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Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of atmospheric-hydrological simulations based on the WRF model and InfoWorks ICM. Results showed that the microphysics scheme could significantly affect spatial and temporal distributions of the simulated torrential rainfall. Generally, the combination of WRF and NSSL schemes produced better performance. Applying the NSSL scheme to the WRF model and combining it with the InfoWorks ICM system can reproduce torrential rainfall and urban flood formations. [ABSTRACT FROM AUTHOR]

Over the last two decades, urban floods and their impacts have been on the rise worldwide, owing to both climatic changes and human activities. The present study examines different at-risk elements, such as residential, commercial, and critical facilities, to evaluate their multi-components of vulnerability to urban floods in Peshawar, Pakistan. Based on the impacts of urban floods, the weightage of each component of the vulnerability for the selected elements at risk is defined. This study presents and uses the modified Fisher's ideal quantity index to combine the different vulnerability components into a single value. Additionally, the Patnaik and Narayan vulnerability index is employed to generalize sector-wise vulnerabilities across the study area. The results show that the old physical infrastructure of commercial and manufacturing units in the Kohati Gate area is highly vulnerable to urban floods, while the residential units are the least susceptible due to their distanced location from the drainage system. In Hayatabad, encroachments along the torrent's sides, affecting housing and educational institutions, contributed to increased vulnerability to urban floods, despite their relatively lower physical vulnerability. The study provides a new platform for understanding the multi-components of vulnerability to urban floods and tackling the challenges posed by urban floods effectively.

Extended Abstract Background: Depending on rainfall systems and conditions influenced by unprincipled human activities, floods cause a lot of damage to natural resources, settlements, and projects every year. Hydrometric stations are often destroyed during floods or small watersheds lack hydrometric stations, which means that the estimation of runoff and maximum flood discharges requires a suitable method to calculate runoff and flood values in these basins. Prevention of this damage is doubly important when the study area overlooks places with a high density of settlements and urban facilities that can threaten the lives of many residents. The amount of runoff in each sub-basin of the watershed overlooking Malayer City was estimated in this study. Methods: The watershed overlooking the city of Malayer with an area of 14,700 hectares is stretched from the north to the northeast of the city. The processing of digital elevation models identified five sub-basins overlooking the city and field visits. The curve number method was used to estimate runoff height and flood volume in each sub-basin. The most intense rainfall event (88 mm per day) with a return period of 30 years was designed as rainfall, considering the amount of previous rainfall 5 days before this event with a cumulative value of 45.2 mm. To calculate the physical parameters of the watershed in this study, topography, geology, vegetation, soil, and land use maps were digitized using a geographic information system. Simultaneous maps of land use and rainfall were provided using Sentinel-2 images, containing 13 spectral bands and a spatial resolution of 10 meters. Utilizing the SCS method, the layers of land use, vegetation, and soil hydrological groups were combined to produce a curve number map. The value of the curve number was corrected for the wet condition based on the rainfall 5 days before and the location of the basin in the previous wet conditions. A weighted curve number approach for each sub-basin was compared with another approach based on the arithmetic mean of the curve number for each sub-basin, as well as a weighted runoff height approach for each curve number. Kirpich's method was used to calculate the concentration time of each sub-basin. In the next step, using the maximum daily rainfall data from the Malayer synoptic station in the statistical period from 1991 to 2021, the rainfall height values were converted to runoff height using the SCS relationship, and then the peak flood discharge was calculated for each sub-basin. Results: The lack of permanent vegetation and the presence of annual grasses are among the reasons for the high potential of the sub-basins in runoff production. As a result, the average curve numbers of arithmetic and weighted average methods are 79.09 and 81.46, respectively, which shows the high capacity of the basin in producing runoff. Converting curve number values to peak flood height and flow using two commonly used methods of curve number calculation and comparing it with the results of the weighted runoff height calculation approach for each curve number in the working units of the study area showed no significant difference. Finally, the runoff height maps and the peak discharge of each sub-basin were drawn. The northern sub-basin of the basin with an average runoff height of 44.32 mm and a peak discharge of 168 m3/s has the highest participation in flood discharge toward the city of Malayer, and sub-basin 4 in the south-eastern part of the basin has the least participation in flood generation. The results of the relationship between the area and runoff height showed that the basins with a larger area did not necessarily contribute the most to the occurrence of floods in all three calculation methods of the curve number and runoff height. Other factors also play a role in these results, such as the extent of rocky outcrops and reduction time of concentration due to the high slope as one of the reasons for this problem. The formation of the longest watercourse in the basin with a length of 16314 meters under Sub-basin 3, which has the highest peak flood flow after Sub-basin 2, is one of the discussed technical points that can handle the rapid discharge of the peak flow of about 100 m3/s. However, the study of its longitudinal slope of about 2% indicates the high risk of flooding caused by the lack of evacuation in rains with a high return period, which in turn is a serious risk for the threats caused by floods in the southeastern part of the city. The intersection of the end part of the main waterway with the exit of other sub-basins and the collection of runoff under the upstream basins are other problems that increase the risk of flooding in the downstream areas. Conclusion: It is suitable to use the curve number method to estimate the amount of runoff produced in the basins overlooking the settlements that do not usually have hydrometric stations. Because of its low-density pastures and rain-fed agriculture, the watershed overlooking Malayer has a high potential for runoff production.

