Flood Modelling Research Articles

Elevating Pakistan’s flood preparedness: a fuzzy multi-criteria decision making approach

In South Asia, Pakistan has a long and deadly history of floods that cause losses to various infrastructures, lives, and industries. This study aims to identify the most appropriate flood risk mitigation strategies that the government of Pakistan should adopt. The assessment of flood risk mitigation strategies in this study is based on certain criteria, which are analyzed using the fuzzy full consistency method. Moreover, flood risk mitigation strategies are evaluated by using the fuzzy weighted aggregated sum product assessment (WASPAS) method, considering previously prioritized criteria. According to the results, lack of governance, lack of funding and resources, and lack of flood control infrastructure are the most significant flood intensifying factors and act as major criteria for assessing flood risk mitigation strategies in Pakistan. Adopting hard engineering strategies (e.g., dams, reservoirs, river straightening and dredging, embankments, and flood relief channels), maintaining existing infrastructure, and adopting soft engineering strategies (flood plain zoning, comprehensive flood risk assessment, and sophisticated flood modeling) are identified as the top three flood risk mitigation strategies by the fuzzy WASPAS method. The highest weight (0.98) was assigned to the adoption of hard engineering strategies to mitigate flood risks. The study introduces a novel dimension by analyzing the real-time impact of the unprecedented 2022 floods, during which approximately one-third of the nation was submerged. This focus on a recent and highly significant event enhances the study’s relevance and contributes a unique perspective to the existing literature on flood risk management. The study recommends that the government of Pakistan should prioritize hard engineering strategies for effective flood risk mitigation. It also recommends that the government should incorporate these strategies in the national policy framework to reduce flood losses in the future.

A hybrid surrogate model for real-time coastal urban flood prediction: An application to Macao

Tropical cyclones could cause co-occurrences of storm tides and rainfall and, therefore, induce flooding hazards, posing high risks to coastal cities. Accurate and timely prediction of flood hazards in time–space domain is essential for flood risk management. In this study, a hybrid surrogate model is proposed for real-time flooding prediction by considering the compound effects of storm tides, rainfall, and drainage outflows. The hybrid surrogate model combines a long short-term memory (LSTM) neural network for predicting drainage outflows with a one-dimensional convolutional neural network (1D CNN) for predicting water depths. The model is tested using performance metrics, and the simulation results agree well with the historical measurements. Moreover, a sensitivity analysis of the drainage outflows using the modified Morris screening method reveals the importance of considering drainage systems in flood modelling, particularly when rainfall return periods are relatively small. The sensitivity indices vary over the computation domain in terms of different driving factors, with positive values corresponding to rainfall-dominated area while negative values corresponding to storm tide-dominated area. The computation cost of the model is quite low, and the fast prediction speed could provide a sound basis for early warning and real-time flood management. The proposed hybrid surrogate model offers a promising function for rapid and accurate flood inundation prediction in coastal cities, enabling more effective flood risk management and emergency response planning.

Using social cartographies for the calibration of two-dimensional hydraulic flood models

Using social cartographies for the calibration of two-dimensional hydraulic flood models

Urban flooding simulation and flood risk assessment based on the InfoWorks ICM model: A case study of the urban inland rivers in Zhengzhou, China.

Urban flooding intensifies with escalating urbanization. This study focuses on Xiong'er river as the study area and couples a 1D/2D urban flooding model using InfoWorks ICM (Integrated Catchment Modeling). Ten scenarios are set respectively with a rainfall return period of 5a 10a, 20a, 50a, and 100a, alongside rainfall durations of 1 and 24 h. Subsequently, the H-V (hazard-vulnerability) method was applied to evaluate urban flooding risk. Three indicators were selected for each of hazard factors and vulnerability factors. The relative weight values of each indicator factor were calculated using the AHP method. The result shows that (1) flood depth, rate, and duration escalate with longer rainfall return periods, yet decrease as the duration of rainfall increases; (2) as the rainfall return period lengthens, the proportion of node overflow rises, whereas it diminishes with longer rainfall durations, leading to an overall overloaded state in the pipeline network; and (3) the distribution in the research area is mainly low-risk areas, with very few extremely high-risk. Medium to high-risk areas are mainly distributed on both sides of the river, in densely built and low-lying urban areas. This study demonstrates that the model can accurately simulate urban flooding and provide insights for flood analyses in comparable regions.

