Evaluating drought effect on MODIS Gross Primary Production (GPP) with an eco-hydrological model in the mountainous forest, East Asia

被引:70
|
作者
Hwang, Taehee [1 ]
Kangw, Sinkyu [2 ]
Kim, Joon [3 ]
Kim, Youngil [4 ]
Lee, Dowon [4 ]
Band, Lawrence [1 ]
机构
[1] Univ N Carolina, Dept Geog, Chapel Hill, NC 27599 USA
[2] Kangwon Natl Univ, Dept Environm Sci, Chunchon 200701, South Korea
[3] Yonsei Univ, Dept Atmospher Sci, Seoul 120749, South Korea
[4] Seoul Natl Univ, Grad Sch Environm Studies, Seoul 151742, South Korea
关键词
drought effect; eco-hydrological model; gross primary productivity; MODIS; RHESSys;
D O I
10.1111/j.1365-2486.2008.01556.x
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Surface soil moisture dynamics is a key link between climate fluctuation and vegetation dynamics in space and time. In East Asia, precipitation is concentrated in the short monsoon season, which reduces plants water availability in the dry season. Furthermore, most forests are located in mountainous areas because of high demand for agricultural land, which results in increased lateral water flux and uneven distribution of plant available water. These climatic and topographic features of the forests make them more vulnerable to drought conditions. In this study, the eco-hydrological model (Regional Hydro-Ecological Simulation System) is validated with various water and carbon flux measurements in a small catchment in Korea. The model is then extended to the regional scale with fine-resolution remote sensing data to evaluate the Moderate Resolution Imaging Radiometer (MODIS) leaf area index and gross primary productivity (GPP) products. Long-term model runs simulated severe drought effect in 2001 well, which is clearly shown in the ring increment data. However, MODIS GPP does not capture this drought effect in 2001, which might be from a simplified treatment of water stress in the MODIS GPP algorithm. This study shows that the MODIS GPP products can potentially overestimate carbon uptake specifically during drought conditions driven by soil water stress.
引用
收藏
页码:1037 / 1056
页数:20
相关论文
共 6 条
  • [1] Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation
    Turner, DP
    Ritts, WD
    Cohen, WB
    Gower, ST
    Zhao, MS
    Running, SW
    Wofsy, SC
    Urbanski, S
    Dunn, AL
    Munger, JW
    REMOTE SENSING OF ENVIRONMENT, 2003, 88 (03) : 256 - 270
  • [2] Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand
    Coops, Nicholas C.
    Black, T. Andy
    Jassal, Rachhpal Paul S.
    Trofymow, J. A. Tony
    Morgenstern, Kai
    REMOTE SENSING OF ENVIRONMENT, 2007, 107 (03) : 385 - 401
  • [3] Evaluation and improvement of MODIS gross primary productivity in typical forest ecosystems of East Asia based on eddy covariance measurements
    He, Mingzhu
    Zhou, Yanlian
    Ju, Weimin
    Chen, Jingming
    Zhang, Li
    Wang, Shaoqiang
    Saigusa, Nobuko
    Hirata, Ryuichi
    Murayama, Shohei
    Liu, Yibo
    JOURNAL OF FOREST RESEARCH, 2013, 18 (01) : 31 - 40
  • [4] Evaluation and improvement of MODIS gross primary productivity in typical forest ecosystems of East Asia based on eddy covariance measurements
    He, Mingzhu
    Zhou, Yanlian
    Ju, Weimin
    Chen, Jingming
    Zhang, Li
    Wang, Shaoqiang
    Saigusa, Nobuko
    Hirata, Ryuichi
    Murayama, Shohei
    Liu, Yibo
    Journal of Forest Research, 2013, 18 (01): : 31 - 40
  • [5] A fine spatial resolution estimation scheme for large-scale gross primary productivity (GPP) in mountain ecosystems by integrating an eco-hydrological model with the combination of linear and non-linear downscaling processes
    Xie, Xinyao
    Li, Ainong
    Tian, Jie
    Wu, Changlin
    Jin, Huaan
    JOURNAL OF HYDROLOGY, 2023, 616
  • [6] Estimating Gross Primary Productivity (GPP) over Rice-Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product
    Duan, Zexia
    Yang, Yuanjian
    Zhou, Shaohui
    Gao, Zhiqiu
    Zong, Lian
    Fan, Sihui
    Yin, Jian
    REMOTE SENSING, 2021, 13 (21)