Hydrological Extremes and Drought Management—Challenges, Innovations, and Solutions

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 3166

Special Issue Editors


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Guest Editor
Institut National de la Recherche Scientifique, Centre Eau Terre Environnement, INRS-ETE, 490 de la Couronne, Québec, QC G1K 9A9, Canada
Interests: climate change; drought monitoring; climate modeling; artificial intelligence; GIS

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Guest Editor
Institut National de la Recherche Scientifique, Centre Eau Terre Environnement, INRS-ETE, 490 de la Couronne, Québec, QC G1K 9A9, Canada
Interests: extreme hydrological events; hydrological forecasting; hydrological variables

Special Issue Information

Dear Colleagues,

The increasing frequency, intensity, and spatial extent of hydrological extremes—such as floods and droughts—have emerged as the most pressing environmental and societal challenges of the 21st century. These events, often exacerbated by anthropogenic climate change and unsustainable land and water use practices, not only disrupt hydrological cycles but also threaten water security, agricultural productivity, public health, ecosystem services, and infrastructure resilience. Moreover, the compound nature of hydro-climatic events—where multiple hazards occur either simultaneously or sequentially—poses an amplified risk that often exceeds the predictive power of conventional analytical frameworks.

The non-stationary behavior of hydrological systems under climate change has rendered traditional frequency analysis, risk assessment, and design protocols insufficient for reliable forecasting and long-term planning. In this context, the integration of advanced technologies—such as artificial intelligence (AI), deep learning (DL), machine learning (ML), Big Data Analytics, remote sensing, and the Internet of Things (IoT)—has revolutionized the landscape of hydrological monitoring, forecasting, and decision-making. These tools offer significant potential for spatiotemporal modeling of drought and flood dynamics, developing early warning systems, uncertainty quantification, and implementing adaptive water resources management strategies.

This Special Issue of Atmosphere, titled “Hydrological Extremes and Drought ManagementChallenges, Innovations, and Solutions”, aims to compile a collection of high-quality studies that explore the intersection of hydrological extremes, novel modeling approaches, and sustainable management solutions. Potential topics include, but are not limited to, the following:

  • Hybrid modeling frameworks that combine physics-based hydrological models with AI/ML methods;
  • The development of integrated hydro-climate models for regional and local drought/flood forecasting;
  • The use of Earth observation data and sensor networks for real-time hazard monitoring;
  • Uncertainty analysis and ensemble projections using CMIP6, CORDEX, or other climate model datasets;
  • The evaluation of adaptation and mitigation strategies under different socio-economic scenarios (e.g., SSPs);
  • Risk mapping and resilience assessments of water systems in arid, semi-arid, and urban regions;
  • The application of spatial statistics, time–frequency analysis, and nonlinear dynamics in hydro-climatology.
  • Compound and cascading hazard analysis under climate extremes

We encourage submissions from a wide range of disciplines, including hydrology, water engineering, climatology, geography, remote sensing, computational modeling, environmental science, and decision sciences. Contributions may include original research articles, comprehensive review papers, methodological advancements, and well-documented case studies.

By bringing together interdisciplinary insights and showcasing cutting-edge methodologies, this Special Issue seeks to deepen our understanding of extreme hydrological events and foster the development of robust, scalable, and adaptive solutions to water-related challenges under a changing climate.

We look forward to receiving your valuable contributions.

Dr. Younes Khosravi
Dr. Taha B. M. J. Ouarda
Guest Editors

Manuscript Submission Information

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Keywords

  • hydrological extremes
  • climate change
  • spatiotemporal modelling
  • integrated hydro-climate modeling
  • machine learning applications
  • water resource resilience

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Published Papers (2 papers)

