Real-time Flood Research Articles

Enhanced flood prediction using LSTM and climate parameters: multi-station analysis of snowmelt-induced flooding in the Red River of the North

ABSTRACT Flooding in cold regions, particularly driven by snowmelt and climate variability, presents complex challenges for accurate prediction and effective risk management. The current study aims to bridge these gaps by integrating all three components – cold region river systems, long short-term memory (LSTM) modeling, and a broader set of climate variables. Specifically, we incorporate additional climate parameters, including air temperature, humidity, solar radiation, and wind speed, into the LSTM model. Using 12 years of hourly water level data (2007–2019) from three USGS stations along the Red River – Pembina, Drayton, and Grand Forks – the model predicts water levels at lead times of 6, 12 h, 1 day, 3 days, and 1 week. The incorporation of climate variables significantly improved short-term prediction accuracy, achieving R2 values of 0.999 for 6-h forecasts across all stations, demonstrating the potential for real-time flood warning systems. For 1-week predictions, R2 values were 0.778 for Grand Forks, 0.816 for Drayton, and 0.864 for Pembina, reflecting a decrease in accuracy with longer prediction horizons. These findings highlight the effectiveness of LSTM models for short-term flood forecasting in snowmelt-prone regions and underscore the need for further refinement to address long-term hydrological forecasting challenges, particularly under variable climatic conditions.

State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models

Abstract. Assimilating either soil moisture or streamflow individually has been well demonstrated to enhance the simulation performance of hydrological models. However, the runoff routing process may introduce a lag between soil moisture and outlet discharge, presenting challenges in simultaneously assimilating the two types of observations into a hydrological model. The asynchronous ensemble Kalman filter (AEnKF), an adaptation of the ensemble Kalman filter (EnKF), is capable of utilizing observations from both the assimilation moment and the preceding periods, thus holding potential to address this challenge. Our study first merges soil moisture data collected from field soil moisture monitoring sites with China Meteorological Administration Land Data Assimilation System (CLDAS) soil moisture data. We then employ the AEnKF, equipped with improved error models, to assimilate both the observed outlet discharge and the merged soil moisture data into the Xin'anjiang model. This process updates the state variables of the model, aiming to enhance real-time flood forecasting performance. Tests involving both synthetic and real-world cases demonstrates that assimilation of these two types of observations simultaneously substantially reduces the accumulation of past errors in the initial conditions at the start of the forecast, thereby aiding in elevating the accuracy of flood forecasting. Moreover, the AEnKF with the enhanced error model consistently yields greater forecasting accuracy across various lead times compared to the standard EnKF.

Development of a Real-Time Flood Prediction and Warning System for Underground Excavation Sites Based on Nomographs

In August 2022, a localized torrential downpour in Seoul highlighted the growing frequency of extreme rainfall events linked to climate change. Simultaneously, rapid urbanization has led to higher population densities and an increase in underground space development and excavation projects, such as the Yeongdong-daero complex transit center and the construction of the Greater Seoul Metropolitan Express Railway (GTX). This study examines the risks to evacuation routes at ‘underground excavation sites’ during heavy rainfall. Based on this analysis, a system for calculating evacuation buffer time and providing real-time flood forecasting and warnings were developed. A testbed was established in a basin susceptible to recurrent flooding, driven by seawater, external water, and internal water. The study modeled excavation sites with depths of less than 10 m, which were the areas currently excluded from intensive underground safety regulations. The flood risks along the evacuation routes were quantified using a depth-velocity product model. To stimulate real-time flood risks, a rainfall scenario was created by combining observed and predicted rainfall data from the weather radar. The real-time flood forecasting and warning system developed in this study, if implemented nationwide in flood-prone areas, could provide timely warnings to site managers and officials, enabling immediate evacuation based on the risk assessments of evacuation routes in different rainfall scenarios.