Zoning and regulation of compound flooding in coastal cities coupled with tracer method

Urban inland rivers play a significant role in mitigating flooding. However, the influence of their discharge on urban flooding affected by different flood drivers remains to be fully explored. Taking a catchment area in Haikou City, Hainan Province, China as an example, in light of the urban flooding model coupled with the water tracer method, this study investigates the degree of inundation in each flood zone under different inland river flows. The flood zones are determined by the flooding simulation results of three cases, and then are verified by the zonation based on the tide tracer approach. Finally, the tracer method is used to study the impact of urban inland rivers on flooding and the connection of hydraulic and water volume between rivers and pipelines. The results indicate that the inundation area decreased by 3.71 % as the river flow increased from 15 to 25 m3/s. In the process of river flow rising from 5 to 45 m3/s, The average flooding depth in the rainfall-affected area climbs slowly from 0.53 m to 0.62 m; In the tidal-affected area, the average flooding depth drops from 0.52 m to 0.12 m when the inland river flow increases from 5 to 25 m3/s, and rises from 0.12 m to 0.54 m when the inland river flow continues from 25 to 45 m3/s; The percentage of pipelines grows from 18.5 % to 28.0 %. This study provides valuable scientific insights and support for flood prevention planning and flood control strategies in coastal cities.

Assessment of CO2 Sequestration Capacity in a Low-Permeability Oil Reservoir Using Machine Learning Methods

The CO2 sequestration capacity evaluation of reservoirs is a critical procedure for carbon capture, utilization, and storage (CCUS) techniques. However, calculating the sequestration amount for CO2 flooding in low-permeability reservoirs is challenging. Herein, a method combining numerical simulation technology with artificial intelligence is proposed. Based on the typical geological and fluid characteristics of low-permeability oil reservoirs in the Liaohe oilfield, the CMG 2020 version software GEM module is used to establish a model for CO2 flooding and sequestration. Meanwhile, a calculation method for the effective sequestration coefficient of CO2 is established. We systematically study the sequestration rules in low-permeability reservoirs under varying conditions of permeability, reservoir temperature, and initial reservoir pressure. The results indicate that, as the permeability and sequestration pressure of the reservoir increase, oil recovery gradually increases. The proportion of structurally bound sequestration volume increases from 55% to 60%. Reservoir temperature has minimal impact on both the recovery rate and the improvement in sequestration efficiency. Sequestration pressure primarily improves sequestration efficiency by increasing the dissolution of CO2 in the remaining oil and water. The calculation chart for the effective sequestration coefficient, developed using artificial intelligence algorithms under multi-factor conditions, enables accurate and rapid evaluation of the sequestration potential and the identification of favorable sequestration areas in low-permeability reservoirs. This approach provides valuable technical support for CO2 flooding and sequestration in pilot applications.

Systematic Literature Review (SLR): Explorations flood systems theory and appropriate modeling

Urban flood risk assessment conducts a comprehensive evaluation of hazard formative factors, hazard formative environment, hazard-affected bodies, and capabilities for disaster prevention and reduction in order to obtain an accurate assessment of flood risk levels. The current study aimed to conduct a systematic review of the existing literature on engagement outcomes, focusing on high-quality studies Each step of the SLR method and their results are explained in detail in the following subsections using SLR performed. Using the SLR method can help answer the objectives of this research in the form of: What type of modeling to overcome flooding This research uses the SLR method which aims to recognize, review, and evaluate Using bibliometric analysis.To achieve the goal of SLR, the variables of interest are organized based on the general characteristics of the article and the specific parameters used general information about the article includes year of publication, type of analysis (quantitative, qualitative, cartographic or mixed), type and scale of research, and the country or region where the research was conducted. The model consists of three components, including rainfall runoff, river channels, and flooding The flow in the channel was generated with a 1D diffusion wave model, and the outflow on the slope was generated with a 2D model.This article showed the basic steps that need to be followed to conduct SLR This method generates topic-specific existing knowledge, trends, and observed gaps and helps to draw relevant conclusions for policy makers and the scientific community. and the appropriate modeling is Press RRI refers to 2D grid-based fluid dynamics and is a model that combines a hydrological model and a flood model.

