基于草原综合顺序分类系统改进CASA模型及其在中国草地NPP估算中的应用
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摘要
植被净第一性生产力（NPP）是指绿色植物在单位面积和单位时间内所累积的有机物数量，是判定生态系统碳汇和调节生态过程的主要因子，在全球变化及碳平衡中扮演着重要的角色。草地NPP是草原生态系统中土、草、畜3个子系统之间及其与气候等外界环境因子之间综合作用的结果，反映了草地植被在自然条件下的生产能力。
草原综合顺序分类系统（CSCS）是目前世界上唯一一个用定量化指标进行植被（主要是草地）分类的方法。CSCS以>0℃年积温（Σθ）和湿润度（K）为分类标准，其分类指标明确且信息量大，对草地畜牧业生产有较多指导意义。CSCS也是第一个可利用计算机定量检索的分类系统。目前已用计算机绘制出甘肃省、中国和北半球的CSCS分类图。将草地NPP与CSCS相结合进行研究是一种研究方法的创新，也是对该分类系统的一个补充和发展。
CASA（Carnegie–Ames–Stanford Approach）模型是由计算植物生产力的方法发展而来的陆地植被NPP全球估算模型。由于积温（能量）和降雨（物质）是影响植物生存的两个关键因素，结合CSCS的定量分类特点和草地NPP的关系，本研究对CASA模型进行改进，建立了基于CSCS草地分类的NPP估算模型。在统一的GIS平台上，将相关的基础图件、MODIS遥感数据、气象观测数据、草地实测数据进行空间叠置分析，对建立的NPP估算模型进行了验证和精度评价。依据改进模型，估算和分析了2004～2008年中国草地NPP值及其时空变化，结合CSCS模拟了中国41个草地类的NPP变化趋势。主要结果和结论如下：
1．模型的改进与验证
将Σθ和K引入CASA模型，改进了该模型中水分胁迫影响系数W x,t的计算方法；同时利用实测数据模拟出中国41个草地类型的最大光能利用率max，使该模型能针对不同草地类型进行模拟。
草地NPP的实测值和改进模型的模拟值基本分布在趋势线附近，二者呈线性相关，相关系数R~2为0.715；41类草地NPP的模拟值，除IID23略低于观测最小值外，其余均落在观测值的范围之内。草地NPP的模拟值与实测值总体平均值接近，分别为503.75gC/m~2/yr和567.312gC/m~2/yr；模拟值的离散程度较小，波动范围小于观测值，其总体标准差（108.598）远低于实测值总体标准差（248.091）。对实测样本点较多的12种草地类型的实测值与模拟值的相关性分析表明，这些类型草地的线性相关趋势较为明显，相关系数为0.54~0.93。
41类草地NPP的模拟值与实测值之间的误差分布有以下规律：总体平均误差为4.85gC/m~2/yr，正向平均误差的样本数多于负向平均误差的样本数，模拟值整体上高于实测值；平均绝对误差介于3.423～241.783，平均为108.428gC/m~2/yr，说明误差的累积值较高；平均相对误差介于-23.3%到51%之间，平均值为7.6%。当样本数量较多时，模拟值与实测值的相对误差较小，这在一定程度上说明了模拟结果的可靠性。
改进CASA模型中的光能转化率介于0.008～0.846之间，平均值为0.345，高于其他研究者的估算结果。其可能原因，一是其最大值和最小值之间的跨度较大，从而使得的平均值较高；二是个别草地类型的m ax估算值较高。
改进CASA模型的估算结果小于Miami模型和Thornthwaite Memorial模型的估算结果。从各草地类型的统计数据与实测值的比较来看，Miami模型和Thornthwaite Memorial模型的估算结果与实测值偏差较大，而本研究模拟的结果具有较高的准确性。改进CASA模型与Miami模型或Thornthwaite Memorial模型的相关性研究表明，改进CASA模型与Miami模型估算结果的相关性较好，相关系数达0.868；改进CASA模型与Thornthwaite Memorial模型相关性稍差，相关系数为0.683。因此，改进CASA模型模拟精度较高，可运用于中国不同草地类NPP的估算。
2．中国草地NPP时空变化分析
（1）空间变化
2004~2008年中国草地NPP年总量为6.79810~9gC/yr，年平均为489.361gC/m~2/yr。中国草地NPP空间分布的基本特点是东高西低，南高北低，从西北向东南逐渐增加。中国区域内的NPP自西北向东南逐渐增大（青藏高原除外）的NPP分布规律与CSCS类的检索图的分布规律吻合度较高，能够体现出K和Σθ的水平和垂直地带性分布规律。
中国草地NPP随经度的递增而逐渐增大，随纬度的增大而逐渐减小，但存在一定的波动性。草地NPP的经度地带性规律与气候的经向变化密切相关。随着经度的递增，气候更有利于植被的生长。与CSCS相对应，在同一热量带上，随着K值的增大，相应的自然景观从荒漠、半荒漠到草原、森林，其NPP呈逐渐增加的趋势。不同纬度草地NPP的分布格局在一定程度上反映了其对热量梯度的响应。随纬度的增加，由热变冷，Σθ降低。与CSCS相对应，在K值相同的情况下，Σθ越低，其NPP值也就越低。
（2）时间变化