Climate change causes erratic rainfall and often results in flooding of urban areas. Floods are hydrometeorological disasters that occur in various regions of Indonesia. Flood vulnerability in urban areas has increased over the past 30 years. Kauman Village is included in the Asri water catchment area, which has an Asri primary channel downstream of the urban area of Nganjuk District. However, from 2019 to 2022, the urban area of Nganjuk District, including Kauman Village, was affected by flooding. Urban floods inundated office areas, schools, housing, and public facilities such as the Nganjuk District General Hospital. The factor indicated as the cause of flooding is the clogging of the drainage channel with rubbish. Therefore, field research and mathematical calculations were conducted to evaluate the discharge capacity of drainage channels in the village. Based on the research, it was found that the existing drainage channel discharge in the research area could not accommodate the planned discharge for the 10-year return period. In addition, there are 33 channels that are unable to accommodate the planned discharge because the channel dimensions are too small, some channels are slightly damaged, sedimentation occurs, and they are blocked by rubbish. Drainage channels that do not function optimally affect urban flooding. Therefore, several efforts have been made to reduce the risk of flooding by changing the dimensions of drainage channels, normalizing drainage channels, and getting used to maintain drainage channels and not throwing rubbish in drainage channels.

Throughout history, natural disasters have caused severe damage to people and properties worldwide. Flooding is one of the most disastrous types of natural disasters. A key feature of flood assessment has been making use of the information derived from remote-sensing imagery from optical sensors on satellites using spectral indices. Here, a study was conducted about a recent spectral index, the Normalised Difference Inundation Index, and a new ensemble spectral index, the Concatenated Normalised Difference Water Index, and two mature spectral indices: Normalised Difference Water Index and the differential Normalised Difference Water Index with four different machine learning algorithms: Decision Tree, Random Forest, Naive Bayes, and K-Nearest Neighbours applied to the PlanetScope satellite imagery about the Brisbane February 2022 flood which is in urban environment. Statistical analysis was applied to evaluate the results. Overall, the four algorithms provided no significant difference in terms of accuracy and F1 score. However, there were significant differences when some variations in the indices and the algorithms were combined. This research provides a validation of existing measures to identify floods in an urban environment that can help to improve sustainable development. [ABSTRACT FROM AUTHOR]

Urban resilience experts have highlighted that frequent extreme weather events are a result of rapid, unplanned development and climate change. To ensure coherent urban planning, effective land governance has been a priority since the introduction of the national urban policy in 2012. However, despite implementing this policy for over a decade, poor land governance continues to negatively impact city development, yet this subject has received limited academic attention. Our study focuses on Santa Maria, a community historically neglected by planning officials and currently facing unregulated urbanization and increasing flood risks. We used qualitative research methods, including key informant interviews and document reviews, to unpack land ownership and management complexities. These complexities have led to "planning by the commons," the creation of green space deserts, and challenges related to residents' mobility and livelihood. Our study affirms that auto-constructed communities like Santa Maria reflect a form of tenacious urbanism that should be acknowledged and respected. Incorporating its logic into proposals for consolidation, informed by the opinions and desires of the residents themselves, can help integrate "planning by the commons" into the urban planning process of Accra. Rather than being viewed as a problem to be cured, this approach should be incorporated into the planning process, incorporating locally specific social functions and traditional structures to ensure equitable and just urban societies. The public sector must recognize that building on local capacity, skills, and expertise is essential in the quest for a resilient city. The experience from Santa Maria provides a great opportunity to nurture local development, fulfill the growing demand for solutions and services without biases, and provide an excellent opportunity for inclusive growth. [ABSTRACT FROM AUTHOR]

