A lagged variable model for characterizing temporally dynamic export of legacy anthropogenic nitrogen from watersheds to rivers
A lagged variable model for characterizing temporally dynamic export of legacy anthropogenic nitrogen from watersheds to rivers
复制标题
用于表征遗留人为氮从流域到河流的时间动态输出的滞后变量模型
DOI:
10.1007/s11356-015-4377-y
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发表时间:
2015-03
影响因子:
5.8
通讯作者:
y A. Dahlgren
中科院分区:
文献类型:
--
作者:
Dingjiang Chen;Yi Guo;Minpeng Hu;R;y A. Dahlgren
Legacy nitrogen (N) sources originating from anthropogenic N inputs (NANI) may be a major cause of increasing riverine N exports in many regions, despite a significant decline in NANI. However, little quantitative knowledge exists concerning the lag effect of NANI on riverine N export. As a result, the N leaching lag effect is not well represented in most current watershed models. This study developed a lagged variable model (LVM) to address temporally dynamic export of watershed NANI to rivers. Employing a Koyck transformation approach used in economic analyses, the LVM expresses the indefinite number of lag terms from previous years’ NANI with a lag term that incorporates the previous year’s riverine N flux, enabling us to inversely calibrate model parameters from measurable variables using Bayesian statistics. Applying the LVM to the upper Jiaojiang watershed in eastern China for 1980–2010 indicated that ~97 % of riverine export of annual NANI occurred in the current year and succeeding 10 years (~11 years lag time) and ~72 % of annual riverine N flux was derived from previous years’ NANI. Existing NANI over the 1993–2010 period would have required a 22 % reduction to attain the target TN level (1.0 mg N L−1), guiding watershed N source controls considering the lag effect. The LVM was developed with parsimony of model structure and parameters (only four parameters in this study); thus, it is easy to develop and apply in other watersheds. The LVM provides a simple and effective tool for quantifying the lag effect of anthropogenic N input on riverine export in support of efficient development and evaluation of watershed N control strategies.
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影响因子:
3.7
作者:
Sheng W;Yu G;Fang H;Jiang C;Yan J;Zhou M
通讯作者:
Zhou M
影响因子:
4
作者:
Howarth, R. W.;Swaney, D. P.;Goodale, C.
通讯作者:
Goodale, C.
影响因子:
5.8
作者:
Cheng Sun;Zhenyao Shen;Ruimin Liu;M. Xiong;F. Ma;O. Zhang;Yangyang Li;Lei Chen
通讯作者:
Cheng Sun;Zhenyao Shen;Ruimin Liu;M. Xiong;F. Ma;O. Zhang;Yangyang Li;Lei Chen
DOI:
10.3133/fs09103
发表时间:
2003
期刊:
--
影响因子:
--
作者:
S. Phillips;B. Lindsey
通讯作者:
S. Phillips;B. Lindsey
影响因子:
12.8
作者:
M. Iqbal
通讯作者:
M. Iqbal