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Urban areas continue to be affected by flooding, necessitating more sustainable and effective adaptation strategies and mitigation initiatives. This study investigates the potential flood reduction capability achieved through implementing various green infrastructures known as low-impact development (LID). The Bendung watershed, in the center of Palembang City, with a total area of 18.37 km2, is used as the study area to evaluate the performance of LID infrastructure in reducing flood parameters, including total runoff volume, peak runoff discharge, runoff coefficient, and flooding area. Five types of LID infrastructure were simulated, namely bio-retention cells, rain gardens, permeable pavements, rain barrels, and recharge wells. The flood simulations were performed using four design storms with 2-, 5-, 10-, and 25-year return periods. Hydrologic and hydraulic modeling and simulations were carried out using PCSWMM Professional 2D, and the results were integrated with ArcMap to map the flood inundation. The results of this study demonstrate that with only 9.81 percent of the area occupied by LIDs, a flood reduction of more than 30% can be achieved. In addition, implementing LIDs can help restore the watershed's hydrological condition to its natural state, as indicated by the decrease in the runoff coefficient. Thus, implementing LIDs in a sustainable urban drainage system must be widely promoted in many urban areas, especially in developed countries like Indonesia. This study can be used as a reference for the local government and authorities to create policies and regulations to establish sustainable flood mitigation measures in Palembang City. [ABSTRACT FROM AUTHOR]

Abstract Floods are recurrent global catastrophes causing substantial disruptions to human life, extensive land degradation, and economic losses. This study aims to identify flood-triggering watershed features and employ a Multi-Criteria Decision-Making (MCDM) approach based on the Analytical Hierarchy Process (AHP) model to delineate flood-prone zones. Weights for various flood-influencing factors (slope, rainfall, drainage density, land-use/land-cover, geology, elevation, and soil) were derived using a 7 × 7 AHP decision matrix, reflecting their relative importance. A Consistency Ratio (CR) of 0.089 (within acceptable limits) confirms the validity of the assigned weights. The analysis identified approximately 128.51 km2 as highly vulnerable to flooding, particularly encompassing the entire stretch of riverbanks within the watershed. Historically, snow avalanches and flash floods have been the primary water-related disasters in the region, posing significant threats to critical infrastructure. In this context, this model-based approach facilitates the proactive identification of susceptible areas, thereby promoting improved flood risk mitigation and response strategies.

Occurrence of natural disaster can never be prevented, and floods are one among the major natural disaster that affects the human life and economy of the country. Considering the global loss due to floods, various government and non-governmental organisations are focusing on minimising the losses and provide emergency measures during floods. Adopting the recent technologies integrating various datasets will assist in providing response strategies before and after disaster. Flood are mostly based on the climatic conditions and environmental factors and the present review focuses on the reviewing the various remote sensing methodologies that are used in mapping and analysing floods. The review carried out examines various remote sensing methodologies adopting multispectral, light detection and ranging and radar datasets for mapping and predicting floods. The review identified the limitations in flood prediction, risk assessment and hazard analysis and suggests a framework that can be adopted for effective mapping of flood extent and in suggesting the regions that the rescue team should focus during a disaster event. [ABSTRACT FROM AUTHOR]

This paper proposes a flood vulnerability index utilizing publicly available data that can be applied to different spatial scales. The index enables the development of an appropriate flood risk management strategy, tailored to those factors of vulnerability that have the greatest impact on the overall flood vulnerability of a given place. Currently, following a shift in the approach to flood protection, vulnerability has become a critical element of disaster mitigation. This index is an important contribution to the existing body of research as it can be applied to flood risk management strategies overcoming the ambiguity of definitions of vulnerability and the dynamic nature of temporal and spatial scales of analysis. The proposed flood vulnerability index is a multifaceted approach, encompassing physical, social, economic, environmental, and institutional attributes. In order to create the index, the literature on flood vulnerability was reviewed, including definitions, research methods, as well as the factors and constituents that are major components of vulnerability. The proposed indicator can be used by policy-makers responsible for flood risk management, especially in EU countries, in order to minimize the negative consequences of floods. Compared to other methods used (i.e., disaster loss estimation or modeling), the flood vulnerability index is easy to use when comparing different areas. [ABSTRACT FROM AUTHOR]

Flood mitigation behavior is essential for effective flood risk management, particularly in Australia, where the federal government has increasingly emphasized individual responsibility for preventing and preparing for flood disasters. Despite this emphasis, around 60% of flood-prone residents hesitate to adopt private mitigation measures. Their reluctance highlights the complexities of decision making regarding the implementation mitigation measures, emphasizing the need for a comprehensive understanding of flood mitigation behavior among Australian residents. In order to address this knowledge gap, we conducted household surveys in flood-prone regions of Australia and used six machine learning algorithms—logistic regression, k -nearest neighbor, support vector machine, random forest, extreme gradient boosting, and artificial neural network—to analyze the proposed framework for flood mitigation behavior. The results of five-fold cross-validation demonstrated that the random forest algorithm provided superior accuracy for predicting protection motivation compared to the other five algorithms. Furthermore, seven of the 37 predictors utilized in the model had a higher impact on protection motivation than the other predictors. These influential predictors included self-efficacy, response efficacy, response costs, key variables within the coping appraisal factor, fear or worry about future flood risk, one variable associated with the threat appraisal factor, past experiences of flooding inside the house linked to the prior flood experience factor, and the implementation of flood mitigation measures by individuals connected to the householder, such as family, friends, and neighbors, which pertains to the social environment factor within the proposed framework for flood mitigation behavior. These predictors were deemed adequate to be used as input variables in the random forest model, because they provided accuracy and performance similar to when all 37 predictors were included. These insights into influential predictors can be used to develop an optimal prediction model for protection motivation in flood-prone areas of Australia and reduce the effort needed to collect the required data during postdisaster surveys. This research is of value to policymakers and floodplain managers in designing effective flood mitigation strategies and promoting the adoption of protective measures in flood-prone communities to reduce flood risk. [ABSTRACT FROM AUTHOR]

Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art risk modeling framework. It uses openly available data to rapidly compute flood inundation footprints of historic and forecasted events for the estimation of associated impacts. For the example of Pakistan, we use this flood model to compute flood depths and extents and employ it to estimate population displacement due to floods. Comparing flood extents to satellite data reveals that incorporating estimated flood protection standards does not necessarily improve the flood footprint computed by the model. We further show that, after calibrating the vulnerability of the impact model to a single event, the estimated displacement caused by past floods is in good agreement with disaster reports. Finally, we demonstrate that this calibrated model is suited for probabilistic impact-based forecasting. [ABSTRACT FROM AUTHOR]

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km−2, 0.13 km−2, 0.25 km−2, 0.30 km−2, and 0.28 km−2 and drainage densities of 0.63 km−1, 0.16 km−1, 0.29 km−1, 0.35 km−1, and 0.33 km−1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning. [ABSTRACT FROM AUTHOR]

