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Advanced Hydrological Modeling for Extreme Events: Floods, Droughts, and Risk...
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Advanced Hydrological Modeling for Extreme Events: Floods, Droughts, and Risk Assessment

A Special Issue of Water (ISSN 2073-4441) belonging to the section "Hydrology".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 1091

Editors

Dr. Muhammad Usman Liaqat

E-Mail Website
Guest Editor
Research Institute for Geo-Hydrological Protection, National Research Council of Italy, 00185 Rome, Italy
Interests: flood and drought risk assessment; remote sensing and GIS; hydrological modelling; machine learning & data-driven modelling
Dr. Stefania Camici

E-Mail Website
Guest Editor
Research Institute for Geo-Hydrological Protection, National Research Council, Via della Madonna Alta 126, 06128 Perugia, Italy
Interests: stochastic generation of spatially distributed rainfall; design flood estimation for water management and flood hazard assessment; analysis of climate change effects on flood frequency; hydraulic risk assessment; levee hydraulic vulnerability assessment

Special Issue Information

Dear Colleagues,

Hydrological extremes such as floods and droughts are intensifying under climate and land use change, challenging the reliability of existing water resources management and risk reduction strategies. This Special Issue focuses on new developments in hydrological modeling that help us understand, simulate, and predict extreme events in different climates, at different scales, and in different types of landforms. We especially welcome papers that help us understand processes better, make predictions more accurate, or help us make decisions about how to deal with climate change and hazards assessment.

Topics of interest include, but are not limited to the following:

  • Physically based, conceptual, and distributed hydrological models for floods and droughts
  • Coupled surface–subsurface and rainfall–runoff modeling for extremes
  • Non‑stationarity, trend and attribution analyses in hydrological extremes
  • Uncertainty analysis, ensemble and scenario‑based modeling
  • Parameter calibration, sensitivity and regionalization methods for extreme events
  • Application of remote sensing and reanalysis data in hydrological models.

We invite original research articles and comprehensive reviews that advance the state of the art in hydrological modeling of extreme events.

Dr. Muhammad Usman Liaqat
Dr. Stefania Camici
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-anonymized 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 modeling
  • extreme events
  • flood simulation
  • drought prediction
  • rainfall–runoff models
  • non-stationarity
  • climate adaptation

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Published Papers (2 papers)

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Research

20 pages, 5072 KB  
Article
Freeze–Thaw Effects on Baffle Friction in Ice–Rock Avalanche Mitigation: Experiments and Numerical Simulations
by Jianjun Liang, Shijie Luo and Kaiyue Zhu
Water 2026, 18(18), 2237; https://doi.org/10.3390/w18182237 - 9 Sep 2026
Abstract
Rock–ice avalanches and repeated freeze–thaw cycles pose coupled challenges to baffle-type mitigation structures in high-altitude cold regions. This study used controlled small-scale pull-out tests to quantify changes in baffle–soil friction over 0–30 freeze–thaw cycles and then calibrated a discrete element method (DEM) model [...] Read more.
Rock–ice avalanches and repeated freeze–thaw cycles pose coupled challenges to baffle-type mitigation structures in high-altitude cold regions. This study used controlled small-scale pull-out tests to quantify changes in baffle–soil friction over 0–30 freeze–thaw cycles and then calibrated a discrete element method (DEM) model to the terminal 30-cycle condition to evaluate baffle geometry, particle size, interparticle cohesion, and pull-out velocity. Moisture redistribution approached equilibrium after approximately 7–10 cycles, whereas the friction response stabilized only after approximately 16 cycles, indicating that hydraulic stabilization preceded mechanical and interfacial stabilization. The friction coefficient decreased from 0.83 before cycling to 0.48 after 30 cycles, corresponding to an attenuation of 42.17%, and the friction force decreased from 130 to 75 N. The decay showed three stages: limited change over 0–3 cycles, accelerated degradation over 3–16 cycles, and a near-plateau thereafter. The DEM results indicate that lateral prop-root projections can increase pull-out resistance by enlarging the mobilized soil volume and enhancing mechanical interlocking; the response also depends nonlinearly on particle size and cohesion. The proposed baffle is therefore presented as a preliminary structural concept rather than a field-ready design. Because the experiments were not performed under complete geometric, kinematic, or dynamic similitude and the DEM calibration represents only one post-freeze–thaw state, the numerical values should be interpreted as laboratory-scale comparative results. Full article
(This article belongs to the Special Issue Advanced Hydrological Modeling for Extreme Events: Floods, Droughts, and Risk Assessment)
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15 pages, 3013 KB  
Article
Forecasting of Macroclimatic Phases Through Stochastic Modeling and Machine Learning: Implications for Regional Hydrological Analysis
by Fernando Oñate-Valdivieso, Paúl Piedra Faicán and Arianna Oñate-Paladines
Water 2026, 18(11), 1358; https://doi.org/10.3390/w18111358 - 3 Jun 2026
Cited by 1 | Viewed by 502
Abstract
Droughts are complex extreme phenomena that severely impact regional development and water availability. Although the influence of interannual and decadal macroclimatic patterns, such as the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on precipitation alteration is widely recognized, current water [...] Read more.
Droughts are complex extreme phenomena that severely impact regional development and water availability. Although the influence of interannual and decadal macroclimatic patterns, such as the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on precipitation alteration is widely recognized, current water management systems lack multivariate predictive approaches to anticipate their phases with sufficient operational lead time. This study developed a predictive framework to project ENSO and PDO phases, establishing an optimal temporal window to forecast drought-triggering conditions. Using monthly historical records, teleconnections were evaluated through cross-correlation and Granger causality. Subsequently, Vector Autoregression (VAR) models and machine learning algorithms (Random Forest) were implemented to project anomalies and classify climatic phases. The Granger causality test demonstrated that ENSO variations statistically precede PDO phase shifts, establishing an optimal forecasting window of three to four months. The VAR model exhibited robust joint explanatory capacity for a continuous four-month projection, while the Random Forest algorithm achieved a predictive accuracy of 52.2% specifically for categorical phase classification at a three-month lead time. It is concluded that this lagged interaction allows for reliable mathematical anticipation, providing an essential analytical framework for exploring regional hydrological dynamics and supporting local preventive water management. Full article
(This article belongs to the Special Issue Advanced Hydrological Modeling for Extreme Events: Floods, Droughts, and Risk Assessment)
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