Recent Advances in Hydrological Modeling

Dear Colleagues,

We would like to draw your attention to a call for papers for the Special Issue “Recent Advances in Hydrological Modeling” in the journal Hydrology (https://www.mdpi.com/journal/hydrology; CiteScore = 3.4).

Hydrological models are important tools that are widely used in water resources operations, planning, and management practices. These models can be classified into different types according to their structure (data-driven/statistical, conceptual, physical, hybrid) or spatial representation (lumped, distributed). Model calibration, validation/verification, sensitivity analysis, and uncertainty analysis are integral parts of the modeling process. In operations, field (in situ and remote sensing) observations are often merged into hydrological models to update model states and/or parameters for improved model performance. Model outputs, particularly ensemble-based outputs, are typically post-processed before being utilized to inform decision-making.

Advances in computing (e.g., cloud-based and parallelization) and information (e.g., artificial intelligence) technologies provide new opportunities for improved hydrological modeling. In addition, ongoing and projected environmental changes (e.g., global warming, intensified extreme events) pose new challenges for hydrological modeling, and call for innovative modeling approaches and integrating modeling across different disciplines. In this context, this Special Issue invites studies covering, but not limited to, the following areas:

• New conceptual, physical, data-driven, or hybrid models that advance our understanding of hydrological processes under a changing climate, at large scales (e.g., national/continental/global), or in ungauged areas.
• Methodological advances and case studies on merging in-situ and remote sensing observations into hydrological models for improved model performance and uncertainty diagnosis.
• Novel approaches and applications on verification or post-processing hydrological model results.
• Ensemble-based approaches to modeling extreme events, climate change, and urban flooding.
• Utilizing multiple techniques for the improved performance of hydrological models (e.g., the combination of data assimilation, machine learning, and pre- or post-processing).
• Novel methodologies to analyze uncertainty, sensitivity, or structural inadequacy associated with hydrological modeling.
• Integrated modeling across different landscapes (e.g., surface/subsurface), multidisciplinary subjects (meteorology, hydrology, hydraulics, hydrodynamics, water quality, etc.), and time scales (minutes to decades).

Dr. Minxue He
Dr. Seong Jin Noh
Dr. Haksu Lee
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.