High-energy events such as tsunamis pose significant threats to coastal infrastructure and buildings, necessitating effective mitigation strategies to minimize damage. Compared to massive construction measures, buffer blocks are increasingly recognized as a potential solution for reducing the impact of such events. Understanding their effectiveness and optimizing their placement configurations is crucial for enhancing coastal resilience. The present study aims to experimentally evaluate the influence of buffer blocks on the depth of tsunami inundation. By comparing different configurations of buffer blocks, this study seeks to identify the most effective one for reducing the inundating flow depth. The laboratory tests revealed that the buffer blocks significantly influence flow depth and its characteristics. For ratios of flow depth to block height (R) below 1.5, the buffer blocks exhibited a delay in the arrival of the flow and effectively reduced the flow depths. However, for R values greater than 1.5, the effectiveness reduced as the flow began to overtop the obstructions. The increase in the flow depth at the buffer blocks was the least for a single-row configuration, while the multiple-row configurations with wider spacing offered higher flow resistance and increased the flow depths in front of the blocks. These findings suggest that strategically configured buffer blocks can substantially enhance coastal protection against high-energy flow events during natural coastal hazards. This study provides critical insights into the design and optimization of buffer block configurations, contributing to improved coastal infrastructure resilience and disaster mitigation strategies. [ABSTRACT FROM AUTHOR]

A disaster community's priority is to ensure the safety of populations during natural hazards. Disaster risk reduction strategies require knowledge of what is hazardous, who is vulnerable, and what is risky. As of now, engineering methods predominate in Russian disaster research, while social science rarely analyzes floods, earthquakes, wildfires, etc. Study aims to test the applicability of new methods for delineating flood hazards, characterizing social vulnerability, and determining overall societal risk in Russia. Multidisciplinary nature makes the problem complex. The research will concentrate on the ten representative counties of the Kuban River basin in a southern Russian region, where at the beginning of the XXI century catastrophic floods led to enormous fatalities and huge economic damage. Flood hazards are assessed based on the county-scale spatial distribution of records produced by a Regional Early Warning System, impact information, and flood management infrastructure. Social vulnerability is investigated using a County Comparable Social Vulnerability Profiling model that encompasses three pillars (physical, socio-economic and awareness), nine themes (age, disability, poverty, etc.), and seventeen census variables (aged 0–19 years, density, one-person household in detached housing, only nine years of school, etc.). Finally, a holistic flood risk map is compiled. The results show that the three riskiest counties require measures to reduce flood hazard and social vulnerability in all phases of disaster risk management (mitigation, preparation, emergency, and rehabilitation). Data-poor nations such as Russia can benefit from these methods, but their use is limited by insufficient flood hazard and census information. [ABSTRACT FROM AUTHOR]

The ability to accurately map tropical wetland dynamics can significantly contribute to a number of areas, including food and water security, protection and enhancement of ecosystems, flood hazard management, and our understanding of natural greenhouse gas emissions. Yet currently, there is not a tractable solution for mapping tropical forested wetlands at high spatial and temporal resolutions at a regional scale. This means that we lack accurate and up-to-date information about some of the world's most significant wetlands, including the Amazon Basin. RadWet-L is an automated machine-learning classification technique for the mapping of both inundated forests and open water using ALOS ScanSAR data. We applied and validated RadWet-L for the Amazon Basin. The proposed method is computationally light and transferable across the range of landscape types in the Amazon Basin allowing, for the first time, regional inundation maps to be produced every 42 days at 50 m resolution over the period 2019–2023. Time series estimates of inundation extent from RadWet-L were significantly correlated with NASA-GFZ GRACE-FO water thickness (Pearson's r = 0.96, p < 0.01), USDA G-REALM lake hight (Pearson's r between 0.63 and 0.91, p < 0.01), and in situ river stage measurements (Pearson's r between 0.78 and 0.94, p < 0.01). Additionally, we conducted an evaluation of 11,162 points against the input ScanSAR data revealing spatial and temporal consistency in the approach (F1 score = 0.97). Serial classifications of ALOS-2 PALSAR-2 ScanSAR data by RadWet-L can provide unique insights into the spatio-temporal inundation dynamics within the Amazon Basin. Understanding these dynamics can inform policy in the sustainable use of these wetlands, as well as the impacts of inundation dynamics on biodiversity and greenhouse gas budgets. [ABSTRACT FROM AUTHOR]

Floods are recurrent global catastrophes causing substantial disruptions to human life, extensive land degradation, and economic losses. This study aims to identify flood-triggering watershed features and employ a Multi-Criteria Decision-Making (MCDM) approach based on the Analytical Hierarchy Process (AHP) model to delineate flood-prone zones. Weights for various flood-influencing factors (slope, rainfall, drainage density, land-use/land-cover, geology, elevation, and soil) were derived using a 7 × 7 AHP decision matrix, reflecting their relative importance. A Consistency Ratio (CR) of 0.089 (within acceptable limits) confirms the validity of the assigned weights. The analysis identified approximately 128.51 km2 as highly vulnerable to flooding, particularly encompassing the entire stretch of riverbanks within the watershed. Historically, snow avalanches and flash floods have been the primary water-related disasters in the region, posing significant threats to critical infrastructure. In this context, this model-based approach facilitates the proactive identification of susceptible areas, thereby promoting improved flood risk mitigation and response strategies. [ABSTRACT FROM AUTHOR]

The impacts of climate change manifest heterogeneously across regions, and in Khovd City, a semi-arid area in Western Mongolia, the escalating threat of flooding is evident through the occurrence of 10 flash floods in the last 30 years. The risk zone, encompassing rivers and flash floods, endangers ca. 32,000 residents, with 750–1,800 traditional nomadic dwellings (ger s) located on the floodplain of the Buyant River during summer. There is a risk of flash floods in the eastern part of Khovd City from the mountains, while the western part is at a higher risk of flooding from the Buyant River. This paper aims at assessing flood hazards through a detailed topographical survey conducted using an Unmanned Aerial Vehicle (UAV). 15,206 aerial photos were collected in Khovd City using the UAV and measured by Real-Time Kinematic (RTK) on 22 Ground Control Points (GCPs). A Digital Elevation Model (DEM) with a resolution of 2.7 meters was generated from the aerial drone mapping data, enabling surface morphology, hydrological and eight-direction pour point model analysis using a Geographic Information System (GIS). The resulting flood hazard map revealed 4 flood risk areas based on flood flow direction and topographical features. Recommendations for local govern and residents include enhancing flood protection facilities for flood disaster prevention on flood risk zones. [ABSTRACT FROM AUTHOR]