Near Real-time Flood Risk Modelling in Response to Increasing Uncertainties in Flood Predictions

In the face of increasing uncertainties and the expected rise in flood events worldwide, this technical note presents an innovative approach for enhancing rapid response capabilities, exemplified during the Kakhovka Dam breach in Ukraine. Utilizing the Tygron Model, our methodology combines open-source data with high performance computing for swift hydrodynamic simulations—a departure from traditional flood risk management techniques. Central to this approach is the strategic use of social media to gather crowd-sourced feedback during the emergency, enhancing the precision and relevance of flood risk information. During the Kakhovka Dam event, our model processed extensive datasets, enabling effective predictions of flood impacts, including extents, velocities, depths, and arrival times. The near real-time modeling capability allowed for dynamic updates using social media inputs, which were of value for emergency responders to optimize response strategies for relief coordination. While the underlying technology is used for flood simulations, its application in emergency response is novel and promising for more adequate disaster response coordination. However, further research and applications are necessary to refine the approach that can ensure real-time flood risk information during a emergency situations.

Urban flood risk assessment using fuzzy logic and real-time flood simulation model – a geomatics techniques

Urban flood risk assessment using fuzzy logic and real-time flood simulation model – a geomatics techniques

Enhancing generalizability of data-driven urban flood models by incorporating contextual information

Abstract. Fast urban pluvial flood models are necessary for a range of applications, such as near real-time flood nowcasting or processing large rainfall ensembles for uncertainty analysis. Data-driven models can help overcome the long computational time of traditional flood simulation models, and the state-of-the-art models have shown promising accuracy. Yet the lack of generalizability of data-driven urban pluvial flood models to both unseen rainfall and distinctively different terrain, at the fine resolution required for urban flood mapping, still limits their application. These models usually adopt a patch-based framework to overcome multiple bottlenecks, such as data availability and computational and memory constraints. However, this approach does not incorporate contextual information of the terrain surrounding the small image patch (typically 256 m×256 m). We propose a new deep-learning model that maintains the high-resolution information of the local patch and incorporates a larger surrounding area to increase the visual field of the model with the aim of enhancing the generalizability of data-driven urban pluvial flood models. We trained and tested the model in the city of Zurich (Switzerland), at a spatial resolution of 1 m, for 1 h rainfall events at 5 min temporal resolution. We demonstrate that our model can faithfully represent flood depths for a wide range of rainfall events, with peak rainfall intensities ranging from 42.5 to 161.4 mm h−1. Then, we assessed the model's terrain generalizability in distinct urban settings, namely, Lucerne (Switzerland) and Singapore. The model accurately identifies locations of water accumulation, which constitutes an improvement compared to other deep-learning models. Using transfer learning, the model was successfully retrained in the new cities, requiring only a single rainfall event to adapt the model to new terrains while preserving adaptability across diverse rainfall conditions. Our results indicate that by incorporating contextual terrain information into the local patches, our proposed model effectively simulates high-resolution urban pluvial flood maps, demonstrating applicability across varied terrains and rainfall events.

Near Real-Time Flood Monitoring Using Multi-Sensor Optical Imagery and Machine Learning by GEE: An Automatic Feature-Based Multi-Class Classification Approach

Flooding is one of the most severe natural hazards, causing widespread environmental, economic, and social disruption. If not managed properly, it can lead to human losses, property damage, and the destruction of livelihoods. The ability to rapidly assess such damages is crucial for emergency management. Near Real-Time (NRT) spatial information on flood-affected areas, obtained via remote sensing, is essential for disaster response, relief, urban and industrial reconstruction, insurance services, and damage assessment. Numerous flood mapping methods have been proposed, each with distinct strengths and limitations. Among the most widely used are machine learning algorithms and spectral indices, though these methods often face challenges, particularly in threshold selection for spectral indices and the sampling process for supervised classification. This study aims to develop an NRT flood mapping approach using supervised classification based on spectral features. The method automatically generates training samples through masks derived from spectral indices. More specifically, this study uses FWEI, NDVI, NDBI, and BSI indices to extract training samples for water/flood, vegetation, built-up areas, and soil, respectively. The Otsu thresholding technique is applied to create the spectral masks. Land cover classification is then performed using the Random Forest algorithm with the automatically generated training samples. The final flood map is obtained by subtracting the pre-flood water class from the post-flood image. The proposed method is implemented using optical satellite images from Sentinel-2, Landsat-8, and Landsat-9. The proposed method’s accuracy is rigorously evaluated and compared with those obtained from spectral indices and machine learning techniques. The suggested approach achieves the highest overall accuracy (OA) of 90.57% and a Kappa Coefficient (KC) of 0.89, surpassing SVM (OA: 90.04%, KC: 0.88), Decision Trees (OA: 88.64%, KC: 0.87), and spectral indices like AWEI (OA: 84.12%, KC: 0.82), FWEI (OA: 88.23%, KC: 0.86), NDWI (OA: 85.78%, KC: 0.84), and MNDWI (OA: 87.67%, KC: 0.85). These results underscore the superior accuracy and effectiveness of the proposed approach for NRT flood detection and monitoring using multi-sensor optical imagery.

