Annual Flood Research Articles

The transport and vertical distribution of microplastics in the Mekong River, SE Asia

Rivers are primary vectors of plastic debris to oceans, but sources, transport mechanisms, and fate of fluvial microplastics (<5mm) remain poorly understood, impeding accurate predictions of microplastic flux, ecological risk and socio-economic impacts. We report on microplastic concentrations, characteristics and dynamics in the Mekong River, one of the world’s largest and polluting rivers, in Cambodia and Vietnam. Sampling throughout the water column at multiple localities detected an average of 24 microplastics m-3 (0.073mgl-1). Concentrations increased downstream from rural Kampi, Cambodia (344km from river mouth; 2 microplastics m-3, 0.006mgl-1), to Can Tho, Vietnam (83km from river mouth; 64 microplastics m-3, 0.182mgl-1) with most microplastics being fibres (53%), followed by fragments (44%) and the most common polymer being polyethylene terephthalate (PET) or polyester. Pathways of microplastic pollution are expected to be from urban wastewater highlighting the need for improved wastewater treatment in this region. On average, 86% of microplastics are transported within the water column and consequently we identified an optimum sampling depth capturing a representative flux value, highlighting that sampling only the water surface substantially biases microplastic concentration predictions. Additionally, microplastic abundance does not linearly follow discharge changes during annual monsoonal floods or mirror siliclastic sediment transport, as microplastic concentrations decrease rapidly during higher monsoon flows. The findings reveal complex microplastic transport in large rivers and call for improved sampling methods and predictive models to better assess environmental risk and guide policy.

Compound-event analysis in non-stationary hydrological hazards: a case study of the Niger River in Niamey

ABSTRACT This study examines compound hydrological hazards in a non-stationary context, specifically focusing on the Niger River in Niamey. The hazard results from the confluence of local Sahelian and more remote Guinean tributaries, displaying seasonal floods. The study first investigates whether Niamey’s annual flood hazard is a compound result of Sahelian and Guinean flows, and then assesses the value of a compound hazard approach versus traditional flood analyses in this region. Analyzing discharge data from 1950 to 2020, the study disentangles Sahelian and Guinean flow impacts. It compares flood return level estimations from three statistical models of varying complexity. Findings confirm Niamey’s hazard as compound, stressing the need to consider both tributaries separately. Incorporating non-stationarity in statistical modeling is crucial, yet fully integrating the compound nature of hazards doesn’t significantly change the quantitative estimation of the hazard in Niamey.

A Novel Time-Varying P-III Distribution Curve Fitting Model to Estimate Design Floods in Three Gorges Reservoir Operation Period

Design floods are traditionally estimated based on the at-site annual maximum flood series, including historical information of hydraulic structures. Nevertheless, the construction and operation of upstream reservoirs undermine the assumption of stationarity in the downstream flood data series. This paper investigates non-stationary design flood estimation considering historical information from the Three Gorges Reservoir (TGR) in the Yangtze River. Based on the property that the distribution function of a continuous random variable increases monotonically, we proposed a novel time-varying P-III distribution coupled with the curve fitting method (referred to as the Tv-P3/CF model) to estimate design floods in the TGR operation period, and we comparatively studied the reservoir indices and parameter estimation methods. The results indicate that: (1) The modified reservoir index used as a covariate can effectively capture the non-stationary characteristics of the flood series; (2) The Tv-P3/CF model emphasizes the fitness of historical information, yielding superior results compared to time-varying P-III distribution estimated by the maximum likelihood method; (3) Compared to the original design values, the 1000-year design peak discharge Qm and 3-day and 7-day flood volumes in the TGR operation period are reduced by approximately 20%, while the 15-day and 30-day flood volumes are reduced by about 16%; (4) The flood-limited water level of the TGR can be raised from 145 m to 154 m, which can annually generate 0.32 billion kW h more hydropower (or increase by 6.8%) during flood season without increasing flood prevention risks.