Floods have devastating consequences for human life and economies worldwide. The present study focuses on flash flooding in the Himalayan environment, specifically in Dharamshala, Himachal Pradesh, India. In July 2021, a low-pressure system developed in the area, intensifying into a deep depression and resulting in extreme rainfall on July 12, 2021. This extreme rainfall event resulted flash flooding, landslides, and extensive damage in Dharamshala. Utilizing satellite observation and geospatial technology, this study examined the heavy rainfall events and the vulnerability of the geology of Dharamshala to flash flooding. The area received a cumulative rainfall of more than 370 mm, with over 35 mm recorded at 09:00 Universal Time Coordinated (UTC). The steep slopes of the region, combined with sudden heavy downpours from multiple extreme rainfall events, contributed to flash flooding and landslides. The consequences were severe, resulting in the loss of seven lives and impacting thousands of people in the study area. The research findings emphasize the significance of satellite-borne remote sensing applications and geospatial technology for real-time short-lived localised rainfall events in the study area. The study also highlights the role of Bhagsu Nala in exacerbating flood risks. This research provides a valuable model for implementing effective disaster mitigation measures. Moreover, it contributes to global efforts to reduce the risk of natural catastrophes, particularly in Himachal Pradesh. The findings offer valuable insights into disaster management and mitigation strategies. [ABSTRACT FROM AUTHOR]

Flood hazard mapping is currently in a transitional phase involving the use of data and methods that were traditionally in the domain of local studies in a regional or nationwide context. Challenges include the representation of local information such as hydrological particularities and small hydraulic structures, as well as computational and labour costs. This paper proposes a methodology of flood hazard mapping that merges the best of the two worlds (local and regional studies) based on experiences in Austria. The analysis steps include (a) quality control and correction of river network and catchment boundary data; (b) estimation of flood discharge peaks and volumes on the entire river network; (c) creation of a digital elevation model (DEM) that is consistent with all relevant flood information, including riverbed geometry; and (d) simulation of inundation patterns and velocities associated with a consistent flood return period across the entire river network. In each step, automatic methods are combined with manual interventions in order to maximise the efficiency and at the same time ensure estimation accuracy similar to that of local studies. The accuracy of the estimates is evaluated in each step. The study uses flood discharge records from 781 stations to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38 000 km. It is argued that a combined local–regional methodology will advance flood mapping, making it even more useful in nationwide or global contexts. [ABSTRACT FROM AUTHOR]

Many communities along rivers in the Northwest Territories do not have water-level gauges, making flood hazard analyses difficult at these sites. These include the communities of Jean Marie River, Tulita and Fort Good Hope on the Mackenzie River, Nahanni Butte on the Liard River and Fort McPherson on the Peel River. However, gauges do exist at other sites upstream and downstream of these communities, from which flood hazard assessments can be extrapolated to the ungauged communities. Reach-based extrapolation becomes particularly challenging when analysing ice-jam flood hazards since data sparsity is an additional challenge at these locations. A simple empirical approach using non-dimensional stage and discharge was implemented, which allowed only a minimum of the required data from all sites to be extracted. From the gauged sites, water-surface elevations and slopes from digital elevation models, channel widths, thalweg elevations and ice thicknesses from under-ice flow measurement surveys and recorded water levels were obtained. As a test case, results from the gauged reach of Fort Simpson were extrapolated to the ungauged reach of Jean Marie River and are presented in this technical note. [ABSTRACT FROM AUTHOR]

Starting on 2 November 2023, some territories in the Region of Tuscany in Italy were hit by exceptionally intense meteorological and calamitous events. The Region of Tuscany's government was immediately at the forefront of relief, assisting the population, and aiding restoration. On 3 November, a national state of emergency was declared due to flooding. Journalistic communication is an essential aspect of disaster mitigation, preparedness, response and recovery. In terms of protecting people and reducing damage, journalists and the media have an important role to play. This article reports an Italian case study analysing the behaviour of local media in cases of natural disaster. Nine focus groups were conducted with local journalists covering the flood emergency. The results highlight the role of social and institutional mediation, rather than mere dissemination, played by the local press in emergency situations, a central element in the construction of a community bond, precisely in moments of insecurity and disorientation. The narration of a disaster from the inside seems to have allowed the emergence and representation of hitherto unknown social realities in Tuscany. The goal of the news coverage was widened, making it possible to respond better to the diverse interests of the reading public and to satisfy in a short time and exhaustively the information needs of individual communities in difficulty. [ABSTRACT FROM AUTHOR]

Flood mitigation efforts (flood fighting), including raising awareness among local residents, have been crucial in Japan to reduce damage during flood events. In this study, interviews were conducted with three flood fighting management bodies on the Yodo and Yamato Rivers, which still have strong flood fighting corps, and field investigations were performed focusing on the actual operation of flood fighting warehouses, which serve as infrastructure bases for flood fighting. Targets included the flood fighting affairs associations on the left and right banks for the Yodo River and that on the right bank for the Yamato River. The survey results showed that with the progress of flood control measures and river improvement, opportunities to conduct actual flood fighting work during floods decreased in all cases in recent years, but flood fighting techniques and knowledge were passed on through exercises and drills. In addition, the flood fighting warehouses were well managed and maintained their functions as material and equipment storage places and as waiting and meeting places for flood fighting corps members. Considering the intensification of external flood forces due to future extreme weather changes, it was deemed that the role of the flood fighting corps is expected to increase in importance, and it was judged that preparations were being made to respond to this trend. On the other hand, there were some challenges in terms of securing a sufficient number of members due to the shortage resulting from the aging of the corps members and maintaining warehouses from a budgetary standpoint. [ABSTRACT FROM AUTHOR]

Wonogiri Regency, located in Indonesia, is an area susceptible to various types of natural disasters, such as floods, landslides, and hurricanes, which pose a significant risk to approximately 90% in this area. Therefore, this study aimed to identify the types of potential disasters in Wonogiri and propose effective strategies for disaster mitigation from 2021 to 2025. This study uses a risk matrix to quantify the risks associated with these potential disasters and utilizes House of Risk (HoR) phase 2 methodology to formulate strategies for disaster risk prevention. This comprehensive analysis has shown seven potential disasters for which nine mitigation strategies have been developed. Among these, the top three strategies, considering the effectiveness value and the degree of difficulty (EDRk) value, in order of priority, are “Increasing multi-stakeholder partnerships in disaster management” (EDRk value 176.20); “Strengthening the legal framework for disaster management” (EDRk value 167.40); and “Conducting socialization and education on disaster mitigation” (EDRk value 111.60). Implementing these strategies is expected to strengthen disaster risk reduction (DRR) in regencies, with a focus on prioritizing the most effective measures. [ABSTRACT FROM AUTHOR]

