Flood Risk Prevention Research Articles

The role of different types of knowledge and expertise in explaining recognition justice in flood defence and flood risk prevention

AbstractFlood risks worldwide are rising and it is increasingly recognised that the impacts of floods are not neutral. Socioeconomic and demographic characteristics determine people's capacity to deal with flood events. These differences in social vulnerability to floods need to be considered in flood risk management (FRM) to prevent the most vulnerable groups from being disproportionately impacted. However, due to a diversification of FRM strategies and the involvement of various policy domains, the experts working on FRM are no longer a homogeneous group. Where FRM was previously dominated by engineers, now various experts are involved that have different disciplinary backgrounds, knowledge bases and approaches to FRM. As a result, they also differ in their recognition of social vulnerability to floods. In this paper, we explore the different types of knowledge and expertise in FRM in three countries (England, Flanders and France), focussing on the strategies of flood defence and flood risk prevention. We characterise the epistemic communities supporting the domains and study to what extent experts differ in their recognition of social vulnerability to floods. We also dive into the mechanisms employed to stimulate integration between experts and consider the extent to which this integration can strengthen recognition justice.

The effect of goal frame and risk perception on digital flood prevention tool acceptability.

To cope with catastrophic floods, people need to be better prepared. In this context, a self-assessment digital tool for habitat vulnerability was developed. To improve its take-up rate, we are looking at the motivations associated with the social acceptability of this tool. The motivations (hedonic-gain-normative), derived from goal-frame theory, as well as elements relating to risk perception, are tested. One, 688 participants (aged between 18 and 87) first read a scenario presenting the application (reflecting either one of the motivations of the Goal Framing Theory or a control scenario with no motivation). After reading one of the scenarios, they completed an online questionnaire, measuring the acceptability of the tool using three measures: a direct one (items from the Technological Acceptability Model: ease of use, perceived usefulness and social influence), an indirect measure (by asking the percentage of neighbours interested in the tool) and a social measure (judgement of a person using the tool). The last part of the questionnaire was about subjects' risk perception. The analyses show that, of all the scenarios, the one involving hedonic motivation leads to the lowest social acceptance of the tool. We also observe that a better risk perception predicts better tool acceptability. Finally, we observe interaction effects between risk perception and motivations, showing that normative motivation is better when risk perception increases and that the control condition is better when risk perception decreases. Goal framing theory is usually used for ecological behaviors. It also appears here as relevant in the field of risk prevention. Although risk perception remains the best predictor of acceptability, these results lead us to conclude that hedonic motivation is not appropriate for the acceptability of a flood risk prevention tool. It is preferable to focus on normative and gain motivations.

How Does Heavy Precipitation of Varying Durations Respond to Urbanization in China?

AbstractHeavy precipitation, which is changing significantly as Earth's climate warms, can result in flooding that seriously damages societies. However, little is known about how heavy precipitation of varying durations responds to the diverse gradients of urban development in China. Through statistical analyses spanning from 1990 to 2021, we have examined shorter‐duration (≤3 days) and longer‐duration (>3 days) heavy precipitation across a spectrum of urban development, encompassing long‐term built‐up (LTB), recently built‐up (RTB), and rural background catchments within each urban agglomeration catchment (UAC) across China. We find that urbanization primarily influences shorter‐duration heavy precipitation, with a more pronounced effect observed in the LTB catchments. Conversely, the influence of urbanization on longer‐duration heavy precipitation appears to be more weakened in the RTB catchments. The intensification of shorter‐duration heavy precipitation induced by urbanization is more pronounced in humid regions and within larger UACs, while the urban effect on longer‐duration heavy precipitation is weaker in humid regions and within larger UACs. Notably, the attribution analysis results of the geographical detector model confirm our findings. Anthropogenic‐related factors (population density, nighttime light data, impervious surface percent, land surface temperature) significantly influence shorter‐duration heavy precipitation in more UACs than natural factors (distance from the coast, wind and elevation), while natural factors dominate longer‐duration events in larger UACs across China. Our results highlight the necessity of considering the spatial difference between the UAC center and UAC periphery for accurate projections and effective prevention of heavy precipitation and potential flood risks in the future.

