Flood Risk Research Articles

Analyzing the association between the hydrodynamics and bank erosion along the Padma River: 2020 monsoon floods

The 2020 Monsoon floods submerged two-thirds of Bangladesh, profoundly affecting the stable char areas along the Padma River. This study addresses the under-explored link between Monsoonal flood hydrology and river morphological changes, introducing a novel approach that integrates one-dimensional steady flow hydraulic modeling, dual-sensor satellite imagery analysis (Sentinel-1 SAR and Sentinel-2 optical), and multiple machine learning techniques to assess flood impacts on bank erosion in the most affected reaches. Multiple linear regression (MLR), random forest (RF), artificial neural networks (ANN), and support vector machines (SVM) along with other statistical analyses, were employed to elucidate relationships between hydraulic variables and bank erosion. Results indicated a gradual decrease in river discharge-related variables from July to October, with peak erosion in July, primarily along the right banks and concave areas. In analyzing the relationships between hydraulic variables and bank erosion, the ANN model outperformed others, identifying flow area, stream power, and shear stress as the most influential predictors through importance analysis. This integrated approach offers a transferable framework for analyzing flood-induced bank erosion, enhancing riverbank erosion understanding crucial for flood risk management strategies in Bangladesh and similar river systems worldwide.

Evaluation of Pedotransfer Functions to Estimate Soil Water Retention Curve: A Conceptual Review

The soil water retention curve (SWRC) is a vital soil property used to evaluate the soil’s water holding capacity, a critical factor in various applications such as determining soil water availability for plants, soil conservation and management, climate change adaptation, and mitigation of flood risks. Estimating SWRC directly in the field and laboratory is a time-consuming and laborious process and requires numerous instruments and measurements at a specific location. In this context, various estimation approaches have been developed, including pedotransfer functions (PTFs), over the past three decades to estimate soil water retention and its associated properties. Despite the efficiencies, PTFs and semi-physical approach-based models often have several limitations, particularly in the dry range of the SWRC. PTFs-based modeling has become a key research topic due to readily available soil data and cost-effective methods for deriving essential soil parameters, which enable more efficient decision-making in sustainable land-use management. Therefore, advancement and adjustment are necessary for reliable estimations of the SWRC from readily available data. This article reviews the evaluation of the current and past PTFs for estimating the SWRC. This study aims to evaluate PTF techniques and semi-physical approaches based on soil texture, bulk density, porosity, and other related factors. Additionally, it also assesses the performance and limitations of various common semi-physical models proposed and developed by Arya and Paris, Haverkamp and Parlange, the Modified Kovács model by Aubertin et al., Chang and Cheng, Meskini-Vishkaee et al., Vidler et al., and Zhai et al. This assessment will be effective for researchers in this field and provide valuable insight into the importance of new PTFs for modeling SWRC.

Modelacion del impacto del deshielo en lo caudales en la cuenca del Lago Mansfiled Hollow en Connecticut, USA

Storm runoff predictions are essential for minimizing flood hazards and increasing resilience to extreme weather events. In this study, an analysis was conducted to simulate snowmelt runoff in the Mansfield Hollow Lake Watershed, which is a tributary of the Thames River watershed in Connecticut, New England. The United States Army Corp of Engineers (USACE) model HEC-HMS was applied to simulate snowmelt runoff during the winter-spring of 2010 and 2019. The Mansfield Hollow Lake Watershed is composed of three main tributaries, namely the Fenton, Mount Hope, and Natchaug Rivers. These runoff simulations and the watershed response to snowmelt are crucial for evaluating the potential impacts of watershed management decisions, particularly during high-flow periods. The HEC-HMS model was calibrated during the 2010 event and validated for the 2019 events. The study found that for the snow storms during 2010 and 2019 events, HEC-HMS model provided highly accurate predictions of snowmelt runoff with R-squared and, Nash - Sutcliffe correlation values exceeding 0.76. These findings highlight the efficacy of HEC-HMS model for simulating snowmelt runoff and demonstrate the utility of such model in predicting and managing flood risks. The results of this study provide valuable insights into the potential impacts of snowmelt runoff and will inform future watershed management decisions in the Mansfield Hollow Lake Watershed and similar regions.

