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"FLOOD RISK"
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A Preliminary Contribution towards a Risk-Based Model for Flood Management Planning Using BIM: A Case Study of Lisbon
Preparing a city for the impact of global warming is becoming of major importance. Adopting climate-proof policies and strategies in response to climate change has become a fundamental element for city planning. To this end, this research considers a multidisciplinary approach, at the local scale, able to connect urban planning and architecture, as a vital base for considering a coastal cities’ ability to control the consequences of climate change, specifically floods. So far, there is a scarcity of research connecting sea ground and land surveys, and this study could become a foundational reference for coastline settlement management using BIM. We found in BIM (Building Information Modeling) a possible tool for managing coastal risk, since it can combine crowdsourced data for geometric and information modeling of the city. The proposed BIM model includes a topography used for 3D thematic maps, a riverbed model, and a waterway model. This model aims to facilitate coordination across separate actors and interests since the urban area model is always updatable and improvable. Focusing on a case study of Lisbon, we developed risk-based 3D maps of the area close to the shoreline of the Tagus River.
Journal Article
Applying the flood vulnerability index as a knowledge base for flood risk assessment
Floods are one of the most common and widely distributed natural risks to life and property worldwide.?There is a need to identify the risk of flooding in flood prone areas to support decisions for flood management from high level planning proposals to detailed design. An important part of modern flood risk management is to assess vulnerability to floods. This assessment can be done only by using a parametric approach.?Worldwide there is a need to enhance our understanding of vulnerability and to also develop methodologies and tools to assess vulnerability.?One of the most important goals of assessing flood vulnerability is to create a readily understandable link between the theoretical concepts of flood vulnerability and the day-to-day decision-making process and to encapsulate this link in an easily accessible tool.?The present book portrays a holistic parametric approach to be used in flood vulnerability assessment and this way to facilitate the consideration of system impacts in water resources decision-making.?The approach was verified in practical applications on different spatial scales and comparison with deterministic approaches. The use of flood vulnerability approach can produce helpful understanding into vulnerability and capacities for using it in planning and implementing projects.
Book
Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS
In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas.
Journal Article
Using risk analysis for flood protection assessment
This book explores the benefits of using risk analysis techniques in the evaluation of flood protection structures, and examines the results of the environmental impact assessment for selected planned flood protection projects. The objective of the book is to propose a methodology for environmental impact assessment in water management. In more detail, flood mitigation measures are investigated with the aim of selecting the best option for the approval process. This methodology is intended to streamline the process of environmental impact assessment for structures in the field of the water management. The book?s environmental impact assessment system for water management structures analyzes the respective risks for different options. The results are intended to support the selection of future projects that pose minimum risks to the environment. Comparison of alternatives and designation of the optimal variant are implemented on the basis of selected criteria that objectively describe the characteristics of the planned alternatives and their respective impacts on the environment. The proposed Guideline for environmental impact assessment of flood protection objects employs multi-parametric risk analysis, a method intended to not only enhance the transparency and sensitivity of the evaluation process, but also successfully addresses the requirements of environmental impact assessment systems in the European Union. These modifications are intended to improve the outcomes of the environmental impact assessment, but may also be applied to other infrastructure projects. The case study proves that the primary aim? to improve transparency and minimize subjectivity in the environmental impact assessment process specific to flood protection structure projects? is met for the planned project in Kruézlov, Slovakia.
Book
How does flood resistance affect learning from flood experiences? A study of two communities in Central China
Property-level flood risk adaptation (PLFRA) has received significant attention in recent years, as flood resilience has become increasingly important in flood risk management. Earlier studies have indicated that learning from flood experiences can affect flood risk perception and the adoption of PLFRA measures; however, it remains unclear whether and how this learning process can be affected by flood control infrastructure—specifically, the level of flood resistance it offers. This study attempts to answer the question: Do people living in environments with different levels of flood resistance learn different lessons from flood experience, manifested in flood risk perception and PLFRA? We present a comparative study of the rural village of Xinnongcun and the urban community of Nanhuyayuan in Central China. In-person interviews with a total of 34 local residents were conducted to understand how flood experiences affect flood risk perception and PLFRA. We find that learning from flood experiences in the highly flood-resistant environment (Nanhuyayuan) does not contribute to flood risk perception but further enhances flood resistance, whereas learning in a less flood-resistant environment (Xinnongcun) leads to a better understanding of flood risk and promotes PLFRA. We argue that flood resistance can affect the learning from flood experiences. High flood resistance can suppress PLFRA through a different learning process that involves learning inertia and path dependency. In the search for flood resilience, this begs society to re-examine the widespread assertion that both structural and nonstructural measures are important in flood risk management.