In recent years, Nature-Based Solutions (NBS) have become instrumental in restoring ecosystems and reducing the negative effects of hard surface developments. Existing studies have shown the potential of NBS in mitigating urban floods and reducing overall hydrogeological risks, especially in high-income areas. However, there is limited knowledge on area-specific NBS implementation and the locational suitability of areas to support different NBS for urban flood mitigation in medium to low-income regions. A methodology based on Spatial Multi-Criteria Analysis (SMCA) has the potential to spatially combine and synthesize information to elicit spatially-specific opportunities for NBS implementation. However, an SMCA tool to determine suitable locations for different NBS measures for urban flood mitigation does not yet exist. In order to address this research gap, this study developed an SMCA methodology to identify different target areas where specific urban flood-related NBS measures can be implemented to minimize urban flood occurrence in Accra, Ghana. The study revealed different locations where urban flood-related NBS measures could be applied. It also demonstrated how specific NBS measures could be tailored to the characteristics of different areas in Accra to ensure effective urban flood mitigation. The suggested methodological approach provides a spatial knowledge base for flood management institutions to integrate NBS into flood management schemes. The approach has application potential for other geographic contexts facing urban flood risks. Future research could focus on possible ways of integrating NBS into the spatial development of cities. [ABSTRACT FROM AUTHOR]

A study is presented for the assessment of different stormwater green infrastructure (SGI) for sustainable stormwater management, both stand-alone and in combination, in the rapidly developing city of Gurugram, India. A decision-making framework was developed for ranking the SGI based on five criteria (social, economic, environmental, technical, and legal and political) encompassing two quantitative and 12 qualitative subcriteria. The assessment of the application of SGI was carried out using Storm Water Management Model (SWMM) software and an expert survey involving participants from varied areas of expertise. The survey outcome determined the criteria weight by employing the analytic hierarchy process method, where technical and social criteria were given the highest (0.37) and the lowest (0.11) weight, respectively. Further, peak flow reduction potential was the most favored subcriteria (82%), while aesthetics was the least favored (58%). The prioritization of a set of predefined alternatives was carried out using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Results indicated that the relative closeness coefficient value for a combination of SGI, vegetated swales plus permeable pavements was 0.93, whereas for green roofs plus permeable pavements it was 0.84, and these were the most efficient alternatives to mitigate urban flooding in the city. The proposed SWMM-based TOPSIS approach can be helpful to decision makers and government bodies in the selection of SGI for urban stormwater management. [ABSTRACT FROM AUTHOR]

Resilience theory has gained significant traction in various urban fields, including natural disasters and risk management or climate change adaptation, and at different organizational levels, including academics, practitioners, and policymakers. It should be considered a complementary approach to sustainable development that enhances cities' capacity to endure future uncertainties and promote rational urban development. However, the lack of a generally accepted definition of resilience hampers understanding and practical implementation in urban services like stormwater management. Conventionally, stormwater services aimed to minimize the impact of rainfall through fail-safe approaches. The resilience approach, on the other hand, embraces a holistic "safe-to-fail" perspective. The existing literature offers diverse approaches to measure flood and stormwater resilience. Still, there is room for the development and improvement of standardized but flexible frameworks for operationalizing resilience in urban drainage and flood management. To address this, a comprehensive resilience framework for urban stormwater services is proposed, entitled RESILISTORM. This framework incorporates a Strategic Dimension and a Performance Dimension, providing segmented and overall resilience ratings that enable utilities to address critical aspects undermining the service's resilience. An open-source digital tool (RESILISTORM-tool) is also introduced to expedite answering, data integration, and visualization analysis of results. [ABSTRACT FROM AUTHOR]

Urban floods induced by the compound effects of climate change and rapid urbanisation are one of the key challenges facing many Chinese cities. In 2013, China initiated the Sponge City Program (SCP) to address this issue via constructing/restoring green-blue infrastructure as Nature-Based Solutions (NBS). Various green and blue spaces have been integrated into traditional grey facilities to mitigate urban floods, provide recreational venues, and enhance the natural aesthetics of urban landscapes. While housing development has been actively included in NBS provision, it remains unclear whether and how homebuying decisions might be influenced by NBS implementation under the framework of the Sponge City Program. Our study uses Guiyang, one of the pilots selected by the central government for the construction of “Sponge City” via implementing NBS. In-depth semi-structured interviews (n = 25) were used to examine whether the multiple benefits generated by newly established green-blue (sponge) facilities would affect residents’ home-purchasing preferences/decisions. We found that the provision of a green and blue facility, and whether it is part of the SCP or not, significantly influences a homebuyers’ choice. This study sheds additional light on the nexus of end users (homebuyers) and NBS provision (developers) under the framework of the Sponge City Program, generating practical implications for stakeholder engagement for NBS implementation to achieve sustainable urbanization.

Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of atmospheric-hydrological simulations based on the WRF model and InfoWorks ICM. Results showed that the microphysics scheme could significantly affect spatial and temporal distributions of the simulated torrential rainfall. Generally, the combination of WRF and NSSL schemes produced better performance. Applying the NSSL scheme to the WRF model and combining it with the InfoWorks ICM system can reproduce torrential rainfall and urban flood formations.

Synoptic fingerprinting of flood driving rainfall events over the Hyderabad city, India were assessed through an unsupervised machine learning technique—self‐organizing maps (SOM) for the period 1979–2020. Flood dates were identified using nonscientific data sources, and then large‐scale climate variables, that is, integrated vapour transport (IVT) and geopotential height at 500 hPa (Z500) along with rainfall at daily scale were considered. SOM was applied on IVT and results indicated large spatial scale‐ and season‐specific mechanisms in addition to local circulation systems—this highlights SOM's ability to cluster circulation mechanisms as per the relevance. The rainy seasons, that is, monsoon and postmonsoon showed well‐known moisture pathways. While synoptic systems can be easily linked with monsoon and postmonsoon mechanisms, it is interesting to note that the local systems exist even during the monsoon and postmonsoon season in addition to the summer season. Spatial patterns of Z500 corresponding to SOM nodes of IVT showed the presence of low‐pressure systems (LPSs), tropical cyclones and trough systems. Further analysis was performed, and a total of 35 atmospheric rivers (ARs) were identified. Plots of back trajectories for the selected AR events indicated that Arabian Sea is the main source of moisture in the monsoon season followed by Bay of Bengal for postmonsoon and tropical cyclones in summer season events. Additionally, moisture from land is also observed as another source of moisture. Back‐trajectory analysis further indicated AR‐LPS interaction during the monsoon and ARs feeding moisture to LPSs. Overall, well‐known and season‐specific synoptic systems that brought significant rainfall to the study region were identified by the SOM. The proposed framework is adaptable for different locations, and as floods in cities across the globe are on the rise, these findings will have a prominent role in urban flood forecasting and management. [ABSTRACT FROM AUTHOR]

This paper addresses the riverine flood events that have occurred in Greece over the last 136 years (i.e., during the 1886–2022 period), focusing, amongst others, on the case of urban floods. The flood record of various sites of the country has been collected and analyzed to determine their spatial and temporal distribution. Greece is a country where flood data and records are very scarce. Therefore, as there is not an integrated catalog of Greek floods spanning from the 19th century to recently, this is the first attempt to create an integrated catalog for Greece. The sources used include published papers, local and regional newspapers and public bodies (mainly the Ministry of Environment and Energy and the official websites of Greek municipalities). Additionally, the main factors responsible for their occurrence have been issued, regarding the country's climatic, geological and geomorphological setting, as well as human interventions. In addition, the atmospheric circulation driving factors of floods are assessed via an unsupervised neural network approach (i.e., Self-Organizing Maps). Based on the results of this research, an online GIS-based database has been created, depicting the areas that have been struck by riverine floods in Greece. By clicking a flood event in the online database, one can view several characteristics, depending on data availability, such as duration and height of the rainfall that caused them and number of fatalities. Long-term trends of mean and extremes seasonal precipitation also linked to the spatial distribution of floods. Our analysis shows that urban floods are a very large portion of the overall flood record, and they mainly occur in the two large urban centers, Athens and Thessaloniki, as well as near large rivers such as Pineios. Autumn months and mainly November are the periods with higher flood hazards, based on past records and cyclonic atmospheric circulation constitutes the principal driving factor. Our results indicate that a flood catalog at national level is of fundamental importance, as it can provide valuable statistical insights regarding seasonality, spatial distribution of floods, etc., while it can also be used by stakeholders and researchers for flood management and flood risk analysis and modelling. [ABSTRACT FROM AUTHOR]

