Climate-sensitive Site Index Model Of Larix Olgensis Henry

Site index is the most significant means for quantitatively evaluating forest standproductivity and widely used in forest growth and yeilds models. However, site index iscommonly considered unchanged. Actually, climate change and practical management areexpected to have an important effect on site index. Under the condition of global changing, it isessential to create climate-sensitive site index model. Larix olgensis Henry is one of the mostsignificant afforestration species in Jilin Province, Northeast of China. In this paper, treeattributes and climatic data were both utilized to establish site index model of Larix olgensisHenry. Tree information containing dominant trees’ age and height were investigated in337plots of Jilin Province in1999. Basic climatic data from1961to2010were downloaded fromChina Meteorological Data Sharing Service System and then interpolated (with the resolution300meters) by a professional interpolation software ANUSPLIN. Due to the complexicity ofinterpolation and calculation of the various climatic data, distributed computing with JAVAlanguage was employed to fulfill the whole process. Two models were developed for siteproductivity projection under climate change.Analysing the interpolation results, we found that annual mean temperature and sumprecipitation both asended during the latest50years. The variation of precipitation among thethree months in summer also increased significantly. In contrast, radiation hours and relativehumidity declined obviously in this peroid.According to the analysis of existing data, linear regression, was choosen to establish siteindex model based on site index and climatic variables. The first climate-sensitive site indexmodel (Model Ⅰ) was created only with climatic factors, which were GSMINTGSRH (theratio of the minimum temperature in growing season and the relative humidity in growingseason), PRATIO (the ratio of the precipitation in growing season and annual meanprecipitation), AMAXT (annually maximum temperature) and GSPDD5((Mean precipitationin growing season multiplied by the accumulated temperature higher than5℃in growing season)/1000). Model Ⅰ could explain47.9%of the variation of site index. The otherclimate-sensitve site index (Model Ⅱ) was composed by three independent variables, whichwere mean age of dominant trees, MAPMTCM (the ratio of Annual mean precipitation andMean temperature in the coldest month) and GSPGSMINT ((Mean precipitation in growingseason multiplied by the minimum temperature in growing season)/1000). Model Ⅱ couldexplain89.2%of the variation of site index. Both models were sensitive to reflect the impact ofclimate on site index, which could be utilized to evaluate and predict site index under globalchange.In terms of good performance of Model Ⅱ, we used Model Ⅱ to predict site indexchange between2000and2010. As the results revealed, during th last10years, site index hasbeen increasing notablely by the effect of climate change.