A comprehensive uncertainty framework for historical flood frequency analysis: a 500-year-long case study

Abstract. The value of historical data for flood frequency analysis has been acknowledged and studied for a long time. A specific statistical framework must be used to comply with the censored nature of historical data, for which only floods large enough to induce written records or to trigger flood marks are usually recorded. It is assumed that all floods which exceeded a given perception threshold were recorded as written testimonies or flood marks. Conversely, all years without a flood record in the historical period are assumed to have had a maximum discharge below the perception threshold. This paper proposes a binomial model that explicitly recognizes the uncertain nature of both the perception threshold and the starting date of the historical period. This model is applied to a case study for the Rhône River at Beaucaire, France, where a long (1816–2020) systematic series of annual maximum discharges is available along with a collection of 13 historical floods from documentary evidence over 3 centuries (1500–1815). Results indicate that the inclusion of historical floods reduces the uncertainty of 100- or 1000-year flood quantiles, even when only the number of perception threshold exceedances is known. However, ignoring the uncertainty around the perception threshold leads to a noticeable underestimation of flood quantile uncertainty. A qualitatively similar conclusion is found when ignoring the uncertainty around the historical period length. However, its impact on flood quantile uncertainty appears to be much smaller than that of the perception threshold.

Understanding bimodal seas, shingle beach response and flood risk at Hayling Island, UK

Beach management through nourishment and annual recycling has been applied for over 37 years to manage flood and coastal erosion risk to 1700 properties at Eastoke, Hayling Island, UK. This form of natural flood risk management has proved successful in avoiding the annual flooding that blighted this area previously. However, there have been unexpected storm events that have caused beach erosion and localised flooding. Using long-term nearshore wave datasets and state-of-the-art statistical methods, new multi-variate extreme wave conditions were derived and applied to assess beach performance. Eastoke beach was found to meet its original design criteria of a 0.5% AEP standard of protection for unimodal wave conditions. However, it experiences greater rollback erosion, wave overwash and therefore flood inundation under certain (but not all) bimodal wave conditions, causing uncertainty around the future standard of protection. Communicating these inconsistencies to practitioners and a non-specialist audience is challenging as we tend to oversimplify, despite every beach and storm being unique. Better understanding of mixed beaches, both in situ and through parametric and numerical models, would reduce uncertainty to ensure communities are resilient to climate change.

Processes and controls of regional floods over eastern China

Abstract. Mounting evidence points to elevated regional flood hazards in a changing climate, but existing knowledge about their processes and controls is limited. This is partially attributed to inadequate characterizations of the spatial extent and potential drivers of these floods. Here we develop a machine-learning-based framework (mainly including the Density Based Spatial Clustering Applications with Noise (DBSCAN) clustering algorithm and a conditional random forest model) to examine the processes and controls of regional floods over eastern China. Our empirical analyses are based on a dense network of stream gauging stations with continuous observations of annual maximum flood peaks (i.e. magnitude and timing) during the period 1980–2017. A comprehensive catalogue of 318 regional floods is developed. We reveal a pronounced clustering of regional floods in both space and time over eastern China. This is dictated by cyclonic precipitating systems and/or their interactions with topography. We highlight contrasting behaviours of regional floods in terms of their spatial extents and intensities. These contrasts are determined by fine-scale structures of flood-producing storms and anomalous soil moisture. While land surface properties might play a role in basin-scale flood processes, it is more critical to capture spatial–temporal rainfall variabilities and soil moisture anomalies for reliable large-scale flood hazard modelling and impact assessments. Our analyses contribute to flood science by better characterizing the spatial dimension of flood hazards and can serve as a basis for collaborative flood risk management in a changing climate.

Regulatory Threshold of Soil and Water Conservation Measures on Runoff and Sediment Processes in the Sanchuan River Basin

Research on the runoff and sediment reduction effects of soil and water conservation measures has always been a topic of interest, which is of great significance for carrying out sustainable strategies for soil and water conservation in the Yellow River Basin. This study aims to find the threshold years of soil and water conservation measures for reductions in runoff and sediment. Through the analysis of various soil and water conservation measures, runoff, sediment, and rainfall data in the Sanchuan River Basin from 1960 to 2019, we determined the threshold years of soil and water conservation measures on runoff and sediment processes using the Hydrology and Lagrange Multiplier method. The results are as follows: The trend in flood season rainfall and annual rainfall in the Sanchuan River Basin is consistent. The 1990s was a turning period in the annual rainfall and flood season rainfall of the Sanchuan River Basin. The 2000s was a turning period of the runoff in the Sanchuan River Basin, while the sediment entered a stable period after 2000. The best periods for reducing runoff and sediment were the initial treatment period (1967–1979) and the centralized treatment period (1980–1996). The runoff and sediment reduction effects of each soil and water conservation measure during the initial treatment period (1967–1979) were terrace (32.8%) &gt; dam (30.1%) &gt; grass (18.6%) &gt; forest (18.5%), while their effects during the centralized treatment period (1980–1996) were grass (53.7%) &gt; terrace (20.7%) &gt; dam (14.6%) &gt; forest (11.0%). The runoff and sediment reduction effects of various soil and water conservation measures during different treatment periods indicate that the runoff reduction effect reached its peak in 2003–2005, while the sediment reduction benefit reached its peak in 2013–2015. Based on the comprehensive benefits of runoff and sediment regulation, 2013–2015 are considered to be the threshold years for various soil and water conservation measures, with the measures covering respective average areas of 4.85 × 104, 17.80 × 104, 1.15 × 104, and 0.82 × 104 hm2. These research results will have a certain significance for the reasonable allocation of soil and water conservation measures and sustainable development in the Yellow River Basin.