The analysis of the influencing factors of flash floods, one of the most destructive natural disasters, is the basis of scientific disaster prevention and mitigation. There is little research considering the influence of tropical cyclones (TCs) and water conservancy projects on flash floods, which cannot be ignored in the island areas where flash floods often occur due to the complex influence of various factors. In this study, under the pressure-state-response framework (PSR framework), the factors affecting the distribution of flash floods on Hainan Island, China, from 1970 to 2010 were quantitatively analyzed by using the geographical detector method. By dividing the time period, give full play to the advantages of the PSR framework and show the evolution process of various factors. Different from inland areas, extreme precipitation and tropical cyclones play a major role in the spatial distribution of flash floods on Hainan Island, China, and the driving force of tropical cyclones is 1.1 times that of extreme precipitation on average. Medium-sized reservoirs play the greatest role in the prevention of flash floods on Hainan Island, and their driving forces reach 0.38 times of extreme precipitation on average, followed by large-sized reservoirs and small-sized reservoirs. Large-sized reservoirs are limited in quantity and have limited effectiveness in preventing flash floods on Hainan Island. Therefore, in the forecasting and risk management of flash flood in the island area, more attention should be paid to the impact of extreme precipitation and TCs, and the role of medium-sized reservoir should be fully exerted. [ABSTRACT FROM AUTHOR]

Flood disasters are destructive especially in prosperous and urbanized estuarine regions, where the flood regime is much more complex due to multiple fluvial–estuarine impacts. The Shenzhen River (SZR), located in one of the most prosperous regions of southern China, is vulnerable to increasing flood risk. Unravelling the influential factors is of particular significance to flood hazards prevention and urban safety for the SZR. Based on the field-measured floods on June 13, 2008 ("2008.06") and August 29, 2018 ("2018.08") with roughly equal magnitudes of rainfall and tide, the changing flood risk in the SZR basin was assessed. Considering the substantial development of tidal flat plants in the past two decades, a physical model of the SZR was built to quantify the impacts of changing river regime on the flood stage. The model covers the whole mainstream of tidal reach and half of the Shenzhen Bay (SZB), which was well calibrated and validated by in situ flow process. Several situations with different ranges of riverine vegetation and estuarine mangrove, including all vegetation (actual situation), half vegetation, no vegetation, mangrove in 2002 and 2018, were modelled to explore flood stage variations in 2-yr and 50-yr return period. The results found that the "2018.08" flood stage was about 1.4 m higher than "2008.06" flood. Moreover, the rainfall–runoff duration in "2018.08" was significantly decreased by 1 h less than that of "2008.06" flood, indicating increased flood risk in the SZR. The flood stage in the middle reach increases by more than 0.6 m driven by the riverine vegetation during the 50-yr return period flood, while the flood stage rises less than 0.1 m for the flood with 2-yr return period. Moreover, the extended estuarine mangrove forest resulted in about 0.2 m flood stage increment in the lower reach. The effects of sea level rise and sediment deposition after channel dredging on the flood risk in the SZR were further discussed. The effects of sea level rise and sediment deposition after channel dredging on flood risk were further discussed in the SZR. Channel infilling probably causes a flood stage increase of approximately 0.5 m for all reaches, while the influence of sea level is relatively slight but cumulative. Sufficient river management and planning, such as seasonal removal of riverine vegetation, mangrove management and regular topography surveys, should be taken into consideration in the near future. [ABSTRACT FROM AUTHOR]

There is a need to acquire the data description of science teachers regarding literacy mitigation in Central Java. However, there is no specific data that informs these conditions. Therefore, the research aims to identify the literacy of flood disaster mitigation among science teachers in the Central Java province. Several 125 science teachers from 6 districts/cities in Central Java were involved as respondents. The flood and tidal disaster literacy instrument consists of 17 items divided into 2 parts. The first part concerns the teacher’s knowledge of floods and tidal waves. The second part contains the teacher’s insights about mitigating flood and tidal disasters. The findings show that the average teacher’s knowledge about floods and tidal waves is good (8.4 of 10). Meanwhile, the average knowledge of science teachers regarding literacy of flood and tidal disaster mitigation is in the sufficient category (11.4 of 18). This fact shows that science teachers’ flood and disaster mitigation literacy needs to be improved. Instilling disaster mitigation literacy, especially floods and tidal waves, can be done by implementing disaster mitigation education in science learning through research and community service. [ABSTRACT FROM AUTHOR]

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Snowmelt floods are highly hazardous meteorological disasters that can potentially threaten human lives and property. Hence, snowmelt susceptibility mapping (SSM) plays an important role in flood prevention systems and aids emergency responders and flood risk managers. In this paper, a method of identifying snowmelt flood hazards is proposed, and a large‐scale snowmelt flood hazard zonation scheme based on historical recordings and multisource remote sensing data is established. To assess the quality of our approach, the proposed model was tested in the cold and arid region of Xinjiang, China. Overall, 140 historical snowmelt flood events and 27 explanatory factors were selected to construct a geospatial dataset for SSM of the contemporary period. GridSearchCV was used to comprehensively search the candidate parameters from the grid of given parameters obtained with the random forest (RF) algorithm. Then, the geospatial dataset was divided into two subsets: 70% for training and 30% for testing. Next, SSM results were obtained with the RF algorithm using optimized parameters. The results indicate that our optimized RF classifier performs well for the task of SSM, with a high AUC value (0.975) for the test dataset. The validation and analysis suggest that the proposed method can efficiently identify snowmelt flood hazards in undersampled arid areas at a regional scale. [ABSTRACT FROM AUTHOR]

Leaky barriers have become an important mitigation option within natural flood management to reduce downstream flood risk. Modelling is a key tool to aid in the design of leaky barrier installations for flood mitigation, but there is limited evidence about the accuracy of model representations of the system. Here, the hydrological model SWAT was combined with a water routing model that simulates multiple leaky barriers as permeable sluice gates. Storage behind individual barriers was quantified using barrier dimensions and LIDAR topography. The model was applied to a series of 27 leaky barriers installed as part of a long‐term manipulation experiment into a 11‐km2 intensive lowland agricultural catchment in Leicestershire, England. Evaluation of the model against flow data collected before and after leaky barrier installation and time‐lapse photography taken across storm events at individual barriers demonstrated robust model performance (Nash‐Sutcliffe efficiency and R2 across 19 validation events were 0.84 ± 0.14 and 0.91 ± 0.08, respectively). Empirical and modelling data were then combined to demonstrate that the 17,700 m3 of water storage provided by the 27 leaky barriers reduced peak flows at the catchment outlet by 22 ± 6% and delayed the peak in flow by up to 5 h for 11 storm events recorded after all barriers had been installed. The volume of storage utilised prior to the flood event was a key factor influencing the reduction in peak flow, and a sensitivity analysis indicated that barriers should be permeable to accelerate drain‐down of the barrier and help to mitigate risk from multiple storm events occurring in sequence. [ABSTRACT FROM AUTHOR]

