Advanced Hydrological Models Applications and Fidelity
A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Hydrogeology".
Deadline for manuscript submissions: closed (25 June 2023) | Viewed by 4068
Special Issue Editors
Interests: hydroclimatology; hydrological modeling; uncertainty analysis; error characterization; water balance; statistical analysis
Special Issue Information
Dear Colleagues,
We would like to draw your attention to a call for papers for the Special Issue “Advanced Hydrological Model Applications and Fidelity” in the journal Geosciences.
Hydrological models are important tools that have a wide range of applications, including water resources management and planning, climate change impact assessment, flood forecasting and drought monitoring, and coupled systems modeling such as water quality, ecohydrology, and socio-hydrology. However, the accuracy and robustness of the hydrological models are subject to challenges imposed by the non-linearity of hydrological processes, scale dependence, spatial heterogeneity, equifinality, and uncertainty. Concurrently, ongoing and projected environmental issues and changes (global warming, intensified extreme events, human activities such as exploitation of natural resources, operations of dam and reservoirs, irrigation, etc.) pose new challenges for developing and evaluating hydrological models. This calls for better and more innovative modeling approaches in a multidisciplinary perspective. Advances in computing and information technologies, together with increasing data availability, have provided new opportunities and potentials for improving and assessing hydrological modeling fidelity.
In this context, this Special Issue invites studies covering, but not limited to, the following areas:
- Improving hydrological process and water management representation in physically based hydrological models for better understanding of hydrological processes and accuracy of hydrological simulations;
- Utilizing different types of data (in situ observations, remote sensing datasets, and reanalysis products) in constraining hydrological models for better understanding of model behaviors and diagnosing model and data discrepancies;
- Developing and exploring novel data-model fusion approaches to integrate multi-source data and hydrological modeling framework for better quantifying water cycle dynamics from regional and global scales;
- Applying advanced and novel methodologies and techniques for evaluating and assessing hydrological performance of models (e.g., data assimilation, machine learning, uncertainty quantification, hybrid modeling);
- Reviewing multi-source data and their associated uncertainties and interaction with models in hydrological model applications, and water balance error analysis.
Dr. Jefferson S. Wong
Dr. Fuad Yassin
Guest Editors
Manuscript Submission Information
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Keywords
- hydrological modeling
- model fidelity
- error analysis
- data-model fusion
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