Data for RTFF can be sourced from various outlets, though sometimes access to these sources can be limited or challenging. RTFF necessitates the modeling of complex distributed systems with high spatial and temporal intricacies. This demands substantial computing resources and may leave limited time for timely early warnings. Significant breakthroughs have occurred in recent decades to address major challenges in the key stages of RTFF, including data collection and preparation, model development, performance assessment, and practical applications.
The objective of this session is to address challenges and advancements in the field by leveraging state-of-the-art techniques, new frameworks, equipment, software tools, hardware facilities, and the integration of existing methods with contemporary algorithms. We will also explore digital innovations and their applications in new pilot studies.
Specifically, this session will concentrate on the following research areas related to RTFF, with a focus on but not limited to:
● Hydrological data collection, analysis, imputation, assimilation and fusion taken from various data sources including ground stations, radar stations, remote sensing (aerial/satellite)
● RTFF modelling including physically/processed-based, conceptually-based, experimentally-based or data-driven modelling such as artificial Intelligence (AI), machine learning (ML)
● Application RTFF for flood alleviation or engagement with the public and authorities, such as early warning and early action systems, digital innovations such as digital twins (DT), or integrated with digital technologies such as augmented reality (AR) and virtual reality (VR).
● The broader implications of RTFF and early warning systems as soft engineering approaches, including their impact on flood risk management, insurance, capacity building, and community resilience.
This research addresses the difficulties to increase lead time on predictions of riverine flooding risks represented by the rainfall in extreme or long-duration weather systems originated from ungauged areas. The methodology explores the opportunity to enhance rainfall data coverage and simplify forecasting tasks by combining the global reach and high resolution of IMERG V07 satellite precipitation products (SPPs) with the ability of deep learning models to capture complex spatiotemporal relationships in time series data.
In a real-world case study, the method applies a long short-term memory (LSTM) model to capture patterns in the historical relationship between IMERG rainfall estimates from selected areas of the Atlantic Ocean and stream level variations in three UK catchments (C1, C2, and C3). The model then utilizes near-real-time (NRT) data from the IMERG early run product to make real-time predictions. Lead times are determined by considering three key factors: the latency of the NRT data, the distance between the catchment and the IMERG data collection point, and the forward speed of the weather system carrying rainfall toward the catchment.
The method was applied to predict stream level variations during two extreme rainfall events and results compared to those obtained from a similar LSTM model using local rain gauge data. Through this comparison, across all catchments the proposed methodology demonstrated significantly smaller prediction errors for lead times exceeding 1.5 hours on both events. For example, with NRT IMERG data, 6.5-hour lead time predictions for C1, C2, and C3 had RMSE values of 19 mm, 21 mm, and 26 mm, respectively, for the 2022 event, and 16 mm, 29 mm, and 45 mm for the 2023 event. In contrast, predictions with the same lead time using rain gauge data resulted in RMSE values of 77 mm, 64 mm, and 59 mm for the 2022 event, and 165 mm, 44 mm, and 112 mm for the 2023 event.
More importantly, considering that during the rainfall events water level rose about 600mm in C1, up to 700 mm in C2 and up to 1000mm in C3, the errors with the proposed methodology remained below 10% of the total water level rise in each catchment on predictions with up to 9 hours lead time. While these are excellent results for real-time applications of flooding forecasts, the 4-hour latency of NRT IMERG data limits the method's applicability for predictions with less than 4 hours lead time and for floods triggered by localized or short-duration rainfall events.
How to cite: Girotto, C., Behzadian, K., Piadeh, F., and Zolgharni, M.: Combining Satellite Precipitation Products and Deep Learning to Increase Lead Times in Real-Time Riverine Flood Risk Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20021, https://doi.org/10.5194/egusphere-egu25-20021, 2025.
Physically based, distributed hydrological model(PBDHM) was proposed for long time, and was regarded to have the potential to improve the flood forecasting accuracy. But unfortunately, this is still in the dream due to some existing challenges, and the biggest one is model parameter determination. Initially, it was assumed that parameter of PBDHM should be derived from the terrain properties directly, such as the DEM, land use/cover(LUC) types and soil types, not calibrated like lumped conceptual model(LCM) by employing optimization algorithm. In fact, PBDHM’s parameter calibration is also infeasible considering its huge number of model parameters, that could be up to millions or even to billions. As there is no “optimal” references for deriving PBDHM’s parameters directly from terrain properties, PBDHM’s capability for real-time flood forecasting has been weakened, so limiting its use mainly in scientific studies. In this study, the author assumes that PBDHM also needs parameter “calibration”, and the theory and framework for PBDHM parameter optimization have been presented. Based on the Liuxihe model, which was proposed for watershed flood forecasting, an automatic parameter optimization algorithm has been proposed by employing Particle Swarm Optimization (PSO). With parameter optimization, flood simulation accuracy of Liuxihe model has been improved largely, and very importantly, its performance is very stable. Not like LCM, model performance fluctuates sharply, thus limiting its capability being used for real-time flood forecasting. From dozens case studies in China, it also has been found that hydrological data from only one flood event is enough for parameter optimization, not like LCM, which requires hydrological data from a series of flood events. This finding is significant particularly for data-poor watershed, which makes PBDHM’s parameter optimization feasible for most of the world watersheds. With this advances, Liuxihe model has been used in several Chinses watersheds for real-time flood forecasting, and successful forecasting have been achieved. These successful implementations have proven that PBDHM has entered a new era for the real world application.
How to cite: Chen, Y.: How far is distributed hydrological model from real-time flood forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2048, https://doi.org/10.5194/egusphere-egu25-2048, 2025.
Floods are among the most devastating extreme events that affect people worldwide. Their frequency and severity are expected to increase as a result of climate change, which requires timely and informed decisions to protect people and reduce economic losses. In Chile, the current flood warning system compares real-time streamflow observations with flood thresholds calculated by the National Water Directorate (DGA) and issues flood warnings only a few hours in advance. Therefore, this work develops a prototype flood early warning system for Andean catchments, using meteorological ensemble forecasts coupled with a hydrological model that provides streamflow forecasts with lead times of up to 10 days.