2004~2008年中国草地NPP年总量分布在5.9910~9～7.3610~9gC/yr之间，年均为428.9～527.5gC/m~2/yr。在5年里，草地NPP除2005年有一定的波动外，总体呈现增加趋势，总量增加了22.9%。月均值分析表明，草地NPP的积累期主要发生在水、热搭配较好的4～10月，其草地NPP总量为957.017gC/m~2，占了全年总量的89.1%。10月中旬至次年的4月中旬，由于气温较低，植物生长缓慢，草地NPP总量仅占到全年NPP总量的11%左右。春（3～5月）、夏（6～8月）、秋（9～11月）和冬（12～2月）四季的草地NPP平均值分别为45.826gC/m~2、136.849gC/m~2、39.935gC/m~2和10.228gC/m~2，各自占全年总量的18.6%、59.6%、17.4%和4.5%。
由年际，月份和季节的NPP变化可知，适宜的水热搭配是草地NPP积累的关键。而Σθ和K为CSCS的分类标准，因而CSCS的草地类与其对应的草地NPP之间存在着必然的联系。
（3）不同草地类型NPP变化
2004~2008年中国草地NPP年均值在不同植被类型中差异显著，最高的为VIF41（亚热潮湿常绿阔叶林类），其次为VIIF42（炎热潮湿雨林类），最低的为IVA4（温暖极干暖温带荒漠类）。研究期间NPP总量最大的是VIF41，其次为VF40（暖热潮湿落叶—常绿阔叶林类）和IF36（寒冷潮湿多雨冻原、高山草甸类），总量最低的是VIA6（亚热极干亚热带荒漠类），这与其分布面积最小相一致。VIF41和VIIF42的K值相同，均大于2.0，而在K值相同的情况下，随着Σθ的减小，其NPP总量也减小。IVA4对应的湿润度级为极干，而Σθ较高，其水热条件不利于植物的生长，导致NPP值较低。这也进一步验证了适宜的水热比才是决定植被NPP高低的关键。
不同草地类型NPP在2004~2008年，除IVB11（暖温干旱暖温带半荒漠类）为下降趋势外，其它类型均呈增长趋势。增长幅度最大的为VD26（暖热微润落叶阔叶林类），达52.7%；其次为IVE32（暖温湿润落叶阔叶林类），增长率为44.2%；IVC18（暖温微干暖温带典型草原类）、IVD25（暖温微润森林草原类）、VC19（暖热微干亚热带禾草、灌木草原类）、VE33（暖热湿润常绿—落叶阔叶林类）和VID27（亚热微润硬叶类和灌丛类）增长幅度相近，均为40%左右；增长幅度最小的为IF36，仅为3.4%。
对41类草地2004~2008年的年均NPP值进行系统聚类分析，可将其划分为3大类。第1大类K值和Σθ均较高，水热比适宜，其NPP值较高；第2大类K值较低，Σθ较高，其NPP值较小。可见，草地NPP与CSCS的类与类组之间耦合度较高。
3．中国草地NPP与其影响因子的关系
（1）NPP与各影响因子的相关性
2004～2008年中国草地年均NPP与NDVI呈显著正相关，相关性最强；其次为降水和K值；NPP与Σθ呈负相关，与太阳辐射相关性最弱。可见，在改进CASA模型中NDVI、降水和K值是草地NPP的主要决定因子。
不同类组草地的NPP与各影响因子的相关性也不尽相同。2004～2008年中国10类天然草地年均NPP值与NDVI、K值和降水均呈正相关；冻原高山草地、温带湿润草地和温带森林草地的年均NPP与太阳辐射和Σθ均呈负相关；冷荒漠草地NPP与Σθ的相关性较低，与NDVI和降水的相关性非常高；半荒漠草地年均NPP与NDVI的相关性较高；斯太普草地年均NPP与降水的相关性较高；亚热带森林草地的年均NPP与降水和K值相关性较强。
滞后相关分析表明，2004～2008年中国草地NPP对NDVI、K和降水均无滞后期，对太阳辐射的响应滞后1个月，对Σθ的响应滞后2个月。草地NPP对太阳辐射和降水均无累积滞后效应，对Σθ累积滞后期为4个月。
偏相关分析表明，当NDVI、太阳辐射、K、Σθ和降水分别作为控制变量时，中国草地NPP与K值的相关系数接近。因此，除K值之外， NDVI、太阳辐射、Σθ和降水对草地NPP值的影响相互干扰，不是相互独立的。各影响因子之间，K值与其它因子的相关性相对较低。NDVI、太阳辐射、Σθ和降水等因子中，NDVI与降水的相关性最高，其次为Σθ和降水。因此，气候变化对草地NPP的影响较复杂，水热搭配良好是草地NPP增长的关键因素。
（2）NPP对影响因子的敏感性
改进CASA模型模拟的中国草地NPP值对NDVI的敏感度最高，其次为Σθ，再次为K值和降水，对太阳辐射最不敏感。CSCS是以K值和Σθ进行量化分类，而这两个参数对NPP值较为敏感。Σθ和K值差异越大，NPP值之间的差异也就越大。因此，这在一定程度上实现了CSCS与草地NPP的耦合。
Net primary productivity (NPP) of vegetation, which is defined as the net flux ofcarbon from the atmosphere into green plants per unit time and unit area, plays crucialroles in global change and carbon balance. NPP is a major determinant of carbonsinks on land and a key regulator of ecological processes. Grassland NPP isdetermined together by soil, grass, and livestock in grassland ecosystem andenvironmental factors such as climate. Grassland NPP can directly reflect theproduction capacity of grassland communities in a natural environment.