This research delves into the intricate dynamics of landslides, emphasizing their consequences on transportation infrastructure, specifically highways and roadway bridges in North Carolina. Based on a prior investigation of bridges in Puerto Rico after Hurricane Maria, we found that bridges above water and situated in valleys can be exposed to both landslide and flooding risks. These bridges faced heightened vulnerability to combined landslides and flooding events due to their low depth on the water surface and the potential for raised flood heights due to upstream landslides. Leveraging a dataset spanning more than a century and inclusive of landslide and bridge information, we employed logistic regression (LR) and random forest (RF) models to predict landslide susceptibility in North Carolina. The study considered conditioning factors such as elevation, aspect, slope, rainfall, distance to faults, and distance to rivers, yielding LR and RF models with accuracy rates of 76.3% and 82.7%, respectively. To establish that a bridge's location is at the bottom of a valley, data including landform, slope, and elevation difference near the bridge location were combined to delineate a bridge in a valley. The difference between bridge height and the lowest river elevation is established as an assumed flooding potential (AFP), which is then used to quantify the flooding risk. Compared to traditional flood risk values, the AFP, reported in elevation differences, is more straightforward and helps bridge engineers visualize the flood risk to a bridge. Specifically, a bridge (NCDOT ID: 740002) is found susceptible to both landslide (92%) and flooding (AFT of 6.61 m) risks and has been validated by field investigation, which is currently being retrofitted by North Carolina DOT with slope reinforcements (soil nailing and grouting). This paper is the first report evaluating the multi-hazard issue of bridges in valleys. The resulting high-fidelity risk map for North Carolina can help bridge engineers in proactive maintenance planning. Future endeavors will extend the analysis to incorporate actual flooding risk susceptibility analysis, thus enhancing our understanding of multi-hazard impacts and guiding resilient mitigation strategies for transportation infrastructure. [ABSTRACT FROM AUTHOR]

Seasonal erosion flooding events present a significant challenge for effective disaster monitoring and land degradation studies. This research addresses this challenge by harnessing the combined capabilities of time-series Landsat and MODIS images to achieve high spatiotemporal-resolution mapping of flooding during such events. The study underscores the critical importance of precise flood monitoring for disaster mitigation and informed land management. To overcome the limitations posed by the trade-off between spatial and temporal resolution in current satellite sensors, we emplyedand theflexible spatiotemporal data fusion (FSDAF) methods to produce synthetic flood images with enhanced spatiotemporal resolutions for mapping by using MODIS and Landsat data from August 29 to September 3, 2016. A comparison was made between flood maps from several post-disaster forecasts based on ground-obtained time-series images of the Tumen River flood in China. According to the FSDAF approach, the input Landsat image of March 25, 2016, and the fused results had a root mean square error (RMSE) of 0.0301, average difference of 0.001, r of 0.941, and structure similarity indexof 0.939, indicating that temporal variation data had been effectively incorporated into a forecast on August 16, 2016. Results also indicated that the FSDAF forecast values are lower than those from the actual Landsat image. The results of the study also showed that the generated images could be effectively used for flood mapping. By using our newly developed simulation model, we were able to produce a comprehensive map of the inundated areas during the event from August 29 to September 3, 2016. This shows that FSDAF holds great potential for flood prediction and study and has the potential to benefit further disaster-related land degradation by combining multi-source images to provide high temporal and spatial resolution remote sensing information. [ABSTRACT FROM AUTHOR]

Storm surge is a natural process that generates flood disasters in coastal zone and causes massive casualties and property losses. Therefore, the 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 on 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 simplified shallow water momentum equation. The wetting and drying algorithm was applied to rectangle grid that iterates through 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 that from a widely used hydrodynamic numerical model, respectively. The comparisons demonstrated that HCA-FM can reproduce the observed inundation distributions, and predict consistent results with the numerical simulation in terms of the inundation extent and submerged depth, with much improved computational efficiency (predict inundation within a few minutes) and high stability. The results reflect significant advancement of HCAFM toward efficient predictions of storm surge inundation and applications at large space scales. [ABSTRACT FROM AUTHOR]

River-crossing bridges in seismically active regions are typically susceptible to two natural hazards – earthquakes and floods. For such bridges, design parameters related to piers may play major roles in bridge multi-hazard performance. In this study, the same was explored for key bridge design parameters – the aspect ratio and longitudinal reinforcement ratio of piers and the differential ground elevation between multiple bents. The multi-hazard condition in the southeast of Nepal was considered as the test-bed hazard condition. The regional seismic hazard was represented using a suite of earthquakes generated based on regional seismic design spectra. Due to the flood hazard in the region, expected pier scour of investigated bridges was estimated from 100-year flood discharge (including climate change projection) in the Koshi River, Nepal. Three-dimensional finite-element models of the chosen bridges, with and without scour and with ±10% variations in the stated design parameters, were developed. Fragility and risk curves of the bridges were developed and compared in order to assess the relative influence of the design parameters on bridge performance. It was found that the aspect ratio and longitudinal reinforcement ratio of piers can significantly influence the multi-hazard performance of riverine bridges. The findings also demonstrate how design parameters may be revised to perform risk-targeted multi-hazard design of bridges. [ABSTRACT FROM AUTHOR]

Under the increasing stormwater events and consequent flood hazards, integrated flood management is increasingly becoming the most essential mitigation strategy to maximize the effectiveness of runoff reduction at the limitation of public budget. The research is aimed at proposed a multi-criteria combination approach to determine spatial intervention prioritization of urban flood based on source tracking analysis. First, Jinhua underpass catchment where an underpass is vulnerable to the impact of stormwater is selected as the case study. Second, source tracking analysis, which combining hydrodynamic model with rainfall-tracking model, is used to obtain the detailed dynamic information including water depth and contribution rate of different source areas. On the basis of source tracking analysis, from the perspective of source-process-end runoff volume management, five types of criteria are presented to evaluate the spatial intervention priority of each sub-area, entropy weight method is applied to combine useful information reflected by criteria 1–5 to form a comprehensive criterion identifying spatial priority of flood management. Notably, the results indicate that criteria extracted by source tracking analysis are highly effective in evaluating spatial priority for urban flood management. Specifically, criteria 1–5 estimate the importance in addressing runoff for different sub-areas from the view of source-progress-end, respectively. Moreover, the comprehensive criterion based on entropy weight method, an objective comprehensive evaluation method, can be used to guide the spatial prioritization of urban flood management for planners and decision-makers with respect to multi-perspective. [ABSTRACT FROM AUTHOR]

The extent and impact of floods in the North Bengal plains show spatial variation from the foothill to the southern part of the floodplain. This phenomenon is further modified and accelerated by anthropogenic impacts such as bridge construction. According to the flood hazard rating chart, there is a chance of flooding both upstream and downstream of bridges, even in the far downstream area close to the confluence of the rivers Lish, Gish, and Chel with river Tista. Depth (m), velocity (m/sec) and debris factor are considered for the computation of the flood hazard rating map. This is further justified by the flood susceptibility map and validated by the Area Under the ROC (Receiver Operating Characteristic curve) Curve (AUC curve). The fuzzy Analytic Hierarchy Process (FUZZY AHP) has been used to assign weights to the selected parameters for the flood susceptibility map. Both the results justify the fact that the downstream section of Chel is more affected than its upstream section. Even the extent of the flood is higher along river Chel in comparison with rivers Lish and Gish. River Gish is affected by flooding throughout its upstream and downstream reaches. However, near bridges flood rate increases and then alteration is found. Mostly agricultural lands in lower flood plains, tea gardens in alluvial fan areas and settlements are under the flood coverage area. It discloses the probable factors like distance from the foothill to the bridge, sediment grain size (D50), volume of discharge (cumec) etc. [ABSTRACT FROM AUTHOR]