Investigating the influence of residents' attitudes, perceptions of risk, and subjective norms on their willingness to engage in flood prevention efforts in Freetown, Sierra Leone

This study investigates the intricate relationships between flood disaster risk perception (RP), attitudes(ATT), subjective norms(SN), and participation in flood prevention activities(IPF), emphasizing the mediating roles of self-efficacy(SE) and bonding social capital(BSC). The research in Freetown, Sierra Leone, involved 702 participants, providing a diverse socio-demographic snapshot crucial for understanding community-based flood risk management. Data analysis revealed that demographic factors like age, gender, education, and income significantly influence flood risk perceptions and mitigation behaviors. Additionally, subjective norms were found to substantially impact both self-efficacy and bonding social capital, affecting participation in flood prevention activities. The study also explored the role of community dynamics and social norms in shaping risk perceptions and intentions to engage in flood risk prevention. It was observed that higher self-efficacy and more robust community bonds lead to increased participation in flood mitigation efforts. The findings offer valuable insights into flood prevention behavior's psychological and social drivers and highlight the importance of community-focused strategies in enhancing flood resilience. The study contributes to the broader understanding of flood risk management in urban settings, particularly in developing countries, and underscores the need for policies and practices that foster individual empowerment and collective community action.

Using supervised machine learning for regional hydrological hazard estimation in metropolitan France

Study regionThis study is carried out for 1929 gauged catchments in France, ranging from 1 to 10,000 km², where quality hydrometric observations are available for flood frequency analysis. Study focusThe regional estimation of hydrological hazards is studied for flood risk management and prevention in hydrology. For gauged catchments, flow quantiles can be estimated from observations using statistical approaches based on suitable probability distributions or simulation approaches based on rainfall-runoff transformation models. For ungauged catchments, the lack of hydrological observations means that we have to extrapolate our knowledge of hazards from gauged catchments to ungauged catchments, using regionalization methods. It is therefore necessary to combine regionalization methods with the implemented hazard estimation approach. In this paper, two popular machine learning methods, Random Forest and Neural Networks, are tested and compared as regionalization methods. A classical regionalization method using multiple linear regression is also applied as a benchmark to evaluate the performance of all configurations. All these regionalization methods are applied to a simulation-based approach (the SHYREG method) and to a statistical-based approach using generalized extreme value distribution (GEV). New hydrological insights•Regionalization approaches based on multiple linear regression have limitations to explain parameters with environmental descriptors in Regional Flood Frequency Analysis (RFFA) domain.•Regionalizing RFFA parameters using Random Forest allows more explanatory variables to be considered through non-linear relationships, resulting in better parameter estimation.•Machine learning techniques can better handle environmental descriptors for regionalization, this providing a notable performance improvement, especially for the statistical approach.•The tested simulation-based approach is less sensitive to the choice of spatial interpolation method than the studied statistical approach.

Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.

Identifying the Contributing Sources of Uncertainties in Urban Flood Vulnerability in South Korea Considering Multiple GCMs, SSPs, Weight Determination Methods, and MCDM Techniques

This study quantified uncertainties involved in assessing the future flood vulnerability in 33 urban areas with population exceeding designated thresholds in South Korea. The driver-pressure-state-impact-response (DPSIR) framework was utilized as the study procedure, integrating social, economic, and environmental factors. In addition, a total of 220 cases of combinations were examined, encompassing twenty general circulation models combined with shared socioeconomic pathway scenarios, five weight determination methods, and three multi-criteria decision-making (MCDM) techniques, as sources of inherent uncertainties in the process. The rankings of urban flood vulnerability (UFV) for the selected cities were comprehensively assessed considering all combinations, followed by an analysis of variance test to investigate contributing sources of uncertainties. As a result, Incheon and Busan were found to be vulnerable to flooding, while Yeongcheon and Andong appeared to be safe cities. Some cities exhibited wide ranges in their rankings, such as Daegu, Yangpyeon, and Jeongeup. The identified contributing sources were weighting (58%), MCDM (27%), and the combination of weighting and MCDM methods together (15%). This study revealed that weight determination methods and MCDM techniques are the primary sources of uncertainties in the assessment of future UFV instead of multiple GCMs and SSPs. This finding underscores the importance for decision-makers and stakeholders to carefully consider these uncertainties for sustainable flood risk management and prevention.

