Vegetation Net Primary Productivity Variation And Its Response To Climate In Buryatiya Republic Russia Based On CASA Model

Vegetation Net Primary Productivity (NPP) is a key component of the terrestrial carbon cycle. As the direct reflection of plant community productivity for a certain natural environment, it is the basis of matter and energy cycle of terrestrial ecosystem. As highlighted during the international negotiation process for the United Nations Framework Convention on Climate Change (UNFCCC), a better grasp upon the controls and distribution of NPP is pivotal for sustainable human use of the biosphere. Based on Remote Sensing, Geographic Information System and Gobal Positioning System, This paper comprehensively used remote sensing data, ground meteorological data, other additional data and improved Carnegie Ames Stanford Approach (CASA) model to estimate vegetation NPP in Buryatiya Republic, Russia from 2000 to 2008. After the comparison and validation with observed data and other NPP product data, the NPP time-series of Buryatiya terrestrial vegetation from 2000 to 2008 was built. Spatio-temporal variations and potential trend of NPP were analyzed in these 9 years, and the relationship between NPP and global climatic change was comprehensively studied. From these researches, some basic conclusions were drawn as follows:1. Improvement of CASA modelCASA model contains three submodels of Photosynthetically Active Radiation, Light Use Efficiency and Soil Water Content, but the parameters of Soil Water Content model are complex. So, there is some difficulty to obtain the reaserch data. This paper simplifys the estimation model via inputing Bio-temperature to model in order to calculate potential evaporation of soil water. With the comparison and validation with observed data and other NPP product data, the result shows: Average value of observed data is 323.69gC·m-2 and estimation data is 355.68gC·m-2. The difference between them is small and the average relative error is 4.94%. Correlation coefficient between observed data and estimation data is 0.88(p<0.01), which prove the improved CASA model can be used to estimate the vegetation NPP in Buryatiya Republic. Precise of improved CASA model is high.2. Comparison and analysis between NDVI and EVIMaximal average value of vegetation NDVI presents in Ivolginskii region, but minimal average value of NDVI in Tarbagataiskii region. Maximal average value of vegetation EVI presents in Tarbagataiskii region, but minimal average value of NDVI in Ivolginskii region. It is very interesting that the opposite phenomenon is found.Vegetation with maximal average NDVI value in Ivolginskii region is forest, but steppe has the minimal NDVI value in this region. In Dzhidinskii region, mixed vegetation of grassland and forest has the maximal average NDVI value, but high mountain vegetation with the minimal NDVI value. In Tarbagataiskii region, vegetation with maximal average NDVI value is forest, and mixed vegetation of grassland and forest has the minimal average NDVI value. In Kyahtinskii region, the phenomenon is opposite to Tarbagataiskii region. Forest has the bigger average NDVI value than steppe in Bichurskii region. In Muhorshibirskii region, forest has the maximal average NDVI value, and steppe with the minimal average NDVI value. Mixed vegetation of swampe and meadow has the maximal average NDVI value, and high mountain vegetation has the minimal average NDVI value in Selenginskii region.Vegetation with maximal average EVI value in Ivolginskii region is mixed vegetation of forest and grassland, but forest has the minimal EVI value in this region. In Dzhidinskii region, mixed vegetation of swampe and meadow has the maximal average EVI value, but mixed vegetation of forest and grassland with the minimal EVI value. In Tarbagataiskii region, vegetation with maximal average EVI value is mixed vegetation of forest and grassland, and steppe has the minimal average EVI value. In Kyahtinskii region, forest has the maximal average EVI value, and mixed vegetation of forest and grassland has the minimal average EVI value. Steppe has the bigger average EVI value than forest in Bichurskii region. In Muhorshibirskii region, steppe has the maximal average EVI value, and forest with the minimal average EVI value. Mixed vegetation of swampe and meadow has the minimal average EVI value, and high mountain vegetation has the maximal average EVI value in Selenginskii region.Forest has the maximal average value of NDVI and EVI, and mixed vegetation of swampe and meadow has the minimal average value of NDVI and EVI in total southern region of Buryatiya. Fluctuation range of maximal value, minimal value, average value and standard deviation of NDVI is obvious bigger than EVI, which can show that NDVI has the superior capacity to distinguish vegetation types on remote sensings objectively and is propitious to remote sensing interpretation and quantitative analysis in future.3. Spatio-temporal distribution pattern of vegetation NPP(1) Annual variation of vegetation NPP: Average value of vegetation NPP in Buryatiya Republic from 2000 to 2008 is 544.29gC·m-2·a-1, and total NPP is 1.91E+14gC·a-1. The trend of vegetation NPP in Buryatiya Republic from 2000 to 2008 is increasing among fluctuation as a whole. The value of vegetation NPP locates lowest point in 2003 with 345.94gC·m-2·a-1, but locates wave crest in 2008 with 668.76gC·m-2·a-1. The increase range is great from 2003 to 2004, 2007 to 2008, but it increase gently in other years. The average increase range of vegetation NPP is 0.39gC·m-2·a-1 in this area from 2000 to 2008.(2) Monthly variation of vegetation NPP: The trend of monthly variation shows: vegetation NPP has the smallest value with 0.002gC·m-2·month-1 per unit area in February, but it has the biggest value with 131.13gC·m-2·month-1 per unit area in July. From January to March, vegetation NPP locates zero per unit area, but it increases rapidly in April and reaches highest point in July. With the climate change, vegetation NPP decreases sharpely from Augest to October, and locates zero per unit area in December.(3) Seasonal variation of vegetation NPP: Vegetation NPP in Spring(March to May), Summer(June to Augest), autumn(September to November) and Winter(December to February next year) are 81.83 gC·m-2, 365.73 gC·m-2, 94.16 gC·m-2 and 0.73 gC·m-2, with proportion of 15.08%, 67.42%, 17.36% and 0.14% of total NPP in all year per unit area. (4) Regional variation of vegetation NPP: Among all the twenty-three regions of Buryatiya Republic, vegetation NPP has lower value in Ulan-Ude city, Okinskiy region on both yearly and monthly level, but it has higher value in Ivolginskii region, Severobaykalsk region, Zakamensk p. region and Bichurskii region.(5) Longitudinal and latitudinal variation of vegetation NPP: Both yearly and monthly level, vegetation NPP in Buryatiya Republic shows"double-humped"distribution pattern on longitudinal scale, and"Singlet"distribution pattern on latitudinal scale. It presents increasing rule fllowing raised longitude and decreasing rule with increased latitude.(6) Spatial variation of vegetation NPP: The regions of increased vegetation NPP locate in northern and western areas in Buryatiya Republic from 2000 to 2008. From southwest to northeast, vegetation represents the trend of increased distinctively, vary indistinctively and increased distinctively. Vegetation NPP changes indistinctively (p>0.05) in most area with proportion of 88.95%, only in 11.05% area, the vegetation NPP changes distinctively (p<0.05). Vegetation NPP shows increased trend with proportion of 75.05%, thereinto, the proportion of increased distinctively (p<0.05) and increased very distinctively (p<0.01) is 9.77%. Vegetation NPP shows decreased trend with proportion of 24.95%, thereinto, the proportion of decreased very distinctively (p<0.01) is 0.23%.(7) NPP Variation of different vegetation: On yearly level, different vegetations NPP present decreased trend from 2000 to 2001, 2002 to 2003, but increased trend from 2001 to 2002, 2003 to 2008. It is similar with the annual variation rule of vegetation NPP from 2000 to 2008. On monthly level, NPP accumulation of different vegetations arise in growing season, but growth arrest from November to March next year. Vegetation NPP accumulation increasing period is April to July, and decreasing period is Augest to October. The rates of increasing and decreasing are great.4. Correlation research between vegetation NPP and climatic factors(1) Correlation analysis between vegetation NPP and climatic factors: with the simple correlation and partial correlation analysis between vegetation NPP and climatic factors in research area, result shows that: on yearly level, there is non-significant correlation (p>0.05) between vegetation NPP and climatic, but it is opposite to (p<0.01) on monthly level.(2) Spatial response of temperature and precipitation to vegetation NPP: In small area of western, southern Buryatiya, baikal shore and large area of northern Buryatiya, vegetation NPP and temperature have significant positive correlation (p<0.05), but vegetation NPP and precipitation have significant positive correlation (p<0.05) in large area of western and northern Buryatiya and significant negative correlation (p<0.05) even very significant negative correlation (p<0.01). It shows that temperature in company with precipitation impact on vegetation NPP in western and northern Buryatiya, but it is mainly controlled by temperature in southern Buryatiya and baikal shore.Based on Remote Sensing data, meteorological data and mathematical model, temporal-spatial variation simulation of vegetation NPP and its correlation with climatic factors were applied. Vegetation between Buryatiya Republic and Northern China has a lot of similarities, because they locate in cold and high-pressure climate circulation system in Siberia. The improved CASA model can be used in vegetation NPP estimation of Northern China and research results have reference significance for ecological cross-border study.