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Flood Risk Identification and Management, 2nd Edition

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 9487

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Dr. Juan Chen

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College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: reservoir operation; flood control operation; risk analysis; water resource allocation and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Floods, as one of the most common natural disasters around the world, cause serious economic loss and even human fatalities. Moreover, there are many uncertainties associated with flood forecast and management that induce risks in flood control decision making. Therefore, risk identification and management are crucial to mitigate flood hazards and disasters in river basins.

Topics of interest for this Special Issue include, but are not limited to, the following:

Understanding and methodologies for risk identification with respect to flood, flood forecast, flood control operation, and decision making;
Flood forecast and operation methodologies dealing with uncertainties and risks;
Risk analysis methods and models for flood, as well as flood forecast, operation, and decision making;
Risk management measures and methods to mitigate flood hazards and disasters considering uncertainties.

Dr. Juan Chen
Guest Editor

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Keywords

flood
flood management
flood control operation
risk identification
risk assessment
risk management
uncertainty

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Related Special Issue

Flood Risk Identification and Management in Water (6 articles)

Published Papers (8 papers)

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Research

21 pages, 7540 KB

Open AccessArticle

Runoff Simulation and Analysis in the Upper Yellow River Basin Using a Budyko–XGBoost Coupled Model

by Ning Qiu, Jia Zhang, Yongwei Liu and Xi Chen

Water 2026, 18(16), 1944; https://doi.org/10.3390/w18161944 - 9 Aug 2026

Viewed by 214

Abstract

The Upper Yellow River (UYR) basin is the predominant runoff-yielding area of the entire watershed. Accurately simulating annual runoff is crucial for water resources management. While the Budyko framework effectively captures long-term hydro-thermal equilibrium, it struggles to represent nonlinear dynamics, flow channel routing, and spatial interconnections. Here, we propose a hybrid physics- and data-driven approach by coupling the Budyko framework (Fu’s equation) with an eXtreme Gradient Boosting (XGBoost) model, integrating upstream channel routing and antecedent storage-lag features using long-term hydrologic observations for nonlinear runoff simulation and driver attribution. To resolve the feature multicollinearity on machine learning attributions, input variables were consolidated into three groups: precipitation driven, evaporation limitation, and flow storage lag. The results demonstrate that the Budyko–XGBoost coupled model enhances annual runoff prediction accuracy compared to the standalone Fu equation and pure XGBoost, raising the coefficient of determination (R²) to 0.63–0.86 (mean R² = 0.75) and capturing both nonlinear dynamics and turning points, alongside reductions of 6.2% in the mean RMSE (18.77 mm) and 11.0% in the MAE (13.66 mm) compared to the pure XGBoost model (mean R² = 0.70, RMSE = 20.01 mm, and MAE = 15.35 mm). Group-level SHAP attributions reveal that flow storage-lag drivers (R_lag and

R_{lag}^{'}

) exert a primary control on runoff evolution across all the stations. Spatially, secondary drivers exhibit heterogeneity: in relatively humid, energy-limited regions (Maqu), high precipitation promotes positive runoff deviations, whereas in arid/semi-arid, water-limited reaches (e.g., Guide, Xunhua, Xiaochuan, and Lanzhou stations), high precipitation is absorbed by severe soil moisture deficits and reservoir interception, exerting a negative effect on runoff deviation. Full article

(This article belongs to the Special Issue Flood Risk Identification and Management, 2nd Edition)

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38 pages, 22896 KB

Open AccessArticle

Ensemble Multi-Criteria Flood Susceptibility Modelling with Spatial Uncertainty Quantification: A Provincial-Scale Application in KwaZulu-Natal, South Africa

by Phumzile Nosipho Nxumalo, Nicholas Byaruhanga, Phindile T. Z. Sabela-Rikhotso, Daniel Kibirige and Philile Mbatha