The methodology implements the Novel Multi-objective Particle Swarm Optimisation (NMPSO) algorithm to calibrate the TUWmodel, a conceptual hydrological model that explicitly accounts for snow processes and rainfall-runoff dynamics. This optimisation framework ensures robust parameter estimation for multiple hydrological objectives, with a focus on better reproducing high streamflows while preserving low-flows dynamics. The calibrated model is then forced with daily mean air temperature and precipitation data from two medium-range meteorological forecast ensembles, namely MSWX-Mid and ECMWF-IFS, comprising 30 and 51 members, respectively. To bias-correct meteorological forcings, an empirical quantile mapping approach is implemented using the daily 5-km Chilean dataset CR2METv2.5 as reference. The efficiency of the system is evaluated in three snow-influenced Andean catchments in southern Chile, which were affected by severe floods events during 2023 and 2024.
The developed prototype is expected to be soon available online to improve medium-range flood forecasting in critical Andean catchments and to provide timely and reliable information to decision makers and the public.
We gratefully acknowledge the financial support of ANID-Fondecyt Regular 1212071 and ANID-PCI NSFC 190018, and ANID/FONDAP 1523A0002.
How to cite: Garces-Figueroa, H. and Zambrano-Bigiarini, M.: Development of a Flood Early Warning System for Critical Catchments in Southern Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13525, https://doi.org/10.5194/egusphere-egu25-13525, 2025.
One of the major challenges in improving flood management is the low reliability and limited availability of rainfall forecasts. This challenge becomes more pronounced in areas affected by rainfall originating in oceanic regions beyond the reach of land-based instruments. In such cases, satellite precipitation products (SPPs) present an alternative source of rainfall data for use in flooding- risk forecasts. To explore this possibility, it is essential to first establish whether there is a relationship between the SPP data and flood risk, and to what extent this relationship can be detected. Therefore, this research explores the historical relationship between rainfall events over the Atlantic Ocean, as captured by IMERG V07 estimates, and stream level variations in four UK catchments. The study utilizes over 20 years of data to perform cross-correlation analyses between stream level records and precipitation estimates from each pixel of the IMERG V07 grid in a selected region of the Atlantic near the UK. The analysis revealed several key insights into these relationships.: A) Each catchment has a distinct historical path for rainfall events moving across the ocean toward the UK, that are related to stream level variations. B) It is possible to identify the regions of the Atlantic that consistently produce the most impactful rainfall events affecting catchments in specific areas of the UK. C) The strongest rainfall-stream level relationships were observed at distances of up to 650 km, which may help to compensate for the 4-hour latency of IMERG V07 early run data, enhancing its suitability for real-time flood forecasting. Such findings are significant as they allow for a more focused approach and the direction of monitoring efforts for flood risk detection toward specific regions of the Atlantic rather than monitoring vast oceanic areas that leads to the processing large amounts of irrelevant data. The next phase of this study focus on applying these findings on the development of a machine learning model able to predict stream level variations based on the long-distance relationship with rainfall events, exploring the potential of earlier risk detection for increasing lead time of flooding forecasts
How to cite: Girotto, C., Behzadian, K., and Piadeh, F.: Historical Correlation Between IMERG V07 Rainfall Estimates Over the Atlantic Ocean and Stream Level Variations in Four UK Catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14571, https://doi.org/10.5194/egusphere-egu25-14571, 2025.
Floods resulting from dike breaches pose significant risks to infrastructure and human safety. This study presents a comprehensive approach for real-time flood mapping, by combining machine-learning-based hydrological models and hydraulic simulations to estimate flood extent and impact following a dike breach in a network of waterways. The methodology integrates climate data, AI-driven hydrological predictions, efficient river flow models, and real-time flood mapping.
The procedure begins with the acquisition of meteorological data, including precipitation observations and forecasts (disaggregated at an hourly resolution). This data is updated at each triggering of the calculation to reflect the most current meteorological conditions. The precipitation data are then fed into an AI-based hydrological model, to predict river discharge values with a 24-hour lead time at key streamflow stations. These discharge predictions constitute the upstream boundary conditions for an efficient 1D staggered-grid hydraulic model of the network of waterways.
The hydraulic model simulates flow processes within the main channels. It is coupled to a model for the morphodynamic evolution of dike breaches. This model is semi-empirical and lumped, to account for the multi-scale nature of the breach process, in which certain failure mechanisms (e.g., slope failures) occur on much smaller spatial scales than those controlling flow dynamics in the channels and floodplains. By using a lumped model for the breach, the need for refining the computational grid in the near-field of the breach is reduced, while still capturing the main effects of complex geotechnical and sediment transport processes involved in dike failures.
The hydraulic model outputs, including computed water levels in the main channel, are used in conjunction with fragility functions representing the resistance of the earth-filled dikes, to determine the likelihood of dike breaches at potential breach locations. For each breach scenario, pre-computed results of a detailed 2D hydraulic model are used to assess the inundation depth, flow velocity, and flood extent across the floodplains. This enables creating dynamic danger maps that are crucial for identifying assets at-risk and estimating impacts (monetary damages). These outputs support the evaluation of potential mitigation measures, such as adjusting weir operations to divert floodwaters from vulnerable areas or redirecting flows toward alternate channels.
The novel procedure proposed here is demonstrated on a case study involving critical waterways in Belgium connecting the Meuse River to the Sea Port of Antwerp. The focus is set on a particular canal segment due to the high population density and presence of industrial infrastructures in the floodplains.
This research is co-funded by the European Union’s Horizon Europe Innovation Actions under grant agreement No. 101069941 (PLOTO project: https://ploto-project.eu/)
How to cite: Hardy, J., Archambeau, P., Mastricci, D., Schmitz, V., Melitsiotis, A., Erpicum, S., Pirotton, M., and Dewals, B.: Real-Time Flood Mapping Considering Dike Breaching, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18058, https://doi.org/10.5194/egusphere-egu25-18058, 2025.
During floods, heavy metal releases from mining pose serious environmental problems that require rapid and effective treatment [1]. Advanced modeling approaches enable cost-effective monitoring and detection, ensuring efficient and cost-effective management of this critical problem [2]. The Příbram Ore Region (Czech Republic), situated ~60 km southwest of Prague, is drained by the Litavka River (56 km, 630 km² watershed). In the region under study, smelting, sediment erosion, and historical silver and base metal ore-field mining all contribute to heavy metal contamination, which is made worse by periodic floods that carry pollutants downstream [3].
