青藏高原森林生产力格局及对气候变化响应的模拟
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摘要
生态系统净第一性生产力NPP (Net Primary Production)指绿色植物在单位时间和单位面积上所积累的有机干物质总量,它既对全球气候变化起着极其重要的作用,同时又对气候变化响应敏感。因此,对区域植被NPP的研究,不仅有利于加深对生态系统的物质循环和能量流动的认识和理解,而且有利于揭示生态系统对全球变化的响应规律及气候变化对生态系统的影响,具有重要意义。
在遥感技术和地理信息系统的支持下,以青藏高原及其周边地区139个气象站点的月降水信息及该地区的数字高程数据(DEM)为基础,对气温、降水等气候因子进行了空间插值分析,为碳储量、生产力的估算和空间分析奠定基础。以250 m分辨率的MODIS卫星数据、地面气象数据、1:5万地形图、1:250万森林分布图和3个区域(西藏、四川青海(两个省合并为一个大区域考虑)、云南)的1086个森林资源清查样地数据为主要数据源,通过分区建模与整个区域总体建模的对比,并引入坡向、坡位和植被类型等定性变量,通过相关分析、共线性诊断和模型模拟,选择相对最佳模型估算整个高原的森林碳储量。利用1991年8月至2000年9月的逐月NOAA\AVHRR数据,研究了青藏高原森林植被净第一性生产力(NPP)的现状,近10年来NPP的时空格局和动态变化特征,建立由遥感数据驱动的高原植被NPP在水热空间上的数学模型,揭示植被生产力对气候变化的响应规律。通过研究,所得主要结论如下:
1.在干季,无论是丰水还是欠水年份,月降水量都比较少,高程对降水量的影响较小,在精度要求不高的情况下,月降水插值可不考虑高程的影响,克里金法的月降水插值精度相对最高;在湿季,月降水量较多,高程的影响较大,混合插值法比局部插值法及克里金插值法的精度高,尤以混合插值法II(多元回归和样条法的综合)的精度最高;干季,整个高原的月降水很少,西部和北部降水最少,东部和南部相对较多,湿季,高原的月降水较多,空间格局表现为由东南到西北递减。
2.在估算青藏高原的碳储量时,定性变量(植被类型、坡向等)的引入大大提高了碳储量模型的估算精度,对数回归模型比线性回归模型的精度高,分区建模也比整个区域总体建模的精度高。
3.在青藏高原,森林类型主要在高原东部和东南部,森林覆盖率约为11.3%,森林平均地上碳储量约为19 t/hm~2。灌木林的平均碳储量相对较低,低于10 t/hm~2,主要位于柴达木盆地、川西高原西部和西藏最南部。青藏高原最东部和最南部的森林碳储量大部分低于50 t/hm~2,岷江流域的森林碳储量大都在100 t/hm~2和150 t/hm~2之间。西藏的森林地上碳储量相对较高,大多高于250 t/hm~2。
4.青藏高原地上月平均生产力最高的森林是林芝一带的暗针叶林,其生产力约在0.5 t/hm~2. m~(-1)至0.6 t/hm~2. m~(-1)之间;其次是云南一带的热带亚热带森林,其生产力约在0.2 t/hm~2. m~(-1)至0.4 t/hm~2. m~(-1)之间;青藏高原东部的生产力处于中等水平,平均约为0.3 t/hm~2. m~(-1);青藏高原东北部的森林生产力较低,一般不超过0.3 t/hm~2. m~(-1)。
5.青藏高原近10年来的森林地上年平均净第一性生产力基本上处于平稳的波动上升状态,从1991年的0.167PgC/a增加到2000年的0.185PgC/a,平均每年增加0.002PgC/a,年平均增加率约为1.1%。10年来青藏高原森林地上净第一性生产力的平均值为0.19PgC/a。
6.在不同的森林植被类型中,针叶林NPP约占整个森林植被的72.96%,其地上NPP总量约为0.37TgC/m (1Tg=1012g);灌木林约占21.62%,其地上NPP总量约为0.11TgC/m;阔叶林的NPP总量相对最小,为0.03TgC/m,约占整个森林植被的5.42%。
7.在云南,森林地上NPP的主要气候驱动因子为降水,且在一定范围降水对NPP起积极促进作用;在青藏高原的西藏和四川、青海等地,气温和降水均为森林地上NPP的主要气候驱动因子,且在一定范围气温对NPP起积极促进作用,降水超过某个限度则起负的促进作用。
NPP of ecosystem refers to the accumulated organic matter gross of green vegetation in unit time and unit area. NPP not only plays an important role in the global climate change, but also is susceptible to the climate change. Therefore, the study on the regional NPP would help to enhance the understanding of matter cycle and energy flowing of ecosystem and reveal the corresponding rules of ecosystem to the global climate change and impacts of global climate change to the ecosystem.