Water 2026, 18(15), 1912; https://doi.org/10.3390/w18151912 - 5 Aug 2026

Viewed by 173

Abstract

Flooding remains a major hydro-meteorological hazard in KwaZulu-Natal, yet province-wide susceptibility assessments incorporating modelling uncertainty are limited. This study develops an ensemble multi-criteria flood susceptibility framework integrating Analytical Hierarchy Process (AHP), fuzzy logic transformation, and frequency ratio (FR) modelling within a cloud-based geospatial environment. Twelve hydro-geomorphological and environmental conditioning factors, including topography, rainfall, land cover, hydrology, and soil proxies, were normalized using percentile scaling. Three independent flood susceptibility models were generated and combined using ensemble mean aggregation, while pixel-wise standard deviation quantified spatial uncertainty. Model validation employed a 10-year historical flood inventory (2015–2025) comprising 65 documented flood locations. The ensemble flood susceptibility index (FSI) ranged from 0.05 to 1.00, with moderate susceptibility zones covering 51.08% of the province. High and very high susceptibility classes occupied 8.36%, indicating spatially concentrated but hydrologically significant risk hotspots. Uncertainty analysis showed low inter-model variability (0.00–0.11), demonstrating strong methodological stability. Validation results confirmed that 73.85% of historical flood points were located within high susceptibility zones, with over 90% captured within overall susceptible classes. The study introduces a hybrid deterministic–fuzzy–probabilistic ensemble modelling approach combined with pixel-level uncertainty mapping and scalable cloud computation. Findings support disaster risk reduction, urban and catchment planning, and early warning system optimization in flood-prone regions. The framework provides a transferable methodology for data-limited environments requiring reliable and uncertainty-aware flood hazard assessment. Full article

(This article belongs to the Special Issue Flood Risk Identification and Management, 2nd Edition)

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27 pages, 11473 KB

Open AccessArticle

Rising Lake Levels as a Distinct Flood Hazard: An Integrated Risk Assessment Framework for Semi-Arid Inland Lake Basins

by Nelly Cherono Kiplangat, Luke Olang, George Thumbi, Gabriel Stecher and Mathew Herrnegger

Water 2026, 18(15), 1887; https://doi.org/10.3390/w18151887 - 3 Aug 2026

Viewed by 292

Abstract

Flooding associated with rising inland lake levels represents a distinct and understudied hazard compared to conventional river or coastal flooding. Unlike riverine floods, lake inundation develops gradually and persists over extended periods, yet integrated flood risk assessments specifically addressing this phenomenon remain scarce. This study uses Lake Baringo in Kenya’s Rift Valley as a case study, a lake that has experienced exceptional water level rises of nearly 10 m since 2010, to assess flood risk through the integration of scenario-based flood hazard modelling, spatial exposure assessment, and household vulnerability analysis. Three lake level scenarios were developed, spanning from maximum observed conditions to a worst-case threshold at the lake’s sill point. Results show that under the worst-case scenario, the lake area could expand by approximately 64%, potentially exposing nearly 18,000 people and 156 km of road infrastructure. A Flood Vulnerability Index (FVI) ranging from 0.58 to 0.73 indicated consistently high socioeconomic vulnerability across all shoreline communities, with vulnerability accounting for 42–53% of overall flood risk across scenarios. Flood risk was highest along the flat southern shoreline, where topography amplifies lateral inundation extent. The findings highlight the importance of integrating lake level dynamics into spatial planning and flood risk management, and the need for improved monitoring, livelihood diversification, and institutional coordination in semi-arid inland lake regions facing increasing hydro-climatic variability. Full article

(This article belongs to the Special Issue Flood Risk Identification and Management, 2nd Edition)

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24 pages, 8694 KB

Open AccessArticle

Research on Stage-Divided Flood-Limited Water Level Under Pre-Release Rules During Flood Season

by Hui Yu, Xinggen Liu, Changyan Li, Yongwen Wang and Qiang Hu

Water 2025, 17(23), 3348; https://doi.org/10.3390/w17233348 - 22 Nov 2025

Viewed by 1076

Abstract

Flood Limited Water Level (FLWL) serves as the core control parameter for the synergistic optimization of flood control operation and beneficial water utilization efficiency in reservoirs during the flood season. Addressing the critical issue of insufficient adaptability in static control schemes, this study innovatively proposes a staged dynamic FLWL regulation model based on pre-release rules. This methodology combines hydrometeorological division theory with frequent flood control mechanisms and establishes a dual-threshold control equation with safe pre-release discharge (q_pre) and effective pre-release duration (t_pre) as sensitive factors. The dynamic FLWL scheme is designed to ensure that no additional risk is imposed on the reservoir and its upstream/downstream regions, and it incorporates a set of hierarchical rules for the strategic pre-release and standard safety modes. Taking the Wuxikou Reservoir in Jiangxi Province as a case study, the safe pre-release discharge value under regular flood conditions and the effective pre-release duration are determined. Additionally, a dynamic FLWL control model is developed according to the reservoir’s characteristics. The verification results demonstrate the significant benefits of the dynamic FLWL model in reducing peak water levels and shortening flood duration. Compared with the original operation plan, the proposed model effectively lowers the maximum water level of the reservoir by 10% and simultaneously shortens the duration of high water levels by nearly 24 h. The research results provide a reference for the efficient utilization of water resources in reservoir basins in monsoon humid areas. Full article