Resilience and Vulnerability of Coastal Communities: A Case Study of Tambak Lorok, Semarang-Indonesia in Facing Flood Risks

This study examines the resilience and vulnerability of the Tambak Lorok coastal community in Semarang to recurring flood events. The research adopts a descriptive qualitative approach to explore the socio-economic, environmental, and infrastructural aspects influencing the community’s adaptive capacity. Through in-depth interviews, field observations, and document analysis, the study identifies key factors contributing to the community’s vulnerability, including reliance on flood-prone economic activities, inadequate housing infrastructure, and limited access to resources. The role of local institutions is also highlighted as a critical factor in enhancing community resilience through training and support programs. Despite ongoing mitigation efforts, such as the construction of levees and drainage improvements, the findings indicate that these measures have not fully protected the community from flood impacts. The study concludes with policy recommendations aimed at improving infrastructure, diversifying economic activities, and strengthening disaster mitigation strategies to enhance the long-term resilience of the Tambak Lorok community. This research contributes valuable insights into the challenges faced by coastal communities in adapting to the increasing risks posed by climate change and environmental hazards.

Mapping Geospatial AI Flood Risk in National Road Networks

Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap’s road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers.

A Multi-Hazard Approach to Climate Migration: Testing the Intersection of Climate Hazards, Population Change, and Location Desirability from 2000 to 2020

Climate change intensifies the frequency and severity of extreme weather events, profoundly altering demographic landscapes globally and within the United States. This study investigates their impact on migration patterns, using propensity score matching and LASSO techniques within a larger regression modeling framework. Here, we analyze historical population trends in relation to climate risk and exposure metrics for various hazards. Our findings reveal nuanced patterns of climate-induced population change, including “risky growth” areas where economic opportunities mitigate climate risks, sustaining growth in the face of observed exposure; “tipping point” areas where the amenities are slowly giving way to the disamenity of escalating hazards; and “Climate abandonment” areas experiencing exacerbated out-migration from climate risks, compounded by other out-migration market factors. Even within a single county, these patterns vary significantly, underscoring the importance of localized analyses. Projecting population impacts due to climate risk to 2055, flood risks are projected to impact the largest percentage of areas (82.6%), followed by heatwaves (47.4%), drought (46.6%), wildfires (32.7%), wildfire smoke (21.7%), and tropical cyclone winds (11.1%). The results underscore the importance of understanding hyperlocal patterns of risk and change in order to better forecast future patterns.

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.

Performance of low impact development techniques in flood hazard mitigation in a closed urbanised catchment for extreme precipitation events

ABSTRACT Sealing of surfaces alters the natural hydrological conditions, thereby increasing the urban flood risk. Rainwater management in urban environments has traditionally been performed using conventional urban drainage systems (UDS). However, low impact development (LID) solutions have been recognised in recent decades as effective in controlling runoff. The present work aims to evaluate the efficiency of LID techniques in flood control in a closed basin characterised by high urbanisation for three extreme precipitation events in the city of Natal, Brazil, using 1D/2D modelling. Two techniques were tested, namely bioretention cells (BCs) and rain barrels (RBs) alone or combined. The existing UDS in the area proved unable to control flooding of the basin. Water depths exceeded 0.5 m in all events. The efficiency of the LID solutions evaluated was extremely low, with reductions of less than 1% for highest hazard class. Combining the techniques produced better performance than implementing isolated techniques.

Probability Distribution Analysis of Annual Maximum Precipitation in the Niger Delta: A Critical Step towards Effective Flood Risk Management

Aims: The study aimed to analyze historical maximum precipitation data from seven cities in the Niger Delta to assess vulnerability to extreme rainfall events, identify optimal probability distribution models for future predictions, and provide insights for flood risk management strategies. Study Design: This is a retrospective analytical study based on historical precipitation data. Place and Duration of Study: The study was conducted across seven major cities in the Niger Delta region of Nigeria, utilizing data collected over several decades. Methodology: Historical maximum precipitation data were statistically analyzed to determine the best-fit probability distribution models. The Kolmogorov-Smirnov (KS) test and Mean Squared Prediction Error (MSPE) were used to validate the suitability of distributions, including Log-Normal, Normal, Log-Pearson III, and Generalized Extreme Value (GEV) for each city. Return periods of 10, 25, 50, and 100 years were calculated to predict future precipitation trends and assess flood risks. Results: Significant variations in annual maximum precipitation were observed across the cities, with Calabar recording the highest peak at 4062.7mm. The Log-Normal distribution was the best fit for Akure and Calabar, while the Normal distribution best described rainfall in Benin City. Log-Pearson III was optimal for Owerri, Umuahia, and Uyo, and GEV best fitted Port Harcourt’s data. High P-values (>0.87) indicated good model fits across the cities. Return period analysis suggested greater risks of extreme precipitation events in coastal cities like Calabar and Port Harcourt. Conclusion: The study underscores the importance of tailored, city-specific flood management strategies in the Niger Delta to mitigate the impact of extreme rainfall events, particularly in the face of climate change.