Climate Change May Increase the Impact of Coastal Flooding on Carbon Storage in China’s Coastal Terrestrial Ecosystems

Climate warming exacerbates the deterioration of soil and degradation of vegetation caused by coastal flooding, impairing ecosystem climate-regulating functions. This will elevate the risk of carbon storage (CS) loss, further intensifying climate change. To delve deeper into this aspect, we aimed to integrate future land use/land cover changes and global mean sea-level rise to assess the impact of coastal floods on terrestrial CS under the effects of climate change. We compared the 10-year (RP10) and 100-year (RP100) return-period floods in 2020 with projected scenarios for 2050 under SSP1-26, SSP2-45, SSP3-70, and SSP5-85. The study findings indicate that CS loss caused by coastal flooding in China’s coastal zones was 198.71 Tg (RP10) and 263.46 Tg (RP100) in 2020. In 2050, under the SSP1-26, SSP2-45, and SSP3-70 scenarios, the CS loss is projected to increase sequentially, underscoring the importance of implementing globally coordinated strategies for mitigating climate change to effectively manage coastal flooding. The value of CS loss is expected to increase in 2050, with an anticipated rise of 97–525% (RP10) and 91–498% (RP100). This highlights the essential need to include coastal flood-induced CS changes in carbon emission management and coastal climate risk assessments.

Floods of Egypt’s Nile in the 21st century

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River’s source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN’s runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs’ output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years.

Combining Geo-Archaeology and Historical Nile Records to Understand Predynastic Settlement Patterns in the Region of the Nile’s First Cataract, Egypt

Abstract This paper aims to contribute new data to understanding early Predynastic settlement patterns in Egypt at the beginning of the state formation process in the 4th millennium bce. It focuses on the early settlements of the First Nile Cataract region. We employ three different datasets to address the issue retrieved from a recent archaeological investigation and drill coring and from Nile records available from archaeological and historical sources. The analysis is performed in a gis-based environment and through inundation modelling. Data suggest early Predynastic settlements at the First Cataract may have been settled all year round. They appear to be deliberately placed above the annual high flood level and have water resources for most of the year. The new geo-archaeological data further suggests that the average high inundation level for the early 4th millennium bce was similar to that documented for the end of the millennium.

Sylvia Doe and the 100-Year Flood by Robert Beatty (review)

Sylvia Doe and the 100-Year Flood by Robert Beatty (review)

Mapping of Flood-Prone and Dengue Fever-Prone Areas in Tahtul Yaman Village, Pelayangan District, Jambi City

Floods are natural events that come suddenly with an uncertain periodicity, except for areas that often experience annual flooding, such as in Tahtul Yemen Village. This flood disaster can cause health problems that threaten society, one of which is the Dangue Hemorrhagic Fever (DHF) outbreak. This activity aims to carry out mapping of flood-prone areas and dengue-prone areas in Tahtul Yemen Village to provide education about the management and prevention of dengue fever in order to improve the health status of the community, especially those in vulnerable areas. This research is exploratory research with an ecological study research design using Geographic Information Systems (GIS). Based on data on flood-prone areas obtained from 12 RTs in Tahtul Yaman Village, 5 RTs had a high percentage of being affected by flooding, 4 RTs had a moderate percentage of being affected by flooding, and 3 RTs had a low percentage of being affected by flooding. Meanwhile, for dengue-prone areas, there are 7 RTs with a low percentage, 3 RTs with a medium percentage, and 2 RTs with a high percentage. In conclusion, it was found that in Tahtul Yaman Subdistrict areas prone to flooding with the highest dominance are in RT 11,12,5,9, and 7 and areas prone to Dengue Hemorrhagic Fever (DHF) are dominant in RT 7 and 9. It is recommended that the areas that have been mapped be included in prone to flooding and Dengue Hemorrhagic Fever (DHF) strengthen drainage systems, green infrastructure, and increase public awareness about disease prevention