An Assessment of Flood Risk in the Gambia: A Data-Driven Approach to Flood Risk Management and Prevention

Coastal African countries, including Gambia, are increasingly vulnerable to a range of natural disasters, notably flooding, which poses significant challenges to sustainable development and safety. This study set out to comprehensively assess the flood risk in Gambia using a data-driven approach, aiming to enhance flood risk management and prevention strategies. Utilizing geospatial data and Geographic Information System (GIS) techniques, the research involved mapping and categorizing flood risk zones across various regions. The analysis focused on the identification and categorization of flood risk zones, assessment of the vulnerability of key infrastructures, health facilities, road networks, and settlements to flooding. The findings reveal that a total of 2,721 square kilometers of land is at flood risk categorized as either high or very high. This constitutes 26.89 percent of the country’s of terrestrial area. A total of 25 critical health infrastructure were in areas of this high flood risk categorization – ranging from general hospitals to clinics. Up to 24.5% of the nation's road network was found to be in this flood risk zone. Of the country’s five administrative regions, the West Coast Region which is home to most of its urban centers was most at risk of flooding. Here, of the 353 settlements in this region, 172 of them fell under the high to very high-risk flood zone categorization, constituting 48.7% of the settlements at a significant level of flood vulnerability. This study enhances the understanding of flood hazards in Gambia, providing essential perspectives for decision makers, city planners, and emergency response teams. This research highlights the need for using data-driven methods in identifying and managing environmental risks. It also advocates for the adoption of long-term strategic planning, infrastructure enhancements, and community-based activities to reduce the effects of floods.

Quantitative assessment of sedimentation in Moroccan dam reservoirs: analysis and projections

The siltation of dam reservoirs is the phenomenon that most embarrasses dam managers and water reservoir operators. In Morocco, this phenomenon affects all the watersheds to different degrees. Dam managers schedule bathymetric measurement companies to estimate the quantities of mud deposited in reservoirs. These companies are neither regular in time nor generalized on the 152 large dams of the Kingdom because of the high cost of the bathymetry operations. In our research work, the bathymetric data available for about a century were collected, checked, corrected, and sorted to be exploited.In the next step, we extrapolated over time and updated the siltation rates for each dam according to the trend curves of the evolution of silted volume. Most of these trend curves were linear. For the nonlinear curves, we looked for explanations throughout history. These are dams whose reservoirs have undergone human intervention such as desilting operations or emptying of reservoirs, or following natural phenomena such as drought or exceptional floods. The research work subsequently made it possible to estimate the annual siltation rates for 53 large dams whose reservoir capacities total approximately 92% of that of the entire country. It also revealed an overall siltation rate of 18.14% for the entire country and rates ranging from 10% to 52% for each watershed. The discussions of these results in this article give explanations on the highest rates, based on the natural data of the watersheds in question. These results will help decision-makers to define the priorities of desilting operations or the construction of new dams, help investors and industrialists wishing to extract and use mud from dam reservoirs as raw material, and can also be used in various scientific fields such as dam reservoir management, flood risk prevention, environmental impact studies, spatial planning, and research and development studies.

Flood risk assessment of slums in Dhaka city

Slums are characterized as some of the most impoverished and vulnerable communities within urban areas. They often face a multitude of challenges, including extreme poverty, social marginalization, and inadequate infrastructure. The poor quality of buildings and the lack of adequate infrastructure further exacerbate the vulnerability of slum dwellers to the impacts of flooding. This study focuses on accurate assessment of flood risk in slum area of the Dhaka metropolitan area, the capital of Bangladesh. The man-machine interactive method was utilized to extract slum information from Sentinel-2A satellite imagery. Thirteen evaluation indicators were selected from the aspects of hazard, sensitivity, exposure, and prevention and mitigation capacity. The fuzzy analytic hierarchy process (FAHP) was employed to determine the relative weights of these indicators. A comprehensive evaluation model for flood disaster risk was established, enabling the determination of the spatial distribution of flood risk in slums of Dhaka city. The results indicate that the flood risk distribution in the slums of Dhaka city exhibits an overall high flood risk. The slums with very high flood risk constitute about 22.035% of the total area and are concentrated in Badda, Khilkhet, and Dakshinkhan districts. Slums with high-risk constitute 41.600% of the total area and are located mainly in Turag, Pallabi, Badda, and Gulshan districts. Those areas with abundant rainfall, dense river networks, and flat topography have higher flood risk in slums compared to other regions. These research findings can be a reference for regional flood risk zoning, prevention, and sustainable development planning.