Climate change is expected to increase the frequency and magnitude of flooding. Furthermore, in recent years, there has been an increase in city extent and population densities. This study considers the metropolitan area of Pamplona (Spain) as a case study. The delta changes in precipitation quantiles in the Arga River catchment, obtained from 12 climate models, have been transformed into peak flow delta changes using the Real-time Interactive Basin Simulator (RIBS) model. Also, three percentiles of flood quantile delta changes are selected. Water depths and flood extents expected in future flood events have been obtained using the two-dimensional Iber hydrodynamic model. The Safer_DAMAGE algorithm has been used to assess flood losses in urban areas at the building scale. The results show that flood losses are expected to be smaller for low return periods and greater for high return periods. [ABSTRACT FROM AUTHOR]

Urban resilience experts have highlighted that frequent extreme weather events are a result of rapid, unplanned development and climate change. To ensure coherent urban planning, effective land governance has been a priority since the introduction of the national urban policy in 2012. However, despite implementing this policy for over a decade, poor land governance continues to negatively impact city development, yet this subject has received limited academic attention. Our study focuses on Santa Maria, a community historically neglected by planning officials and currently facing unregulated urbanization and increasing flood risks. We used qualitative research methods, including key informant interviews and document reviews, to unpack land ownership and management complexities. These complexities have led to “planning by the commons,” the creation of green space deserts, and challenges related to residents' mobility and livelihood. Our study affirms that auto-constructed communities like Santa Maria reflect a form of tenacious urbanism that should be acknowledged and respected. Incorporating its logic into proposals for consolidation, informed by the opinions and desires of the residents themselves, can help integrate “planning by the commons” into the urban planning process of Accra. Rather than being viewed as a problem to be cured, this approach should be incorporated into the planning process, incorporating locally specific social functions and traditional structures to ensure equitable and just urban societies. The public sector must recognize that building on local capacity, skills, and expertise is essential in the quest for a resilient city. The experience from Santa Maria provides a great opportunity to nurture local development, fulfill the growing demand for solutions and services without biases, and provide an excellent opportunity for inclusive growth.

Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.

Urban floods have been highly prominent natural disasters occurring in catchments across the globe, causing financial loss and damage to buildings. This necessitates effective and sustainable mitigation mechanisms. In this context, flood-susceptibility-based building risk (FSBR), a combined index for evaluating flood susceptibility and building risk simultaneously to understand the impact of the flood, is proposed by fusing XGBoost (facilitates flood susceptibility) and Hydrologic Engineering Center River Analysis System 2D (enables building risk) in climate change situations. The methodology is applied to Greater Hyderabad Municipal Corporation, India. Six combinations of FSBR, namely, high building risk and high flood susceptibility (HH), high and medium (HM), medium and medium (MM), medium and high (MH), low and medium (LM), and low and high (LH) are employed to study the urban floods. The total affected areas for HH, HM, MH, MM, LH, and LM are 63.40 km2 (52.627%), 28.92 km2 (24%), 9.52 km2 (7.9%), 4.81 km2 (3.99%), 9.26 km2 (7.686%), and 4.56 km2 (3.79%) (totalling 120.47 km2). The number of corresponding buildings is 182,178, 84,136, 46,238, 22,691, 48,092, and 23,781. Waterproofing as a mitigation measure is considered. The total cost of waterproofing is Rs 4,964.60 cr. HIGHLIGHTS Machine learning and hydraulic modelling are complemented to derive an indicator, flood-susceptibility-based building risk (FSBR), for the urban catchment.; Six combinations of FSBR are employed to study urban floods.; Waterproofing as a mitigation measure is studied.;

Floods in urban areas have become a common problem globally. Extensive research has been done to understand the changes in urban areas that are resulting in floods, such as Land Use Land Cover changes and insufficient subsurface sewer systems. However, many of these studies have concentrated on the city-level changes causing increased flood events. Hence, this paper explores the impact of temporal changes in Land Use Land Cover (LULC) at the geographic unit (meso) level on the flood response for inland cities. LULC changes for two watersheds, Ulsoor and Bellandur in Bengaluru, are assessed using Remote Sensing data and GIS, and flood metrics are calculated using the Hydrologic Engineering Center Hydrologic Modeling System (HEC HMS) between the years 2003 and 2021. Changes in LULC classes are correlated with the changes in flood metrics. Results indicate that the LULC changes were similar in the two watersheds but with varying rates of change. While changes in built-up areas impacted flood metrics in the Ulsoor watershed, vegetation and open land changes impacted the Bellandur watershed.

