Flood Management Strategies Research Articles

A hybrid SWAT-ANN model approach for analysis of climate change impacts on sediment yield in an Eastern Himalayan sub-watershed of Brahmaputra

The current study focuses on analyzing the impacts of climate change and land use/land cover (LULC) changes on sediment yield in the Puthimari basin, an Eastern Himalayan sub-watershed of the Brahmaputra, using a hybrid SWAT-ANN model approach. The analysis was meticulously segmented into three distinct time spans: 2025–2049, 2050–2074, and 2075–2099. This innovative method integrates insights from multiple climate models under two Representative Concentration Pathways (RCP4.5 and RCP8.5), along with LULC projections generated through the Cellular Automata Markov model. By combining the strengths of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) techniques, the study aims to improve the accuracy of sediment yield simulations in response to changing environmental conditions. The non-linear autoregressive with external input (NARX) method was adopted for the ANN component of the hybrid model. The adoption of the hybrid SWAT-ANN approach appears to be particularly effective in improving the accuracy of sediment yield simulation compared to using the SWAT model alone, as evidenced by the higher coefficient of determination value of 0.74 for the hybrid model compared to 0.35 for the standalone SWAT model. In the context of the RCP4.5 scenario, during 2075-99, the study noted a 29.34% increase in sediment yield, accompanied by simultaneous rises of 42.74% in discharge and 27.43% in rainfall during the Indian monsoon season, spanning from June to September. In contrast, under the RCP8.5 scenario, for the same period, the increases in sediment yield, discharge, and rainfall for the monsoon season were determined to be 116.56%, 103.28%, and 64.72%, respectively. The present study's comprehensive analysis of the factors influencing sediment supply in the Puthimari River basin fills an important knowledge gap and provides valuable insights for designing proactive flood and erosion management strategies. The findings from this research are crucial for understanding the vulnerability of the Puthimari basin to climate and land use changes, and by incorporating these findings into policy and decision-making processes, stakeholders can work towards enhancing resilience and sustainability in the face of future hydrological and environmental challenges.

Flood risk zoning of cascade reservoir dam break based on a 1D-2D coupled hydrodynamic model: A case study on the Jinsha-Yalong River

Large-scale earthquakes and huge landslide surges may cause the failure of cascade reservoirs, resulting in massive downstream flooding. The flood risk zoning of cascade reservoir dam breaks is crucial for local governments to formulate emergency plans and for individuals to garner their awareness of the flood risks. This paper presents a flood risk zoning approach based on a one-dimensional and two-dimensional coupled hydrodynamic model. The flooding area simulated by the coupled hydrodynamic model was divided into a Deadly Zone (D-zone) and an Escape Zone based on whether there is sufficient time for the public to evacuate the danger area under a specific evacuation generation time (EGT). A case study was conducted on the Jinsha-Yalong River (JYR), a confluence river located in Panzhihua City, to map the risk zones. This study considered a dam-break flood in Yalong River encountering two river flood scenarios in Jinsha River (scenario A: the annual average flow; scenario B: a 1000-year return period flood). Two evacuation scenarios (EGT of 30 and 60 min) were also considered; thereafter, four combined evacuation scenarios (A-EGT30, A-EGT60, B-EGT30, and B-EGT60) were simulated. The coupled hydrodynamic model is demonstrated to be effective through the analysis of water balance and hydrographs of special section discharge. The results show that: (1) in comparison to scenario A, scenario B reached its maximum flood extent approximately 5 min earlier, signifying that flood spread speed increases with the increase in flood magnitude; (2) the D-zone area was 127.7 % larger in scenario A-EGT60 compared to A-EGT30 and 120.8 % larger in scenario B-EGT60 compared to B-EGT30. These findings underscore the importance of flood scenarios and EGT on flood risk zones. The proposed risk zoning method can help government agencies in the JYR and major river regions worldwide with flood management and emergency strategy development.

