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Reservoir Control Operation and Water Resources Management, 2nd Edition

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 8395

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Special Issue Editors

Dr. Yuxue Guo

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Guest Editor

Institute of Water Science and Engineering, Civil Engineering, Zhejiang University, Hangzhou 310058, China
Interests: reservoir control operation; water resources management; climate change and adaption; system analysis optimization; hydrological modelling; uncertainty; artificial intelligence
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Dr. Jingkai Xie

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Guest Editor

Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore
Interests: water systems planning and control; climate risk assessment; terrestrial hydrology; sustainable groundwater management; machine learning applications; complex adaptive human-earth systems; remote sensing

Dr. Hao Chen

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Guest Editor

School of Hydraulic Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
Interests: hydrological model; simulation of hydrological processes; long-term forecasts; artificial intelligence models; ecological flow assessment; baseflow separation

Special Issue Information

Dear Colleagues,

Water managers and governments worldwide are facing similar challenges: how to meet the growing demands for water, food, and energy sustainably in a changing environment. Efficient reservoir operation techniques are vital for water resources and energy development and utilization. However, uncertainties have always characterized reservoir operations due to the inevitable uncertainty caused by various factors, such as measurement errors, model structure and parameter diversity, and climatic and hydrologic variability, among others. These uncertainties pose significant risks, particularly in light of current and future uncertainties related to climate change and rapid societal, ecological, and economic changes.

Successful operations of reservoirs and water resources require a comprehensive understanding of modeling-related uncertainties and the integrative application of artificial intelligence technology to generate sustainable solutions for water, food, and energy systems in a changing environment.

With the success of the first volume of the Special Issue "Reservoir Control Operation and Water Resources Management", the second volume of this special issue include but are not limited to the following: (I) water, food, and energy systems, (II) reservoir control operation, (III) integrated water resources management, (IV) extreme weather events, (V) risk assessment and reduction, (VI) modeling uncertainties and their effects, and (VII). artificial intelligence methods

Original field and experimental research papers, review papers and case studies are invited for submission.

Dr. Yuxue Guo
Dr. Jingkai Xie
Dr. Hao Chen
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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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.

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Keywords

water, food, and energy
reservoir operation
water resources management
changing environment
uncertainty
risk
artificial intelligence

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Reservoir Control Operation and Water Resources Management in Water (11 articles)

Published Papers (9 papers)

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Research

22 pages, 6361 KiB

Open AccessArticle

An Analysis of the Current Situation of Ecological Flow Release from Large- and Medium-Sized Reservoirs in the Southeastern River Basins of China

by Yijing Chen, Hui Nie, Gaozhan Liu, Jiongling Xiao, He Qiu, Bingjiao Xu, Hao Chen, Saihua Huang and Huawei Xie

Water 2025, 17(3), 451; https://doi.org/10.3390/w17030451 - 6 Feb 2025

Viewed by 684

Abstract

Ecological flow is a crucial determinant of river ecosystem well-being and aquatic ecosystem stability. Large- and medium-sized reservoirs, with flood prevention, irrigation, and power generation functions, necessitate a scientifically devised ecological flow release plan for river ecosystem conservation and water quality amelioration. This study centered on three reservoirs in the Jiaojiang River Basin of Zhejiang Province, China. Using measured outflow data, the hydrological approach was initially adopted to calculate individual reservoir ecological flows. Subsequently, the entropy weight method was employed to ascertain the most suitable ecological flow. Ecological flow grade thresholds were then established to formulate the optimal release scheme. The outcomes demonstrated that the average ecological flows of Xia’an, Lishimen, and Longxi reservoirs were 1.90 m³/s, 1.95 m³/s, and 0.42 m³/s, respectively. The multi-year average ecological flow assurance rates were 62.53%, 77.72%, and 56.94%, successively. The entropy weighted downstream optimal ecological flows were 2.10 m³/s, 2.28 m³/s, and 0.44 m³/s. During periods when the monthly ecological flow assurance rate was below 60%, the three reservoirs implemented schemes of installing ecological siphons, renovating water diversion systems, and using post-dam ecological units, respectively. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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18 pages, 12010 KiB