To address this, the present study applied Fick’s second law technique in MATLAB 2019b simulations to find emissions of the heavy metals based on time and spatial variations in the case study. The present simulation assumes: (1) constant diffusion coefficients for heavy metals in flood condition is assumed equal to 0.8 km²/day; (2) initial concentrations derived from flux-to-suspended particulate matter (SPM) ratios (Cd: 74 kg, Pb: 2954 kg, Zn: 5811 kg with SPM 2400 tons) [3]; (3) a uniform spatial distribution initially set to zero; (4) boundary concentrations at the source (Cd/SPM = 0.0308 mg/L, Pb/SPM = 1.231 mg/L, Zn/SPM = 2.421 mg/L) [3]; (5) Fick’s Second Law solved via explicit finite difference; (6) no external sources or sinks within the 2 km, 2 h simulation.
The simulation demonstrates the dispersion of heavy metals (Cd, Pb, Zn) in a river during a flood event, assuming a uniform flux rate of 0.8 km²/day for all metals. Initial concentrations at the source are determined from flux-to-SPM ratios: Zn has the highest concentration (2.42 mg/L), followed by Pb (1.23 mg/L) and Cd (0.03 mg/L). As dispersion progresses, concentrations decrease and spread downstream, with the uniform flux rate ensuring comparable dispersion rates across all metals. Spatial profiles reveal a rapid decline in concentrations within the first 2 km, with Zn maintaining the highest overall spread due to its larger initial flux. Temporal heat maps show that, despite equal diffusion rates, Zn and Pb exhibit more extensive downstream spread due to their higher initial concentrations, while Cd remains more localized. These results emphasize the role of initial fluxes and source concentrations in determining heavy metal distribution during flood events. The uniform flux rate assumption simplifies the transport dynamics, providing insights into contamination spread and highlighting the need for monitoring strategies to mitigate environmental risks in mining-impacted regions.
Keywords: Uranium mining; Early-Warning; Hazardous materials; Fate and Transporte; Flood.
References
1. Foulds, S.A., Brewer, P.A., Macklin, M.G., Haresign, W., Betson, R.E. and Rassner, S.M.E., 2014. Flood-related contamination in catchments affected by historical metal mining: an unexpected and emerging hazard of climate change. Science of the Total Environment, 476, pp.165-180.
2. Hakim, D.K., Gernowo, R. and Nirwansyah, A.W., 2024. Flood prediction with time series data mining: Systematic review. Natural Hazards Research, 4(2), pp.194-220.
3. Žák, K., Rohovec, J. and Navrátil, T., 2009. Fluxes of heavy metals from a highly polluted watershed during flood events: a case study of the Litavka River, Czech Republic. Water, Air, and Soil Pollution, 203, pp.343-358.
How to cite: Gheibi, M., Palušák, M., Silvestri, D., Černík, M., and Wacławek, S.: Prediction of Heavy Metal Emissions from Uranium Mines to Water Resources During Flood Disasters Using Fick's Second Law Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10555, https://doi.org/10.5194/egusphere-egu25-10555, 2025.
Accurate reservoir inflow estimation is the foundation of flood forecasting and control. However, traditional inflow estimation methods based on water balance ignore the effects of observational errors in water level and dynamic reservoir capacity. These methods will cause significant fluctuation, generating negative inflow values over short intervals, and cannot capture lateral flows. To address these challenges, the study proposes a novel reservoir inflow estimation method combining the Rauch-Tung-Striebel (RTS) smoother to account for water level observation errors and a 1D hydraulic model (1D-HM) to account for dynamic reservoir capacity. Firstly, a potential inflow ensemble is stochastically generated. Then, multiple 1D-HMs are conducted to simulate water levels under different potential inflow scenarios. Finally, the RTS smoother is employed to update the inflow ensemble and historical inflow records based on the differences between observed and simulated water levels. The estimation of smoothed upstream inflow and lateral inflow is achieved through rolling filtering. The proposed method is validated through numerical experiments and a real-world case study of the Three Gorges Reservoir. The results show that: (1) In numerical experiments, the proposed method outperforms other comparative methods under various conditions, including errors in water levels, dynamic reservoir capacities, and lateral flows. (2) In the real case study, the proposed method can generate no-fluctuation reservoir inflow and lateral flow estimates at 15-minute intervals.
How to cite: Liu, Y. and Liu, P.: Estimation of reservoir inflow considering water level observation errors and dynamic reservoir capacity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14333, https://doi.org/10.5194/egusphere-egu25-14333, 2025.
Flood management is a critical challenge, especially in areas where climate change and urbanisation have altered the level of risk from floods [1]. The conventional approach to flood risk assessment is usually insensitive to behavioural couplings between human behaviour and flood dynamics, which severely affect the result of any management strategy [2]. On the other hand, behavioural simulation models such as agent-based modelling (ABM), present a promised alternative by allowing bottom-up explorations of flood management scenarios [3]. The present study tries to fill this gap by proposing activity-based ABM devised to evaluate flood management strategies for different flood risk scenarios in a UK case study. The model uses real-time travel data from the Google Maps application program interface (API) intending to simulate individual behavioural dynamics realistically in terms of movement patterns and responses in case of flooding. By integrating these behavioural insights with flood risk maps and infrastructural data, the model assesses the effectiveness of interventions such as flood warnings, evacuation plans, and adaptive infrastructure. The findings of this research demonstrate how ABMs can be used to inform decision-makers, contributing to the improvement of both short-term flood preparedness and response as well as long-term planning for infrastructure development. This study illustrates how dynamic and realistic modelling of interactions between humans and their environment can reveal the role of ABMs in advancing flood resilience planning.
References
[1] Piadeh, F., Behzadian, K. & Alani, A.M., (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, 127476.
[2] Bakhtiari, V., Piadeh, F., Chen, A., Behzadian, K. (2024). Stakeholder Analysis in the Application of Cutting-Edge Digital Visualisation Technologies for Urban Flood Risk Management: A Critical Review. Expert Systems with Applications, p.121426.
[3] Zhuo, L., & Han, D. (2020). Agent-based modelling and flood risk management: a compendious literature review. Journal of Hydrology, 585, 124755.
