<sup>2</sup> = 0.950， P<0.05，n= 10）优于与年均温的相关性（R2 = 0.399， P<0.05，n=10），说明降水可能是中国陆地生态系统年ET变化的主要决定因子。中国陆地生态系统年ET表现出明显的空间分布格局：从西北地区到东南地区呈三级阶梯逐渐增加，与相应的降水分布格局类似；而基于不同植被类型和气候带的年ET表现出一定的地带性规律。从时间格局上看，ET的年内变化主要表现为单峰形式，而年际变化和相应植被类型及气候条件有关。全国年ET变化趋势在空间上有很大的异质性。 |
| [18] | Thin plate smoothing splines provide accurate, operationally straightforward and computationally efficient solutions to the problem of the spatial interpolation of annual mean rainfall for a standard period from point data which contains many short period rainfall means. The analyses depend on developing a statistical model of the spatial variation of the observed rainfall means, considered as noisy estimates of standard period means. The error structure of this model has two components which allow separately for strong spatially correlated departures of observed short term means from standard period means and for uncorrelated deficiencies in the representation of standard period mean rainfall by a smooth function of position and elevation. Thin plate splines, with the degree of smoothing determining by minimising generalised cross validation, can estimate this smooth function in two ways. First, the spatially correlated error structure of the data can be accommodated directly by estimating the corresponding non-diagonal error covariance matrix. Secondly, spatial correlation in the data error structure can be removed by standardising the observed short term means to standard period mean estimates using linear regression. When applied to data both methods give similar interpolation accuracy, and error estimates of the fitted surfaces are in good agreement with residuals from withheld data. Simplified versions of the data error model, which require only minimal summary data at each location, are also presented. The interpolation accuracy obtained with these models is only slightly inferior to that obtained with more complete statistical models. It is shown that the incorporation of a continuous, spatially varying, dependence on appropriately scaled elevation makes a dominant contribution to surface accuracy. Incorporating dependence on aspect, as determined from a digital elevation model, makes only a marginal further improvement. |
| [19] | |
| [20] | ABSTRACT To eliminate the confusion in the definition of leaf area index ( L ) for non-flat leaves, the projection coefficients of several objects including spheres, cylinders, hemicircular cylinders, and triangular and square bars are investigated through mathematical derivation and numerical calculation for a range of ellipsoidal angular distributions. It is shown that the projection coefficient calculated based on half the total intercepting area is close to a constant of 0.5 when the inclination angle of the objects is randomly (spherically) distributed, whereas the calculated results based on the object's largest projected area are strongly dependent on the shape of the objects. Therefore, it is suggested that the leaf area index of non-flat leaves be defined as half the total intercepting area per unit ground surface area and that the definition of L based on the projected leaf area be abandoned. |
| [21] | Effective leaf area index (LAI(e)) in the semi-arid Pinus halepensis plantation, located between arid and semi-arid climatic zones at the edge of the Negev and Judean deserts, was measured bi-annually during four years (2001-2004) and more intensively (monthly) during the following two years (2004-2006) by a number of non-contact optical devices. The measurements showed a gradual increase in LAI(e) from similar to 1 (+/- 0.25) to similar to 1.8 (+/- 0.11) during these years. All instruments, when used properly, gave similar results that were also comparable with actual leaf area index measured by litter collection and destructive sampling and allometric estimates. Because of the constraint of clear sky conditions, which limited the use of the fisheye type sensors to times