ABSTRACT Knowing the status of emergency capacity for disaster risk reduction helps the government and stakeholders to minimize vulnerabilities and disaster risk. However, there is no widely applied methodology for emergency capacity assessment. This study develops a multidimensional framework integrating vulnerability, susceptibility, and adaptability assessments to evaluate regional flood emergency capacity. The information quantity method, the Maxent model, and the entropy‐weighted TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) are used to assess the regional vulnerability, susceptibility, and adaptability of disaster emergency capacity, respectively. These capacity features are then integrated into the overall disaster capacity using the analytic hierarchy process (AHP)‐entropy method. The exemplary application in Zengcheng District (ZCD), China shows the effectiveness of the proposed method. The identified vulnerability hotspots of flood over ZCD are located in Xintang and southern Yongning, driven by their high road density, GDP per capita, residential disposable income, and high population density. Susceptibility modeling revealed land use, Vegetation Index, and elevation as the dominant drivers, with Xintang exhibiting the highest risk. Adaptability assessments highlighted superior resilience in Paitan, Zhongxin, and Shitan, driven by enhanced disaster‐preparedness investments. Integrated analysis prioritized emergency resource storage capacity, drainage capacity, and emergency rescue capacity as critical determinants of flood response capacity. Lower emergency capacity is concentrated in Zhongxin, Xiaolou, Zhenguo, and Zhucun. This paper constructs a multidimensional assessment framework covering the entire chain of pre‐disaster prevention, in‐disaster response, and post‐disaster recovery, to systematically evaluate a region's comprehensive emergency capacity, providing potentially feasible insights for urban areas vulnerable to flood disasters.

ABSTRACT Urban road system resilience is critical for managing extreme flood events, yet existing assessments predominantly focus on static indicators that capture inherent system capacity rather than dynamic responses to disturbances. This study proposes a dynamic resilience analysis framework that tracks urban road system performance under progressive flood intensities using three core indicators: road service availability (infrastructure condition), road network accessibility (user impact), and recovery speed (managerial effectiveness). Applied to Xiamen, a coastal city in China, the framework simulates four flood inundation scenarios (0.335, 1, 3, and 9 m) to monitor system‐flood interactions and identify critical thresholds. Results reveal that structural damage does not necessarily imply functional disruption, and high road density provides service redundancy, maintaining user accessibility even when some roads are flooded. The analysis identifies a current resilience threshold at 1 m inundation, which can be elevated through infrastructure upgrades. These findings demonstrate that dynamic assessment complements static approaches by revealing critical flood thresholds and vulnerable zones to enable targeted interventions—from emergency response prioritization to long‐term adaptation planning. The proposed framework offers a transferable methodology for coastal cities to diagnose their own resilience thresholds and guide evidence‐based investments, with minimal data requirements making it applicable in data‐scarce contexts.

ABSTRACT The work proposes the development of a Lattice Boltzmann (LB) Cascaded collision operator (CO) model with two unexplored main purposes: reproducing the dynamics of multilayer shallow water flows and modeling urban floods with a porosity approach. The Cascaded CO has been already proved to enhance accuracy and stability in low‐viscosity flows, like water, outperforming other mesoscopic models. Its adaptation to a multilayer model allows capturing vertical variations in hydrodynamic quantities of large‐scale geophysical flows. To simulate n‐layer flow dynamics, the model CaLB‐N solves n shallow water equations, one for each layer, with coupling terms representing the mutual interactions between layers. Capturing localized flow features in large scale flood events is still a challenge: in complex morphology like urban areas, the dimension of the city dominion is many orders of magnitude larger than buildings, thus they can hardly be taken into account. The introduction of a new sub grid porosity‐based LB model (CaLB‐P) allows facing this issue, considering in a specific source term the portion of a computational cell that is available for flow. The model is implemented in C and parallelized to achieve computational speeds suitable for large‐scale applications. This optimization allows for high‐resolution simulations while substantially reducing processing time. The applicability of the suggested model for hydraulic engineering is explored, paving the way for new approaches to realistically model complex water flows.

ABSTRACT Flood forecasting has undergone significant development over the past two decades, with researchers exploring a range of innovative approaches to quantify both the hazards and risks associated with flood events across a range of magnitudes and frequencies. Recent advances in computing power and data availability have enhanced prediction capabilities, shifting the field towards more sophisticated, data‐driven techniques. This paper reviews these methodologies, tracing their evolution from classical models to modern data‐driven approaches, while highlighting recent progress in data sources and data integration. Our bibliometric analysis indicates that the number of data‐driven flood modelling approaches reported in the literature has increased by approximately fourfold since 2015, underscoring the accelerating trend in flood forecasting research. The focus is on data‐centric techniques, innovations, and ongoing challenges, with special emphasis on unconventional data sources (e.g., commercial microwave links) and their role in flood forecasting. By reviewing traditional and cutting‐edge methods, the discussion identifies underexplored gaps and emerging opportunities within the existing literature, highlighting the trajectory that this fast‐emerging field is following.

ABSTRACT Urban areas are increasingly vulnerable to pluvial floods, a threat that is exacerbated by climate change and urbanisation. Current risk assessment methods often lack scalability and practical applicability across diverse urban contexts and varying capacities. This study introduces a novel comprehensive framework for multi‐level pluvial flood risk assessment, which provides a systematic approach to evaluating hazard, exposure, and vulnerability across citywide, catchment, and sub‐catchment levels. By integrating geographic information systems with adaptive modelling techniques, the framework enables resource‐efficient prioritisation and dynamic risk management tailored to urban development and climate change scenarios. Case studies based on Estonian cities illustrate how this approach can guide effective flood risk mitigation at various spatial scales.

