Flood Frequency Research Articles

Sea Level Rise and Coastal Erosion in Miami

Purpose: The aim of the study was to analyze the sea level rise and coastal erosion in Miami. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Sea level rise and coastal erosion in Miami are growing environmental concerns due to the city's low elevation and proximity to the ocean. Studies have shown that rising sea levels, driven by climate change, are accelerating the erosion of Miami's coastline, threatening infrastructure, homes, and ecosystems. The increased frequency of tidal flooding and storm surges exacerbates the problem, making coastal areas more vulnerable. Unique Contribution to Theory, Practice and Policy: Resilience theory, social vulnerability theory, ecosystem-based adaptation (EBA) Theory may be used to anchor future studies on sea level rise and coastal erosion in Miami. Implement local ICZM strategies that involve stakeholders in decision-making processes, ensuring that community needs are met while enhancing coastal resilience. Advocate for policies that support adaptive management practices and funding for local ICZM initiatives.

Experimental Investigation of Bridge Scour under Pressure Flow Conditions

Recent studies have revealed that the frequency and magnitude of floods tend to increase due to climate change. Hence, excessive scouring due to flood events puts river bridges at greater risk of failure. This paper presents the initial findings of an experimental study to improve the understanding of the main characteristics of bridge pier scour under pressurized flow encountered during flooding. The experiments were carried out in four main groups according to two deck alignments with circular and oblong pier shapes. For each group of experiments, thirty-six tests were conducted under partially and fully pressurized flow conditions using four approach flow depths and three discharge values. The validity of the structured design approach for pier scour estimation implemented in the guidelines was investigated. The results showed that the bridge pier scour depths were up to 29.4% and 49.4% greater than the sum of the vertical contraction and local scour depths for 100 L/s for partially and fully pressurized flow conditions, respectively. However, as the discharge increased to 120 L/s, the bridge pier scour depth became 38.3% and 17.8% smaller than the sum of the vertical contraction and local scour depths for partially and fully pressurized flow, respectively. So, the structured design approach was determined to be safe for high discharge values. Furthermore, it was found that tests with a circular pier resulted in higher bridge pier scour depths than the sum of the vertical contraction and local scour depths up to 19.3% even for 120 L/s. Conversely, smaller bridge pier scour depths than the sum of the vertical contraction and local scour depths were observed up to 17.8% for tests with oblong piers. Thus, it can be concluded that the pier shape has a profound effect on scour holes and oblong piers cause smaller scour depths than circular piers in pressurized flow conditions. This study showed that the flow–pier–deck interaction significantly affects the depth and width of the scour hole, especially for small discharges and fully pressurized flow conditions.

Exposing disparities in flood adaptation for equitable future interventions in the USA

As governments race to implement new climate adaptation solutions that prepare for more frequent flooding, they must seek policies that are effective for all communities and uphold climate justice. This requires evaluating policies not only on their overall effectiveness but also on whether they benefit all communities. Using the USA as an example, we illustrate the importance of considering such disparities for flood adaptation through a FEMA dataset of ~ 2.5 million flood insurance claims. We use CausalFlow, a causal inference method based on deep generative models, to estimate the treatment effect of flood adaptation interventions based on a community’s income, racial demographics, population, flood risk, educational attainment, and precipitation. We find that the program saves communities $5,000–15,000 per household. However, these savings are not evenly spread across communities. For example, for low-income communities savings sharply decline as flood-risk increases in contrast to their high-income counterparts. Even among low-income communities, savings are >$6,000 per household higher in predominantly white communities. Future flood adaptation efforts should go beyond reducing losses overall and aim to equitably support communities in the race for climate adaptation.

Factors affecting household evacuation decision making in response to disaster: Case study from the 2021 South Kalimantan floods, Indonesia

Understanding the factors that influence evacuation decisions is crucial for optimizing resource allocation and formulating an effective emergency plan, particularly in the communication and implementation of warnings. To assist with this endeavour, this study investigates whether selected variables affected evacuation decision-making during the 2021 South Kalimantan, Indonesia, Floods. This is achieved through identifying significant factors in a binomial regression model constructed using a resident survey during the extreme floods of January 2021 and the more typical frequent floods of December 2021. The survey captures sociodemographic, capacity-related, hazard-related and warning-related factors from 375 residents including 205 in West Banjarmasin (urban area) and 170 in Tabuk River (rural area). It was found that families with partial ownership of their house (renting or living with another family) and those with a higher number of members are less likely to evacuate, as are those with a lack of knowledge about evacuation shelters and routes. External factors influencing people's evacuation decisions included the flood height and duration as well as whether there was an unofficial warning to evacuate. The findings suggest the following actions could assist with encouraging safety-seeking behaviour through evacuation: ensure evacuation instructions reach larger families and households and that they are relevant for them; include evacuation instructions with warnings; and conduct regular evacuation drills that include information about the flood risk map and the location and route to relevant evacuation shelters.

Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting

ABSTRACT The utilisation of modelling tools in hydrology has been effective in predicting future floods by analysing historical rainfall and inflow data, due to the association between climate change and flood frequency. This study utilised a historical dataset of monthly inflow and rainfall for the Terengganu River in Malaysia, and it is renowned for its hydrological patterns that exhibit a high level of unpredictability. The evaluation of the predictive precision and effectiveness of the Optimised Decision Tree ODT model, along with the RF and GBT models, in this study involved analysing several indicators. These indicators included the correlation coefficient, mean absolute error, percentage of relative error, root mean square error, Nash-Sutcliffe efficiency, and accuracy rate. The research results indicated that the ODT and RF models performed better than the GBT model in predicting monthly inflows. The ODT model, as well as the RF and GBT models, showed validation results with average accuracies of 94%, 91%, and 92%, respectively. The R² values were 90.2%, 84.8%, and 96.0%, respectively, and the NES values ranged from 0.92 to 0.94. The results of this research have greater implications, extending beyond the forecasting of monthly inflow rates to encompass other hydro-meteorological variables that depend exclusively on historical input data.

Evaluation of probability distribution methods for flood frequency analysis in the Jhelum Basin of North-Western Himalayas, India

The Kashmir Valley has frequently endured devastating floods, presenting significant challenges for flood management due to unpredictable flood magnitudes and limited pre-disaster preparedness. A major difficulty in addressing these challenges is the lack of reliable flood frequency analysis (FFA) for effective planning and mitigation. This study seeks to overcome these issues by employing a rigorous quantitative analysis of annual peak discharge data over a 51-year period (1971–2021). One key challenge was the presence of low outliers, which could compromise the integrity of the data. To address this, the Multiple Grubbs-Beck test was applied to remove these outliers before conducting FFA. The study's original achievement lies in its application of multiple distribution models which include Gumbel (EV1), Generalized Extreme Variations (GEV), Log-Normal, Log Pearson III (LP III), Gamma and Normal distribution. Goodness-of-fit tests, including Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Chi-square at the 5 % significance level, along with visualization techniques such as Probability plots (PP), Quantile plots (QQ), and Probabilistic distribution (PD) graphs, were used to identify the most suitable distribution methods. The Log Pearson Type III (LP-III) was identified as the best fit for the Sangam gauge site (Upper Jhelum), the gamma distribution for Ram Munshibagh (Middle Jhelum), and the Generalized Extreme Value (GEV) and LP-III for Asham (Lower Jhelum). For Sangam, the estimated discharges for 2, 5, 10, 50, 100, 150, 200, and 250-year return periods were 549.63, 1028.43, 1471.34, 2907.64, 3758.92, 4338.61, 4790.99, and 5167.23 cumecs, respectively, using LP-III. For Ram Munshibagh, the discharges were 602.13, 911.03, 1107.04, 1512.12, 1674.35, 1767.0, 1831.87, and 1881.74 cumecs using the gamma distribution. For Asham, the discharges were 685.8, 998.0, 1193.3, 1593.2, 1750.6, 1839.4, 1901.0, and 1948.0 cumecs using the GEV distribution. The findings indicate that the Jhelum River cannot accommodate excess discharge for return periods of 5 years or more, underscoring the need for enhanced flood management strategies.

An Urban Flood Model Development Coupling the 1D and 2D Model with Fixed-Time Synchronization

Due to climate change, the frequency and intensity of torrential rainfall in urban areas are increasing, leading to more frequent flood damage. Consequently, there is a need for a rapid and accurate analysis of urban flood response capabilities. The dual-drainage model has been widely used for accurate flood analysis, with minimum time step synchronization being commonly adopted. However, this method has limitations in terms of speed. This study applied the hyper-connected solution for an urban flood (HC-SURF) model with fixed-time step flow synchronization, validated its accuracy using laboratory observation data, and tested its effectiveness in real urban watersheds with various synchronization times. Excellent performance was achieved in simulating real phenomena. In actual urban watersheds, as the synchronization time increased, the errors in surcharge and discharge also increased due to the inability to accurately reflect water level changes within the synchronization time; however, overall, they remained minimal. Therefore, the HC-SURF model is demonstrated as a useful tool for urban flood management that can be used to advantage in real-time flood forecasting and decision-making.

