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Advances and Major Achievements in China’s Digital Twin River Basin...
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Advances and Major Achievements in China’s Digital Twin River Basin Development

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2487

Editors

Prof. Dr. Yesen Liu

E-Mail Website
Guest Editor
China Institute of Water Resources and Hydropower Research, Beijing, China
Interests: digital twin watershed; machine learning; big data; flood control; knowledge graph; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Yueqin Zhu

E-Mail Website
Guest Editor
National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, China
Interests: digital twin watershed; urban flooding; emergency decision-making; flood control; remote sensing; geographic information system; artificial intelligence
Dr. Yaohuan Huang

E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Interests: remote sensing; GIS; deep learning; object detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since 2021 (China's 14th Five-Year Plan period), China has launched the construction of digital twin water conservancy systems to achieve smart management goals. Currently, annual investment in digital twin water conservancy exceeds CNY 10 billion and continues to grow. Water conservancy management departments across the country have significantly enhanced the intelligence of operations such as flood control, water resource management, and project scheduling through developing digital twin river basins, digital twin water networks, and digital twin engineering projects. Technologies such as machine learning, knowledge graphs, large language model, remote sensing, drones, high-performance computing, and virtual simulation have been deeply integrated. This has led to the development of software products including remote sensing monitoring platforms, knowledge platforms, data foundation management platforms, digital twin platforms, and integrated decision support platforms for flood control projects. This Special Issue is dedicated to showcasing China's exemplary cases and experiences in digital twin water resources management. Submissions are encouraged to cover the following aspects: case (technology) background, technical framework, application targets, achieved outcomes, and areas for improvement.

Prof. Dr. Yesen Liu
Dr. Yueqin Zhu
Dr. Yaohuan Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • China
  • digital twin hydrology
  • knowledge graph
  • large language model
  • virtual simulation
  • satellite remote sensing
  • flood control
  • water resources
  • water network

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Published Papers (3 papers)

