EN中文

自然资源学报 >

2016 , Vol. 31 >Issue 8: 1275 - 1288

DOI: https://doi.org/10.11849/zrzyxb.20151064

资源生态

渭河流域植被WUE遥感估算及其时空特征

展开
  • 1. 北京师范大学 a. 地表过程与资源生态国家重点实验室,b. 资源学院,北京 100875;
    2. 陕西师范大学旅游与环境学院,西安 710119
位贺杰（1988- ）,男（汉族）,河南项城人,博士研究生,主要从事生态系统服务研究。E-mail:shanxishidawhj@163.com *通信作者简介：董孝斌（1973- ）,男（汉族）,河北张家口人,博士,教授,主要研究方向为系统生态学与可持续农业。E-mail:dong_xiaobin@163.com

收稿日期: 2015-10-07

  修回日期: 2016-02-22

  网络出版日期: 2016-08-20

基金资助

国家自然科学基金项目（41271549）; 国家科技支撑计划项目（2012BAD14B03）; 中央高校基本科研业务费专项资金（2014KJJCB33）

收起

Estimating the Spatio-temporal Characteristic of Vegetation Water Use Efficiency over Weihe River Basin

Expand
  • 1. a. State Key Laboratory of Earth Surface Processes and Resource Ecology, b. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China;
    2. College of Tourism and Environment, Shaanxi Normal University, Xi’an 710119, China

Received date: 2015-10-07

  Revised date: 2016-02-22

  Online published: 2016-08-20

Supported by

National Natural Science Foundation of China, No.41271549; China S & T Supporting Programme, No.2012BAD14B03; Fundamental Research Funds for the Central Universities, No.2014KJJCB33

Fold

摘要

论文基于估算NPP的CASA模型和估算ET的三角形模型对水分限制因子算法进行改进的基础上,构建了由NPP子模型和ET子模型组成的WUE遥感估算模型,以2010年相关MODIS影像和气象参量为数据源,实现了渭河流域WUE的估算,并对WUE的时空特征及其与年内气温、降雨的关系进行了分析。研究表明：1）WUE模拟结果与通量观测数据以及生态系统模型模拟结果均具有一定的可比性,各模型模拟结果存在差异可能与WUE定义、模拟区域、使用数据源以及使用植被覆盖分类底图等存在差异有关;2）渭河流域WUE年内分布呈现微“双峰”型格局,以8月最高,春、夏、秋、冬四季WUE分别为0.57、1.05、0.66、0.12 gC·m-2·mm-1,呈现夏季>秋季>春季>冬季的特征;3）渭河流域WUE空间分布呈现子午岭、黄龙山、六盘山以及秦岭北坡等林区高,西安市建成区、子流域上游低植被覆盖区以及局部旱作农业区低的分异特征;4）渭河流域尺度上,WUE随年内气温和降雨的变化均呈现5阶段的变化特征,但变化形式存在差异。

关键词： 净初级生产力（NPP）; 水分生产效率（WUE）; 渭河流域; 蒸散发（ET）

本文引用格式

位贺杰, 张艳芳, 董孝斌, 鲁纳川, 王雪超 . 渭河流域植被WUE遥感估算及其时空特征[J]. 自然资源学报, 2016 , 31(8) : 1275 -1288 . DOI: 10.11849/zrzyxb.20151064

Abstract

Water Use Efficiency (WUE) can be used to describe the relationship between “water loss” and “carbon fixation” of plants in the process of photosynthesis, which is an important variable to link ecosystem carbon and water cycles. Estimating WUE with remote sensing data can enhance our ability to reveal how global change affects water and carbon cycles. Based on triangle model and CASA model which is used to estimate evapotranspiration (ET) and net primary production (NPP), this paper constructs the WUE remote sensing estimation models with improved water limiting parameters. Using the constructed model, this paper acquires the WUE of vegetation over the Weihe River Basin in 2010 based on MODIS imagery and meteorological data. Then this paper studies the relationship between WUE and temperature or precipitation. Results are shown as follows: 1) The results of WUE estimated by different WUE models are different because these models are based on different definitions of WUE, different simulation areas, different data sources or different vegetation classifications. 2) The monthly variation of WUE roughly shows a double peak pattern over the Weihe River Basin in 2010 with the highest value in August. The seasonal WUE shows that the maximum value is in summer (1.05 gC·m-2·mm-1), followed by autumn (0.66 gC·m-2·mm-1), spring (0.57 gC·m-2·mm-1) and winter (0.12 gC·m-2·mm-1). 3) The spatial distribution of WUE shows that the high value pixels are in the forest region of Ziwuling, Huanglong Mountain, Liupan Mountain and the northern slope of Qinling, while the low value pixels are in the built-up regions of Xi’an city, low vegetation coverage regions of upper basin and some dry farming areas. 4) With the increase of temperature, the change of WUE can be divided into five stages, which are essentially invariant, slightly increased, rapid increased, stable and declined. With the increase of precipitation, the change of WUE over the Weihe River Basin also can be divided into five stages, which are rapidly increased, slowly increased, stable, slowly declined and rapidly declined.

