As climate change intensifies hydrologic extremes, the need for near real-time urban flood prediction becomes critical. Pluvial flooding occurs when intense rainfall overwhelms urban drainage systems, involving complex hydrodynamic interactions between surface runoff and subsurface sewer flow—known as dual drainage. Capturing the spatiotemporal evolution of these processes requires detailed representations of flow patterns, inundation propagation, and runoff accumulation. However, physics-based hydrodynamic models, while effective at resolving the fine-scale dynamics of flood events, face significant computational limitations, particularly for large urban areas or high-resolution domains. To address this challenge, we propose a deep learning-based urban flood prediction model that integrates surface runoff dynamics with sewer network interactions. The model is developed using training data generated from physics-based 1D-2D hydrodynamic simulations that capture interactions between 2D surface flow and 1D sewer network flow. The Oncheoncheon River catchment in Busan, South Korea—a region frequently impacted by urban flooding—serves as the study area. Various synthetic rainfall scenarios are used to train the model, ensuring its ability to generalize across different extreme rainfall events. Model validation against historical flood events shows that the deep learning model accurately predicts flood evolution patterns while significantly reducing computational time compared to traditional hydrodynamic models. This study demonstrates the potential of deep learning-based approaches to enhance real-time urban flood prediction and provides valuable insights for developing efficient, data-driven disaster management strategies.

Integrating deep learning techniques into hydrology has opened a new way to improve urban flood modeling, with various solutions being developed to address urban flood problems driven by climate change and urbanization. However, predicting urban inundation in near real-time for large urban areas remains challenging due to computational demands and limited data availability. This work proposes a deep learning-based super-resolution framework that enhances the spatial resolution of process-based urban flood modeling outputs using convolutional neural networks (CNNs) while improving computational efficiency. This study investigates the interaction between deep learning model architecture and the underlying physical processes to improve prediction accuracy and robustness in urban pluvial flood mapping. The methodology will be applied to various urban flood scenarios, including extreme rainfall events and hurricane-induced flooding, and its performance will be evaluated through quantitative indicators and sensitivity analyses. The applicability and scalability of this model will also be discussed. In particular, strategies to enhance model reliability and integrate additional hydrological information under extreme conditions will be explored. The study will further address uncertainty estimation in deep learning-based super-resolution models and scalability challenges associated with super-resolution approaches for large-scale flood simulations. The findings aim to demonstrate the potential of deep learning as an innovative tool in hydrological modeling and to enable more effective flood risk management strategies.

Accurate high-resolution precipitation is critical for hydrological modelling, climate impact assessment, and flood risk analysis, yet reanalysis products like ERA5 often lack the necessary spatial detail required at regional scales. This study investigates machine learning-based super-resolution techniques for precipitation downscaling, specifically examining scale-dependency and uncertainty.We test several downscaling strategies, including convolutional neural networks with channel‑attention mechanisms and generative diffusion models. Precipitation fields are downscaled from coarse-resolution ERA5 inputs (0.25° resolution) to finer spatial resolutions using gridded observational datasets as reference: E‑OBS (0.125°) for pan‑European evaluation and, for selected regions, higher‑resolution products such as EMO‑1 (~1 km). By considering multiple scale factors, we adopt a scale‑aware framework that quantifies how downscaling skill and the associated uncertainty in super-resolution machine learning methods vary with spatial resolution and with the choice of reference dataset.Model evaluation combines conventional accuracy metrics with diagnostics of field structure, focusing on spatial heterogeneity, intensity‑dependent behaviour (including extremes), and robustness across seasons and climatic regimes. We also discuss how scale‑dependent changes in precipitation variability and spatial structure can inform uncertainty characterisation for machine‑learning downscaling and guide its use in regional hydrological modelling and flood‑risk assessments across Europe.

Accurate flood risk assessments are critical for mitigating the impacts of pluvial flooding in densely populated urban areas. However, conventional flood modeling approaches often face limitations due to the lack of measurement information. To address this challenge, we develop, implement, and evaluate a novel framework that integrates crowdsourced data, such as citizen observations, with process-based modeling to enhance the accuracy of urban flood risk assessments. The proposed method utilizes indicator co-kriging techniques to merge citizen-sourced data with auxiliary variables, including inundation maps generated from 1D-2D urban flood models driven by high-resolution radar rainfall estimates. The framework is applied to the Oncheon River catchment in Busan, South Korea, a region highly vulnerable to pluvial flooding due to its urbanization and complex hydrological conditions. To evaluate the method, synthetic citizen observation data were generated based on inundation maps. These synthetic experiments assess the influence of the spatial distribution and quality of citizen observations on urban flood risk predictions. This study examines the integration of citizen observations into urban flood modeling workflows to address uncertainties in models and observations. In particular, we investigate the extent to which distributed citizen observations enhance prediction accuracy and analyze the effects of model bias on the reliability of flood risk assessments. The study quantitatively evaluates the effects of citizen data quality and spatial distribution on the accuracy of urban flood risk mapping. Furthermore, a sensitivity analysis is conducted for co-kriging parameters, focusing on semivariogram model selection and its influence on prediction accuracy.