Flood Depth Research Articles

Automated geovisualization of flood disaster impacts in the global South cities with open geospatial data sets and ICEYE SAR flood data

Flooding is a highly destructive natural disaster affecting millions of people annually, causing substantial global economic losses. Especially in the Global South, vulnerable populations with limited resources for flood protection and recovery suffer the most. However, effective flood impact analysis and standardized geospatial data processing and visualization is lacking, hindering effective disaster risk reduction. This study employs Python to develop a standardized, automated analysis and visualization model to address flood impacts after a high magnitude event anywhere in the world by using global geospatial data sets.In order to test its applicability two relevant cases, with different locations and scales, were selected, (Bangkok, Thailand, and Tula de Allende, Mexico). In both cases the automated flood model efficiently processed flood extent and depth data from ICEYE SAR images, added the population estimates from the German Aerospace Center, and key infrastructure elements from OpenStreetMap. The output of the automated process is presented as web-based interactive maps, offering insights into flooding severity and impacts with minimal user input. This quick approach (processing time between 41 s and 10,5 min) facilitates timely responses by first responders, with great potential to aid and improve the efficiency of international humanitarian efforts.By leveraging global high-resolution geospatial data for local-level analysis, this study demonstrates the versatility and time-saving benefits of this automated analysis and visualization. Automated models standardize results, minimizing human errors, and enabling consistent historical flood data comparison. Even though the databases are gathered from trusted sources, additional evaluation of uncertainties of the results with field data should be considered in further development of this tool. Nevertheless, this research highlights the worldwide potential, and especially the Global South, of automated global geospatial analysis to improve disaster impact reduction by enhancing efficient response efforts.

Flood forecasting based on radar precipitation nowcasting using U-net and its improved models

Accurate and timely short-term precipitation nowcasting is important for achieving reliable flood forecasting. The data-driven approaches have performed well in radar echo extrapolation to nowcast precipitation. In this paper, U-Net and its improved models, including SmaAt-Unet, Nested-Unet, and U-Net 3Plus were applied to perform radar echo extrapolation and precipitation nowcasting for 0.5 h, 1 h, and 2 h lead times of typical rainfall processes. The nowcasted precipitation was used as input to the HEC-HMS hydrological model for flood forecasting to compare the effect of different structural improvements to U-Net on the accuracy of flood forecasting. The results demonstrated that Nested-Unet and U-Net 3Plus aided in enhancing the accuracy of the extrapolation of moderate intensity radar echoes. With fewer discrepancies and better correlation with measured rainfall, the U-Net and U-Net 3Plus precipitation nowcasting results also produced improved flood forecasting outcome. The precipitation nowcasting and flood forecasting for SmaAt-Unet were slightly worse than other models; the relative errors of both flood peak and depth for Nested-Unet at 0.5 h lead time were less than 20 %, showing a good performance. Moreover, in a separate control experiment, the accuracy of the echo extrapolation was significantly decreased when convolutional block attention module (CBAM) was added to each model. However, all models have better extrapolation accuracy than the basic ConvLSTM. In general, Nested-Unet and U-Net 3Plus were helpful to improve the accuracy of precipitation nowcasting and flood forecasting, and the forecasted flood with 0.5 h and 1 h lead times could match the actual flood processes, but the peak discharge from nowcasting with 2 h lead time were severely underestimated, while the peak occurrence time could be forecasted correctly. These conclusions and attempts can provide effective guidelines for regional precipitation nowcasting and flood forecasting.

Impacts of sea level rise on the cultural heritage of oceanic islands: Modeling twenty-first century scenarios in the Canary archipelago

