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The Kabul River Basin (KRB) is witnessing frequent flood and drought events that influence food production and distribution. The KRB is one of the world's poorest regions regarding food security. Food security issues in the KRB include shifts in short-term climate cycles with significant river flow variations that result in inadequate water distribution. Due to the lack of hydro-infrastructure, low irrigation efficiency, and continuing wars, the Afghanistan portion of the KRB has experienced low agricultural land expansion opportunities for food production. This research assesses the relationship between flood mitigation, flow balances, and food production and, cumulatively, assesses the social and economic well-being of the population of the KRB. SWAT modeling and climate change (CCSM4) implications are utilized to assess how these relationships impact the social and economic well-being of the population in the KRB. The intricacies of transboundary exchange and cooperation indicate that the conservation of ~38% of the water volume would nearly double the low flows in the dry season and result in the retention of ~2B m3/y of water for agricultural developmental use. Results show that the peak flood flow routing in reservoirs on the Afghanistan side of the KRB would have a substantial positive impact on agricultural products and, therefore, food security. Water volume conservation has the potential to provide ~44% more arable land with water, allowing a ~51% increase in crop yield, provided that improved irrigation efficiency techniques are utilized. [ABSTRACT FROM AUTHOR]

Purpose – This paper contributes to discourse about complex disasters by applying cultural lenses to the study of coastal infrastructure (such as seawalls and dikes), thus departing from studies that focus on characterising, assessing, and predicting the physical resilience of hard structural forms that dominate knowledge about coastal infrastructure. Design/methodology/approach – This ethnographic study nuances Philippine coastal infrastructure through examining the material registers of a seawall bordering an island inhabited by artisanal fisherfolk. By “material registers”, this research refers to the socially informed ways of regarding and constructing material configurations and how the latter are enacted and resisted. Data collection was accomplished through focus groups with community leaders, on-site and remote interviews with homeowners, and archival research to further understand the spatial and policy context of the structure. Findings – The discussion focuses on the seawall’s three material registers (protection, fragility, and misrecognition) and reveals how infrastructure built for an island community of fisherfolk simultaneously fulfils, fails, and complicates the promise of disaster resilience. Research limitations/implications – This research demonstrates the potential of “material registers”, a term previously used to analyse architecture and housing, to understand the technopolitics of infrastructure and how materially informed tensions between homeowners' and state notions of infrastructure contribute to protracted experiences of disaster and coastal maladaptation. Practical implications – This research signposts the need for disaster risk reduction, climate adaptation, and sustainable development policies that legitimize the construction of infrastructure to recognize the latter's relationship and impact on multiple sphere of coastal life, including housing and citizenship implications. Social implications – This research highlights how infrastructure for coastal disaster risk management implicates geographically informed power relations within a community fisherfolk and between their “small” island community and more politically and economically dominant groups. Originality/value – Whereas studies of coastal infrastructure are focused on quantitative and predictive research regarding hard structural forms in megacities, this study apprehends disaster complexity through examining the cultural and contested nature of infrastructure for coastal flood management in an island community of fisherfolk.

Indonesia's high vulnerability to flood disasters has significant social and educational impacts, especially on secondary school students. This research addresses the need for an effective flood disaster literacy tool by developing a credible and validated test instrument designed for high school geography education. Using the ADDIE model, this research involved the stages of review, creation, implementation, testing, and assessment. The study involved a sample of 30 students from SMA Negeri 1 Beber, Cirebon Regency, who were assessed for their understanding through a specially designed flood disaster literacy test. The results showed a mix of correct and incorrect answers, with notable variations in students' understanding. The test instrument showed high validity and reliability, with a KR-21 coefficient of 0.963, confirming its robustness. Despite a few invalid items, the instrument effectively evaluated key aspects of flood disaster literacy, including knowledge, attitudes and practical skills. The findings highlight strengths and areas for improvement in students' understanding, emphasising the need for targeted educational strategies. This research contributes to improving geography education by integrating disaster literacy and offers a foundation for future curriculum development.

This study evaluates community participation in flood disaster mitigation efforts in Wetee Village, Sidenreng Rappang Regency. Employing a descriptive qualitative approach, data were gathered through structured observations and in-depth interviews with community members and officials responsible for disaster management. The interview data were subsequently analyzed using NVivo 12 Plus software. The study focuses on community empowerment strategies implemented by the Sidenreng Rappang Regional Disaster Management Agency, using an eight-step community participation model to assess engagement levels. Findings reveal that the community’s involvement in flood mitigation remains largely at a quasi-participation level, characterized by limited engagement in planning and supervisory processes. This restricted level of participation is seen to hinder the overall effectiveness of disaster mitigation initiatives. The study recommends promoting community participation from pseudo-participation to a ‘delegated power’ level to improve outcomes, facilitating comprehensive involvement in planning, execution, and oversight. These findings underscore the critical role of collaborative efforts across various stakeholders to enhance disaster preparedness and response capacities at the local level, ultimately fostering a more resilient community.

Rapid urbanization and development have led to a significant number of Malaysians living in flood-prone areas, exacerbating the impact of natural disasters such as floods. This study aims to identify the factors influencing community resilience both before and after flood events in Temerloh, Pahang. Data were collected via questionnaires distributed to 350 flood victims. Key findings reveal that long-term residents exhibit higher resilience due to local knowledge and preparedness strategies. The study highlights the importance of integrating local experiences into flood management policies to enhance community resilience. The results reveal that large flood-prone areas experience frequent flooding, contributing to considerable community distress. This distress is somewhat alleviated by the support provided by government agencies and NGOs, which includes relief efforts, emergency services, and flood defense construction. Additionally, community involvement in flood management was found to have a statistically significant impact (p < 0.05). The study highlights the need for enhanced flood management strategies that address psychological aspects to reduce negative impacts on the community.

This study used the Best-Worst Method (BWM) and the Game Theory Algorithm (GTA) to prioritize and determine critical sub-watersheds based on flood generation potential in the Sehezar and Dohezar Watersheds, Iran. The necessary information was collected through semi-structured interviews conducted with stakeholders. Based on their indigenous, technical, and political knowledge, stakeholders prioritized the sub-watersheds in flood generation potential. Then, BWM and Condorcet Game Theory algorithms were used to prioritize the sub-watersheds based on stakeholders' voting. Finally, the sub-watersheds were classified into high, moderate, and low potential based on flood generation. Based on BWM in the Sehezar Watershed, sub-watershed 9, and in the Dohezar Watershed, sub-watershed 10, respectively, with weights of 0.253 and 0.283, had the highest flood generation potential. The prioritization pattern in the policymakers' group was similar to that of residents, but the difference between the two groups was that policymakers prioritized sub-watershed 12. The difference between the stakeholders' opinions of executive experts and policymakers was 33.3 and 50%, respectively. In the Dohezar Watershed, approximately 33% had a high flood generation potential. Meanwhile, in the Sehezar Watershed, 22% of the area showed a high potential for flood generation. In conclusion, based on field observation and anecdotal evidence, sub-watersheds 9 and 1 needed to be in better condition in terms of vegetation. Also, glacial floods in summer have made these two sub-watersheds based on flood generation to be the priority. Finally, the results of the BWM confirmed that this method provided better prioritization based on the consensus of stakeholders than GTA. [ABSTRACT FROM AUTHOR]

