Historical Flood Research Articles

Multi-scenario modelling framework for afforestation strategies to mitigate runoff and flooding across storm return periods in tropical large river basins: the Upper Citarum case

ABSTRACT Flooding is a frequent disaster in Indonesia, especially in the Upper Citarum River Basin, causing substantial damage. This study assesses the impact of afforestation on runoff, peak discharge and flood risks under different storm conditions using hydrological (HEC-HMS) and hydraulic (HEC-RAS) models. Six afforestation scenarios were analyzed: No Forest (NF), Medium Afforestation (MF), Bandung's 2029 spatial plan (F-2029), Riverbank Afforestation (RF), spatial plan for 2029 (SP-2029) and No Built-up (NB), with Land Cover 2021 (LC-2021) as the baseline. Rainfall return periods (2, 5, 10, 20, 50 and 100 years) were generated from 32 years of rainfall data. The results show land cover changes significantly influence runoff, with urbanization increasing flood risks and afforestation improving water retention. Riverbank afforestation (RF) and reduced built-up areas (NB) were the most effective in mitigating flood risks, while reduced forest cover (NF) and urban expansion (SP-2029) exacerbated flooding. Validation with historical flood data highlighted flood-prone areas like Margahayu, Bojongsoang, Dayeuhkolot and Baleendah. Frequent but less severe floods were noted in 2- and 5-year return period scenarios. These findings emphasize the critical role of afforestation and land cover management in reducing flood risks and enhancing resilience in the Upper Citarum Basin.

Development of a Block-Scale Spatial Flood Vulnerability Index—Case Study: Morelia, Mexico

The study of urban floods is increasingly crucial due to their growing frequency and impact on densely populated areas, often characterized by inadequate drainage and located in flood-prone zones. The consequences extend beyond physical damage, significantly affecting economies and livelihoods, necessitating substantial economic resources for recovery and infrastructure rebuilding. Urban planning now must integrate flood risk management, emphasizing not only infrastructural resilience but also comprehensive policies that address environmental and social vulnerabilities to better prepare and protect urban environments against future flood risks. This study addresses the critical issue of urban flood vulnerability through a focused analysis of Morelia, a city known for its susceptibility to flooding due to its geographical and hydrological characteristics and accelerated urban growth. Employing a multifaceted approach that integrates hydrological, socio-economic, and land use data within a Geographic Information Systems (GIS) framework, the research develops a Spatial Flood Vulnerability Index (SFVI). This index is meticulously applied at the urban block level, offering a precise mapping of flood risks across the city. By correlating the SFVI results with historical flood data, the study identifies the most vulnerable areas in Morelia, which are primarily impacted due to their proximity to water bodies, economic density, and infrastructural settings. The methodology not only highlights immediate flood risks but also aids in strategic urban planning to enhance resilience against future flooding events. This paper contributes a novel approach to flood risk assessment, providing a replicable model for similarly affected cities worldwide, aiming to balance structural measures with strategic planning tailored to local needs.

Implementation of Geodesign Framework for Development of Flood Resilient Hill Town: A Case Study

Flooding is a natural disaster characterized by the inundation of land areas due to the overflow of water from its usual channels. Various natural and man-made factors are responsible for the occurrence of floods and the damage caused by them. Floods have become more dangerous in the Himalayan region because of complex topographical features and extreme climatic conditions. In recent years, the Kullu district of Himachal Pradesh has experienced a number of devastating floods due to its proximity to the Beas River and the hilly terrain which caused collective damage to man-made as well as natural resources, leading to loss of lives and property. Recognizing the urgent need for flood resilience, the present study aims to develop a flood-resilient town through a geodesign framework that will reduce the impact of floods in hilly areas. The study uses a three-stage geodesign framework that involves data collection, data analysis, and geodesign-based flood-resilient plan modeling. In the first stage, the data pertaining to historical floods and geospatial data of the study area was collected. In the second stage, flood susceptibility mapping was done to identify vulnerable communities and assess critical infrastructure at risk based on the analytical hierarchy process (AHP). In the third stage, interactive three-dimensional (3D) modeling was done in ESRI CityEngine to develop a flood-resilient model for the study area which integrates geospatial data, flood simulation, land-use planning, and stakeholders’ engagement. The results of this study show that by focusing on flood resilience, this model offers a valuable insight and practical approach for shaping the development in hilly areas that can withstand and adapt to the challenges of increasing flood occurrences.