The impact of reservoirs with seasonal flood limit water level on the frequency of downstream floods

Reservoirs alter the flood propagation process. Considering that many reservoirs have different flood limit water levels and flood control storage capacities during different flood seasons, we propose a modified reservoir index. In this study, we construct four types of non-stationary statistical models, taking time t, antecedent rainfall P, and the modified Reservoir Index MRI as candidate explanatory variables. Then we compare the simulation effects of different models. The equivalent reliability method and the extreme value distribution method considering the flood staging are proposed to calculate the flood design values at different return periods for downstream sites of the reservoir. The calculation results at the Huangzhuang Hydrological Station in the lower Hanjiang River in China show that the flood occurrence time in the upper Hanjiang River and its corresponding reservoir seasonal flood limit level and flood control storage capacity directly affect the flood in the downstream river. The non-stationary model incorporating P and MRI as explanatory variables achieves the best fit for the flood. Compared with the existing reservoir indices, MRI can better describe the impact of the upstream reservoir group on the flood sequence. The calculation results of the equivalent reliability method and the extreme value distribution method are comparable. The impact of the staged setting of flood limit water levels should be fully considered. The results of the equal reliability method and the extreme value distribution method can provide a reference for the design of flood control projects.

The Impact of Catastrophic Floods on Macroinvertebrate Communities in Low-Order Streams: A Study from the Apennines (Northwest Italy)

Floods are normal components of many river regimes and, as such, they exert a significant influence at the ecosystem level. In recent decades, however, climate change has increased the frequency and intensity of floods, with serious consequences for lotic biota, particularly benthic macroinvertebrates, due to their limited mobility and sensitivity to disturbance. The impact of floods varies according to different biological parameters including the characteristics of the macrobenthic communities (taxonomic composition, morphology, behaviour, and life history traits) on one hand and various nonbiological parameters such as flood intensity, artificialisation of the river bed, the presence of dams, and many other factors on the other. Understanding these dynamics is pivotal to improve the effective management and conservation of aquatic ecosystems in the context of current climate change. The aim of this short communication is to evaluate the impact of a catastrophic flood on the macroinvertebrate community of a low-order Appennine stream (NW Italy). This will provide data regarding the varying impacts on different taxa and the recovery pattern of this significant component of the ecosystem.

Nivar cyclone-related urban flood vulnerability in Velachery municipality, Chennai

ABSTRACT The Velachery Municipality is highly vulnerable to natural disasters such as Storms, and floods due to its geo-environmental settings. It is evident that the Nivar cyclone has severely hit and caused flooding in the Velachery region during the fourth week of November 2020. Along with the increasing urbanization, it is also expected to experience severe impacts of climate change soon. Urbanization exerts additional environmental stress and amplifies the impact of natural disasters. The study area has been divided into 3 clusters based on the geographical location. The survey has been carried out in 180 households and data was statically analyzed. Indicators such as exposure, sensitivity, and adaptive capacity were grouped and aligned with available data. Data were aggregated using a composite index and Urban Flood Vulnerability was compared to Cluster wise. The results reveal an extreme variability in terms of flood magnitude and frequency in Velachery. The vulnerability spider diagram suggests that Cluster 2 is more vulnerable in terms of less capacity to cope, and Cluster 3 is more vulnerable in terms of Exposure, Health, Economic susceptibility, physical susceptibility, less capacity to anticipate, cope and recover. Overall Cluster 3 has higher flood vulnerability compared to other clusters. The identification of flood-vulnerable zones has been done in the study and recommended flood resilience measures for Velachery Municipality, Chennai.