Identification of influencing factors and risk assessment of underground space flooding in the mountain city

As a consequence of the process of urbanization, there is an increasing reliance on and exploitation of underground spaces. Moreover, these areas are susceptible to urban waterlogging, which can be difficult to drain, potentially leading to significant risks. At present, there are few simulations in hydrodynamic models that consider underground spaces. The risk of inundation of underground spaces remains an uncertain phenomenon. This study conceptualized underground spaces as storage areas, with the aim of simulating the spatial distribution of flood risk for different return periods. We studied the flooding processes and characteristics of three underground space types. The results indicate that the flood evolution simulated by the simplified modeling method that incorporates underground space is more aligned with the actual situation. To further analyze the risk distribution of urban waterlogging, we choose the XGBoost model and SHapley Additive exPlanations (SHAP) method to assess the influence of specific indicators on the occurrence of urban waterlogging in mountainous regions. Our analysis revealed that key factors include density of road (DR), flow velocity (V), density of pipes (DP), elevation (DEM), and inundation height (H), with contribution rates of 23.13 %, 17.06 %, 14.67 %, 10.12 %, and 9.04 %, respectively. This analysis clarified the risk of underground inundation within the study area, which can serve as a reference point for the formulation of flood control and drainage strategies.

Integrated assessment for groundwater quality and flood vulnerability in coal mining regions.

Coal mining activities greatly damage water resources, explicitly concerning water quality. The adverse effects of coal mining and potential routes for contaminants to migrate, either through surface water or infiltration, into the groundwater table. Dealing with pollution from coal mining operations is a significant surface water contamination concern. Consequently, surface water resources get contaminated, harming nearby agricultural areas, drinking water sources, and aquatic habitats. Moreover, the percolation process connected with coal mining could alter groundwater quality. Subsurface water sources can get contaminated by toxins generated during mining activities that infiltrate the soil and reach the groundwater table. The aims of this study are the creation of models and the provision of proposals for corrective measures. Twenty-five scenarios were simulated using MODFLOW; according to the percolation percentage and contamination, 35% of the study area, i.e., the middle of the research area, was the most affected. About 38.08% of the area around the mining zones surrounding Margherita is prone to floods. Agricultural areas, known for applying chemical fertilizers, are particularly vulnerable, generating a risk of pollution to surrounding water bodies during flooding. The outputs of this research contribute to identifying and assessing flood-vulnerable regions, enabling focused measures for flood risk reduction, and strengthening water resource management.

Armed conflict as a catalyst for increasing flood risk

Abstract Armed conflict has many adverse impacts beyond violence such as increasing risks of natural hazards. Analyses of the interactions between flood risks and armed conflict are essential for developing effective policies and strategies to address both challenges. This study aims to develop conceptual and analytical socio-hydrological frameworks for assessing how armed conflict can impact flood risks. The frameworks postulate a link between armed conflict and flood vulnerability, given that armed conflict creates unique challenges that exacerbate the effects of floods. Our conceptual framework identifies routes through which armed conflict affects vulnerability to floods, such as damage to infrastructure, population displacement and density, weak governance, and less awareness, resulting in lower resilience, higher susceptibility, and increased flood vulnerability and risk. Our analytical framework uses flood modeling to evaluate flood hazards and incorporates spatial data related to armed conflict zones, nighttime light, population classification by age, land price, land cover, and rural/urban areas classification. We take Khartoum, the capital city of Sudan, as a case study in view of its armed conflict that erupted in 2023. By highlighting the linkages between armed conflict and flood risk, this study contributes to conceptualizing the broader interlinkages between conflict and environmental systems. The study emphasizes the importance of integrating conflict analysis with disaster risk management strategies. We encourage collaboration between humanitarian, environmental, and security sectors to improve preparedness, response, and resilience in conflict-affected regions. While our analysis for Khartoum is based on conflict zones in the early stages of the conflict and uses simple estimates for conflict vulnerability contribution, the proposed frameworks provide groundwork for assessing changes in flood risk in Sudan and other conflict regions around the world.

