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"Watershed–Urban" Flooding and Waterlogging Disasters

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

Deadline for manuscript submissions: closed (24 April 2026) | Viewed by 7797

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Dr. Jinjun Zhou

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Guest Editor

College of Architecture & Civil Engineering, Beijing University of Technology, Beijing 100124, China
Interests: urban hydrology; flood disaster; hydrologic process; urban waterlogging; urban rainwater harvesting; stormwater management; disaster risk assessment
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Dr. Zhuoran Luo

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Guest Editor

School of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing 102206, China
Interests: urban water cycle; climate change; hydrometeorology; hydrothermal coupling; basin hydrology
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Dr. Shengwei Pei

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Guest Editor

Centre for Water Systems, Department of Engineering, University of Exeter, Exeter, UK
Interests: stormwater management; operation optimization; artificial intelligence
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Special Issue Information

Dear Colleagues,

Collaborative research on basin–urban flood disasters is of crucial significance and is an urgent necessity for enhancing disaster prevention and mitigation capabilities. Extreme rainfall and flood evolution in the upstream basin directly constrain the flood risk of the urban area, while the hardened urban surface and drainage behavior have significantly changed the runoff generation and collection patterns of floods. This dynamic interaction often leads to a sharp amplification of disasters in the intersection area. Collaborative research can integrate natural and social systems, integrate meteorological–hydrological–water dynamic models and urban infrastructure data, and systematically reveal the disaster-causing mechanism of the "basin–urban" composite system, accurately simulating the spread of floods and the process of internal flooding. The main research topics include river basin floods, urban waterlogging, urban flood disaster, urban stormwater management, urban low-impact development, sponge city design and construction, urban hydrological cycles, urban water security, urban water resources, urban rainwater harvesting, urban water environments and water ecology, cause analysis of urban flood disasters, disaster loss evaluation, rainstorm–flood emergency technology, urban rainstorm–flood countermeasures, risk assessment and management of urban flood disasters, etc. Research articles, review articles, or other articles in related fields and research topics are highly welcome.

Dr. Jinjun Zhou
Dr. Zhuoran Luo
Dr. Shengwei Pei
Guest Editors

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Keywords

river basin flood
urban hydrology
urban flood
urban waterlogging
low-impact development
sponge city
urban stormwater management
urban flood damage
flood hazard risk

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Related Special Issue

"Watershed–Urban" Flooding and Waterlogging Disasters, 2nd Edition in Water

Published Papers (9 papers)

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Research

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22 pages, 32463 KB

Open AccessArticle

Flood Risk Prediction Framework Considering Combined Effects of Rainfall, Tide and Land Surface Changes Under a Non-Stationary Environment in a Coastal City

by Hongshi Xu, Jiahao Zhang, Huiliang Wang, Yongle Guan, Yuhe Deng and Yongjie Zhou

Water 2026, 18(10), 1237; https://doi.org/10.3390/w18101237 - 20 May 2026

Abstract

Coastal cities are prone to flooding due to extreme rainfall, rising sea levels, and urbanization. This study develops a non-stationary flood risk prediction framework for a coastal city to assess the combined effects of rainfall, tide, and land surface change on future flood inundation and socioeconomic risk. Future rainfall was predicted by integrating the time-varying parameter distribution (TVPD) model with CMIP6 data through a genetic algorithm; future tides were estimated using the TVPD model; and land use in 2035 was simulated using the Markov–PLUS model. Flood inundation and the associated socioeconomic risks were then evaluated. The results showed that the integrated rainfall prediction approach reduced RMSE by 13.4% compared with the individual models. The land use simulation also showed acceptable performance, with a Kappa coefficient of 0.79 and an FOM value of 0.15. Under the combined effects of rainfall, tide, and land use change, the future peak inundation volume increased by 19.97% on average relative to the baseline period, while the affected population and economic losses increased by 72,603 people and US$12.61 billion, respectively. These results indicate that flood risk in coastal cities may be substantially exacerbated under a non-stationary environment, and the proposed framework can provide support for future flood risk assessment and adaptation planning. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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19 pages, 16655 KB

