Digital Twin-Enabled Hydrological and Hydropower Systems for Reservoir Inflow Modelling
A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 23
Special Issue Editors
Interests: hydropower systems; reservoir inflow modelling; water resources management; optimization algorithms
Interests: water Resources; hydropower; statistical methods; artificial intelligence
Interests: water resources management; small hydropower plants; reservoir operation; optimization algorithms; renewable energy systems; pumped storage hydropower
Special Issues, Collections and Topics in MDPI journals
Interests: hydropower; GIS-based calculation; artificial intelligence; water-energy-food nexus
Interests: hydropower; water-energy-food-ecosystem nexus; water policy; water resilience; dams and reservoirs
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Reservoir inflow modelling plays a central role in hydrological forecasting, hydropower operation, and climate-resilient water resources management. However, increasing climate variability, shifting precipitation regimes, and hydrological non-stationarity pose significant challenges to traditional process-based models. At the same time, advances in hydroinformatics, hybrid physics–AI approaches, and remote sensing provide powerful new tools for improving prediction skill. Digital Twin frameworks—linking observations, models, data assimilation, and machine learning—offer a transformative perspective for understanding and forecasting watershed response and reservoir inflows under dynamic environmental conditions.
The goal of this Special Issue is to collect original research articles and review papers that provide new insights into Digital Twin-enabled hydrological and hydropower systems for reservoir inflow modelling. This topic is fully aligned with the scope of Hydrology, as it integrates surface water hydrology, hydroinformatics, climate impacts, modelling techniques, and water resources management.
This Special Issue welcomes manuscripts related to the following themes:
- Digital Twin frameworks in hydrology and hydropower;
- Reservoir inflow modelling under climate variability;
- Hybrid physics–AI hydrological modelling;
- Data assimilation and real-time forecasting;
- Remote sensing precipitation and catchment forcing;
- Uncertainty quantification and non-stationarity analysis;
- Hydropower operation and integrated water–energy systems.
We look forward to receiving your original research articles and reviews.
Dr. Eliza Isabela Tică
Dr. Angela Neagoe
Dr. Bogdan Popa
Dr. Raquel López-Fernández
Dr. Emanuele Quaranta
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly 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 1800 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
- digital twins
- reservoir inflow modelling
- hydrological modelling
- hydropower systems
- climate variability
- hybrid physics–AI models
- data assimilation
- remote sensing precipitation
- forecasting strategies
- water resources management
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