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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


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Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: catchment hydrology; hydrologic modeling; hydrodynamic modeling; hydrologic forecasting; flood forecasting; data assimilation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor Assistant
College of Agricultural Science and Engineering, Hohai University, Nanjing 210024, China
Interests: catchment hydrology; hydrologic modeling; hydrologic forecasting; flood forecasting; data assimilation

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

Manuscript Submission Information

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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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Published Papers (2 papers)

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Research

10 pages, 1104 KiB  
Article
Comparative Analysis of Extreme Flood Characteristics in the Huai River Basin: Insights from the 2020 Catastrophic Event
by Youbing Hu, Shijin Xu, Kai Wang, Shuxian Liang, Cui Su, Zhigang Feng and Mengjie Zhao
Water 2025, 17(12), 1815; https://doi.org/10.3390/w17121815 - 17 Jun 2025
Viewed by 97
Abstract
Catastrophic floods in monsoon-driven river systems pose significant challenges to flood resilience. In July 2020, China’s Huai River Basin (HRB) encountered an unprecedented basin-wide flood event characterized by anomalous southward displacement of the rain belt. This event established a new historical record with [...] Read more.
Catastrophic floods in monsoon-driven river systems pose significant challenges to flood resilience. In July 2020, China’s Huai River Basin (HRB) encountered an unprecedented basin-wide flood event characterized by anomalous southward displacement of the rain belt. This event established a new historical record with the three typical hydrological stations (Wangjiaba, Runheji, and Lutaizi sections) along the mainstem of the Huai River exceeded their guaranteed water levels within 11 h and synchronously reached peak flood levels within a 9-h window, whereas the inter-station lag times during the 2003 and 2007 floods ranged from 24 to 48 h, causing a critical emergency in the flood defense. By integrating operational hydrological data, meteorological reports, and empirical rainfall-runoff model schemes for the Meiyu periods of 2003, 2007, and 2020, this research systematically dissects the 2020 flood’s spatial composition patterns. Comparative analyses across spatiotemporal rainfall distribution, intensity metrics, and flood peak response dynamics reveal distinct characteristics of southward-shifted torrential rain and flood variability. The findings provide critical technical guidance for defending against extreme weather events and unprecedented hydrological disasters, directly supporting revisions to flood control planning in the Huai River Ecological and Economic Zone. Full article
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22 pages, 1823 KiB  
Article
Heavy Rainfall Probabilistic Model for Zielona Góra in Poland
by Marcin Wdowikowski, Monika Nowakowska, Maciej Bełcik and Grzegorz Galiniak
Water 2025, 17(11), 1673; https://doi.org/10.3390/w17111673 - 31 May 2025
Viewed by 524
Abstract
The research focuses on probabilistic modeling of maximum rainfall in Zielona Góra, Poland, to improve urban drainage system design. The study utilizes archived pluviographic data from 1951 to 2020, collected at the IMWM-NRI meteorological station. These data include 10 min rainfall records and [...] Read more.
The research focuses on probabilistic modeling of maximum rainfall in Zielona Góra, Poland, to improve urban drainage system design. The study utilizes archived pluviographic data from 1951 to 2020, collected at the IMWM-NRI meteorological station. These data include 10 min rainfall records and aggregated hourly and daily totals. The study employs various statistical distributions, including Fréchet, gamma, generalized exponential (GED), Gumbel, log-normal, and Weibull, to model rainfall intensity–duration–frequency (IDF) relationships. After testing the goodness of fit using the Anderson–Darling test, Bayesian Information Criterion (BIC), and relative residual mean square Error (rRMSE), the GED distribution was found to best describe rainfall patterns. A key outcome is the development of a new rainfall model based on the GED distribution, allowing for the estimation of precipitation amounts for different durations and exceedance probabilities. However, the study highlights limitations, such as the need for more accurate local models and a standardized rainfall atlas for Poland. Full article
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