Systematic Literature Review (SLR): Explorations flood systems theory and appropriate modeling

Urban flood risk assessment conducts a comprehensive evaluation of hazard formative factors, hazard formative environment, hazard-affected bodies, and capabilities for disaster prevention and reduction in order to obtain an accurate assessment of flood risk levels. The current study aimed to conduct a systematic review of the existing literature on engagement outcomes, focusing on high-quality studies Each step of the SLR method and their results are explained in detail in the following subsections using SLR performed. Using the SLR method can help answer the objectives of this research in the form of: What type of modeling to overcome flooding This research uses the SLR method which aims to recognize, review, and evaluate Using bibliometric analysis.To achieve the goal of SLR, the variables of interest are organized based on the general characteristics of the article and the specific parameters used general information about the article includes year of publication, type of analysis (quantitative, qualitative, cartographic or mixed), type and scale of research, and the country or region where the research was conducted. The model consists of three components, including rainfall runoff, river channels, and flooding The flow in the channel was generated with a 1D diffusion wave model, and the outflow on the slope was generated with a 2D model.This article showed the basic steps that need to be followed to conduct SLR This method generates topic-specific existing knowledge, trends, and observed gaps and helps to draw relevant conclusions for policy makers and the scientific community. and the appropriate modeling is Press RRI refers to 2D grid-based fluid dynamics and is a model that combines a hydrological model and a flood model.

Hydrodynamic modeling of dam breach floods for predicting downstream inundation scenarios using integrated approach of satellite data, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE)

Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination (R2), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination (R2) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods.

Pluvial flood modeling for coastal areas under future climate change – A case study for Prince Edward Island, Canada

It has been increasingly understood that pluvial flooding poses a substantial risk to numerous communities across the globe. This is especially relevant for the Canadian province of Prince Edward Island (PEI), which is susceptible to a compound flood consisting of both inland and coastal flooding. Despite various studies, a comprehensive pluvial flood model still lacks that addresses the complex interplay of compound floods. Therefore, this research aims to bridge the gap in flood mitigation by developing a pluvial flood model for PEI’s coastal communities. The Intensity-Duration-Frequency (IDF) curves under current and future climatic conditions were used to portray rainfall intensity over the study area corresponding to 10-year, 25-year, 50-year, and 100-year return periods. In addition, a hydraulic model (HEC-RAS 2D) was used to drive pluvial flood maps based on two configurations, including detailed flood maps for major municipalities and an island-wide level. The validated results showed consistency in model simulation when compared to observations. The high-resolution flood maps produced by this study can support the development of flood mitigation and adaptation strategies in PEI and other parts of the world.

A probabilistic model for flood forecasting based on hydrological data in the state of Maranhão, Brazil

A probabilistic model for flood forecasting based on hydrological data in the state of Maranhão, Brazil

IHST (Integrated, Holistic, Spatial, and Thematic) Flood Management Model: The Integration of Flood Model, Green Infrastructure and Non-Structural Mitigation in the Urban Area of Barabai City