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Research

32 pages, 9074 KB  
Article
A New Framework for Comprehensive Flood Risk Assessment Under Non-Stationary Conditions Using GIS-Based MCDM Modeling
by Reşat Gün and Muhammet Yılmaz
Atmosphere 2026, 17(1), 62; https://doi.org/10.3390/atmos17010062 - 3 Jan 2026
Viewed by 1582
Abstract
Flood risk has been increasing due to the effects of climate change, frequent rainfall, and urbanization. Therefore, flood risk assessments in urban areas are important issues for the mitigation of flood disaster and sustainable development. Although there has been an increase in studies [...] Read more.
Flood risk has been increasing due to the effects of climate change, frequent rainfall, and urbanization. Therefore, flood risk assessments in urban areas are important issues for the mitigation of flood disaster and sustainable development. Although there has been an increase in studies on flood risk, there remains a scarcity of research examining the effects of rainfall at different return periods on flood risk under non-stationary conditions in Geographic Information System (GIS) - and multi-criteria decision-making model (MCDM)-based flood risk assessments. To address this gap, this study integrated MCDM-based flood hazard mapping techniques with rainfall quantiles calculated for different return periods under non-stationary conditions to identify and prioritize flood risk areas in Izmir, Türkiye. Firstly, to analyze the current flood risk, the Analytical Hierarchy Process (AHP) was integrated into the GIS and the VIseKriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) approach was used to determine the flood risk priority of 165 points. The results showed that Buca, Menderes, Bornova, Kemalpaşa, Çeşme, Torbalı, Menemen, Seferihisar, and Çiğli were identified as high-flood-risk areas. The VIKOR results indicate that the highest-flood-risk points are R91 (Çeşme), R153 (Buca), and R93 (Çeşme). For a thorough flood risk assessment, the rainfall estimates obtained with the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) at 10-, 20-, 50-, and 100-year return levels under non-stationary conditions were re-weighted with AHP and were incorporated into the hazard criteria, and flood risk analyses were performed for four scenarios. The results showed that as return periods increase, high-risk areas expand, while low-risk areas shrink. Specifically, the proportion of very-low-risk areas declined from 15.12% for the 10-year return period to 13.92% for the 100-year return period, whereas the proportion of very-high-risk areas increased from 6.73% to 7.53% over the same return period levels. VIKOR results, unlike the VIKOR findings for the current case, revealed that points R55, R56, and R54 in Kemalpaşa had the highest flood risk in four scenarios. Full article
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38 pages, 11071 KB  
Article
Accuracy Assessment of Remote Sensing-Derived Evapotranspiration Products Against Eddy Covariance Measurements in Tensift Al-Haouz Semi-Arid Region, Morocco
by Yassine Manyari, Mohamed Hakim Kharrou, Vincent Simonneaux, Saïd Khabba, Lionel Jarlan, Jamal Ezzahar and Salah Er-Raki
Atmosphere 2025, 16(12), 1407; https://doi.org/10.3390/atmos16121407 - 17 Dec 2025
Viewed by 803
Abstract
Evapotranspiration (ET) is challenging to measure directly, motivating the use of remote sensing products as alternatives. We evaluated five high-resolution (≤1 km) global ET products (SSEBop, MOD16, ETMonitor, PMLv2, and FAO’s WaPOR) against five eddy covariance (EC) measurements in Morocco’s semi-arid Tensift Al-Haouz [...] Read more.
Evapotranspiration (ET) is challenging to measure directly, motivating the use of remote sensing products as alternatives. We evaluated five high-resolution (≤1 km) global ET products (SSEBop, MOD16, ETMonitor, PMLv2, and FAO’s WaPOR) against five eddy covariance (EC) measurements in Morocco’s semi-arid Tensift Al-Haouz region, with observations spanning from 2006 to 2019. These five products were selected because they offer the finest spatial resolution (around 1 km or less) among freely downloadable global ET datasets, making them well-suited for comparison with local EC flux tower data. The study area was chosen for its reliable ground-truth EC stations, extensive knowledge of local irrigation practices, and a semi-arid climate that provides a rigorous testbed for ET model evaluation in water-limited conditions. Precipitation observations were included to assess each product’s sensitivity to soil moisture and precipitation-driven ET variations, particularly to identify which models respond to rainfall and irrigation inputs (i.e., differences between rainfed and irrigated fields). Results indicate that PMLv2 achieved the best agreement with EC (R2 up to 0.65, RMSE as low as 0.4 mm/day, and PBIAS under 10% at most sites), followed by WaPOR and SSEBop which captured seasonal ET patterns (R2 ~0.3–0.5) with moderate bias (~20–30%). In contrast, ETMonitor and MOD16 underperformed, showing larger errors (RMSE ~1–2.5 mm/day) and substantial underestimation biases (e.g., MOD16 PBIAS ~50–80% in irrigated sites). These findings underscore the impact of algorithmic differences and highlight PMLv2, SSEBop, and WaPOR as more reliable options for estimating ET in semi-arid agricultural regions lacking in situ measurements. Full article
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