Fluvial floods are destructive hazards that affect millions of people worldwide each year. Forecasting flood events and their potential impacts therefore is crucial for disaster preparation and mitigation. Modeling flood inundation based on extreme value analysis of river discharges is an alternative to physical models of flood dynamics, which are computationally expensive. We present the implementation of a globally applicable, open-source fluvial flood model within a state-of-the-art natural catastrophe modeling framework. It uses openly available data to rapidly compute flood inundation footprints of historic and forecasted events for the estimation of associated impacts. At the example of Pakistan, we use this flood model to compute flood depths and extents, and employ it to estimate population displacement due to floods. Comparing flood extents to satellite data reveals that incorporating estimated flood protection standards does not necessarily improve the flood footprint computed by the model. We further show that, after calibrating the vulnerability of the impact model to a single event, the estimated displacement caused by past floods is in good agreement with disaster reports. Finally, we demonstrate that this calibrated model is suited for probabilistic impact-based forecasting. [ABSTRACT FROM AUTHOR]

In the present multi-hazard-prone times, the dynamics of vulnerability across time, space, and different hazards emerges as an intriguing but challenging research topic. Within multi-hazards, both the impacts of hazards and the mitigation strategies can augment vulnerabilities, adding layers to the complexity of multi-risk assessments. Delving into these intricacies, this study aims to i) explore the multi-hazard impacts of the co-occurrent powerful river flood events and the COVID-19 pandemic in Romania, taking as reference 2020 and 2021, and ii) to analyse the trajectories in rising vulnerability that result from impacts and adaptation options, as well as their implications. The proposed framework relies on an Impact Chain that was enhanced to include new elements (i.e., augmented vulnerabilities and derived impacts) and links (i.e., connections that describe the augmentation of vulnerability); which were also used to rank the vulnerabilities based on their augmentation. The Impact Chain draws on various data and information sources, including scientific literature, the feedback of first responders, reports, legislative documents, official press releases, and news reports. This research work makes a significant contribution to the field of DRR by broadening the purpose of the Impact Chain, transforming it into a first-hand, semi-qualitative tool for analysing vulnerability dynamics. [ABSTRACT FROM AUTHOR]

This Conference Paper uses descriptive-qualitative research to describe and analyze factors forming community resilience affected by floods. The determination of informants is determined deliberately on flood-affected communities using criteria according to the purpose of the study. Primary data were obtained from in-depth observations and interviews, while secondary data were obtained from library sources and relevant data. The data collection techniques used were observation, interviews, and documentation, while the data were analyzed using descriptive-qualitative analysis through several stages, namely data reduction, data presentation, and conclusions. The results of this study show that factors forming the resilience of the community to flood disasters are: (1) Value factors that have existed in the community for many years in the flooded land, namely mutual assistance; (2) Economic factors, finding alternative jobs or coping strategies by flood-affected communities; (3) Social factors, namely knowledge and skills to adapt to flood disasters through non-formal training and counselling on disaster and disaster mitigation from their experience or obtained through social media and mass media, such as television and radio; (4) Institutional factors, namely socialization of early flood warning, socialization of flood disaster mitigation, appeals for the prohibition of throwing garbage in rivers and essential food assistance before and during the occurrence of flood by the relevant government; and (5) Infrastructure factors that include the construction of facilities and infrastructure such as river dredging, drainage construction, and river cliff protection walls. [ABSTRACT FROM AUTHOR]

Large-scale two-dimensional hydrodynamic modeling at high resolution is still rarely performed because of its high computational cost and the lack of topographical data for some areas. Despite this, such modeling has been performed for the Odra River, the second largest river in Poland. This river has a high potential for flooding, which has been severely experienced many times in history, most recently in 1997 and 2010, when floods caused large losses. Since then, many different types of activities have been executed in order to reduce the risk of flooding. The paper presents a 2D modeling concept created during these activities. Given that the river valley is up to several kilometers wide, and consists of many complex topographical features and hydrotechnical facilities, a cascade of 25 2D models in MIKE21 software was developed. It covers a 600 km long section of the Odra River and an area of 5700 km2 in total. A regular grid resolution of 4–6 m was used in the modeling. The models were applied for numerous purposes, first for the elaboration of flood hazard and flood risk maps for larger cities, and then for the verification of historic flood data and stage–discharge relations at gauge stations, as well as the verification of design discharges via flood routing. Other important uses were the evaluation of the effectiveness of flood mitigating works, including the feasibility study for the Racibórz reservoir, and the assessment of flood hazard due to embankment failure or ice jamming. Selected applications, as well as practical aspects of the model's preparation and use, are presented. [ABSTRACT FROM AUTHOR]

The dam is a crucial river-crossing structure that ensures a sustainable water supply and offers numerous benefits. However, the potential hazard of dam failure is an imminent threat that could materialize unexpectedly. To comprehend the potential impact of dam break flood and identify vulnerable areas, it is essential to conduct rigorous analysis and simulate various dam failure scenarios. This comprehensive assessment is invaluable for informed land use planning and the development of effective emergency response plans. Therefore, this study aimed to analyze flood inundation resulting from the hypothetical failure of Gongseng dam, using Iber model. The modeling approach relied on a two-dimensional finite volume shallow water model, guided by specialized software. The scenarios for Gongseng dam break showed inundation areas of 12.57 km² and 7.55 km² for overtopping and piping failure, respectively. Overtopping failure resulted in the highest discharge, with Von Thun method causing severe damage due to wide break dimensions, and eventually leading to catastrophic consequences. However, this study showed that Froehlich method provided the most rational prediction for break parameters. In contrast to the other methods focusing solely on water height behind dam, Froehlich equation considered both the volume and height at the time of failure. Implementing dam break analysis held the potential to benefit downstream communities by providing inundation maps, thereby aiding in the mitigation of flood risks. Particularly, in situations with limited data and resources, as shown in this study, the cost-effective modeling method proposed could be an attractive option for simulating extreme flood induced by dam break. [ABSTRACT FROM AUTHOR]

Flash floods are sudden flood disasters that can be triggered by several factors, one of which is landslides that occur in the upper watershed. In Bogor Regency, there are 14 sub-districts located in the Upper Cisadane watershed area that are prone to flash flood disasters. This study aimed to determine the social, economic, physical, and environmental vulnerability assessment of the community in the Upper Cisadane watershed area based on the modification assessment from Regulation of The Head of National Disaster Management Authority Number 12 of 2012. This vulnerability assessment is part of disaster risk assessment, which is an approach to show the potential negative impacts of a disaster that occurs in an area. This potential negative impact can be seen in the potential number of lives exposed, property loss, and environmental damage. According to the vulnerability index, the Upper Cisadane watershed has high and very high classes of flash flood vulnerability, with ten (10) sub-districts having high vulnerability index classes, and fourteen (14) districts having very high vulnerability index classes. The level of vulnerability in the sub-districts is influenced by the level of social, physical, and economic vulnerability, which has high to very high classes compared to other sub-districts. The vulnerability index class maps from this study are expected to be used as references for local governments and related parties in regional spatial planning and flash flood disaster mitigation planning. [ABSTRACT FROM AUTHOR]