An Impact-Based Flood Forecasting System for Citizen Empowerment

This work addresses the critical issue of flooding, a significant natural hazard, consistently ranked highest in the 2023 World Risk Index. The annual onslaught of tropical cyclones and the associated abnormal rainfall threaten lives, and destroy crops and property, thereby causing great economic damage, demanding urgent and science-based decision-making. We introduce an impact-based flood forecasting system as a proactive anticipatory action (i.e., AA) measure, linking climate services and disaster risk management to mitigate extreme weather impacts. Developed by the University of the Philippines Resilience Institute, the National Operational Assessment of Hazards (NOAH) Center, and Gerry Bagtasa of the Institute of Environmental Science and Meteorology, this system advances disaster resilience efforts, leveraging science-based forecasts to prioritize vulnerable barangays (villages). Unlike traditional early warning systems, the proposed automated system predicts flooding one day in advance and assesses exposure levels based on population distribution. By utilizing global weather forecast models locally calibrated for Philippine conditions and 100-year rain return flood hazard maps for the Philippines, the system forecasts river inundated areas, enabling local government units (LGUs) and humanitarian organizations to prioritize preparations for communities and allocate resources during flooding events effectively. This system enhances the efficiency of disaster response planning, fostering a proactive and impactful strategy to address the recurring threat of flooding in the country. The system also underscores the role of open data in disaster resilience, exemplified by NOAH’s system to disseminating big data, which aligns with open governance principles. By encouraging transparency and stakeholder collaboration, data exchange is promoted, providing actionable information that fosters collaboration between the LGUs, humanitarian organizations, and other stakeholders. Cognizant of the importance of community engagement in disaster resilience, the newly developed impact-based flood forecasting system encourages community involvement by providing easily accessible information, which can be used to validate the forecasts based on local knowledge and experience. This research contributes to the urgent call for anticipatory action in the face of escalating extreme weather events globally.

Assessment of the extent of community preparedness to flood risk in Jigawa State, Nigeria

Jigawa State faces annual flood disasters that have resulted in significant loss of life and property, putting many communities at risk. This study aim to evaluate the preparedness for flood risk in Jigawa State, Nigeria. A sample of 666 respondents was selected for a self-administered questionnaire, with 601 fully completed. The collected data were analyzed descriptively to determine the preparedness index. (PI). From the result of the findings, a majority (77.7%) of the respondents agreed that the community is aware of flood risks. There was also a provision of a neighborhood directory (97.8%), local shelter (75.9%) and emergency contacts (55.4%) during floods. The PI (92.6%, 98.6% 84.1% and 85.1% respectively), are above the 65% threshold and hence considered ‘prepared’ in this regard; but, ‘somewhat prepared’ (PI of 61.7%) regarding awareness of flood disaster risk management strategies; ‘and unprepared’ (PI of 55.5%, 50.5%, 50.5%, 50.8% and 54.3% respectively) in terms of community organization of public education and training of flood risk reduction (66.6%); engagement on rehearsals/emergency drills (51.6%); good community-based warning system (51.4%); and a standalone system for property protection measures during floods (52.4%); as well as an active recovery plan (62.9%). It was recommended that proper embankment protection and water regulation outlets be established. Additionally, real-time flood forecasting and effective early warning systems should be developed. Communities should be assessed to identify flood risk zones before construction activities begin, or alternatively, resettlement to safer areas should be considered for those living in flood-prone regions.

Triple exposure: the geographic correlation between flood risk, climate skepticism, and social vulnerability in the United States

Abstract This study investigates the geographic correlation between flood risk, climate skepticism, and social vulnerability across the United States. Our results reveal a systematic underestimation of flood risk in the Federal Emergency Management Agency’s (FEMA) Flood Insurance Rate Maps, especially in Appalachia, parts of New England, and the Northwest. These three regions face two additional risks: high levels of social vulnerability and skepticism about climate change. Nationally, there is a statisically significant correlation (0.19, p &lt; 0.005) between flood risk and climate change skepticism, which increases (0.28, p &lt; 0.005) in regions with high FEMA undercounts and elevated flood risk. Climate change skepticism manifests as distrust in science, an underestimation of property and community risk, and a resistance to mitigation and adaptation efforts. Indicators of social vulnerability, such as poverty rates, physical disabilities, unemployment, households in mobile homes, and lack of vehicle access, are especially pronounced in Appalachia. Addressing this geographically-embedded triple exposure—flood risk, social vulnerability, climate change skepticism— requires strategies to enhance local resilience. These include revising the 100-year floodplain categorization in FEMA’s National Flood Insurance Program to better reflect climate change, conducting public education campaigns in vulnerable communities, and scaling-up financial assistance for flood mitigation and adaptation projects.