How to cite: Najjar-Ghabel, S., Behzadian, K., Piadeh, F., and Ardakanian, A.: Flood Management Strategies Using Agent-Based Modelling and Public APIs: A Case Study in the UK, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17415, https://doi.org/10.5194/egusphere-egu25-17415, 2025.
Flash floods cause substantial hazards, particularly in regions with limited hydro-meteorological data availability that hinder the development of forecasting models and post-hazard impact assessments. The absence of comprehensive on-ground datasets regarding flood hazard characteristics, exposure elements, and vulnerability can impede accurate evaluations and effective risk management strategies. With advanced technology, integrating remotely sensed imagery products with machine learning can enhance flash flood prediction capabilities in data-scarce regions. This study applies remote sensing and machine learning techniques to enhance the identification of rainfall sources that cause flash floods and improve inundation detection in Lodwar Town, Kenya. Considering the area's frequent flash floods, this methodology is crucial for assessing flood risks and the sudden and severe impacts on the local community. This analysis used remotely sensed rainfall products, CHIRPS, MSWEP, IMERG, and TAMSAT, and Normalized Difference Water Index (NDWI) from Aqua MODIS satellite representing flood-inundated locations. Correlation analysis was conducted between rainfall and NDWI at a daily timescale for 2002-2022.
The results show that among the rainfall products, CHIRPS and MSWEP showed better performance in terms of 0-day lag time correlation with NDWI values of Lodwar town with a 0.51 correlation coefficient. To enhance the predictive capabilities of the NDWI in Lodwar Town, a machine learning technique with the Decision Tree Regressor model was applied to the finer spatial resolution CHIRPS rainfall data. The findings indicate that the model improved the correlation coefficient between rainfall and NDWI to 0.64 with a 0-day lag time, demonstrating its effectiveness in identifying potential rainfall areas causing flooding in the town. These are in the west, north-west, and south-west of Lodwar Town. Rainfall observed in identified flash flood source areas with elevations ranging from 508m to 648m can lead to rapid flooding in the town. This flooding occurs with a 0-day lag time, as the town is situated at approximately 500m elevation. If forecasted rainfall data from the identified areas that trigger flash floods is available, this study showed that it is possible to anticipate potential flooding events in the town. The methodology proposed in this study is particularly important in regions that lack comprehensive hydro-meteorological datasets that can support needed information to prepare and minimize the impacts of flash floods.
How to cite: Taye, M. T., Lakew, H. B., Lino, O., and Dyer, E.: Curtailing flash flood impacts on vulnerable communities in data-scarce regions through the utilization of digital innovations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4234, https://doi.org/10.5194/egusphere-egu25-4234, 2025.
Flooding poses significant risks to communities, necessitating timely and effective warning systems [1]. Social media platforms like Twitter provide a real-time avenue for gathering public insights during such events [2-3]. This study investigates the relationship between flood warning and alert systems announced by the Environment Agency and X(Twitter) trends for the specific area of River Pinn, located in Ruislip, London, UK. This study employs a systematic approach to explore the interplay between social media activity and flood-related warnings. Keywords such as rainfall, rain, flooding, and flood were identified and used to extract relevant tweets associated with the River Pinn, Ruislip, UK. Data collection involved geotagging techniques and temporal filters to ensure spatial relevance and focus on periods of flood warnings issued by the Environment Agency. The extracted tweets were analysed for temporal trends and spatial distribution to assess their alignment with rainfall events and flooding status.
To investigate the temporal dynamics, cross-correlation analysis was performed between the volume of Twitter activity and the timeline of actual flooding events. Rainfall data from official meteorological sources were also incorporated into the analysis to ensure accurate mapping of precipitation to flooding events. The study further examined whether Twitter activity could act as a predictive tool, evaluating how far in advance users' tweets reflect flood-related concerns compared to observed flood warnings.
The analysis revealed a 30-minute lag between the onset of rainfall and the appearance of related Twitter activity, indicating that social media trends align closely with the progression of real-world weather conditions. More notably, Twitter users exhibited the ability to predict potential flooding events up to one hour in advance. This anticipatory behavior suggests that individuals, through collective observation and situational awareness, recognise the likelihood of flooding before it becomes a reality.The spatial distribution of tweets also highlighted localised concerns, reinforcing the value of geotagged data in enhancing situational awareness for specific areas like Ruislip. These findings underscore the viability of integrating social media insights into flood warning systems, offering a cost-effective and real-time supplement to traditional methods.
This study demonstrates the potential of Twitter as a dynamic tool for flood detection and early warning, with implications for improving emergency response strategies. By harnessing user-generated data, authorities can enhance the effectiveness of flood management systems and better protect at-risk communities.
References
[1] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.
[2] Piadeh, F., Ahmadi, M., Behzadian, K. (2020). A Novel Planning Policy Framework for the Recognition of Responsible Stakeholders in the of Industrial Wastewater Reuse Projects. Journal of Water Policy, 24 (9), pp. 1541–1558.
[3] Bakhtiari, V., Piadeh, F., Chen, A., Behzadian, K. (2024). Stakeholder Analysis in the Application of Cutting-Edge Digital Visualisation Technologies for Urban Flood Risk Management: A Critical Review. Expert Systems with Applications, p.121426.
How to cite: Piadeh, F. and Piadeh, F.: Leveraging Twitter Trends for Early Flood Detection: A Case Study of Ruislip, UK, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3580, https://doi.org/10.5194/egusphere-egu25-3580, 2025.
Posters on site: Tue, 29 Apr, 10:45–12:30 | Hall A
Flooding is one of the most destructive natural disasters worldwide, causing significant socio-economic losses, disruption of critical infrastructure, and loss of lives. The increasing frequency and intensity of floods due to climate change and rapid urbanisation have underscored the need for advanced flood management strategies [1]. While traditional flood risk assessment methods primarily focus on deterministic approaches to predict flood extents and impacts, they often overlook the dynamic interplay between human behaviour and flood dynamics [2,3]. This limitation prevents the development of effective flood management strategies that reflect real-world complexities [4]. This review identifies key research gaps, such as the limited exploration of cascading failures in critical infrastructure and the need for multi-agent collaboration in large-scale flood scenarios. It also outlines opportunities for future development, including the use of synthetic population generation and participatory modelling to enhance the realism and applicability of ABMs.