Flood hazard mapping is currently in a transitional phase involving the use of data and methods that were traditionally the domain of local studies in a regional or nation-wide context. Challenges include the representation of local information such as hydrological particularities and small hydraulic structures, and computational and labour costs. This paper proposes a methodology of flood hazard mapping that merges the best of the two worlds (local and regional studies) based on experiences in Austria. The analysis steps include (a) quality control and correction of river network and catchment boundary data; (b) estimation of flood discharge peaks and volumes on the entire river network; (c) creation of a digital elevation model (DEM) that is consistent with all relevant flood information, including river bed geometry; (d) simulation of inundation patterns and velocities associated with a consistent flood return period along the entire river network. In each step, automatic methods are combined with manual interventions in order to maximise the efficiency and at the same time ensure estimation accuracy similar to that of local studies. The accuracy of the estimates is evaluated in each step. The study uses flood discharge records from 781 stations to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38000 km. It is argued that a combined local-regional methodology will advance flood mapping, making it even more useful in nation-wide or global contexts. [ABSTRACT FROM AUTHOR]

Recent studies show how geomorphic descriptors, retrieved from digital elevation models (DEMs), can be used for flood hazard mapping. As they strictly depend on the accuracy of the input DEMs and reference flood hazard maps used for training, DEM-based flood hazard models may display severe inconsistencies. Our study shows the application of two advanced DEM-based models to a large study area, and presents two main innovative points. First, the delicate tasks of appropriately selecting the input DEM and flood hazard map are specifically addressed with newly defined methods. Second, the ability of DEM-based models to exploit their natural features to enhance flood hazard mapping over the study region is investigated. Our results show (a) the benefits of considering multiple geomorphic descriptors, (b) the potential of DEM-based models for completing the information of imperfect reference flood hazard maps, and (c) the advantages of continuous representation of hazard over binary flood maps. [ABSTRACT FROM AUTHOR]

Sea level is a key element of global-scale climatic changes with significant coastal impacts. The northeastern Mediterranean Sea (Aegean, Ionian, and Cretan Seas: AICS) consists of extended low-lying coastal zones exposed to coastal inundation due to seawater level increases. The variability of the Sea Level Anomaly (SLA) in the AICS has been evaluated with the use of satellite-derived observations from 1993 to 2021. The general SLA trend over the AICS domain was 3.6 mm/year, which is higher than that of the overall Mediterranean Sea. The coastal inundation variability along the AICS coastline, specifically in 20 characteristic study areas, was investigated based on the long-term satellite-derived sea level observations, high-resolution land elevation data, and numerical simulations, conducted with a coastal inundation (CoastFLOOD) model. The coastal zones were categorized based on the frequency of occurrence and percentage coverage of flooding incidents during the 29-year period, while the interannual trends and the seasonality of inundated littoral and inland areas also showed intense spatial variation. High flooding levels occurred at coastal areas of either extended low-lying areas and/or high sea level elevations. The coastal zones were categorized based on a heuristic Coastal Inundation Hazard Index (CIHI: 1–5 ranking) that was determined by the occurrence frequency of severe floods (temporal component) and their impact area (spatial component). Our study findings provide vital information about the coastal inundation conditions and the respective hazard level of the AICS, useful to support enhanced coastal zone management for the improvement of coastal protection and decisions about coastal hazards' mitigation. [ABSTRACT FROM AUTHOR]

The abundant precipitation over Southern China during the pre–flood season (PFS) [i.e., April–June (AMJ)] has important socioeconomic impacts on this densely populated region. Using observational and reanalyzed datasets, this study explores how El Niño affected the subsequent PFS precipitation over Southern China during 1961–2020. The results show that the El Niño–related anomalies in sea surface temperature forced a northwestern Pacific anomalous anticyclone (NPAAC) in the decaying AMJ. This NPAAC featured southwesterly wind anomalies in its northwestern flank, which could transport moisture from the South China Sea, and accompanying the NPAAC there was abnormal descending motion over the tropical western Pacific, resulting in weakened regional Hadley circulation with abnormal ascending motion over subtropical East Asia. Before the 1990s, this abnormal ascending motion was located mainly to the east of Southern China with insignificant impacts on the PFS precipitation there. In contrast, after the early 1990s, El Niño–related warm sea surface temperature anomalies were stronger and longer-lasting with westward extension. This enhanced the NPAAC with a decadal westward extension, and consequently, the anomalous regional Hadley circulation was more evident over Southern China after the early 1990s during the El Niño decaying AMJ, causing strong abnormal upward motion and excessive precipitation there. The present results emphasize an enhancing influence of El Niño on the subsequent PFS precipitation over Southern China since the early 1990s, offering better understanding of the interannual precipitation variability over Southern China and with important implications for regional seasonal climate prediction. Significance Statement: The precipitation over Southern China during the pre–flood season (PFS) contributes between 40% and 50% of the local annual precipitation, with severe floods occurring frequently and tremendous socioeconomic impacts on the region, including on its agriculture, water resources, food security, ecosystems, disaster mitigation, infrastructure construction, and human health. This study reveals a decadal enhancement in the effect of El Niño on the subsequent PFS precipitation over Southern China since the early 1990s. This is ascribed to a decadal enhancement of El Niño–related warm sea surface temperature anomalies over the equatorial central Pacific, as a result of El Niño warming occurring more frequently over the central Pacific against the backdrop of global warming. To the extent that the central Pacific El Niño events would occur even more frequently for the projected future warming scenarios, a stronger effect of El Niño on the PFS precipitation over Southern China would be expected, implying potentially enhanced seasonal predictability of the regional climate in the future. [ABSTRACT FROM AUTHOR]

Landslides and flash floods are significant natural hazards with substantial risks to human settlements and the environment, and understanding their interconnection is vital. This research investigates the hazards of landslides and floods in two adopted basins in the Yamaguchi and Shimane prefectures, Japan. This study utilized ten environmental variables alongside categories representing landslide-prone, non-landslide, flooded, and non-flooded areas. Employing a machine-learning approach, namely, a LASSO regression model, we generated Landslide Hazard Maps (LHM), Flood Hazard Maps (FHM), and a Composite Hazard Map (CHM). The LHM identified flood-prone low-lying areas in the northwest and southeast, while central and northwest regions exhibited higher landslide susceptibility. Both LHM and FHM were classified into five hazard levels. Landslide hazards predominantly covered high- to moderate-risk areas, since the high-risk areas constituted 38.8% of the study region. Conversely, flood hazards were mostly low to moderate, with high- and very high-risk areas at 10.49% of the entire study area. The integration of LHM and FHM into CHM emphasized high-risk regions, underscoring the importance of tailored mitigation strategies. The accuracy of the model was assessed by employing the Receiver Operating Characteristic (ROC) curve method, and the Area Under the Curve (AUC) values were determined. The LHM and FHM exhibited an exceptional AUC of 99.36% and 99.06%, respectively, signifying the robust efficacy of the model. The novelty in this study is the generation of an integrated representation of both landslide and flood hazards. Finally, the produced hazard maps are essential for policymaking to address vulnerabilities to landslides and floods. [ABSTRACT FROM AUTHOR]