Integrating Hydrological Models for Improved Flash Flood Risk Assessment and Mitigation Strategies in Northeastern Thailand

This study focuses on assessing flash flood risks in Northeastern Thailand, particularly within the Lam Saphung, Phrom, and Chern River Basins, which are highly susceptible to flash floods and debris flows. Using the HEC-RAS hydraulic model integrated with GIS tools, the research analyzes historical and scenario-based flood events to evaluate the impact of land use changes and hydrological dynamics. The model was calibrated and validated with statistical metrics such as R2 values ranging from 0.745 to 0.994 and NSE values between 0.653 and 0.893, indicating strong agreement with the observed data. This study also identified high-risk areas, with up to 5.49% and 5.50% increases in flood-prone areas in the Phrom and Chern River Basins, respectively, from 2006 to 2019. Key findings highlight the critical role of proactive risk management and targeted mitigation strategies in enhancing community resilience. The integration of advanced hydraulic modeling with detailed datasets enables precise flood hazard mapping, including flood depths exceeding 1.5 m in certain areas and high-risk zones covering up to 105.2 km2 during severe flood events. These results provide actionable insights for emergency response and land use planning. This research significantly contributes to hydrological risk assessments by advancing modeling techniques and delivering practical recommendations for sustainable flood management. The outcomes are particularly relevant for stakeholders, including urban planners, emergency management officials, and policymakers, who aim to strengthen resilience in vulnerable regions. By addressing the complexities of flash flood risk assessments with robust quantitative evidence, this study not only enhances the understanding of flood dynamics, but also lays the groundwork for developing adaptive strategies to mitigate the adverse impacts of flash floods, safeguarding both communities and infrastructure in the region.

Relationship Between Urbanization–Induced Land Use Changes and Flood Risk: Case Study in Chiang Mai, Thailand

Urban flooding has long been a critical issue, particularly in rapidly urbanizing regions of developing countries, where land use changes—especially the conversion of rice paddies into urban areas—have significantly increased flood risks. This study investigated the impact of urbanization on flood risk taking Chiang Mai, Thailand, as a case study. Based on historical flood data, the study identified and analyzed frequent flood–prone areas in Chiang Mai during the period from 1990 to 2018. By integrating the Rainfall–Runoff–Inundation (RRI) model simulation results and the remote sensing data, the research quantified dynamics in flood risk, exposure, and vulnerability across these frequent flood–prone areas. The findings demonstrated that the conversion of high–exposure paddy fields into urban areas markedly elevated area flood risks, primarily due to the reduction in water retention capacity and the inheritance of the high–exposure characteristics of paddy fields. This study highlighted the importance of integrating sustainable urban planning and land management strategies in rapidly urbanizing regions. Furthermore, this study examined the feasibility of adopting flood characteristics quantification in frequent flood–prone areas as a systematic approach to analyze the dynamic interplay between flood risks and urbanization in developing countries.

Mimořádné projevy počasí v Jeseníkách

The article describes detailed overview of significant meteorological events in the Jeseníky region, including extreme precipitation, floods, and temperature fluctuations from around 1500 to the present. In addition to the actual data from the CHMI database, historical flood events are included, which are better documented than meteorological extremes. The literature evidence is supplemented by values specific for the Jeseníky region, so the paper provides a broad overview of climatic extremes with an emphasis on long-term observations and historical contexts that reveal exceptional climatic situations in this part of the Czech Republic.

Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML

Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims to evaluate the flash flood susceptibility in the Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by the H2O automated ML platform. The best-performing model was used to generate a flash flood susceptibility map, and its interpretability was analyzed using the Shapley Additive Explanations (SHAP) tree interpretation method. The results revealed that the top four models, including both single and ensemble models, demonstrated high accuracy in the tests. The flash flood susceptibility map generated by the best-performing eXtreme Randomized Trees (XRT) model showed that 8.92%, 12.95%, 15.42%, 31.34%, and 31.37% of the study area exhibited very high, high, moderate, low, and very low flash flood susceptibility, respectively, with approximately 74.9% of the historical flash floods occurring in areas classified as moderate to very high susceptibility. The SHAP plot identified topographic factors as the primary drivers of flash floods, with the importance analysis ranking the most influential factors in such descending order as DEM, topographic wetness index, topographic position index, normalized difference vegetation index, and average multi-year precipitation. This study demonstrates the benefits of interpretable machine learning, which can provide guidance for flash flood mitigation.

