Recent Advances on Physically-Based and Data Driven Models in Watershed Science and Engineering
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 6328
Special Issue Editors
Interests: flood propagation; rainfall-runoff modeling; river networks; hazard communication; surface irrigation; impacts of climate change; lidar; soil erosion and sediment transport
Special Issues, Collections and Topics in MDPI journals
Interests: computational hydraulics; flood modeling; river engineering; urban drainage; machine learning; uncertainty quantification; water resources management
Special Issues, Collections and Topics in MDPI journals
Interests: computational hydraulics; numerical methods; shallow water equations; high-performance computing; sediment and pollutant transport; rainfall-runoff modeling; optimization and control; hydraulic structures modeling
Interests: hydro-environmental modelling; flood risk management; extreme flood modeling; evacuation planning; nature-based solutions; digital twins; pollution modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Over the last few decades, the understanding of water-related processes in natural/urban catchments and coastal areas has been significantly improved by means of physically-based distributed models, based on the fundamental laws of conservation of mass, energy and momentum at multiple spatio-temporal scales. These models are still evolving due to the 1) advances in mathematical derivation of hydrological and hydrodynamic processes, 2) the potentiality of mining flood data from several sources, such as the application of satellite-based products, the accessibility of range of sensors, the use of social media, etc., which reduce uncertainties in model parametrization and calibration, and 3) the increasing use of parallel computing techniques, especially for applications in large basins.
In addition to the advances in physically-based models, data-driven modeling based on computational intelligence and machine-learning methodologies has drawn mass research interest in hydrological and hydrodynamic simulation, since data availability has also increased. Machine learning techniques are powerful tools for understanding complex, nonlinear relations and have been applied in hydrological–hydrodynamic-related fields. For example, artificial neural networks, decision trees, and kernel methods (e.g., support vector machines; Gaussian process regression) have been widely explored.
The goal of this Special Issue is to collect high-quality and innovative scientific papers that describe cutting-edge research on the development and applications of physically-based and data-driven models in watershed science and technology in a broader sense.
The topics of interest include, but are not limited to, the following:
- Runoff mechanism;
- Short-term forecasting and inundation mapping of natural hazards;
- Urban flood modeling;
- Climate change impacts;
- Sediment and solute transport;
- Hydrometeorology ;
- Water losses in the catchment (evapotranspiration, infitration and interception).
Dr. Pierfranco Costabile
Dr. Vasilis Bellos
Dr. Mario Morales-Hernández
Dr. Reza Ahmadian
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
- physically-based models
- data-driven models
- machine learning
- high-performance computing
- watershed hydrology
- flood risk management
- high-resolution flood modeling
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