Comprehensive and sequential classification system of grasslands (CSCS) is aunique vegetation classification system (mainly for grassland) that is dependent onquantitive measurement indexes. In CSCS, differentiation between class groups isdetermined by an integrated index of>0oC average annual cumulative temperature(Σθ) and moisture index (K). Thus, the classification indicators of CSCS are clear andinvolve a great amount of information and it can be used as a reference for animalhusbandry on the grasslands. Additionally, CSCS is the first classification system thatmay retrieve quantitively by computer. Up to data, the distribution maps for grasslandclasses in Gansu province, China, and northern hemisphere have been completed byCSCS, respectively. The study of coupling of simulating grassland NPP and CSCS isnot only a creative research method, but also is supplement and development forCSCS.
Carnegie Ames Stanford Approach (CASA) model is a process-based ecosystemdepiction of NPP, which has taken full account of environmental conditions andvegetation characters. As cumulative temperature (energy) and precipitation(substance) are two key factors that affect vegetation exist, the CASA model havebeen improved based on the relationship of the quantitive classification of CSCS andgrassland NPP. As a result, we established a new model for grassland NPP in thispaper. On the uniform GIS platform, the spatial overlay analysis of basic map,MODIS data, observed meteorological data, and measured data was carried out. Afterthe comparison with observed data and other NPP product data, the validation andprecision of the improved CASA model were analyzed. Then, the grassland NPP from2004to2008in China and its spatio-temporal variations were estimated and analyzed.We also simulated the potential trend of NPP of41grassland classes in China based on CSCS. The followings are the main results and conclusions of this study.
1. The improvement and validation of CASA model
Here, Σθ and K in CSCS were introduced in CASA model, and the calculation ofW x,t in model was improved. To estimate the NPP of different grassland classesby the improved model, the maximum light-use efficiency (max) of41grasslandclasses in China was simulated according to the measured data. The values ofobserved data and estimation data of the improved model were distributed round thetrend line. There were likely linear relationship between observed data and estimationdata and the correlation coefficient (R~2) was0.715. The estimation data of41grassland classes was included in the range of observed data except IID23. Averagevalue of observed data was503.75gC/m~2/yr and estimation data was567.312gC/m~2/yr, indicating the difference between them was small. The variation andfluctuation of the simulated values was small, and its total standard error was108.598,which was lower than that of observed data (248.091). Correlation analysis ofobserved data and estimation data from12grassland classes which have manysamples revealed that the linear correlation trend was obvious (R2=0.54~0.93).The characters of errors distribution of observed data and estimation data from41grassland classes are as follows:(1) total average error was4.85gC/m~2/yr; thesamples of positive average error were more than those of negative average error;estimation data was higher than observed data.(2) the average absoluteness error was3.423～241.783, and its average was108.428gC/m~2/yr, indicating that theaccumulation value of error was relative high; the average relative error was-23.3%~51%, and its average was7.6%.(3) when the number of samples was enough,the relative error between observed data and estimation data was small, suggestingthat the estimation results were reliable.
The light-use efficiency () in the improved CASA model was0.008～0.846,and its average was0.345, which were higher than that estimated by other researchers.The reason for this may be:(1) the span between the maximum value and theminimum value was high, resulting in the average of was high;(2) the estimationvalue ofmaxof some grassland classes was high.The estimation data of the improved model was lower than that of Miami model andThornthwaite Memorial model. The difference between the estimation data of Miamimodel or Thornthwaite Memorial model and the observed data was high, while theestimation data of the improved model was more precise. Statistically significant relationships were determined between the improved CASA model and Miami modelor Thornthwaite Memorial model. The results showed that there were likelysignificant (high) correlations between the improved CASA model and Miami model(R2=0.868). The improved model had relative low correlations with ThornthwaiteMemorial model, and its correlation coefficient was0.683. This confirmed that theprecision of the improved CASA model is enough for the estimation of differentgrassland classes in China.
2. Spatio-temporal variations of NPP in China
(1) Spatial variations
The annual total and average of grassland NPP in China were6.79810~9gC/yrand489.361gC/m~2/yr from2004to2008, respectively. There were clearly strongregional variations in grassland NPP. The grassland NPP in east was higher than thatin west, and that in south was higher than that in north. It increased from NorthwestChina toward Southeast China except Tibetan Plateau. This was consistent with theindex chart for determining grassland class in CSCS, which may show thecharacteristic of horizontal and vertical distribution of Σθ and K.
The grassland NPP increased with the increasing longitude and the decreasinglatitude, although there were some fluctuations. The characteristic of longitudezonation of grassland NPP had a highly correlation with longitudinal variabilities ofclimate. Climate becomes more benefit for the vegetation growth with the increasinglongitude. In CSCS, with the suitable natural landscape from desert and semidesert tomeadow and forest with the increasing K value, the NPP has an increasing trend. Thedistribution pattern of grassland NPP of different latitude reflected the NPP responseto thermal gradient. With the increasing latitude, climate became from hot to cold, andΣθ decreased accordingly. NPP decreased with the decreasing Σθ when K value wasthe same.