Urban system interactions during flood events: a system dynamics modeling approach

ABSTRACT Floods cause significant damage in cities due to their intensity, frequency, and vulnerability, which are driven by complex internal and external factors. Dynamic behavior of urban response during a flood event is difficult to measure due to urban systems’ complexity and non-linear behavior. This study simulates the flood response dynamics of people, inundated waters, and financial expenditure during a flood event to visualize the dynamics of urban flood events using Colombo City, Sri Lanka, as a case study. Sensitivity analysis was conducted for pumping infrastructure and flood relief expenditure to view the variation of flood damage using Monte Carlo simulation. Results revealed that pumping units and the initial flood relief budget significantly impact the flood recovery of inundated areas. The results were validated using flood water pumping statistics and hydrology models conducted for historical flood events. The system dynamics framework is useful for combining multiple parameters in the flood modeling process to understand the complex relationships between population, flood inundation, and financial relief programs. This framework can be expanded to quantify the effects of mitigation and adaptation strategies in flood-vulnerable cities to optimize the decision-making process.

Urgent call: Environmental mismanagement after historical flood amplifies Rio Grande do Sul’s ecological crisis in south of Brazil

Urgent call: Environmental mismanagement after historical flood amplifies Rio Grande do Sul’s ecological crisis in south of Brazil

Modeling of maximum runoff characteristics of small rivers in the mountain permafrost zone

Climate change leads to the transformation of hydrological processes in the mountain permafrost regions, increasing the frequency and intensity of catastrophic floods. An example is the Magadan Oblast — an important region for the Russian economy, exposed to the risk of floods. The aim of the study was to calculate the maximum water discharge of three small rivers in the Magadan Oblast — the Krivulya, Ambardakh and Susuman rivers — using pluviograph data and the hydrological model Hydrograph. The model was verified on daily intervals for the periods 1966–1994 for the Krivulya stream and 1966-1987 for the Susuman and Ambardakh rivers. Modelling results have shown satisfactory correlation with the observed daily and mean annual values. The median values of the Nash-Sutcliffe coefficient varied from 0.52 to 0.62. The model parametrization for three main types of the landscapes was developed based on the data of the Kolyma Water-Balance station. The modeling was carried out for the historical floods of August 16, 1986 for the Susuman and Ambardakh rivers (maximum observed instant discharges were 393 m3/s and 74.7 m3/s, calculated 1-hourly discharge — 420 m3/s and 78.5 m3/s, respectively) and July 26, 1984 for the Krivulya Stream (observed instant discharge — 14.2 m3/s, calculated 1-hourly discharge — 16.5 m3/s). The relative error of simulated and observed maximum discharges did not exceed 20%. The results of the study confirm the possibility of applying deterministic hydrological modeling on an intra-day interval using pluviograph data. However, the main limitation for the mountain cryolithozone remains the lack of precipitation data. A promising solution may be the use of climate model data, which, in combination with runoff formation models, opens up new opportunities for assessing and predicting maximum runoff characteristics.

Reconstructing the 1968 River Chew flash flood: merging a HEC-RAS 2D hydraulic modelling approach with historical evidence

The devastating 1968 flash flood in the River Chew, South-West of England, serves as a stark reminder of the unpredictable nature of such natural disasters and highlights the importance of natural hazard assessments. The uncertain and often incomplete historical data, and the limited field measurements at the time hindered our understanding of this event. By integrating historical evidence, including technical reports, newspapers, literature, and eyewitness accounts, with advanced hydraulic modelling (HEC-RAS 2D), this study reconstructs the 1968 flash flood. A sensitivity analysis of the computational methodologies in HEC-RAS, examining various governing equations and numerical methods, introduces an additional dimension to this research. The results verify a maximum flow rate of 165 m3/s at the Compton Dando hydrometric station, marking a 65% increase from the previous official estimate. This update aligns with over 90% of the historical flood marks observed. Findings suggest recalibrating hydrological models, revising risk assessments, and updating flood frequency analyses in the study area. This novel framework confronts the challenges of uncertain and incomplete historical records through a reverse engineering methodology to reconstruct missing peak discharges. The study also presents a new methodological blueprint that can be replicated for reconstructing historical flash flood events in various regions.

GOOGLE EARTH ENGINE FOR FLOOD MAPPING USING SENTINEL-1 GRD SAR IMAGES AND IMPACT ASSESSMENT ON SOCIO-ECONOMIC FACTORS: A CASE STUDY IN DA NANG

This study exploits the high computing performance of the Google Earth Engine (GEE) cloud computing platform to process Sentinel 1 images to rapidly establish a flood map in the coastal city of Da Nang through the historic flood in October 2022. The flood map is eventually exported to a shapefile and overlaid with socio-economic data in a GIS environment to assess the impact of flooding on socio-economic activities. The results indicate that the entire city has 10.505 ha flooded, concentrated in the Northwest and Southeast of Hoa Vang district, Lien Chieu district, and the results of geo-spatial statistical analysis also show that 189.161 km of flooded roads, 184 flooded residential areas, 12 flooded commercial service-school-hospital areas, and 9,786.81 ha of flooded agricultural land. The consequences were considerable damage to property, houses, crops and serious impacts on the lives and livelihoods of Da Nang residents.