Agent-based models (ABM) have emerged as a transformative tool in addressing these gaps, offering a bottom-up approach to simulating individual and collective behaviours during flood events. By representing individuals, groups, or entities as autonomous agents with distinct decision-making rules, ABMs provide valuable insights into how human behaviors influence, and are influenced by, flood risks and interventions. Recent advancements have enhanced the utility of ABMs, particularly their integration with real-time data, which are sources that enable the dynamic simulation of human mobility and interactions under varying flood conditions. Additionally, the coupling of ABMs with hydrological and flood-forecasting models has created comprehensive frameworks for evaluating proactive and reactive flood management strategies. Despite these advancements, challenges remain in the broader adoption of ABMs. Computational complexity, the need for extensive data to calibrate and validate models, and the difficulty of capturing long-term behavioural adaptations are significant hurdles. Furthermore, there is a growing need for the integration of machine learning and cloud computing methods to improve the scalability, accuracy, and predictive power of ABMs. By providing a detailed evaluation of current methodologies, challenges, and future directions, this study underscores the transformative potential of ABMs in advancing adaptive and resilient flood management strategies. The findings are particularly relevant for policymakers, urban planners, and emergency responders seeking to design targeted, effective interventions that reduce flood impacts and improve community resilience.
References
[1] Ferdowsi, A., Piadeh, F., Behzadian, K., Mousavi, S., Ehteram, M. (2024). Urban Water Infrastructure: A Critical Review on Climate Change Impacts and Adaptation Strategies. Urban Climate, 58, p.102132.
[2] Girottoa, C., Piadeh, F., Bakhtiari, V., Behzadian, K., Chen, A., Campos, L., Zolgharni, M. (2024). A Critical Review of Digital Technology Innovations for Early Warning of Water-Related Disease Outbreaks Associated with Climatic Hazards, International journal of disaster risk reduction, 100, p.104151.
[3] Anshuka, A., Ogtrop, F., Sanderson, D., Leao, S.Z. (2022). A systematic review of agent-based model for flood risk management and assessment using the ODD protocol. Natural Hazards, 112(3), pp.2739-2771.
[4] Zhuo, L., Han, D. (2020). Agent-based modelling and flood risk management: a compendious literature review. Journal of Hydrology, 585, p.124755.
How to cite: Behzadian, K., Najjar-Ghabel, S., and Ardakanian, A.: Advancing Flood Management Strategies: A Review of Agent-Based Models in Flood Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20698, https://doi.org/10.5194/egusphere-egu25-20698, 2025.
Hydrological models often require gridded atmospheric fields, yet such datasets, particularly in high-resolution and near real-time, are often unavailable. Al- though precipitation is the most dominant variable in hydrological processes, temperature can influence river flow by influencing snowmelt, leading to snowmelt floods. Daily temperature data are often insufficient to capture floods, highlight- ing the importance of hourly temporal resolution. Currently, there is a lack of reliable real-time, hourly gridded temperature data for Germany. The DWD provides historical hourly temperature that is part of the HOSTRADA product [1]. However, the data are updated monthly by including the data from the pre- vious month. Near real-time hourly station records for temperature are freely available from the DWD. We assume that the average hourly temperature has smooth spatial and temporal distributions, facilitating a reliable interpolation with only 512 active stations.
This study investigates the interpolation of hourly station temperature data to generate a high-resolution historical and near-real-time gridded temperature dataset. The methods explored include ordinary kriging (OK) and external drift kriging (EDK) with topographical elevation as the drift variable. Various variogram models were considered for both methods. Cross-validation [2] was used to select the best interpolation method and determine an optimal distance for interpolation. The performance of the interpolated dataset, particularly EDK with an exponential variogram, was also validated against HOSTRADA from 1995 to 2023. The comparison yielded a root mean square error of 0.8◦C, demonstrating the robustness of the method. Based on this evaluation, a near real-time gridded temperature dataset is generated to serve as input to the hourly configuration of mHM for an operational flood prediction within HI- CAM II project.
In practice, the interpolation was performed using EDK software developed first by Samaniego et al. [3] which is implemented in Fortran and supports parallel computation. Its robustness and efficiency make it well-suited for pro- cessing and interpolating station data in large domains and in operational set- tings, ensuring timely and reliable outputs for hydrological application such as operational flood impact-based forecasting.
References
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[1] S Kr ̈ahenmann, A Walter, S Brienen, F Imbery, and A Matzarakis. High- resolution grids of hourly meteorological variables for germany. Theoretical and Applied Climatology, 131:899–926, 2018.
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[2] Steffen Zacharias, Heye Bogena, Luis Samaniego, Matthias Mauder, Roland Fuß, Thomas Pu ̈tz, Mark Frenzel, Mike Schwank, Cornelia Baessler, Klaus Butterbach-Bahl, et al. A network of terrestrial environmental observatories in germany. Vadose zone journal, 10(3):955–973, 2011.
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[3] Luis Samaniego, Rohini Kumar, and Conrad Jackisch. Predictions in a data-sparse region using a regionalized grid-based hydrologic model driven by remotely sensed data. Hydrology Research, 42(5):338–355, 2011.
How to cite: Mohannazadeh Bakhtiari, M., Najafi, H., Modiri, E., Rakovec, O., and Samaniego Eguiguren, L.: Creating a Near Real-Time 1-km Hourly Mean Temperature Gridded Dataset for Operational Flood Forecasting in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19023, https://doi.org/10.5194/egusphere-egu25-19023, 2025.
Flood forecasting models are essential tools for mitigating the impacts of extreme hydrological events by providing early warnings and actionable insights. This study evaluates the performance of the Today's Earth (TE) model, JAXA's land surface simulation system developed under joint research with the University of Tokyo, by comparing its predictions against observed water level data from the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) measuring stations in Japan. The study also compares the results to Google’s SOTA hydrological model using the Google Runoff Reanalysis & Reforecast (GRRR) dataset. The GRRR model features a 7-day (168-hour) forecast window, while Today's Earth offers a shorter forecast window of 39 hours. The analysis focuses on major flood events in Japan (2020–2024), including typhoons and heavy precipitation events, to examine trends and accuracy in flood predictions over different lead times. This evaluation identifies strengths and areas for improvement in operational forecasting across diverse hydrological scenarios.