(2) Temporal variations
From2004to2008, the annual total NPP of grassland in China ranges from5.9910~9to7.3610~9gC/yr, and its annual average ranges from428.9to527.5gC/m~2/yr. The trend of grassland NPP in China from2004to2008was increaseexcept some fluctuation, and the NPP increased by22.9%in these5years. From thetrend of monthly variation, it can be drawn that the NPP accumulative period wasmainly between April and October when the combination of water and thermal is in agood condition for grassland vegetation growth. The quantity of NPP in these seven months was957.017gC/m~2, which was about89.1%of the annual total. FromOctober to April in the next year, its NPP was about11%of the annual total for thetemperature is too low to inhibit grassland vegetation grow. The grassland NPP inspring, summer, autumn and winter was45.826gC/m~2,136.849gC/m~2,39.935gC/m~2and10.228gC/m~2, respectively, which was about18.6%,59.6%,17.4%and4.5%each of the total annual NPP.
According to the annual, monthly and seasonal variation of grassland vegetationNPP, the suitable combination of water and thermal plays key roles in grasslandvegetation growth. In CSCS, differentiation between class groups is determined by anintegrated index of moisture (K-value) and temperature (Σθ). Thus, there existsinherent relationship between grassland classes in CSCS and its NPP.
(3) NPP variation of different grassland classes
The variations of annual average NPP in different grassland classes weresignificant in China from2004to2008. VIF41(sub-tropical perhumid evergreenbroad leaved forest) had the highest annual average NPP value, and then VIIF42(tropical-perhumid rain forest). However, IVA4(warm temperate-extrarid warmtemperate zonal desert) had the lowest annual average NPP. During the experiment,VIF41had the highest total NPP value, and then VF40(warm-perhumiddeciduous-evergreen broad leaved forest) and IF36(frigid perhumid rain tundra,alpine meadow), VIA6(Subtropical-extrarid subtropical desert) had the lowest totalNPP, it agree with its minimum area. The K-values of VIF41and VIIF42were same,which were both greater than2.0. When K-value is same, the total grassland NPPdecreased with the decreasing Σθ. As the humidity grades of IVA4is extrarid and itsΣθ is high, the combination of water and thermal is not good for grassland vegetationgrowth, resulting in low NPP. The results further confirmed the idea that the suitablecombination of water and thermal is a key factor for vegetation NPP.
The NPP showed a decrease from2004to2008for all grassland classes exceptIVB11(warm temperate-arid warm temperate zonal semidesert). VD26(warm-subhumid deciduous broad leaved forest) had the greatest increase range of52.7%, then IVE32(warm temperate-humid deciduous broad leaved forest,44.2%).IVC18(warm temperate semiarid warm temperate typical steppe), IVD25(warmtemperate-subhumid forest steppe), VC19(warm-semiarid subtropicalgrasses-fruticous steppe), VE33(warm-humid evergreen-deciduous broad leavedforest), and VID27(subtropical-subhumid sclerophyllous forest) all had the similar increase range of about40%. IF36had the lowest increase range of3.4%.
System clustering analysis was conducted based on the annual average NPP of41grassland classes from2004to2008. The41grassland classes were clustered intothree groups: the groups1had high NPP for its high K-values and Σθ and suitableratio of moisture and temperature. However, the NPP of the groups2was low for itslow K-values and high Σθ. Therefore, the degree of coupling between grassland NPPand the classes and super-classes in CSCS was high.
3. Relationship between grassland NPP in China and its influencefactors
(1) Correlation analysis between NPP and influence factors
There were significant positive linear correlations between the annual averageNPP in China from2004to2008and NDVI, precipitation, and K-values. Σθ had anegative direct effect on NPP. NPP had the highest correlation with NDVI and had theweakest correlation with solar radiation. The above results demonstrated that NDVI,precipitation, and K-values are the main factors for grassland NPP in the improvedCASA model.
The correlation between NPP of various grassland super-classes and its influencefactors are different. The NPP of10zonal grassland super-class groups in China from2004to2008had positive correlation with NDVI, precipitation, and K-values. Intundra alpine steppe, temperate zonal humid grassland, and temperate zonal foreststeppe, annual NPP had negative correlation with solar radiation and Σθ. The NPP offrigid desert had relatively high correlation with NDVI and precipitation, while it hadrelatively weak correlation with Σθ. In semidesert, there were high correlationbetween annual NPP and NDVI. The annual NPP of steppe had high correlation withprecipitation. In sub-tropical zonal forest steppe, annual NPP had high correlationwith precipitation and K-values.
According to the lag-linear correlations analysis, the response of grassland NPPin China from2004to2008to NDVI, precipitation, and K-values did not show a timelag effect. However, the time lag as to responses of NPP to solar radiation and Σθ wasaround1month and2months, respectively. There was no accumulated time lag effectof solar radiation and precipitation on grassland NPP. The accumulated time lagperiods as to responses of NPP to Σθ were4months.The results of partial correlation analysis showed that when NDVI, solar radiation,K-values, Σθ and precipitation were regarded as control varies respectively, the correlation coefficients between grassland NPP in China and K-values were all similar.Thus, the effects of NDVI, solar radiation, Σθ and precipitation on grassland NPPwere interaction but not independent. Among various influence factors, the correlationcoefficients between K-values and other factors were relatively low. NDVI andprecipitation had the highest correlation, and Σθ and precipitation had the second highcorrelation coefficients. Therefore, the effects of climate changes on grassland NPPwere complex, and the integration of moisture and thermal is the key factors for theincrease of grassland NPP.
(2) Sensitive analysis on NPP to its influence factors
NDVI was the most sensitive variable to the grassland NPP in China simulatedby the improved CASA model in general in this study, followed by Σθ, K-values,precipitation, and solar radiation. K-values and Σθ were used to quantitiveclassification in CSCS, and these two parameters were sensitive to NPP. Thedifferences of NPP values of different grassland classes increased with the increasingdifference of Σθ and K-values. In conclusion, the coupling of CSCS and grasslandNPP has been completed to a certain extent in this study.