Oceanic islands constitute 0.05% of emerged land masses but account for 2% of UNESCO World Heritage Sites. Physical constraints have determined that the history of these remote territories is marked by globally unique forms of colonization and human cultures. A significant part of this heritage, located in coastal areas, is threatened by the mean sea level rise (SLR) due to anthropogenic climate change. This paper presents a regional approach to the risk estimation of SLR in the cultural heritage of oceanic islands, taking the Canary Islands (Spain) as an example. The Canary Islands is one of the most prominent oceanic archipelagos in the world (∼1500 km of coastline), where cultural heritage, preferentially located in the coastal areas (up to 50% in the first 1 km), nicely represents the two major stages of human occupation of islands (aboriginal and European). Through process-based modeling, high resolution flood maps were obtained for 20 climate scenarios and complete coverage of the archipelago. These maps were combined with detailed planimetries of 343 officially designated cultural heritage sites, applying damage functions to relate flood depth with the type of construction and the historical period. The results indicate potential damage to 55 cultural heritage sites of the Canary Islands which could be significant in 15 of them. For these cases, it is recommended to study specific adaptation measures aimed at mitigating the flood risk, such as moving the heritage assets away from the hazard areas or reinforcing and protecting the structures to reduce their vulnerability.

Sunny-Day Flooding and Mortality Risk in Coastal Florida

Sea-level rise is likely to worsen the impacts of hurricanes, storm surges, and tidal flooding on coastal access to basic services. We investigate the historical impact of tidal flooding on mortality rates of the elderly population in coastal Florida using administrative records of individual deaths, demographics, and residential location combined with tidal gauge and high-resolution elevation data. We incorporate data capturing storm and precipitation events into our empirical model to distinguish between disruptions from routine sunny-day flooding and less predictable tropical storm–induced flooding. We find that a 1-standard-deviation (20-millimeter) increase in tidal flooding depth increases mortality rates by 0.46% to 0.60% among those aged 65 or older. Our estimates suggest that future sea-level rises may contribute to an additional 130 elderly deaths per year in Florida relative to 2019, all else being equal. The enhanced risk is concentrated among residents living more than nine minutes away from the nearest hospital. Results suggest that tidal flooding may augment elderly mortality risk by delaying urgent medical care.

The global long-term effects of storm surge flooding on human settlements in coastal areas

People in low-lying coastal areas live under the great threat of damage due to coastal flooding from tropical cyclones. Understanding how coastal population settlements react to such events is of high importance for society to consider future adaptation strategies. Here we generate a new global hydrodynamic data set on tropical cyclone-generated storm surge flooding for the period 1851–2020. By combining this data with spatial data on human populations, we analyze the influence of the depth of storm surge flooding on the rural, urban, and total populations in low elevation coastal zones from 1941–2010. We find that in response to a one standard deviation increase in storm surge flooding depth (0.43 m), the exposed population in a 10×10 km low elevation coastal zone decreases by around 970 individuals on average per decade. This reduction corresponds to 9% of the average population living in an exposed grid cell. Tropical cyclone generated wind speed and rainfall do not influence the relocation of coastal populations. The majority of the threatened population lives in Eastern, South–Eastern, and Southern Asia. We show that the exposed coastal population appears to have adapted over time by reducing its exposure in recent decades. This finding applies to all regions other than North America, Oceania, and Western Asia.

Exposure and physical vulnerability to geo-hydrological hazards in rural environments: A field-based assessment in East Africa

Geo-hydrological hazards (landslides and floods) are often associated with large impact on physical infrastructures, affecting strongly the exposed population. Understanding in detail the nature of exposure and what determines the extent of impact is a prerequisite for building resilient structures and reducing vulnerability. However, such information is rarely available, especially in rural communities where people are often the most vulnerable. In this study we document the impact of landslide and flood hazards in 17 sub-counties in the mid- and south-western parts of Uganda between May 2019 and March 2021 through extensive fieldwork involving local stakeholders. We quantify the economic value of the physical damage of landslide and flood hazards on exposed buildings, roads and bridges. We then determine statistically the factors that influence the physical vulnerability of the elements at risk based on damage ratios, i.e. monetary value of each individual building damage divided by its initial construction value. A total of 91 and 212 buildings were documented from the landslide and flood hazard affected zones, respectively. Out of the 91 landslide affected buildings, 71 % were completely destroyed and 4 % severely damaged, while 19 % had minor-to-no damage at all. For floods, 37 % of the buildings were completely destroyed and 6 % were severely damaged, while 53 % had minor-to-no damage. The impact of landslides and floods is unevenly distributed across the study sub-counties and the extent of loss they cause also vary with exposed elements. Based on parameters that could be documented through our field-based approach, we conclude that while the physical vulnerability of buildings to landslides is equally associated with both landslide size and building structural characteristics, the physical vulnerability due to floods is largely linked to the flood depth. This quantitative study therefore shows the need for plans, policies and strategies geared towards improving the building resistance to landslides and controlling settlements in flood prone places.