Several cities are facing an increasing flood risk due to the coupled effect of climate change and urbanization. Non-structural protection strategies, such as Early Warning Systems (EWSs), have demonstrated significant potential in mitigating hydraulic risk and often become the primary option when the implementation of structural measures is impeded by the complexities of urban environments. This study presents a new EWS designed specifically for fluvial floods in the city of Palermo (Italy), which is crossed by the Oreto River. The system is based on the preliminary definition of various Flood Event Scenarios (FESs) as a function of typical precursors, such as rainfall forecasts, and antecedent wetness and river flow conditions. Antecedent conditions are derived from real-time water stage observations at an upstream river section, while rainfall forecasts are provided by the Italian National Surveillance Meteorological Bulletins with a preannouncement time of up to 36 h. An innovative feature of the system is the use of rainfall Depth–Duration Thresholds to predict the expected hydrograph peak, significantly reducing warning issuing times. A specific FES, immediately accessible from a pre-built library, can be linked to any combination of precursors. Each FES predicts the timing and location of the first points of flooding; flood-prone areas and water depths; and specific hazard maps for elements typically exposed in cities, such as people, vehicles, and buildings. The EWS has been tested on a historical flood event, demonstrating satisfactory accuracy in reproducing the location, extent, and severity of the flood. [ABSTRACT FROM AUTHOR]

Introduction Floods cause financial losses and countless lives in the world every year. Identifying flood-prone areas is one of the basic steps in flood management. In the past, careful observation and note-taking of the mechanism of occurrence and the natural course of the cause and effect of the effective factors that led to the final occurrence of the phenomenon in a chain manner helped to understand the pattern and process of the occurrence to some extent. Because the processes of creating floods are numerous and affected by various factors and the flood phenomenon is multidimensional and dynamic, all-natural, human, and organizational-management factors affect the occurrence, intensity, extent, and continuity of floods. So far, many efforts have been made to use data-mining models and artificial intelligence in the spatial prediction of floods. The models try to better and more accurately estimate the distribution of the flood phenomenon by examining the relationships between each flood event (dependent factor) and the set of underlying and stimulating factors (independent factors) and fitting them to educational evidence. Since the applicability of the flexible discriminant analysis model has not been fully investigated in the field of flood susceptibility prediction, this research quantitatively evaluated its performance using real-world flood data. Materials and Methods Based on the availability of periodic history of flood events, the Zarrineh-Rood Watershed of Kurdistan Province, Iran, was chosen as the study area. It was tried to select the factors based on different criteria such as familiarity with the process of flood inundation, ease of data preparation, having the most spatial variability at the regional level (not uniform), and containing the most information for the model should be selected to separate areas with different levels of flood susceptibility. Thirteen diverse geo-environmental factors including elevation, aspect, slope percent, land use, drainage density, lithology, plan curvature, profile curvature, mean annual precipitation, soil texture, stream power index, distance from the stream, and topographic wetness index were used as independent variables. The maps of elevation, aspect, slope percent, plan and profile curvatures, stream power index, and topographic wetness index were produced using a digital elevation model. Hydrological layers including distance from the stream and drainage density were produced using the stream network layer. The location of the flooding events was also collected as the dependent variable. The spatial data of flooding were randomly divided into two groups of training and validation with a ratio of 70:30. After running the model (i.e., Flexible Discriminant Analysis) based on the training group, the flood susceptibility map was produced. The validation of the model results was conducted using the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS) metrics. Results and Discussion The results indicated that the FDA model with the value of AUROC= 0.96 and TSS= 0.86 efficiently and accurately produced the flood susceptibility map. The flexible nature of the model in the selection of regression equations, as well as the possibility of weighting, and determining the priority of the evidence of presence over the evidence of absence, are among the special capabilities of the FDA model, which many machine learning models lack. Using probability distribution estimation algorithms in the model is very important and can not only extract the hidden spatial pattern of occurrence from a set of data but also help to predict flood-prone areas in datascarce Watersheds. Based on the results, about 14% (62 thousand ha) of the study area was categorized in the high and very high flood susceptibility zones, which include the northern, northwestern, and southeastern areas. Spatial analysis of the flood susceptibility map showed that in total 25897 ha (18.12%) of agricultural lands, 343 ha (50.91%) of garden lands, and 2126 ha (39.93%) of residential areas located in high and very high susceptible zones. Considering the successfulness of the FDA model in goodness-of-fit and validation phases, the flood susceptibility map can be used as a basis for planning flood control and management measures. Conclusion The findings of this study proved that the flexible discriminant analysis model provides the possibility of processing diverse and big geo-environmental data to predict the flood susceptibility of Watersheds and it had a high efficiency in this context. There is a lot of spatial correspondence between the vegetation status map and the flood susceptibility map; in such a way that the places that had a high flood susceptibility degree, their upstream areas were generally destroyed in terms of vegetation. The results of this research showed that a significant area of the Zarineh-Rood Watershed had a high and very high flood potential, which was characterized by the interaction of low slope and flat areas, formations and soils with low penetration and dense drainage network, and more importantly, flood-prone areas located in the northern, northwestern and southeastern parts of the Watershed. The situation of the flood probability of the Zarineh-Rood Watershed has been determined and managers and decision-makers must put the critical areas in the priority of flood management programs. More flood-driver factors are suggested to be used as predictor variables in flood susceptibility modeling in future studies. On the other hand, it is very important to determine the role of predictor variables in the flood susceptibility degree at the Watershed scale, which can be investigated in future research. [ABSTRACT FROM AUTHOR]

Rapid urbanization and development have led to a significant number of Malaysians living in flood-prone areas, exacerbating the impact of natural disasters such as floods. This study aims to identify the factors influencing community resilience both before and after flood events in Temerloh, Pahang. Data were collected via questionnaires distributed to 350 flood victims. Key findings reveal that long-term residents exhibit higher resilience due to local knowledge and preparedness strategies. The study highlights the importance of integrating local experiences into flood management policies to enhance community resilience. The results reveal that large flood-prone areas experience frequent flooding, contributing to considerable community distress. This distress is somewhat alleviated by the support provided by government agencies and NGOs, which includes relief efforts, emergency services, and flood defense construction. Additionally, community involvement in flood management was found to have a statistically significant impact (p < 0.05). The study highlights the need for enhanced flood management strategies that address psychological aspects to reduce negative impacts on the community. [ABSTRACT FROM AUTHOR]

Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies. [ABSTRACT FROM AUTHOR]

Because of its low‐lying location, urbanization, and inadequate infrastructure, Jakarta (Indonesia) has experienced an increase in annual flooding events, rising from an average of five significant floods per year in the 1990s to over 20 annually (2010–2020). With climate change exacerbating extreme weather events, Jakarta encounters escalating risks of flooding. Although the recurrent flooding is exacerbated by non‐point source (NPS) of pollution such as urban runoff and agricultural discharge that contribute to 40% of total pollutants leading to flood‐related issues in Jakarta, none has investigated this research gap. To reflect its novelty, this work explores the implications of climate change on the annual flooding in Jakarta by focusing on NPS and analyzes their impacts from social perspectives. This work also underscores the implications of flooding on livelihoods, health, and social cohesion in Jakarta. Focus group discussion with affected residents was used to shed light on the coping strategies employed in response to recurrent floods, ranging from community‐based initiatives to reliance on informal networks. The empirical findings show that the implications of flooding extend beyond physical damages. Displacement of communities, loss of livelihoods, disruption of essential services, and increased health risks are among the social impacts experienced by local residents. Vulnerable populations, including low‐income communities residing in informal settlements, bear their consequences. Economic losses from flooding amount to USD 500 million annually, impacting over 1 million residents. However, recent interventions have led to a 15% reduction in peak flood levels and a 20% reduction in flood duration in affected areas. Community resilience has also improved, with a 25% increase in flood insurance coverage and a 20% rise in community response initiatives. Overall, this study highlights that climate change exacerbates annual flooding in Jakarta, significantly impacting vulnerable communities through NPS pollution. Addressing the challenges requires integrated approaches combining effective pollution control, resilient infrastructure, and community engagement to mitigate social and long‐term environmental impacts. Practitioner Points: Climate‐induced flooding disproportionately affects vulnerable communities in Jakarta.Non‐point source pollution from urban runoff contributes to the severity of flooding in Jakarta.Waterborne diseases, disruption of livelihoods, and reduced access to clean water are major concerns identified in the study.The study highlights the importance of community‐based adaptation strategies to mitigate the impact of flooding and pollution. [ABSTRACT FROM AUTHOR]

Northern Bangladesh is more vulnerable to climatic variability, flash floods, upstream heavy rainfall, early floods during the pre-monsoon period, two or three times flooding in a certain year, and poverty as well. This paper explicitly focuses on the socio-environmental challenges associated with the 2017 flood disaster in northern Bangladesh. This study is based on mixed methods. Almost data of the study are collected from primary sources (household survey, FGD) and the remaining data are obtained from authorized sources. The devastating floods in 2017 inundated one-third of Bangladesh, destroyed nearly 700,000 homes and damaged 4,680,000 hectares of cropland. In addition, the socio-economic conditions of the 2017 flood victim HHs in northern Bangladesh are sub-standard compared to other regions of Bangladesh. Some sort of variables (geophysical environment and human interventions) make floods vulnerable. Therefore, social and economic security, rebuilding damaged infrastructures and institutions, public awareness, and regional and delta planning are crucial parts of the mitigation of the effects of disastrous floods in northern Bangladesh. [ABSTRACT FROM AUTHOR]

Given the increasing frequency and severity of floods caused by climate change, there is a pressing requirement for creative ways to improve public comprehension and control of hydrological phenomena. Contemporary technology provides unparalleled possibilities to transform this domain. This project investigates the possibilities for merging gaming engines and digital twins to enhance flood education, data visualization, and interactive monitoring. This study proposes the utilization of immersive digital twins to enhance the comprehension of hydrological and hydraulic systems. The suggested method utilizes game engines to generate dynamic and interactive models that connect raw data to practical insights, enabling a more profound understanding of flood dynamics. This study underscores the wide-ranging usefulness of digital twins in various watersheds by focusing on the development of advanced monitoring systems, the benefits of improved data visualization, and educational outreach. The incorporation of real-time data via IoT technology considerably improves the significance and precision of these virtual models. This novel approach seeks to refashion flood management approaches by cultivating well-informed stakeholders and advocating for effective environmental education, ultimately leading to more resilient and prepared communities. An immersive digital twin of the real world can assist decision-makers technically, psychologically, and mentally by making complex phenomena easier to understand and visualize, thanks to real-time data and simulations that keep the information up-to-date, consequently leading to a more precise and intuitive decision-making process. [ABSTRACT FROM AUTHOR]

The city of Bangkok is built on an elaborate network of waterways shaped by its historical settlement process, underscoring the profound bond between humans and the natural landscape. In light of Bangkok's rapid urban transformation and its status as one of Southeast Asia's most vulnerable coastal metropolises, this paper seeks to explore the intricacies of Bangkok's waterway landscape by examining how stakeholders address its value. This research draws insights from qualitative data collection involving government agencies, experts, practitioners, NGOs, and residents from three distinct waterway communities. The paper identifies distinct value categories within Bangkok's waterways, classified as diminishing, absent, and evolving values. These values reflect the complex landscape surrounding the waterways and their relationship with flood management and heritage preservation. The study underscores the limitations of the values expressed by government agencies and in policy documents while highlighting the potential contributions of other stakeholders in enhancing waterway management. This evidence emphasises the necessity of multi-stakeholder involvement and the application of values in decisions when developing urban resilient alternatives to the 'business-as-usual' model prevalent in Bangkok. [ABSTRACT FROM AUTHOR]

The floods of the Lahdar River cause repeated inundations and damage to road infrastructures, particularly crossing structures in the territorial center of Had Msila. Our study involved the application of various methods to estimate flood flows for different return periods along the Lahdar River. The selected flows were chosen after a comparative analysis of values calculated by the different methods used. These results served as the basis for hydraulic modeling aimed at assessing water levels to establish risk zone mapping. This step is crucial in flood risk assessment. Two main approaches were distinguished: hydrometeorological methods, based on regional parameters derived from rainfall data, and empirical methods, used in the absence or with limited data on flood flows in a given region. Hydraulic modeling was carried out using two software programs: a Geographic Information System (GIS) such as Arc-GIS, and a specific river modeling software like Hec-Ras, allowing for the numerical representation of the natural state of the territory. The results obtained serve as the foundation for all river hydraulic modeling, thereby facilitating flood prediction and hydrological risk management in floodplains. Modeling Lahdar River floods in the studied sections enables the prediction of flood risk and its impacts on constructions and infrastructure in the Had Msila Center. [ABSTRACT FROM AUTHOR]