Hydrological and hydrodynamic modelling for flood management: A case study of the Yamuna River Basin in Delhi

Study regionYamuna River (Delhi), India. Study focusThe anthropogenic activities within the vicinity of the floodplain reduce the river's margin and subsequently alter the magnitude of the river's flow. The encroachment of riverbeds leads to waterlogging and flooding in urban areas, thereby causing damage to property, human life, etc. It necessitates a comprehensive study of the floodplain and changes in its proximity such as encroachment of floodplains to carry out any further activities with certainty. This study employs a two-dimensional model to simulate the Yamuna River's (YR) hydrodynamic characteristics, focusing on India's Delhi region. New hydrological insightsSimulated flood flows are employed to evaluate floods of once in 10, 20, 25, and 30-year return periods using the flood frequency analysis for 1951–2013. The model validation results indicated that the model could mimic the flood depth in YR. Simulation results revealed that the floodplain's encroachment had increased the severity of the floods. The increase in the extremeness of flooding events, i.e., from once in a 10-year return period to a 30-year return period event, is expected to increase the areas at risk of floods by 12 %. The model also offers a potential platform for evaluating other alternatives, such as further encroachment, for a business-as-usual scenario or for restoring the Yamuna floodplains. With such a comprehensive perspective, floodplains' role enhances river basin resilience to climate and anthropogenic changes and increases flood safety.

Seasonal Controls on Microbial Depolymerization and Oxidation of Organic Matter in Floodplain Soils.

Floodplain soils are vast reservoirs of organic carbon often attributed to anaerobic conditions that impose metabolic constraints on organic matter degradation. What remains elusive is how such metabolic constraints respond to dynamic flooding and drainage cycles characteristic of floodplain soils. Here we show that microbial depolymerization and respiration of organic compounds, two rate-limiting steps in decomposition, vary spatially and temporally with seasonal flooding of mountainous floodplain soils (Gothic, Colorado, USA). Combining metabolomics and -proteomics, we found a lower abundance of oxidative enzymes during flooding coincided with the accumulation of aromatic, high-molecular weight compounds, particularly in surface soils. In subsurface soils, we found that a lower oxidation state of carbon coincided with a greater abundance of chemically reduced, energetically less favorable low-molecular weight metabolites, irrespective of flooding condition. Our results suggest that seasonal flooding temporarily constrains oxidative depolymerization of larger, potentially plant-derived compounds in surface soils; in contrast, energetic constraints on microbial respiration persist in more reducing subsurface soils regardless of flooding. Our work underscores that the potential vulnerability of these distinct anaerobic carbon storage mechanisms to changing flooding dynamics should be considered, particularly as climate change shifts both the frequency and extent of flooding in floodplains globally.

Flood risk awareness and transportation mode choice in communities with varying flooding frequencies: The cases of Bozkurt, Kastamonu, Turkey and Malanday, Marikina, Philippines

This study investigates the post-flood transportation mode choices (TMC) of communities exposed to floods of varying frequencies, focusing on Bozkurt, Turkey, recently afflicted by a major flood, and Malanday, Philippines, which endures an average of 10 floods annually. The study considers 17 factors affecting TMC, including age, occupation, income, housing type, flood experience, education level, changes in travel time, primary transportation mode, duration of residence, gender, marital status, relocation status, personal losses, disruption of daily activities, time to return to routine, satisfaction with road maintenance, and satisfaction with flood measures. Data were collected through surveys and analysed using regression analysis.The findings indicate that personal and cultural characteristics, and flood frequency, have limited effects on TMC. The most impactful factors on post-flood TMC are changes in travel time (42.11 %), personal losses (23.75 %), and satisfaction with road maintenance (11.30 %). The study demonstrates that flood risk awareness is directly related to education level and flood experience. In Malanday, where 84 % of the population are university or high school graduates, floods are regarded as routine events. In contrast, in Bozkurt, where only 37 % have similar education levels, the community experienced panic under flood conditions, adversely affecting decision-making processes.The research provides insights into how communities exposed to floods adjust their TMC, offering essential information for mitigating flood risks and increasing flood awareness. Moreover, the findings deliver suggestive information for local governments and planners in developing more effective flood management and transportation planning strategies.

Comprehensive Zoning Strategies for Flood Disasters in China

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management.