Flood Mapping Through Satellite Images Using Deep Learning

Flooding is a pervasive natural disaster that significantly impacts agriculture, infrastructure, and communities, particularly in rural and agricultural regions. Traditional methods of flood assessment and compensation are often manual, time-intensive, and prone to inaccuracies, resulting in delayed aid and inequitable resource distribution. With advancements in technology, satellite imagery and artificial intelligence (AI) provide new opportunities for real-time flood mapping and automated disaster response. This project proposes a Real-Time Flood Mapping and Compensation System using the Attention U-Net deep learning model. Satellite imagery is analysed to detect and segment flood-affected regions with high precision. The Attention U-Net architecture enhances segmentation accuracy by focusing on the most relevant regions in the images while ignoring irrelevant or noisy areas, such as clouds or non-flooded landscapes. The system integrates geospatial data on land boundaries to assess flood severity for individual properties, enabling fair and data-driven compensation for affected landowners. This project holds significant potential for transforming flood disaster management by reducing response times, fostering transparency, and empowering affected communities to recover more effectively. The integration of advanced AI and geospatial technologies marks a critical step toward mitigating the impacts of natural disasters in a rapidly changing climate

Incorporating Dynamic Drainage Supervision into Deep Learning for Accurate Real-Time Flood Simulation in Urban Areas

Urban flooding has become a prevalent issue in cities worldwide. Urban flood dynamics differ significantly from those in natural watersheds, primarily because of the intricate drainage systems and the high spatial heterogeneity of urban surfaces, which pose considerable challenges for accurate and rapid flood simulation. In this study, an urban drainage-supervised flood model (UDFM) for urban flood simulation is proposed. The urban flood process is decoupled into drainage routing and surface flood inundation. On the basis of physical and deep learning drainage models, a hybrid module combining deep learning and dimensionality reduction algorithm is adopted to convert the 1D drainage overflow process into a high-resolution, spatiotemporal 2D pluvial flooding process. Compared with existing state-of-the-art surrogate models for rapid flood simulation, the UDFM more comprehensively and accurately represents the role of drainage systems in urban flood dynamics, providing high-resolution predictions of flood depth and velocity. When applied to a highly urbanized district in Shenzhen, UDFM-deep learning demonstrated real-time predictive capabilities and high accuracy, particularly in simulating flow velocity, with average Nash efficiency coefficients improved by 0.112 and 0.251 compared with those of a response surface model (RSM) and a low-fidelity model (LFM), respectively. These findings underscore the critical importance of drainage system overflow in urban surface flood simulations. The UDFM enhances accuracy, flexibility, interpretability, and extensibility without requiring additional physical model construction. This research introduces a novel hierarchical surrogate model structure for urban flood simulation, offering valuable insights for rapid flood warning and risk management in urban environments.