Unravelling flood risk in the Rel River watershed, Gujarat using coupled earth observations, multi criteria decision making and Google Earth Engine

Socioeconomic developments, ineffective drainage systems, and insufficient river control, all contribute to significant loss of property and life due to the constant threat of floods. Therefore, controlling flood threats across Rel River, Dhanera, Gujarat has become even more crucial due to floods causing strain throughout the area during monsoon season. The different 52 micro-watersheds were formed across the study region using earth observations for the estimation of flood hazards, vulnerability, and risk. The AHP-MCDM was employed to assign priority rank, weightage, and risk category for each micro watershed. The flood hazard zone was mapped and its vulnerability was characterized in different categories varying from very low to very high. The normalized weights of each factor i.e, hazard indicator (soil, elevation, slope, flow accumulation, rainfall) and vulnerability indicator (LULC, distance from the hospital, population density map) were estimated employing the AHP-MCDM technique whereas LULC along with most of other factors were derived from GEE. The integration of vulnerability and hazard indicators, provides insights into understanding the flood sensitivity, facilitating the preparation of the flood risk map. 20 micro-watersheds were susceptible to high to very high risk and covered an area of 213.15 km2 whereas 32 micro-watersheds were in the range of very low to moderate category which covers the area of 228.41 km2. Therefore, the integration of GEE and spectral indices for obtaining various hazard & vulnerability indicators, which were prioritized and ranked using AHP is a unique methodology, facilitating a robust evaluation of flood risk mapping.

A Coupled Human and Natural Systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation

To address the challenge of escalating urban flood risk and the deficiency in effective flood emergency management, this study introduces a novel Coupled Human and Natural Systems (CHANS) modelling framework that employs hierarchical reinforcement learning to optimise mobile pump scheduling and placement for urban flood risk mitigation. The CHANS framework integrates hydrodynamic and agent-based models within a multi-GPU computing environment for high-resolution, real-time flood inundation modelling and risk assessment to enrich Reinforcement Learning (RL) training. In the application to Ninh Kieu District in Can Tho City, Vietnam, the new RL-enabled modelling framework is used to evaluate optimal mobile pumping strategies for concurrent pluvial flooding and post-flooding events against the traditional deployment approaches. Results demonstrate that RL-based strategies can significantly enhance flood risk reduction, outperforming traditional methods by achieving 2× and 4× improvements in the concurrent and post-flooding periods/events, respectively. Incorporating human factors and adapting to local conditions, the RL agent provides valuable insights into mobile pump scheduling and deployment strategies. Sensitivity analysis confirms the robustness of the CHANS modelling framework and underscores the role of RL in optimising mobile pump scheduling and placement where traditional rule-based strategies are challenging.

A novel ANN-CA and MCDA integrated framework for predicting urban expansion and its implications on future flood risk, Accra Metropolis

Urban development in African countries significantly impacts environmental sustainability and city resilience, particularly in flood risk management. The Accra Metropolis, in particular, faces increasingly prevalent annual floods that disproportionately affect the urban poor living in informal settlements, resulting in significant loss of life and disruption of livelihoods. This study developed a GIS-based flood risk mapping framework that integrates artificial neural network (ANN) and cellular automata (CA) modelling with multi-criteria decision analysis. This hybrid approach was employed to predict flood scenarios for the Metropolis in 2032 and 2042. The framework incorporated six hydro-geomorphological indicators influencing extreme events: LULC, slope, elevation, drainage density, soil type, and proximity to rivers. The study analysed LULC projections, revealing a trend of substantial urban expansion with an estimated 10.9% increase in built-up areas within the next 20 years. This growth is expected to significantly heighten future flood risk in both the central upstream and downstream portions of the Metropolis, particularly in low-lying informal settlements close to the Odaw River and Korle Lagoon. Model performance was validated by ROC curve analysis, with AUC values of 0.927 and 0.922 for 2032 and 2042, respectively, which resonates with the records of previous flood distribution studies of the area. This study highlights the crucial need for sustainable urban development, improved infrastructure, and proactive flood mitigation measures in Accra to assist urban planners, policymakers, and stakeholders in managing flood risks effectively.