Resilience has gained significant prominence in the management of climate-related shocks and the minimization of disaster risks. Assessing flood resilience is, therefore, crucial in identifying areas lacking support and opportunities for development. Comprehensive risk assessments are urgently required, especially in areas prone to floods. By applying the Analytical Hierarchy Process (AHP), stakeholder meetings, and keyword analysis, this study aims to assess the flood resilience of Phra Nakhon Sri Ayutthaya Province (PNSAP) in Thailand, an area at high risk of flooding and home to a UNESCO World Heritage Site. According to the Arup and Rockefeller Foundation's City Resilience indicators, the key elements for building resilience in PNSAP include effective emergency response services, comprehensive hazard monitoring and risk assessment, and emergency medical care. In addition, the focus group discussions and keyword analysis identified the following critical components for strengthening flood resilience: "financial aspects", "community awareness and preparedness", "multi-stakeholder collaboration", "citizen engagement", and "urban flood monitoring and data management." Moreover, to gain insight into the perceptions of people in local communities, the results of a household survey (n = 552) indicated that approximately one-third of the respondents had never engaged in flood preparedness activities. Neither the gender nor the education level of the respondents significantly influenced their engagement in flood preparedness. The respondents also perceived that infrastructure flexibility, affordable transport networks, flood monitoring and data management, the adequate provision of critical services, and reliable communication systems in building resilience were important during a flood disaster. These results provide valuable insight into the community's perception of the effectiveness of disaster risk reduction mechanisms for building flood resilience in PNSAP and can serve as a useful guide for future resilience-building initiatives. By addressing these factors, PNSAP could enhance its resilience to the impacts of flooding and contribute to the global field of flood management and resilience building. [ABSTRACT FROM AUTHOR]

Context: Rapid urbanisation and economic growth have intensified challenges such as water scarcity and flood management in urban areas. Addressing these issues requires enhanced water-management strategies to support sustainable urban development. Aims: This study aims to refine the framework for water-saving initiatives in cities, crucial for implementing stringent water-management practices and addressing complex water challenges, thereby contributing to sustainable economic and social progress. Methods: Using the analytic hierarchy process, a comprehensive evaluation model for urban water-resource management was developed. This model integrates an evaluation index system that considers social, economic and environmental benefits, promoting sustainable development and the circular economy. Key results: The developed model successfully enables both quantitative and qualitative assessments of urban water-resource management strategies. It offers a more streamlined and accurate methodology for evaluating the regional water-resource carrying capacity, surpassing the precision and usability of existing models. Conclusions: The study underscores the critical role of advanced water-resource management strategies in urban environments. It demonstrated that integrated approaches are essential for addressing the multifaceted challenges of urban water management and for promoting urban sustainability. Implications: This research provides actionable insights for policymakers, highlighting the necessity of incorporating comprehensive evaluation models in urban water management. By emphasising the integration of social, economic and environmental benefits, it supports the advancement of sustainable water-use practices and promotes ecological and social equity. This study introduces a new model for evaluating urban water management, blending social, economic and environmental factors. The model helps cities better assess and manage their water resources, promoting sustainability and economic progress. By improving water-saving strategies and addressing urban water challenges, the research offers valuable insights for policymakers aiming for sustainable urban development. This article belongs to the collection Ecological Monitoring and Assessment of Freshwater Ecosystems: New Trends and Future Challenges. [ABSTRACT FROM AUTHOR]

Climate change resulting in frequent flooding events have caused catastrophic effects to Small Island Developing States (SIDS) in recent times. Rainfall events have become less predictable in recent years. This paper presented the findings of a project that evaluated permeable pavement systems (PPS) using low-carbon materials, recycled aggregates, and carbon-negative aggregates within its structure as a sustainable drainage system (SuDS). It replicates typical drainage systems, reducing the surface runoff volumetric rates and retaining stormwater pollutants from downstream runoff. Low carbon permeable pavements and Carbon Negative pavement systems are novel structural pavements implementing materials which can withstand the same axial loading as the conventional pavements and can enhance stormwater quality by water treatment through filtration and infiltration between sub-base layers. Carbonnegative aggregates utilise patented technology that converts secondary waste products into high-quality aggregates based on a process that absorbs CO2 into the pavement materials. This paper evaluates two pilot-scaled Low-CarbonPorous Asphalt Pavement (LC-PAP) systems versus two carbon-negative interlocking concrete block permeable pavement systems (CN-ICB-PPS) on the overall environmental and structural performance. It was found that the pavement systems achieved similar permeability for stormwater remediation results using a combination of virgin aggregates, recycled aggregates, and carbon-negative aggregates for the CN-ICB-PPS and LC-PAP. The pavement systems utilising greater content of carbon-negative aggregates displayed a higher water infiltration rate when compared to the CN-ICB-PPS because of the sub-base design implemented. The LC-PAP systems could achieve the necessary strength at a lower cost, implementing low-carbon recycled materials and carbon-negative aggregates forming 70 % of sub-base layer of the pavements. For the LC-PAP system, ammonium, nitrates, colour, BOD and COD from the stormwater influent decreased significantly when compared to outflow water samples from the CN-ICB-PPS. Due to the variations in the top layers of the pavements with very small pore-spaces, this ensured a greater pollutant retention rate improving the overall stormwater quality being discharged from the pavement. The CN-ICB-PPS displaced a slight decrease in ammonium, nitrates, and colour over the period of study. Moreover, the LC-PAP contained higher content of low-carbon materials and recycled aggregates, placed above the saturation zone of the pavement, allowing some stormwater pollutants to filtrate easily through the pavement structure. [ABSTRACT FROM AUTHOR]

Constructed levees are designed to protect anthropogenic developments from destructive flooding events, but their construction has reduced groundwater recharge, increased flood risk severity under levee failure, increased the incision of river channels, and deteriorated riparian habitat. To reverse these impacts, levee setbacks are often designed to reduce flood risk and provide the opportunity to restore ecohydrological function, while groundwater recharge is rarely considered because it may require relatively detailed groundwater system analysis. In this study, we evaluated 100 heterogeneous hydrogeology realizations to estimate recharge with high-conductivity pathways (HCPs) under varying flood flows for a range of levee setback distances to identify the trade-offs in groundwater recharge and floodplain habitat. We find that on a regional scale, total recharge potential increases with setback distance, with the largest gains up to 1,400 m where there are outcropping HCPs and sufficient flow to inundate more of the setback area. In contrast, the recharge per unit area (i.e., the average daily recharge divided by setback area) generally decreases as levee setback increases, but there are local increases in the recharge per unit area at 1,400 m where HCP recharge may sufficiently offset the larger area. There is a median 10%–40% reduction in peak streamflow with increasing setback distance, which would aid flood risk reduction, but the increased area leads to decreasing depth due to flow losses and increased spreading of flood water. Ultimately, the decision for levee setback distance will depend on local conditions and management goals, as we find that increasing recharge will reduce the floodplain depth necessary for ecosystem function. Our results highlight the opportunity to consider groundwater recharge benefits in levee setback feasibility studies in semi-arid regions impacted by floods and groundwater overdrafts so that setback distance designs can achieve integration of flood risk reduction, riparian habitat, and groundwater recharge.