Climatological context of the mid-November 2021 floods in the province of British Columbia, Canada

The Canadian province of British Columbia (BC) is subjected to large-scale, destructive floods. The most dramatic was a mid-November 2021 event when atmospheric rivers (ARs) linked to high-intensity storms caused heavy rainfall in southwestern BC, triggering catastrophic flooding. This study examines 37 floods from 2000 to 2021 using information from over 250 climatological stations and compares events with the mid-November 2021 flood. The dates of the floods showed a bi-modal pattern: a primary season (spring to early summer, 16 floods) and a secondary season (fall to early winter, 21 floods). Five mechanisms controlled these floods: heavy rainfall, rapid snowmelt, severe ice jam, rain-on-snow, and a mixture of snowmelt and ice jam; the mid-November 2021 flood was mainly driven by heavy rainfall. Of the 37 floods, those affected by either heavy rainfall (18 floods) or rain-on-snow (10 floods) were used to derive a relationship between the average daily precipitation amount over the duration of an event and the associated integrated water vapour transport IVT¯. Flood events showed a strong linear relationship between these variables with R2≥0.85, p < 0.05, and values of these parameters were significantly higher for the mid-November 2021 flood than for > 90% of the others, although they were not the highest. The mid-November 2021 flood was also one of the four rainfall-related floods that occurred in the secondary season with IVT‾ > 400 kg m−1 s−1. The frequency of flood events over the last five years of the study period has slightly decreased when considering flood events with unknown insured cost. In contrast, insured costs of these events have increased, suggesting that present-day floods are becoming more impactful and may require changes to flood management strategies to reduce costs.

Assessment of flood disaster and management strategies in the lower Brahmaputra valley of Assam

Floods are common in every rainy season in the Brahmaputra valley. Several districts in Assam experience flooding every year. One of the flood-prone districts in Assam is Bongaigaon, which is situated in the lower reach of Brahmaputra River. The present work aims to examine the threats associated with floods and the role of government in management strategies. For the study, primary and secondary data have been gathered from different sources, including the District DC office (DDMA branch), Assam Secretariat office (ASDMA Branch), Sentinel 1, ICIMOD and field surveys and have been analyzed with the help of different tools. In this study, 2015, 2017, 2019, 2021 and 2022 flood hazard scenarios of the district have been considered for the case evaluation. The result shows that flooding in this area has a wide range of negative impacts. Furthermore, different efforts have notably been made by the district and state administration to reduce the impact and promote sustainable management.

Flash Flood Risk Assessment in the Asir Region, Southwestern Saudi Arabia, Using a Physically-Based Distributed Hydrological Model and GPM IMERG Satellite Rainfall Data

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m3/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m3/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.

Strategic Integration of Catchment Level Natural and Structural Methods of Sustainable Flood Management: A Case Study of River Wharfe Catchment Area

Water has a crucial place in the advent of humankind the flourishing of mega population centres, and is an essential source of food, water transportation, and irrigation. The anthropogenic activities in taming the natural water streams to the optimum benefit of human beings disturb natural flood plains, ecology and habitat. The channelisation of streams and hydromodifications in dams, barrages or reservoirs result in climatic variations locally/ regionally and impact transborder stream flow. Researchers have been endeavouring to restore the flood plains to their natural conditions. Still, huge hydromodifications and the development of megacities right in the flood plains or adjacent to the streams have resulted in irreversible disturbances to the natural lay of ground/ landscape. Therefore, to avoid flooding disasters, further structural interventions are undertaken to augment the natural flood prevention methods using advanced materials like cement concrete, steel, and polymers rather than increasing the emissions of greenhouse gases. Considering the strategic necessity of engineering structures as an integrated catchment level solution to augment the natural methods, the researchers/ engineers are now focussing on the use of sustainable, eco-friendly materials and demountable/ hydraulic structures to minimise the carbon footprints of hydromodifications and to decrease the obstruction to the natural flow of streams by using the flood prevention structures/ gates/ walls/ reservoirs only in case of disastrous flooding and otherwise keeping them unemployed during normal stream discharges. This study has been used to review sustainable flood management using natural and structural techniques in the Wharf River catchment in the UK, reviewing the existing research/ flood management schemes giving the pictorial coverage. The study suggests that natural flood management techniques have restricted application parameters and must be augmented by engineering structures to achieve effective flood management against heavy flooding. Low CO2 embodied greener infrastructure structural materials containing supplementary cementitious materials (SCMs) can be a beneficial option for an environmentally friendly flood management strategy.