Open AccessArticle

Landslide-Induced Wave Run-Up Prediction Based on Large-Scale Geotechnical Experiment: A Case Study of Wangjiashan Landslide Area of Baihetan Reservoir, China

by Lei Tian, Jie Lei, Pengchao Mao and Wei-Chau Xie

Water 2025, 17(3), 304; https://doi.org/10.3390/w17030304 - 22 Jan 2025

Viewed by 706

Abstract

When a landslide mass enters a water body, it generates waves that propagate along the river channel, climb up upon reaching the riverbank, and impact nearby residential areas. To investigate the characteristics of wave run-up on a three-dimensional terrain, this study established a large-scale 3D physical model with a scale of 1:150 (dimensions: 64 m × 40 m × 3 m) based on the geological features of a specific amphibious landslide. The results show that the landslide-induced waves can partially inundate nearby residential areas. The unique terrain formed by the combination of residential areas and the southern riverbank amplifies the wave run-up height. A predictive formula was used to estimate the wave run-up height during wave convergence. This study provides valuable insights for predicting wave run-up heights in three-dimensional terrains. Considering the influence of different water levels on wave run-up, the study can be used to optimize water level regulation. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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19 pages, 1710 KiB

Open AccessArticle

Predicting the Dynamic of Debris Flow Based on Viscoplastic Theory and Support Vector Regression

by Xinhai Zhang, Hanze Li, Yazhou Fan, Lu Zhang, Shijie Peng, Jie Huang, Jinxin Zhang and Zhenzhu Meng

Water 2025, 17(1), 120; https://doi.org/10.3390/w17010120 - 4 Jan 2025

Viewed by 726

Abstract

The prediction of debris flows is essential for safeguarding infrastructure and minimizing the economic losses associated with the hazards. Traditional empirical and theoretical models, while providing foundational insights, often struggle to capture the complex and nonlinear behaviors inherent in debris flows. This study aims to enhance debris flow prediction by integrating theoretical modeling with data-driven approaches. We model debris flow as a viscoplastic fluid, employing the Herschel–Bulkley rheological model to describe its behavior. By combining the kinematic wave model with lubrication theory, we develop a comprehensive theoretical framework that encapsulates the mechanical physics of debris flows and identifies key governing parameters. Numerical solutions of this theoretical model are utilized to generate an extensive training dataset, which is subsequently used to train a support vector regression (SVR) model. The SVR model targets slide depth and velocity upon impact, using explanatory variables including yield stress, material density, source area depth and length, and slope length. The model demonstrates high predictive accuracy, achieving coefficients of determination

R^{2}

of 0.956 for slide depth and 0.911 for slide velocity at impact. Additionally, the relative residuals

σ

are primarily distributed within the range of −0.05 to 0.05 for both slide depth and slide velocity upon impact. These results indicate that the proposed hybrid model not only incorporates the fundamental physical mechanisms governing debris flows but also significantly enhances predictive performance through data-driven optimization. This study underscores the critical advantage of merging physical models with machine learning techniques, offering a robust tool for improved debris flow prediction and risk assessment, which can inform the development of more effective early warning systems and mitigation measures. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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23 pages, 2017 KiB

Open AccessArticle

Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management

by Annalise Sara Hooper, Sarah R. Christofides, Fredric M. Windsor, Sophie E. Watson, Peter Kille and Rupert G. Perkins

Water 2025, 17(1), 79; https://doi.org/10.3390/w17010079 - 31 Dec 2024

Viewed by 875

Abstract

Geosmin and 2-methylisoborneol (2-MIB) are secondary bacterial metabolites that create an earthy-musty taste and odour (T&O) in drinking water. Both compounds exhibit low odour thresholds and are the leading causes of customer complaints to water companies worldwide. Water companies must predict spikes in T&O concentrations early to intervene before these compounds reach the treatment works. Cyanobacteria are key producers of T&O in open waters, yet the influence of broader microbial and algal communities on cyanobacterial T&O events remains unclear. This study identified T&O risk indicator taxa using next-generation sequencing of bacterial (16S rRNA) and algal (rbcL) communities in three reservoirs in Wales, UK. Ordination analysis of these communities revealed clustering according to assigned T&O concentration levels, identifying T&O signature communities. Random Forest (RF) analyses accurately classified samples for high and low concentrations of geosmin and 2-MIB, demonstrating the biological consortium’s predictive power. Based on shared ecological traits of bacterial and algal taxa, we propose five categories corresponding to different magnitudes of T&O risk. Indicator taxa in T&O risk categories can then be used to predict T&O events, empowering water companies first to optimise treatment response and, importantly, to determine triggers before an event to evidence preventative intervention management. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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18 pages, 6076 KiB