Climate change and the inherent instability of cities create a significant threat to urban systems, and the impacts of climate change may worsen existing vulnerabilities in cities. In this study, we propose spatial solutions to enhance resilience in Korean cities facing climate change and minimize damages from natural disasters. To achieve this, we developed a vulnerability analysis model considering inadequate facilities and public-space utilization. We analyzed climate vulnerability under future scenarios, focusing on heat waves, urban flooding, and property damage in Korea, which are increasing in occurrence. By incorporating various climate-change-induced natural disaster factors into climate exposure variables, we identified current vulnerabilities within the context of future scenarios, allowing for the utilization of sensors and optimal monitoring locations for early preparedness. Future research could further establish criteria for designating natural disaster prevention districts and regulating development activities within such zones, as well as determining the best locations for heat wave and flood disaster prevention sensors. [ABSTRACT FROM AUTHOR]

The present study is aimed to perform flood susceptibility mapping in the rapidly developing Gurugram district of India. Major flood-triggering factors, namely, slope, elevation, land use land cover, precipitation, drainage density, and soil texture, were selected, and their relative weights were assessed using the Analytical Hierarchy Process method through an expert survey. The highest weightage was attributed to precipitation (34%), while the least (7%) was assigned to elevation and soil texture each. Flood hazard maps were developed using overlay analysis in the Geographic Information System (GIS) environment to provide a quantitative assessment of flood vulnerability covering the time periods of 1985–1994, 1995–2004, 2005–2014, 2015–2019, and 2020–2021. A surface runoff estimation was done using the GIS-aided Natural Resource Conservation Service-Curve Number method for the past thirty-seven years (1985–2021) to explore how land use alterations have impacted surface runoff. The results from flood hazard maps and surface runoff estimation together revealed that the northern part of the district is the most likely to experience flooding. This was in concurrence with the locations that were inundated in the recent past. To the best of our knowledge, no current studies have used these two approaches together to detect flood-prone areas. The developed methodology can be easily employed in regions with limited data for urban drainage planning and undertaking disaster management measures by prioritising areas depending on the likelihood of flood occurrence. The inclusion of more flood-triggering factors and encouraging micro-level studies can be done as part of future research. [ABSTRACT FROM AUTHOR]

Climate change and natural disasters caused by hydrological, meteorological, and climatic phenomena have a significant impact on cities. Russia, a continental country with a vast territory of complex geographic–ecological environments and highly variable climatic conditions, is subject to substantial and frequent natural disasters. On 29 June 2019, an extreme precipitation event occurred in the city of Tulun in the Irkutsk oblast, Russian Federation, which caused flooding due to the increase in the water level of the Iya River that passes through the city, leaving many infrastructures destroyed and thousands of people affected. This study aims to determine the flooded areas in the city of Tulun based on two change detection methods: Radiometric Rotation Controlled by No-change Axis (RCNA) and Ratioing, using Sentinel 2 images obtained before the event (19 June 2019) and during the flood peak (29 June 2019). The results obtained by the two methodologies were compared through cross-classification, and a 98% similarity was found in the classification of the areas. The study was validated based on photointerpretation of Google Earth images. The methodology presented proved to be useful for the automatic precession of flooded areas in a straightforward, but rigorous, manner. This allows stakeholders to efficiently manage areas that are buffeted by flooding episodes. [ABSTRACT FROM AUTHOR]

Abstract Global climate change and sea level rise have led to increased losses from flooding. Accurate prediction of floods is essential to mitigating flood losses in coastal cities. Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity. In this study, we proposed a hybrid modeling approach for rapid prediction of urban floods, coupling the physically based model with the light gradient boosting machine (LightGBM) model. A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model (PCSWMM). The variables related to rainfall, tide level, and the location of flood points were used as the input for the LightGBM model. To improve the prediction accuracy, the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation. Taking Haidian Island, Hainan Province, China as a case study, the optimum values of the learning rate, number of estimators, and number of leaves of the LightGBM model are 0.11, 450, and 12, respectively. The Nash-Sutcliffe efficiency coefficient (NSE) of the LightGBM model on the test set is 0.9896, indicating that the LightGBM model has reliable predictions and outperforms random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN). From the LightGBM model, the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area. The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.