(This article belongs to the Special Issue Flood Risk Identification and Management, 2nd Edition)

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19 pages, 8715 KB

Open AccessArticle

Research on Optimizing Rainfall Interpolation Methods for Distributed Hydrological Models in Sparsely Networked Rainfall Stations of Watershed

by Dinggen Feng, Yangbo Chen, Ping Jiang and Jin Ni

Water 2025, 17(22), 3237; https://doi.org/10.3390/w17223237 - 13 Nov 2025

Cited by 1 | Viewed by 1215

Abstract

Rainfall stations in small and medium-sized river basins in China are sparsely distributed and unevenly spaced, resulting in insufficient spatial representativeness of precipitation data and posing challenges to the accuracy of flood forecasting. Spatial interpolation methods for rainfall data are a key tool for bridging the gap between discrete rainfall station data and continuous surface rainfall data; however, their applicability in flood forecasting for small and medium-sized river basins with sparse rainfall stations requires further investigation. Taking the Hezikou basin as the study area and focusing on the Liuxihe model, this study analyzes the distribution characteristics of the seven rainfall stations in the basin and the interpolation effectiveness of the original Thiessen Polygon Interpolation (THI) method in the model. It compares and discusses the applicability of the THI, the Inverse Distance Weighting (IDW) method, and the Trend Surface Interpolation (TSI) method in flood forecasting for this basin. Different rainfall station distribution scenarios (full coverage, upstream only, downstream only, single rainfall station) were set up to study the performance differences in each method under extremely sparse conditions. The results indicate that, under the sparse condition of only 0.0068 rainfall stations per square kilometer in the Hezikou basin, IDW interpolation yields the best flood forecasting results, with model Nash–Sutcliffe Efficiency (NSE) values all above 0.85, Kling–Gupta Efficiency (KGE) values exceeded 0.78, and the Peak Relative Error (PRE) was controlled within 0.09, significantly outperforming THI and TSI. Additionally, as rainfall station sparsity increased, IDW exhibited the smallest decline in performance, showing a weak negative correlation (p ≤ 0.05) between prediction performance and rainfall station sparsity, demonstrating stronger adaptability to sparse scenarios. When station information is extremely limited, IDW performs more stably than THI and TSI in terms of certainty coefficients (NSE, KGE) and flood peak error control. The Inverse Distance Weighting method (IDW) can provide reliable rainfall spatial interpolation results for flood forecasting in small and medium-sized basins with sparse rainfall stations. Full article

(This article belongs to the Special Issue Flood Risk Identification and Management, 2nd Edition)

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35 pages, 7892 KB

Open AccessArticle

Nature-Based Solutions for Flood Risk Reduction in Lethem and Tabatinga, Guyana: An Integrated Approach

by Temitope D. Timothy Oyedotun, Esan Ayeni Hamer, Linda Johnson-Bhola, Stephan Moonsammy, Oluwasinaayomi Faith Kasim and Gordon A. Nedd