Open AccessArticle

Multivariate Joint Risk Assessment of Small- and Medium-Sized River Flood in Arid and Semi-Arid Regions Based on Vine Copula

by Boyan Sun, Xiaomin Liu, Guoqing Wang, Ping Miao, Kang Xie and Hongli Ma

Water 2026, 18(9), 1098; https://doi.org/10.3390/w18091098 - 3 May 2026

Viewed by 848

Abstract

Flood risk assessment is essential for flood control and disaster mitigation in arid and semi-arid river basins, where conventional univariate and bivariate frequency analyses struggle to capture nonlinear dependence among flood variables and often underestimate extreme synergistic risks. This study focuses on the Wulanmulun River Basin in Inner Mongolia and employs long-term observations from the Zuanlongwan and Wangdaohengta hydrological stations. A trivariate D-vine Copula model was constructed to jointly characterize peak discharge, total flood volume, and water level. Optimal vine structures differ between the stations (Qp–H–W and W–Qp–H) and outperform traditional Copula models in representing extreme joint risks. The ternary joint return periods reveal two distinct flood risk transmission modes, “jump” and “accumulation”, and joint exceedance probabilities under low, medium, high, and ultra-high-risk scenarios are 6.4%, 31.95%, 37.64%, and 5.75% at Zuanlongwan, and 4.7%, 35.24%, 45.78%, and 0.53% at Wangdaohengta, indicating concentration in medium-to-high risk ranges. The validation at Longtouguai Station showed an error RSME of 0.0630 and an R² of 0.905, confirming the reliability of the model framework. These results indicate that the proposed framework can effectively capture multivariate flood dependencies and provide a scientific basis for flood control design, risk zoning, and emergency management of small and medium rivers in arid and semi-arid regions. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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22 pages, 4494 KB

Open AccessArticle

Risk-Driven Multi-Objective Synergistic Optimization of Grey-Green Infrastructure in High-Density Urban Areas

by Houying Xin, Soon-Thiam Khu, Xiaotian Qi, Pei Yu and Mingna Wang

Water 2026, 18(8), 934; https://doi.org/10.3390/w18080934 - 13 Apr 2026

Viewed by 491

Abstract

High-density urban areas face a critical trade-off between limited land resources and intensifying flood risks. This study develops a grey-green infrastructure (GGI) optimization framework that integrates hazard–exposure–vulnerability (H-E-V) risk assessment, surrogate modelling, and NSGA-III to simultaneously minimize cost, maximize flood control, and enhance water environmental benefits. The Suqian City case study reveals: (1) Grey-green coupling significantly outperforms single green infrastructure (GI), providing an additional 7.07–23.34 percentage points in flood risk control rate (FRCR). While GI reaches a performance bottleneck at 78.59% FRCR under extreme events, the GGI configuration maintains a high efficiency of >92.74%. (2) Risk-informed spatial targeting effectively reclassifies urban vulnerability. Under a 20-year return period, high-risk and medium-high risk areas are reduced by 80.99% and 52.15%, respectively. The validated surrogate models ensure high optimization efficiency with

R^{2}

values exceeding 0.85. This framework provides a methodologically transferable decision-support tool for sponge city construction, demonstrating that strategic spatial allocation is as vital as infrastructure capacity for urban flood risk management. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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32 pages, 13227 KB

Open AccessArticle

Multifractal Analysis of Monthly Precipitation in a Semi-Arid Region of Central Mexico: Guanajuato, 1981–2016

by Jorge Luis Morales Martínez, Victor Manuel Ortega Chávez, Guillermo Sosa-Gómez, Juana Edith Lozano Hernández, Xitlali Delgado-Galvan and Juan Manuel Navarro Céspedes

Water 2026, 18(8), 911; https://doi.org/10.3390/w18080911 - 11 Apr 2026

Cited by 1 | Viewed by 518

Abstract

This study characterizes the multifractal structure of monthly precipitation in the semi-arid state of Guanajuato, Mexico, using Multifractal Detrended Fluctuation Analysis with quadratic detrending (MFDFA-2). We analyze 65 quality-controlled meteorological stations covering the period 1981–2016. All series exhibit multifractality, with generalized Hurst exponents [...] Read more.

h (2) = 0.568 \pm 0.065

indicating predominantly persistent dynamics and long-term positive autocorrelation (64.6% of stations). The multifractal spectrum width (

Δ α

) ranges from 0.15 to 0.72 (mean = 0.2423), revealing substantial spatial variability in scaling complexity. K-means clustering based on multifractal features identifies the following four hydroclimatic groups: one random cluster (29.2% of stations) and three persistence-dominated clusters (70.8%), with coherent spatial organization. These findings provide new insights into the temporal scaling properties of precipitation in semi-arid regions and have important implications for water resource management and regionalized drought-risk assessment. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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25 pages, 8326 KB