The methodology integrates a comprehensive dataset of water level observations and forecast outputs, with an emphasis on lead time-dependent peak timing and magnitude error. Forecasts were evaluated based on their ability to capture observed flood peaks, with errors in both peak magnitude and timing quantified for varying lead times. Performance metrics such as Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Pearson correlation were calculated for each location and lead time.
Results reveal that forecast accuracy varies significantly with lead time, playing a critical role in the effectiveness of early warning systems. While Google's hydrological model performs better under normal flow conditions, Today's Earth model performs considerably better in both peak magnitude and timing during flood events. The GRRR dataset consistently underestimated peak magnitudes, highlighting the difficulty of forecasting extremes. Another key finding is the marked improvement in forecasting accuracy for the Today’s Earth model between 2021 and 2022, which coincides with the incorporation of observed precipitation data. Initial results indicate a significant enhancement in peak flow timing predictions following this update. This study evaluates how this modification improved forecasting results, emphasising the potential to refine TE’s algorithms and integrate additional observational data.
This research provides actionable insights into flood prediction reliability and demonstrates the value of leveraging Japan’s extensive network of water level gauges. Findings contribute to ongoing efforts to enhance flood forecasting systems globally and highlight the importance of targeted evaluations for improving model performance. The study's implications extend to disaster risk management, operational forecasting practices, and the broader pursuit of climate-resilient water management strategies.
How to cite: Wolf, M., Yamamoto, K., Liu, Y., and Yoshimura, K.: Evaluating the Performance of Flood Forecasting Models in Japan (2020-2024): Insights from Today's Earth, MLIT Observations and Google's SOTA hydrological model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14212, https://doi.org/10.5194/egusphere-egu25-14212, 2025.
The widespread implementation of small-scale hydraulic engineering structures has profoundly modified the underlying surface characteristics of watersheds, leading to significant changes in rainfall-runoff processes. Understanding the mechanisms by which small reservoirs influence runoff generation and routing, as well as developing effective simulation methods, is crucial for enhancing flood forecasting accuracy at the watershed scale. This research seeks to construct aggregated reservoirs based on the topological relationships of small storage bodies and to integrate these with existing hydrological models, thereby improving the precision of flood forecasting in humid watershed regions.
This study simplifies the diverse topological structures of small reservoirs into three foundational connection units: single-reservoir, series, and parallel configurations. These units are systematically combined into mixed configurations that realistically reflect the spatial distribution of storage bodies within watersheds. Furthermore, a multi-stage weir flow discharge scheme, specifically designed for aggregated reservoirs, is proposed based on field conditions, and the corresponding reservoir outflow equations are formulated. By coupling this newly developed reservoir storage-discharge module with the traditional lumped Xin'anjiang model, an improved version of the model is created, incorporating the regulatory effects of small reservoirs. To evaluate the performance of the improved Xin'anjiang model, 13 flood events were analyzed. Results demonstrated a substantial enhancement in simulation accuracy, with the average Nash-Sutcliffe efficiency coefficient improving by 0.27 during the calibration period and 0.40 during the validation period compared to the original model. Notably, the improved model excelled in simulating floods early in the flood season or following extended dry spells. However, its ability to simulate mid-to-late-season or multi-peak floods showed comparatively modest improvements. Additionally, the model's simulation accuracy was observed to decrease as flood magnitude increased.
Compared to traditional hydrological models that exclusively consider natural watershed processes, those incorporating aggregated reservoir storage and discharge dynamics offer a more nuanced representation of watershed hydrology. By significantly enhancing flood forecasting accuracy during the critical flood season, the improved model not only mitigates the impacts of flood disasters but also bolsters local water resource management capabilities.
How to cite: Wang, Y., Zhong, P., Zhu, F., Qian, X., Wang, B., and Han, Y.: Mechanisms and simulation methods for the impact of small reservoirs on watershed hydrological processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1336, https://doi.org/10.5194/egusphere-egu25-1336, 2025.
In order to estimate quantitative precipitation estimation (QPE) with high accuracy for flood forecasting, the quantitative uncertainty of heavy rainfall observations of radar must be identified in the spatiotemporal aspect. Considering the beam attenuation and the height of precipitation detected by radio waves, the accuracy of observations tends to be higher in short-range areas where the degree of beam attenuation is less and the observation height is low in order to estimate accurate precipitation used for ground flood forecasting. However, there have not been many cases where the error of precipitation estimation according to distance and altitude has been individually quantified and evaluated. Against this background, this study analyzed 22 major heavy rainfall events observed by five S-band dual-polarization radars in 2016 to quantify the reflectivity error according to observation distance and altitude, and derived the reflectivity error according to distance and altitude separately using Specific Differential Phase (Kdp). The analysis results showed that the average distance error of rainfall radar was approximately 10% or less up to 100 km and exceeded 30% above 150 km. The radar average elevation error was found to be approximately 10% or less for the second elevation angle from the ground among the six operating elevation angles, 20% for the third and above, and over 50% for the fourth and above. And the changes in observation accuracy during the heavy rainfall according to the observation range of 300km and 150km were compared through experiments. The experimental results showed that the cumulative reflectivity of the 150km observation was large when the distance from the radar was less than 75km, and the cumulative reflectivity of the 150km observation was large when the distance was more than 75km. This study is expected to contribute to establishing an appropriate rainfall radar observation strategy when operating a rainfall radar for the purpose of accurate quantitative rainfall observation for flood forecasting.
Acknowledgments
This research was supported by a grant(2022-MOIS61-003(RS-2022-ND634022)) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).
How to cite: Hwang, S., Yoon, J., Kang, N., and Kim, S.: Separation and estimation of reflectivity errors according to distance and altitude using Specific Differential Phase, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8803, https://doi.org/10.5194/egusphere-egu25-8803, 2025.
The classification of precipitation types using weather radar plays a crucial role in improving the accuracy of weather forecasts and preparing for natural disasters. However, for accurate precipitation prediction using weather radar, additional data preprocessing steps, such as quality control and removal of non-meteorological echoes, are essential.