In the support of Remote Sensing and Geographic Information System, based on the temperature and precipitation data measured at 139 stations and the DEM data of the Tibetan Plateau, the map of monthly temperature and precipitation on the plateau were drawn, which would be the elements of the carbon storage and NPP. MODIS data with 250m spatial resolution, climate data, 1km DEM, 1:2,500,000 forest map and 1086 plot data of National Forest Inventory (NFI) of the three test areas (Tibet, Yunnan and Sichuan) were used to estimate the above-ground forest carbon storage in Tibetan Plateau. Dummy variables,such as aspect and vegetation types derived from DEM, NFI and forest map of Tibetan Plateau, were used to the carbon model. To get a more accurate model of estimating forest carbon storage, the Plateau was divided into three separated sub-areas according to its physical geography characteristics and sub-area models were compared with the whole Plateau model. By the use of monthly NOAA\AVHRR data from August, 1991 to September, 2001 with spatial resolution of 8km, the spatial pattern and dynamics of annual NPP in recent 10 years were analyzed.The relationships of NPP and climate data were established to simulate the impacts of climate changes to the Plateau NPP. The results could be obtained as follows:
1. In the dry seasons, the monthly precipitation is low no matter wet years or dry years, and the best results for dry monthly precipitation mapping were obtained using Kriging interpolation. In the wet seasons, the monthly precipitation was highly affected by the altitude, so the two mixed methods got better results than the corresponding other three methods. Furthermore, the mixed method II (the combination of Multiple Regression and Splines) got the best result. The spatio-temporal patterns of the Tibet Plateau in the precipitation maps were discussed. The precipitation in plateau’s west and north is fairly low and Plateau’s east and south is less low for the dry seasons. The wet monthly precipitation decreases as the spatial variation from southeast to northwest.
2. The dummy variables improved the precision of the forest carbon storage models dramatically. When the forest carbon storage models included dummy variables, the determining coefficients (R2) of the linear models were increased from 0.20, 0.24, 0.16 to 0.48, 0.35, 0.33 in Tibet, Yunnan and Sichuan respectively. When the linear regression models were changed to logarithmic models, R2 of Tibet, Yunnan and Sichuan were increased from 0.23, 0.30, 0.14 to 0.60, 0.65 and 0.59, respectively.
3. In Tibetan Plateau, the forest is mainly distributed on eastern part with forest cover-of about 11.3 % in 2002, and the mean above-ground forest carbon storage was about 19 t/hm~2. The carbon storage of shrub is fairly low, less than 10 t/hm~2, which is located in the Qaidam basin, the western Sichuan Plateau and the southernmost part of Tibet. The forest carbon storage in the easternmost and southeasternmost of Tibetan Plateau was mostly below 50 t/hm~2. In the Minjiang River watershed, the carbon storage is about between 100 t/hm~2 and 150 t/hm~2. In Tibet, the above-ground forest carbon storage was much higher, with more than 250 t/hm~2.
4. The highest forest aboveground NPP was located in the closed coniferous forest in Linzhi of Tibet, the forest aboveground NPP is between 0.5 t/hm~2. m~(-1) and 0.6 t/hm~2. m~(-1). The next is the tropical and subtropical forest in Yunnan, the forest above-ground NPP is between 0.2 t/hm~2. m~(-1) and 0.4 t/hm~2. m~(-1). The forest above-ground NPP in the eastern Plateau is at the middling level, with the mean NPP of 0.3 t/hm~2. m~(-1). The forest aboveground NPP in the northeastern Plateau is fairly low, which is no more than 0.3 t/hm~2. m~(-1).
5. The forest aboveground annual NPP of recent 10 years were in a fairly steady moving up state, which took change from 0.167PgC/a of 1991 to 0.185PgC/a of 2000. The annual NPP increased 0.002PgC/a of every year, and the annual increasing rate was about 1.1%. In recent 10 years, the forest aboveground average annual NPP was about 0.19PgC/a.
6. In different forest vegetation types, coniferous forest NPP accounted for about 72.96% of the whole Plateau forest vegetation, and the total NPP of it was about 0.37TgC/m. The shrub forest accounted for about 21.62%, and the total NPP of it was about 0.11TgC/m. The total NPP of broad-leaf forest was fairly little, which was about 0.03TgC/m, which accounted for 5.42% of the whole Plateau.