Open AccessArticle

Flood Season Division Using Statistical Analysis Methods and Verifying by Regional Rainy Characteristics

by Xiaoya Wang, Shenglian Guo, Sirui Zhong, Mengyue Wang and Xin Xiang

Water 2024, 16(24), 3677; https://doi.org/10.3390/w16243677 - 20 Dec 2024

Viewed by 959

Abstract

Seasonal variation information is very important information for reservoir operation and water resources management. Traditional flood season division relies primarily on statistical methods and does not consider the specific regional rainy patterns. This study used several statistical methods to divide the flood season for the Danjiangkou Reservoir basin in the upper Han River, and verified the results by considering the regional characteristics of the Meiyu and Autumn Rain in the Yangtze River basin. The whole flood season is finally divided into three periods: the summer flood season (20 June to 10 August), a transition period (11 August to 31 August), and the autumn flood season (1 September to 10 October). The Meiyu occurs mainly in June and July, which can produce large floods. The latest end date of the Meiyu is on 8 August, which signals a reduction of flood prevention pressure in the downstream Han River. After 10 August, the Danjiangkou Reservoir flood prevention storage can be released gradually. Autumn Rain occurs from late August to mid-September, and contributes significantly flow discharge, which is an opportunity for reservoir early refill operations. This study will provide a practical approach for flood seasonal division in other regions with seasonal rainfall characteristics. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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18 pages, 1933 KiB

Open AccessArticle

Spatial and Temporal Evolution of Water Resource Disparities in Yangtze River Economic Zone

by Guanghui Yuan, Haobo Ni, Di Liu and Hejun Liang

Water 2024, 16(24), 3664; https://doi.org/10.3390/w16243664 - 19 Dec 2024

Viewed by 727

Abstract

The process of urbanization, which leads to increased population density, changes in land use patterns, and heightened demand for industrial and domestic water use, exacerbates the contradiction between the supply and demand of water resources. This study examines the discrepancies between the supply and demand of water resources amidst urbanization, utilizing data from 110 cities within the Yangtze River Economic Belt (YREB) spanning from 2012 to 2021. The research employs the projection pursuit clustering model and the Dagum Gini coefficient method to evaluate the developmental status of water resources. While the Yangtze River Delta (YRD) region maintains a leading position with a water resources development score of 9.827 in 2023, there is a 2.2% increase in intra-regional disparity. The water resources development score for the City Cluster in the Middle Reaches of the Yangtze River (CCRYR) has experienced a decline, from 8.263 in 2012 to 8.016 in 2021; however, a reduction in intra-regional disparities has been observed since the implementation of the 2016 Outline of the Yangtze River Economic Belt Development Plan (YREBP), which suggests the policy’s efficacy. The Chengdu-Chongqing Economic Zone (CCEZ), despite its initially lower level of development, has demonstrated significant growth, with scores rising from 7.036 in 2012 to 7.347 in 2021. Collectively, the water resources development in the YREB exhibits an upward trend, yet the development remains uneven. The CCRYR shows a catching-up effect because of the YREBP, and the differences in other regions are widening. The research results provide decision-making support for water resources planning and management, and are of great significance in promoting the sustainable use of water resources. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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19 pages, 7461 KiB

Open AccessArticle

A Combined Landslide Displacement Prediction Model Based on Variational Mode Decomposition and Deep Learning Algorithms