Water 2025, 17(16), 2435; https://doi.org/10.3390/w17162435 - 18 Aug 2025

Cited by 1 | Viewed by 3136

Abstract

This study presents a comprehensive assessment and strategic framework for implementing Nature-Based Solutions (NBSs) to mitigate flooding in Lethem and Tabatinga, Region 9 of Guyana. The communities are increasingly vulnerable to flooding due to climate variability, hydrological dynamics, and socio-economic factors. A mixed-methods approach, comprising hydrological modelling and observation, a questionnaire survey with a sample of households in both communities, and interviews with municipal administrators, was utilised to acquire data for the study. The study utilised the Statistical Package for Social Sciences (SPSS) to analyse the socio-economic impacts of flooding in the two communities. The results revealed that recent events, such as the significant floods of 2022, have prompted an urgent need for sustainable management strategies. Community engagement efforts, supported by data analysis through remote sensing technology, identified flood-prone areas and vulnerable populations, including women, the elderly, and persons with disabilities. Chi-Square testing was conducted to determine mutual dependence between the communities’ livelihood activities and disruptions to income and working days, and their ability to deal with flooding. Based on the results, the farmers were the group that the highest inability to deal with flooding. Existing infrastructure, including drainage systems and emergency response initiatives led by the Civil Defence Commission, has contributed to improved flood management; however, limitations persist, particularly in urban planning and land use practices. This study underscores the detailed process of implementing and adopting NBS approaches, such as flood conveyance solutions and water storage and bio-retention solutions. These solutions can improve water quality, preserve ecosystems, and enhance community well-being while reducing flood risks. Applying these solutions in the targeted communities promises to bolster ecological resilience, support climate adaptation, and reduce the incidence and the impact of floods in the sampled communities. Full article

(This article belongs to the Special Issue Flood Risk Identification and Management, 2nd Edition)

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19 pages, 1167 KB

Open AccessArticle

A Reservoir Group Flood Control Operation Decision-Making Risk Analysis Model Considering Indicator and Weight Uncertainties

by Tangsong Luo, Xiaofeng Sun, Hailong Zhou, Yueping Xu and Yu Zhang

Water 2025, 17(14), 2145; https://doi.org/10.3390/w17142145 - 18 Jul 2025

Cited by 5 | Viewed by 1415

Abstract

Reservoir group flood control scheduling decision-making faces multiple uncertainties, such as dynamic fluctuations of evaluation indicators and conflicts in weight assignment. This study proposes a risk analysis model for the decision-making process: capturing the temporal uncertainties of flood control indicators (such as reservoir maximum water level and downstream control section flow) through the Long Short-Term Memory (LSTM) network, constructing a feasible weight space including four scenarios (unique fixed value, uniform distribution, etc.), resolving conflicts among the weight results from four methods (Analytic Hierarchy Process (AHP), Entropy Weight, Criteria Importance Through Intercriteria Correlation (CRITIC), Principal Component Analysis (PCA)) using game theory, defining decision-making risk as the probability that the actual safety level fails to reach the evaluation threshold, and quantifying risks based on the First-Order Second-Moment (FOSM) method. Case verification in the cascade reservoirs of the Qiantang River Basin of China shows that the model provides a risk assessment framework integrating multi-source uncertainties for flood control scheduling decisions through probabilistic description of indicator uncertainties (e.g., Zmax1 with μ = 65.3 and σ = 8.5) and definition of weight feasible regions (99% weight distribution covered by the 3σ criterion), filling the methodological gap in risk quantification during the decision-making process in existing research. Full article

(This article belongs to the Special Issue Flood Risk Identification and Management, 2nd Edition)

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21 pages, 3192 KB

Open AccessArticle

Flood Regional Composition Considering Typical-Year and Multi-Site Flood Source Characteristics

by Yun Wang, Sirui Zhong, Shenglian Guo, Bokai Sun and Xiaoya Wang

Water 2025, 17(7), 1106; https://doi.org/10.3390/w17071106 - 7 Apr 2025

Viewed by 989

Abstract

The construction and operation of reservoirs have significantly altered the downstream flow regime, and the flood regional composition (FRC) method has been widely used to estimate design flood considering the regulation impact of upstream cascade reservoirs. This paper proposes a novel flood regional composition based on the proper orthogonal decomposition (FRC-POD) method that comprehensively takes into account typical-year flood differences and the multi-site flood source characteristics. The proposed method is applied at Cuntan hydrologic station in the upper Yangtze River and compared with the typical-year flood composition (TYFC) method and the most likely flood regional composition (MLFRC) method. The results show the following: (1) The proposed FRC-POD method can identify main flood sources in the design section and pay more attention to floods from the mainstream and the uncontrolled interval basin. (2) Compared with the originally designed values, the 1000-year design peak discharge and 3 d, 7 d, and 15 d flood volumes estimated by the FRC-POD method are decreased by 41.3%, 40.2%, 36.6%, and 34.7%, respectively. (3) Current FRC methods depend on the selected typical-year flood events and have several solutions, while the proposed method has only one final solution, which is more reasonable in practical application. (4) A comparative study proves that the FRC-POD method could obtain rational design flood estimation and is worth further study. Full article

(This article belongs to the Special Issue Flood Risk Identification and Management, 2nd Edition)

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