Integrated Hydrological Modeling for Watershed Analysis, Flood Prediction, and Mitigation Using Meteorological and Morphometric Data, SCS-CN, HEC-HMS/RAS, and QGIS

Flooding is a natural disaster with extensive impacts. Desert regions face altered flooding patterns owing to climate change, water scarcity, regulations, and rising water demands. This study assessed and predicted flash flood hazards by calculating discharge volume, peak flow, flood depth, and velocity using the Hydrologic Engineering Centre-River Analysis System and Hydrologic Modelling System (HEC-HMS and HEC-RAS) software. We employed meteorological and morphological data analyses, incorporating the soil conservation service (SCS) curve number method for precipitation losses and the SCS-Hydrograph for runoff transformation. The model was applied to two drainage basins (An-Nawayah and Al-Rashrash) in southeastern Cairo, Egypt, which recently encountered several destructive floods. The applied model revealed that 25-, 50-, and 100-year storms produced runoff volumes of 2461.8 × 103, 4299.6 × 103, and 5204.5 × 103 m3 for An-Nawayah and 6212 × 103, 8129.4 × 103, and 10,330.6 × 103 m3 for Al-Rashrash, respectively. Flood risk levels, categorised as high (35.6%), extreme (21.9%), and medium (21.12%) were assessed in low- and very-low-hazard areas. The study highlighted that the areas closer to the Nile River mouth faced greater flood impacts from torrential rain. Our findings demonstrate the effectiveness of these methods in assessing and predicting flood risk. As a mitigation measure, this study recommends the construction of five 10 m high dams to create storage lakes. This integrated approach can be applied to flood risk assessment and mitigation in comparable regions.

Predicting Typhoon Flood in Macau Using Dynamic Gaussian Bayesian Network and Surface Confluence Analysis

A typhoon passing through or making landfall in a coastal city may result in seawater intrusion and continuous rainfall, which may cause urban flooding. The urban flood disaster caused by a typhoon is a dynamic process that changes over time, and a dynamic Gaussian Bayesian network (DGBN) is used to model the time series events in this paper. The scene data generated by each typhoon are different, which means that each typhoon has different characteristics. This paper establishes multiple DGBNs based on the historical data of Macau flooding caused by multiple typhoons, and similar analysis is made between the scene data related to the current flooding to be predicted and the scene data of historical flooding. The DGBN most similar to the scene characteristics of the current flooding is selected as the predicting network of the current flooding. According to the topography, the influence of the surface confluence is considered, and the Manning formula analysis method is proposed. The Manning formula is combined with the DGBN to obtain the final prediction model, DGBN-m, which takes into account the effects of time series and non-time-series factors. The flooding data provided by the Macau Meteorological Bureau are used to carry out experiments, and it is proved that the proposed model can predict the flooding depth well in a specific area of Macau under the condition of a small amount of data and that the best predicting accuracy can reach 84%. Finally, generalization analysis is performed to further confirm the validity of the proposed model.

Suitability of the height above nearest drainage (HAND) model for flood inundation mapping in data-scarce regions: a comparative analysis with hydrodynamic models

Unprecedented floods from extreme rainfall events worldwide emphasize the need for flood inundation mapping for floodplain management and risk reduction. Access to flood inundation maps and risk evaluation tools remains challenging in most parts of the world, particularly in rural regions, leading to decreased flood resilience. The use of hydraulic and hydrodynamic models in rural areas has been hindered by excessive data and computational requirements. In this study, we mapped the flood inundation in Huron Creek watershed, Michigan, USA for an extreme rainfall event (1000-year return period) that occurred in 2018 (Father’s Day Flood) using the Height Above Nearest Drainage (HAND) model and a synthetic rating curve developed from LIDAR DEM. We compared the flood inundation extent and depth modeled by the HAND with flood inundation characteristics predicted by two hydrodynamic models, viz., HEC-RAS 2D and SMS-SRH 2D. The flood discharge of the event was simulated using the HEC-HMS hydrologic model. Results suggest that, in different channel segments, the HAND model produces different degrees of concurrence in both flood inundation extent and depth when compared to the hydrodynamic models. The differences in flood inundation characteristics produced by the HAND model are primarily due to the uncertainties associated with optimal parameter estimation of the synthetic rating curve. Analyzing the differences between the HAND and hydrodynamic models also highlights the significance of terrain characteristics in model predictions. Based on the comparable predictive capability of the HAND model to map flood inundation areas during extreme rainfall events, we demonstrate the suitability of the HAND-based approach for mitigating flood risk in data-scarce, rural regions.

Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method

Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method

A spatial–temporal deep learning-based warning system against flooding hazards with an empirical study in Taiwan

With climate change intensifying, increasingly extreme rainfall events which lead to flooding are occurring at a higher frequency. As such, it is crucial to develop and implement useful flood forecasting models which can provide early knowledge of the severity of flood risks and allow decision makers to take preventive and response actions which reduce human casualties. This study, carried out in collaboration with the National Science and Technology Center for Disaster Reduction (NCDR) of Taiwan, utilizes deep learning techniques combined with attention mechanisms to establish a data-driven flood forecasting framework which can serve as an early warning system for flooding hazards. The overall framework, which utilizes historical flood depth data from an array of flood sensors while engineering additional features from KRID (estimation Rainfall with Integrated radar-gauge Data) observed rainfall data consists of two phases. The first phase is a flood event detection model built using a classification-based random forest algorithm. The second phase is a flood depth prediction model which utilizes a spatial–temporal attention mechanism combined with a transformation-gated long short-term memory (LSTM).This novel model, known as the Spatial–Temporal Attention Transformation-Gated LSTM (STA-TGLSTM), is capable of predicting flood levels over the next three hours. In an empirical study based in Changhua County, Taiwan, STA-TGLSTM achieves the lowest RMSE losses for predicting the next three hours of flood levels compared to baseline machine learning models. Notably, the accuracy of predicting the flood sensor depth for the first hour (T+1) reaches 76% when STA-TGLSTM is applied. In comparison to traditional hydrological models, this data-driven model offers greater computational efficiency, providing future flood depth information within seconds. Moreover, as demonstrated in the empirical study, by visualizing the weight values of the spatial–temporal attention mechanism, valuable insights into the relationship between each flood sensor and the spatiotemporal variables can be readily gained.

An overview of flood risk components: The Land subsidence case of Pekalongan City

Pekalongan City is located on the northern coast of Java Island, where climate crises, floods, and tidal waves pose significant threats exacerbated by rising sea levels and land subsidence. This study aims to mitigate of flood future losses and emphasizes its potential as input for Indonesian regulators to enhance current flood hazard classifications. This study conducts a comprehensive flood risk analysis, emphasizing a detailed review of flood hazard classification quantitatively with flood hazard mapping. The results showed flood hazard mapping, comparing NDMA, MPW, and proposed classifications, indicates a tendency for MPW to overestimate hazards, particularly categorizing depths of 0.5 m and above as “High”. The flood depth according to MPW with land subsidence produces a flood inundation area of 14,145 Ha. This shows that land subsidence recorded in risk mapping can increase the overall hazard level. This study proposed flood depth and velocity classification, balancing NDMA’s underestimation and MPW’s overestimation, offering a more comprehensive approach.

Application of Spatial Model the Contribution of Land Subsidence Caused by Palm Oil Plantations Land Clearing to the Escalating Flood Risk in the Trumon Area, South Aceh Regency, Indonesia

The Trumon watershed one of critical watershed in Aceh Province had an increase in historical flood episodes during the past 25 years. This is consistent with the expansion of oil palm plantations within the 53,653 Ha watershed basin dominated by peatlands. According to earlier research, changes in land use in peatland regions may cause subsidence. This research looks at the connection between flood events and the subsidence of ground caused by oil palm plantation land clearing. The satellite imagery interpretation reveals a remarkable land use change of previously 5091.25 Ha peatland forest observed in 1995, to become a 13,753.43 Ha palm oil plantation in 2021. From DEMs analysis, those changes are associated with a subsiding land elevation up to 1.23 m. Despite being less than 20% of the watershed area, the land subsidence effect striking change in the watershed boundary, i.e., from only 53,653 Ha to 786,675 Ha which led to an increase in flood inundation from 3,443.04 Ha to 47,924.92 Ha. and flood depth from 0.5 – 1.0 m, to up to 4.0 m. This current situation has made the Trumon area subjected to frequent floods, i.e., from a five-year return period to become an annual event.

