Historical Flood Research Articles

Reconstruction of the historic flood of 1813 on rivers in the mountains of the Western Carpathians (Slovakia)

ABSTRACT The catastrophic flood of 1813 that occurred in the Western Carpathians region was the largest natural disaster ever recorded here. The 1812/1813 winter season was very cold with abundant snow cover. Spring and summer months were also rich in liquid precipitation, further increasing soil saturation and catchment runoff. Torrential rains were reported to occur from 24 to 26 August when two deep low-pressure systems met over the Western Carpathians above the headwaters of large rivers like Váh and Hron. This resulted in a flood with the flood wave being 10 m above the long-term river stage. In this study we summarize the findings from our archival research to report on the causes, severity and extent of the 1813 flood. Using three-dimensional terrain modelling we have reconstructed the culmination water level in two towns affected by the flood from the rivers Váh and Hron using historical maps and preserved flood marks.

Simulation of floods under the influence of effective factors in hydraulic and hydrological models using HEC-RAS and MIKE 21

Simulating floods and creating vulnerability maps are essential in urban watershed management systems. The present research aimed to simulate floods and assess floodplain risk in the Khalkai watershed located in Gilan Province, Iran. This river is characterized by extensive agricultural areas and urban areas that become flooded during most rainy seasons. Hence, an accurate assessment of flood extent and behavior is necessary. This study employs HEC-HMS, HEC-RAS, and MIKE 21 for advanced hydrodynamic modeling, focusing on two-dimensional (2D) capabilities essential for urban areas and flood is simulated with probability return periods of 50, 100, and 500 years. The research objectives include comparing HEC-RAS and MIKE 21 to identify the most effective flood simulation model, evaluating the impact of hydrological and hydraulic factors, the influence of GPS topographic mapping compared with DEMs extracted from satellite images and assessing land-use changes on flood behavior. Model accuracy is validated against historical flood data, revealing that MIKE 21 effectively addresses challenges related to grid resolution and flood delineation. Results indicate that while HEC-RAS provides a broader extent of flooding, both models offer critical insights into flood depth and flow characteristics. This research contributes to enhanced flood risk management by illustrating the implications of land-use changes on flood events, offering valuable insights for policymakers in mitigating future flood impacts and developing sustainable urban strategies.

Mitigating flood risk at Shah Alam, Malaysia for sustainable development

The natural environment and infrastructure are severely damaged by floods, which are becoming frequent occurrences. It may not be able to stop floods given the impending climate change and variability of the weather. Yet mapping flood risk can help with both mitigation and prevention of flooding. The Shah Alam experiences flash floods yearly, which is why it was chosen. An interactive approach to decision-making under multi-criteria decision analysis (MCDA), the analytical hierarchy process (AHP), is used. The flood risk zone is divided into five categories very low-risk (32.38%), low-risk (22.25%), moderate-risk (23.17%), high-risk (18.87%), and very high-risk (3.33%) respectively. Based on the data gathered for this study, the economic losses are identified, low-risk areas (RM 2091), moderate-risk areas (RM 2343), and high-risk areas (RM 2728). The historical flood events occur in the Seksyen 36, 18, 17, and most of the southern part of Shah Alam, Malaysia. Rainfall and the Klang River have the biggest effects on these regions, with water drainage systems being critical in the Shah Alam area to reduce the risk of floods. When analysing flood risks in the Shah Alam region and throughout the nation, important stakeholders both state and non-state actors can benefit greatly from the flood susceptibility map provided by this study.

Microplastic Chemostasis and Homogeneity During a Historic Flood on the Mississippi River

Microplastic Chemostasis and Homogeneity During a Historic Flood on the Mississippi River

The interplay between the urban development of Rome (Italy) and the Tiber River floods: A review of two millennia of socio-hydrological history.

The urban development of Rome (Italy) has been intertwined with the dynamics of the Tiber River since its foundation. In this review paper, we analyse more than 2500years of flood history and urban development to untangle the dynamics of flood risk and assess the resulting socio-hydrological phenomena. Until the 1800s, urban dwellers living in the riparian areas of the Tiber River were accustomed to frequent flooding. From the 1900s, the construction of flood walls reshaped the co-evolution of hydrological, economic, political, technological, and social processes. As a result, while the probability of flooding is currently very low, its potential adverse consequences would be catastrophic. From the analysis of the long-term feedback between urban development of Rome and flood events from ancient times to present days, it emerges the crucial need for an effective flood risk mitigation strategy that combines structural and non-structural measures. In particular, heightened flood risk awareness and preparedness to cope with rare but potentially devastating events is key to alleviate flood risk.

