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基于Biome-BGC模型的青藏高原五道梁地区NPP变化及情景模拟
引用本文:李传华,韩海燕,范也平,曹红娟,王玉涛,孙皓.基于Biome-BGC模型的青藏高原五道梁地区NPP变化及情景模拟[J].地理科学,2019,39(8):1330-1339.
作者姓名:李传华  韩海燕  范也平  曹红娟  王玉涛  孙皓
作者单位:西北师范大学地理与环境科学学院,甘肃兰州,730070;西北师范大学地理与环境科学学院,甘肃兰州,730070;西北师范大学地理与环境科学学院,甘肃兰州,730070;西北师范大学地理与环境科学学院,甘肃兰州,730070;西北师范大学地理与环境科学学院,甘肃兰州,730070;西北师范大学地理与环境科学学院,甘肃兰州,730070
基金项目:国家自然科学基金项目资助(41761083);国家自然科学基金项目资助(41661084)
摘    要:以“气候变暖”为标志的全球气候变化对青藏高原生态系统产生强烈影响,利用参数本地化的生物地球化学模型(Biome-BGC)对五道梁地区草地生态系统进行模拟,研究了该区域1961~2015年净初级生产力(net primary productivity,NPP)的变化,并进行了情景模拟。结果表明:五道梁地区近55 a草地年均NPP为67.94 g/(m 2·a),呈显著上升趋势,主要是由生长季延长以及9月份生物量快速增长造成。在该地区,温度是草地NPP的主导因子,降水变化在40%以内对生产力影响不显著;温度和降水交互影响NPP,对单一影响有放大作用,暖湿条件下NPP对气候变化响应更加明显。

关 键 词:草地生态系统  生物地球化学模型  气候变暖  参数本地化
收稿时间:2018-07-03
修稿时间:2018-10-22

NPP Change and Scenario Simulation in Wudaoliang Area of the Tibetan Plateau Based on Biome-BGC Model
Li Chuanhua,Han Haiyan,Fan Yeping,Cao Hongjuan,Wang Yutao,Sun Hao.NPP Change and Scenario Simulation in Wudaoliang Area of the Tibetan Plateau Based on Biome-BGC Model[J].Scientia Geographica Sinica,2019,39(8):1330-1339.
Authors:Li Chuanhua  Han Haiyan  Fan Yeping  Cao Hongjuan  Wang Yutao  Sun Hao
Institution:College of Geography and Environment Science, Northwest Normal University,Lanzhou 730070, Gansu, China
Abstract:The Tibetan Plateau is the largest plateau in China and the highest altitude in the world. Due to its unique natural conditions, this area not only directly affects the climate change in East Asia, but also has a great impact on the northern hemisphere. However, the Wudaoliang site on this Plateau has not been studied independently. The purpose of this study is to use Biome-BGC model to simulate NPP changes in Wudaoliang from 1961 to 2015. In addition, scenario simulation is also conducted to explore the response of NPP to climate change under different scenarios. Biome-BGC model has strong rationality and systematicness. It is a biogeochemical model in daily for studying the physiological processes and interactions of vegetation and soil energy, water, carbon and nitrogen in regional and global scale ecosystems. The input data of the model are meteorological data, initialized file data, atmospheric CO2 concentration data for many years, vegetation parameter data. The model has been widely applied. In order to improve the accuracy, some parameters, such as leaf C/N ratio and soil particle size, were also measured. In August 2017, the measured samples of grassland vegetation in the flourishing season were validated by directly converting the measured samples into biomass, and compared with previous studies. The verification results showed that the simulated NPP is acceptable and applicable to this area. NPP of Wudaoliang alpine grassland ranged from 57.6 to 79.8 g/(m 2·a), with an average of 67.94 g/(m 2·a), showing an overall growth trend (P<0.01), with an average annual growth rate of 0.18%/a. According to the criterion of Odum ecosystem productivity, the study area is the lowest productivity ecosystem. NPP increased by 5.9%, 11.77%, 17.57% and 23.32% respectively along with the temperature increased by 0.5℃, 1℃, 1.5℃ and 2℃ than the normal scenario. The greater the temperature rise, the more obvious the increase of NPP. In the scenarios of precipitation increasing and decreasing by 20% and 40%, the average NPP changed by 1.13%, 2.14%, 1.19% and -2.45% than normal scenarios. Increasing precipitation could promote the growth of NPP, but decreasing precipitation showed inhibition effect. The combined scenarios of T2W20+, T2W40+, T2W20- and T2W40- increased by 24.54%, 25.68%, 21.79% and 19.94% respectively than normal scenarios. Under the combined scenarios, the NPP increase is higher than that of a single increase in moderate precipitation scenarios. Biome-BGC model with localized parameters can better simulate the net primary productivity of grassland in Wudaoliang area of Tibetan Plateau. In the past 55 years, the net primary productivity of Wudaoliang grassland showed a significant upward trend, which was mainly caused by the extension of growing season and the rapid growth of biomass in September. In this area, temperature increase is beneficial to productivity increase. Precipitation increase is beneficial to productivity acceleration. Precipitation decrease inhibits NPP accumulation. Precipitation change within 40% has no significant effect on productivity. Temperature and precipitation interact with NPP and have an amplifying effect on a single effect.
Keywords:grassland ecosystem  Biogeochemical model  climate warming  localized parameters  
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