Measuring rising heat and flood risk along the belt-and-road initiative

China’s global infrastructure financing flagship, the Belt and Road Initiative (BRI) encompasses countries hosting over 60% of the global population and one-third of worldwide GDP. It is based mainly on long-term loans that will mature decades into the future, and timely repayments are only possible if they remain commercially viable. But despite its vast global scope, little is known about the climate risks that could imperil the operations of BRI projects over the next few decades, and, consequently, threaten their long-term sustainability. We narrow this gap by estimating the impacts of future climate change on 217 BRI projects across 70 countries and 9 sectors in two dimensions. First, the effects of increased heat stress on human physical work capacity are calculated using the wet bulb globe temperature and an assessment of the workload for each selected BRI project. Second, the potential structural damages from more frequent flooding incidents are measured by return period (RP), where a shorter RP signals heightened risk. Both have direct impacts on human productivity and infrastructural integrity, which are essential to maintaining the operational viability and financial stability of BRI projects. We compared projected changes on both measures for the mid- and late-twentieth centuries (2041–2060 and 2081–2100) to the historical baseline (1981–2010). We found that BRI projects face escalating vulnerability to climatic risks on both counts. The results underscore a broad variance across different future carbon emission scenarios measured under the Shared Socioeconomic Pathways (including SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). BRI’s aggregated climatic risks are substantially elevated under a high carbon emission scenario compared to a low emission scenario. By the end of the twentieth century, labor workability under SSP3-7.0 (31%) could potentially decrease three times more compared to SSP1-2.6 (10%). Under an intermediate-emission scenario (SSP2-4.5), floods with a historical return period of 10 years could have a return period of 5 years in the future. Significantly hampering the utilization and economic return generation potential of infrastructure projects. In addition, regional geography contributes to risk heterogeneity, with 100-year floods occurring every 15 years in South Asia and every 24 years in Sub-Saharan Africa. Such climate risk implications, potentially overlooked by development financiers, represent significant risks to the sustenance of the BRI, estimated to be worth $1 trillion.

Assessment of Climate Change Impact on Flood Hazard Zones

AbstractThere have been many destructive pluvial and fluvial floods in Poland and the projection of increasing flood hazards in the future is a reason of considerable concern. The maps of river hazard zones are changing over time, and understanding these changes is of primary importance for flood risk reduction and climate change adaptation. This article aims to assess the impact of climate change on the spatial extent and depth classes of flood hazard zones for a selected reach of the River Warta in the western part of Poland. To this end, we integrated the Soil &amp; Water Assessment Tool (SWAT) hydrological model of the Warta River Basin with the 1D hydraulic model HEC-RAS of the selected reach. The climate change effect was quantified based on the coupled model simulations forced with bias-corrected projections from the EURO-CORDEX project. Flood hazard maps were developed for two townships along the River Warta (Oborniki and Wronki), three greenhouse gas concentration scenarios (one for the baseline scenario in the reference period, 1971–2000; one for RCP 4.5 and one for RCP 8.5, for the time horizon 2021–2050) and for three return periods (10-, 100- and 500-year floods). Based on the ensemble mean, the increase in the flooded area projected in the future is more pronounced for RCP8.5 than for RCP4.5. This unique combination of software and data enabled the transformation of climate change impact into the land surface part of the hydrological cycle and assessment of changes in flood hazard and opens the way to assess the potential increases in the economic losses in the future.