Urban floods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban floods are frequently observed in many parts of the world. Flood events in Chennai city are a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover are focused in the present study. The present study aims at mapping flooded region using Sentinel 1A datasets over Adyar watershed. Series of Sentinel 1A image is collected before, during and after floods for mapping the extent of flood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of flooded extent from the SAR datasets. Analysis performed over the Adyar watershed provided promising results in the extraction of flooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of flooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of flooded pixels. [ABSTRACT FROM AUTHOR]

Rapid urbanization has altered the regional hydrological processes, posing a great challenge to the sustainable development of cities. The TVGM-USWM model, a new urban hydrological model considering the nonlinear rainfall-runoff relationship and the flow routing in an urban drainage system, was developed in this study. We employed this model in the Huangtaiqiao drainage basin of Jinan City, China, and examined the impact of land cover changes due to urbanization on rainfall-runoff processes. Two urbanization scenarios were set up in the TVGM-USWM model during the design rainfall events with different return periods. Results showed that (1) the TVGM-USWM model demonstrated good applicability in the study area, and the RNS values of the flood events are all greater than 0.75 in both calibration and validation periods; (2) the proportion of impervious areas increased from 44.65% in 1990 to 71.00% in 2020, and urbanization played a leading role in the process of land cover change and manifested itself as a circular extensional expansion; and (3) urbanization showed a significant amplifying effect on the design flood processes, particularly for relatively big floods with small frequency, and the impact of urbanization on the time-to-peak of the design flood gradually decreased as the frequency of the design rainfall decreased. The results of this study can provide technical support for flood mitigation and the construction of a sponge city in Jinan City. [ABSTRACT FROM AUTHOR]

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Technological evolution survey allowed the broad use of 2D mathematical models for flood simulation. However, it is possible that the answer required for a given problem does not need a 2D approximation or even does not configure a 2D surface solution. Urban flood simulations may fall in this second case, since urban structures may interact with flood flows and introduce discontinuities in the surface solution. This study aims to highlight the discussion about the physical interpretation and the modeller role as key elements in the interpretation and representation of physical systems. To support this proposal, the MODCEL, a Quasi-2D flow-cell model, was used in two different ways: in a detailed raster approach, similarly to usual 2D model uses; and in an alternative conceptual and interpretive way, using larger cells representing homogeneous portions of the territory. The modelling results showed that equivalent responses can be obtained. Although other models can be used and could offer different absolute results, the relative analysis offered sufficient support to the research hypothesis that a physical-based process conducted by a conscient modeller is crucial for model reliability and optimization. HIGHLIGHTS Interpreting the physical reality and recognizing model simplifications are key points to guarantee simulation quality.; How much of a detailed model is really needed to produce proper answers?; The use of Quasi-2D flow models built in a conceptual and interpretive way can be an alternative to the use of more complex and data demanding 2D models.;

One of the most common types of natural disaster, floods can happen anywhere on Earth, except in the polar regions. The severity of the damage caused by flooding can be reduced by putting proper management and protocols into place. Using remote sensing and a geospatial methodology, this study attempts to identify flood-vulnerable areas of the central district of Duhok, Iraq. The analytical hierarchy process (AHP) technique was used to give relative weights to 12 contributing parameters, including elevation, slope, distance from the river, rainfall, land use land cover, soil, lithology, topographic roughness index, topographic wetness index, aspect, the sediment transport index, and the stream power index in order to calculate the Flood Hazard Index (FHI). The relative importance of each criterion was revealed by a sensitivity analysis of the parameter values. This research developed a final flood susceptibility map and identified high-susceptible zones. This was classified anywhere from very low to very high classifications for its potential flood hazard. The generated map indicates that 44.72 km2 of the total land area of the study area in Duhok city has a very high susceptibility to flooding, and that these areas require significant attention from government authorities in order to reduce flood vulnerability. [ABSTRACT FROM AUTHOR]

Simulated free surface transients in periodic urban layouts have been reported to be self-similar in the space-time domain when averaged on the scale of the building period. Such self-similarity is incompatible with the head loss model formulae used in most porosity-based shallow water models. Verifying it experimentally is thus of salient importance. New dam-break flow laboratory experiments are reported, where two different configurations of idealized periodic buildings layouts are explored. A space-time analysis of the experimental water level fields validates the self-similar character of the flow. Simulating the experiment using the two-dimensional shallow water model also yields self-similar period-averaged flow solutions. Then, the Single Porosity (SP), Integral Porosity (IP) and Dual Integral Porosity (DIP) models are applied. Although all three models behave in a similar fashion when the storage and connectivity porosities are close to each other, the DIP model is the one that upscales best the refined 2D solution. [ABSTRACT FROM AUTHOR]

