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Advances in Extreme Hydrological Events Modeling

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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 13137

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Special Issue Editor

Dr. Majid Mirzaei

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Guest Editor

Department of Environmental Science and Technology, University of Maryland, College Park, MD 20742, USA
Interests: climate extremes; climate change; flood modeling; uncertainty analysis; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue of Water, titled "Advances in Extreme Hydrological Events Modeling", aims to provide a scientific platform for exploring innovative methodologies and advanced tools to enhance the understanding, prediction, and management of extreme hydrological events. These phenomena, including extreme precipitation, droughts, floods, and streamflow extremes, represent critical challenges under the influence of a changing climate.

We invite high-quality research contributions focusing on the frequency, intensity, and spatial distribution of extreme precipitation events. Submissions that advance the analysis of streamflow extremes and their recurrence intervals are particularly welcome, as these metrics are essential for understanding the dynamics of riverine systems during extreme events. Furthermore, we invite contributions utilizing machine learning, deep learning, and innovative approaches to advance the prediction, analysis, and management of extreme precipitation, droughts, floods, and streamflow variability. This Special Issue emphasizes the significance of uncertainty analysis in modeling extreme hydrological events. We seek research that quantifies uncertainties in precipitation, streamflow, and other hydrological extremes, providing robust frameworks for improved predictive modeling and risk assessments. Innovative approaches integrating uncertainty quantification into hydrological and climate models are particularly valuable.

Additionally, we encourage submissions that showcase the application of data assimilation techniques to improve the accuracy of extreme event predictions. These approaches are critical for integrating observational data with model outputs, enabling real-time forecasting and enhanced decision-making capabilities. We welcome research that advances our understanding of drought dynamics and flood events, including their spatiotemporal characteristics, causative factors, and long-term trends. Contributions that investigate the impacts of climate change on these hydrological extremes, along with adaptive strategies to mitigate their adverse effects, are highly relevant to this Special Issue. By bringing together a diverse collection of original research articles, comprehensive review papers, and practical case studies, this Special Issue aims to foster interdisciplinary collaboration and advance the scientific knowledge required to address the complex challenges of extreme hydrological events.

Dr. Majid Mirzaei
Guest Editor

Manuscript Submission Information

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Keywords

extreme hydrological events
extreme precipitation
frequency analysis
drought
floods
climate change impacts
uncertainty analysis
data assimilation
hydrological modeling
machine learning
deep learning

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Published Papers (6 papers)

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Research

Jump to: Review

30 pages, 65437 KB

Open AccessArticle

Transboundary Aquifer Vulnerability: Modeling Future Groundwater Decline in the Nubian Sandstone Aquifer (Al Kufrah Basin, Libya)

by Abdalraheem Huwaysh, Fadoua Hamzaoui and Nawal Alfarrah

Water 2026, 18(8), 987; https://doi.org/10.3390/w18080987 - 21 Apr 2026

Viewed by 538

Abstract

Groundwater in arid and semi-arid regions is increasingly stressed by low rainfall, high evaporation, population growth, agricultural expansion, and climate change. A critical question is whether non-renewable aquifers can sustain rising water demand without irreversible decline. This study addresses that question for the Al Kufrah Basin in southeastern Libya, part of the Nubian Sandstone Aquifer System, the world’s largest fossil aquifer. A three-dimensional groundwater flow model (MODFLOW-2000) was calibrated using data from more than 1000 production wells and 32 piezometers spanning 1968–2022. The model was applied to simulate groundwater behavior under five scenarios extending to 2050, including the planned development of 150 new wells. The results indicate that over 85% of withdrawals are derived from aquifer storage rather than boundary inflows. While regional water levels remain relatively stable over the 25-year horizon, localized drawdowns of up to 11 m are expected near new well fields. These findings highlight short-term resilience but point to long-term vulnerability, as continued reliance on non-renewable reserves without recharge will ultimately lead to depletion. The study underscores the need for adaptive management, climate-resilient water strategies, and regional cooperation to ensure the sustainable use of this transboundary aquifer under increasing environmental and socio-economic pressures. Full article

(This article belongs to the Special Issue Advances in Extreme Hydrological Events Modeling)

