摘要
水分利用效率是深入理解生态系统水碳循环耦合关系的重要指标。西南高山地区是响应气候变化的重点区域,研究西南高山地区水分利用效率动态及其对气候变化的响应,对于评估区域碳水耦合关系及对全球气候变化的响应具有重要意义。应用生态系统模型CEVSA(Carbon Exchange between Vegetation,Soil,and the Atmosphere)估算了1954—2010年西南高山地区水分利用效率(Water use efficiency,WUE)的时空变化,分析了其对气候变化的响应。结果表明:(1)西南高山地区1954—2010年水分利用效率均值为1.13 g C mm-1m-2。3种主要植被类型草地、常绿针叶林和常绿阔叶林的WUE分别为1.35、1.14、0.99 g C mm-1m-2。在空间分布上,WUE与海拔显著正相关(r=0.156,P<0.05),而与温度则显著负相关(r=-0.386,P<0.01)。(2)在时间尺度上,1954—2010年西南高山地区整体WUE降低趋势显著(P<0.01),变动区间为0.83-1.46g C mm-1m-2,平均每年下降0.006g C mm-1m-2。整体WUE年际变化与温度呈显著负相关(r=-0.727,P<0.01),与降水量相关性不显著;整体WUE下降主要原因是温度上升引起的ET增加速率大于NPP增加速率。(3)1954—2010年西南高山地区3种主要植被类型草地、常绿针叶林及常绿阔叶林WUE均显著下降(P<0.01),下降速度分别为-1.03×10-2、-6.17×10-3、-1.37×10-3g C mm-1m-2a-1。西南高山地区76.3%格点WUE年际变化与温度显著负相关(P<0.05),34.1%格点WUE年际变化与降水量显著正相关(P<0.05)。草地和常绿针叶林WUE年际变化与温度显著负相关(r=-0.889,P<0.01;r=-0.863,P<0.01),与降水量相关性不显著。由于西南高山地区降水较为丰富,且过去57年降水变化不显著,因此该地区WUE的时空格局主要受温度变化的影响。1954—2010年期间温度升高造成的ET增加显著高于NPP的增加是该地区WUE下降的主要原因。未来需要获取更高空间分辨率的气候、土壤、植被数据,从而更加准确和精确地模拟
The various types of ecosystems and complex landforms found in the cold alpine area of southwestern China makethis region ideal for researching regional responses to global climate change. Therefore,to evaluate the responses of regional carbon and water cycles to climate change; it is of great importance to investigate the response of water use efficiency（WUE） to the climate in this region. A process-based ecosystem model,Carbon Exchange between Vegetation,Soil,and the Atmosphere（ CEVSA）,was used to estimate temporal and spatial variations of WUE in the terrestrial ecosystems in the alpine area of southwestern China during 1954—2010. First,we ran the model using the average climate data from 1954 to2010 until an ecological equilibrium was reached,then we conducted dynamic simulations with climate data at a time-step of 10 days during the same period. Moreover,the correlation coefficients between WUE and climate variables were calculated to analyze the relative effects of temperature and precipitation on variations of WUE. To achieve the results,various types of computer software were used,such as ANUSPLIN4. 1,Fortran 95,Arcgis9. 3,and SPSS18. 0. The results showed that the average WUE in the studied region was 1. 13 g C mm- 1m- 2during 1954—2010. The mean WUE of three main vegetation types included 1. 35 g C mm- 1m- 2for herbaceous cover,1. 14 g C mm- 1m- 2for evergreen needle-leaf tree cover,and 0. 99 g C mm- 1m- 2for evergreen broadleaf tree cover. In spatial distribution,significant positive correlations were found between the annual WUE and altitude（ r = 0. 156,P〈0. 05）,and significant negative correlation was found between the annual WUE and annual mean temperature（ r =- 0. 386,P〈0. 01）. Moreover,the annual mean WUE in the entire region showed a significantly decreasing trend at a rate of 0. 006 g C mm- 1m- 2a- 1（ P〈0. 01）. Significant negative correlations were found between the annual mean WUE and annual mean temperature（ r =- 0. 727,P〈0. 01）,and no significant correlati
出处
《生态学报》
CAS
CSCD
北大核心
2016年第6期1515-1525,共11页
Acta Ecologica Sinica
基金
林业公益性行业科研专项(201404201)
国家自然科学基金(31370463
31070398)
公益性科研院所基本科研业务费专项(CAFRIFEEP201411)