Investigating the influence of urban morphology on pluvial flooding: Insights from urban catchments in England (UK)

As climate change intensifies, cities globally are experiencing more severe rainfall and frequent pluvial floods. Urban expansion is altering the permeability of the land, thus increasing the risk of flooding. This study investigates the impact of urban morphology on pluvial floodwater distribution in 15 urban catchments across England, UK, to provide an analysis of how urban morphology influences flood magnitude. Using a cellular automata-based model, pluvial flood simulations were conducted for catchments characterized by diverse urban morphologies. Then a series of machine learning models were adopted to reveal the relationships between the morphological characteristics of urban configurations (e.g., building footprints, impervious surfaces, street network, topography) and pluvial flooding. These models were used to identify and quantify the effects of key urban morphological indicators on pluvial flooding. The results indicate that, although the total area of impervious surfaces plays the most significant role in floodwater distribution, the edge density (ED) of building footprints and impervious surfaces also influences this process. Synthetic experiments with an exemplary urban fabric show that decreasing “ED of building footprint” and increasing “ED of impervious surface” can mitigate flood volume by up to 6.3 % at 100 % drainage efficiency and 7.8 % at 50 % efficiency. The results of this study are anticipated to aid urban planners and policymakers in developing strategies for implementing flood-resilient cities.

Optimized ensemble-based flood hazard mapping in low altitude subtropical riverine terrane

Among various natural hazards, those induced by hydrological and meteorological factors have emerged as the most frequent and damaging. India is at the second from the top, after Bangladesh, of flood hazard-affected countries of the world, causing immense damage to life and property every year. The Middle Ganga Plain in the country is one of the most severely affected regions due to the convergence of Himalayan rivers in the Ganga (Ganges) plain. This study focuses on optimizing novel ensemble models to enhance the mapping of flood hazard susceptibility in specific topoclimatic setting, characterized by a low-altitude-range, sub-tropical monsoonal climate, and a riverine floodplain environment of the tectonically active Middle Ganga Plain. This region forms part of the Ganga Foreland Basin prone to frequent floods. We employ a comprehensive flood inventory and twelve selected flood conditioning factors and is the first study that has developed these ensemble models in low relief riverine floodplain environmental setting with high resolution geomorphology as one of the conditioning factors. Four artificial intelligence ensemble models were employed in this work: Logistic Regression-Evidential Belief Function (LR-EBF), Logistic Regression-Frequency Ratio (LR-FR), Multi-Layer Perceptron-Evidential Belief Function (MLP-EBF), and Multi-Layer Perceptron-Frequency Ratio (MLP-FR). Results showed that LR-based ensembles outperformed MLP-based ensembles in the studied topoclimatic setting, with LR-EBF model achieving 87.2% and 84.7% success and prediction rates respectively. Our results reveal consistent performance differences between LR-based ensembles with FR and EBF models across different environmental settings. MLP-based ensembles, particularly with Frequency Ratio, displayed more significant performance variations. The findings underscore the importance of considering specific topoclimatic and altitudinal range environments when applying flood hazard susceptibility models. Our study also provides valuable insights for planning and policymaking in flood management practices in similar topoclimatic settings worldwide.

Assessment of flood vulnerability in Osun River Basin using AHP method

Flooding is a global natural disaster that occurs when water rises over normal levels and damages infrastructure, buildings, and land. Lately, a substantial rise has occurred in the frequency and severity of floods in Nigeria due to urbanization, population growth and climate change. This study aims to identify areas in the Osun River Basin (ORB) in southwest Nigeria that are at risk of flooding as a result of increased rainfall patterns that can induce river flooding. 10 flood factors contributing to flood susceptibility were obtained around the study area. These remote sensing data were analyzed using a weighted overlay on ArcGIS. The Analytic Hierarchy Process (AHP) was particularly applied in analysing the flood factors and creating the flood susceptibility maps. Results obtained showed that flood events are probable in areas along the river bank, some areas that are low-lying terrains and areas where there is high rainfall. Ogun State falls within the areas with the lowest digital elevation, therefore the state is very highly susceptible to flooding from the tributaries of the Osun River. Areas such as Ijebu North, Ijebu North East, Ijebu East and Ijebu Ode were identified as highly susceptible to flooding from the maps created. This study will further help stakeholders and policymakers in reducing the impact of flooding in these areas.