Near real-time flood forecasting system for the Greater Chao Phraya River Basin

Floods are major natural disasters that cause severe problems and extensive damage to socioeconomic and ecological systems. Estimating and forecasting flood inundation are crucial for early warning, flood mitigation, and adaptation measures to manage risk and mitigate damage. This study proposes a near real-time (NRT) flood forecasting system using a rainfall–runoff–inundation model for the Greater Chao Phraya River Basin in Thailand, which covers large regions and specific high-risk areas. The NRT flood forecasting system operates through preprocessing, processing, and post-processing steps. Initially, the flood simulation model estimates river discharge and inundation depth using observed data such as rainfall, evaporation, and dam releases. Subsequently, the flood forecasting model simulates flood events based on 10-day forecasted rainfall and previous simulation results, producing discharge hydrographs and flood inundation maps. The performance of the NRT flood forecasting system was evaluated using observed daily discharge and inundation areas from satellite images during flood events in October 2023. Overall, despite the absence of notable deviations in the timing and magnitude of the forecasts, the hydrographs demonstrate the capability of the system to predict general discharge trends. Results provide crucial information for early emergency responses, potentially minimizing flood damage and enhancing the accuracy and precision of future system developments. This study lays the groundwork for further development, aiming toward a highly accurate and reliable flood forecasting system.

Climate change and urban flooding: assessing remote sensing data and flood modeling techniques: a comprehensive review

This review critically examines the current understanding of climate change impacts on urban flooding, synthesizing recent findings to provide a comprehensive overview of how rising temperatures, sea-level rise, and increased extreme weather events influence urban flooding. Additionally, it discusses various flood modeling techniques, outlining their advantages and disadvantages. Covering a broad spectrum of studies published between 2007 and 2024, selected for their relevance and approach, this review highlights several key findings. Climate change significantly exacerbates urban flooding disasters, with remote sensing emerging as an indispensable tool for climate change and flood monitoring and prediction. Despite these advancements, notable gaps remain in the literature. There is a conspicuous lack of long-term impact studies and limited discourse on the efficacy of existing mitigation strategies. Additionally, the review underscores the deficiency of real-time flood monitoring systems specifically designed for urban areas. These gaps highlight an urgent need for targeted research and the development of adaptive management practices to enhance flood prediction and management in urban settings. Future research must focus on advancing real-time monitoring systems, integrating remote sensing technologies more effectively, and developing comprehensive flood models. The review also emphasizes the importance of interdisciplinary approaches that merge hydrology, climatology, urban planning, and technology. By consolidating existing research, this review serves as a valuable resource for researchers and policymakers. It underscores the critical need for innovative flood modeling techniques and adaptive management strategies to tackle the challenges posed by climate change. This synthesis not only enriches the current body of knowledge but also provides clear directions for future investigations, emphasizing the imperative for improved prediction, preparedness, and response mechanisms in urban flooding management.

Small-grid urban flood prediction model using Twitter data and population GPS data - an example of the 2019 Nagano city flood

In this study, a small-grid urban flood prediction model integrating Twitter data and population GPS data was constructed using the 2019 Nagano City flood as an example, and the validity of these two data for the model was determined. Using natural language processing techniques, Twitter data was filtered to extract real-time information relevant to flooding. At the same time, geographic information processing techniques were applied to analyze the population GPS data and obtain the distribution of the local population. Based on these two types of data, we combined with terrain, land use, traffic and infrastructure data related to flooding, a real-time flood prediction model was constructed using the random forest algorithm with a basic unit of a 70 m × 70 m grid. An analysis of the model accuracy showed that, the model that included both GPS and Twitter data showed an improvement in prediction accuracy of about 8% compared to flood prediction models that do not have these data sources. This indicated that the integrated use of Twitter and GPS data allowed us for a more accurate representation of the dynamic characteristics of flood disasters, thereby improving the performance of real-time flood prediction models and increasing real-time awareness of flood events. This approach provided effective flood monitoring methods for disaster management authorities.

