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Reservoir Control Operation and Water Resources Management, 2nd Edition
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Reservoir Control Operation and Water Resources Management, 2nd Edition

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 March 2025 | Viewed by 1263

Special Issue Editors

Dr. Yuxue Guo

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Jingkai Xie

E-Mail Website
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

E-Mail Website
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.

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

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

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Related Special Issue

Published Papers (2 papers)

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Research

24 pages, 5359 KiB  
Article
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 470
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 [...] Read more.
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  
Article
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 528
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 [...] Read more.
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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