Special Issue "Advances in Hydrological Forecasting"
Deadline for manuscript submissions: 31 December 2020.
Interests: hydrological; hydraulic; hydrodynamic; and water quality modeling;climate change;stochastic modeling; deep learning
Interests: hydrological modeling; data assimilation; ensemble modeling; climate change; extreme events; post-processing; precipitation; soil moisture
Interests: hydrological modeling and forecasting; coupled human-hydrologic systems planning; optimization; uncertainty; high-performance computing; decision-making support
Hydrological forecasting is of primary importance to better inform decision-making on flood management, drought mitigation, water system operations, water resources planning, and hydropower generation, among others. Typical hydrological forecasting translates single deterministic or an ensemble of short, intermediate, and long lead-time meteorological forecasts into estimates of hydrological variables of interest (e.g., streamflow, river stage, snowmelt, etc.) via forecast models at the corresponding temporal scales. These models range from process-based hydrological models to purely data-driven models. Model predictive skill and uncertainty are normally verified by comparing archived forecasts to field data or in a hindcasting mode. During forecasting, real-time in situ or remote sensing measurements for forecast hydrological variables can be assimilated into the forecast model to update model states or parameters for improved forecasts. Before being disseminated for operational use, hydrological forecasts are often post-processed to best reflect the perceptions of forecasters on the future state of those forecast variables. Although there has been immense progress in forecasting systems, services, and sensors to date, hydrological forecasting today faces convoluted challenges induced by the increasing trend of extreme events (calibration/verification), changing basin climate and hydrology (non-stationarity), and demands of a unified and versatile hydrological forecasting system operating at local to continental scales (hyper-resolution large-scale forecasting). To understand and advance science and practices of present hydrological forecasting, this Special Issue invites studies on the following:
- Methodological advances in hydrological forecasting, including innovative forecasting methods (e.g., machine learning-based or hybrid), data assimilation, post-processing, and uncertainty analysis techniques;
- Research and development of hyper-resolution large-scale hydrological forecasting, urban hydrological modeling, and forecasting of hydrological extremes;
- Incremental analysis of utilizing new observations, modules, or atmospheric model outputs in hydrological forecasting systems;
- Performance evaluation or verification of current operational hydrological forecast systems, services, or products at different scales and forecast horizons via large sample analysis, long-term hindcasting, or real-time forecasting;
- Latest applications of deterministic or probabilistic hydrological forecasts in decision-support practices.
Dr. Minxue He
Dr. Haksu Lee
Dr. Sungwook Wi
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 papers will be 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. Forecasting is an international peer-reviewed open access quarterly 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 1000 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.
- Hydrological forecasting
- machine learning
- data assimilation
- uncertainty assessment
- forecast verification
- hyper-resolution modeling
- hydrological extremes
- decision support