Key words： evapotranspiration (ET); net primary production (NPP); water use efficiency (WUE); Weihe River Basin

参考文献

[1] FISCHER R A, TURNER N C. Plant productivity in the arid and semiarid zones [J]. Annual Review of Plant Physiology, 1978, 29(1): 277-317.
[2] 张良侠, 胡中民, 樊江文, 等. 区域尺度生态系统水分利用效率的时空变异特征研究进展 [J]. 地球科学进展, 2014, 29(6): 691-699.

[3] MARTIN B, THORSTENSON Y R. Stable carbon isotope composition (δ 13 C), water use efficiency, and biomass productivity of Lycopersicon esculentum , Lycopersicon pennellii , and the F1 hybrid [J]. Plant Physiology, 1988, 88(1): 213-217.
[4] KOZLOWSKI T T, KRAMER P J, PALLARDY S G. The physiological ecology of woody plants [J]. Tree Physiology, 1991, 8(2): 213.
[5] TIAN H, CHEN G, LIU M, et al. Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895-2007 [J]. Forest Ecology and Management, 2010, 259(7): 1311-1327.
[6] TIAN H, LU C, CHEN G, et al. Climate and land use controls over terrestrial water use efficiency in monsoon Asia [J]. Ecohydrology, 2011, 4: 322-340.
[7] VANLOOCKE A, TWINE T E, ZERI M, et al. A regional comparison of water use efficiency for miscanthus, switchgrass and maize [J]. Agricultural and Forest Meteorology, 2012, 164: 82-95.
[8] MO X, LIU S, LIN Z, et al. Regional crop yield, water consumption and water use efficiency and their responses to climate change in the North China Plain [J]. Agriculture, Ecosystems & Environment, 2009, 134(1): 67-78.
[9] 莫兴国, 刘苏峡, 林忠辉, 等. 华北平原蒸散和 GPP 格局及其对气候波动的响应 [J]. 地理学报, 2011, 66(5): 589-598.

[10] 卢玲, 李新, 黄春林. 中国西部植被水分利用效率的时空特征分析 [J]. 冰川冻土, 2007, 29(5): 777-784.

[11] 徐晓桃. 黄河源区NPP及植被水分利用效率时空特征分析 [D]. 兰州: 兰州大学, 2008.

[12] 张春敏, 梁川, 龙训建, 等. 江河源区植被水分利用效率遥感估算及动态变化 [J]. 农业工程学报, 2013, 29(18): 146-155.

[13] JIANG L, ISLAM S. Estimation of surface evaporation map over southern Great Plains using remote sensing data [J]. Water Resources Research, 2001, 37(2): 329-340.
[14] BATRA N, ISLAM S, VENTURINI V, et al. Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains [J]. Remote Sensing of Environment, 2006, 103(1): 1-15.
[15] JIANG L, ISLAM S, GUO W, et al. A satellite-based daily actual evapotranspiration estimation algorithm over South Florida [J]. Global and Planetary Change, 2009, 67(1): 62-77.
[16] 赵晓松, 刘元波, 吴桂平. 基于遥感的2000—2009年鄱阳湖流域蒸散特征及影响因子研究 [J]. 长江流域资源与环境, 2013, 22(3): 369-378.

[17] 赵安周, 朱秀芳, 刘宪锋, 等. 1965—2013 年渭河流域降水时空变化分析 [J]. 自然资源学报, 2015, 30(11): 1896-1909.

[18] POTTER C S, RANDERSON J T, FIELD C B, et al. Terrestrial ecosystem production: A process model based on global satellite and surface data [J]. Global Biogeochemical Cycles, 1993, 7(4): 811-841.
[19] FIELD C B, RANDERSON J T, MALMSTRÖM C M. Global net primary production: Combining ecology and remote sensing [J]. Remote Sensing of Environment, 1995, 51(1): 74-88.
[20] 朱文泉. 中国陆地生态系统植被净初级生产力遥感估算及其与气候变化关系的研究 [D]. 北京: 北京师范大学, 2005.