Extracting Wetlands in Coastal Louisiana from the Operational VIIRS and GOES-R Flood Products

Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Baseline Imager (GOES-R ABI) flood products have been widely used by the National Weather Service (NWS) for river flood monitoring, and by the Federal Emergency Management Agency (FEMA) for rescue and relief efforts. Some water bodies, like wetlands, are detected as water but not marked as permanent or normal water, which may result in their misclassification as floodwaters by VIIRS and GOES-R flood products. These water bodies generally do not cause significant property damage or fatalities, but they can complicate the identification of truly hazardous floods. This study utilizes the severe Louisiana flood event caused by Hurricane Ida to demonstrate how to differentiate wetlands from real-hazard flooding. Since Hurricane Ida made landfall in 2021, and there was no major flood event in 2022, VIIRS and ABI flood data from 2021 and 2022 were selected. The difference in annual total flooding days between 2021 and 2022 was calculated and combined with long-time flood frequency to distinguish non-hazard floodwaters due to wetlands identified from real-hazard floods caused by the hurricane. The results were compared with the wetlands from the change detection analysis. The confusion matrix analysis indicated an accuracy of 91.58%, precision of 89.97%, and F1-score of 76.63% for the VIIRS flood products. For the GOES-R ABI flood products, the confusion matrix analysis yielded an accuracy of 86.88%, precision of 97.49%, and F1-score of 75.21%. The accuracy and F1-score values for the GOES-R ABI flood products are slightly lower than those for the VIIRS flood products, possibly due to their lower spatial resolution, but still within a feasible range.

Flood frequency analysis in the lower Burhi Dehing River in Assam, India using Gumbel Extreme Value and log Pearson Type III methods

Flood is a widespread climate-related hazard with the potential to occur in almost any geographical location where fluvial processes are active and can occur due to the involvement of multiple factors. Flood frequency analysis (FFA) is considered a valuable hydrologic tool to know the magnitude and frequency of the recurrence of floods. In this research, we have examined the Lower Burhi Dehing River (LBDR) in Assam, India, which frequently experiences severe flooding due to its meandering course induced by heavy monsoon rainfall. Therefore, the present research aims to analyze past flood events and current trends and predict future flood frequencies using Gumbel’s extreme value distribution and Log Pearson Type III Method. In this particular investigation, 25 years of annual maximum peak discharge data from 1972 to 1997 was employed to examine the discharge records of LBDR. This research estimated the return periods of different flood magnitudes, quantifying the likelihood of future floods with varying degrees of severity. In this regard, the return periods are estimated at 2, 5, 10, 20, 50, 100, and 200 years. The study reveals that the largest flood event occurred in 1972, with a discharge of 1134.4 m3/s, and the smallest flood event occurred in 1997, with a discharge of 214.65 m3/s. The 2-year flood is a relatively common event with a 50% probability of occurring in any given year, characterized by a moderate discharge of 1384.90 m3/s and minor impacts of 0.3665. The 5-year flood, occurring with a 20% chance annually, brings a significantly higher water level of 2008.81 m3/s and moderate-to-severe impacts of 1.4999, potentially causing widespread flooding. In the 10-year and 50-year return period, which has a probability of returning 10% and 2% chance annually, the discharge will reach 2421.89 m3/s and 3331.01 m3/s, respectively. The outcome of this research will sustainably guide policymakers and environmental planners to mitigate flood hazards.

Flood Risks Analysis in Upton Upon Severn: Assessing the Baseline Conditions and Future Scenarios for 100 and 1000-year Flood Events with a 2050 Climate Change Perspective.

This paper investigates the flood risks in Upton upon Severn, a town in Worcestershire, England, which is prone to fluvial flooding from the River Severn. Historical events, such as the significant floods of 1947 and 2007, highlight the town’s vulnerability and underscore the importance of analysing current and future flood risks. This research uses a one-dimensional hydraulic model, using Flood Modeller and QGIS software, to assess baseline flood conditions and predict the potential impacts of severe and extreme flood events, including 1 in 100-year and 1 in 1000-year flood scenarios. Additionally, it examines the projected effects of climate change by incorporating a 20% increase in flood risk estimates for the year 2050. The model results reveal that the baseline scenario poses limited flood risks, with only 17 properties affected, and key infrastructure like the A4104 road remains passable. However, under the 1 in 100-year event, approximately 336 properties are at risk, with significant road blockages and a flooded area of 5.09 Km2. The 1 in 1000-year flood scenario predicts a peak flow of 1807 m3/s, threatening more properties and covering a larger area of land. When climate change is factored into the 1 in 100-year scenario, the risks become similar to those of the 1 in 1000-year event, with 500 properties and key infrastructure, including the fire station and primary school, at risk and the A4104 road becoming impassable. These findings provide vital insights for flood risk management, especially given the possible effects of climate change, and stress the need for long-term solutions to flooding.