of twilight, the fisheye techniques were less useful. The tracing radiation and architecture of canopies system, which includes specific treatment of two levels of clumpiness of the sparse forest stand, was used successfully for the intensive monitoring. The mean clumpiness index, 0.61, is considered representative for the specific environment. Finally, the LAI(e) measurements at the start of each season were used to constrain phenology-based estimates of annual LAI(e) development, resulting in a continuous course of LAI(e) in the forest over the five-year period. Intra-seasonal LAI(e) variation in the order of 10% of total LAI(e) predicted by the model was also observed in the intensive TRAC measurements, giving confidence in the TRAC system and indicating its sensitivity and applicability in woodlands even with low LAI(e) values. The results can be important for forest management decision support as well as for use in evaluation of remote sensing techniques for forests at the lowest range of LAI(e) values. (C) 2011 Elsevier B.V. All rights reserved. |
| [22] | The world's forests influence climate through physical, chemical, and biological processes that affect planetary energetics, the hydrologic cycle, and atmospheric composition. These complex and nonlinear forest-atmosphere interactions can dampen or amplify anthropogenic climate change. Tropical, temperate, and boreal reforestation and afforestation attenuate global warming through carbon sequestration. Biogeophysical feedbacks can enhance or diminish this negative climate forcing. Tropical forests mitigate warming through evaporative cooling, but the low albedo of boreal forests is a positive climate forcing. The evaporative effect of temperate forests is unclear. The net climate forcing from these and ot |
| [23] | . 森林是重要的陆地生态系统,其叶面积指数(leafareaindex,LAI)是决定该生态系统与大气之间物质和能量交换的关键参数.利用MOD09A1及MCD43A1数据和基于4尺度几何光学模型的反演算法,生成了中国森林2000~2010年每8天的500mLAI产品,并利用6个典型森林样区的LAI观测数据对该LAI产品进行了验证.在此基础上,分析了2000~2010年间我国森林LAI的时空分布特征及其与温度和降水之间关系.结果表明,利用MODIS数据反演生成的500mLAI产品具有可靠的质量,6个典型森林样区的验证精度达到70%以上;2000~2010年间,我国东北、华北、中南部地区森林LAI呈增加趋势,但在东南部和西南地区森林LAI呈下降趋势,主要原因是2008~2010年该地区的LAI明显下降.森林LAI年平均值与年平均气温在东北地区正相关,在西南地区负相关;在华北和中南部地区LAI年平均值与年降水量正相关.2001和2009年的异常气候导致我国秦岭和淮河以南地区的森林LAI明显低于常年,部分地区的LAI年平均值较正常年份下降1.0左右. 森林是重要的陆地生态系统,其叶面积指数(leafareaindex,LAI)是决定该生态系统与大气之间物质和能量交换的关键参数.利用MOD09A1及MCD43A1数据和基于4尺度几何光学模型的反演算法,生成了中国森林2000~2010年每8天的500mLAI产品,并利用6个典型森林样区的LAI观测数据对该LAI产品进行了验证.在此基础上,分析了2000~2010年间我国森林LAI的时空分布特征及其与温度和降水之间关系.结果表明,利用MODIS数据反演生成的500mLAI产品具有可靠的质量,6个典型森林样区的验证精度达到70%以上;2000~2010年间,我国东北、华北、中南部地区森林LAI呈增加趋势,但在东南部和西南地区森林LAI呈下降趋势,主要原因是2008~2010年该地区的LAI明显下降.森林LAI年平均值与年平均气温在东北地区正相关,在西南地区负相关;在华北和中南部地区LAI年平均值与年降水量正相关.2001和2009年的异常气候导致我国秦岭和淮河以南地区的森林LAI明显低于常年,部分地区的LAI年平均值较正常年份下降1.0左右. |
| [24] | . 土地覆盖变化是陆地生态系统变化的重要组成部分与驱动因素。在全球变化、生态环境建设、经济高速发展等因素的影响下，21世纪前十年中国土地覆盖发生了显著变化，对此变化的监测和分析不但能支持中国碳源/汇的评估和碳收支估算，还可为生态环境变化评估提供基础数据。本研究在面向对象（object-based）的分类技术支持下，利用LandsatTM/ETM数据和HJ-1卫星数据，结合大量外业调查数据生产了30m分辨率的2000年、2010年中国土地覆盖数据（ChinaCover）；采用像元二分法生产了植被覆盖度数据。利用这两个数据集对中国土地覆盖10年的变化特点进行了分析。结果表明，人工表面和林地呈增加趋势，而耕地、湿地和草地面积呈减少的趋势；人工表面的快速增加和耕地面积的大规模减少是这一时期中国土地覆盖变化的最主要特点；土地覆盖类型转换中，耕地转换为人工表面的区域主要集中在我国中东部地区，耕地转换为林地和草地的区域主要分布在退耕还林还草的重点区域，耕地的扩张主要来自三江平原和新疆绿洲的农业开发。以植被覆盖度为评估指标显示森林、灌丛和草地质量总体呈上升趋势，但在汶川地震重灾区、横断山以及武夷山等局部地区的森林质量呈退化趋势；塔里木盆地周围、青藏高原东部、太行山、吕梁山等地区的灌丛植被覆盖度有所下降；内蒙古中部、青藏高原西南部、新疆天山南部、呼伦贝尔等地区的草地出现退化现象。 土地覆盖变化是陆地生态系统变化的重要组成部分与驱动因素。在全球变化、生态环境建设、经济高速发展等因素的影响下，21世纪前十年中国土地覆盖发生了显著变化，对此变化的监测和分析不但能支持中国碳源/汇的评估和碳收支估算，还可为生态环境变化评估提供基础数据。本研究在面向对象（object-based）的分类技术支持下，利用LandsatTM/ETM数据和HJ-1卫星数据，结合大量外业调查数据生产了30m分辨率的2000年、2010年中国土地覆盖数据（ChinaCover）；采用像元二分法生产了植被覆盖度数据。利用这两个数据集对中国土地覆盖10年的变化特点进行了分析。结果表明，人工表面和林地呈增加趋势，而耕地、湿地和草地面积呈减少的趋势；人工表面的快速增加和耕地面积的大规模减少是这一时期中国土地覆盖变化的最主要特点；土地覆盖类型转换中，耕地转换为人工表面的区域主要集中在我国中东部地区，耕地转换为林地和草地的区域主要分布在退耕还林还草的重点区域，耕地的扩张主要来自三江平原和新疆绿洲的农业开发。以植被覆盖度为评估指标显示森林、灌丛和草地质量总体呈上升趋势，但在汶川地震重灾区、横断山以及武夷山等局部地区的森林质量呈退化趋势；塔里木盆地周围、青藏高原东部、太行山、吕梁山等地区的灌丛植被覆盖度有所下降；内蒙古中部、青藏高原西南部、新疆天山南部、呼伦贝尔等地区的草地出现退化现象。 |