草原综合顺序分类系统（CSCS）是目前世界上唯一一个用定量化指标进行植被（主要是草地）分类的方法。CSCS以>0℃年积温（Σθ）和湿润度（K）为分类标准，其分类指标明确且信息量大，对草地畜牧业生产有较多指导意义。CSCS也是第一个可利用计算机定量检索的分类系统。目前已用计算机绘制出甘肃省、中国和北半球的CSCS分类图。将草地NPP与CSCS相结合进行研究是一种研究方法的创新，也是对该分类系统的一个补充和发展。
CASA（Carnegie–Ames–Stanford Approach）模型是由计算植物生产力的方法发展而来的陆地植被NPP全球估算模型。由于积温（能量）和降雨（物质）是影响植物生存的两个关键因素，结合CSCS的定量分类特点和草地NPP的关系，本研究对CASA模型进行改进，建立了基于CSCS草地分类的NPP估算模型。在统一的GIS平台上，将相关的基础图件、MODIS遥感数据、气象观测数据、草地实测数据进行空间叠置分析，对建立的NPP估算模型进行了验证和精度评价。依据改进模型，估算和分析了2004～2008年中国草地NPP值及其时空变化，结合CSCS模拟了中国41个草地类的NPP变化趋势。主要结果和结论如下：
1．模型的改进与验证
将Σθ和K引入CASA模型，改进了该模型中水分胁迫影响系数W x,t的计算方法；同时利用实测数据模拟出中国41个草地类型的最大光能利用率max，使该模型能针对不同草地类型进行模拟。
草地NPP的实测值和改进模型的模拟值基本分布在趋势线附近，二者呈线性相关，相关系数R~2为0.715；41类草地NPP的模拟值，除IID23略低于观测最小值外，其余均落在观测值的范围之内。草地NPP的模拟值与实测值总体平均值接近，分别为503.75gC/m~2/yr和567.312gC/m~2/yr；模拟值的离散程度较小，波动范围小于观测值，其总体标准差（108.598）远低于实测值总体标准差（248.091）。对实测样本点较多的12种草地类型的实测值与模拟值的相关性分析表明，这些类型草地的线性相关趋势较为明显，相关系数为0.54~0.93。
41类草地NPP的模拟值与实测值之间的误差分布有以下规律：总体平均误差为4.85gC/m~2/yr，正向平均误差的样本数多于负向平均误差的样本数，模拟值整体上高于实测值；平均绝对误差介于3.423～241.783，平均为108.428gC/m~2/yr，说明误差的累积值较高；平均相对误差介于-23.3%到51%之间，平均值为7.6%。当样本数量较多时，模拟值与实测值的相对误差较小，这在一定程度上说明了模拟结果的可靠性。
改进CASA模型中的光能转化率介于0.008～0.846之间，平均值为0.345，高于其他研究者的估算结果。其可能原因，一是其最大值和最小值之间的跨度较大，从而使得的平均值较高；二是个别草地类型的m ax估算值较高。
改进CASA模型的估算结果小于Miami模型和Thornthwaite Memorial模型的估算结果。从各草地类型的统计数据与实测值的比较来看，Miami模型和Thornthwaite Memorial模型的估算结果与实测值偏差较大，而本研究模拟的结果具有较高的准确性。改进CASA模型与Miami模型或Thornthwaite Memorial模型的相关性研究表明，改进CASA模型与Miami模型估算结果的相关性较好，相关系数达0.868；改进CASA模型与Thornthwaite Memorial模型相关性稍差，相关系数为0.683。因此，改进CASA模型模拟精度较高，可运用于中国不同草地类NPP的估算。
2．中国草地NPP时空变化分析
（1）空间变化
2004~2008年中国草地NPP年总量为6.79810~9gC/yr，年平均为489.361gC/m~2/yr。中国草地NPP空间分布的基本特点是东高西低，南高北低，从西北向东南逐渐增加。中国区域内的NPP自西北向东南逐渐增大（青藏高原除外）的NPP分布规律与CSCS类的检索图的分布规律吻合度较高，能够体现出K和Σθ的水平和垂直地带性分布规律。
中国草地NPP随经度的递增而逐渐增大，随纬度的增大而逐渐减小，但存在一定的波动性。草地NPP的经度地带性规律与气候的经向变化密切相关。随着经度的递增，气候更有利于植被的生长。与CSCS相对应，在同一热量带上，随着K值的增大，相应的自然景观从荒漠、半荒漠到草原、森林，其NPP呈逐渐增加的趋势。不同纬度草地NPP的分布格局在一定程度上反映了其对热量梯度的响应。随纬度的增加，由热变冷，Σθ降低。与CSCS相对应，在K值相同的情况下，Σθ越低，其NPP值也就越低。
（2）时间变化
2004~2008年中国草地NPP年总量分布在5.9910~9～7.3610~9gC/yr之间，年均为428.9～527.5gC/m~2/yr。在5年里，草地NPP除2005年有一定的波动外，总体呈现增加趋势，总量增加了22.9%。月均值分析表明，草地NPP的积累期主要发生在水、热搭配较好的4～10月，其草地NPP总量为957.017gC/m~2，占了全年总量的89.1%。10月中旬至次年的4月中旬，由于气温较低，植物生长缓慢，草地NPP总量仅占到全年NPP总量的11%左右。春（3～5月）、夏（6～8月）、秋（9～11月）和冬（12～2月）四季的草地NPP平均值分别为45.826gC/m~2、136.849gC/m~2、39.935gC/m~2和10.228gC/m~2，各自占全年总量的18.6%、59.6%、17.4%和4.5%。