Intelligent IoT-Based Flood Monitoring and Alerting System using Raspberry Pi

Abstract: Floods are one of the most destructive natural disasters, causing significant loss of life and property. Early detection and timely intervention are crucial in mitigating the impacts of floods. The "Intelligent IoT-Based Flood Monitoring and Alerting System" is an innovative solution that leverages the power of the Internet of Things (IoT) to monitor water levels and predict potential flooding events. This system uses low-cost and easily accessible hardware such as a Raspberry Pi, sensors, LCD, buzzer and a cloud-based platform to provide real-time flood monitoring. The system continuously collects data on water levels, environmental conditions, and other relevant metrics, and processes this data to issue early warnings and alerts to local authorities and residents. Through an intuitive web interface, users can monitor flood-prone areas and receive notifications when water levels reach dangerous thresholds. The system aims to enhance disaster preparedness, reduce human and material losses, and improve the efficiency of flood management

Assessment of the extent of community preparedness to flood risk in Jigawa State, Nigeria

Jigawa State faces annual flood disasters that have resulted in significant loss of life and property, putting many communities at risk. This study aim to evaluate the preparedness for flood risk in Jigawa State, Nigeria. A sample of 666 respondents was selected for a self-administered questionnaire, with 601 fully completed. The collected data were analyzed descriptively to determine the preparedness index. (PI). From the result of the findings, a majority (77.7%) of the respondents agreed that the community is aware of flood risks. There was also a provision of a neighborhood directory (97.8%), local shelter (75.9%) and emergency contacts (55.4%) during floods. The PI (92.6%, 98.6% 84.1% and 85.1% respectively), are above the 65% threshold and hence considered ‘prepared’ in this regard; but, ‘somewhat prepared’ (PI of 61.7%) regarding awareness of flood disaster risk management strategies; ‘and unprepared’ (PI of 55.5%, 50.5%, 50.5%, 50.8% and 54.3% respectively) in terms of community organization of public education and training of flood risk reduction (66.6%); engagement on rehearsals/emergency drills (51.6%); good community-based warning system (51.4%); and a standalone system for property protection measures during floods (52.4%); as well as an active recovery plan (62.9%). It was recommended that proper embankment protection and water regulation outlets be established. Additionally, real-time flood forecasting and effective early warning systems should be developed. Communities should be assessed to identify flood risk zones before construction activities begin, or alternatively, resettlement to safer areas should be considered for those living in flood-prone regions.

Real-time flood forecasting and uncertainty analysis: a case study of the Krong H’nang hydropower reservoir

ABSTRACT Early and reliable flood forecasting is crucial for mitigating the impacts of floods and ensuring the safe operation of irrigation and hydroelectric facilities. Traditional methods often use a calibrated event-based rainfall-runoff model with a limited number of simulations, making it difficult to measure parameter uncertainties accurately. This paper introduces a new method for estimating the parameters of the HEC-HMS model and evaluating the uncertainties in real-time flood forecasting. First, the Latin Hypercube Sampling (LHS) procedure is used to efficiently sample the parameter values throughout the feasible parameter space. Next, thousands of parameter sets are simulated using a deterministic rainfall-runoff model, generating numerous initial solutions for the Shuffled Complex Evolution (SCE-UA) algorithm to search and evolve. Additional parameter sets are then generated as the search evolves, leading to new simulations until the algorithm reaches the convergence criteria. The optimal solutions are selected based on predefined multiple objective functions. Uncertainty of the forecast results is also computed using the Generalized Likelihood Uncertainty Estimation (GLUE) method and represented as confidence intervals. The method was applied to forecast flood hydrographs at the Krong H’nang hydropower reservoir in Vietnam, using data from 33 flood events from 2016 to 2021. The results demonstrate that the program can effectively forecast the flood hydrograph up to 4 hours in advance (the Kling-Gupta efficiency KGE > 0.8 and the absolute relative volume error |VE| < 10%). The observed values fall within the uncertainty range of Q5%-Q95%. Compared to the conventional method, which uses a fixed parameter set value, this approach is superior and therefore can assist policymakers and operators in more effectively managing reservoir operations.

Influence of Rainfall Patterns on Rainfall–Runoff Processes: Indices for the Quantification of Temporal Distribution of Rainfall

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies.