Climate‐driven runoff variability in semi‐mountainous reservoirs of the Vietnamese Mekong Delta: Insights for sustainable water management

AbstractThe Mekong Delta, South East Asia's ‘rice bowl’, sustains more than 18 million people through its agricultural output. This yield is secured by efficient water management systems but is susceptible to climatic changes. As Vietnam's policies aim to optimize the delta's semi‐mountainous regions reliant on rain‐fed agriculture, this study investigates drought risks and climate change impacts on runoff in the O Ta Soc and O Tuk Sa reservoirs, An Giang Province, Vietnam. Using simulation models, we determined runoff volumes for specific rainfall return periods and climate scenarios for the 2030s and 2050s. Using the storm water management model (SWMM), we simulated the reservoir water balance considering rainfall, evaporation and infiltration. Our findings suggest potentially increased runoff and reservoir storage due to intensified monsoons and reduced off‐season rainfall. The 4.77 km2 drainage of the O Ta Soc reservoir could benefit from this, while the 2.55 km2 drainage of the O Tuk Sa watershed may require alternative water‐sourcing strategies. This research offers insights for drought predictions, flood management and water strategies in An Giang. To refine these predictions, future research should consider upcoming rainfall patterns.

Projected performance of green infrastructure strategies for flood mitigation in the Ganges-Brahmaputra-Meghna delta

Background: The Ganges-Brahmaputra-Meghna (GBM) delta – the world’s most populous river delta – faces heightened susceptibility to the rise in flooding disasters due to climate change, impacting millions annually. Current flood management strategies are unsustainable and ineffective, and resilient flood management is needed. A promising alternative is the strategic implementation of green infrastructure (GI) applications, which have proven effective in flood management in other regions. Methods: An analysis of the region’s past and future vulnerability to flooding is conducted. Then, green infrastructure performance metrics from regions with similar climatic conditions are extrapolated for the GBM. Green roofs, permeable pavements, and rain gardens were identified as the most suitable GI types for the GBM. Finally, computer simulations were employed to analyze the performance of different implementations of GI within a model city. Results: The simulations showed that 0% green rooftop coverage, 100% permeable pavement coverage, and 40% rain garden coverage were the most feasible GI layout. This configuration resulted in the most preferable balance between cost effectiveness and reduced runoff. Green rooftops were minimized due to high installation costs relative to their retention capacity, whereas permeable pavements and rain garden coverage were maximized. Conclusions: The studies show GI’s potential for flood mitigation and resilience in the GBM region. GI initiatives align with the region's flood mitigation policies and are thus feasible to implement with aid from government incentives. Furthermore, the computer program developed for this analysis could serve as a valuable tool for assessing GI implementation limits and offering guidance to policymakers.

Hydrogeomorphological approach for flood analyses at high- detailed scale: Narrow rivers with broad complex alluvial plains

Fluvial flood risk management requires precise knowledge of flooding processes; thus, there is a need to carry out a detailed hydrological, hydraulic, and geomorphologic analysis to understand floods and the factors that influence them. Although the integration of geomorphological methods and hydrological-hydraulic modelling is known to be crucial for flood risk analysis, it remains a challenge to apply these approaches to the understanding of floods in small and medium-sized catchments, focusing on complex fluvial systems that have been highly modified. In this work, a hydrogeomorphological methodology was developed to analyse fluvial floods in narrow rivers with broad complex alluvial plains. The procedure comprises different phases that combine hydrologic-hydraulic modelling and a geomorphological break-down. Critically, this allows for a detailed analysis of existing relationships among erosional and depositional fluvial landforms and hydraulic parameters, comprising geometric sections, discharges, depths, flood extents and, therefore, flood hazards. The approach involves the analysis of specific flood events beforehand and the study of fluvial floods associated with different probabilities of occurrence afterwards. The information employed in the present study incorporates high- detailed inputs comprising hourly time series of hydrometeorological data, LiDAR data to extract high-precision terrain information, fluvial geomorphic maps and flood event extents based on Sentinel-2 imagery. We demonstrate this methodology in the Carrión River, located in the province of Palencia, Spain, whose characteristics include a wide complex alluvial plain, reaches highly modified by anthropic interventions and recurrent flood episodes. This hydrogeomorphological approach allowed the integration of geomorphological knowledge and hydrologic-hydraulic modelling in flood analysis. As a result, it led to a deeper comprehension of the processes of inundation, which would be challenging to thoroughly understand without incorporating a holistic method, an essential factor for defining effective flood management strategies.

Can geomorphic flood descriptors coupled with machine learning models enhance in quantifying flood risks over data-scarce catchments? Development of a hybrid framework for Ganga basin (India).