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.

Urban Flood Resilience Evaluation Based on Heterogeneous Data and Group Decision-Making.

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state-response model, and social-economic-natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses.

Trends of forest cover and prospects of Great Ethiopian Renaissance Dam (GERD) to Ethiopia and the downstream countries

The need for forest products, agricultural expansion, and dependency on biomass for the household energy source has largely influenced Ethiopia’s forest resources. Consequently, the country lost its forest resources to less than 6% until the millennium. In this study, quantitative and qualitative historical data analysis was employed to understand the socioeconomic benefits of large dam construction to Ethiopia and downstream countries. Moreover, remotely sensed data was also used to analyze the trends of vegetation cover change in the Nile catchment since the commencement of the dam; focusing on areas where there are high settlement and urban areas. It was identified that Ethiopia has one of the lowest electricity consumption per capita in Africa; about 91% of the source of household energy supply depends on fuelwood today and more than 55.7% of the population does not have access to electricity. The normalized difference vegetation index result shows an increment of vegetation area in the Nile catchment and a reduction of no vegetation area from 2011–2021 by 37.1%; which is directly related to the protection of the dam catchment for its sustainability in the last decade. The hydroelectric dam construction has prospects of multi-benefit to Ethiopia and downstream countries either through the direct benefit of hydropower energy production, related socioeconomic values, and reducing risks of destructive flood from Ethiopian highlands. Generally, it explains the reason why to not say ‘No’ to the reservoir as it is an ever more vital tool for fulfilling growing energy demand and supporting ecological stability.

An Analysis of Factors That Influence the Success of Flood Risk Management in Selected River Basins (FMSRB) Program in Banten Province and Maluku Province

FMSRB Program, under CPIU Directorate General of Regional Development Ministry of Home Affairs has successfully built 2,907 units of infrastructure, consisting of: (1) 152 units of retaining walls; (2) 24 units of reservoirs; (3) 265 units of drainages; (4) 56 units of rainfall harvesting system; (5) 46 units of retention wells; (6) 2,351 units of biopori; and (7) 13 units evacuation routes. The construction of infrastructure is in collaboration with the establishment of 4 KMSB forums in the regency, 140 KMSBs, and 140 waste banks in the community level. The success of program is primarily determined by 4 factors, including the success in the human resources management, collaboration among stakeholders, collaboration of structural and non-structural approaches, as well as the partnership development. Quantitatively, it is discovered that 55.70% of impact value indicates a very significant influence on flood control, whereas 36.80% states influential. For the construction of reservoir 57.50% of respondents state that it is high influence, whereas 33% state influential. In terms of rainfall harvesting system, 54.70% of respondents state highly influential, while 31.10% state influential. In Biopori, 60.40% of respondents state highly influential, whereas only 28.30% state influential. For retention well, 52.80% of respondents state very influential and only 38.70% states influential. In the meantime, the status of constructed infrastructure assets indicates that 65.10% are very good and 17.90% are good. Furthermore, the human resources management contributes β 0.141 with the influence of p-0,038 to the sustainability empowerment mediation. Collaboration among stakeholder is very good (59.40%) and good (25.50%). About gender and empowerment-based collaboration model, 65% of respondents fully agree and confirm, whereas only 35% agree. Meanwhile, collaboration among stakeholders is very helpful according to 65% of respondents and only 35% state helpful. As for structural and non-structural approaches collaboration, 82% of respondents state very helpful, and only 18% state helpful. For partnership, 59% of respondents fully agree with the partnership developed by FMSRB Program that uses the collaboration principle, and 41% agree. For sustainable program, advanced facilitation from the stakeholders to KMSB Forum, KMSB and Waste Bank is necessary. They will oversee utilization, maintenance, and development of the infrastructure for controlling flood.

Global population datasets overestimate flood exposure in Sweden

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies.