Using Hydrological Modeling for Spatio-Temporal Analysis of Rainfall Signatures
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 1026
Special Issue Editors
Interests: catchment hydrology; hydrologic modeling; hydrodynamic modeling; hydrologic forecasting; flood forecasting; data assimilation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Rainfall signatures, encompassing the spatial and temporal distribution of precipitation events, are pivotal for understanding and managing water resources, agricultural planning, flood forecasting, and mitigating the impacts of climate variability. Hydrological modeling serves as a fundamental tool for analyzing these rainfall patterns, enabling the simulation of runoff processes, evapotranspiration, soil moisture dynamics, and watershed responses. Advances in hydrological modeling techniques, coupled with high-resolution spatial data and temporal datasets, have enhanced our ability to dissect complex rainfall signatures and their implications on hydrological systems. This Special Issue aims to compile cutting-edge research that leverages hydrological models to perform spatio-temporal analyses of rainfall signatures, addressing both methodological innovations and practical applications. We welcome the topics listed below and other scientific results related to this Special Issue:
- Development and enhancement of hydrological models;
- High-resolution spatial and temporal rainfall data assimilation;
- Rainfall-runoff modeling;
- Impact of climate change on rainfall patterns;
- Data assimilation and uncertainty quantification in hydrological modeling;
- Application of deep learning and AI in hydrological rainfall analysis;
- Urban hydrology and rainfall signature analysis;
- Remote sensing in rainfall analysis.
Prof. Dr. Zhijia Li
Guest Editor
Dr. Junfu Gong
Guest Editor Assistant
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Keywords
- hydrological modeling
- rainfall patterns
- spatio-temporal analysis
- data assimilation
- uncertainty quantification
- deep learning
- urban hydrology
- catchment hydrology
- remote sensing
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