BEW-YOLOv8: A deep learning model for multi-scene and multi-scale flood depth estimation

As global climate change intensifies, the frequency of extreme weather events, including urban flooding, has risen, posing a significant threat to the safety of urban areas. Efficient and precise flood depth monitoring is crucial for a dynamic understanding of disaster conditions, enabling informed decision-making and reducing the potential for significant loss of life and property. Although object detection models have shown promise in estimating flood depths and have achieved notable results, enhancements are still needed in applications across multi-scene and multi-scale. This research enhances the YOLOv8 model by integrating Bidirectional Feature Pyramid Network (BiFPN), Effective Squeeze and Excitation (EffectiveSE), and Wise-IoU (WIoU), targeting submerged cars to develop the BEW-YOLOv8 model. Testing shows that the BEW-YOLOv8 model improves Precision, Recall, F1 Score, mAP@0.5, and mAP@0.5–0.95 by 14.1%, 2.2%, 7.9%, 8.7%, and 6.3%, respectively, compared to the unmodified YOLOv8 model. Compared with other existing models, BEW-YOLOv8 achieves the best performance with less than one-tenth of the parameters. Furthermore, this study verifies that the BEW-YOLOv8 model holds considerable promise for further enhancements with dataset expansions. Its precision and dependability make the BEW-YOLOv8 model well-suited for real-time flood depth assessment across multiple scenes and scales. The model’s capability for real-time processing supports urgent flood response needs, offering solid technical assistance for managing urban floods and mitigating disaster risks.

An Urban Flood Model Development Coupling the 1D and 2D Model with Fixed-Time Synchronization

Due to climate change, the frequency and intensity of torrential rainfall in urban areas are increasing, leading to more frequent flood damage. Consequently, there is a need for a rapid and accurate analysis of urban flood response capabilities. The dual-drainage model has been widely used for accurate flood analysis, with minimum time step synchronization being commonly adopted. However, this method has limitations in terms of speed. This study applied the hyper-connected solution for an urban flood (HC-SURF) model with fixed-time step flow synchronization, validated its accuracy using laboratory observation data, and tested its effectiveness in real urban watersheds with various synchronization times. Excellent performance was achieved in simulating real phenomena. In actual urban watersheds, as the synchronization time increased, the errors in surcharge and discharge also increased due to the inability to accurately reflect water level changes within the synchronization time; however, overall, they remained minimal. Therefore, the HC-SURF model is demonstrated as a useful tool for urban flood management that can be used to advantage in real-time flood forecasting and decision-making.

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.

Enhanced rainfall nowcasting of tropical cyclone by an interpretable deep learning model and its application in real-time flood forecasting

Reliable Tropical Cyclone (TC) rainfall and flood forecasts play an important role in disaster prevention and mitigation. Numerous studies have demonstrated the promising performance of deep learning in hydrometeorological forecasts. However, few studies have investigated the potential enhancement of advanced TC track forecasts in predicting rainfall and induced flood. In this study, a novel rainfall nowcasting model (TCRainNet) is developed by fusing TC track characteristics with antecedent rainfall in a Convolution LSTM to predict hourly rainfall with a lead time of 6 h. The nowcasts are subsequently used to drive an event-based Xin’anjiang hydrological model for real-time flood forecasting. The model performance is interpretated by the occlusion sensitivity approach, and the propagation of errors from TC track forecasts to flood forecasts is quantified. The results underscore the superiority of TC track characteristics as input features for rainfall nowcasts, as indicated by a Mutual Information value of up to 0.51. The generated nowcasts are found to have averaged Probability of Detection (POD) and Critical Success Index (CSI) greater than 0.27 and 0.2 respectively. The Mean Absolute Error (MAE) of the nowcasts falls below 2.6 mm, which is only 46 % of the ECMWF operational high-resolution forecasts. The rainfall-driven flood forecasts have NSE greater than 0.7 and PBIAS smaller than 20 % with lead time up to + 4 h. It is shown that the position error of 0.45° and intensity error of 10 hPa&7.8 m/s in TC track forecasts generally result in 0.9 mm degradation in rainfall forecasts and 10% decline in the accuracy of rainfall-driven flood forecasts. The effectiveness of our method presents favorable applicability in advancing disaster mitigation efforts.