Altitude characteristics in the response of rain-on-snow flood risk to future climate change in a high-latitude water tower

Global warming is profoundly impacting snowmelt runoff processes in seasonal freeze-thaw zones, thereby altering the risk of rain-on-snow (ROS) floods. These changes not only affect the frequency of floods but also alter the allocation of water resources, which has implications for agriculture and other key economic sectors. While these risks present a significant threat to our lives and economies, the risk of ROS floods triggered by climate change has not received the attention it deserves. Therefore, we chose Changbai Mountain, a water tower in a high-latitude cold zone, as a typical study area. The semi-distributed hydrological model SWAT is coupled with CMIP6 meteorological data, and four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) are selected after bias correction, thus quantifying the impacts of climate change on hydrological processes in the Changbai Mountain region as well as future evolution of the ROS flood risk. The results indicate that: (1) Under future climate change scenarios, snowmelt in most areas of the Changbai Mountains decreases. The annual average snowmelt under SSP126, SSP245, SSP370, and SSP585 is projected to be 148.65 mm, 135.63 mm, 123.44 mm, and 116.5 mm, respectively. The onset of snowmelt is projected to advance in the future. Specifically, in the Songhua River (SR) and Yalu River (YR) regions, the start of snowmelt is expected to advance by 1–11 days. Spatially, significant reductions in snowmelt were observed in both the central part of the watershed and the lower reaches of the river under SSP585 scenario. (2) In 2021–2060, the frequency of ROS floods decreases sequentially for different scenarios, with SSP 126 > SSP 245 > SSP 370 > SSP 585. The frequency increments of ROS floods in the source area for the four scenarios were 0.12 days/year, 0.1 d/yr, 0.13 days/year, and 0.15 days/year, respectively. The frequency of high-elevation ROS events increases in the YR in the low emission scenario. Conversely, in high emission scenarios, YR high-elevation ROS events will only increase in 2061–2100. This phenomenon is more pronounced in the Tumen River (TR), where floods become more frequent with increasing elevation.

Assessing Community Resilience to Flood Disasters: A Case Study of Temerloh, Pahang

Rapid urbanization and development have led to a significant number of Malaysians living in flood-prone areas, exacerbating the impact of natural disasters such as floods. This study aims to identify the factors influencing community resilience both before and after flood events in Temerloh, Pahang. Data were collected via questionnaires distributed to 350 flood victims. Key findings reveal that long-term residents exhibit higher resilience due to local knowledge and preparedness strategies. The study highlights the importance of integrating local experiences into flood management policies to enhance community resilience. The results reveal that large flood-prone areas experience frequent flooding, contributing to considerable community distress. This distress is somewhat alleviated by the support provided by government agencies and NGOs, which includes relief efforts, emergency services, and flood defense construction. Additionally, community involvement in flood management was found to have a statistically significant impact (p < 0.05). The study highlights the need for enhanced flood management strategies that address psychological aspects to reduce negative impacts on the community.

Facies architecture, pedogenesis and palynology of the Olenekian dryland setting – A sedimentary record of Early Triassic climate oscillations in the SE Germanic Basin

The Early Triassic period was characterised by significant climatic perturbations driven by generally high global temperatures. Several significant shifts in the stable carbon and oxygen isotopes were recognised in the marine settings in the Olenekian age deposits that were linked to short-lasting but relatively high-amplitude climatic events. The record of these events in the continental strata is far less understood due to the stratigraphic incompleteness of such deposits. However, in the Holy Cross Mountains (Poland), the Olenekian continental deposits are well preserved, and sedimentation was relatively continuous, making them an ideal candidate for studying the effects of climate changes and their preservation in continental settings. As such, sedimentological analysis of five wells and one outcrop (over 530 m logged in total), integrated with new and legacy palynological data, allowed the reconstruction and dating of major steps in the palaeoenvironmental evolution of the SE part of the Central European (Germanic Basin), and its comparison with the marine-based climatic models for the Early Triassic. The lower and middle Olenekian stage is represented by fine-grained deposits of dry floodplain interbedded with sandstones deposited within fluvial distributary channels and terminal splays. The palaeosols are mainly represented by aridisols (calcisols), indicating a relatively dry, though not extreme, climate as some water was necessary to support relict plant cover. Towards the mid-Spathian times, the climate became more humid - lacustrine deposits appeared, and palaeosols displayed features of an elevated water table (gleyed vertisols and inceptisols). Frequent storms and floods also affected the lacustrine sedimentation, reflecting volatile climatic conditions. In the late Spathian time, continental sedimentation continued, and the dominance of vertisols and more stacked fluvial channels in the sequence is linked with an increased seasonality, with more pronounced humid seasons. The continental setting lasted until the latest Spathian time, when a marine transgression occurred, marking a new phase in the basin development.