Quantifying flood risks through a cascade of hydraulic-cum-hydrodynamic modelling is data-intensive and computationally demanding- a major constraint for economically struggling and data-scarce low and middle-income nations. Under such circumstances, geomorphic flood descriptors (GFDs), that encompass the hidden characteristics of flood propensity may assist in developing a nuanced understanding of flood risk management. In line with this, the present study proposes a novel framework for estimating flood hazard and population exposure by leveraging GFDs and Machine Learning (ML) models over severely flood-prone Ganga basin. The study incorporates SHapley Additive exPlanations (SHAP) values in flood hazard modeling to justify the degree of influence of each GFD on the simulated floodplain maps. A set of 15 relevant GFDs derived from high-resolution CartoDEM are forced to five state-of-the-art ML models; AdaBoost, Random Forest, GBDT, XGBoost, and CatBoost, for predicting flood extents and depths. To enumerate the performance of ML models, a set of twelve statistical metrics are considered. Our result indicates a superior performance of XGBoost (κ = 0.72 and KGE = 82%) over other ML models in flood extent and flood depth prediction, resulting in about 47% of the population exposure to high-flood risks. The SHAP summary plots reveal a pre-dominance of Height Above Nearest Drainage during flood depth prediction. The study contributes significantly in comprehending our understanding of catchment characteristics and its influence in the process of sustainable disaster risk reduction. The results obtained from the study provide valuable recommendations for efficient flood management and mitigation strategies, especially over global data-scarce flood-prone basins.

Slower-decaying tropical cyclones produce heavier precipitation over China

The post-landfall decay of tropical cyclones (TC) is often closely linked to the magnitude of damage to the environment, properties, and the loss of human lives. Despite growing interest in how climate change affects TC decay, data uncertainties still prevent a consensus on changes in TC decay rates and related precipitation. Here, after strict data-quality control, we show that the rate of decay of TCs after making landfall in China has significantly slowed down by 45% from 1967 to 2018. We find that, except the warmer sea surface temperature, the eastward shift of TC landfall locations also contributes to the slowdown of TC decay over China. That is TCs making landfall in eastern mainland China (EC) decay slower than that in southern mainland China (SC), and the eastward shift of TCs landfall locations causes more TCs landfalling in EC with slower decay rate. TCs making landfall in EC last longer at sea, carry more moisture upon landfall, and have more favorable dynamic and thermodynamic conditions sustaining them after landfall. Observational evidence shows that the decay of TC-induced precipitation amount and intensity within 48 h of landfall is positively related to the decay rate of landfalling TCs. The significant increase in TC-induced precipitation over the long term, due to the slower decay of landfalling TCs, increases flood risks in China’s coastal areas. Our results highlight evidence of a slowdown in TC decay rates at the regional scale. These findings provide scientific support for the need for better flood management and adaptation strategies in coastal areas under the threat of greater TC-induced precipitation.

REMOTE SENSING AND GOOGLE EARTH ENGINE FOR RAPID FLOOD MAPPING AND DAMAGE ASSESSMENT: A CASE OF TYPHOON GONI (ROLLY) AND VAMCO (ULYSSES)

Abstract. Flooding is a devastating natural disaster with global repercussions, affecting human life, crop production, and infrastructure. The increasing frequency and unpredictability of flood events, driven by climate change, highlight the urgent need for rapid and accurate flood mapping and damage assessment. This study explores the use of Google Earth Engine, a web-based platform that utilizes satellite imagery and geospatial data, for flood mapping and damage assessment. By incorporating localized datasets, such as region-specific land cover maps and population density information, the developed workflow provides a robust tool for assessing flood extent, identifying high-risk areas, and estimating the impact on cropland and urban centers. Key findings showed that Cagayan province presented the highest potential flood area, covering approximately 55,063 hectares. This province also had the largest population at risk, with around 39,440 individuals potentially exposed to the effects of flooding. Moreover, the cropland in Cagayan province experienced the most substantial impact, with around 91.26% being affected due to the swelling of the Cagayan River. Camarines Sur experienced severe devastation in urban areas, with about 269 hectares affected by flooding from the Bicol River. This study's findings contribute to a comprehensive understanding of flood impacts beyond the extent of flooding, facilitating informed decision-making, emergency response planning, and targeted interventions to mitigate the effects of floods on communities, agriculture, and urban areas. The approach presented here can be applied in other regions, supporting improved flood management and mitigation strategies for national.