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Research

24 pages, 3863 KB  
Article
An Integrated Framework for Dam-Break Flood Risk Assessment Considering Hydraulic Hazard and Socioeconomic Vulnerability Using Hydrodynamic Modeling, GIS, and Fuzzy Comprehensive Evaluation
by Zifeng Lin, Jinbao Sheng, Jiankang Chen and Zhenhan Du
Water 2026, 18(15), 1822; https://doi.org/10.3390/w18151822 - 27 Jul 2026
Viewed by 392
Abstract
Dam-break floods pose severe threats to downstream urban areas due to their sudden onset, rapid propagation, and potentially catastrophic socioeconomic consequences. This study develops a multi-indicator fuzzy comprehensive evaluation framework for urban flood risk assessment under dam-break scenarios. The framework integrates hydrodynamic simulation [...] Read more.
Dam-break floods pose severe threats to downstream urban areas due to their sudden onset, rapid propagation, and potentially catastrophic socioeconomic consequences. This study develops a multi-indicator fuzzy comprehensive evaluation framework for urban flood risk assessment under dam-break scenarios. The framework integrates hydrodynamic simulation outputs with geographic information system-based spatial analysis to characterize spatial variations in flood risk. It incorporates flood hazard factors (inundation depth, arrival time, and inundation duration) derived from a coupled one-dimensional/two-dimensional hydrodynamic model and socioeconomic vulnerability factors (population density and road network density) derived from spatial statistics. Indicator weights were determined using a combined Analytic Hierarchy Process and entropy-weight method, and risk levels were obtained through membership-function calculation and spatial overlay analysis. The framework was applied to the Dongpu and Dafangying reservoirs in Hefei, China, under a scenario of simultaneous dam failure during a probable maximum flood. Results show that, compared with hazard-only assessment, the comprehensive evaluation substantially reduced the extent of high-risk areas and altered their spatial distribution. Very high-risk zones were concentrated along the upstream main channel, where high-velocity floodwaters coincide with dense population and economic activity. Flood arrival time and population density were identified as the most influential indicators in the proposed risk assessment framework. The proposed framework provides a more comprehensive and spatially refined tool for urban dam-break flood risk management and emergency decision-making. Full article
(This article belongs to the Special Issue Advances and Major Achievements in China’s Digital Twin River Basin Development)
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17 pages, 26204 KB  
Article
Development and Application of a River–Sewer Water Level Correlation Model for Identifying Inflow and Infiltration Diagnosis: A Case Study of Zhongshan’s Regional Sewage Network
by Xingquan Xu, Lincheng Ma, Zhenchong Li, Mengfan Wu, Nan Sun, Hao Wen, Bin Li and Wei Song
Water 2026, 18(15), 1811; https://doi.org/10.3390/w18151811 - 25 Jul 2026
Viewed by 409
Abstract
Identifying inflow and infiltration (I/I) in urban sewer networks is challenging due to nonlinear hydraulic interactions and time-lag effects between river stages and pipeline water levels. This study proposes a multi-scale fusion correlation model integrating Dynamic Time Warping (DTW), Pearson correlation, and Spearman [...] Read more.
Identifying inflow and infiltration (I/I) in urban sewer networks is challenging due to nonlinear hydraulic interactions and time-lag effects between river stages and pipeline water levels. This study proposes a multi-scale fusion correlation model integrating Dynamic Time Warping (DTW), Pearson correlation, and Spearman rank correlation coefficients. The framework evaluates water level sequences across temporal windows (2, 6, and 12 h) under flexible displacement constraints (1 and 2 h), utilizing a dynamic weight-allocation mechanism based on sequence volatility and data density. Leveraging a 12-month monitoring dataset from 513 sensing devices in Zhongshan City, China, the model was evaluated on 36 typical water-level sequences. It achieved a classification accuracy of 91.7% for low-correlation sequences and perfect accuracy (100%) for both medium- and high-correlation levels. Furthermore, practical deployment in the Shaxi–Qijiang Highway section and Yicheng Area successfully isolated multiple vulnerable pipe segments suffering from Baishiyong River (tidal) water intrusion, yielding an empirical field-verification hit rate of 83.3%. The results demonstrate that the proposed framework effectively overcomes temporal asynchrony and nonlinear hydraulic noise, providing a robust, data-driven diagnostic tool for urban drainage infrastructures. Full article
(This article belongs to the Special Issue Advances and Major Achievements in China’s Digital Twin River Basin Development)
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23 pages, 1630 KB  
Article
Research on the Construction and Application of a Water Conservancy Facility Safety Knowledge Graph Based on Large Language Models
by Cui Li, Yu Wang, Lei Gao and Qiaoyan Ding
Water 2026, 18(7), 840; https://doi.org/10.3390/w18070840 - 1 Apr 2026
Viewed by 943
Abstract
Water conservancy safety management faces several challenges. These include the integration of multi-source heterogeneous data and inefficient knowledge utilization. To address these issues, this study proposes a knowledge graph (KG) construction method that combines ontology modeling with large language models (LLMs). First, an [...] Read more.
Water conservancy safety management faces several challenges. These include the integration of multi-source heterogeneous data and inefficient knowledge utilization. To address these issues, this study proposes a knowledge graph (KG) construction method that combines ontology modeling with large language models (LLMs). First, an ontology for water conservancy facility safety is constructed, encompassing four core elements: agencies and personnel, engineering equipment, risks and hidden dangers, and systems and processes. Subsequently, a KG-LLM-GraphRAG architecture is designed, which optimizes the knowledge extraction effectiveness of LLM through ontology-constrained prompt templates and utilizes the Neo4j graph database for knowledge storage and multi-hop reasoning. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches in entity-relationship extraction tasks. The resulting KG supports hazard identification, emergency decision-making, and knowledge reuse, offering an efficient tool for organizing and reasoning in water conservancy safety management, strongly propelling the digital transformation of the water conservancy industry. Full article
(This article belongs to the Special Issue Advances and Major Achievements in China’s Digital Twin River Basin Development)
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