由年际，月份和季节的NPP变化可知，适宜的水热搭配是草地NPP积累的关键。而Σθ和K为CSCS的分类标准，因而CSCS的草地类与其对应的草地NPP之间存在着必然的联系。
（3）不同草地类型NPP变化
2004~2008年中国草地NPP年均值在不同植被类型中差异显著，最高的为VIF41（亚热潮湿常绿阔叶林类），其次为VIIF42（炎热潮湿雨林类），最低的为IVA4（温暖极干暖温带荒漠类）。研究期间NPP总量最大的是VIF41，其次为VF40（暖热潮湿落叶—常绿阔叶林类）和IF36（寒冷潮湿多雨冻原、高山草甸类），总量最低的是VIA6（亚热极干亚热带荒漠类），这与其分布面积最小相一致。VIF41和VIIF42的K值相同，均大于2.0，而在K值相同的情况下，随着Σθ的减小，其NPP总量也减小。IVA4对应的湿润度级为极干，而Σθ较高，其水热条件不利于植物的生长，导致NPP值较低。这也进一步验证了适宜的水热比才是决定植被NPP高低的关键。
不同草地类型NPP在2004~2008年，除IVB11（暖温干旱暖温带半荒漠类）为下降趋势外，其它类型均呈增长趋势。增长幅度最大的为VD26（暖热微润落叶阔叶林类），达52.7%；其次为IVE32（暖温湿润落叶阔叶林类），增长率为44.2%；IVC18（暖温微干暖温带典型草原类）、IVD25（暖温微润森林草原类）、VC19（暖热微干亚热带禾草、灌木草原类）、VE33（暖热湿润常绿—落叶阔叶林类）和VID27（亚热微润硬叶类和灌丛类）增长幅度相近，均为40%左右；增长幅度最小的为IF36，仅为3.4%。
对41类草地2004~2008年的年均NPP值进行系统聚类分析，可将其划分为3大类。第1大类K值和Σθ均较高，水热比适宜，其NPP值较高；第2大类K值较低，Σθ较高，其NPP值较小。可见，草地NPP与CSCS的类与类组之间耦合度较高。
3．中国草地NPP与其影响因子的关系
（1）NPP与各影响因子的相关性
2004～2008年中国草地年均NPP与NDVI呈显著正相关，相关性最强；其次为降水和K值；NPP与Σθ呈负相关，与太阳辐射相关性最弱。可见，在改进CASA模型中NDVI、降水和K值是草地NPP的主要决定因子。
不同类组草地的NPP与各影响因子的相关性也不尽相同。2004～2008年中国10类天然草地年均NPP值与NDVI、K值和降水均呈正相关；冻原高山草地、温带湿润草地和温带森林草地的年均NPP与太阳辐射和Σθ均呈负相关；冷荒漠草地NPP与Σθ的相关性较低，与NDVI和降水的相关性非常高；半荒漠草地年均NPP与NDVI的相关性较高；斯太普草地年均NPP与降水的相关性较高；亚热带森林草地的年均NPP与降水和K值相关性较强。
滞后相关分析表明，2004～2008年中国草地NPP对NDVI、K和降水均无滞后期，对太阳辐射的响应滞后1个月，对Σθ的响应滞后2个月。草地NPP对太阳辐射和降水均无累积滞后效应，对Σθ累积滞后期为4个月。
偏相关分析表明，当NDVI、太阳辐射、K、Σθ和降水分别作为控制变量时，中国草地NPP与K值的相关系数接近。因此，除K值之外， NDVI、太阳辐射、Σθ和降水对草地NPP值的影响相互干扰，不是相互独立的。各影响因子之间，K值与其它因子的相关性相对较低。NDVI、太阳辐射、Σθ和降水等因子中，NDVI与降水的相关性最高，其次为Σθ和降水。因此，气候变化对草地NPP的影响较复杂，水热搭配良好是草地NPP增长的关键因素。
（2）NPP对影响因子的敏感性
改进CASA模型模拟的中国草地NPP值对NDVI的敏感度最高，其次为Σθ，再次为K值和降水，对太阳辐射最不敏感。CSCS是以K值和Σθ进行量化分类，而这两个参数对NPP值较为敏感。Σθ和K值差异越大，NPP值之间的差异也就越大。因此，这在一定程度上实现了CSCS与草地NPP的耦合。
Net primary productivity (NPP) of vegetation, which is defined as the net flux ofcarbon from the atmosphere into green plants per unit time and unit area, plays crucialroles in global change and carbon balance. NPP is a major determinant of carbonsinks on land and a key regulator of ecological processes. Grassland NPP isdetermined together by soil, grass, and livestock in grassland ecosystem andenvironmental factors such as climate. Grassland NPP can directly reflect theproduction capacity of grassland communities in a natural environment.