Conceptual Design of a Flood-Adjusted Land Value Index (FALVI) Methodology for Urban Areas: A Study Case at Bandung City, Indonesia

Addressing these water management challenges requires a comprehensive and integrated approach. Floods and other water-related challenges in urban areas can have an impact on land values. However, the lack of studies has developed a comprehensive index methodology related to examining floods and land value relationships for urban areas. Therefore, the main purpose of this study is to develop a comprehensive index methodology related to examining floods and land value relationships for urban areas that is called a Flood-Adjusted Land Value Index (FALVI) Methodology. This paper illustrates the importance of the proposed FALVI methodology to determine the relationship between flood events and land value. Important variables within three main aspects—environmental, socio, and historical flood variables—would be elaborated and measured by GIS-based analysis. It provides a more accurate and thorough assessment of property values by taking flood risk variables into account throughout the valuation process. This methodology is also regarded as an essential methodology for examining floods and land value links in metropolitan areas. FALVI can help guide government strategies on flood management, land use planning, and catastrophe risk reduction. By identifying high-risk locations, governments can prioritize flood mitigation measures and enact restrictions that prevent development in susceptible areas. Urban areas in certain watershed systems can be kept viable for the long term by carefully reviewing this methodology and implementing suitable land management strategies.

Flood risk assessment combining the historical disaster statistics method with the index system method

ABSTRACT Flood risk assessment is an important aspect of flood management, and we combined the historical disaster statistics method with the index system method to assess the flood risk in Hubei Province, China. Our methodology includes collecting historical flood disaster data to assess the flood hazard by calculating degree and trend of disaster from flood disasters in each geographical unit. Meanwhile, we selected relevant indicators such as elevation difference, distance to the water body, gross domestic product (GDP), population, and proportion of the construction land to measure flood susceptibility, and the weights for these indicators are determined combining the analytic hierarchy process (AHP) and the entropy weight method (AHP_entropy). Then, a risk assessment model is developed by integrating the hazard and susceptibility at a high-resolution grid scale of 1 km×1 km. The results show that about 55.6% of the area of Hubei Province falls into the medium-high risk category.

The Role of the Great Plains Low‐Level Jet in Moisture Transport, Streamflow, and Flooding in the Missouri River Basin

AbstractThe Missouri River Basin, one of the primary agricultural regions in North America, is prone to large hydroclimatic variability and extreme events. The Great Plains low‐level jet (GPLLJ) is one of the major sources of moisture transport into this region, and concerns over moisture extremes already seen in the interior U.S. have been linked to changes in GPLLJ. However, the relationship between GPLLJ and precipitation in this region remains uncertain, with studies showing conflicting results that may be related to distinct types of jet events. Here, we use daily GPLLJ data to study connections between different types of jet events and precipitation, integrated water vapor transport, and synoptic conditions to better understand GPLLJ‐streamflow linkages. We assess GPLLJ conditions during historical flood events and potential future changes using CMIP6 projections. We find: (a) in May–September, GPLLJ is associated with 29%–46% of monthly precipitation and with increased precipitation intensity; (b) GPLLJ has a consistently positive impact on Missouri River streamflow over the warm season; (c) coupled jets have a larger impact on moisture transport than uncoupled jets, but streamflow impact is reduced, likely due to seasonal timing; (d) unusual jet conditions preceded historical warm‐season flood events; and (e) future projections indicate relatively small changes in GPLLJ meridional wind velocities along with large increases in moisture transport driven by humidity increases. Although future streamflow will be partially dependent on snowmelt from the upper basin, the projected intensification of warm‐season moisture transport to the Missouri River suggests possible increases in future flood risk.

Simulating flood risk in Tampa Bay using a machine learning driven approach

Machine learning (ML) models can simulate flood risk by identifying critical non-linear relationships between flood damage locations and flood risk factors (FRFs). To explore it, Tampa Bay, Florida, is selected as a test site. The study’s goal is to simulate flood risk and identify dominant FRFs using historical flood damage data as target variable, with 16 FRFs as predictor variables. Five different ML models such as decision tree (DT), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and random forest (RF) were adopted. RF classifies 2.42% of Tampa Bay as very high risk and 2.54% as high risk, while XGBoost classifies 3.85% as very high risk and 1.11% as high risk. Moreover, the communities reside at low altitudes and near the waterbodies, with dense man-made infrastructure, are at high flood risk. This study introduces a comprehensive framework for flood risk assessment and helps policymakers mitigate flood risk.