This paper will assess the feasibility and implementation of an engineered wetland to fight climate change driven grape stock waterlogging and subsequent disease in the Rahovec wine region of the Republic of Kosovo. We will utilize a vineyard in Rahovec which has known disease and waterlogging challenges to determine the extent and examine the geomorphic conditions that caused nearby grape farmers to lose nearly all produce due to infection spread. The hypothesis is that climate change-driven anomalous rain patterns combined with insufficient drainage caused field waterlogging, in these stagnant areas, inferior stock selection led to rapid spread of disease. Within the framework of climate change, adding drainage is a less viable option as compared to an engineered wetland that can emanate healthy microfauna into adjacent and downhill vineyards. To understand the geomorphology, we will conduct drainage basin assessment which will provide watershed information on the location and soils analysis. Field surveys of the damage to vineyard's stocks will be conducted to understand the extent and severity of the disease spread, which will be geographically analyzed to understand risk hotspots. Finally, a proposed construction project will be designed and estimated to provide an understanding of the farmers capabilities to fund the design. An understanding will be developed for the disease impacts occurring with the theoretical engineered wetlands function of replacing the waterlogged disease breading grounds with positive functioning ecosystem building climate change resilience for vineyards.

Floods are frequent natural hazards that cause widespread destruction, particularly in low-elevated areas. This study focuses on identifying flood susceptible zones in the Kashmir Valley, known for historical flooding attributed to the overflow of the Jhelum River. Various Multi-Criteria Decision Making (MCDM) techniques, including Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijai Compromission Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS), were employed in this research. A total of 17 multidimensional factors were considered, and multicollinearity tests revealed no correlation among these factors. The results of the MCDM models indicate that areas along the Jhelum River are classified under very high flood susceptible zone. Specifically, Srinagar city is consistently classified under very high flood susceptible zone by all three models. Approximately 4.27 %, 9.67 %, and 5.39 % of the total area were identified as very high susceptible areas by TOPSIS, VIKOR, and EDAS, respectively. The models exhibited robust performance, as evidenced by the Area Under the Curve of the Receiver Operating Characteristics curve (AUC-ROC). Notably, VIKOR demonstrated excellent performance among the three models in generating flood susceptible maps. The favorable outcomes of these models underscore their potential application in similar regions facing comparable challenges. This study carries significant implications for policymakers, administrators, and local authorities involved in flood management within the Kashmir Valley. The insights provided can inform proactive measures and strategies to mitigate the impact of floods and enhance the overall resilience of the region.

This study focuses on demonstrating the effectiveness and efficiency of the Stochastic Sequential Model Predictive Control (MPC) framework within the context of the Hjartdøla hydropower system. Multistage MPC, while effective in managing uncertainty, poses challenges due to its high computational demands and complex optimal control problems, particularly in applications requiring long-term forecasting, such as hydropower systems. Through a comparative simulation study with multistage MPC, this paper highlights the superior feasibility and computational speed of the Stochastic Sequential MPC framework. This work contributes to the broader understanding of MPC applications in hydropower systems.

Trash screens are used to prevent debris from entering critical parts of rivers. However, debris can accumulate on the screen and generate floods. This makes their monitoring critical both for maintenance and flood modeling purposes (e.g., local forecasts may change because the trash screen is blocked). We developed three novel deep learning methods for trash screen maintenance management consisting of automatically detecting trash screen blockage using cameras: a method based on image classification, a method based on image similarity matching, and a method based on anomaly detection. To facilitate their use by end users, these methods are designed so that they can be directly applied to any new trash screen camera installed by the end users. We have built a new dataset of labeled trash screen images to train and evaluate the efficiency of our methods, in terms of both accuracy and implications for end users. This dataset consists of 80,452 trash screen images from 54 cameras installed by the Environment Agency (UK). This work demonstrates that trash screen blockage detection can be automated using trash screen cameras and deep learning, which could have an impact on both trash screen management and flood modeling. HIGHLIGHTS Development and analysis of three deep learning-based trash screen monitoring methods using cameras, tailored for various investment levels.; Binary classification: ready-to-use, no additional labeling or retraining required.; Image similarity matching: requires minimal user annotation for improved accuracy.; Anomaly detection: able to use unlabeled datasets.; Network and dataset provided to support further research.;

Abstract As climate change increases the risk of extreme rainfall events, concerns over flood management have also increased. To recover quickly from flood damage and prevent further consequential damage, flood waste prediction is of utmost importance. Therefore, developing a rapid and accurate prediction of flood waste generation is important in order to reduce disaster. Several approaches of flood waste classification have been proposed by various researchers, however only a few focus on prediction of flood waste. In this study, a Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) approach is adapted to address these challenges. Two different raw datasets were obtained from the “Advancing Sustainable Materials Management: Facts and Figures 2015” source. The datasets were for 9 years (1960, 1970, 1980, 1990, 2000, 2005, 2010, 2014, 2015), and are labelled as the materials generated in the Municipal Waste Stream from 1960 to 2015 and the materials Recycled and Composted in Municipal Solid Waste from 1960 to 2015. The waste types were grouped as paper and paperboard (PP), glass (GI), metals (Mt), plastics (PI), rubber and leather (RL), textiles (Tt), wood (Wd), food (Fd), yard trimmings (YT) and miscellaneous inorganic wastes (IW).

This paper explores the use of the rain-on-grid (or direct rainfall) method for flood risk assessment at a basin scale. The method is particularly useful for rural catchments with small vertical variations and complex interactions with man-made obstacles and structures, which may be oversimplified by traditional hydrologically based estimations. The use of a hydrodynamic model solving mass and momentum conservation equations allows the simulation of runoff over the watershed at a basin scale. As a drawback, more detailed and spatially distributed data are needed, and the computational time is extended. On the other hand, a smaller number of parameters is needed compared to a hydrological model. Roughness and rainfall loss coefficients need to be calibrated only. The direct rainfall methodology was here implemented within the two-dimensional HEC-RAS model for the low-land rural, and ungauged, watershed of the Terdoppio River, Northern Italy. The resulting hydrographs at the closing section of the watershed were compared to synthetic design hydrographs evaluated through pure hydrological modelling, showing agreement on the peak discharge values for the low-probability scenarios, but not on the total volumes. The results in terms of water depth and flow velocity maps were used to create flood hazard maps using the Australian Institute for Disaster Resilience methodology. The Index of Proportional Risk model was then adopted to generate a basin-scale flood risk map, by combining flood hazard maps, damage functions for different building-use classes, and the value of reconstruction and content per unit area. [ABSTRACT FROM AUTHOR]