Performance assessment for the integrated green-gray-blue infrastructure under extreme rainfall scenarios

Synergistic allocation of urban stormwater infrastructure is critical to flood risk prevention and control under extreme rainfall events. This study focuses on the interaction regularity of green, gray, and blue infrastructure in the process of runoff retention and discharge under extreme rainfall scenarios. Three strategies, namely, gray infrastructure, green-gray infrastructure, and green-gray-blue infrastructure, are proposed to analyze the effectiveness of different scenarios on flood risk control capabilities of urban drainage systems and to determine the key influencing factors of the three strategies. The results show that green-gray-blue infrastructure demonstrates synergy in improving the efficiency of urban drainage systems. Under gravity drainage conditions, the backwater jacking caused by high river water levels has a certain impact on the smooth discharge of stormwater runoff in urban drainage systems. This case study identified a sensitive range in the influence of water level on runoff control and drainage function and an adaptive range in which the flood increases slightly with the water level increase. These present notable rules in improving the synergistic effect of blue infrastructure in the integrated urban green-gray-blue infrastructure system.

Comparison of Two Hydrological Models, the HEC-HMS and Nash Models, for Runoff Estimation in Michałówka River

Floods are among the most devastating natural disasters in small suburban catchments. These phenomena, causing loss of life and massive property damage, pose a serious threat to the economy. Hydrological modeling is extremely important in terms of climate change, and the use of appropriate modeling can be a useful tool for flood risk prevention and mitigation. Rainfall–runoff modeling requires the selection of an appropriate hydrological model in order to obtain satisfactory results. Hydrological models are used in water resource planning and management to estimate catchment runoff. Small uncontrolled catchments play a particularly important role in hydrological phenomena, since changes in them affect flows in the recipient. Hydrologists are particularly interested in developing hydrological models that can be made with a minimum of data and parameters. Nash models and the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) are examples of simple and most practical hydrologic models. These models were used in this paper to study geographic and qualitative changes in precipitation runoff due to land cover changes. The modeling was carried out for two spatial aspects relating to the years 1940 and 2018. The model allowed for the simulation of the river flow that can occur under different rainfall probabilities. The analysis of the results was used to evaluate the hydrological models used. The hundred-year flow modeled with the Nash model for 1940 was 13.4 m3∙s−1, whereas the second model gave slightly lower flow values. In addition, modeling the flow for 2018 (after changing the land cover) highlighted the increase in the flow value for both models, where again the flow volume was slightly higher for the Nash model and amounted to about 19 m3∙s−1. The flow differences for individual models were not too large. This made it possible to conclude that the simulated outflow hydrographs are in good agreement, and this means that the models accurately reproduce the flow of the Michałówka River. The study showed that rapid urbanization adversely affects hydrological processes. In addition, the study showed that a well-distributed model can outperform a global flood forecasting model, especially in terms of magnitude, as in the current study example.

Extremal Analysis of Flooding Risk and Its Catastrophe Bond Pricing

Catastrophic losses induced by natural disasters are receiving growing attention because of the severe increases in their magnitude and frequency. We first investigated the extreme tail behavior of flood-caused economic losses and maximum point precipitation based on the peaks-over-threshold method and point process (PP) model and its extreme tail dependence. We found that both maximum point precipitation and direct economic losses are well-modeled by the PP approach with certain tail dependence. These findings were further utilized to design a layered compensation insurance scheme using estimated value-at-risk (VaR) and conditional VaR (CVaR) among all stakeholders. To diversify the higher level of losses due to extreme precipitation, we designed a coupon paying catastrophe bond triggered by hierarchical maximum point precipitation level, based on the mild assumption on the independence between flood-caused risk and financial risk. The pricing sensitivity was quantitatively analyzed in terms of the tail risk of the flood disaster and the distortion magnitude and the market risk in Wang’s transform. Our trigger process was carefully designed using a compound Poisson process, modeling both the frequency and the layered intensity of flood disasters. Lastly, regulations and practical suggestions are provided regarding the flood risk prevention and warning.