Abstract The January 2021 Floods paralyzed the entire Hulu Sungai Tengah Region, South Kalimantan Province, Indonesia, submerging 20,553 houses, causing minor damage to 2,973 houses, severe damage to 268 houses, and resulting in losses exceeding 53 million dollars. The Barabai Urban Area was one of the locations severely affected by flood. The flood management was conducted in the Barabai urban area to predict and visualize water movement during flooding, aiding in understanding and mitigating flood risks, as well as developing effective flood management strategies. The flood management was carried out adopted the IHST Model, which is the abbreviation from Integrated, Holistic, Spatial, and Thematic. The activities involve HECRAS 6.2 application, utilizing Google Earth terrain data, river geometry data, and flood discharge analysis based on the Nakayasu method. The analysis results highlighted the need for both structural and non-structural flood interventions. Non-structural interventions aiming to reduce vulnerability and enhance community capacity in facing flood. Structural developments involved the construction of storm water retention ponds and river embankments at specific locations based on the flood modelling results. The concept of polder and river embankment development was approached through green infrastructure principles, as flood management solutions should not only facilitate rapid water drainage but also improve soil absorption to prevent prolonged inundation.

On detailed representation of flood defences and flow-wave coupling in coastal flood modelling

Coastal flooding is projected to become more severe over the 21st century, necessitating effective adaptation, which in turn requires detailed local scale information that can only be provided by detailed numerical modelling. The current lack of information on flood protection measures and the high resource requirements of traditional hydrodynamic models presents concurrent challenges for detailed coastal flood modelling. But how comprehensive do the representation of coastal flood defences and hydrodynamic forcing need to be for adequately accurate modelling of coastal flooding? Here, we attempt to answer this question through strategic numerical simulations of the flooding that occurred at Île de Ré (France) during the Xynthia storm (2010), using the flexible mesh model Delft3D FM, with an over-land grid resolution of ~10 m. The model is validated against the flood extents observed in Île de Ré during Xynthia. We use three levels of detail in flood defence representation: a 5 m resolution DEM (i.e. base case DEM), the same 5 m DEM augmented with defences extracted from a 1 m DEM and Google Earth images (i.e. moderately augmented DEM), and the moderately augmented DEM further augmented with in-situ measurements of flood defences (i.e. highly augmented DEM). Simulations with these three DEMs are performed with and without flow-wave coupling (thus, 6 simulations in total), and results are analysed in terms of four flood indicators: maximum flood depths, flood extents, flood current velocities and flood damages. Our analysis indicates that both detailed representation of flood defences and the inclusion of waves have substantial effects on coastal flood modelling at local scale, with the former having a more pronounced effect. The return on the investment in implementing highly detailed in-situ measurements to represent flood defences appears to be low in this case, and adequately accurate results are obtained with a moderately augmented DEM. The combined effect of using the moderately augmented DEM together with waves, relative to using the base case DEM without waves, is to decrease maximum flood depths (up to 2 m), flood extent (by ~10%), maximum current velocities (in ~50% flooded area) and total flood damage (by ~27% or ~€ 188 million).

Large-scale flood modeling and forecasting with FloodCast

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptive flood modeling and forecasting framework that can perform at large scales, namely FloodCast. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the multi-satellite observation module, a real-time unsupervised change detection method and a rainfall processing and analysis tool are proposed to harness the full potential of multi-satellite observations in large-scale flood prediction. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced, benefiting from the absence of a requirement for training data in physics-informed neural networks (PINNs) and featuring a fast, accurate, and resolution-invariant architecture with Fourier neural operators. To adapt to complex river geometries, we reformulate PINNs in a geometry-adaptive space. GeoPINS demonstrates impressive performance on popular partial differential equations across regular and irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. This model employs sequence-to-sequence learning and hard-encoding of boundary conditions. Next, we establish a benchmark dataset in the 2022 Pakistan flood using a widely accepted finite difference numerical solution to assess various flood simulation methods. Finally, we validate the model in three dimensions - flood inundation range, depth, and transferability of spatiotemporal downscaling - utilizing SAR-based flood data, traditional hydrodynamic benchmarks, and concurrent optical remote sensing images. Traditional hydrodynamics and sequence-to-sequence GeoPINS exhibit exceptional agreement during high water levels, while comparative assessments with SAR-based flood depth data show that sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with smaller simulation errors. The experimental results for the 2022 Pakistan flood demonstrate that the proposed method enables high-precision, large-scale flood modeling with an average MAPE of 14.93 % and an average Mean Absolute Error (MAE) of 0.0610 m for 14-day water depth simulations while facilitating real-time flood hazard forecasting using reliable precipitation data.

