Advances and challenges in multi-scale water environment system modeling: from process simulation to a novel simulator architecture
文献类型：期刊论文
| 作者 | Xia, Rui1,2; Chen, Sheng1,2; Ding, Yan1,2; Sun, Mingdong1,2; Wu, Yali1,2; Shi, Kaifang1,2; Cai, Yajing1,2; Zhang, Kai1,2; Chen, Yan1,2; Zou, Lei3 |
| 刊名 | ECOLOGICAL MODELLING
 |
| 出版日期 | 2026-04-01 |
| 卷号 | 514页码:111498 |
| 关键词 | Water ecological environment model Multi-model coupling Complex system simulators Source-stream-network-sink Artificial intelligence |
| ISSN号 | 0304-3800 |
| DOI | 10.1016/j.ecolmodel.2026.111498 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Water ecological environment models serve as essential scientific tools for watershed ecological governance and management, yet they still exhibit notable limitations in systematicity, accuracy, and adaptability when addressing complex multi-media and cross-scale ecosystems. Current research lacks a systematic synthesis of the evolutionary pathways of multi-scale models and has not fully integrated the strengths of artificial intelligence (AI) and mechanistic modeling, which constrains breakthroughs in water ecological system simulation from methodology to application. This paper systematically reviews the development trajectories and typical applications of water ecological environment models across different scales-including watersheds, rivers, lakes/ reservoirs, urban water systems, and marine environments-proposes a source-flow-network-sink multi-process coupled systemic architecture, and explores pathways for integrating AI and environmental foundation models into simulation and prediction. The study finds that water ecological simulation in China urgently needs to shift from imported applications toward independent innovation and standardized development. Priority should be given to developing multi-model coupling architectures with independent intellectual property, establishing localized parameter databases, and deeply incorporating AI and big-data methods in model calibration, prediction, and uncertainty quantification. Furthermore, the research highlights that building intelligent simulator systems and promoting their operational application is a critical pathway for enhancing ecological risk early-warning and decision-support capabilities. |
| URL标识 | 查看原文 |
| WOS关键词 | ARTIFICIAL-INTELLIGENCE ; NEURAL-NETWORKS ; PREDICTION ; QUALITY ; ACCURACY ; UNCERTAINTY |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001681920600001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/220953]  |
| 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 | Zou, Lei |
| 作者单位 | 1.Chinese Res Inst Environm Sci, Natl Key Lab Environm Criteria & Stand & Risk Mana, Beijing, Peoples R China; 2.Chinese Res Inst Environm Sci, Natl Engn Lab Lake Water Pollut Control & Ecol Res, Beijing, Peoples R China; 3.Chinese Acad Sci, Inst Key Lab Terr Water Cycle & Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xia, Rui,Chen, Sheng,Ding, Yan,et al. Advances and challenges in multi-scale water environment system modeling: from process simulation to a novel simulator architecture[J]. ECOLOGICAL MODELLING,2026,514:111498. |
| APA | Xia, Rui.,Chen, Sheng.,Ding, Yan.,Sun, Mingdong.,Wu, Yali.,...&Zou, Lei.(2026).Advances and challenges in multi-scale water environment system modeling: from process simulation to a novel simulator architecture.ECOLOGICAL MODELLING,514,111498. |
| MLA | Xia, Rui,et al."Advances and challenges in multi-scale water environment system modeling: from process simulation to a novel simulator architecture".ECOLOGICAL MODELLING 514(2026):111498. |
入库方式： OAI收割
来源：地理科学与资源研究所
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