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22 pages, 3860 KB

Open AccessArticle

Drought–Flood Abrupt Alternation in the Heilongjiang River Basin Under Climate Change: Spatiotemporal Patterns, Drivers, and Projections

by Fengli Huang, Jianyu Jing, Changlei Dai and Peng Qi

Water 2025, 17(23), 3436; https://doi.org/10.3390/w17233436 - 3 Dec 2025

Viewed by 1817

Abstract

Climate change has exacerbated the occurrence of complex extreme hydrological events in high-latitude cold regions, among which drought–flood abrupt events (DFAAEs) threaten food and water security, and accurately predicting their future evolution remains a key challenge. This study used the Community Water Model (CWatM) hydrological model, combined with five CMIP6 climate models, to simulate runoff datasets for historical periods (1985–2014) and future shared socioeconomic pathways (SSPs: SSP126, SSP370, SSP585: 2015–2100). We calculated the DFAA index (DFAAI), analyzed the spatiotemporal distribution patterns and predicted future trends of DFAAEs in the Heilongjiang River Basin, and explored their climatic driving mechanisms. The main conclusions are as follows: (1) Under SSPs, precipitation and evaporation increase from northwest to southeast, and temperature increases from north to south; hotspots expand inland. By 2100, annual precipitation will reach 655, 700, and 720 mm; mean air temperature will rise by 3, 6, and 7 °C; and annual evapotranspiration will reach 460, 515, and 521 mm. (2) Relative to the historical period, DFAAEs increase from 5.9 to 6.6, 7.1, and 7.5 events per year (SSP126/370/585). Coverage rises from 10.6% to 12.7%, 17.1%, and 19.0%, while mean intensity remains 1.8–2.0. Across both the historical period and SSPs, the shares of light (69–74%), moderate (20–24%), and severe (6–8%) events are stable. (3) Principal Component 1 (PC1,62.9%) reflects a precipitation-dominated wetting mode with synchronous increases in evapotranspiration and is the primary driver of DFAAI variability. PC2 (20.3%) captures an energy-related mode governed mainly by evapotranspiration and indirectly modulated by air temperature, providing a secondary contribution. These results clarify DFAA mechanisms and inform water-resources security planning in the Heilongjiang River Basin. Full article

(This article belongs to the Special Issue Advances in Extreme Hydrological Events Modeling)

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15 pages, 2349 KB

Open AccessArticle

Evaluating IMERG Satellite Precipitation-Based Design Storms in the Conterminous U.S. Using NOAA Atlas Datasets

by Kenneth Okechukwu Ekpetere, Xingong Li, Jude Kastens, Joshua K. Roundy and David B. Mechem

Water 2025, 17(17), 2602; https://doi.org/10.3390/w17172602 - 3 Sep 2025

Viewed by 1553

Abstract

Probable Maximum Storms (PMS) are synthetic design storms represented by idealized hyetographs. They play a critical role in assessing extreme rainfall events over extended durations and are widely applied in the hydraulic design of infrastructure such as dams, culverts, and bridges. PMS provide essential input for estimating Probable Maximum Floods (PMF), vital for analyzing worst-case flood scenarios with the potential to cause catastrophic loss of life and property. Despite their importance, the estimation of design storms at ungauged locations, particularly across synoptic scales, remains a major scientific and engineering challenge. This study addresses this gap by utilizing the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) dataset, which provides near-global estimated precipitation coverage. IMERG’s 24 h design storm hyetographs (expressed as cumulative percentage of precipitation throughout a 24 h period) were modeled and compared with similar reference data from NOAA Atlas 14 across twenty-eight regions and seven larger zones covering most of the conterminous United States (CONUS). Across the regions, the average root mean square error (RMSE) was 3.7%, with a mean relative bias (RB) of 1.4%. The mean normalized storm loading index (NSLI) from NOAA Atlas 14 was −7.7%, indicating that 57.7% of the total precipitation was received during the first 12 h of the storm, whereas IMERG storms exhibited a mean NSLI of −4.1%, suggesting they are also frontloaded but to a lesser extent. Across the broader zones, the mean RMSE was 4.8% and the mean RB was 1.1%. The mean NSLI values were −9.7% for NOAA Atlas 14 and −5.7% for IMERG, again indicating that IMERG storms are less frontloaded. When design storm families were estimated corresponding with different degrees of frontloading (corresponding to the 10, 20, …, 90% deciles of NSLI), the 40th to 60th percentile range exhibited the strongest agreement between IMERG and NOAA Atlas 14 hyetographs. Full article

(This article belongs to the Special Issue Advances in Extreme Hydrological Events Modeling)

