Flood Distribution Research Articles

Spatial-Temporal Distribution and Vulnerability Level of Tidal Flooding in the Coastal City of Semarang

Spatial-Temporal Distribution and Vulnerability Level of Tidal Flooding in the Coastal City of Semarang

Comprehensive Assessment of Large-Scale Regional Fluvial Flood Exposure Using Public Datasets: A Case Study from China

China’s vulnerability to fluvial floods necessitates extensive exposure studies. Previous large-scale regional analyses often relied on a limited set of assessment indicators due to challenges in data acquisition, compounded by the scarcity of corresponding large-scale flood distribution data. The integration of public datasets offers a potential solution to these challenges. In this study, we obtained four key exposure indicators—population, built-up area (BA), road length (RL), and average gross domestic product (GDP)—and conducted an innovative analysis of their correlations both overall and locally. Utilising these indicators, we developed a comprehensive exposure index employing entropy-weighting and k-means clustering methods and assessed fluvial flood exposure across multiple return periods using fluvial flood maps. The datasets used for these indicators, as well as the flood maps, are primarily derived from remote sensing products. Our findings indicate a weak correlation between the various indicators at both global and local scales, underscoring the limitations of using singular indicators for a thorough exposure assessment. Notably, we observed a significant concentration of exposure and river flooding east of the Hu Line, particularly within the eastern coastal region. As flood return periods extended from 10 to 500 years, the extent of areas with flood depths exceeding 1 m expanded markedly, encompassing 2.24% of China’s territory. This expansion heightened flood risks across 15 administrative regions with varying exposure levels, particularly in Jiangsu (JS) and Shanghai (SH). This research provides a robust framework for understanding flood risk dynamics, advocating for resource allocation towards prevention and control in high-exposure, high-flood areas. Our findings establish a solid scientific foundation for effectively mitigating river flood risks in China and promoting sustainable development.

Analyzing the spatial scale effects of urban elements on urban flooding based on multiscale geographically weighted regression

In the context of rapid urbanization and the frequent occurrence of extreme rainfall in cities, the risk of flooding in the future will further increase, and the problem of urban flooding cannot be ignored. Urban elements exhibit significant spatial heterogeneity, which largely determines the spatial distribution differences in urban flooding. Therefore, it is important to clarify the scale of influence of different urban elements and explore their scale effects on urban flooding to accurately assess the risk of urban flooding. Taking Zhengzhou City, China, as the study area, this study analyzed the urban elements associated with urban flooding, quantified the scale of the influence of urban elements on flooding using multiscale geographically weighted regression (MGWR), and further explored the spatial scale effects of urban elements on urban flooding. The results showed that MGWR can better fit the spatially non-uniform distribution of urban flooding and that the scale of the influence of urban elements on urban flooding can be reflected by the bandwidth of MGWR. The results of MGWR indicated that the bandwidths of elevation, number of drainage outfalls (NDO1Abbreviations: RRP, Rainfall return period; DDR, Distance to river; NDO, Number of drainage outfalls; ILMFCP, Input level of materials for flood control projects; PRL, Proportion of residential land; PCL, Proportion of commercial land; PIL, Proportion of industrial land; PPSL, Proportion of public service land; PRA, Proportion of road area; PGL, Proportion of green land; PD, Population density; MI, Manpower input; GDP, Gross domestic product; ILER, Investment level in education and research.1), distance to river (DR), Gross domestic product (GDP), proportion of residential land (PRL), and proportion of commercial land (PCL) were small, and their influence scales were localized. In contrast, the influence of rainfall return period (RRP), slope, proportion of industrial land (PIL), proportion of public service land (PPSL), proportion of road area (PRA), proportion of green land (PGL), input level of materials for flood control projects (ILMFCP), population density (PD), manpower input (MI), and investment level in education and research (ILER) were at global scales. The most influential factors for urban flooding were RRP, PD, and MI. Slope, DR, and PRA had less influence on urban flooding. This study helps improve the effectiveness of urban flood prevention and mitigation efforts.