City-scale high-resolution flood models and the role of topographic data: a case study of Kathmandu, Nepal

Topographic data is a fundamental input to flood hazard models and controls the quality of the outputs. However, open-access global digital elevation models (DEMs) are dated and limited to 30 m resolution, which hinders modelling efforts in urban or topographically complex environments. We used the flood prone and expanding city of Kathmandu, Nepal, to evaluate the impact of topographic data source and resolution on flood model outputs. All DEMs evaluated featured spatially correlated topographic sinks with depths exceeding 20 m that required hydrological conditioning before being used in flood modelling. Incomplete hydrological conditioning appeared related to the overestimation of flood extent and therefore limited agreement when comparing a global 90 m resolution flood hazard model with a bespoke city-scale model at 10 m resolution (F1 score = 0.40). Instead, we found that the height above nearest drainage (HAND) metric was better able to replicate the higher resolution flood map as an indicator of flood susceptibility requiring only topographic information as an input. We also found that the computationally efficient FastFlood model was able to match the inundation extent (F1 score = 0.79) and flood depths (mean absolute error and root mean square error of 0.46 and 0.76 m respectively) of a published 10 m physics-based flood hazard model whilst requiring 212 times less computation time. Our analysis demonstrated that mapping city-scale flood inundation required hydrologically conditioned high-resolution topographic data but not physically complex flood models, highlighting the need for greater availability of high quality open access topographic data.

2D Hydrodinamic Modelling of Flood Inundation Scenarios of Krueng Peuto River at North Aceh Regency of Aceh Province

Krueng Peuto sub-watershed, is a part of the Krueng Keureuto watershed in North Aceh that frequently experiences flooding. At the end of 2021, an embankment broke on the Peuto River and caused river overflow and submerged hundreds of houses in 33 villages around the sub-watershed. This study aims to analyze the critical circumstance of the Krueng Peuto sub-watershed which potentially lead to Peuto River overflow, using a 2D hydrodynamic model Geo-HECRAS. The validation of flood distribution points is based on historical floods conditions, i.e. depth and area of flood inundation, with modeling results for return periods scenarios of 2, 5, 10, 25, 50, and 100 years. From the modelling results, the flood inundation area at the 25-year return period was 25.514 km2, with a flood depth of 0-1 m covering an area of 8.35 km2 (32.73%), a depth of 1-2 m covering an area of 6.71 km2 (26.33%), and a depth of >2 m covering an area of 10.44 km2 (40.95%). The validation results of the 25-year return period flood inundation with flood distribution points show that in the flood inundation area there are 17 flood distribution points or the flood inundation area already represents 81% of flood distribution points.

Flood Risk Management in the Upper Caroni River Basin

Flooding is more common than earthquakes and hurricanes, leaving casualties, infrastructure damage, and economic losses in its wake. The October 2018 floods in Trinidad heightened public concern about the ability of public authorities to manage floods. This study investigates the impact of five combinations of detention basins and embankments on the cost of physical damage to residential buildings in the Caroni River Basin. Further comparison of the maximum flood depth before and after the implementation of structural mitigation combinations was conducted for typical design return periods of 5, 25, 50, and 100 years. The results showed that strategically introducing detention ponds along the Aripo and Guanapo Rivers caused the greatest reduction in flood damage cost (12.86%). Evidence also suggests that attenuation of flood waters by detention ponds is a better alternative to flood protection by embankment-only options

Soil Quality Assessment of Cultivating Flooded Rice on Histosol Under Varying Flood Depths

Soil Quality Assessment of Cultivating Flooded Rice on Histosol Under Varying Flood Depths

Flood Risk Mapping of Kankai Basin: A Case Study of Shivasatakshi Municipality and Kankai Municipality

