The prediction of tidal waves is essential for improving not only our understanding of the hydrological cycle at the boundary between the land and ocean but also energy production in coastal areas. As tidal waves are affected by various factors, such as astronomical, meteorological, and hydrological effects, the prediction of tidal waves in estuaries remains uncertain. In this study, we present a novel method that can be used to improve short-term tidal wave prediction using a fixed-lag smoother based on sequential data assimilation (DA). The proposed method was implemented for tidal wave predictions of the estuary of the Nakdong River. As a result, the prediction accuracy was improved by 63.9% through DA and calibration using regression. Although the accuracy of the DA diminished with the increasing forecast lead times, the 1 h lead forecast based on DA still showed a 44.4% improvement compared to the open loop without DA. Moreover, the optimal conditions for the fixed-lag smoother were analyzed in terms of the order of the smoothing function and the length of the assimilation window and forecast leads time. It was suggested that the optimal DA configuration could be obtained with the 8th-order polynomial as the smoothing function using past and future DA assimilation windows assimilated 6 h or longer.

In this study, we analyzed the impact of model spatial resolution on streamflow predictions, focusing on high-resolution scenarios (<1 km) and flooding conditions at catchment scale. Simulation experiments were implemented for the Geumho River catchment in South Korea using Weather Research and the Forecasting Hydrological Modeling System (WRF-Hydro) with spatial resolutions of 100 m, 250 m, and 500 m. For the estimation of parameters, an automatic calibration tool based on the Model-Independent Parameter Estimation and Uncertainty Analysis (PEST) method was utilized. We assessed the hydrological predictions across different spatial resolutions considering calibrated parameters, calibration runtime, and accuracy of streamflow before and after calibration. For both Rainfall Events 1 and 2, significant improvements were observed after event-specific calibration in all resolutions. Particularly for 250 m resolution, NSE values of 0.8 or higher were demonstrated at lower gauging locations. Also, at a 250 m resolution, the changes in the calibrated parameter values ($REFKDT$) were minimized between Rainfall Events 1 and 2, implicating more effective calibration compared to the other resolutions. At resolutions of 100 m and 500 m, the optimal parameter values for the two events were distinctively different while more computational resources were required for calibration in Event 2 with drier antecedent conditions.

This study investigates the impact of spatial resolution on urban pluvial flood modelling, focusing on the implications of high-resolution topography data for flood inundation mapping. We employed the physically-based urban flood model, H12, to simulate a past pluvial flooding event that occurred from 6-8 December 2015 in a sub-watershed in Portland, Oregon. We compared the temporal evolution of inundation maps from a benchmark 1 m resolution model with those from coarser resolutions (ranging from 2 m to 50 m). We evaluated the accuracy of the modelling results using water depth error measures and grid-based inundation extent error metrics to investigate the main causes of the differences. Our results show that the accumulated inundated water volume significantly increases with coarser grid resolutions, leading to larger discrepancies in pluvial flood inundation maps across the modelling domain. Additionally, coarser-resolution grids result in the overprediction of inundation extents, except in the 2 m model. Accuracy metrics decrease with coarser modelling grid resolutions; the hit rate (H) and critical success index (C) both decline as resolution coarsens. Notably, H rapidly drops below 0.7 and C to 0.5 or less when the resolution exceeds 7 m. Furthermore, multi-directional flow path analyses reveal that the spatial ranges of inundation expand as grid resolution becomes coarser. On the other hand, computational efficiency improves as grid resolutions become coarser. One of the main causes of accuracy degradation is the inherent limitation of coarse-resolution data in depicting topographical details. Although there is no single optimal resolution that suits all urban conditions, it is important to note that grid resolutions must be fine enough to accurately represent major urban features, such as road networks, which affect water flows during flood events.

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.

High-resolution urban flood modelling is increasingly critical for disaster mitigation, but simulations remain computationally expensive, particularly when applying meter-scale grids over large spatial domains. Such computational constraints often restrict the practical use of high-resolution simulations in operational forecasting and scenario-based analyses. To address this challenge, this study investigates the use of multi-GPU acceleration to improve computational efficiency in large-scale urban flood simulations. We present a multi-GPU implementation of the H12 2D urban flood model based on an MPI–OpenACC framework. The H12 2D model is a physics-based two-dimensional urban flood model that supports CPU-based parallel execution and is extended here to GPU architectures. The proposed approach employs directive-based parallelization. This approach allows a single code base to be executed on both CPU and GPU systems without extensive code modification. Domain decomposition is managed using MPI, while computationally intensive kernels are offloaded to GPUs through OpenACC directives. This hybrid design ensures portability across heterogeneous high-performance computing environments and enables efficient use of multiple GPUs. We evaluate performance using spatial resolutions ranging from 1 to 20 m over two contrasting domains: an urban catchment in downtown Portland, Oregon (USA), and a downstream reach of the Han River basin (Republic of Korea). This study will discuss how computational performance varies with model resolution, domain size, and the distribution of computational workload across multiple GPUs, with a focus on scalability and parallel efficiency. The improved computational efficiency achieved in this study can support pseudo real-time urban flood prediction for early warning applications. In addition, the proposed framework facilitates large-scale, high-resolution simulations that can be used to generate ground-truth datasets for the development and validation of physics-informed or data-driven flood prediction models.