Detection of flood trends and drivers in the Taihu Basin, China

Study regionTaihu Basin, China Study focusFloods threaten humans, the environment, economic activity, and infrastructure. In this study, a new trend test and flood-frequency methods were adopted to detect extreme floods and their distributions based on flood-event identification. To fully understand the phased process of the influence of human activities on extreme hydrological processes, 12 copula functions were employed creatively in combined static and dynamic time-varying correlation aspects between extreme precipitation and floods. New hydrological insights for the regionAlthough both significant and insignificant increasing trends of the annual maximum water level in all three hydrological districts were examined, the periodic oscillations of all the stations were similar. Thus, it was significant to fully detect the periodical variation of floods. Extreme floods occurred mainly in the 1990s, as measured by frequency estimates. Generally, the nonstationary response relationship between heavy rain and an extreme water level was gradually strengthened; that is, a certain magnitude precipitation seemed to induce a greater-intensity flood event as time passed. Through the identification of historical flood events and the analysis of the rise and fall processes of floods, we found that the main reason for variation in the response relationship was the increase in the water level before the rising stage, rather than the water level rising in the Taihu Basin. Our study findings further existing knowledge on the regional flood-control design standard and can ensure the coexistence of humans and water systems in the future.

Modeling Flood Hazards in Ambon City Watersheds: Case Studies of Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah and Wai Ruhu

Flood hazard modeling in watersheds is an important step in natural disaster risk mitigation, especially in vulnerable areas such as Ambon City. This research focused on the Wai Batu Gantung, Wai Batu Gajah, Wai Tomu, Wai Batu Merah, and Wai Ruhu watersheds, using JRC Global Surface Water Mapping Layers data, NASA SRTM Digital Elevation 30 m data, and USGS Landsat 8 Level 2, Collection 2, Tier 1 data analyzed on the Google Earth Engine (GEE) platform. Prediction of built-up land in flood-prone areas was conducted by utilizing flood history analysis, hydrological modeling, and flood zone mapping. The results show that flood hazard modeling provides a better understanding of flood risk, assists in the development of safer land use planning, and increases public awareness of flood risk in Ambon City. It is hoped that the results of this research can contribute to flood risk management and sustainable regional development in the future.

Spatial Variation of Changes in Extreme Discharge Seasonality Across the Northeastern United States

ABSTRACTThe Northeast United States exhibits significant spatial heterogeneity in flood seasonality, with spring snowmelt‐driven floods historically dominating northern areas, while other regions show more varied flood seasonality. While it is well documented that since 1996 there has been a marked increase in extreme precipitation across this region, the response of flood seasonality to these changes in extreme precipitation and the spatial distribution of these effects remain uncertain. Here we show that, historically, snowmelt‐dominated northern regions were relatively insensitive to changes in extreme precipitation. However, with climate warming, the dominance of snowmelt floods is decreasing and thus the extreme flood regimes in northern regions are increasingly susceptible to changes in extreme precipitation. While extreme precipitation increased everywhere in the Northeastern United States in 1996, it has since returned to near pre‐1996 levels in the coastal north while remaining elevated in the inland north. Thus, the inland north region has and continues to experience the greatest changes in extreme flooding seasonality, including a substantial rise in floods outside the historical spring flood season, particularly in smaller watersheds. Further analysis reveals that while early winter floods are increasingly common, the magnitude of cold season floods (Nov‐May) have remained unchanged over time. In contrast, warm season floods (June‐Oct), historically less significant, are now increasing in both frequency and magnitude in the inland north. Our results highlight that treating the entire Northeast as a uniform hydroclimatic region conceals significant regional variations in extreme discharge trends and, more generally, climate warming will likely increase the sensitivity of historically snowmelt dominated watersheds to extreme precipitation. Understanding this spatial variability in increased extreme precipitation and increased sensitivity to extreme precipitation is crucial for enhancing disaster preparedness and refining water management strategies in affected regions.