Geomorphic changes after the 2021 Central European flood in the Ahr Valley by LiDAR-based differences

BackgroundIn July 2021, destructive floods in Western Europe were triggered by enormous precipitation rates related to a low-pressure system named "Bernd." These catastrophic events led not only to major damage to infrastructure, severe economic losses, and the loss of lives but also to significant landscape changes and modifications. Here, we focus, as a case study, on the flood aftermath of the Ahr Valley in Rhineland-Palatinate state in western Germany, as it was one of the most affected and destroyed regions by the flood. We utilize high-resolution Digital Terrain Models (DTMs) based on airborne Light Detection and Ranging (LiDAR) that were taken shortly before and after the flood to investigate insights into geomorphic changes. ResultsBy calculating Digital Terrain Models of Difference (DoD), we are able to quantify volumetric and areal changes caused by erosional and depositional processes for different sites in the Ahr Valley. Due to the morphology of the narrow Ahr Valley, most of the erosion and deposition is located within the deeply incised canyon of the Ahr River. The comprehensive analysis reveals notable morphological modifications throughout the study area, with a calculated erosion/deposition areal ratio of 0.46 and an erosion/deposition volumetric ratio of 0.63. Our findings indicate massive deposition regarding both areal and volumetric. We selected six different locations along the Ahr Valley that showcase distinct aspects of flood-induced fluvial morpho-dynamics. Deposition occurred mainly in point bars and downstream of destroyed artificial levees, in a braided river style.ConclusionOur investigations contribute to an overview and assessment of the morphological response to the destructive flood in the Ahr Valley. The results emphasize the necessity for implementing effective flood management strategies, as most of the urban areas in the Ahr Valley were flooded. Moreover, our results provide valuable insights into the impacted areas, highlighting vulnerable locations for flood-related erosion and deposition. This information could contribute to future mitigation and protection efforts, aiding in the development of comprehensive strategies to minimize the impact of similar events in the future.

Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach.

Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2's high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July-about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts.

Life cycle assessment of green–grey coastal flood protection infrastructure: a case study from New Orleans

The study compared the life cycle environmental impacts of three coastal flood management strategies: grey infrastructure (levee), green–grey infrastructure (levee and oyster reef), and a do-nothing scenario, considering the flood damage of a single flooding event in the absence of protection infrastructure. A case study was adopted from a New Orleans, Louisiana residential area to facilitate the comparison. Hazus software, design guidelines, reports, existing projects, and literature were utilized as foreground data for modelling materials. A process-based life cycle assessment was used to assess environmental impacts. The life cycle environmental impacts included global warming, ozone depletion, acidification, eutrophication, smog formation, resource depletion, ecotoxicity, and various human health effects. The ecoinvent database was used for the selected life cycle unit processes. The mean results show green–grey infrastructure as the most promising strategy across most impact categories, reducing 47% of the greenhouse gas (GHG) emissions compared to the do-nothing strategy. Compared to grey infrastructure, green–grey infrastructure mitigates 13%–15% of the environmental impacts while providing equivalent flood protection. A flooding event with a 100-year recurrence interval in the study area is estimated at 34 million kg of CO2 equivalent per kilometre of shoreline, while grey and green–grey infrastructure mitigating such flooding is estimated to be 21 and 18 million kg, respectively. This study reinforced that coastal flooding environmental impacts are primarily caused by rebuilding damaged houses, especially concrete and structural timber replacement, accounting for 90% of GHG emissions, with only 10% associated with flood debris waste treatment. The asphalt cover of the levee was identified as the primary contributor to environmental impacts in grey infrastructure, accounting for over 75% of GHG emissions during construction. We found that there is an important interplay between grey and green infrastructure and optimizing their designs can offer solutions to sustainable coastal flood protection.

Geospatial analysis of flood risk hazard in Zambezi Region, Namibia

The recent decade has seen an increase in frequency and intensity of flood risk globally especially in less developed countries. This has been attributed to many factors such as population growth, urbanization, climate change, increasing precipitation, and poor solid waste management among others. This study used quantitative analysis to identify and characterise flood-prone areas in Kabbe and Kaltima in the Zambezi region. We estimated major factors influencing flood events in the study area. We estimated this by incorporating the analytical hierarchy process (AHP) and GIS-based multi-criteria decision-making to map and identify flood-prone areas in Kabbe and Katima. AHP was employed to ascertain the weight of each criterion taken into consideration for the susceptibility mapping. We analysed ten factors elevation, slope, distance to river, rainfall, topographic wetness index, distance to road, drainage density, land use land cover, modified soil adjusted vegetation index, and soil which are closely associated with flood occurrence in the study area. The flood factors maps were categorized into five susceptibility levels. We used field data by interviewing key informants and community members to validate the GIS analysis. The result from the flood susceptibility map indicates that 46 % of areas in Kaltima and Kabbe have a low susceptibility to flood, 56.04 % are moderate, 43.33 % are high and 17 % are very highly susceptible to flood. The areas susceptible to flooding are mostly low-laying areas in Kabbe which are gentle slopes and are sitting at approximately 921–935 m below sea level. Speaking with stakeholders in the area, they confirmed that communities such as Ihaha, Isize, Mbalasinte, and Kalumnesa are highly susceptible to flooding. The community members confirm that they experience flooding every year with devastating impacts on their lives and livelihoods. We recommend localized flood management strategies that cater to the needs of the communities and develop strategies to improve preparedness, response, and mitigation. Increasing government funding for flood risk management in the area can increase the capacity of the community for flood risk management through training and awareness