Comprehensive and sequential classification system of grasslands (CSCS) is aunique vegetation classification system (mainly for grassland) that is dependent onquantitive measurement indexes. In CSCS, differentiation between class groups isdetermined by an integrated index of>0oC average annual cumulative temperature(Σθ) and moisture index (K). Thus, the classification indicators of CSCS are clear andinvolve a great amount of information and it can be used as a reference for animalhusbandry on the grasslands. Additionally, CSCS is the first classification system thatmay retrieve quantitively by computer. Up to data, the distribution maps for grasslandclasses in Gansu province, China, and northern hemisphere have been completed byCSCS, respectively. The study of coupling of simulating grassland NPP and CSCS isnot only a creative research method, but also is supplement and development forCSCS.
Carnegie Ames Stanford Approach (CASA) model is a process-based ecosystemdepiction of NPP, which has taken full account of environmental conditions andvegetation characters. As cumulative temperature (energy) and precipitation(substance) are two key factors that affect vegetation exist, the CASA model havebeen improved based on the relationship of the quantitive classification of CSCS andgrassland NPP. As a result, we established a new model for grassland NPP in thispaper. On the uniform GIS platform, the spatial overlay analysis of basic map,MODIS data, observed meteorological data, and measured data was carried out. Afterthe comparison with observed data and other NPP product data, the validation andprecision of the improved CASA model were analyzed. Then, the grassland NPP from2004to2008in China and its spatio-temporal variations were estimated and analyzed.We also simulated the potential trend of NPP of41grassland classes in China based on CSCS. The followings are the main results and conclusions of this study.
1. The improvement and validation of CASA model
Here, Σθ and K in CSCS were introduced in CASA model, and the calculation ofW x,t in model was improved. To estimate the NPP of different grassland classesby the improved model, the maximum light-use efficiency (max) of41grasslandclasses in China was simulated according to the measured data. The values ofobserved data and estimation data of the improved model were distributed round thetrend line. There were likely linear relationship between observed data and estimationdata and the correlation coefficient (R~2) was0.715. The estimation data of41grassland classes was included in the range of observed data except IID23. Averagevalue of observed data was503.75gC/m~2/yr and estimation data was567.312gC/m~2/yr, indicating the difference between them was small. The variation andfluctuation of the simulated values was small, and its total standard error was108.598,which was lower than that of observed data (248.091). Correlation analysis ofobserved data and estimation data from12grassland classes which have manysamples revealed that the linear correlation trend was obvious (R2=0.54~0.93).The characters of errors distribution of observed data and estimation data from41grassland classes are as follows:(1) total average error was4.85gC/m~2/yr; thesamples of positive average error were more than those of negative average error;estimation data was higher than observed data.(2) the average absoluteness error was3.423～241.783, and its average was108.428gC/m~2/yr, indicating that theaccumulation value of error was relative high; the average relative error was-23.3%~51%, and its average was7.6%.(3) when the number of samples was enough,the relative error between observed data and estimation data was small, suggestingthat the estimation results were reliable.
The light-use efficiency () in the improved CASA model was0.008～0.846,and its average was0.345, which were higher than that estimated by other researchers.The reason for this may be:(1) the span between the maximum value and theminimum value was high, resulting in the average of was high;(2) the estimationvalue ofmaxof some grassland classes was high.The estimation data of the improved model was lower than that of Miami model andThornthwaite Memorial model. The difference between the estimation data of Miamimodel or Thornthwaite Memorial model and the observed data was high, while theestimation data of the improved model was more precise. Statistically significant relationships were determined between the improved CASA model and Miami modelor Thornthwaite Memorial model. The results showed that there were likelysignificant (high) correlations between the improved CASA model and Miami model(R2=0.868). The improved model had relative low correlations with ThornthwaiteMemorial model, and its correlation coefficient was0.683. This confirmed that theprecision of the improved CASA model is enough for the estimation of differentgrassland classes in China.
2. Spatio-temporal variations of NPP in China
(1) Spatial variations
The annual total and average of grassland NPP in China were6.79810~9gC/yrand489.361gC/m~2/yr from2004to2008, respectively. There were clearly strongregional variations in grassland NPP. The grassland NPP in east was higher than thatin west, and that in south was higher than that in north. It increased from NorthwestChina toward Southeast China except Tibetan Plateau. This was consistent with theindex chart for determining grassland class in CSCS, which may show thecharacteristic of horizontal and vertical distribution of Σθ and K.
The grassland NPP increased with the increasing longitude and the decreasinglatitude, although there were some fluctuations. The characteristic of longitudezonation of grassland NPP had a highly correlation with longitudinal variabilities ofclimate. Climate becomes more benefit for the vegetation growth with the increasinglongitude. In CSCS, with the suitable natural landscape from desert and semidesert tomeadow and forest with the increasing K value, the NPP has an increasing trend. Thedistribution pattern of grassland NPP of different latitude reflected the NPP responseto thermal gradient. With the increasing latitude, climate became from hot to cold, andΣθ decreased accordingly. NPP decreased with the decreasing Σθ when K value wasthe same.
(2) Temporal variations
From2004to2008, the annual total NPP of grassland in China ranges from5.9910~9to7.3610~9gC/yr, and its annual average ranges from428.9to527.5gC/m~2/yr. The trend of grassland NPP in China from2004to2008was increaseexcept some fluctuation, and the NPP increased by22.9%in these5years. From thetrend of monthly variation, it can be drawn that the NPP accumulative period wasmainly between April and October when the combination of water and thermal is in agood condition for grassland vegetation growth. The quantity of NPP in these seven months was957.017gC/m~2, which was about89.1%of the annual total. FromOctober to April in the next year, its NPP was about11%of the annual total for thetemperature is too low to inhibit grassland vegetation grow. The grassland NPP inspring, summer, autumn and winter was45.826gC/m~2,136.849gC/m~2,39.935gC/m~2and10.228gC/m~2, respectively, which was about18.6%,59.6%,17.4%and4.5%each of the total annual NPP.
