Improving Dynamic Vegetation Modeling in Noah-MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau
文献类型：期刊论文
| 作者 | Shu, Zunyun1,2,3,7; Zhang, Baoqing6; Tian, Lei5,6; Zhao, Xining1,2,3,4 |
| 刊名 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
 |
| 出版日期 | 2022-10-16 |
| 卷号 | 127期号:19页码:25 |
| ISSN号 | 2169-897X |
| 关键词 | Noah-MP vegetation dynamics parameter optimization data assimilation |
| DOI | 10.1029/2022JD036703 |
| 通讯作者 | Zhao, Xining(zxn@nwsuaf.edu.cn) |
| 英文摘要 | Accurate modeling of vegetation dynamics is needed to improve our understanding of and ability to predict the impacts of vegetation changes on terrestrial water-energy-carbon cycles. Parameter optimization (PO) and data assimilation (DA) are widely used to improve the performance of dynamic vegetation modules in land surface models (LSMs). However, their effectiveness is unclear. Here we analyze their impacts on the performance of the dynamic vegetation module of the Noah with multiparameterization options (Noah-MP) LSM over the Chinese Loess Plateau, which is an ideal study case because it is a large region that has undergone dramatic vegetation change. We first optimize these parameters that strongly affect the predicted vegetation dynamics based on the results of sensitivity analysis using PO. In addition, we evaluate the effect of DA by assimilating leaf area index (LAI) remote sensing data into Noah-MP without PO. Finally, we investigate the effect of applying PO and DA together. PO increases the predicted rates of carbon assimilation and turnover and thus reduces the underestimation of LAI and the lag in vegetation seasonality. DA has a weaker impact than PO: it only reduces the root mean squared error (RMSE) of the predicted LAI in around 49.76% of the studied region and is mainly beneficial in the growing phase. Combining PO and DA compensate the limitations of each other, and gives the most significant reduction in RMSE (median: -0.24 m(2)/m(2)) and increase in R-2 (+0.44). The improved vegetation dynamics with different optimization methods thus improve the modeling of water and carbon cycle processes. |
| WOS关键词 | LAND-SURFACE MODEL ; LEAF-AREA INDEX ; SOIL-MOISTURE ; INFORMATION-SYSTEM ; DEPTH RETRIEVALS ; WATER FLUXES ; CARBON ; CLIMATE ; SENSITIVITY ; MULTIPLE |
| 资助项目 | National Natural Science Foundation of China[42022001] ; National Natural Science Foundation of China[42001029] ; National Natural Science Foundation of China[42125705] ; National Natural Science Foundation of China[41877150] ; National Natural Science Foundation of China[42041004] |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | AMER GEOPHYSICAL UNION |
| WOS记录号 | WOS:000865481200001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/185632]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhao, Xining |
| 作者单位 | 1.Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, Yangling, Shaanxi, Peoples R China 2.Minist Educ, Yangling, Shaanxi, Peoples R China 3.Chinese Acad Sci, Res Ctr Soil & Water Conservat & Ecol Environm, Yangling, Shaanxi, Peoples R China 4.Northwest A&F Univ, Inst Soil & Water Conservat, Yangling, Shaanxi, Peoples R China 5.Lanzhou Univ, Inst Green Dev Yellow River Drainage Basin, Lanzhou, Peoples R China 6.Lanzhou Univ, Coll Earth & Environm Sci, Minist Educ, Key Lab Western Chinas Environm Syst, Lanzhou, Peoples R China 7.Univ Chinese Acad Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shu, Zunyun,Zhang, Baoqing,Tian, Lei,et al. Improving Dynamic Vegetation Modeling in Noah-MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2022,127(19):25. |
| APA | Shu, Zunyun,Zhang, Baoqing,Tian, Lei,&Zhao, Xining.(2022).Improving Dynamic Vegetation Modeling in Noah-MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,127(19),25. |
| MLA | Shu, Zunyun,et al."Improving Dynamic Vegetation Modeling in Noah-MP by Parameter Optimization and Data Assimilation Over China's Loess Plateau".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 127.19(2022):25. |
入库方式： OAI收割
来源：地理科学与资源研究所
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