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23 pages, 6710 KB

Open AccessArticle

Extreme Precipitation Dynamics and El Niño–Southern Oscillation Influences in Kathmandu Valley, Nepal

by Deepak Chaulagain, Ram Lakhan Ray, Abdulfati Olatunji Yakub, Noel Ngando Same, Jaebum Park, Anthony Fon Tangoh, Jong Wook Roh, Dongjun Suh, Jeong-Ok Lim and Jeung-Soo Huh

Water 2025, 17(9), 1397; https://doi.org/10.3390/w17091397 - 6 May 2025

Cited by 3 | Viewed by 3950

Abstract

Understanding historical climatic extremes and variability is crucial for effective climate change adaptation, particularly for urban flood management in developing countries. This study investigates historical precipitation trends in the Kathmandu Valley, Nepal, focusing on precipitation frequency, intensity, and the influence of the El Niño–Southern Oscillation (ENSO), using extreme precipitation indices and the precipitation concentration index (PCI). The results reveal sharply fluctuating short-term precipitation from 1980 to 2022, with the exception of an increasing trend during spring (1.17 mm/year) and a decreasing trend in November and December. Trends in extreme precipitation indices are mixed: RX7day shows an increasing trend of 0.1 mm/year, with decadal analysis (1980–2001 and 2002–2022) indicating similar upward patterns. In contrast, RX1day, RX3day, RX5day, and R95pTOT exhibit inconsistent trends, while R99pTOT demonstrates a decreasing trend over the full period (1980–2022). Although the number of days with precipitation ≥ 35 mm has declined, the increasing trend in 7-day maximum precipitation, coupled with no significant change in total annual precipitation and highly variable short-term rainfall, points to a rising risk of unexpected extreme precipitation events. Precipitation patterns in the Kathmandu Valley remain highly irregular across seasons, except during summer. ENSO exhibits a negative correlation with annual precipitation, extreme precipitation indices, and the PCI but shows a positive correlation with the annual and summer PCI as well as 1-day maximum precipitation, emphasizing its significant influence on precipitation variability. These findings highlight the urgent need for targeted climate adaptation strategies and provide valuable insights for hydrologists, meteorologists, policymakers, and urban planners to enhance climate resilience and improve flood management in the Kathmandu Valley. Full article

(This article belongs to the Special Issue Advances in Extreme Hydrological Events Modeling)

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29 pages, 5493 KB

Open AccessArticle

Effectiveness of Water-Sensitive Urban Design Techniques on Stormwater Quantity Management at a Residential Allotment Scale

by Samira Rashetnia, Ashok K. Sharma, Anthony R. Ladson, Dale Browne and Ehsan Yaghoubi

Water 2025, 17(6), 899; https://doi.org/10.3390/w17060899 - 20 Mar 2025

Cited by 3 | Viewed by 3762

Abstract

Rapid population growth and urbanization are transforming natural landscapes into built environments, resulting in increased stormwater runoff, which poses significant challenges for local governments to manage. Water-Sensitive Urban Design (WSUD) techniques have been implemented to enhance urban stormwater quality, but their effectiveness in managing stormwater quantity and quality across different scales remains uncertain. This study examines the capacity of various WSUD approaches to reduce stormwater runoff volume and peak flow rates in a residential allotment transitioning from a single dwelling to a redeveloped condition with two dwellings. The tested techniques included a rainwater tank, infiltration trench, rain garden, vegetated swale, and permeable pavement. For storm events with a 1-in-5-year Annual Recurrence Interval (ARI)—aligning with typical piped drainage design standards—peak flow rates were reduced by 90% in the redeveloped scenario. Smaller storm events, up to a 1-in-1-year ARI, were frequently eliminated, thereby minimizing disturbances to waterways caused by frequent runoff discharges. Among the tested techniques, the combination of a rainwater tank, rain garden, and infiltration trench demonstrated the greatest effectiveness in reducing stormwater runoff volume and peak flow rates despite considerations of life cycle costs. These findings highlight the potential of integrated WSUD techniques in addressing urban stormwater management challenges. Full article

(This article belongs to the Special Issue Advances in Extreme Hydrological Events Modeling)

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Review

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42 pages, 2880 KB

Open AccessReview

Multiscale Modeling of Sediment Transport During Extreme Hydrological Events: Advances, Challenges, and Future Directions

by Jun Xu and Fei Wang

Water 2026, 18(9), 1004; https://doi.org/10.3390/w18091004 - 23 Apr 2026

Viewed by 509

Abstract

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

(This article belongs to the Special Issue Advances in Extreme Hydrological Events Modeling)

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