Abstract: As the “Third Pole” of the world,the Qinghai-Tibetan Plateau,China is located at 74°-104°E and 25°-40°N.It is known for its high altitude,complex terrain,and harsh climate.The Qinghai-Tibetan Plateau is one of the most sensitive regions to global climate change.It can affect the productivity of plateau’s ecosystem.Calculating the net primary productivity of the Qinghai-Tibetan Plateau ecosystem is very important for accurately estimating the global carbon cycle.As NPP cannot be directly measured on a regional or global scale,so modelbased estimation is the only way to proceed.In this study,we used the CEVSA model to eatimate the NPP in the Tibetan Plateau between 2000 and 2014, and analyzed the spatiotemporal patterns and trends of NPP with M-K Trend test method,the Sen’s slope estimation method,and the Pearson correlation coefficient method.The model was based on a 0.1°×0.1° resolution map of vegetation types,soil texture data,and daily meteorological data.The results indicated as follows:(1) the NPP for the Qinghai-Tibetan Plateau decreased from southeast to northwest and was consistent with the trend of water-heat distribution.The results were similar to those obtained by ZHOU Caiping et al,who applied a combination of terrestrial ecosystem model and MODIS data to estimate the net primary productivity of the Qinghai-Tibetan Plateau.In the spatial distribution,the NPP of the forests in the east and southeast was between 600 and 1 200 gC·m^-2·a^-1 and the NPP of the central grassland and meadows was from 200 to 400 gC·m^-2·a^-1.In the western and northern deserts,the NPP was limited by the moisture and temperature.(2) the annual average temperature increase had a significant positive effect,while the precipitation decrease had a significant negative effect on the Qinghai-Tibetan Plateau’s NPP.The annual NPP was positively correlated with annual mean temperature over 82.24% of the region,while negatively correlated with annual precipitation over 49.31% of the region.Therefore,temperature is considered to be the dominant factor determining spatial variations in NPP.The predecessors also obtained similar result.For example,LIU Gang et al. analyzed the spatiotemporal variation of net primary productivity and climate controls in China from 2001 to 2014.Based on their results,the correlation analysis between NPP and meteorological elements indicated that NPP was positively correlated with temperature in the Changbai Mountain area,QinghaiTibetan Plateau,and southern areas.(3) from 2000 to 2014,the trend of increasing NPP was consistent with the changes in temperature.The precipitation showed a slight decrease change.A period of warming accompanied by a decrease in precipitation contributed to the trend of a gradual increase of NPP in Qinghai-Tibetan Plateau.Therefore,improving our ability to accurately describe the response of NPP to climate changes will provide a better understanding of terrestrial ecosystem responses to global changes.However,there are still some issues to be solved,such as the uncertainty of NPP prediction.These uncertainties mainly include the driving variables and parameters in the model.Overall,to predict the impact of climate change on ecosystems at the regional level,modeling uncertainty can be reduced by increasing the spatial resolution of the driving variables.In addition,optimizing the model parameters can also reduce the uncertainty in the model simulation.

Key words: CEVSA model')">

CEVSA model, net primary productivity, climate change, Tibetan Plateau