Understanding and Assessing Flood Risk Management in Balochestan Iran: With Integrated River Basin Approach, Future Direction

A holistic perspective on changing rainfall-driven flood risk is provided for the late 20th and early 21st centuries. Balochistan is exposed to different types of floods that cause severe economic losses, damage to infrastructures, and loss of lives. Reliable information on the drivers, patterns and dynamics of flood risk is crucial for the identification, prioritization and planning of risk reduction and adaptation measures. Here, we present a systematic review of existing flood risk assessments in Balochistan. We evaluate the current status, persisting gaps, and challenges regarding the understanding and assessment of flood risk in this large province. A society well-aware of risks must not only give attention to the prevention of flood risks but must also consider disaster management, i.e. minimizing casualties and flood damages, and enhancing recovery. Balochistan has No solid network of levees along any of the rivers that protect the many low-lying polders from flooding, however the government trying to build few in Sarbaz river from Shirgwaz to Kolani and in Kajo river from Kalat to Dalgan. Well, nature is unpredictable, extreme events may happen, and absolute protection against flooding cannot be offered. It is common practice to perform technical and economic analyses to determine the feasibility of flood protection plans and also institutional and administrative aspects are addressed. But how the people that live in the polders feel about the flood risk and protection plans seldom gets attention.

Understanding and Assessing Flood Risk Management in Balochestan Iran: With Integrated River Basin Approach, Future Direction

A holistic perspective on changing rainfall-driven flood risk is provided for the late 20th and early 21st centuries. Balochistan is exposed to different types of floods that cause severe economic losses, damage to infrastructures, and loss of lives. Reliable information on the drivers, patterns and dynamics of flood risk is crucial for the identification, prioritization and planning of risk reduction and adaptation measures. Here, we present a systematic review of existing flood risk assessments in Balochistan. We evaluate the current status, persisting gaps, and challenges regarding the understanding and assessment of flood risk in this large province. A society well-aware of risks must not only give attention to the prevention of flood risks but must also consider disaster management, i.e., minimizing casualties and flood damages, and enhancing recovery. Balochistan has No solid network of levees along any of the rivers that protect the many low-lying polders from flooding, however the government trying to build few in Sarbaz river from Shirgwaz to Kolani and in Kajo river from Kalat to Dalgan. Well, nature is unpredictable, extreme events may happen, and absolute protection against flooding cannot be offered. It is common practice to perform technical and economic analyses to determine the feasibility of flood protection plans and also institutional and administrative aspects are addressed. But how the people that live in the polders feel about the flood risk and protection plans seldom gets attention.

Green Space Optimization Strategy to Prevent Urban Flood Risk in the City Centre of Wuhan

Changing the water permeability ratio of urban underlying surface helps alleviate urban flood. This paper designs the swale identification experiment to modify the flood-submerging simulation experiment based on the SCS-CN model and proves that the results generated by the modified experiment better reflect the realities. The modified flood-submerging simulation experiment is then applied to downtown Wuhan to obtain the quantitative data. The data are used to quantify the catchment capacities of the lots. Based on the rainfall collection capacities, the maximum surface rainfall runoff volume that would not cause flood is arrived at using the rainfall runoff formula. The maximum runoff volume represents the rainwater storage capacities of the lot based on the proportion of the green space that is identified within the study area. The results suggest that this rainwater storage capacity evaluation model works efficiently to identify the urban areas with flood risks and provides the rainwater runoff thresholds for different areas. Adjustments in the spatial patterns and proportions of the green space help ensure that the rainwater runoff volume is below the thresholds, thus contributing to the prevention and control of the urban flood risks.

Assessment of flood prone zones in the Tarkwa mining area of Ghana using a GIS-based approach

Flood is one of the most imperious natural hazards that can cause economic damages to infrastructures and natural ecosystems. In view of the extensive mining activities in the Tarkwa area, there has been an increase in human intervention of natural water bodies via degradation of vegetation in the watershed area, inappropriate land-use change, soil erosion and expansion of paved surface area for settlement and industrialisation. This has increased the degree of flood susceptibility within the area, with major flood incidents recorded on a timely basis. In this study, a GIS-based approach was utilised to delineate flood-prone zone within the Tarkwa mining area using Digital Elevation Models (DEMs). A DEM-derived morphologic features including local slope and streamflow network were developed using ArcGIS. The flood susceptible map was generated by merging the slope map and the stream network maps. The results indicate that about 42.59% of the study area were highly prone to flooding. The areas susceptible to flooding were mainly made up of agricultural lands with high usage of chemical fertilizers for farming, which may leach into nearby rivers, streams, lakes and groundwater during flood occurrence. It is understood from this study that the mining areas within the Tarkwa Nsuaem municipality were less susceptible to flooding. The outcome of this study provides a simplified method of mapping and assessment of flood susceptible areas which can be used as a reference for flood risk management, prevention, and reduction of natural disasters in the study area.