This study aimed to develop an algorithm for classifying atmospheric hydrometeors using dual-polarization variables from weather radar and artificial intelligence (AI) technology. Various AI models were compared and evaluated to select the model with the best performance and examine its applicability. This is expected to contribute to improving early warning systems for hazardous weather phenomena such as heavy rain or snowstorms.
Acknowledgments
This research was supported by a grant(2022-MOIS61-003(RS-2022-ND634022)) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).
How to cite: Kang, N., Hwang, S., Yoon, J., and Kim, S.: Development of an AI-Based Precipitation Type Classification Algorithm Using Dual-Polarization Variables from Weather Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10563, https://doi.org/10.5194/egusphere-egu25-10563, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot A
EGU25-3543 | Posters virtual | VPS9
Real-time Transportation-Based Flood Warning System: A Case Study in Downtown LondonTue, 29 Apr, 14:00–15:45 (CEST) vPoster spot A | vPA.19
Flooding has posed a significant challenge to urban infrastructure, necessitating effective and real-time risk management strategies [1]. One of the most devastating impacts is on urban transportation, where disruption can lead to significant economic losses or even human casualties [2-3]. This study has focused on the key financial and commercial areas in downtown London, where an innovative system has been developed to integrate real-time flood risk forecasting with traffic data visualisation and dynamic decision support for emergency response and resource allocation. First, with access to the Google Maps API, real-time and forecast traffic data have been collected for local streets. Then, these datasets can facilitate a 15-minute resolution forecast for the next 8 hours, enabling an in-depth understanding of traffic flow patterns during flood events. Furthermore, by employing flood forecasting measures on these real-time datasets, streets at risk of inundation can be identified faster, with their traffic conditions assessed accordingly.
A key aspect of this study is to consider different factors dynamically for weighting and prioritising streets. On one hand, pre-existing factors such as road hierarchy, connectivity, access to critical facilities, land use, infrastructure vulnerability, and proximity to evacuation zones are converted into dynamic factors by attaching a temporal variable to these pre-existing factors. On the other hand, real-time dynamic ones include flood depth, traffic congestion, accessibility for emergency services, and community needs reported. The integration of all these factors leads to the development of a transportation-based decision support system (TBDSS) tailored to urban flood management. The TBDSS has facilitated the allocation of emergency resources, prioritisation of street reopening, and planning for evacuation or relief operations. For instance, streets connecting to hospitals or shelters have been given higher priority, while those serving industrial or low-density areas have been weighted lower. As such, our proposed system can dynamically adjust priorities based on evolving flood and traffic conditions, ensuring optimal response strategies.
The findings have demonstrated the feasibility of leveraging real-time data and advanced modeling to enhance urban flood resilience. By combining flood risk maps, traffic forecasts, and a comprehensive prioritisation framework, this approach has provided a promising tool for urban planners and emergency responders.
[1] Piadeh, F., Behzadian, K., Alani, A. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.
[2] Gao, G., Ye, X., Li, S., Huang, X., Ning, H., Retchless, D., Li, Z. (2024). Exploring flood mitigation governance by estimating first-floor elevation via deep learning and google street view in coastal Texas. Environment and Planning B: Urban Analytics and City Science, 51(2), 296-313.
[3] Naghedi, S. N., Piadeh, F., Behzadian, K., and Hemmati, M.: Unveiling the Interplay: Flood Impacts on Transportation, Vulnerable Communities, and Early Warning Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13189, https://doi.org/10.5194/egusphere-egu24-13189, 2024.
How to cite: Naghedi, R., Piadeh, F., Huang, X., and Wu, M.: Real-time Transportation-Based Flood Warning System: A Case Study in Downtown London, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3543, https://doi.org/10.5194/egusphere-egu25-3543, 2025.
EGU25-20713 | Posters virtual | VPS9 | Highlight
A Digital Twin Framework for Real-Time Flood Monitoring and Multidimensional Prediction: A case study in IranTue, 29 Apr, 14:00–15:45 (CEST) | vPA.20
Digital twins, virtual representations of physical systems, integrate sensor data and predictive models to enable real-time simulation and analysis. They are instrumental in monitoring weather, infrastructure health, and water levels, particularly in flood management. By modeling mitigation techniques, forecasting risks, and enhancing emergency responses, digital twins improve decision-making, reduce economic losses, and enhance public safety in flood-prone areas [1][2]. This study developed a digital twin system to monitor and forecast flood disasters in western Iran. The system combined multidimensional sensor data on temperature, flood flow, vegetation cover, and water levels using an offline databank. Time-series analysis tracked trends, while a linear regression-based predictive model estimated future flood conditions. Threshold values for flood warnings and high-risk alerts were defined using hydrological principles and environmental data [3]. Game theory concepts were employed to optimize flood management strategies by modeling interactions among stakeholders, including authorities, responders, and communities. A non-cooperative game theory approach simulated conflicting objectives, such as minimizing economic losses and optimizing resource allocation. Stable solutions were identified through the Nash equilibrium, ensuring no stakeholder could unilaterally improve outcomes. Visualization dashboards presented time-series data, risk levels, and stakeholder strategies, facilitating informed decision-making. Simulation results demonstrated the system's effectiveness in flood risk assessment. Water levels remained below the 2.5-meter warning threshold but rose significantly during simulated abnormal conditions. In later stages, some areas approached the 3.0-meter high-risk threshold, indicating zones vulnerable to flooding. Flood flow rates frequently exceeded the 40 m³/s threshold, with peaks above 60 m³/s, highlighting the need for continuous flow monitoring. Temperature fluctuations were minimal, consistently below the 25°C threshold, suggesting limited influence on flood risks during the study. However, vegetation cover often fell below the 30% threshold, correlating with increased flood risks and reinforcing its importance in mitigation. The system effectively categorized risk levels, with most instances classified as "Normal" or "Warning." High-risk alerts were concentrated during elevated water levels and flows. This research highlights the potential of digital twins for real-time flood monitoring and collaborative decision-making, providing a robust framework to enhance disaster resilience.
Keywords: Digital Twin; Flood Risk Assessment; Game Theory; Predictive Modeling; Multidimensional Data Analysis.
