首页 | 本学科首页   官方微博 | 高级检索  
   检索      

2000-2015年中国陆地生态系统蒸散时空变化及其影响因素
引用本文:牛忠恩,胡克梅,何洪林,任小丽,张黎,葛蓉,李攀,郑涵,朱晓波,曾纳.2000-2015年中国陆地生态系统蒸散时空变化及其影响因素[J].生态学报,2019,39(13):4697-4709.
作者姓名:牛忠恩  胡克梅  何洪林  任小丽  张黎  葛蓉  李攀  郑涵  朱晓波  曾纳
作者单位:中国科学院地理科学与资源研究所, 生态系统网络观测与模拟重点实验室, 北京 100101;中国科学院大学, 北京 100049,大连海事大学, 大连 116026,中国科学院地理科学与资源研究所, 生态系统网络观测与模拟重点实验室, 北京 100101,中国科学院地理科学与资源研究所, 生态系统网络观测与模拟重点实验室, 北京 100101,中国科学院地理科学与资源研究所, 生态系统网络观测与模拟重点实验室, 北京 100101,中国科学院地理科学与资源研究所, 生态系统网络观测与模拟重点实验室, 北京 100101;中国科学院大学, 北京 100049,中国科学院大学, 北京 100049;中国科学院地球化学研究所, 贵阳 550002,中国科学院地球环境研究所, 黄土与第四纪地质国家重点实验室, 西安 710061,西南大学地理科学学院, 遥感大数据应用重庆市工程研究中心, 重庆 400715,中国科学院地理科学与资源研究所, 生态系统网络观测与模拟重点实验室, 北京 100101;中国科学院大学, 北京 100049
基金项目:国家重点研发计划项目(2016YFC0500204)
摘    要:准确量化区域蒸散时空格局及其影响因素对理解陆地生态系统碳水循环具有十分重要的意义。近年来中国经历了严重的空气污染及气候波动,亟须探讨蒸散的时空变化及其影响因素。基于PT-JPL(Priestly-Taylor Jet Propulsion Laboratory)模型,集成遥感数据和气象数据模拟了中国陆地生态系统2000-2015年蒸散,并分析其时空变化及影响因素。结果表明:1)参数优化后PT-JPL模型可解释蒸散年际变化的68%,优于原始模型及MODIS蒸散产品;2)中国地区多年平均蒸散为440.16 mm/a,呈东南沿海到西北内陆逐渐递减的空间格局;3)2000-2015年蒸散整体呈轻微下降趋势(slope=6.48 Gt/a,P=0.17),但具有年代际差异,2000-2010年中国地区蒸散呈显著下降趋势(slope=21.05,P < 0.01),占全国蒸散总量45.05%的内蒙古地区、甘新地区、黄土高原地区及青藏地区解释了61.88%的年际变化;2010-2015年呈轻微上升趋势(slope=10.48,P=0.71),各地区均无显著变化趋势;4)辐射主导了蒸散的年代际差异,分别解释了2010年前后蒸散下降及上升趋势的51.45%、85.26%。蒸散呈显著变化趋势的内蒙古地区、黄土高原地区及青藏地区主要受辐射控制,甘新地区主要受降水和温度的影响。

关 键 词:蒸散  PT-JPL模型  时空格局  中国陆地生态系统
收稿时间:2018/3/9 0:00:00
修稿时间:2019/3/9 0:00:00

The spatial-temporal patterns of evapotranspiration and its influencing factors in Chinese terrestrial ecosystem from 2000 to 2015
NIU Zhongen,HU Kemei,HE Honglin,REN Xiaoli,ZHANG Li,GE Rong,LI Pan,ZHENG Han,ZHU Xiaobo and ZENG Na.The spatial-temporal patterns of evapotranspiration and its influencing factors in Chinese terrestrial ecosystem from 2000 to 2015[J].Acta Ecologica Sinica,2019,39(13):4697-4709.
Authors:NIU Zhongen  HU Kemei  HE Honglin  REN Xiaoli  ZHANG Li  GE Rong  LI Pan  ZHENG Han  ZHU Xiaobo and ZENG Na
Institution:Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China,Dalian Maritime University, Dalian 116026, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China,Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China,University of Chinese Academy of Sciences, Beijing 100049, China;Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550002, China,State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi''an 710061, China,Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China and Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:Accurately quantifying the spatial and temporal patterns of regional evapotranspiration and their influencing factors has significant implications for understanding carbon and water cycles in the terrestrial ecosystem. Based on MODIS remote sensing data and meteorological data from 2000 to 2015, we used the modified Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) model to simulate evapotranspiration and its spatial distribution in the Chinese terrestrial ecosystem and analyzed the temporal and spatial variation of evapotranspiration and its influencing factors. The results revealed the following. 1) The observation data demonstrated that the PT-JPL model with optimized parameters illustrated 68% of the seasonal variation and spatial distribution patterns of evapotranspiration better than the original model and the MODIS evapotranspiration product. 2) The mean evapotranspiration over the years was 440.16 mm/a with obvious spatial heterogeneity, gradually decreasing from the southeast coast to the inland of the northwest. 3) The total evapotranspiration showed a slight downward trend (slope = 6.48 Gt/a, P=0.17) from 2000 to 2015; however, there was an interdecadal difference as the evapotranspiration significantly decreased from 2000 to 2010 (slope = 21.05 Gt/a, P < 0.01). The Inner Mongolia region, Loess Plateau region, Gan-xin region, and Qinghai-Tibet region accounted for 45.05% of the total national evapotranspiration and 61.88% of the interannual change; these regions showed a slightly increasing but insignificant trend from 2010 to 2015 (slope = 10.48 Gt/a, P=0.71). 4) Radiation was the main influencing factor of the trend of evapotranspiration before and after 2010, accounting for 51.45% and 85.26% of the total, respectively.
Keywords:evapotranspiration  PT-JPL model  spatial-temporal patterns  Chinese terrestrial ecosystem
点击此处可从《生态学报》浏览原始摘要信息
点击此处可从《生态学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号