Global climate change and sea level rise have led to increased losses from flooding. Accurate prediction of floods is essential to mitigating flood losses in coastal cities. Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity. In this study, we proposed a hybrid modeling approach for rapid prediction of urban floods, coupling the physically based model with the light gradient boosting machine (LightGBM) model. A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model (PCSWMM). The variables related to rainfall, tide level, and the location of flood points were used as the input for the LightGBM model. To improve the prediction accuracy, the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation. Taking Haidian Island, Hainan Province, China as a case study, the optimum values of the learning rate, number of estimators, and number of leaves of the LightGBM model are 0.11, 450, and 12, respectively. The Nash-Sutcliffe efficiency coefficient (NSE) of the LightGBM model on the test set is 0.9896, indicating that the LightGBM model has reliable predictions and outperforms random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN). From the LightGBM model, the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area. The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency. [ABSTRACT FROM AUTHOR]

The paper makes a case-study analysis of the annually flooded Dibrugarh town of the Brahmaputra valley of India’s northeast, and looks at floods as disasters in the context of how women negotiate, adapt and survive an annual phenomenon of flood. The paper has tried to interweave multiple narratives arising from differentiated gendered roles played by women in the pre-flood, during floods as well as in the post-flood periods, as collected through interviews and discussions to showcase how women use their agency to make place within as well as outside the community through dialogues, shared experiences and inter-community social capital. It discusses gendering space through the changing lived spaces of a household in the flooded and non-flooded period for four different communities united by flood. The first section of the paper looks at the City as a site for recurring disaster. The second section discuss agency of women as an emanating tool to face, adapt and survive disaster, expanding beyond herself to family and community. The third section presents the spatialities of adaptation manifested through built-form, arising as an outcome of gendered vulnerability and adaptation practices, as translated onto the spaces they inhabit, throughout the flood-cycle. [ABSTRACT FROM AUTHOR]

Many urban areas in tropical Southeast Asia, e.g., Bangkok in Thailand, have recently been experiencing unprecedentedly intense flash floods due to climate change. The rapid flood inundation has caused extremely severe damage to urban residents and social infrastructures. In addition, urban Southeast Asia usually has inadequate capacities in drainage systems, complicated land use patterns, and a large vulnerable population in limited urban areas. To reduce the urban flood risk and enhance the resilience of vulnerable urban communities, it has been of essential importance to develop real-time urban flood forecasting systems for flood disaster prevention authorities and the urban public. This paper reviewed the state-of-the-art models of real-time forecasting systems for urban flash floods. The real-time system basically consists of the following subsystems, i.e., rainfall forecasting, drainage system modelling, and inundation area mapping. This paper summarized the recent radar data utilization methods for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. This paper also dealt with available technologies for modelling, e.g., digital surface models (DSMs) for the finer urban terrain of drainage systems. The review indicated that an obstacle to using process-based hydraulic models was the limited computational resources and shorter lead time for real-time forecasting in many urban areas in tropical Southeast Asia. The review further discussed the prospects of data-driven AI models for real-time forecasting systems. [ABSTRACT FROM AUTHOR]

In urban basins, localized torrential rain increases the water level of rivers in an extremely short time, thereby leading to flooding within an hour. Therefore, to achieve early evacuation, the water level should be accurately forecasted. The outflow process in urban areas employs the sewer system to discharge the water back to rivers. However, the data for the sewer system are not freely available, and it requires much work and time to design a physical model based on such data. Thus, a vector autoregressive model to develop a water level forecast system that uses observed rainfall and water level is being used. Additionally, this model was used to ensure information conducive to evacuation approximately 20 min in advance and to assess its forecast accuracy, despite the very limited data—water levels at one point and average rainfall at another—without the need to build a physical model such as that which is used in sewer pipe calculations. Compared to the observed water level, the calculated water level increased faster; and thus, the forecast leaned toward safety in evacuation. Furthermore, the data from past five torrential rainfall events to achieve a stable forecast; this method can be applied to basins with limited observation data. Therefore, these results indicate that this method can be applied as a water level forecast method for basins with an extremely fast flood arrival time. [ABSTRACT FROM AUTHOR]