by Mengcheng Sun, Yuxue Guo, Ke Huang and Long Yan

Water 2024, 16(23), 3503; https://doi.org/10.3390/w16233503 - 5 Dec 2024

Cited by 1 | Viewed by 1013

Abstract

Accurate landslide displacement prediction is an essential prerequisite for early warning systems aimed at mitigating geological hazards. However, the inherent nonlinearity and dynamic complexity of landslide evolution often hinder forecasting performance. Previous studies have frequently combined signal decomposition techniques with individual machine learning methods to enhance prediction reliability. To address the limitations and uncertainties associated with individual models, this study presents a hybrid framework for displacement forecasting that combines variational mode decomposition (VMD) with multiple deep learning (DL) methods, including long short-term memory neural network (LSTM), gated recurrent unit neural network (GRU), and convolutional neural network (CNN), using a cloud model-based weighted strategy. Specifically, VMD decomposes cumulative displacement data into trend, periodic, and random components, thereby reducing the non-stationarity of raw data. Separate DL networks are trained to predict each component, and the forecasts are subsequently integrated through the cloud model-based combination strategy with optimally assigned weights. The proposed approach underwent thorough validation utilizing field monitoring data from the Baishuihe landslide in the Three Gorges Reservoir (TGR) region of China. Experimental results demonstrate the framework’s capacity to effectively leverage the strengths of individual forecasting methods, achieving RMSE, MAPE, and R values of 12.63 mm, 0.46%, and 0.987 at site ZG118, and 20.50 mm, 0.52%, and 0.990 at site XD01, respectively. This combined approach substantially enhances prediction accuracy for landslides exhibiting step-like behavior. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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24 pages, 5359 KiB

Open AccessArticle

Quartile Regression and Ensemble Models for Extreme Events of Multi-Time Step-Ahead Monthly Reservoir Inflow Forecasting

by Jakkarin Weekaew, Pakorn Ditthakit, Nichnan Kittiphattanabawon and Quoc Bao Pham

Water 2024, 16(23), 3388; https://doi.org/10.3390/w16233388 - 25 Nov 2024

Viewed by 1387

Abstract

Amidst changing climatic conditions, accurately predicting reservoir inflows in an extreme event is challenging and inevitable for reservoir management. This study proposed an innovative strategy under such circumstances through rigorous experimentation and investigations using 18 years of monthly data collected from the Huai Nam Sai reservoir in the southern region of Thailand. The study employed a two-step approach: (1) isolating extreme and normal events using quantile regression (QR) at the 75th, 80th, and 90th quantiles and (2) comparing the forecasting performance of individual machine learning models and their combinations, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Multiple Linear Regression (MLR). Forecasting accuracy was assessed at four lead times—3, 6, 9, and 12 months—using ten-fold cross-validation, resulting in 16 model configurations for each forecast period. The results show that combining quantile regression (QR) to distinguish between extreme and normal events with hybrid models significantly improves the accuracy of monthly reservoir inflow forecasting, except for the 9-month lead time, where the XG model continues to deliver the best performance. The top-performing models, based on normalized scores for 3-, 6-, 9-, and 12-month-ahead forecasts, are XG-MLR-75, RF-XG-80, XG-75, and XG-RF-75, respectively. Another crucial finding of this research is the uneven decline in prediction accuracy as lead time increases. Notably, the model performed best at t + 9, followed by t + 3, t + 12, and t + 6, respectively. This pattern is influenced by model characteristics, error propagation, temporal variability, data dynamics, and seasonal effects. Improving the accuracy and efficiency of hybrid model forecasting can greatly enhance hydrological operational planning and management. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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23 pages, 3116 KiB

Open AccessArticle

Assessing Flood Risks in Coastal Plain Cities of Zhejiang Province, Southeastern China

by Saihua Huang, Weidong Xuan, He Qiu, Jiandong Ye, Xiaofei Chen, Hui Nie and Hao Chen

Water 2024, 16(22), 3208; https://doi.org/10.3390/w16223208 - 8 Nov 2024

Viewed by 869

Abstract

Constructing a precise and effective evaluation index system is crucial to flood disaster prevention and management in coastal areas. This study takes Lucheng District, Wenzhou City, Zhejiang Province, southeastern China, as a case study and constructs an evaluation index system comprising three criterion levels: disaster-causing factors, disaster-gestation environments, and disaster-bearing bodies. The weights of each evaluation index are determined by combining the Analytic Hierarchy Process (AHP) and the entropy method. The fuzzy matter-element model is utilized to assess the flood disaster risk in Lucheng District quantitatively. By calculating the correlation degree of each evaluation index, the comprehensive index of flood disaster risk for each street area is obtained, and the flood disaster risk of each street area is classified according to the risk level classification criteria. Furthermore, the distribution of flood disaster risks in Lucheng District under different daily precipitation conditions is analyzed. The results indicate that: (1) the study area falls into the medium-risk category, with relatively low flood risks; (2) varying precipitation conditions will affect the flood resilience of each street in Lucheng District, Wenzhou City. The flood disaster evaluation index system and calculation framework constructed in this study provide significant guidance for flood risk assessment in coastal plain cities. Full article

(This article belongs to the Special Issue Reservoir Control Operation and Water Resources Management, 2nd Edition)

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