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

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

An updated reference table for extreme flood hydrology methods at UK dams

There are several possible methods available for extreme flood estimation applicable for UK dams and reservoirs. New models continue to be released to calculate both rainfall and runoff. This paper provides a short summary of the methods that are currently recommended for use in reservoir flood studies, and the methods that have been recommended in the past. This is intended to help inform reservoir owners, panel engineers and hydrologists of current best practice and the extent of changes in methodology since historical flood studies were undertaken. A simplified approach for sensitivity testing of multiple methods is proposed.

FLOOD HAZARD ESTIMATION AND EVALUATION IN LAGOS STATE USING MACHINE LEARNING TECHNIQUES

Floods threaten human life, infrastructure, and economic stability in Lagos State, Nigeria. Accurate flood hazard estimation is crucial for effective flood risk management and mitigation. This study employs machine learning techniques to estimate flood hazards in Lagos State. A dataset comprising historical flood events, meteorological factors (rainfall, temperature, humidity), topographical features (elevation, slope, land cover), and socioeconomic variables (population density, urbanization) is compiled. Feature selection and engineering techniques are applied to optimize model performance. Flooding is the most frequent and destructive natural catastrophe that may happen anywhere in the globe. The frequency and severity of flooding events have increased worldwide in recent years due to climate change and human activity. Flooding has caused widespread death and devastation of property, farms, and vegetation in several emerging Nigeria, including Lagos State, and has forced the relocation of many more. Flooding has been Lagos State’s most common natural disaster during the last decade. Modern machine learning methods have shown great promise for improving flood Estimation and Evaluation. The optimum machine learning algorithm for flood Estimation and Evaluation is debated. To reduce the harm caused by floods, finding better ways to anticipate their occurrence is crucial. This paper initially applied 4 machine learning algorithms (Support Vector Machine SVM, Classification and Regression Trees CART, K-Nearest Neighbors KNN, and 4. Generalized Linear Model Network GLMNET) on the default dataset. The results reveal fair accuracy (over 60%) and kappa values (< 0.4). The same ML algorithms were again applied to the transformed dataset using the Boxcox transformation technique; the accuracy and kappa values improved but not significantly. Finally, Models for predicting floods were implemented using 3 different ensemble algorithms: Bagged CART (Bootstrap Aggregating BAG), Random Forest (RF), and Stochastic Gradient Boosting ( Gradient Boosting Machine GBM). Compared to the other three models, the performance of RF (Area Under the Curve AUC = 0.93) and BAG (AUC = 0.92) indicated superior accuracy.

Risk assessment of flash flood under climate and land use and land cover change in Tianshan Mountains, China

Tianshan Mountains in China (CTM) is one of the flash flood-prone areas worldwide, constraining economic development and threatening human safety. We assessed the short-term and long-term future flash flood risk in CTM using a multi-criteria model combined with future climate and Land use and land cover (LULC). The short-term risk assessment results indicated that moderate, high, and extremely high risk areas were mainly located in the northern foothills of the Tianshan Mountain, the southern Junggar Basin, and the northern Tarim Basin, accounting for about 10 % of the total area. The areas classified as high and extremely high risk were distributed in Ili (10,100 km2), Changji (4838 km2), Bortala (1783 km2), and Urumqi (1505 km2). The spatial distributions of future long-term and short-term flash flood risk are similar. There will be an increase in the flash flood risk from 2020 to 2030, mainly in Ili, Changji, and Hami. The flash flood risk in the CTM will decrease under different scenarios from 2030 to 2070. Historical flash flood data from the 1940s to the 2010s indicated that the results of the risk assessment are reasonable. Abundant precipitation from the westerly circulation and the trumpet-shaped topography drove the flash floods in Bortala-shuanghe(BS), Ili, Changji (west), and Urumqi-wujiaqv(UW). Snowmelt flash floods are another important flash flood type in Changji (West) and UW. The dense water system and the undulating terrain led to rainstorm flash floods in high mountainous areas of Aksu and Kashgar, Hami, Kizilsu, and Changji (east). Cultivated land, population, and roads are important risk receptors. The oases, water resource constraints, and the urban scale discontinuity led to the spatial displacement between hazard and risk. The flash flood risk change in the future is more closely related to LUCC than climate change. This study provides support for flash flood risk assessment and management in CTM and other arid inland areas under changed climate and LULC.