Floods are probably the most frequent, widespread, and disastrous hazards in the world. It causes loss of life, casualties, financial loss, and displacement. Heavy monsoons, fragile geography, and constructions along the embankments cause the river to flood. The increase of population and squatter settlement of landless people living at the bank of the river has tremendous pressure on encroachment of flood plain making them vulnerable to flood damage. This study aims to develop HEC-RAS models for the Kankai River basin for preparation of 1D flood plain maps, vulnerability, risk assessment of RCC building in Shivasatakshi Municipality ward 10, and Kankai Municipality ward 4. Flood frequency analysis for 2, 10, 25, 50, 100, 200 and 1000 years return was carried out by Log Pearson III, Gumbel and Log Normal method based on maximum instantaneous flow recorded at Mainachuli station. The result of flood frequency analyzed by Log Normal method for 2, 10, 25, 50, 100, 200 and 1000 years return period floods shows higher among the results obtained using different methods. The flooding impact of the Kankai River being a large catchment with braided river form inundates vast downstream flood plain regions during high flood levels. With numerous people living along the river and downstream plains, flood risk is predominately high in the Kankai River catchment. With increasing flood intensity, higher flood depth increases and lower flood depth decreases. The inundation of a large area of Shivasatakshi Municipality ward 10 and Kanaki Municipality ward 4 at the bank of the Kankai river indicates that human lives and the physical infrastructure will be more vulnerable to flood disasters in the future. According to a study, the largest areas affected by floods with return periods of 2, 10, 25, 50, 100, 200, 1000 years, respectively, are 1843.24 ha, 1967.96 ha, 2016.32 ha, 2049.48 ha, 2079.64 ha, 2109.4 ha and 2176.08 ha of the whole basin. As a result, the study may help in future disaster planning and management.

Influence of flooding variability on the development of an Amazonian peatland

ABSTRACTPeat in the Pastaza–Marañón Foreland Basin (PMFB), northern Peru, forms beneath open wetlands, palm swamps, pole forests and seasonally flooded forests. These vegetation communities may represent different successional stages of peatlands, but the spatiotemporal patterns of peatland development in Amazonia are still poorly understood. We present a new geochemical and palaeoecological record spanning the last c. 4330 years from an open peatland (San Roque, core SAR_T3_03_B). Our results suggest the persistence of predominantly herbaceous vegetation communities at the core site since the start of peat accumulation (c. 3180 cal a bp). Micro‐X‐ray fluorescence core scanning provides evidence for episodes of fluvially derived minerogenic input and simultaneous increases in flood‐tolerant taxa relating to intervals of increased frequency and depth of riverine flooding. The establishment of Mauritia flexuosa palms from around 440 cal a bp coincided with a shift to lower flooding depth and frequency which continues to the present day. This study reveals the role of flooding variability in shaping peatland development and influencing vegetation succession in the PMFB, underlining the need to understand natural environmental variability for the conservation of these ecosystems due to their vital contributions to ecosystem services and carbon storage.

Integration of an improved transformer with physical models for the spatiotemporal simulation of urban flooding depths

Study regionThe Caochangmen area in the Gulou District, Nanjing City, China, is a typical flood-prone and well-defined region. Study focusThis paper addresses the limitations of the current "single-driver" approach in simulating urban waterlogging, proposing a modeling methodology that integrates physical coupling models with data-driven techniques. The objective is to conduct comprehensive simulation research on urban flooding depth that leverages the respective strengths of both physical and data-driven models. A 1D pipe network runoff model and 2D surface runoff model were coupled to create an integrated model under a physical coupling framework. Based on a dataset of urban-rainfall-induced flooding depths, two data-driven modules were designed: a convolutional neural network (CNN) and improved transformer. To address the parameter uncertainty in the “model-data” coupling approach, two optimization methods were proposed for optimizing sensitive physical model parameters and the performance of the deep learning models, facilitating the spatiotemporal simulation of urban flood depths. New hydrological insights for the regionThe proposed physically calibrated module exhibits a nearly linear relationship with observed water depths, confirming the suitability of the calibrated parameters for the physical driver of the integrated model within the study area. The improved transformer integrated model outperformed the CNN integrated model, achieving excellent efficiency and high accuracy. This research employed a dual-engine driving approach, wherein physical models and data are mutually integrated and mutually reinforced. This demonstration underscores the potential of the synergy between deep learning and physical knowledge in urban flooding simulation studies.