A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh

Flash floods are catastrophic global events, especially in northeast Bangladesh, and assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional geographic system-based flash flood susceptibility mapping struggles to capture flash floods’ non-linear and complex nature. However, machine learning models have recently emerged as an efficient alternative to address these limitations. This study evaluates four machine learning models—Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest – Gradient Boosting (RFGB) Hybrid, and Categorical Boosting (CatBoost)—for flash flood susceptibility. It categorizes areas into five susceptibility levels: very low, low, moderate, high, and very high. Covering 24,424.25 km2 across eight districts, the study uses 400 points (200 flood and 200 non-flood) for training and validation, based on field investigation, historical flood information, Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar data using Google Earth Engine, and insights from the local people. The models’ predictive performances are evaluated by incorporating topographical attributes and rainfall indices and using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score, with 70% of the data for training and 30% for testing. The ANN model performs best with rainfall indices, achieving high accuracy (maximum AUC = 0.802). The RFGB hybrid model shows excellent training accuracy (AUC ≥ 0.971) but suffers from overfitting during validation (AUC ≤ 0.674), requiring careful hyperparameter adjustment. The CatBoost model effectively uses both rainfall indices and terrain features, achieving AUC = 0.701 in training and AUC = 0.667 in validation. The ANN model conservatively includes the largest area (2198.3 km2) under ’very high’ susceptibility. This study’s flash flood susceptibility maps, which include rainfall extremes, are more robust than those without, helping local administrative authorities and national flood practitioners prepare for flash floods.

Study on flood level forecasting of tidal reach in Puyang River basin

Abstract The downstream section of the Puyang River is a tidal river reach subject to both upstream floods and downstream tides. This combined impact results in an unstable relationship between water level and discharge at the forecast station, making flood level forecasting challenging. This paper took the Linpu station on the Puyang River in Zhejiang Province as the research object. Based on the historical flood data, the cause of high water levels at Linpu station was analyzed. The flood levels at Linpu station were decomposed into flood increments and basic tidal levels. Using the multiple regression method, the prediction formula for upstream flood-induced water level increments was obtained. A flood level forecasting method based on the flood increment was developed. The result of the scheme accuracy evaluation indicates that this method has good forecasting performance. The flood level forecasting method for the tidal channel proposed in this paper has a simple structure and high accuracy, which provides a scientific basis for the prevention and control of flood and drought hazards and the decision-making of project scheduling in the tidal river.

Geospatial Analysis of Abiotic and Biotic Conditions Associated with Leptospirosis in the Klaten Regency, Central Java, Indonesia.

The Klaten Regency, Central Java Province, Indonesia, is a leptospirosis endemic area. The purpose of this study is to spatially describe the abiotic and biotic environmental factors that contributed to the incidence of leptospirosis in the Klaten Regency in 2018. This was a descriptive observational with a cross-sectional approach conducted in the Klaten Regency, Central Java, in 2019 with 59 respondents. The results revealed that the percentage of abiotic environmental factors such as poor waste disposal facilities, poor gutter conditions, rivers < 200 m, and flooding history, namely 35.6%, 41.2%, 54.2%, and 6.8%, respectively. The highest leptospirosis cases occurred in April 2018, with 325 mm of rainfall, an average temperature of 27 °C, an average humidity of 82.3%, and an altitude of 100-200 MASL (79.7%). Meanwhile, biotic factors included rat nest existence (100%), having pets at risk (32.2%), and ≥three types of vegetation (79.7%). The main result confirmed that all leptospirosis cases had rat nests throughout the respondent's house. This finding emphasizes the importance of rat pest control programs by establishing cross-sectoral collaboration with the Department of Agriculture and educating the public to also play a role in environmental cleanliness in controlling rats.

Flood hazard assessment using machine learning and hydrodynamic modeling: case study in the Vu Ga–Thu Bon basin in Vietnam

ABSTRACT The objective of this study was the construction of a theoretical framework to evaluate flood hazards by integrating machine learning and hydrodynamic modeling in Vietnam's Vu Gia–Thu Bon basin. MIKE FLOOD was used to simulate historical floods in 2017 in order to obtain flood depth and velocity. Support vector machine-stochastic gradient descent (SVM-SGD), decision trees (DT), and dagging (DA) were used to determine flood susceptibility. Flood hazard was constructed by combining the flood depth, velocity, and susceptibility using the analytic hierarchy process technique. The statistical indices AUC, RMSE, MAE, and NASH were used to evaluate the precision of the hydrodynamic and machine learning models. The results showed that hydrodynamic modeling was highly accurate, with a NASH value of 0.86. The proposed models achieved AUC values of 0.98 for SVM-SGD, 0.93 for DT, and 0.92 for DA. The results showed that 7.59% of the flood zones is located in the very low flood hazard zone, 108.2 km2 in the low flood hazard zone, 24.59% in the moderate flood hazard zone, 22.53% in the high flood hazard zone, and 10.01% in the very high flood hazard zone.