Open AccessArticle

Research on Restoring Urban Flood Community Resilience Based on Hydrodynamic Models

by Mian Wang, Ruirui Sun, Huanhuan Yang, Hao Wang, Ding Jiao and Gaoqing Lv

Water 2026, 18(8), 903; https://doi.org/10.3390/w18080903 - 9 Apr 2026

Viewed by 552

Abstract

Global climate change continues to intensify, leading to an increase in extreme meteorological disasters characterized by high intensity, frequency, and extensive impact. Chinese cities are facing increasingly severe flood disaster risks. As the fundamental unit of the urban system, scientifically quantifying a community’s post-disaster recovery capacity provides a crucial basis for formulating disaster prevention and mitigation strategies. Existing research has largely focused on either quantitative resilience assessment of communities or the functional recovery of specific systems within communities, falling short of meeting the quantitative needs for assessing community functional recovery after flood disasters. Given this, this paper aims to construct a community functional recovery model based on different land use types to precisely quantify the recovery trajectory of community functions. First, the MIKE 21 two-dimensional hydrodynamic model is employed to simulate 100-year and 200-year flood scenarios, obtaining dynamic inundation data at the community scale. Subsequently, a semi-Markov process is adopted to model the recovery of individual buildings, with the aggregated building functions within the community summarized to derive building recovery curves. A road network topology model is constructed using the Space L method, and network global efficiency is applied to quantify community road functionality. Green space functional loss is quantified based on the percentage of inundated areas. Finally, calculation is performed based on the proposed dual-layer computational framework consisting of a connectivity layer and a functional layer, and the overall community functional recovery curve after the disaster is generated, thereby achieving precise quantification of the recovery process. The research findings indicate that increased disaster intensity significantly amplifies functional losses and recovery delays. Concurrently, distinct land use types exert markedly different impacts on community recovery. This study quantitatively reveals the phased dominant roles of various land use types throughout the community recovery process, providing a scientific basis for formulating phased, prioritized resilience enhancement strategies. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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20 pages, 2737 KB

Open AccessArticle

Hydro–Meteorological Coupled Runoff Forecasting Using Multi-Model Precipitation Forecasts

by Zhanyun Zhu, Yue Zhou, Xinhua Zhao, Yan Cheng, Qian Li and Weiwei Zhang

Water 2026, 18(5), 638; https://doi.org/10.3390/w18050638 - 7 Mar 2026

Viewed by 546

Abstract

Accurate runoff forecasting is essential for effective water resource management, hydropower operation, and flood risk mitigation. In this study, daily inflow runoff in the Xin’an River Basin, eastern China, was simulated using four ensemble learning models: Gradient Boosting Decision Tree (GBDT), XGBoost, CatBoost, and Stacking. Among them, the CatBoost model achieved the best performance, with a correlation coefficient (CC) exceeding 0.97, Nash–Sutcliffe efficiency (NSE) above 0.95, and reduced RMSE and MAE compared with the currently operational hydrological model. To extend the forecast lead times, two hydro–meteorological coupled models were developed by integrating the CatBoost model with a single numerical weather prediction model (EC) and a dynamically weighted multi-model ensemble precipitation forecast system (OCF). The coupled models were evaluated for lead times up to 240 h. The forecast skill value was highest within 96 h, with CC values above 0.80 and NSE around 0.50. The OCF-coupled model demonstrated improved reliability for lead times of 48–96 h, whereas the EC-driven forecasts performed better within the first 48 h. Case studies during the 2021–2022 flood seasons confirmed that the coupled framework accurately reproduced flood evolution and peak discharge dynamics, demonstrating its practical value for medium-range runoff forecasting in humid river basins. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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21 pages, 10379 KB

Open AccessArticle

Spatial Optimization of Urban-Scale Sponge Structures and Functional Areas Using an Integrated Framework Based on a Hydrodynamic Model and GIS Technique