Analysis of Deformation and Flood Inundation Spatial Distribution Using Sentinel-1 SAR Data for The 2021 South Kalimantan Floods

Abstract Major flood disasters that have occurred in Indonesia, one of which occurred in South Kalimantan in early 2021. This flood disaster was caused by high rainfall intensity. There are several relationships between rainfall, flood, and deformation. The objective of this study is to monitor deformation in flood inundation that occurred during the flood disaster in South Kalimantan. Observations with direct measurements in the field provide high data accuracy, but in its implementation there are limitations. Therefore, we utilize the development of Synthetic Aperture Radar (SAR) technology in observing flood inundation spatial distribution and deformation in South Kalimantan. Using SAR cannot be as accurate as direct measurements in the field, but it is effective in saving time, can be applied to wide area coverage, and can be observed multitemporally. The SAR data used is Sentinel-1 imagery with C-band which can be freely accessed and has high spatial and temporal resolution. In flood mapping with backscatter using change detection, the flood inundation distribution was 2022.558 km2. The results show deformation variations in the form of land subsidence of up to 15.9 cm in the flood distribution area.

A novel ANN-CA and MCDA integrated framework for predicting urban expansion and its implications on future flood risk, Accra Metropolis

Urban development in African countries significantly impacts environmental sustainability and city resilience, particularly in flood risk management. The Accra Metropolis, in particular, faces increasingly prevalent annual floods that disproportionately affect the urban poor living in informal settlements, resulting in significant loss of life and disruption of livelihoods. This study developed a GIS-based flood risk mapping framework that integrates artificial neural network (ANN) and cellular automata (CA) modelling with multi-criteria decision analysis. This hybrid approach was employed to predict flood scenarios for the Metropolis in 2032 and 2042. The framework incorporated six hydro-geomorphological indicators influencing extreme events: LULC, slope, elevation, drainage density, soil type, and proximity to rivers. The study analysed LULC projections, revealing a trend of substantial urban expansion with an estimated 10.9% increase in built-up areas within the next 20 years. This growth is expected to significantly heighten future flood risk in both the central upstream and downstream portions of the Metropolis, particularly in low-lying informal settlements close to the Odaw River and Korle Lagoon. Model performance was validated by ROC curve analysis, with AUC values of 0.927 and 0.922 for 2032 and 2042, respectively, which resonates with the records of previous flood distribution studies of the area. This study highlights the crucial need for sustainable urban development, improved infrastructure, and proactive flood mitigation measures in Accra to assist urban planners, policymakers, and stakeholders in managing flood risks effectively.

Numerical Analysis of Flood Invasion Path and Mass Flow Rate in Subway Stations under Heavy Rainfall Conditions

The occurrence of extreme weather, such as heavy rainfall and sudden increases in precipitation, has led to a notable rise in the frequency of flooding in subway stations. By conducting numerical simulations of flood disasters in subway stations under heavy rainfall conditions and gaining insights into the patterns of flood invasion inside the stations, it is possible to develop practical and feasible drainage designs for the stations. This paper employs the computational fluid dynamics (CFD) method, utilising the volume of fluid function (VOF) method and the renormalization k-ε group model within the vortex viscosity model. The complete process of flood invasion into subway stations with varying water levels (1500 mm, 2000 mm, and 2450 mm) is modelled, and the distribution of floods at different times under varying operational conditions is analysed to identify the evolutionary patterns of station flood history. The simulation calculations yielded the mass flow rate time history curve at the tunnel entrance and exit, which was then subjected to an analysis of its development trend over time. The total accumulated water in the subway station is calculated by integrating the difference in mass flow rate between the entrance and the tunnel exit, using the mass flow rate curve. In conclusion, the paper proposes drainage measures that provide valuable insights into pumping strategies when floodwaters infiltrate subway stations. The results indicate that the speed of flood spreading in subway stations increases with higher groundwater levels, and that the mass flow rate of floodwater entering the tunnels increases over time, eventually reaching a stable state. It was observed that, at certain times, the mass flow rate of floodwater into the tunnels exhibited a linear relationship with time.

Historic Flood Events and Current Flood Hazard in Ulaanbaatar City, Central Mongolia

Climate change will increase the frequency of extreme weather events, alter rainfall patterns, and exacerbate flood disasters in Ulaanbaatar City. Here we combine aerial and satellite imagery with cadastral data, to scrutinize the historical trajectory of rainfall patterns and flood disasters in Ulaanbaatar over the past six decades. The study focusses on the causative factors behind historical floods, current flood conditions, the geographical distribution of floods, land ownership in floodprone areas, and the spatial allocation of fences and buildings based on social conditions. Over the last 60 years, Ulaanbaatar received a total of 16,780 mm of precipitation, with a staggering 80.5% of this total occurring during the summer season. Over this period, the city has endured about ten significant flood disasters. The most severe and destructive events occurred in 1966, 1982, 1994, 2003, 2009, and 2023 as river basins and mountain flash floods. These flood events claimed at least 220 lives, affected around 46,000 households, and caused economic losses of ca. 3.3 million U.S. dollars. Our study identifies several flood hazard areas along the Tolgoit, Selbe, Uliastai, and Tuul River valleys, which define a flood buffer zone extending 200 m from their banks, encompassing 59 khoroos of 7 districts in Ulaanbaatar. There are 27,970 fences and 12,887 buildings in the 200 m buffer zone, which is 66.5% of all fence unit area, and 46.3% of the total building, situated within the identified flood risk areas. In response to these findings, we emphasize the urgent need for comprehensive long-term strategy for sustainable flood management based on disaster resilence.