Health cadres and community leaders’ empowerment to become flood disaster preparedness cadres (Kader Siaga Bencana, KSB) still needs to be improved. The study aimed to assess flood management practices among families with disaster-vulnerable groups, as well as KSB candidates (posyandu/health cadres, community leaders) in flood management initiatives in flood-prone areas. The study used a cross-sectional design of 100 families with flood-prone groups and 100 health cadres and community leaders. They were selected using simple random sampling in five selected villages of Jakarta city affected by floods and Disaster Alert Villages (Kasmpung Siaga Bencana, DAV). Most family respondents had a low knowledge level of DAV, and inadequate knowledge of flood warning system, emergency response plan, and flood management practices, despite having good family support. Health cadres and community leaders as KSB candidates were assessed using four parameters of knowledge on KSB – flood early warning system, emergency response plan, natural hazards and flood knowledge, and flood management practice. With their sound understanding of natural hazards and floods, health cadres and community leaders were 4.52 times more likely to have good flood management practices compared to those with inadequate knowledge. Both elements of the community have the potential to be KSB because of good disaster knowledge and the presence of the flood early warning system in the community. Intervention studies to increase the knowledge and practical ability of KSB in disaster management are also needed. Contribution: The research assessed the initial readiness of health cadres and community leaders to become disaster preparedness cadres in flood management. It is necessary to provide training to develop their expertise as KSB.

IntroductionFlood is one of the most destructive natural disasters that has a negative impact on social, economic and environmental dimensions. Floods usually occur following a prolonged period of rain or snowmelt in combination with unfavorable conditions. In this regard, all over the world, the occurrence of floods has intensified by 40% in the last two decades. In Asia, almost 90% of all human casualties caused by natural disasters are due to floods. The increase in flooding is usually due to increased environmental degradation such as urbanization, increased population growth, and deforestation. Periodic and regular occurrences of floods over a certain timeframe significantly amplify the detrimental impacts on living organisms. Urban areas in close proximity to rivers bear the brunt of these damages, owing to high population density, economic infrastructure, and transportation networks. However, these consequences can be alleviated through meticulous vulnerability analysis. One of the primary objectives pursued by researchers and policymakers is the precise modeling and zoning of floods to mitigate associated risks. Consequently, a myriad of methods and approaches have been devised for flood risk modeling and zoning to address this pressing issue. Among them, hydrological methods such as rainfall-runoff modeling and data-based techniques, which are unable to comprehensively analyze rivers and flood zones due to their one-dimensional nature. This is despite the fact that the morphology of the river is not stable and due to its high erosion potential, it also has a dynamic characteristic. In addition, these methods require fieldwork and large budgets for data collection. Hence, comprehensive flood management is necessary to reduce these effects. Therefore, this study was conducted with the aim of identifying areas sensitive to the risk of flooding in Famnat watershed located in Gilan province. Fomanat watershed is located in Gilan province and is considered a part of the first grade watershed of the Central Plateau. This area is located in the range of 36.89 to 37.57 degrees north latitude and 48.77 to 49.69 degrees east longitude. This region has an area of 3595 square kilometers, the highest point of which is 3088 meters and the lowest point is -69 meters. Materials and Methods To carry out the current research, firstly, by reviewing the sources and history of the research, as well as knowing the region, a map and layers of information related to the factors affecting flood susceptibility zoning were prepared. These layers include land use map, slope degree, geology, distance from waterway, digital map of height, direction, shape of land curvature, land curvature profile, rainfall and topographic humidity index, which are created using the collected data and also various additions in the environment. Geographic information system (Arcgis 10.4) was prepared. In this regard, machine learning models such as generalized linear model (GLM), multivariate adaptive regression model (MARS) and classification and regression tree model (CART) were used to zone the sensitivity of the watershed to floods. Also, among 100 flood events, 70% (70) were considered for training and 30% (30) for validation. In the following, using field survey and review of previous studies, 10 factors influencing the occurrence of floods in the watershed area were identified and used. Finally, the area under the ROC curve and the TSS index were used to evaluate the models.Results and Discussion The results of the evaluation of the most important factors affecting the sensitivity of the watershed to floods indicated that the distance from the river, the height and the curvature profile had the greatest impact on the sensitivity of the region, and on the other hand, the factors of slope, geology and topographic humidity index had the least impact. Based on the obtained results, the areas covered by very low, low, medium, high and very high classes in the CART model were 26.6, 17.6, 21.2, 0.1 and 34.0%, respectively. These results for the GLM model were 13.6, 12.7, 16.2, 25.1 and 32.4 percent, respectively. Based on the obtained results, the CART model performed better than other models, so that AUC for MARS model was equal to 0.76, CART model was equal to 0.9 and GLM model was equal to 0.84. Also, the better performance of CART model compared to other models was confirmed by other indicators. So, based on TSS, MARS model equal to 0.52, CART model equal to 0.77 and GLM model equal to 0.66 were obtained.ConclusionImplementing the findings of this study can facilitate the adoption of effective management strategies to mitigate losses and casualties. Moreover, in developing nations grappling with restricted access to hydrogeological and soil data, the utilization of geographic information systems (GIS) and data mining techniques assumes a pivotal role in conducting comprehensive studies. These technologies offer valuable insights and support decision-making processes, enabling proactive measures to address flood risks and enhance disaster resilience in vulnerable regions.

Inundation and flooding are problems that often occur in many cities worldwide, including university campuses. This research aims to examine the existing drainage conditions on the Universitas Islam Riau (UIR) campus and identify solutions to overcome inundation and flooding that probably occur on the UIR campus in extreme conditions. The method in this study uses the EPA's SWMM (Storm Water Management Model) 5.2 software simulation program. The results indicate that the current drainage system is unable to handle rain discharge during extreme conditions. In this study, three improvements are set up to analyze hydrological characteristics. The first improvement is the traditional improvement by changing the existing channel dimensions. The second improvement is using Low Impact Development (LID) technology, in which the combination of 14 infiltration wells and 7 Biopore Infiltration Holeare applied. The third improvement is using the combination of 3 LID: Infiltration well, Biopore infiltration Hole, and Rain Water Harvesting (RWH). From the results, runoff in the improvement one was reduced by 21.5%. The second improvement, where 2 LID was used, reduced the runoff by 51.5%; finally, the third improvement reduced the runoff by 57.6%. So, the scenario that is most effective in overcoming inundation or flooding is the third scenario. Furthermore, the improvement is in line with the water conservation strategy. The findings of this research can serve as a valuable resource for future studies, contributing to the advancement of SDGs 6, 9, and 11, specifically targeting better water management, resilient infrastructures, and sustainable cities and communities. Moreover, the result can be a guide for the decision maker, especially on the UIR Campus and Campus worldwide, as an effective strategy for water conservation and to prevent flooding in similar universities.