Spatial variability of rainfall: deciphering flood characteristics and model precision

ABSTRACT This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogeneous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR’s role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy.

Research on CO2-WAG in Thick Reservoirs: Geological Influencing Factors and Random Forest Importance Evaluation.

In the development process of thick reservoirs, the impact of various geological factors on the effectiveness of the CO2 water alternating gas (CO2-WAG) flooding technology remains unclear. This paper establishes multiple CO2-WAG flooding models for thick reservoirs to study the effects of sedimentary rhythm, dip angle, matrix permeability, high-permeability streaks (HPS), and barrier layers on the effectiveness of CO2-WAG flooding and then uses the random forest algorithm to rank the importance of these geological factors. The results show that different geological factors have varying degrees of impact on the distribution of water and gas migration and recovery rates during the CO2-WAG flooding process. The ranking of the importance of various factors obtained by reservoir numerical simulations and the random forest algorithm is HPS, sedimentary rhythm, dip angle, matrix permeability, and barrier layers. These research findings will provide effective guidance and a reference for the optimal selection of CO2-WAG flooding schemes for similar thick reservoirs under different geological conditions.

Development of a novel storm surge inundation model framework for efficient prediction

Abstract. Storm surge is a natural process that causes flood disasters in coastal zones and results in massive casualties and property losses. Therefore, storm surge inundation is of major concern in formulating appropriate strategies for disaster prevention and mitigation. However, traditional storm surge hydrodynamic models have large limits with respect to computational efficiency and stability in practical applications. In this study, a novel storm surge inundation model was developed based on a wetting and drying algorithm established from a simplified shallow-water momentum equation. The wetting and drying algorithm was applied to a rectangular grid that iterates through a cellular automata algorithm to improve computational efficiency. The model, referred to as the Hydrodynamical Cellular Automata Flood Model (HCA-FM), was evaluated by comparing the simulations to regional field observations and to a widely used hydrodynamic numerical model. The comparisons demonstrated that HCA-FM can reproduce the observed inundation distributions and predict results that are consistent with the numerical simulation in terms of the inundation extent and submerged depth with much improved computational efficiency (predicting inundation within a few minutes) and high stability. The results reflect significant advancement of HCA-FM toward efficient predictions of storm surge inundation and applications at large spatial scales.

Evaluation of key flood risk drivers under climate change using a bottom-up approach

Our understanding of the key drivers of change in flood risk due to climate change remains incomplete. Here, to understand and quantify the key drivers of change in flood risk, we present a framework to undertake a ‘bottom-up’ (‘scenario-neutral’) climate change impact study on flood risk using an event-based flood model that considers non-stationarity in rainfall extremes and catchment wetness. A key advantage of this approach is that by using an event-based model, which explicitly represents key flood drivers, we can directly understand how changes in these drivers will influence changes in flood peaks. The utility of this modelling framework is demonstrated by applying it to one temperate and one tropical catchment in Australia, focusing on the sensitivity of frequent (1 in 5 annual exceedance probability, AEP) and rare (1 in 50 AEP) flood peaks to changes in thermodynamic and dynamic drivers of rainfall extremes, and changes in catchment processes, which are quantified by changes in rainfall losses.Response surfaces of the catchment sensitivity are first produced and demonstrate that increases in rainfall intensity drive increases in the flood peak. Smaller flood peaks in drier catchments are most sensitive to changes in rainfall losses, with the modulating impact of changes in losses decreasing as the runoff ratio increases. Climate model projections of these drivers are then superimposed on these surfaces to identify plausible shifts in flood risk under climate change. Several climate model ensemble members imply a decrease in future flood peaks, most notably 14 % of ensemble members suggest future decreases for the 1 in 5 AEP event for the temperate catchment. Despite large agreement on the direction of change, we found large variability in the expected magnitude of change for both frequent and rare events with flood peaks projected to increase between 0 % and 45 % for the tropical catchment and between −10 % to more than 50 % for the temperate catchment under RCP8.5 in 2085, highlighting the deep uncertainty associated with projecting flood risk under climate change. We believe this study acts as proof of concept for how a bottom-up climate change assessment can be undertaken within an event-based flood modelling framework, and provides insights to help us better understand and communicate the key drivers of changes to flood risk into the future.