One of the important themes in the issue of urban ecology is how to build a resilient urban society. Urban resilience is defined as the capacity of a city's individuals, communities, institutions, businesses, and systems to survive, adapt, and grow against continuous stress and major shocks. However, efforts to communicate the various urban resilience programs still need to be improved. This study aims to develop a campaign medium and dissemination of flood disaster mitigation programs through visual communication approaches in the form of graphic novel. This graphic novel narrates the activities of one of the disaster preparedness communities in carrying out flood disaster mitigation actions in the city of Surakarta, Indonesia. This graphic novel's creation applied a codesign method, meaning that the process involves all stakeholders in the flood mitigation program (especially community members). The codesign method implemented its three principles: collaborative, inclusive, and people-centered. The application of the codesign method in creating the graphic novel has responded to and considered the local community's social and cultural context. [ABSTRACT FROM AUTHOR]

When multiple atmospheric rivers (ARs) occur in rapid succession, the combined effect on the hydrologic system can lead to more flooding and damage than would be expected from the individual events. This temporally compounding risk is a source of growing concern for water managers in California. We present a novel moving average‐based definition of AR "sequences" that identifies the time periods of elevated hydrologic hazard that occur during and after consecutive AR events. This marks the first quantitative evaluation of when temporal compounding is contributing to AR flood risk. We also assess projected changes in sequence frequency, intensity, and duration in California using the Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and EArth system Research (GFDL SPEAR) global coupled model. Sequence frequency increases over time and is fairly uniform across the state under both intermediate (SSP2–4.5) and very high (SSP5–8.5) emissions scenarios, with the largest changes occurring by the end of the century (+0.72 sequences/year in SSP2–4.5, +1.13 sequences/year in SSP5–8.5). Sequence intensity and duration both see increases in the medians and extreme values of their respective distributions relative to the historical baselines. In particular, "super‐sequence" events longer than 60 days are projected to occur 2–3x more frequently and to emerge in places that have never seen them in the historical record. In a world where California precipitation is becoming more variable, our definition of sequences will help identify when and where hydrologic impacts will be most extreme, which can in turn support better management of the state's highly variable water resources and inform future flood mitigation strategies. Plain Language Summary: Atmospheric rivers (ARs) are a type of storm that are vital to water resources in the western United States, but can also cause significant flooding and damage. Back‐to‐back AR events have historically been a source of concern for water managers because the compound effect of multiple events together can increase the probability of damaging floods. We present a definition of AR "sequences" that identifies periods of time where the likelihood of compound effects is increased. We look at the relationship between sequences, runoff, and soil moisture in California and show that sequences are in fact aligning with time windows of elevated hydrologic hazard in the historical record. We then look at sequences in two future climate projections and find that sequence frequency, intensity, and duration are all projected to increase with increasing emissions levels. In particular, "super‐sequences" more than 60 days long are projected to become two to three times more frequent across all of California. Our definition of sequences captures and communicates new information about the risk associated with temporally compounding hydrologic events in present and future climates. Key Points: We introduce atmospheric river (AR) sequences as a way to measure the hydrologic hazard from temporally compounding (back‐to‐back) ARsAR sequences in Geophysical Fluid Dynamics Laboratory Seamless System for Prediction and EArth system Research (GFDL SPEAR) model projections increase in frequency, intensity, and duration in California by the end of the century"Super‐sequences" over 60 days long drive the projected increase in frequency and present a growing water management threat in California [ABSTRACT FROM AUTHOR]

The Shishapangma region, situated in the middle of the Himalayas, is rich in glacial lakes and glaciers. Hence, glacial lake outburst floods (GLOFs) have become a top priority because of the severe threat posed by GLOFs to the downstream settlements. This study presents a comprehensive analysis of GLOF hazards using multi-source remote sensing datasets and designs a flood model considering the different breaching depths and release volumes for the Galong Co region. Based on high-resolution optical images, we derived the expanding lake area and volume of glacial lakes. We monitored deformation velocity and long-term deformation time series around the lake dam with Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The glacier thinning trend was obtained from the difference in the Digital Elevation Model (DEM). We identified potential avalanche sources by combining topographic slope and measurable deformation. We then carried out flood modeling under three different scenarios using the hydrodynamic model HEC-RAS for Galong Co, which is formed upstream of Nyalam. The results show that the Nyalam region is exposed to high-intensity GLOFs in all scenarios. The larger breaching depth and release volumes caused a greater flow depth and peak discharge. Overall, the multiple remote sensing approaches can be applied to other glacial lakes, and the modeling can be used as a basis for GLOF mitigation. [ABSTRACT FROM AUTHOR]

The development of probabilistic maps associated with lava flow inundation is essential to assess hazard in open vent volcanoes, especially those that have highly urbanized flanks. In this study we present the new lava flow hazard map linked to the summit eruptions of Mt. Etna, which has been developed using a probabilistic approach that integrates statistical analyses of the volcanological historical data with numerical simulations of lava flows. The statistical analysis of volcanological data (including vent location, duration and lava volumes) about all summit eruptions occurred since 1998 has allowed us both to estimate the spatiotemporal probability of future vent opening and to extract the effusion rate curves for lava flow modelling. Numerical simulations were run using the GPUFLOW model on a 2022 Digital Surface Model derived from optical satellite images. The probabilistic approach has been validated through a back-analysis by calculating the fit between the expected probabilities of inundation and the lava flows actually emplaced during the 2020-2022 period. The obtained map shows a very high probability of inundation of lava flows emitted at vents linked to the South East Crater, according to the observation of the eruptive dynamics in the last decades. [ABSTRACT FROM AUTHOR]

Modifications to land can serve to jointly reduce risks of floods and droughts to people and to ecosystems. Whether land modifications are implemented will depend on the willingness and ability of a diversity of actors. This article reviews the state of knowledge on land modification use in areas exposed to dual hydrologic risks and the land owners, managers, and users who directly make decisions about action on lands they control. The review presents a typology of land modifications and explains how land modifications interact with the hydrological cycle to reduce risks. It then addresses the roles and perspectives of the land owners, managers, and users undertaking land modifications, summarizing theories explaining motivations for, as well as barriers to and enablers of, land modification implementation. The analysis reveals geographical differences in narratives on land modifications as well as knowledge gaps regarding variation across actors and types of land modifications. [ABSTRACT FROM AUTHOR]