Research on multi-dimensional aspects of dam engineering is gaining momentum because of massive flood destructions in lives, ecosystem and development including Wivenhoe dam flood in 2011, Oroville dam’s spillway incident 2017 and the Europe’s widespread flooding in 2021. The aim of this study is to capture and review research activities in dam science. A case study of the Wivenhoe dam Brisbane Australia, focusing on its design, catchment, water storage and flood mitigation capacity, management procedure, climate change and historical rainfall pattern has been carried out to conclude why such a catastrophic flood event happened in Brisbane in January 2011. Reviewed literatures throughout the world indicated that dam failures are primarily associated with improper design, inadequate monitoring in construction period and poor management (operational) practices. In the case of the Wivenhoe dam flood, the report of the Flood Commission of Inquiry Australia was that the dam was operated so that its flood mitigation was near optimal. Whilst the operators were found to be at fault for not following the Operation Manual, it was found that the manual was confusing and difficult to follow, and therefore, they were cleared of all liability. Hence, it is difficult to conclude what would have actually happened if the Wivenhoe dam operators had released more water earlier, author’s reasoned outlook about the flood mitigation measures used at the time is to ask the question “was reasonable discretion” used during the flood.

Social values of land use are often excluded when undertaking integrated flood management as they are harder to quantify. To fill this research gap, a geographic information system application called Social Values for Ecosystem Services was used to assess, map and quantify the perceived social values of flood-prone land use in Kuala Selangor, Malaysia. This approach was based on a non-monetary value index (VI) calculated from responses to a quantitative social survey on the public's attitude and preference towards flood management across different land uses. The study outcome is the geospatial representation of flood-prone land use with their social values, which local communities perceive as crucial for flood management. The VI was influenced by elevation and slope, with lower elevations and flatter slopes associated with higher values. Farmland is highly favoured by the local community for flood management, whereas oil palm and rubber plantations are opposed. Tourism received the highest monetary allocations from survey respondents, with the popular firefly park consistently associated with the highest social values. This practical framework contributes to integrated flood management in facilitating decision-makers to evaluate land-use trade-offs by considering their social values when prioritising flood mitigation measures or investments. HIGHLIGHTS Quantifying social land-use values is challenging compared to economic and environmental aspects in flood management.; A public geographic information system app modelled these values based on public attitudes.; Some land uses were seen as more crucial for flood management due to higher local social values.; This aids decision-makers to evaluate land-use trade-offs when prioritising flood mitigation measures or investments.;

Inundation and flooding are problems that often occur in many cities worldwide, including university campuses. This research aims to examine the existing drainage conditions on the Universitas Islam Riau (UIR) campus and identify solutions to overcome inundation and flooding that probably occur on the UIR campus in extreme conditions. The method in this study uses the EPA's SWMM (Storm Water Management Model) 5.2 software simulation program. The results indicate that the current drainage system is unable to handle rain discharge during extreme conditions. In this study, three improvements are set up to analyze hydrological characteristics. The first improvement is the traditional improvement by changing the existing channel dimensions. The second improvement is using Low Impact Development (LID) technology, in which the combination of 14 infiltration wells and 7 Biopore Infiltration Holeare applied. The third improvement is using the combination of 3 LID: Infiltration well, Biopore infiltration Hole, and Rain Water Harvesting (RWH). From the results, runoff in the improvement one was reduced by 21.5%. The second improvement, where 2 LID was used, reduced the runoff by 51.5%; finally, the third improvement reduced the runoff by 57.6%. So, the scenario that is most effective in overcoming inundation or flooding is the third scenario. Furthermore, the improvement is in line with the water conservation strategy. The findings of this research can serve as a valuable resource for future studies, contributing to the advancement of SDGs 6, 9, and 11, specifically targeting better water management, resilient infrastructures, and sustainable cities and communities. Moreover, the result can be a guide for the decision maker, especially on the UIR Campus and Campus worldwide, as an effective strategy for water conservation and to prevent flooding in similar universities. [ABSTRACT FROM AUTHOR]

Introduction Flood is one of the most destructive natural disasters that has a negative impact on social, economic and environmental dimensions. Floods usually occur following a prolonged period of rain or snowmelt in combination with unfavorable conditions. In this regard, all over the world, the occurrence of floods has intensified by 40% in the last two decades. In Asia, almost 30% of all human casualties caused by natural disasters are due to floods. The increase in flooding is usually due to increased environmental degradation such as urbanization, increased population growth, and deforestation. Periodic and regular occurrences of floods over a certain timeframe significantly amplify the detrimental impacts on living organisms. Urban areas in close proximity to rivers bear the brunt of these damages, owing to high population density, economic infrastructure, and transportation networks. However, these consequences can be alleviated through meticulous vulnerability analysis. One of the primary objectives pursued by researchers and policymakers is the precise modeling and zoning of floods to mitigate associated risks. Consequently, a myriad of methods and approaches have been devised for flood risk modeling and zoning to address this pressing issue. Among them, hydrological methods such as rainfall-runoff modeling and data-based techniques, which are unable to comprehensively analyze rivers and flood zones due to their one-dimensional nature. This is despite the fact that the morphology of the river is not stable and due to its high erosion potential, it also has a dynamic characteristic. In addition, these methods require fieldwork and large budgets for data collection. Hence, comprehensive flood management is necessary to reduce these effects. Therefore, this study was conducted with the aim of identifying areas sensitive to the risk of flooding in Famnat watershed located in Gilan province. Fomanat watershed is located in Gilan province and is considered a part of the first grade watershed of the Central Plateau. This area is located in the range of 36.83 to 37.57 degrees north latitude and 48.77 to 43.63 degrees east longitude. This region has an area of 3535 square kilometers, the highest point of which is 3088 meters and the lowest point is -63 meters. Materials and Methods To carry out the current research, firstly, by reviewing the sources and history of the research, as well as knowing the region, a map and layers of information related to the factors affecting flood susceptibility zoning were prepared. These layers include land use map, slope degree, geology, distance from waterway, digital map of height, direction, shape of land curvature, land curvature profile, rainfall and topographic humidity index, which are created using the collected data and also various additions in the environment. Geographic information system (Arcgis 10.4) was prepared. In this regard, machine learning models such as generalized linear model (GLM), multivariate adaptive regression model (MARS) and classification and regression tree model (CART) were used to zone the sensitivity of the watershed to floods. Also, among 100 flood events, 70% (70) were considered for training and 30% (30) for validation. In the following, using field survey and review of previous studies, 10 factors influencing the occurrence of floods in the watershed area were identified and used. Finally, the area under the ROC curve and the TSS index were used to evaluate the models. Results and Discussion The results of the evaluation of the most important factors affecting the sensitivity of the watershed to floods indicated that the distance from the river, the height and the curvature profile had the greatest impact on the sensitivity of the region, and on the other hand, the factors of slope, geology and topographic humidity index had the least impact. Based on the obtained results, the areas covered by very low, low, medium, high and very high classes in the CART model were 26.6, 17.6, 21.2, 0.1 and 34.0%, respectively. These results for the GLM model were 13.6, 12.7, 16.2, 25.1 and 32.4 percent, respectively. Based on the obtained results, the CART model performed better than other models, so that AUC for MARS model was equal to 0.76, CART model was equal to 0.9 and GLM model was equal to 0.84. Also, the better performance of CART model compared to other models was confirmed by other indicators. So, based on TSS, MARS model equal to 0.52, CART model equal to 0.77 and GLM model equal to 0.66 were obtained. Conclusion Implementing the findings of this study can facilitate the adoption of effective management strategies to mitigate losses and casualties. Moreover, in developing nations grappling with restricted access to hydrogeological and soil data, the utilization of geographic information systems (GIS) and data mining techniques assumes a pivotal role in conducting comprehensive studies. These technologies offer valuable insights and support decision-making processes, enabling proactive measures to address flood risks and enhance disaster resilience in vulnerable regions. [ABSTRACT FROM AUTHOR]