Utilization of Sentinel-1 Imagery for Mapping the Distribution of Floods in the Putussibau Kota Subdistrict and Surrounding Areas

Floods are a disaster that often occurs in the Putussibau Kota sub-district, West Kalimantan, because of its location where the Kapuas River passes. However, flood disaster mitigation efforts in this area are often hampered by the lack of information on flood distribution maps and impact predictions. This research utilizes the change detection and thresholding (CDAT) method applied to Sentinel-1 SAR data to map the distribution of floods in the Putussibau Kota sub-district and its surroundings, as well as analyze its impact on infrastructure and population. Next, the impact of the flood is calculated using an overlay technique between the flood map and the exposure map. The research results show that the flood distribution map for the August 2021 event had an accuracy of 0.76 and a kappa coefficient of 0.52. Next, the results of the flood impact evaluation showed that 37 km of roads, 2,700 buildings, and 11,700 people were affected by this incident. This analysis can be used to assist local governments in future flood mitigation efforts.

Named Entity Recognition Using Conditional Random Fields for Flood Detection In Gerbang Kertosusila Based Twitter Data

The national strategic area Gerbang Kertosusila East Java should be aware of floods. One of the existing efforts is to place flood sensors at several flood-prone points. However, that way is constrained by the need for more equipment to handle the many needy areas. So it is necessary to develop technology for the dissemination of flood information. Dissemination of flood information was quickly obtained from social media Twitter. One way is to use Twitter's text data source for a Named Entity Recognition model to help detect flood events and their locations. The Named Entity Recognition (NER) model was constructed using the Conditional Random Fields (CRFs) method to achieve research objectives. This research adds slang word handling at the preprocessing stage to improve model performance and the use of the BIO format in the labeling process and POS Tagging in the Feature Extraction process. Evaluation results with five Kfolds, 80% training data, and 20% test data show that the NER CRFs model performs excellently with a Precision of 0.981, Recall of 0.926, and f-measure of 0.950 so that these results can help the community and government regarding the information on the distribution of floods.

Satellite Image Analysis Approach for Identifying Flood Impacts in DKI Jakarta

Jakarta, as the capital city of Indonesia, is one of the major cities in the world that is inevitably affected by disasters resulting from climate change. The topographical characteristics of Jakarta, situated in a low-lying area, make it susceptible to floods during the rainy season. In 2020, Jakarta experienced a rainfall of 377 mm/day, marking the highest precipitation during the period of 1866 to 2020. To mitigate the impact of flood disasters, the availability of flood distribution data is crucial. The process of identifying flood distribution utilizes Sentinel-1 radar satellite imagery with the Normalized Difference Sigma-Naught Index (NDSI) method. NDSI is sensitive to open water bodies due to changes in land surface properties during floods. Based on NDSI analysis, the flood distribution in DKI Jakarta was identified on January 2, 2020, covering 17,38% of the total area of DKI Jakarta, which is 661,52 km2.

Modelling multidecadal variability in flood frequency using the Two-Component Extreme Value distribution

The Two-Component Extreme Value (TCEV) distribution is traditionally known as the exact distribution of extremes arising from Poissonian occurrence of a mixture of two exponential exceedances. In some regions, flood frequency is affected by low-frequency (decadal) climate fluctuations resulting in wet and dry epochs. We extend the exact distribution of extremes approach to such regions to show that the TCEV arises as the distribution of annual maximum floods for Poissonian occurrences and (at least two) exponential exceedances. A case study using coastal basins in Queensland and New South Wales (Australia) affected by low-frequency climate variability, shows that the TCEV produces good fits to the marginal distribution over the entire range of observed values without the explicit need to resort to climate covariates and removal of potentially influential low values. Moreover, the TCEV reproduces the observed dog-leg, a key signature of different flood generation processes. A literature review shows that the assumptions underpinning the TCEV are conceptually consistent with available evidence on climate and flood mechanisms in these basins. We provide an extended domain of the TCEV distribution in the L-moment ratio diagram to account for the wider range of parameter values encountered in the case study and show that for all basins, L-skew and L-kurtosis fall within the extended domain of the TCEV.