Statistical Prediction of Extreme Storm Surges Based on a Fully Supervised Weather-Type Downscaling Model

Increasing our capacity to predict extreme storm surges is one of the key issues in terms of coastal flood risk prevention and adaptation. Dynamically forecasting storm surges is computationally expensive. Here, we focus on an alternative data-driven approach and set up a weather-type statistical downscaling for daily maximum storm surge (SS) prediction, using atmospheric hindcasts (CFSR and CFSv2) and 15 years of tidal gauge station measurements. We focus on predicting the storm surge at La Rochelle–La Pallice tidal gauge station. First, based on a sensitivity analysis to the various parameters of the weather-type approach, we find that the model configuration providing the best performance in SS prediction relies on a fully supervised classification using minimum daily sea level pressure (SLP) and maximum SLP gradient, with 1° resolution in the northeast Atlantic domain as the predictor. Second, we compare the resulting optimal model with the inverse barometer approach and other statistical models (multi-linear regression; semi-supervised and unsupervised weather-types based approaches). The optimal configuration provides more accurate predictions for extreme storm surges, but also the capacity to identify unusual atmospheric storm patterns that can lead to extreme storm surges, as the Xynthia storm for instance (a decrease in the maximum absolute error of 50%).

Analytic hierarchy process (AHP) applied to flood susceptibility in São José dos Campos, São Paulo, Brazil

The changes in environmental factors in a hydrographic basin, mainly in the urban environment, enable different scenarios than the change of parameters in climatic, physical, political, and socioeconomic factors. Those changes may have immediate effects on indigenous populations, often making them vulnerable to the risk of flooding. This article evaluates the vulnerability to flooding in the municipality of São José dos Campos, State of São Paulo, Brazil using multicriteria analysis and geoprocessing techniques. Quantum GIS (QGIS) was used in the manipulation and processing of vector and matrix data related to the terrain slope, hydrography, geotechnics, and data history of flooding (2009 - 2018) in São José dos Campos. The hierarchy and criteria weighting processes were carried out, as well as the zoning of areas at risk of flooding. Thus, it was found that 23.81% and 2.17% of the municipal territory has high and very high susceptibility to flooding. It can be concluded that the combined use of multicriteria analysis and geoprocessing is an effective contribution to assist the management of public policies in order to obtain more assertive decisions in the prevention or reduction of flood risks.

Evaluating the Impacts of Future Urban Expansion on Surface Runoff in an Alpine Basin by Coupling the LUSD-Urban and SCS-CN Models

Effective evaluations of the future urban expansion impacts (UEI) on surface runoff in alpine basins are full of challenges due to the lack of reliable methods. Our objective was to provide a new approach by coupling the Land Use Scenario Dynamics-urban (LUSD-urban) and Soil Conservation Service-Curve Number (SCS-CN) models to estimate the future UEI on surface runoff. Taking the Qinghaihu-Huangshui basin (QHB) in the Tibetan Plateau, China, as an example, we first applied the SCS-CN model to quantify the surface runoff in 2000 and 2018 and analyzed the changes in surface runoff. Next, we applied the LUSD-urban model to simulate urban expansion under five localized shared socioeconomic pathways (SSPs) from 2018 to 2050. Finally, we assessed the UEI on surface runoff in the QHB from 2018 to 2050. We found that coupling the LUSD-urban and SCS-CN models could effectually evaluate the future UEI on surface runoff. Compared with the combination of the Future Land Use Simulation (FLUS) and SCS-CN models, our method reduced the absolute evaluation errors from 3.40% and 11.78% to 0.18% and 4.23%, respectively. In addition, the results showed that future urban expansion will have severe impacts on surface runoff in the valley region. For example, as a result of urban expansion, the surface runoff in the Huangzhong, Xining, and Datong catchments will increase by 4.90–9.01%, 4.25–7.36%, and 2.33–3.95%, respectively. Therefore, we believe that the coupled model can be utilized to evaluate the future UEI on surface runoff in alpine basins. In addition, the local government should pay attention to flood risk prevention, especially in the valley region, and adopt reasonable urban planning with soft and hard adaptation measures to promote the sustainable development of alpine basins under rapid urban expansion.