Assessment of urban inundation and prediction of combined flood disaster in the middle reaches of Yellow river basin under extreme precipitation

Watershed Cities often face the dual flood risks of upstream floods and urban waterlogging. This compound disaster is caused by the interaction of multiple factors (topography, storms, upstream water and urban drainage, etc.), and is particularly affected by extreme rainfall events and the large and small drainage carrying capacity. To explore the combined effects of extreme rainfall and drainage systems, this study applied the Chicago rainfall hyetograph method and constructed design under different return periods. A comprehensive model combining the verified SWMM and LISFLOOD hydraulic models was utilised to build one-dimensional and two-dimensional coupled watershed urban flood models, simulating inundation depth and duration. By considering urban rainfall, runoff, topography, and drainage system characteristics, the flood processes under different extreme rainfall scenarios were simulated, and the node overflow and inundation depth were obtained. A flood risk assessment of watershed cities was conducted by combining methods such as the HR threshold method, evaluation level, and threshold division. In this study, Huyi District of Xi’an City, China, was used as the research area. The main research findings show: (1) the precipitation design values for different return periods were calculated based on the proposed method, with amount for a 100-year event being 87.196 mm, suitable for composite flood simulation; (2) applying the SWMM-LISFLOOD coupled model constructed for midstream urban areas ensured the accurate simulation of composite flood processes. Under different extreme rainfall return period scenarios, the percentage of flood inundation depth exceeding 1 m caused by multiple disaster factors increased from 0.73 % to 5.19 %; (3) extreme rainfall leads to different levels of internal flooding risk. When the return period of extreme rainfall is 5 years, the area inundated by composite floods in the research area is 67.21 km2. In comparison, when the return period of extreme rainfall is 50 years, the area inundated by composite floods in the research area significantly increases to a total inundation area of 286.85 km2. This study is expected to provide scientific references for flood prevention planning, composite disaster risk management, and urban resilience building in watershed urban areas while significantly impacting modern economic growth, social stability, and the safety of watershed cities.

Enhancing resilience: Integrating future flood modeling and socio-economic analysis in the face of climate change impacts

As climate change intensifies, future floods will become more severe in some areas with geographic variation, necessitating that local and regional governments implement systems to provide information for climate adaptation, particularly for vulnerable populations. Therefore, we aimed to develop a methodology to identify areas that are at an increased risk from future floods and independently socially vulnerable. In this study, 100-year recurrence interval flood extents and depths were estimated using an ensemble of six independent Coupled Model Intercomparison Project Phase 6 climate models for a past and future period under the highest-emissions climate scenario. The flood inundation results were related to social vulnerability for two selected study areas in the Mississippi River Basin. The range of flood extents and depths for both time periods were estimated, and differences were evaluated to determine the effects from climate change. To identify at-risk areas, the relationship between the spatial distribution of flood depths and vulnerability was then assessed. Finally, an analysis of the current and future damages on infrastructure from flooding on residential housing was performed to determine whether damages are correlated with higher vulnerability areas. Results show in every flooding scenario, flood extents and depths are increasing in the future compared with the past, ranging from an increase of 6 to 76 km2 in extent across both locations. A statistically significant relationship between spatial clusters of flooding and of vulnerability was found. The infrastructure analysis found that residential structures in the most vulnerable census tracts are 6 to 59 times more likely to experience moderate damage compared with the least vulnerable tracts depending on scenario. Overall, a framework was established to holistically understand the hydrologic and socioeconomic impacts of climate change, and a methodology was developed to use for allocating resources at the local scale.