The impact of tropical cyclones and water conservancy projects on island's flash floods.

The analysis of the influencing factors of flash floods, one of the most destructive natural disasters, is the basis of scientific disaster prevention and mitigation. There is little research considering the influence of tropical cyclones (TCs) and water conservancy projects on flash floods, which cannot be ignored in the island areas where flash floods often occur due to the complex influence of various factors. In this study, under the pressure-state-response framework (PSR framework), the factors affecting the distribution of flash floods on Hainan Island, China, from 1970 to 2010 were quantitatively analyzed by using the geographical detector method. By dividing the time period, give full play to the advantages of the PSR framework and show the evolution process of various factors. Different from inland areas, extreme precipitation and tropical cyclones play a major role in the spatial distribution of flash floods on Hainan Island, China, and the driving force of tropical cyclones is 1.1 times that of extreme precipitation on average. Medium-sized reservoirs play the greatest role in the prevention of flash floods on Hainan Island, and their driving forces reach 0.38 times of extreme precipitation on average, followed by large-sized reservoirs and small-sized reservoirs. Large-sized reservoirs are limited in quantity and have limited effectiveness in preventing flash floods on Hainan Island. Therefore, in the forecasting and risk management of flash flood in the island area, more attention should be paid to the impact of extreme precipitation and TCs, and the role of medium-sized reservoir should be fully exerted.

Understanding tectonics from fluvial topography by using the stochastic‐threshold incision model: Theory and application to the Dadu River basin, eastern Tibetan Plateau

AbstractThe flood distribution and bedrock incision threshold exert important influences on the erosion efficiency, which can result into a nonlinear scaling between channel steepness (normalized channel gradients for the downstream increases in drainage area) and erosion rates. Here, we explore the incorporation of both river incision threshold and discharge probability distribution into erosion efficiency to construct a stochastic‐threshold incision model equation to simulate river power incision and the evolution of fluvial topography. Assuming time‐dependent tectonic uplift, we develop a closed‐form analytical solution to the model equation and derive a linear inverse procedure to retrieve relative uplift rate history from river longitudinal profiles. Application of the inverse scheme to the Dadu River basin in the eastern Tibetan Plateau produces a history with three‐phase increases in the uplift/incision rates during the Cenozoic. We use the inversion results to forward the time‐changed river long profile, which indicates the existence of paleo relief before the Oligocene and significant contributions of the late Miocene regional uplift and early Pleistocene river capture on the present topography.

Monitoring Changes in the Jakarta Flood Disaster with Using Sentinel 2A Imagery

DKI Jakarta Province is one of the provinces in Indonesia with a relatively high incidence of flooding since its establishment in the Dutch East Indies era because geomorphologically Jakarta is a floodplain area. On the other hand, land pressure by increasing urbanization in the city of Jakarta results in the increasingly limited availability of decent housing land for the community so that lands that should be water catchment areas and riverbanks are transformed into densely populated flying areas which ultimately reduces water absorption capacity in the Jakarta area. To obtain fast data in identifying flood-affected areas, the technique of identifying the potential extent and distribution of flooding by utilizing the remote sensing NDWI (Normalized Difference Water Index) method and/or using the Modified Normalized Difference Water Index (MNDWI) method from the free data Sentinel 2A satellite can be useful to provide data input to relevant parties in DKI Jakarta Province. Opportunities and constraints The application of NDWI and/or MNDWI techniques will greatly assist mapping activities and identification of the distribution of flood areas as an alternative input to policymakers for action and anticipation of areas with high flood potential. This research is one of the first steps and input to the DKI Jakarta Government in making strategies and roadmaps for flood mitigation in DKI Jakarta.

Analysis model of flood spatiotemporal change characteristics based on k-means clustering algorithm