ABSTRACT The study prioritizes sub‐watersheds (SWs) from the Taleqan Watershed in Iran based on Flood Generation Potential (FGP) using Multi‐Criteria Decision Making (MCDM) approaches, including Condorcet, Borda scoring, Fallback bargaining algorithms (Game Theory [GT]), and Best‐Worst Method (BWM), and compares the results with observed discharge data. Based on a review of reliable resources, 30 geo‐environmental criteria were identified to prioritize FGP. Quantitative values for each criterion were then calculated for all SWs. The Principal Component Analysis (PCA) was employed to reduce the FGP conditioning criteria. Next, using the decision matrix based on the selected criteria, three GT algorithms and BWM were applied, and the SWs were prioritized accordingly. FGP maps were then created using all four approaches, with results categorized into five classes, namely, very low, low, moderate, high, and very high. Finally, the prioritization patterns from the MCDM approaches were compared against observed discharge data from all 18 SWs to select the optimal method. The results from all four MCDM approaches indicated that SW5 had the highest potential for flood generation. The BWM approach classified about 56% of the study watershed as having low or very low FGP. In contrast, GT identified only 45% of the area in this category, with approximately 39% of SWs classified with high or very high FGP. The comparison of means using the One‐Way ANOVA test revealed insignificant differences between the MCDM approaches and the observed discharge data. However, the similarities between the results of the Condorcet, Borda scoring, Fallback bargaining, and BWM approaches, compared with the observed discharge data, were 27.78%, 27.78%, 33.33%, and 44.44%, respectively. Accordingly, the BWM approach with the highest similarity was chosen as the optimal model. The summary indicated that the BWM approach outperformed the GT algorithms in mapping flood generation compared to the observed flood discharge data.

ABSTRACT Flood events are the most common weather‐related hazard in Europe and Spain, comprising 41% of such events between 2001 and 2020. Mediterranean catchments, with steep slopes and short river courses, are particularly vulnerable to intense convective rainfall, often triggering flash floods. To address this risk, the University of the Balearic Islands developed RiscBal, an innovation ecosystem featuring a high‐resolution Multi‐Hazard Early Warning System. Its core, RiscBal‐Warnings, integrates real‐time data from 56 discharge‐monitoring stations and 32 rainfall/soil moisture stations, forming the RiscBal‐Control network. These stations are positioned in high‐risk and historically flood‐prone areas. This paper focuses on the innovation management behind RiscBal's design and integration into regional governance. Through interdisciplinary collaboration, stakeholder co‐creation, and institutional alignment, RiscBal demonstrates how managing innovation can translate scientific knowledge into actionable, context‐sensitive solutions. The system's performance was tested during the August 15, 2024, flash flood in Es Mercadal, Menorca, providing critical lead time for emergency response. However, issues like telecommunication gaps and early‐stage hydrological modeling prompted improvements, including redundant systems. Riscbal's modular and interoperable design supports polycentric risk governance and continuous feedback between academia, government, and municipalities. Adaptable to other Mediterranean and global flood‐prone regions, it offers a replicable framework for climate resilience. The paper also explores adoption challenges, emphasizing trust, usability and resource constraints.

ABSTRACT Flooding is a disruptive and devastating natural hazard for communities all around the world. To combat the negative effects of flooding, it has become a global priority to implement and research flood early warning systems (FEWS). However, previous research did not comprehensively examine both the technological and social dimensions of FEWS nor the knowledge of their availability and status around the world. To address these gaps, this paper completes a narrative review and synthesizes best practices in FEWS warning and system design, and introduces a “FEWS Around the World” repository which catalogs 3–4 examples per region across six continents. Our analysis shows that while most FEWS include design features outlined in the literature such as timeliness, human capacity, and integration, fewer implement novel components related to impact‐based warnings, participatory science, bottom‐up approaches, advanced technology, and maintaining a preparedness fund, highlighting gaps and opportunities for improvement. Examples demonstrate how these characteristics manifest in diverse contexts, from community‐based systems in Nepal to AI‐driven systems like Google's Flood Hub. By bridging design principles with observed global practices, this paper is designed to aid researchers, practitioners, and communities in people‐centered FEWS development, implementation, and operation, improving resilient flood risk management.

ABSTRACT Discharge modeling utilizing novel deep learning techniques is highly recommended due to their high efficacy in modeling nonlinear time series. In this study, a hybrid discharge model is developed, termed wavelet‐based long short‐term memory (WLSTM), by integrating wavelet transform and Long Short‐Term Memory (LSTM). This technique focuses on improving discharge prediction by effectively denoising the input data and amplifying the most relevant temporal patterns for the model. However, since LSTM models depend on underlying data patterns, their performance can be significantly affected by the intensity of nonlinearity in hydrological time series. To address this, we introduce a novel method using ApEn (Approximate Entropy) to quantify nonlinearity intensity. Then, we applied Fuzzy Clustering to classify nonlinearity into weak, moderate, and high nonlinearity categories. The performance of both LSTM and WLSTM is evaluated using daily discharge data from 16 hydrometric stations in Hesse, Germany, for the period 2000–2017. The results notably show a remarkable reduction of 66.43% for Root Mean Squared Error (RMSE) and of 45.49% for Mean Absolute Percentage Error (MAPE) for WLSTM performance compared to LSTM. Furthermore, WLSTM increased R ‐squared ( R 2 ) by 2.06%. This research acknowledges that there is a direct correlation between the streamflow nonlinearity and WLSTM accuracy. With increasing nonlinearity intensity, WLSTM captures the complexity of streamflow patterns more effectively. RMSE is 0.1194, 0.0836, 0.0547 and R 2 is 0.9976, 0.9990, 0.9994 for weak, moderate, and high nonlinearity groups, respectively. This study highlights the importance of streamflow nonlinearity analysis in improving flood forecasting and risk management.