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing untold losses. This study modelled the flood susceptibility of Calabar Metropolis using frequency ratio and geographical information system (GIS). Direct field observation and key informant approaches were used to obtain 127 previous and current flood locations in the study area from which a database of historical flood occurrence was developed. Using the geostatistical analyst tool within a geographical information environment the flood locations were split into two parts in the ratio of 70:30 percent for the training and testing processes, respectively. Eight flood conditioning factors (elevation, slope, aspect, curvature topographic position index, topographic wetness index, land cover and normalized difference vegetation index) were extracted from SRTM-30m DEM and Landsat 8 data accordingly and used in the geospatial analysis of flood occurrence within the GIS. The frequency ratio model was then developed using the training dataset. The final product was the flood susceptibility map of the study area. The results revealed that 9.3 percent of the study area was considered to have very high flood susceptibility, 19.97 percent was classified as high flood susceptibility while 31.79 percent was under moderate risk area. About 39 percent of the study area was classified at least under low flood susceptibility. The order of contribution of the conditioning factors to the model were topographic position index (TPI), elevation, normalized difference vegetation index (NDVI), land cover, curvature, aspect, slope, and topographic wetness index (TWI). The validation of the model using the receiver operating characteristics (ROC) curve returned an AUC value of 74.81 percent. Hence, the result was acceptable for the prediction of flood locations in the study area. The findings would assist urban planners, environmental managers, risk reduction and management agencies, land developers, policy makers, and indeed the general public.

A synergic approach using the model and remote sensing data for flood monitoring in under-observed transboundary rivers

ABSTRACT The southern plain of Nepal recognized as the ‘granary of Nepal’, confronts recurrent monsoon-induced flooding, posing a substantial threat to its pivotal role as a major agricultural contributor to the national economy. As an analysis, this study employs advanced satellite imagery to delineate historical floods in nine flood-prone transboundary basins and compares the rainfall-induced model-based inundation in the West Rapti Basin (WRB) to validate the result. The extent of flooding was mapped between 2015 and 2022 using Sentinel-1 Synthetic Aperture Radar data processed on Google Earth Engine. Hydrodynamic modelling centred on the WRB, incorporated daily measured precipitation data with varying return periods over a 10 m resolution digital elevation model generated through an in situ survey. The model was calibrated for the August 2017 flood event with Nash–Sutcliffe efficiency greater than 70% and validation reasonably with satellite-derived flood maps with Cohen's Kappa value of 0.58 and an overall accuracy metric of 0.84. This synergic approach integrates climatology, remote sensing data, and hydraulics to monitor transboundary river floods in Nepal where precise hydro-meteorological data are limited, thus, offering continuous all-weather monitoring.

Agnes, Revisited: methods and principles for community-engaged research on historic flood disasters

Agnes, Revisited: methods and principles for community-engaged research on historic flood disasters

Evaluation of Floods Susceptibility Models Based on Different Pairwise Parameters in the Analytical Hierarchy Process: Case Study Cilemer and Ciliman Watersheds

This study investigated flood vulnerability in the Ciliman and Cilemer Watersheds, situated in Banten Province, and employs a spatial multi-criteria-integrated approach, with a specific focus on the Analytical Hierarchy Process (AHP). Two distinct scenarios, which have different parameter priority, were compared: one based on expert judgment for pairwise parameter comparisons (scenario-1) and the other derived from historical flood occurrences in high and very high vulnerability areas (scenario-2). Seven parameters, including elevation, slope, precipitation, geologic, soil type, land use, and distance to streams were weighted substantially different between the two scenarios. The study validated the flood vulnerability scenarios by contrasting them with historical flood data. Scenario-2 exhibited a closer agreement with the historical flood points during validation, particularly in very high vulnerability areas. Elevation and slope are identified as pivotal factors influencing flood vulnerability: low elevations and gentle slopes increased vulnerability, while higher slopes decreased flood susceptibility.

Buffalo Bayou Watershed Near Katy Flood Analysis

Buffalo Bayou, originating near Katy, Texas, is a vital watercourse within the Houston metropolitan area, traversing diverse urban, suburban, and rural landscapes. This paper investigates the flood mechanisms impacting the Buffalo Bayou watershed, focusing on significant flood events and various flood mitigation methods. Key historical floods, such as those caused by Tropical Storm Allison (2001), the Memorial Day Flood (2015), the Tax Day Flood (2016), and Hurricane Harvey (2017), underscore the region’s vulnerability to intense rainfall and storm systems. By examining a 20-year period of daily streamflow and climate data, this study employs the bucket model to simulate long-term discharge and predict flood occurrences. The results highlight the critical need for effective flood mitigation strategies, including the use of reservoirs, green infrastructure, and infrastructure improvements. The study aims to enhance flood management practices and community resilience within the Buffalo Bayou watershed, offering insights into the dynamic interplay between natural hydrological processes and human interventions.