References
[1] Ghaith, M., Yosri, A., & El-Dakhakhni, W. (2021, May). Digital twin: a city-scale flood imitation framework. Canadian Society of Civil Engineering Annual Conference (pp. 577–588). Singapore: Springer Nature Singapore.
[2] Gheibi, M., & Moezzi, R. (2023). A Social-Based Decision Support System for Flood Damage Risk Reduction in European Smart Cities. Quanta Research, 1(2), 27–33.
[3] Kreps, D. M. (1989). Nash equilibrium. In Game Theory (pp. 167–177). London: Palgrave Macmillan UK.
How to cite: MohammadZadeh, F., Eghbalian, H., Gheibi, M. G., Yeganeh-Khaksar, R., Ghazikhani, A., and Behzadian, K.: A Digital Twin Framework for Real-Time Flood Monitoring and Multidimensional Prediction: A case study in Iran, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20713, https://doi.org/10.5194/egusphere-egu25-20713, 2025.
EGU25-3504 | Posters virtual | VPS9
Asset-based Dynamic Flood Risk Assessment: Case Study of London DowntownTue, 29 Apr, 14:00–15:45 (CEST) | vPA.21
Asset-based Dynamic Flood Risk Assessment: Case Study of London Downtown
Flooding poses significant risks to urban centres, with particular challenges faced by business hubs where disruptions can have devastating consequences on national and global economies [1]. Business hubs are the lifeblood of national and global economies. During flood events, businesspeople encounter disruptions that not only obstruct daily operations but also ripple through supply chains and financial systems [2-3]. This study emphasises the importance of protecting critical assets in Downtown London, a vital business hub, to mitigate economic and social impacts during floods. Through a watershed-based approach, Downtown London, a vibrant business hub with numerous critical assets, has been selected as the case study area. The district contains key commercial buildings and infrastructure that are vital to economic and social continuity. Using Digimap and Verisk, essential commercial buildings and critical assets are pinpointed based on their usage and significance. These tools facilitate generating an accurate map of assets requiring priority attention during flood events.
The proposed decision support system (DSS) is developed to aid risk management authorities, including policy-makers, decision-makers, and technical staff. The system operates on two key bases. Real-time population density data for critical assets is obtained using Google API. This data helps evaluate the human vulnerability component during flood scenarios. A flood forecasting system is integrated to predict water levels at 15-minute intervals for the coming hours. This system provides granular and actionable insights into evolving flood conditions. For each critical asset, two risk values are computed: one based on population density and another on forecasted water levels. These values are combined to derive a dynamic risk level for each time step, enabling authorities to respond effectively. The integration of real-time data and predictive modeling in the DSS offers a comprehensive framework for flood risk assessment. By prioritising critical assets based on dynamic risk levels, authorities can implement targeted preparedness and response measures such as early warnings and evacuation plans. This approach ensures both human safety and economic resilience. The findings have demonstrated the feasibility of applying real-time data and cutting-edge modeling to enhance urban flood resilience. By combining flood risk maps, real-time population density, and a comprehensive prioritisation framework, this approach provides a promising tool for urban planners and emergency responders to protect critical business assets and ensure economic continuity during flood events.
References
[1] Bakhtiari, V., Piadeh, F., Behzadian, K. and Kapelan, Z. (2023). A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management. Sustainable Cities and Society, p.104958.
[2] Bakhtiari, V., Piadeh, F., Chen, A.S. and Behzadian, K. (2024). Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review. Expert Systems with Applications, 236, p.121426.
[3] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.
How to cite: Bakhtiari, V. and Piadeh, F.: Asset-based Dynamic Flood Risk Assessment: Case Study of London Downtown, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3504, https://doi.org/10.5194/egusphere-egu25-3504, 2025.
EGU25-3526 | Posters virtual | VPS9
Community-based flood early warning system: Current practice and Future directionsTue, 29 Apr, 14:00–15:45 (CEST) | vPA.22
Social media applications have emerged as reliable communication channels, especially when traditional methods falter [1]. Their integration into emergency management presents significant advantages, including enhanced situational awareness during unfolding events, rapid dissemination of news and alerts to broader audiences, and improved coordination among decision-makers and stakeholders [2]. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring [3]. To better understand these advantages and challenges, a comprehensive review and analysis of the literature on the application of social media in this field was conducted. Social media facilitates participatory and collaborative structures, enabling collective knowledge-building in public information and warning systems. To realise this vision, the authors examined, 73 studies conducted from 2014 to 2024 to systematically evaluate the current literature surrounding communication on social media and the latest research in social media informatics related to disasters. This review identified key challenges within existing studies. The articles included 23 related to pluvial floods, 12 related to fluvial floods, 17 related to storm floods and 21 paper that were unspecified The majority of the studies were conducted in China, followed by the United States. Various software platforms, including Twitter, YouTube, and other social media networks, were analysed. Data extraction from these platforms was performed using Python programming. The study periods ranged from 1 to 3,650 days. These findings serve as guidance for researchers examining the relationship between social media and disaster management. They aim to develop the use of social networks during disasters, analyse patterns, and create programming to identify best practices for utilising social media in times of crisis. In the future, a mapping framework and tool can be developed to automatically extract information from social media through text and image analysis. By integrating this data with other available information sources, it will be possible to generate more accurate inundation maps in real-time. It is essential to recognise that information about floods obtained from social media may be incomplete during communication interruptions. To address this issue, future research should prioritise integrating big data from urban Internet of Things networks and improving communication infrastructure repairs. By adopting this strategy, we can collect more comprehensive disaster information to enhance flood emergency response effectiveness.
References
[1] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.
[2] Piadeh, F., Ahmadi, M., Behzadian, K. (2020). A Novel Planning Policy Framework for the Recognition of Responsible Stakeholders in the of Industrial Wastewater Reuse Projects. Journal of Water Policy, 24 (9), pp. 1541–1558.
[3] Bakhtiari, V., Piadeh, F., Chen, A., Behzadian, K. (2024). Stakeholder Analysis in the Application of Cutting-Edge Digital Visualisation Technologies for Urban Flood Risk Management: A Critical Review. Expert Systems with Applications, p.121426.
How to cite: Panahi, A., Karkhaneh, N., and Piadeh, F.: Community-based flood early warning system: Current practice and Future directions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3526, https://doi.org/10.5194/egusphere-egu25-3526, 2025.