by Mengxiao Jin, Quanyi Zheng, Yu Shao, Yong Tian, Jiang Yu and Ying Zhang

Water 2026, 18(2), 262; https://doi.org/10.3390/w18020262 - 19 Jan 2026

Cited by 2 | Viewed by 613

Abstract

Rapid urbanization has exacerbated urban-stormwater challenges, highlighting the critical need for coordinated surface-water and groundwater management through rainfall recharge. However, current sponge city construction methods often overlook the crucial role of underground aquifers in regulating the water cycle and mostly rely on simplified engineering approaches. To address these limitations, this study proposes a spatial optimization framework for urban-scale sponge systems that integrates a hydrodynamic model (FVCOM), geographic information systems (GIS), and Monte Carlo simulations. This framework establishes a comprehensive evaluation system that synergistically integrates surface water inundation depth, geological lithology, and groundwater depth to quantitatively assess sponge city suitability. The FVCOM was employed to simulate surface water inundation processes under extreme rainfall scenarios, while GIS facilitated spatial analysis and data integration. The Monte Carlo simulation was utilized to optimize the spatial layout by objectively determining factor weights and evaluate result uncertainty. Using Shenzhen City in China as a case study, this research combined the “matrix-corridor-patch” theory from landscape ecology to optimize the spatial structure of the sponge system. Furthermore, differentiated planning and management strategies were proposed based on regional characteristics and uncertainty analysis. The research findings provide a replicable and verifiable methodology for developing sponge city systems in high-density urban areas. The core value of this methodology lies in its creation of a scientific decision-making tool for direct application in urban planning. This tool can significantly enhance a city’s climate resilience and facilitate the coordinated, optimal management of water resources amid environmental changes. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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16 pages, 3166 KB

Open AccessArticle

Prophet-Based Artificial Intelligence Versus Seasonal Auto-Regressive Models for Flood Forecasting with Exogenous Variables

by Adya Aiswarya Dash and Edward McBean

Water 2025, 17(24), 3551; https://doi.org/10.3390/w17243551 - 15 Dec 2025

Viewed by 1018

Abstract

Accurate stream flow forecasting is essential for flood risk management and preparedness. This study compares two forecasting approaches: (a) the Seasonal Auto-Regressive Integrated Moving Average with Exogenous Regressors (SARIMAX), a classical statistical model, and (b) Prophet, a decomposable time-series forecasting model that incorporates seasonality and exogenous predictors. Forecasts were generated for 15-day and 3-day horizons and evaluated using uncertainty bounds, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). Results indicate that SARIMAX was less effective at capturing the observed variability, producing wide uncertainty (177.7%) and high errors (MAE = 153.73; RMSE = 207.10) with a negative R² (–4.42). At shorter horizons, its performance remained limited (uncertainty = 28.04%; MAE = 61.52; RMSE = 94.88; R² = –0.14). In contrast, Prophet achieved significantly lower uncertainty (16%), high accuracy (R² = 0.95), and exceptional performance on short-term forecasts (R² = 0.99). Conventional procedures such as SARIMAX have long been relied upon by engineers for their interpretability, and remain important as part of a strategy; however, they fail to represent nonlinear dynamics and exogenous influences now captured effectively by AI-based models. These findings highlight Prophet’s superiority across horizons and its promise for enhancing operational flood forecasting through its ability to effectively capture non-linear dynamics and exogenous influences. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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Review

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21 pages, 2952 KB

Open AccessReview

A Review of Urban Flood Disaster Chain Research: Causes, Identification, and Assessment

by Xichao Gao, Pengfei Wang, Zhiyong Yang, Weijia Liang, Wangqi Lou and Jinjun Zhou

Water 2025, 17(23), 3344; https://doi.org/10.3390/w17233344 - 22 Nov 2025

Viewed by 2548

Abstract

Urban flood disasters have become one of the most significant natural hazards under the dual pressures of rapid urbanization and intensified climate change. With the increasing interconnection among urban subsystems, these disasters often evolve into urban flood disaster chains, characterized by cascading failures across infrastructure, environment, and society. Current research hotspots mainly focus on three key aspects: the formation mechanisms, identification methods, and risk assessment approaches of urban flood disaster chains. In terms of formation mechanisms, most studies qualitatively describe the triggering and transmission processes of cascading events, revealing how interactions among hazard-inducing factors, disaster-formative environments, and disaster receptor generate chain reactions. Identification methods are categorized into four paradigms: qualitative identification based on experiential reasoning, semantic identification driven by data, structural identification through model inference, and behavioral identification using simulation modeling. Risk assessment approaches include historical disaster analysis, indicator-based evaluation models, uncertainty models, numerical simulation models, and intelligent algorithm models that integrate machine learning with physical simulations. The review finds that, due to the scarcity and heterogeneity of disaster chain event data, existing studies lack a unified quantitative framework to represent the mechanisms of urban flood disaster chains, as well as dynamic identification and assessment methods that can adapt to their evolutionary processes. Future research should focus on developing integrated mathematical paradigms, enhancing multisource data fusion and causal reasoning, and constructing hybrid models to support real-time risk assessment for urban flooding disaster chains. Full article

(This article belongs to the Special Issue "Watershed–Urban" Flooding and Waterlogging Disasters)

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