Advances in Calibration, Sensitivity and Uncertainty Analysis of Hydrological Models

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 25 May 2024 | Viewed by 120

Special Issue Editors


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Guest Editor
Water Security Program, CSIRO Land and Water, Brisbane, QLD 4001, Australia
Interests: groundwater modelling and management; uncertainty analysis; optimization; water resources management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Water Security Program, CSIRO Environment, Sydney, NSW 2015, Australia
Interests: water resources; machine learning; hydrological engineering

Special Issue Information

Dear Colleagues,

Hydrological models play an important role in developing improved understanding and managing water resources in a changing world. Accurate predictions of changes in run off, river flow, groundwater recharge and levels and other hydrological variables and processes like surface water – groundwater interaction are essential for informed decision-making in water resource management, and climate change impact assessment.

Process-based and data-driven models of different kinds and complexities are used for such purposes these days. Reliability of predictions made by such models depends on how well they honour data and/or simulate the underlying processes that are important for making the predictions of interest. Sensitivity analysis, calibration and uncertainty analysis of models can help model simplification, optimal parameterisation, history matching and quantify predictive uncertainties of the model caused by model structure and parameters. This special issue invites submissions that focus on advances in methods and application of calibration, sensitivity analysis, and uncertainty quantification in hydrological modeling.

Novel approaches and applications of local and global sensitivity analysis for improving model structure and parameters, computationally efficient approaches for model calibration and data assimilation including the use of Machine Learning based techniques are of interest. Contributions investigating quantification and propagation of model uncertainties for risk assessment improved decision making under uncertainty are also welcome.

Dr. Sreekanth Janardhanan
Dr. Stephanie Clark
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

  • hydrological models
  • sensitivity
  • calibration
  • uncertainty

Published Papers

This special issue is now open for submission.
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