<p>In order to analyze the spatiotemporal change characteristics of regional flood disaster, analyze the main change characteristics and hydrological response of precipitation distribution in Guangxi, and improve the comprehensive utilization efficiency of water resources, this paper introduces the k-means clustering algorithm to design an analysis model of spatiotemporal change characteristics of flood disaster. First of all, obtain the flood data in Guangxi and the observation data of the national meteorological station within 5 km, complete the collection of the basic data of mountain flood disasters. Based on the collected data, an analysis was conducted on the spatial and temporal distribution of flash floods in the Guangxi region. Secondly, the Kriging spatial interpolation method was used to analyze the spatial distribution of precipitation data in Guangxi. The Mann-Kendall trend test was then employed to examine the trend of precipitation-related statistical parameters over time. Additionally, wavelet theory was applied to analyze the time series of annual precipitation and precipitation with different durations in Guangxi. Subsequently, the k-means clustering algorithm was introduced to construct a model for analyzing the spatiotemporal characteristics of flood changes, determining the concentration and duration of precipitation in different years in the region. Finally, analyze the spatiotemporal change characteristics of flood events in different seasons under various indicators, and realize the analysis of flood spatiotemporal change characteristics. The research results indicate that the Frank Copula function fits the best correlation between annual precipitation and temperature, and can better characterize the correlation between the two. The Frank Copula function has the best fitting effect on the correlation between precipitation and temperature in autumn in Guilin, summer in Nanning, and summer and winter in Beihai. In Guangxi Zhuang Autonomous Region, the annual precipitation shows a gradually decreasing trend, especially at R and P stations. In summary, the Frank Copula function can effectively characterize the correlation and trend of precipitation and temperature in different seasons and regions of Guangxi.</p>

Impact of the correlation between the flood of upstream tributaries on the frequency distribution of downstream flood—A case study in Fuhe River of Lake Poyang

Impact of the correlation between the flood of upstream tributaries on the frequency distribution of downstream flood—A case study in Fuhe River of Lake Poyang

Assessing land subsidence and analyzing tidal flooding in Tangerang, Banten

The increase in ocean temperature causes the expansion of seawater volume, resulting in an increase in sea level rise. The phenomenon of land subsidence also exacerbates the occurrence of tidal floods in coastal areas of Indonesia. This has prompted the need for a study of land subsidence and the distribution of tidal floods in Tangerang as a basis for taking anticipatory steps to reduce the negative impacts. The methods used for estimating land subsidence involved the SAR Sentinel-1A. The research utilized a total of 170 data points, spanning from 2017 until 2022. Data processing was carried out using the Parallel Small Baseline Subset method. The supporting data used in this study included SRTM data, tidal range, rainfall data, wind speed and direction. The results of this study reveal that the city of Tangerang has a maximum deformation value of -10.8 cm per year in the Periuk Sub District. Meanwhile, Tangerang Regency experienced land subsidence at a rate of -8.6 cm per year in Kosambi Sub District. Significant subsidence deformations occurred on the northeast side of Tangerang District and the southeast side of Tangerang City. Based on data analysis, it is evident that the total area inundated by tidal floods in Tangerang covers 33.267 hectares, with the largest affected area being in Pakuhaji District, spanning 9,262 hectares.

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.

ГИДРОЛОГИЧЕСКОЕ ЦИФРОВОЕ ПРОГНОЗНОЕ МОДЕЛИРОВАНИЕ ЗОН ЗАТОПЛЕНИЯ ТЕРРИТОРИИ СЕВЕРНОГО КАЗАХСТАНА НА ОСНОВЕ ВЫСОКОДЕТАЛЬНОЙ ЦИФРОВОЙ МОДЕЛИ РЕЛЬЕФА

The study presents the results of assessing the spatial distribution of floods, forecasting and modeling flood zones of territories under water as a result of changes in the hydrological state of water bodies.

Assessing the influence of human activities on flash flood susceptibility in mountainous regions of Vietnam

Flash floods pose a significant and escalating threat to life, infrastructure, and property in the mountainous regions of Vietnam. Over the past two decades, Vietnam has experienced rapid socio-economic development, making it one of the fastest-growing countries in the region. However, this progress has been accompanied by urbanization, land cover conversion, and reductions in natural forest coverage, exacerbating the risk of flash floods. The objective of our study was to evaluate the quantitative influence of human activities on flash flood susceptibility from 2001 to 2022. Employing various machine learning algorithms, we analyzed 15 independent factors and examined 452 historical data points to predict the probability of flash floods and build flash flood susceptibility maps for the period of 2013–2022. By assuming similar weather conditions that trigger flash floods, we also estimated the probability and spatial distribution of flash floods for 2001–2020. To assess the anthropogenic impact, we conducted a detailed analysis of changes in land use patterns and employed indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). These factors played a significant role in shaping the differences in flash flood probability between the two time periods. The results demonstrated alarming trends, revealing a notable increase in the probability of flash floods by 7.69% and 4.01% in areas classified as high and very high susceptibility, respectively, over the past two decades. These findings provide tangible and robust evidence that policymakers can utilize to evaluate the implications of ongoing socio-economic growth. Furthermore, they serve as a critical foundation for formulating sustainable development plans that prioritize mitigating the future escalating risk of flash floods.