Examining the Effects of a Flood Event in the Lower Ceyhan Basin in 1980 Using Historical Satellite Data

Analysis of past flood events contributes forecasting of effects of future flood events. Flood maps have been created in order to assess flood hazards in planning projects and to identify flood-inundated regions with flood damage following flood occurrences. Flood mapping in the context of flood monitoring enables development of flood management strategies to protect life and property. Although conventional terrestrial observations and measurements in flood control have been constrained by topographical and meteorological circumstances, remote sensing provides decision support with quick analysis capability. The flood event that occurred in the Lower Ceyhan Basin of Turkey in 1980 was examined in this work utilizing satellite-based remote sensing techniques, and flood inundation areas were calculated using NDWI (Normalized Difference Water Index). As a result, it was determined that 3493.45 ha in the north of Karataş in the Lower Ceyhan Plain, 7799.42 ha between Bahçe, Akdeniz, and Yumurtalık, 7404.9 ha around Çatalpınar and Yakapınar in the Lower Ceyhan Plain, and approximately 24890 ha in the Upper Ceyhan were affected by the flood event in 1980.

Reevaluating the benefit of flood risk management for flood-prone livelihoods

As flood risk is projected to increase, effective flood risk management (FRM) is crucial not only for mitigating direct flood damage but also for contributing to the long-term improvement of livelihoods and hence the socioeconomic development of flood-prone areas in developing countries. While many studies have assessed avoided damage as the benefit of flood countermeasures, few recognize the impact on improvements in livelihood resulting from risk reduction. We present a novel framework that captures the dynamic relationship between decreasing flood risk perception and improvements in household livelihoods. Using the case of flood-prone Bago, Myanmar, we apply this framework to evaluate 127 flood management strategies under different assumptions of future climate change and socioeconomic features. We show that the benefits of improved livelihoods, when quantified alongside avoided flood losses, have a significant impact enough to change the cost-efficiency rankings of flood management strategies. The findings highlight the importance of incorporating livelihood improvements into FRM decision making to promote the sustainable development in flood-prone areas.

Spatial assessment of flood vulnerability and waterlogging extent in agricultural lands using RS-GIS and AHP technique-a case study of Patan district Gujarat, India.

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district's land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation.

Employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed: implications for flood management

Abstract. Extreme rainfall events in the Chi watershed of northeastern Thailand have significant implications for the safe and economic design of engineered structures and effective reservoir management. This study investigates the characteristics of extreme rainfall events in the watershed and their implications for flood risk management. We apply extreme value theory to historical maximum cumulative rainfall data for consecutive rainy days from 1984 to 2022. The generalized Pareto distribution (GPD) was used to model the extreme rainfall data, with the parameters estimated using maximum likelihood estimation (MLE) and linear moment estimation (L-ME) methods based on specific conditions. The goodness-of-fit tests confirm the suitability of the GPD for the data, with p values exceeding 0.05. Our findings reveal that certain regions, notably Udon Thani, Chaiyaphum, Maha Sarakham, Tha Phra Agromet., Roi Et, and Sisaket provinces, show the highest return levels for consecutive 2 d (CONS-2) and 3 d (CONS-3) rainfall. These results underscore the heightened risk of flash flooding in these regions, even with short periods of continuous rainfall. Based on our findings, we developed 2D return level maps using the Q-geographic information system (Q-GIS) program, providing a visual tool to assist with flood risk management. The study offers valuable insights for designing effective flood management strategies and highlights the need for considering extreme rainfall events in water management and planning. Future research could extend our findings through spatial correlation analysis and the use of copula functions. Overall, this study emphasizes the importance of preparing for extreme rainfall events, particularly in the era of climate change, to mitigate potential flood-related damage.