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Advancing Sustainable Hydrological Modelling and Smart Water Resource Management
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Advancing Sustainable Hydrological Modelling and Smart Water Resource Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 30 August 2026 | Viewed by 3511

Editors

Prof. Dr. Zengchuan Dong

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: sustainable development; management and utilisation of water resources; hydro-logical regime; ecological and environmental systems
Special Issues, Collections and Topics in MDPI journals
Dr. Wenzhuo Wang

E-Mail Website
Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: hydrological model; machine learning
Dr. Jitao Zhang

E-Mail Website
Guest Editor Assistant
College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China
Interests: water resources allocation; reservoir operation

Special Issue Information

Dear Colleagues,

This Special Issue centres on the advancement of sustainable hydrological modelling and smart water resource management in the context of climate change. It aims to explore cutting-edge methodologies that integrate machine learning, data assimilation, and hybrid modelling frameworks to improve the accuracy, robustness, and adaptability of hydrological predictions. Emphasis is placed on the development of intelligent scheduling and allocation strategies that can dynamically respond to spatiotemporal variability and uncertainty in water systems. This Special Issue seeks to bridge the methodological gap between physical models and data-driven techniques, providing novel insights into model fusion, scenario-based regulation, and real-time decision making. By highlighting interdisciplinary research at the intersection of hydrology, climate science, and artificial intelligence, this collection will complement the existing literature with forward-looking approaches and contribute to building resilient and adaptive water systems for a changing world.

Prof. Dr. Zengchuan Dong
Dr. Wenzhuo Wang
Guest Editors

Dr. Jitao Zhang
Guest Editor Assistant

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

  • climate change
  • sustainable hydrological modelling
  • smart water resource management
  • machine learning
  • model integration
  • real-time decision making
  • data-driven approaches
  • adaptive basin management

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Published Papers (4 papers)