Resilience to future floods through flood memory approach: An example from West Bengal, India

Managing flood situations is a combination of structural, non-structural, and socio-ecological approaches, where the latest includes an operative involvement from the local community, based on their experiences in past flood episodes. The instances of considering this indigenous knowledge and perception while planning for the flood mitigational framework in India are neither very popular a practice nor they are frequently exemplified - but while looking at the case studies from all over the world, this could be a powerful tool for gauging flood scenarios and the living with it. This paper aims to inspect the ways and approaches through which a local community memorizes the fury of floods, how this information can be put in a quantitative way, and can be realistically functioning for coping with future flood consequences. Focusing on the flood memory approach for community resilience, we present the findings of archival research, site-specific folklores and mythological stories (cultural), social methods, verbal methods, exploration of flood markers (structural), and campaigns and exhibitions (educational). A digital questionnaire-based survey was conducted in the most flood-suffering wards of the Kolkata Municipal Corporation area in West Bengal, and upon receiving the reactions, a Flood History Volatility Scale (FHVS) has been devised to test how the strength of an individual's flood memory varies due to some controlling factors. Based on the results obtained from memory work and perceiving the importance of community knowledge during the survey, a comprehensive flood risk management approach has been proposed for flood risk reduction and developing community resilience.

Geo-Referenced Databases and SWOT Analysis for Assessing Flood Protection Structures, Measures, and Works at a River Basin Scale

This research aims to evaluate the operational effectiveness of current flood protection infrastructure and measures in a flood-prone area using geo-referenced information systems and SWOT analysis. To achieve this, all existing flood protection measures and works in the case study basin, namely Strymonas River basin in Greece, were mapped and recorded. These data, along with water-related spatial information, were stored in a geo-referenced database created within an open-source GIS environment. Additionally, the system was populated with the basin’s recorded historic floods, derived from the European Union’s Floods Directive implementation process. The outputs of the research, which include a spatial comparison of flood protection measures and works with flood event occurrences as well as analyses of the figures, density, and locations of flood protection works, were evaluated as an integrated system and further processed using SWOT analysis. The latter was informed by questionnaire results, and the identified strengths and weaknesses of the flood protection infrastructure were used to explore potential opportunities and threats, which could respectively reinforce or jeopardize the basin’s capacity to effectively respond to future floods. The research framework can be applied to any river basin and could provide important assets in flood protection planning at a basin scale.

Holocene extreme flood events in the middle reaches of the Lancang‒Mekong River basin recorded by a high-altitude lake in southwestern China

The Lancang‒Mekong River (LMR) basin is a region particularly susceptible to floods induced by extreme precipitation. However, owing to limited historical flood records, it is difficult to understand the processes and mechanisms of extreme floods in the LMR basin. In the present study, sediments from a high-altitude lake (Lake Baima, 4700 m above sea level) located in the upper-middle reaches of the LMR are used to reconstruct climate and hydrology variability since the last deglaciation based on a range of climate proxies and methods, including loss-on-ignition, grain size, X-ray diffraction, and X-ray fluorescence. Through the investigation of modern and historical floods in the LMR basin, we combine the climate proxies with hydrological frequency curves to reconstruct the paleoflood sequence during the Holocene. Our results suggest that the temperature and precipitation in southwestern China increased during the Holocene compared to the last deglaciation, indicating a response to changes in Northern Hemisphere summer solar insolation. In addition, Holocene sediments of Lake Baima are primarily composed of brown background layers and numerous light yellow/gray event layers. These event layers are further identified as flood events based on sedimentary facies, redness, and main exogenous elements content and their rate of change. The results show that extreme flood events in the LMR basin occurred more frequently during the early and late Holocene, with seven once-in-1000-year floods during the two intervals; while there were low-frequency flood periods during the mid-Holocene. The occurrence of Holocene floods may be related to external forcing, such as the abrupt changes in total solar irradiance (TSI) and volcanic activity, as well as the internal variability of the wider climate system including the Pacific Decadal Oscillation (PDO) and El Niño‒Southern Oscillation (ENSO). When the TSI was low, the Intertropical Convergence Zone (ITCZ) in the Northern Hemisphere shifted northward, and the Western Pacific Subtropical High (WPSH) moved eastward, accompanied with a negative phase of the PDO and La Niña-like phase of the ENSO. The air-sea interactions increased sea surface temperatures, leading to high precipitation and increasing flood risk in the LMR basin.