According to the annual, monthly and seasonal variation of grassland vegetationNPP, the suitable combination of water and thermal plays key roles in grasslandvegetation growth. In CSCS, differentiation between class groups is determined by anintegrated index of moisture (K-value) and temperature (Σθ). Thus, there existsinherent relationship between grassland classes in CSCS and its NPP.
(3) NPP variation of different grassland classes
The variations of annual average NPP in different grassland classes weresignificant in China from2004to2008. VIF41(sub-tropical perhumid evergreenbroad leaved forest) had the highest annual average NPP value, and then VIIF42(tropical-perhumid rain forest). However, IVA4(warm temperate-extrarid warmtemperate zonal desert) had the lowest annual average NPP. During the experiment,VIF41had the highest total NPP value, and then VF40(warm-perhumiddeciduous-evergreen broad leaved forest) and IF36(frigid perhumid rain tundra,alpine meadow), VIA6(Subtropical-extrarid subtropical desert) had the lowest totalNPP, it agree with its minimum area. The K-values of VIF41and VIIF42were same,which were both greater than2.0. When K-value is same, the total grassland NPPdecreased with the decreasing Σθ. As the humidity grades of IVA4is extrarid and itsΣθ is high, the combination of water and thermal is not good for grassland vegetationgrowth, resulting in low NPP. The results further confirmed the idea that the suitablecombination of water and thermal is a key factor for vegetation NPP.
The NPP showed a decrease from2004to2008for all grassland classes exceptIVB11(warm temperate-arid warm temperate zonal semidesert). VD26(warm-subhumid deciduous broad leaved forest) had the greatest increase range of52.7%, then IVE32(warm temperate-humid deciduous broad leaved forest,44.2%).IVC18(warm temperate semiarid warm temperate typical steppe), IVD25(warmtemperate-subhumid forest steppe), VC19(warm-semiarid subtropicalgrasses-fruticous steppe), VE33(warm-humid evergreen-deciduous broad leavedforest), and VID27(subtropical-subhumid sclerophyllous forest) all had the similar increase range of about40%. IF36had the lowest increase range of3.4%.
System clustering analysis was conducted based on the annual average NPP of41grassland classes from2004to2008. The41grassland classes were clustered intothree groups: the groups1had high NPP for its high K-values and Σθ and suitableratio of moisture and temperature. However, the NPP of the groups2was low for itslow K-values and high Σθ. Therefore, the degree of coupling between grassland NPPand the classes and super-classes in CSCS was high.
3. Relationship between grassland NPP in China and its influencefactors
(1) Correlation analysis between NPP and influence factors
There were significant positive linear correlations between the annual averageNPP in China from2004to2008and NDVI, precipitation, and K-values. Σθ had anegative direct effect on NPP. NPP had the highest correlation with NDVI and had theweakest correlation with solar radiation. The above results demonstrated that NDVI,precipitation, and K-values are the main factors for grassland NPP in the improvedCASA model.
The correlation between NPP of various grassland super-classes and its influencefactors are different. The NPP of10zonal grassland super-class groups in China from2004to2008had positive correlation with NDVI, precipitation, and K-values. Intundra alpine steppe, temperate zonal humid grassland, and temperate zonal foreststeppe, annual NPP had negative correlation with solar radiation and Σθ. The NPP offrigid desert had relatively high correlation with NDVI and precipitation, while it hadrelatively weak correlation with Σθ. In semidesert, there were high correlationbetween annual NPP and NDVI. The annual NPP of steppe had high correlation withprecipitation. In sub-tropical zonal forest steppe, annual NPP had high correlationwith precipitation and K-values.
According to the lag-linear correlations analysis, the response of grassland NPPin China from2004to2008to NDVI, precipitation, and K-values did not show a timelag effect. However, the time lag as to responses of NPP to solar radiation and Σθ wasaround1month and2months, respectively. There was no accumulated time lag effectof solar radiation and precipitation on grassland NPP. The accumulated time lagperiods as to responses of NPP to Σθ were4months.The results of partial correlation analysis showed that when NDVI, solar radiation,K-values, Σθ and precipitation were regarded as control varies respectively, the correlation coefficients between grassland NPP in China and K-values were all similar.Thus, the effects of NDVI, solar radiation, Σθ and precipitation on grassland NPPwere interaction but not independent. Among various influence factors, the correlationcoefficients between K-values and other factors were relatively low. NDVI andprecipitation had the highest correlation, and Σθ and precipitation had the second highcorrelation coefficients. Therefore, the effects of climate changes on grassland NPPwere complex, and the integration of moisture and thermal is the key factors for theincrease of grassland NPP.
(2) Sensitive analysis on NPP to its influence factors
NDVI was the most sensitive variable to the grassland NPP in China simulatedby the improved CASA model in general in this study, followed by Σθ, K-values,precipitation, and solar radiation. K-values and Σθ were used to quantitiveclassification in CSCS, and these two parameters were sensitive to NPP. Thedifferences of NPP values of different grassland classes increased with the increasingdifference of Σθ and K-values. In conclusion, the coupling of CSCS and grasslandNPP has been completed to a certain extent in this study.
引文
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