Journal Article
Flood hazard assessment in Chenab River basin using hydraulic simulation modeling and remote sensing
This paper analyses flood frequency and performs flood simulation modeling along the Chenab River from Trimmu–Panjnad reach, to simulate flood 2014 and identify resultant flood inundated areas, under different return periods of floods. The aim of this study is to assist policymakers in designing efficient flood mitigation policies for the Chenab River, Pakistan which has been frequently hit by floods, especially in September 2014. Flood frequency analysis was carried out using log-Pearson type III (LP3) distributions to estimate peak flows with various return periods. The peak floods were incorporated into the Hydrologic Engineering Centre River Analysis System (HEC-RAS) model to predict the relevant flood levels for river stretches from Trimmu to Panjnad reach. The HEC-RAS model outcomes were integrated with ArcGIS to prepare flood risk maps that helped in identifying different flood-vulnerable areas. Two flood risk zones were developed; low to moderate and high to very high flood risk zones. The simulation analysis of a 50-year flood period showed that about 400% of the land would be submerged when compared to normal river flow. The simulation of the flood 2014 extent was found to clearly match the MODIS images provided by the United Nations Satellite Centre (UNOSAT). The surface areas of floods having different return periods, were also estimated. The utilization of the HEC-RAS model for simulating the 2014 flood, presents an opportunity for flood policymakers to enhance their understanding and formulate effective risk reduction strategies in the Chenab River basin.
Journal Article
Aggregation bias and its drivers in large‐scale flood loss estimation: A Massachusetts case study
Large‐scale estimations of flood losses are often based on spatially aggregated inputs. This makes risk assessments vulnerable to aggregation bias, a well‐studied, sometimes substantial outcome in analyses that model fine‐grained spatial phenomena at coarse spatial units. To evaluate this potential in the context of large‐scale flood risk assessments, we use data from a high‐resolution flood hazard model and structure inventory for over 1.3 million properties in Massachusetts and examine how prominent data aggregation approaches affect the magnitude and spatial distribution of flood loss estimates. All considered aggregation approaches rely on aggregate structure inventories but differ in whether flood hazard is also aggregated. We find that aggregating only structure inventories slightly underestimates overall losses (−10% bias), and when flood hazard data is spatially aggregated to even relatively small spatial units (census block), statewide aggregation bias can reach +366%. All aggregation‐based procedures fail to capture the spatial covariation of inputs distributions in the upper tails that disproportionately generate total expected losses. Our findings are robust to several key assumptions, add important context to published risk assessments and highlight opportunities to improve flood loss estimation uncertainty quantification.
Journal Article
Allowances for evolving coastal flood risk under uncertain local sea-level rise
Estimates of future flood hazards made under the assumption of stationary mean sea level are biased low due to sea-level rise (SLR). However, adjustments to flood return levels made assuming fixed increases of sea level are also inadequate when applied to sea level that is rising over time at an uncertain rate. SLR allowances—the height adjustment from historic flood levels that maintain under uncertainty the annual expected probability of flooding—are typically estimated independently of individual decision-makers’ preferences, such as time horizon, risk tolerance, and confidence in SLR projections. We provide a framework of SLR allowances that employs complete probability distributions of local SLR and a range of user-defined flood risk management preferences. Given non-stationary and uncertain sea-level rise, these metrics provide estimates of flood protection heights and offsets for different planning horizons in coastal areas. We illustrate the calculation of various allowance types for a set of long-duration tide gauges along U.S. coastlines.
Journal Article