摘要
利用中国植被净第一性生产力ＮＰＰ（ｎｅｔｐｒｉｍａｒｙｐｒｏｄｕｃｔｉｖｉｔｙ）的散点测量数据及中国一般气象站气候记录资料，并根据附近气象站数据进行地理位置相关插值，采用Ｐｅｎｍａｎ方法计算潜在蒸散，用Ｔｈｏｒｎｔｈｗａｉｔｅ的土壤水分处理方法，计算了ＮＰＰ测定位点的水分平衡。结果表明ＮＰＰ与生长季实际蒸散、总蒸散、潜在蒸散显著相关，与干燥度成负相关。从生长季的实际蒸散、干燥度指标两参数出发，建立了ＷＢＩＮＰＰ模型：ＮＰＰ＝２．５５·ＧＡＥ·ｅ－４．２０９２２－１．９６６５ＡＩ。该模型考虑了植被生产力形成机制，与ＮＰＰ实测值相关性高达０．８４２，高于国际上著名ＮＰＰ模型Ｍｉａｍｉ模型、ＴｈｏｒｎｔｈｗａｉｔｅＭｅｍｏｒｉａｌ模型和Ｃｈｉｋｕｇｏ模型（相关系数为０．６５５～０．７３２）。最后利用ＥＩＳ软件对全国潜在ＮＰＰ分布格局进行了分析，从东南到西北逐渐减少，最大值出现在海南和台湾，大于２２ｔ·ｈｍ－２·ａ－１，长江中下游地区１２～１６ｔ·ｈｍ－２·ａ－１，华北平原为８～１２ｔ·ｈｍ－２·ａ－１，东北地区为４～８ｔ·ｈｍ－２·ａ－１；草原地区为２ｔ·ｈｍ－２·ａ－１左右，干旱荒漠小于２ｔ·ｈｍ－２·ａ－１。
The relationship between net primary productivity (NPP) of natural potential vegetation in China and its climate control has been studied. First, site water balance indices are calculated by means of climate station document interpolation. NPP field document is strongly correlated with actual evapotranspiration in growth period, annual actual evapotranspiration, annual potential evapotranspiration and water deficiency indices. Then according to the mechanism of NPP accumulation, a WBINPP model is developed basing on a collection of NPP field document all over the country and site water balance indices interaction: NPP=2.55 GAE·e-4.20922-1.9665·AI. Comparing with other NPP models, such as Miami, Thornthwaite Memorial and Chikugo, WBINPP model is more rational and strongly fit to field doccment, the correlation coefficient is 0.845. According to WBINPP model, a spatial pattern analysis for NPP in China is conducted by means of a GIS software, EIS, and the result shows NPP is declined from Southeastern China to Northwestern China. NPP of the greatest value is above 22 t·hm-2·a-1 in the region located in Hainan and Taiwan Province; while with different values of 12～16, 8～12, 4～8 t·hm-2·a-1 in the region of middle and lower reaches of Yangtze River, the Northern China plain and the Northeastern Region in China, respectively. In the Northern steppe zone NPP is about 2 t·hm-2·a-1 and in the dry desert zone is lower than 2 t·hm-2·a-1.
基金
国家自然科学基金
关键词
植被
净第一性生产力
水分平衡指数
分布
模拟
Climatevegetation, Net primary productivity, Water balance indices, Global change
