Integrated Methodology for Urban Flood Risk Mapping at the Microscale in Ungauged Regions: A Case Study of Hurghada, Egypt

Urban flood numerical modeling and hydraulic performance of a drainage network: A case study in Algiers, Algeria

Urban sewer system management is challenging due to its higher vulnerability to flooding caused by rapid urbanization and climate change. For local decision-makers, storm water management is essential for urban planning and development. Therefore, the main objective of this study is to develop a numerical model for the sewerage network of the central catchment area of Algiers since it has experienced frequent overflows during the winter season. For this purpose, to model the sewerage networks, the model was built by coupling ArcGIS with MIKE URBAN. Its calibration and validation were performed using real-time measurements with a time step of 15 min. The model was evaluated by several statistical indicators, such as the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS). The model results showed acceptable model performance, with an NSE superior to 0.50, R2 of approximately 0.63, RMSE of 7%, and PBIAS of 10% during the validation of the model. The performance parameters prove the reliability of the developed model. The employed model can be applied in other regions and could be helpful for policymakers and managers to improve flood mitigation measures based on the model prediction of the sewerage network.

AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat, India

Flooding is one of the major natural catastrophic disasters that causes massive environmental and socioeconomic destruction. The magnitude of losses due to floods has prompted researchers to focus more on robust and comprehensive modeling approaches for alleviating flood damages. Recently developed multi-criteria decision making (MCDM) methods are being widely used to construct decision-making process more participatory, rational, and efficient. In this study, two statistical MCDM approaches, namely the analytical hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), have been employed to generate flood risk maps together with hazard and vulnerability maps in a GIS framework for Navsari city in Gujarat, India, to identify the vulnerable areas that are more susceptible to inundation during floods. The study area was divided into 10 sub areas (i.e., NC1 to NC10) to appraise the degree of flood hazard, vulnerability and risk intensities in terms of areal coverage and categorized under 5 intensity classes, viz., very low, low, moderate, high, and very high. A total of 14 flood indicators, seven each for hazard (i.e., elevation, slope, drainage density, distance to river, rainfall, soil, and flow accumulation) and vulnerability (i.e., population density, female population, land use, road network density, household, distance to hospital, and literacy rate) were considered for evaluating the flood risk. Flood risk coverage evaluated from the two approaches were compared with the flood extent computed from the actual flood data collected at 36 random locations. Results revealed that the TOPSIS approach estimated more precise flood risk coverage than the AHP approach, yielding high R² values, i.e., 0.78 to 0.95 and low RMSE values, i.e., 0.95 to 0.43, for all the 5 risk intensity classes. The sub areas identified under "very high" and "high" risk intensity classes (i.e., NC1, NC4, NC6, NC7, NC8, and NC10) call for immediate flood control measures with a view to palliate the extent of flood risk and consequential damages. The study demonstrates the potential of AHP and TOPSIS integrated with GIS towards precise identification of flood-prone areas for devising effective flood management strategies.

Big Data in Criteria Selection and Identification in Managing Flood Disaster Events Based on Macro Domain PESTEL Analysis: Case Study of Malaysia Adaptation Index

The impact of Big Data (BD) creates challenges in selecting relevant and significant data to be used as criteria to facilitate flood management plans. Studies on macro domain criteria expand the criteria selection, which is important for assessment in allowing a comprehensive understanding of the current situation, readiness, preparation, resources, and others for decision assessment and disaster events planning. This study aims to facilitate the criteria identification and selection from a macro domain perspective in improving flood management planning. The objectives of this study are (a) to explore and identify potential and possible criteria to be incorporated in the current flood management plan in the macro domain perspective; (b) to understand the type of flood measures and decision goals implemented to facilitate flood management planning decisions; and (c) to examine the possible structured mechanism for criteria selection based on the decision analysis technique. Based on a systematic literature review and thematic analysis using the PESTEL framework, the findings have identified and clustered domains and their criteria to be considered and applied in future flood management plans. The critical review on flood measures and decision goals would potentially equip stakeholders and policy makers for better decision making based on a disaster management plan. The decision analysis technique as a structured mechanism would significantly improve criteria identification and selection for comprehensive and collective decisions. The findings from this study could further improve Malaysia Adaptation Index (MAIN) criteria identification and selection, which could be the complementary and supporting reference in managing flood disaster management. A proposed framework from this study can be used as guidance in dealing with and optimising the criteria based on challenges and the current application of Big Data and criteria in managing disaster events.

Modeling, mapping and analysis of urban floods in India-a review on geospatial methodologies

An increasing trend of urban floods in India from past several years causes major damages on Indian cities. By 2050, more than half of the population in the developing countries like India are expected to migrate to urban regions. Urbanization is triggered in developing countries as people migrate to cities in search of employment opportunities resulting in formation of new slums. With high density of population concentration in cities, urban floods are triggered leading to a significant impact of human life and economy of the country. The review focuses on addressing the urban flood occurrence in India and its relationship with population growth climate change. The study also describes the impact of urban floods to the environment and integrated methodologies adopted over decades for the prediction and effective mitigation and management during a disaster event.

A Framework for Calculating Peak Discharge and Flood Inundation in Ungauged Urban Watersheds Using Remotely Sensed Precipitation Data: A Case Study in Freetown, Sierra Leone

As the human population increases, land cover is converted from vegetation to urban development, causing increased runoff from precipitation events. Additional runoff leads to more frequent and more intense floods. In urban areas, these flood events are often catastrophic due to infrastructure built along the riverbank and within the floodplains. Sufficient data allow for flood modeling used to implement proper warning signals and evacuation plans, however, in least developed countries (LDC), the lack of field data for precipitation and river flows makes hydrologic and hydraulic modeling difficult. Within the most recent data revolution, the availability of remotely sensed data for land use/land cover (LULC), flood mapping, and precipitation estimates has increased, however, flood mapping in urban areas of LDC is still limited due to low resolution of remotely sensed data (LULC, soil properties, and terrain), cloud cover, and the lack of field data for model calibration. This study utilizes remotely sensed precipitation, LULC, soil properties, and digital elevation model data to estimate peak discharge and map simulated flood extents of urban rivers in ungauged watersheds for current and future LULC scenarios. A normalized difference vegetation index (NDVI) analysis was proposed to predict a future LULC. Additionally, return period precipitation events were calculated using the theoretical extreme value distribution approach with two remotely sensed precipitation datasets. Three calculation methods for peak discharge (curve number and lag method, curve number and graphical TR-55 method, and the rational equation) were performed and compared to a separate Soil and Water Assessment Tool (SWAT) analysis to determine the method that best represents urban rivers. HEC-RAS was then used to map the simulated flood extents from the peak discharges and ArcGIS helped to determine infrastructure and population affected by the floods. Finally, the simulated flood extents from HEC-RAS were compared to historic flood event points, images of flood events, and global surface water maximum water extent data. This analysis indicates that where field data are absent, remotely sensed monthly precipitation data from Integrated Multi-satellitE Retrievals for GPM (IMERG) where GPM is the Global Precipitation Mission can be used with the curve number and lag method to approximate peak discharges and input into HEC-RAS to represent the simulated flood extents experienced. This work contains a case study for seven urban rivers in Freetown, Sierra Leone.

Voluntary Local Review Framework to Monitor and Evaluate the Progress towards Achieving Sustainable Development Goals at a City Level: Buraidah City, KSA and SDG11 as A Case Study

Around the world, cities are on the front lines of sustainable development. They are responsible for more than 70% of global carbon emissions. Many of these cities are experiencing dangerous levels of pollution, underemployment, and health disparities. Since 2015, 193 countries have endorsed the 17 Sustainable Development Goals (SDGs), intended to help address a wide range of challenges affecting cities and ultimately secure the resources for their next generations. All states are expected to present the national progress towards the SDGs through a Voluntary National Review (VNR). Despite the importance of the cities within this framework, only a handful of them worldwide have actively begun to review and assess progress towards these SDGs on a city scale. This paper seeks to develop a Voluntary Local Review (VLR) framework to assess and evaluate the progress of cities towards contributing to the SDGs. This framework has been developed by localizing the international and national frameworks to measure the performance of cities as they advance towards achieving the SDGs. Such a framework can serve as a tool for benchmarking progress on different aspects of sustainable development and help urban planners and policymakers prioritize policies and actions to improve urban quality of life. This framework is applied to monitor and evaluate the progress of the city of Buraidah in Saudi Arabia, as it strives towards achieving the targets of SDG11 (“Make cities and human settlements inclusive, safe, resilient and sustainable”).

An Overview of Multi-Criteria Decision Analysis (MCDA) Application in Managing Water-Related Disaster Events: Analyzing 20 Years of Literature for Flood and Drought Events

This paper provides an overview of multi-criteria decision analysis (MCDA) applications in managing water-related disasters (WRD). Although MCDA has been widely used in managing natural disasters, it appears that no literature review has been conducted on the applications of MCDA in the disaster management phases of mitigation, preparedness, response, and recovery. Therefore, this paper fills this gap by providing a bibliometric analysis of MCDA applications in managing flood and drought events. Out of 818 articles retrieved from scientific databases, 149 articles were shortlisted and analyzed using a Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) approach. The results show a significant growth in MCDA applications in the last five years, especially in managing flood events. Most articles focused on the mitigation phase of DMP, while other phases of preparedness, response, and recovery remained understudied. The analytical hierarchy process (AHP) was the most common MCDA technique used, followed by mixed-method techniques and TOPSIS. The article concludes the discussion by identifying a number of opportunities for future research in the use of MCDA for managing water-related disasters.

A Methodological Approach towards Sustainable Urban Densification for Urban Sprawl Control at the Microscale: Case Study of Tanta, Egypt

When a high need for new residences coincides with an insufficient area of obtainable land within cities, urban sprawl occurs. Although densification is a well-known policy for controlling urban sprawl, one of the main challenges faced by researchers is that of determining urban densification potentials and priorities at the city scale. This paper aims to establish a methodology to facilitate decision-making regarding urban densification using five different methods. The proposed methodology utilizes high-quality city strategic plans (CSPs) and urban regulation documents and adopts geographic information systems (GISs) to determine and map the potential areas for densification. Multiple sustainability parameters, including environmental, economic, and social parameters, are selected, and weighted using an analytical hierarchy process (AHP) to prioritize the densification sites. The proposed method is tested in Tanta, Egypt, which has suffered due to agricultural losses of approximately 10 km2 within the last 50 years. The results credibly demonstrate the means by which to accommodate approximately 428% of the anticipated population increase in Tanta by 2027 and thereby save more than 53% of the approved deducted agricultural lands under the current urban regulations. Generally, this methodology offers a new model to optimize urban densification, which can be effective in urban management to achieve city resilience.