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Research

19 pages, 4457 KB  
Article
Machine-Learning Multi-Model Integration for Future Precipitation and Water Management Implications in the Yangtze River Basin
by Lan Yang, Shengnan Zhu, Yanan Sun, Zhuozheng Li, Wei Gao and Zhongxu Li
Water 2026, 18(13), 1536; https://doi.org/10.3390/w18131536 - 23 Jun 2026
Viewed by 337
Abstract
Reliable estimates of future precipitation are essential for adaptive water management in large river basins. This study presents a machine-learning approach that combines six CMIP6 models to examine precipitation changes in the Yangtze River Basin. ERA5 monthly precipitation for 1979–2025 served as the [...] Read more.
Reliable estimates of future precipitation are essential for adaptive water management in large river basins. This study presents a machine-learning approach that combines six CMIP6 models to examine precipitation changes in the Yangtze River Basin. ERA5 monthly precipitation for 1979–2025 served as the reanalysis reference. The random forest model incorporated individual model outputs, ensemble statistics, geographic variables, and monthly cyclic terms. It was trained with data from 1979–2009, evaluated for 2010–2014, and then applied to the period 2015–2099 under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Compared with the simple multi-model mean, the proposed method showed better agreement with ERA5 and generally smaller reconstruction errors during the validation period. Annual precipitation is projected to increase under all three pathways, with the largest increase under SSP5-8.5. Precipitation remains concentrated from May to August, while spring totals and intra-annual variability increase more clearly under high-emission conditions. Mean precipitation remains highest in the humid middle and lower reaches, while the magnitude and significance of future trends vary across the basin. Inter-model spread remains greater than the differences among emission pathways and reaches 85.92 mm under SSP5-8.5 during 2071–2099. These results represent uncertainty-aware climate estimates rather than verified forecasts. They can support flood-risk assessment, reservoir planning, and adaptive water management in the Yangtze River Basin. Full article
(This article belongs to the Special Issue Advancing Sustainable Hydrological Modelling and Smart Water Resource Management)
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26 pages, 8090 KB  
Article
Eco-Socioeconomic Coordination and Driving Mechanisms in an Inland River Basin Under a Major Water Transfer Project: A Case Study of the Shiyang River Basin
by Mi Zhang, Zengchuan Dong, Daoli Wang, Yizhou Jiang, Jitao Zhang and Wenzhuo Wang
Water 2026, 18(11), 1293; https://doi.org/10.3390/w18111293 - 26 May 2026
Viewed by 364
Abstract
Arid inland river basins are constrained by severe water scarcity and fragile ecosystems. Although large-scale water transfer projects are critical interventions, studies of their comprehensive impacts on eco-socioeconomic systems remain limited. To address this gap, this study proposes an integrated assessment framework. A [...] Read more.
Arid inland river basins are constrained by severe water scarcity and fragile ecosystems. Although large-scale water transfer projects are critical interventions, studies of their comprehensive impacts on eco-socioeconomic systems remain limited. To address this gap, this study proposes an integrated assessment framework. A global Remote Sensing Ecological Index (gRSEI) was developed by incorporating a salinity indicator, employing optimal indicator selection, and utilizing a full-period global normalization strategy. A Gridded Socioeconomic Index (GSEI) was constructed by integrating nighttime light (NTL), population (POP), and gross domestic product (GDP) data. The coupling coordination degree (CCD) model, spatial autocorrelation analysis, and the optimal parameters-based geographical detector (OPGD) were applied to analyze spatial patterns across subregions. Focusing on the Shiyang River Basin (SYRB), this study analyzed the spatiotemporal responses and coupling coordination of the eco-socioeconomic system to the 2001 Jingdian Phase II Water Transfer Project. Results indicate that ecological quality improved significantly after the water transfer, with gRSEI increasing from 0.225 to 0.334. Socioeconomic development also improved overall. The eco-socioeconomic system exhibited high coupling but moderate coordination. The coupling degree (C) and coordination degree (D) increased from 0.824 and 0.370 to 0.852 and 0.442, respectively, with clear regional heterogeneity. The water transfer project shifted the dominant driver of coordinated development from water-related factors to land cover. This study provides a practical framework for assessing ecological and socioeconomic dynamics and their interactions in arid basins under major water transfer project interventions. Full article
(This article belongs to the Special Issue Advancing Sustainable Hydrological Modelling and Smart Water Resource Management)
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12 pages, 1497 KB  
Article
Deriving Implicit Optimal Operation Rules for Reservoirs Based on TgLSTM
by Ran He, Wenhao Jia and Zhengzhe Qian
Water 2025, 17(14), 2059; https://doi.org/10.3390/w17142059 - 10 Jul 2025
Cited by 2 | Viewed by 962
Abstract
With the continuous improvement of reservoir projects and the advancement of digital twin basin initiatives in China, rapidly and accurately generating long-term practical reservoir operation schedules has become a key priority for stakeholders. This study proposes a Theory-guided Long Short-Term Memory (TgLSTM) model [...] Read more.
With the continuous improvement of reservoir projects and the advancement of digital twin basin initiatives in China, rapidly and accurately generating long-term practical reservoir operation schedules has become a key priority for stakeholders. This study proposes a Theory-guided Long Short-Term Memory (TgLSTM) model to extract optimal reservoir operation rules accurately and reliably. Concretely, TgLSTM integrates data-fitting accuracy with the physical constraints of an operation, e.g., water level constraints and minimal discharge constraints, to address the low credibility often observed in conventional LSTM networks. Using the Three Gorges Reservoir during the dry season as a case study, a multi-year hydrological series optimized by particle swarm optimization (PSO) was used to train the TgLSTM network and derive optimized operation rules. Results show that TgLSTM efficiently generates operation schemes close to the theoretical optimum, achieving power generations of 4.27 × 1010 kW·h and 4.19 × 1010 kW·h in two test years, with deviations of only 4.20% and 2.33%, respectively. Compared to traditional LSTM models, TgLSTM is more reliable as it captures key operational characteristics such as terminal water levels and water level fluctuations, maintaining an average ten-day drawdown depth below 1.5 m—significantly lower than the 7 m fluctuations observed with conventional LSTM. Furthermore, comparative analyses against SwR, BP–ANN, and SVM confirm that TgLSTM offers a moderate performance in absolute metrics but is the best to simulate the constrained reservoir operation. Full article
(This article belongs to the Special Issue Advancing Sustainable Hydrological Modelling and Smart Water Resource Management)
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17 pages, 5984 KB  
Article
Correction of Pump Characteristic Curves Integrating Representative Operating Condition Recognition and Affine Transformation
by Yichao Chen, Yongjun Zhao, Xiaomai Li, Chenchen Wu, Jie Zhao and Li Ren
Water 2025, 17(13), 1977; https://doi.org/10.3390/w17131977 - 30 Jun 2025
Cited by 1 | Viewed by 1085
Abstract
To address the need for intelligent scheduling and model integration under spatiotemporal variability and uncertainty in water systems, this study proposes a hybrid correction method for pump characteristic curves that integrates data-driven techniques with an affine modeling framework. Steady-state data are extracted through [...] Read more.
To address the need for intelligent scheduling and model integration under spatiotemporal variability and uncertainty in water systems, this study proposes a hybrid correction method for pump characteristic curves that integrates data-driven techniques with an affine modeling framework. Steady-state data are extracted through adaptive filtering and statistical testing, and representative operating conditions are identified via unsupervised clustering. An affine transformation is then applied to the factory-provided characteristic equation, followed by parameter optimization using the clustered dataset. Using the Hongze Pump Station along the eastern route of the South-to-North Water Diversion Project as a case study, the method reduced the mean blade angle prediction error from 1.73° to 0.51°, and the efficiency prediction error from 7.32% to 1.30%. The results demonstrate improved model accuracy under real-world conditions and highlight the method’s potential to support more robust and adaptive hydrodynamic scheduling models, contributing to the advancement of sustainable and smart water resource management. Full article
(This article belongs to the Special Issue Advancing Sustainable Hydrological Modelling and Smart Water Resource Management)
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