7. On the Plateau of Yunnan, the primary climate drive factor of forest aboveground NPP is precipitation, which is positive to NPP in certain precipitation scope. On the Plateau of Tibet, Sichuan and Qinghai, temperature and precipitation are both primary drive factors, and temperature is positive to NPP and precipitation is negative to NPP.
在遥感技术和地理信息系统的支持下,以青藏高原及其周边地区139个气象站点的月降水信息及该地区的数字高程数据(DEM)为基础,对气温、降水等气候因子进行了空间插值分析,为碳储量、生产力的估算和空间分析奠定基础。以250 m分辨率的MODIS卫星数据、地面气象数据、1:5万地形图、1:250万森林分布图和3个区域(西藏、四川青海(两个省合并为一个大区域考虑)、云南)的1086个森林资源清查样地数据为主要数据源,通过分区建模与整个区域总体建模的对比,并引入坡向、坡位和植被类型等定性变量,通过相关分析、共线性诊断和模型模拟,选择相对最佳模型估算整个高原的森林碳储量。利用1991年8月至2000年9月的逐月NOAA\AVHRR数据,研究了青藏高原森林植被净第一性生产力(NPP)的现状,近10年来NPP的时空格局和动态变化特征,建立由遥感数据驱动的高原植被NPP在水热空间上的数学模型,揭示植被生产力对气候变化的响应规律。通过研究,所得主要结论如下:
1.在干季,无论是丰水还是欠水年份,月降水量都比较少,高程对降水量的影响较小,在精度要求不高的情况下,月降水插值可不考虑高程的影响,克里金法的月降水插值精度相对最高;在湿季,月降水量较多,高程的影响较大,混合插值法比局部插值法及克里金插值法的精度高,尤以混合插值法II(多元回归和样条法的综合)的精度最高;干季,整个高原的月降水很少,西部和北部降水最少,东部和南部相对较多,湿季,高原的月降水较多,空间格局表现为由东南到西北递减。
2.在估算青藏高原的碳储量时,定性变量(植被类型、坡向等)的引入大大提高了碳储量模型的估算精度,对数回归模型比线性回归模型的精度高,分区建模也比整个区域总体建模的精度高。
3.在青藏高原,森林类型主要在高原东部和东南部,森林覆盖率约为11.3%,森林平均地上碳储量约为19 t/hm~2。灌木林的平均碳储量相对较低,低于10 t/hm~2,主要位于柴达木盆地、川西高原西部和西藏最南部。青藏高原最东部和最南部的森林碳储量大部分低于50 t/hm~2,岷江流域的森林碳储量大都在100 t/hm~2和150 t/hm~2之间。西藏的森林地上碳储量相对较高,大多高于250 t/hm~2。
4.青藏高原地上月平均生产力最高的森林是林芝一带的暗针叶林,其生产力约在0.5 t/hm~2. m~(-1)至0.6 t/hm~2. m~(-1)之间;其次是云南一带的热带亚热带森林,其生产力约在0.2 t/hm~2. m~(-1)至0.4 t/hm~2. m~(-1)之间;青藏高原东部的生产力处于中等水平,平均约为0.3 t/hm~2. m~(-1);青藏高原东北部的森林生产力较低,一般不超过0.3 t/hm~2. m~(-1)。
5.青藏高原近10年来的森林地上年平均净第一性生产力基本上处于平稳的波动上升状态,从1991年的0.167PgC/a增加到2000年的0.185PgC/a,平均每年增加0.002PgC/a,年平均增加率约为1.1%。10年来青藏高原森林地上净第一性生产力的平均值为0.19PgC/a。
6.在不同的森林植被类型中,针叶林NPP约占整个森林植被的72.96%,其地上NPP总量约为0.37TgC/m (1Tg=1012g);灌木林约占21.62%,其地上NPP总量约为0.11TgC/m;阔叶林的NPP总量相对最小,为0.03TgC/m,约占整个森林植被的5.42%。
7.在云南,森林地上NPP的主要气候驱动因子为降水,且在一定范围降水对NPP起积极促进作用;在青藏高原的西藏和四川、青海等地,气温和降水均为森林地上NPP的主要气候驱动因子,且在一定范围气温对NPP起积极促进作用,降水超过某个限度则起负的促进作用。
NPP of ecosystem refers to the accumulated organic matter gross of green vegetation in unit time and unit area. NPP not only plays an important role in the global climate change, but also is susceptible to the climate change. Therefore, the study on the regional NPP would help to enhance the understanding of matter cycle and energy flowing of ecosystem and reveal the corresponding rules of ecosystem to the global climate change and impacts of global climate change to the ecosystem.