[21] XIAO X, ZHANG Q, SALESKA S, et al. Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest [J]. Remote Sensing of Environment, 2005, 94(1): 105-122.
[22] XIAO X, HOLLINGER D, ABER J, et al. Satellite-based modeling of gross primary production in an evergreen needle leaf forest [J]. Remote Sensing of Environment, 2004, 89(4): 519-534.
[23] JIANG L, ISLAM S. A methodology for estimation of surface evapotranspiration over large areas using remote sensing observations [J]. Geophysical Research Letters, 1999, 26(17): 2773-2776.
[24] LIANG S, SHUEY C J, RUSS A L, et al. Narrowband to broadband conversions of land surface albedo: II. Validation [J]. Remote Sensing of Environment, 2003, 84(1): 25-41.
[25] RUSHTON K R, WARD C. The estimation of groundwater recharge [J]. Journal of Hydrology, 1979, 41(3): 345-361.
[26] MORAN M S, JACKSON R D, RAYMOND L H, et al. Mapping surface energy balance components by combining Landsat Thematic Mapper and ground-based meteorological data [J]. Remote Sensing of Environment, 1989, 30(1): 77-87.
[27] 李贵才. 基于 MODIS 数据和光能利用率模型的中国陆地净初级生产力估算研究 [D]. 北京: 中国科学院研究生院, 2004.

[28] 陶波, 李克让, 邵雪梅. 中国陆地净初级生产力时空特征模拟 [J]. 地理学报, 2003, 58(3): 372-380.

[29] 孙睿, 朱启疆. 中国陆地植被净第一性生产力及季节变化研究 [J]. 地理学报, 2000, 55(1): 36-45.

[30] PRINCE S D, HASKETT J, STEININGER M, et al. Net primary production of US midwest croplands from agricultural harvest yield data [J]. Ecological Applications, 2001, 11(4): 1194-1205.
[31] 位贺杰, 张艳芳, 朱妮, 等. 基于MOD16数据的渭河流域地表实际蒸散发时空特征 [J]. 中国沙漠, 2015, 35(2): 414-422.

[32] LAW B E, FALGE E, GU L, et al. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation [J]. Agricultural and Forest Meteorology, 2002, 113(1): 97-120.
[33] ITO A, INATOMI M. Water-use efficiency of the terrestrial biosphere: A model analysis focusing on interactions between the global carbon and water cycles [J]. Journal of Hydrometeorology, 2012, 13(2): 681-694.
[34] ZHU Q, JIANG H, PENG C, et al. Evaluating the effects of future climate change and elevated CO 2 on the water use efficiency in terrestrial ecosystems of China [J]. Ecological Modelling, 2011, 222(14): 2414-2429.
[35] ZHANG Z, JIANG H, LIU J, et al. Modeling the spatial-temporal dynamics of water use efficiency in Yangtze River Basin using IBIS model [J]. Acta Ecologica Sinica, 2011, 31(5): 246-253.
[36] KUGLITSCH F G, REICHSTEIN M, BEER C, et al. Characterization of ecosystem water-use efficiency of European forests from eddy covariance measurements [J]. Biogeosciences Discussions, 2008, 5(6): 4481-4519.
[37] HU Z, YU G, FU Y, et al. Effects of vegetation control on ecosystem water use efficiency within and among four grassland ecosystems in China [J]. Global Change Biology, 2008, 14(7): 1609-1619.
[38] WEVER L A, FLANAGAN L B, CARLSON P J. Seasonal and interannual variation in evapotranspiration, energy balance and surface conductance in a northern temperate grassland [J]. Agricultural and Forest Meteorology, 2002, 112(1): 31-49.
[39] 国志兴, 王宗明, 刘殿伟, 等. 三江平原农田生产力时空特征分析 [J]. 农业工程学报, 2009, 25(1): 249-254.

[40] 徐浩杰, 杨太保. 黄河源区植被净初级生产力时空变化特征及其对气候要素的响应 [J]. 资源科学, 2013, 35(10): 2024-2031.

[41] 徐永明, 赵巧华, 巴雅尔, 等. 基于 MODIS 数据的博斯腾湖流域地表蒸散时空变化 [J]. 地理科学, 2012, 32(11): 1353-1357.
Options
文章导航

/

〈 〉