Simulating and predicting lake dynamics by fusing HBV modeling, machine learning approach and remote sensing data
文献类型：期刊论文
| 作者 | Naeem, Muhammad2,3; Zhang, Yongqiang3; Ma, Ning3; Tang, Zixuan2,3; Miao, Ping1; Tian, Xiaoqiang4; Li, Congcong3; Huang, Qi2,3; Xu, Zhenwu3; Wang, Longhao2,3 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 663页码:134303 |
| 关键词 | Hydrological dynamics HBV model Machine learning Random Forest CA-Markov model Climate change Lake area prediction Water resource management |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2025.134303 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | This study provides a comprehensive analysis of the hydrological dynamics and land use changes in the Hongjiannao Lake Basin from 1990 to 2023, with projections extending to 2060. By integrating advanced hydrological modeling Hydrologiska Byrans Vattenbalansavdelning (HBV), a machine learning algorithm Random Forest (RF), Cellular Automata (CA) Markov, and remote sensing data, this research offers a robust framework for understanding the interactions between climate change, anthropogenic activities, and ecosystem responses. The historical analysis revealed remarkable fluctuations in the lake's area, including a 25.5 % reduction between 2000 and 2011, followed by a recovery from 2012 to 2023. The lake area increased by 26.2 % during the recovery phase, highlighting a partial reversal of decline. Projections indicate that, under various future climate scenarios, the lake area could increase by 29 % by 2060, showcasing the resilience of the ecosystem despite ongoing climate and anthropogenic pressures. The RF model demonstrated strong predictive capabilities, with R2 values of 0.92 during 1990-2013 calibration and 0.76 during 2014-2023 validation, coupled with root mean square errors of 0.12 km2 and 0.26 km2, respectively. Additionally, the CA-Markov model predicted vegetation growth and urbanization, highlighting potential for significant landscape changes. These findings stress the need for water management strategies to preserve the lake's ecological health, advocating for the integration of climate, land use, and hydrological factors in management plans for sustainable conservation and restoration in semi-arid regions. |
| URL标识 | 查看原文 |
| WOS关键词 | TIBETAN PLATEAU ; CO LAKE ; EVAPORATION ; WATER ; COVER |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001582482000003 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217494]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Zhang, Yongqiang |
| 作者单位 | 1.River & Lake Protect Ctr, Ordos Water Conservancy Bur, Ordos 017000, Inner Mongolia, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 4.Flood & Drought Disaster Prevent Technol Ctr, Ordos Water Conservancy Bur, Ordos 017000, Inner Mongolia, Peoples R China |
| 推荐引用方式 GB/T 7714 | Naeem, Muhammad,Zhang, Yongqiang,Ma, Ning,et al. Simulating and predicting lake dynamics by fusing HBV modeling, machine learning approach and remote sensing data[J]. JOURNAL OF HYDROLOGY,2025,663:134303. |
| APA | Naeem, Muhammad.,Zhang, Yongqiang.,Ma, Ning.,Tang, Zixuan.,Miao, Ping.,...&Huang, Zhen.(2025).Simulating and predicting lake dynamics by fusing HBV modeling, machine learning approach and remote sensing data.JOURNAL OF HYDROLOGY,663,134303. |
| MLA | Naeem, Muhammad,et al."Simulating and predicting lake dynamics by fusing HBV modeling, machine learning approach and remote sensing data".JOURNAL OF HYDROLOGY 663(2025):134303. |
入库方式： OAI收割
来源：地理科学与资源研究所
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