In the support of Remote Sensing and Geographic Information System, based on the temperature and precipitation data measured at 139 stations and the DEM data of the Tibetan Plateau, the map of monthly temperature and precipitation on the plateau were drawn, which would be the elements of the carbon storage and NPP. MODIS data with 250m spatial resolution, climate data, 1km DEM, 1:2,500,000 forest map and 1086 plot data of National Forest Inventory (NFI) of the three test areas (Tibet, Yunnan and Sichuan) were used to estimate the above-ground forest carbon storage in Tibetan Plateau. Dummy variables,such as aspect and vegetation types derived from DEM, NFI and forest map of Tibetan Plateau, were used to the carbon model. To get a more accurate model of estimating forest carbon storage, the Plateau was divided into three separated sub-areas according to its physical geography characteristics and sub-area models were compared with the whole Plateau model. By the use of monthly NOAA\AVHRR data from August, 1991 to September, 2001 with spatial resolution of 8km, the spatial pattern and dynamics of annual NPP in recent 10 years were analyzed.The relationships of NPP and climate data were established to simulate the impacts of climate changes to the Plateau NPP. The results could be obtained as follows:
1. In the dry seasons, the monthly precipitation is low no matter wet years or dry years, and the best results for dry monthly precipitation mapping were obtained using Kriging interpolation. In the wet seasons, the monthly precipitation was highly affected by the altitude, so the two mixed methods got better results than the corresponding other three methods. Furthermore, the mixed method II (the combination of Multiple Regression and Splines) got the best result. The spatio-temporal patterns of the Tibet Plateau in the precipitation maps were discussed. The precipitation in plateau’s west and north is fairly low and Plateau’s east and south is less low for the dry seasons. The wet monthly precipitation decreases as the spatial variation from southeast to northwest.
2. The dummy variables improved the precision of the forest carbon storage models dramatically. When the forest carbon storage models included dummy variables, the determining coefficients (R2) of the linear models were increased from 0.20, 0.24, 0.16 to 0.48, 0.35, 0.33 in Tibet, Yunnan and Sichuan respectively. When the linear regression models were changed to logarithmic models, R2 of Tibet, Yunnan and Sichuan were increased from 0.23, 0.30, 0.14 to 0.60, 0.65 and 0.59, respectively.
3. In Tibetan Plateau, the forest is mainly distributed on eastern part with forest cover-of about 11.3 % in 2002, and the mean above-ground forest carbon storage was about 19 t/hm~2. The carbon storage of shrub is fairly low, less than 10 t/hm~2, which is located in the Qaidam basin, the western Sichuan Plateau and the southernmost part of Tibet. The forest carbon storage in the easternmost and southeasternmost of Tibetan Plateau was mostly below 50 t/hm~2. In the Minjiang River watershed, the carbon storage is about between 100 t/hm~2 and 150 t/hm~2. In Tibet, the above-ground forest carbon storage was much higher, with more than 250 t/hm~2.
4. The highest forest aboveground NPP was located in the closed coniferous forest in Linzhi of Tibet, the forest aboveground NPP is between 0.5 t/hm~2. m~(-1) and 0.6 t/hm~2. m~(-1). The next is the tropical and subtropical forest in Yunnan, the forest above-ground NPP is between 0.2 t/hm~2. m~(-1) and 0.4 t/hm~2. m~(-1). The forest above-ground NPP in the eastern Plateau is at the middling level, with the mean NPP of 0.3 t/hm~2. m~(-1). The forest aboveground NPP in the northeastern Plateau is fairly low, which is no more than 0.3 t/hm~2. m~(-1).
5. The forest aboveground annual NPP of recent 10 years were in a fairly steady moving up state, which took change from 0.167PgC/a of 1991 to 0.185PgC/a of 2000. The annual NPP increased 0.002PgC/a of every year, and the annual increasing rate was about 1.1%. In recent 10 years, the forest aboveground average annual NPP was about 0.19PgC/a.
6. In different forest vegetation types, coniferous forest NPP accounted for about 72.96% of the whole Plateau forest vegetation, and the total NPP of it was about 0.37TgC/m. The shrub forest accounted for about 21.62%, and the total NPP of it was about 0.11TgC/m. The total NPP of broad-leaf forest was fairly little, which was about 0.03TgC/m, which accounted for 5.42% of the whole Plateau.
7. On the Plateau of Yunnan, the primary climate drive factor of forest aboveground NPP is precipitation, which is positive to NPP in certain precipitation scope. On the Plateau of Tibet, Sichuan and Qinghai, temperature and precipitation are both primary drive factors, and temperature is positive to NPP and precipitation is negative to NPP.
引文
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