Today, watersheds have changed under the influence of various environmental and human factors, and their expected performance has decreased in terms of meeting human needs, providing natural responses, and reducing environmental risks. However, evaluating health status and its impact on various factors has not been given enough attention. To this end, an attempt has been made to evaluate the health and ecological security of the subwatersheds of the Sharghonj Watershed using the pressure‐state‐response approach emphasizing flood‐related issues in the region. Toward that, 21 preliminary hydrological, anthropogenic, natural, and climatic criteria were selected. The preliminary variables were then finalized based on the results of the collinearity test and the data availability. The final variables were appropriately allocated to pressure, state, and response (PSR) indices, and associated health and ecological security indices were determined at the subwatershed scale. The results showed that the PSR ranged from 0.56 to 0.86, 0.46 to 0.84, and 0.35 to 0.96, respectively. Health and ecological security also varied from 0.53 to 0.83 and 0.27 to 1.01 in different subwatersheds. Eventually, the weighted mean PSR indices for the Sharghonj Watershed were 0.72, 0.67, and 0.73, respectively. The weighted mean health and ecological security indices were 0.70 and 0.68, respectively. The research results indicated that the dynamic of various variables related to floods considerably affects the health of the Sharghonj Watershed. It could be concluded from the results that the Sharghonj Watershed is highly flood‐susceptible, whose health and ecological security status was mainly controlled by the damages due to the destruction of residences and orchards by floods in the past few years. The results of the current study provide an appropriate roadmap to the decision makers and planners for the better management of the watershed resources in the region through allocating adequate financial support and implementation measures to priority subwatersheds. Highlights: Flood‐based health of the Sharghonj Watershed, Iran, was levied using 21 criteria.Barberry orchards played an essential role in determining watershed health.Proper land‐use and floodplain management affected watershed health positively.The health and security changes of the Sharghonj Watershed were dissimilar.Flood damages mainly controlled the health and ecological security status. Recommendations for Resource Managers Determining the determinant factors on flood‐related issues in the study watershed to accordingly implement appropriate flood control measures.A problem‐oriented approach and in‐depth conceptualization of pressures and management policies applied to watersheds are essential in integrated watershed management.Designating the subwatershed‐specific best management scenarios based on the health and ecological security conditions in a technically productive and economically efficient strategy.Focusing on priority subwatershed to shortly adopt appropriate measures before worsening the governing conditions. [ABSTRACT FROM AUTHOR]

One of measures to reduce the flood impact in Indonesia has been done by flood emergency response action plan. The action plan is generated from decision making process assisted by instrument such computer models which have capability in producing scenarios and decision to be taken. This research is conducted to identify how the written procedures (delivered by international and national agencies) related to flood emergency response, being accommodated by those (computer) models. By using qualitative analysis, the element procedures of an effective flood emergency response model have been reviewed. The result shows that the detailed information such human and material resources, facilities information, and warning time-lag, have not been properly and entirely accommodated by the (computer) model. Highlights are also given to propose more effective instrument to improve the management of flood crisis. [ABSTRACT FROM AUTHOR]

The scarcity of measured hydrological data poses a challenge in many developing countries, stemming from insufficiently established gauging stations. Due to the mentioned issue, it is crucial to develop models capable of conducting reliable simulations of runoff behavior, particularly for ungauged catchments. Understanding the intricate relationships in rainfall-runoff modeling is essential for estimating peak flows, a critical aspect in formulating water resources management strategies, which can aid in water resource management and planning. In areas prone to floods performing, an extensive hydrological study becomes necessary. This study determined the outflow discharge at the outlet point of the Bessre Valley Ungauged Catchment (41.4 km2) using the Watershed Modeling System, used by reliable hydrological standards as a graphical interface integrating with the Hydrologic Modeling System (HEC-HMS). Bessre Valley watershed is one of the flood-prone watersheds in the Duhok governorate, mainly due to the terrain’s steep slopes at the upper north and east of the catchment. The catchment was delineated by a Geographic Information System (GIS). Its properties were extracted from a 12.5 m × 12.5 m Digital Elevation Model (DEM), which evaluates the hydrological response of a watershed to two significant storm events: a real rainfall event in March 2020 and a hypothetical 100-year return period event by dividing the watershed into ten sub-basins. Achieving a Nash-Sutcliffe efficiency of 0.895 indicates a high accuracy between observed and simulated peak flows of the real rainfall event of March 2020, underscoring the model's reliability for hydrological predictions. Also, comparing the HEC-HMS model and the Rational Method of (100 YRP event) for calculating peak discharges revealed a mere 2.2% error. Furthermore, the study explores the potential for building additional dams based on discharge volumes from specific sub-basins to enhance flood control and water storage capabilities.

Climate change is a serious global issue causing more extreme weather patterns, resulting in more frequent and severe events like urban flooding. This review explores the connection between climate change and urban flooding, offering statistical, scientific, and advanced perspectives. Analyses of precipitation patterns show clear changes, establishing a strong link between climate change and the heightened intensity of global rainfall events. Hydrological modeling and case studies provide compelling scientific evidence attributing urban flooding to climate-induced changes. Urban infrastructure, including transportation networks and critical facilities, is increasingly vulnerable, worsening the impact on people's lives and businesses. Examining adaptation strategies, the review highlights the need for resilient urban planning and the integration of green infrastructure. Additionally, it delves into the role of advanced technologies, such as artificial intelligence, remote sensing, and predictive modeling, in improving flood prediction, monitoring, and management. The socio-economic implications of urban flooding are discussed, emphasizing unequal vulnerability and the importance of inclusive policies. In conclusion, the review stresses the urgency of addressing climate-induced urban flooding through a holistic analysis of statistical trends, scientific evidence, infrastructure vulnerabilities, and adaptive measures. The integration of advanced technologies and a comprehensive understanding of socio-economic implications are essential for developing effective, inclusive strategies. This review serves as a valuable resource, offering insights for policymakers, researchers, and practitioners striving for climate-resilient urban futures amid escalating climate change impacts.

Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies.