The Influence of Hazard Maps and Trust of Flood Controls on Coastal Flood Spatial Awareness and Risk Perception

Understanding the impact of digital, interactive flood hazard maps and flood control systems on public flood risk perception could enhance risk communication and management. This study analyzed a survey of residents living near California’s Newport Bay Estuary and found that self-rated nonspatial perceptions of dread or concern over future flood impacts were positively associated with spatial awareness of flood-prone areas. Trust in flood control systems was associated with greater spatial flood hazard awareness but weaker nonspatial dread or concern, suggesting residents who witnessed and trust flood control systems developed a confident sense of flood-prone areas and that this confidence reduced the overall nonspatial sense of flood dread and concern. Viewing a flood hazard map eliminated differences in spatial hazard awareness between subgroups that existed prior to viewing a map, and viewing a map with estimated flood depth and greater spatial differentiation was associated with higher levels of postmap spatial awareness.

Download Full-text

Building hazard maps with differentiated risk perception for flood impact assessment

Natural Hazards and Earth System Science ◽

10.5194/nhess-20-2647-2020 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2647-2663

Author(s):

Punit K. Bhola ◽

Jorge Leandro ◽

Markus Disse

Keyword(s):

Risk Perception ◽

Impact Assessment ◽

Flood Hazard ◽

Flood Forecasting ◽

Exceedance Probability ◽

Generation Process ◽

Hazard Maps ◽

Flood Impact ◽

The Impact ◽

Flood Hazard Maps

Abstract. In operational flood risk management, a single best model is used to assess the impact of flooding, which might misrepresent uncertainties in the modelling process. We have used quantified uncertainties in flood forecasting to generate flood hazard maps that were combined based on different exceedance probability scenarios. The purpose is to differentiate the impacts of flooding depending on the building use, enabling, therefore, more flexibility for stakeholders' variable risk perception profiles. The aim of the study is thus to develop a novel methodology that uses a multi-model combination of flood forecasting models to generate flood hazard maps with differentiated exceedance probability. These maps take into account uncertainties stemming from the rainfall–runoff generation process and could be used by decision makers for a variety of purposes in which the building use plays a significant role, e.g. flood impact assessment, spatial planning, early warning and emergency planning.

Download Full-text

Combination of geomorphic classifiers through Machine Learning-based techniques for flood hazard assessment

10.5194/egusphere-egu21-4723 ◽

2021 ◽

Author(s):

Andrea Magnini ◽

Michele Lombardi ◽

Simone Persiano ◽

Antonio Tirri ◽

Francesco Lo Conti ◽

...

Keyword(s):

Machine Learning ◽

Hazard Assessment ◽

Water Depth ◽

Flood Hazard ◽

Research Centre ◽

Hazard Mapping ◽

Hazard Maps ◽

Flood Hazard Assessment ◽

Flood Prone Areas ◽

Flood Hazard Maps

Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (i.e.&#160;land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource-intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein &#8220;DEM-based methods&#8221;) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km2, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (i) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.&#160;

Download Full-text

Assessing the global risk of climate change to re/insurers using catastrophe models and hazard maps

10.5194/egusphere-egu2020-5323 ◽

2020 ◽

Author(s):

Sarah Jones ◽

Emma Raven ◽

Jane Toothill

Keyword(s):

Climate Change ◽

Sea Level ◽

Flood Risk ◽

River Flow ◽

Climate Model ◽

Flood Hazard ◽

Insurance Industry ◽

Global Climate Model ◽

Hazard Maps ◽

The Impact

In 2018 worldwide natural catastrophe losses were estimated at around USD $155 billion, resulting in the fourth-highest insurance payout on sigma records, and in 2020 JBA Risk Management (JBA) estimate 2 billion people will be at risk to inland flooding. By 2100, under a 1.5&#176;C warming scenario, the cost of coastal flooding alone as a result of sea level rise could reach USD $10.2 trillion per year, assuming no further adaptation. It is therefore imperative to understand the impact climate change may have on global flood risk and insured losses in the future.The re/insurance industry has an important role to play in providing financial resilience in a changing climate. Although integrating climate science into financial business remains in its infancy, modelling companies like JBA are increasingly developing new data and services to help assess the potential impact of climate change on insurance exposure.We will discuss several approaches to incorporating climate change projections with flood risk data using examples from research collaborations and commercial projects. Our case studies will include: (1) building a national-scale climate change flood model through the application of projected changes in river flow, rainfall and sea level to the stochastic event set in the model, and (2) using Global Climate Model data to adjust hydrological inputs driving 2D hydraulic models to develop climate change flood hazard maps.These tools provide outputs to meet different needs, and results may sometimes invoke further questions. For example: how can an extreme climate scenario produce lower flood risk than a conservative one? Why may adjacent postcodes' flood risk differ? We will explore the challenges associated with interpreting these results and the potential implications for the re/insurance industry.

Download Full-text

Delineating flood prone areas using a statistical approach

10.7287/peerj.preprints.1937v2 ◽

2016 ◽

Author(s):

Ivan Marchesini ◽

Mauro Rossi ◽

Paola Salvati ◽

Marco Donnini ◽

Simone Sterlacchini ◽

...

Keyword(s):

Statistical Approach ◽

Learning Algorithm ◽

Flood Hazard ◽

Environmental Data ◽

Training Data ◽

Hazard Maps ◽

Elevation Data ◽

Terrain Elevation ◽

Flood Prone Areas ◽

Flood Hazard Maps

Floods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geo-environmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.

Download Full-text

Pengendalian Pemanfaatan Ruang Kawasan Terdampak Banjir Di Perkotaan Sinjai

Journal of Urban Planning Studies ◽

10.35965/jups.v1i2.49 ◽

2021 ◽

Vol 1 (2) ◽

pp. 147-164

Author(s):

Muhammad Muhammad Afandi Naser ◽

Murshal Manaf ◽

Tri Budiharto

Keyword(s):

Spatial Pattern ◽

Urban Areas ◽

Flood Control ◽

Flood Hazard ◽

Descriptive Analysis ◽

Disaster Mitigation ◽

Qualitative Descriptive ◽

Spatial Use ◽

High Flood ◽

Flood Prone Areas

Abstract. This study aims to explain the characteristics of flood-affected areas, in order to analyze land suitability and spatial use in flood-affected areas and to formulate the concept of controlling the spatial use of flood-affected areas. This research is qualitative-quantitative with the analysis techniques used are scoring analysis, superimpose analysis, qualitative descriptive analysis and space envelope analysis. The results show that there are three classifications of flood hazard, namely low, medium and high, where in the high flood-prone areas in Sinjai city there are five villages, namely Balangnipa Village, Biringere Village, Bongki Village, Lappa Village and Samataring Village. The results of the second research objective were obtained from the overlay prone to flooding and the spatial pattern of the Sinjai urban RDTR, where the dominant spatial pattern of high flood prone areas is in the housing zone which covers an area of 564,185 hectares. The direction of the strategic concept based on three classifications of flood hazard in Sinjai urban areas is proposed in the form of disaster mitigation in the form of recommendations for flood control in accordance with the characteristics of flood-prone areas, and in controlling spatial use in the form of zoning regulations and permit proposals at the research location granting land use permits for each area prone to high, medium and low flood disasters. Abstrak. Penelitian ini bertujuan untuk menerangkan karakteristik kawasan terdampak banjir, guna menganalisis kesesuaian lahan dan pemanfaatan ruang pada kawasan terdampak banjir dan merumuskan konsep pengendalian pemanfaatan ruang kawasan terdampak banjir. Penelitian ini bersifat kualitatif-kuantitatif dengan teknik analisis yang digunakan adalah analisis skoring, analisis superimpose, analisis deskriptif kualitatif dan analisis amplop ruang. Hasil penelitian diketahui bahwa terdapat tiga klasifikasi kerawanan banjir yaitu rendah, sedang dan tinggi yang dimana pada kawasan rawan banjir tinggi di perkotaan Sinjai terdapat di lima kelurahan yaitu Kelurahan Balangnipa, Kelurahan Biringere, Kelurahan Bongki, Kelurahan Lappa dan Kelurahan Samataring. Adapun hasil tujuan penelitian kedua yang didapat dari overlaynya rawan banjir dan pola ruang RDTR perkotaan Sinjai, dimana yang berdominan pada pola ruang kawasan rawan banjir tinggi terdapat di zona perumahan yang luasnya sebesar 564.185 Ha. Arahan konsep strategi berasarkan tiga klasifikasi kerawanan banjir di kawasan perkotaan Sinjai diusulkan dalam bentuk mitigasi bencana berupa rekomendasi pengendalian banjir yang sesuai dengan karakteristik pada kawasan rawan banjir, dan pada pengendalian pemafaatan ruang berupa peraturan zonasi dan usulan perizinan di lokasi penelitian dapat disimpulkan bahwa terdapat perbedaan perilaku pemberian perizinan penggunaan lahan pada setiap kawasan rawan bencana banjir tinggi, sedang maupun rendah.

Download Full-text

Floods in Siberia: geographical and statistical analysis for the period of climate change

Vestnik of Saint Petersburg University Earth Sciences ◽

10.21638/spbu07.2021.103 ◽

2021 ◽

Vol 66 (1) ◽

Author(s):

Natalia V. Kichigina ◽

Keyword(s):

Climate Change ◽

Statistical Analysis ◽

Flood Hazard ◽

Hazard Maps ◽

Current Position ◽

Snow Melting ◽

Natural Factor ◽

Ice Jams ◽

Runoff Analysis ◽

The Impact

In Siberia, floods are one of dangerous natural disaster. The danger of floods varies under the climatic and anthropogenic changes, as well as socio-economic development. The aim was to study the current position of problems associated with flood hazard. A key to understanding the flood situation is geographical and statistical analysis of the floods for the period of climate change (1985-2019). Such analyzes addresses the following aspects: study of flood genesis and recurrence, the severity of the impact for floods of different genesis; maxima runoff analysis as the principal cause of floods; analysis of the spatial distribution of settlements vulnerable to flooding; analysis of the ice jams and ice dams as a specific natural factor causing the floods in Siberia; assessment of the degree of danger, and identification of areas with the different integral flood danger. In Siberia, more than 1400 settlements are flooded at regular intervals. Most of them are concentrated in the southern, most developed territories in the Ob, Tom and Yeniseiy basins. In Siberia, rainfall, mixed (from snow melting with rainfall) and ice-dam floods are the most dangerous. They have the highest recurrence and severity of the impact. The greatest floods risk is in the most populated and economically developed southern regions within the Ob, Lena and Yenisey rivers and Lake Baikal basins. Territories with the highest risk of floods were determined. For the Baikal region, one of the most developed territories of Siberia, the flood hazard was determined for all administrative districts. Flood hazard maps for Siberian regions can be the basis for developing the flood adaptation strategies.

Download Full-text

Hydraulic Efficiency of Green-Blue Flood Control Scenarios for Vegetated Rivers: 1D and 2D Unsteady Simulations

Water ◽

10.3390/w13192620 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2620

Author(s):

Giuseppe Francesco Cesare Lama ◽

Matteo Rillo Migliorini Giovannini ◽

Alessandro Errico ◽

Sajjad Mirzaei ◽

Roberta Padulano ◽

...

Keyword(s):

Best Management Practices ◽

Urban Areas ◽

Flood Control ◽

Management Practices ◽

Flood Hazard ◽

Control Area ◽

Flood Hazards ◽

Hydraulic Efficiency ◽

Peak Reduction ◽

The Impact

Flood hazard mitigation in urban areas crossed by vegetated flows can be achieved through two distinct approaches, based on structural and eco-friendly solutions, referred to as grey and green–blue engineering scenarios, respectively; this one is often based on best management practices (BMP) and low-impact developments (LID). In this study, the hydraulic efficiency of two green–blue scenarios in reducing flood hazards of an urban area crossed by a vegetated river located in Central Tuscany (Italy), named Morra Creek, were evaluated for a return period of 200 years, by analyzing the flooding outcomes of 1D and 2D unsteady hydraulic simulations. In the first scenario, the impact of a diffuse effect of flood peak reduction along Morra Creek was assessed by considering an overall real-scale growth of common reed beds. In the second scenario, riverine vegetation along Morra Creek was preserved, while flood hazard was mitigated using a single vegetated flood control area. This study demonstrates well the benefits of employing green–blue solutions for reducing flood hazards in vegetated rivers intersecting agro-forestry and urban areas while preserving their riverine ecosystems. It emerged that the first scenario is a valuable alternative to the more impacting second scenario, given the presence of flood control areas.

Download Full-text

Delineating flood prone areas using a statistical approach

10.7287/peerj.preprints.1937v1 ◽

2016 ◽

Author(s):

Ivan Marchesini ◽

Mauro Rossi ◽

Paola Salvati ◽

Marco Donnini ◽

Simone Sterlacchini ◽

...

Keyword(s):

Statistical Approach ◽

Learning Algorithm ◽

Flood Hazard ◽

Environmental Data ◽

Training Data ◽

Hazard Maps ◽

Elevation Data ◽

Terrain Elevation ◽

Flood Prone Areas ◽

Flood Hazard Maps

Download Full-text

Fluvial flooding hazard assessment in Northern Italy: potential and informativeness of different geomorphic classifiers

10.5194/egusphere-egu2020-19760 ◽

2020 ◽

Author(s):

Attilio Castellarin ◽

Simone Persiano ◽

Caterina Samela ◽

Andrea Magnini ◽

Stefano Bagli ◽

...

Keyword(s):

Flood Hazard ◽

Numerical Models ◽

The United States ◽

Northern Italy ◽

Economic Losses ◽

Steady Increase ◽

Threshold Values ◽

Hazard Maps ◽

Flood Prone Areas ◽

Flood Hazard Maps

The steady increase of economic losses and social consequences caused by flood events in Europe, as a result of the combined effects of anthropization (e.g. land-use and land-cover changes) and climate change, calls for updated and efficient technologies for assessing pluvial, fluvial and coastal flood hazards and risks. In this context, the EIT-Climate KIC SaferPLACES () project aims at exploring and developing innovative and simplified modelling techniques to assess and map flood hazard and risk in urban environments under current and future climates. Concerning fluvial flooding, detailed inundation maps can be accurately obtained by means of hydrological and hydraulic numerical models, whose application, though, is often very resource intensive. For this reason, consistent and harmonized national flood hazard maps are still lacking in many countries of the world. Several studies have proved that flood-prone areas can be delineated by considering linear binary geomorphic classifiers, which are computed by analysing Digital Elevation Models, DEMs, and whose threshold values are calibrated relative to existing hydraulic flood hazard maps. One of these indices, the so-called Geomorphic Flood Index (GFI), was recently shown to be cost-effective, reliable and efficient for identifying flood-prone areas in several test sites in the United States, Africa and Europe. As part of the activities of SaferPLACES, in this study we test different geomorphic classifiers (GFI included) for the identification of flood-prone areas in a wide area in Northern Italy (c.a. 100000 km2, including Po, Adige, Brenta-Bacchiglione and Reno river basins). We refer to the recently compiled MERIT (Multi-Error-Removed Improved-Terrain) DEM, a 3sec-resolution (~90m at the equator) DEM developed by removing multiple error components from existing spaceborne DEMs. As reference maps for the calibration, we select the flood hazard maps provided by (i) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Center (JRC) of the European Commission. Our study confirms the better performances of GFI compared to other geomorphic classifiers, also providing useful information regarding the sensitivity of GFI threshold values relative to different reference hazard maps; it also suggests as a promising avenue for future researches the combination of multiple geomorphic indices through data-driven approaches and artificial intelligence.

Download Full-text

Impact of Flood Control Systems on the Probability Distribution of Floods

10.5194/hess-2021-47 ◽

2021 ◽

Author(s):

Salvatore Manfreda ◽

Domenico Miglino ◽

Cinzia Albertini

Keyword(s):

Probability Distribution ◽

Control Systems ◽

Flood Control ◽

Mathematical Formulation ◽

River Basins ◽

Flood Mitigation ◽

Mathematical Framework ◽

Flood Peak ◽

Detention Basins ◽

The Impact

Abstract. Detention dams are one of the most effective practices for flood mitigation. Therefore, the impact of these structures on the basin hydrological response is critical for flood management and the design of flood control structures. With the aim to provide a mathematical framework to interpret the effect of flow control systems on river basin dynamics, the functional relationship between inflows and outflows is investigated and derived in a closed-form. This allowed the definition of a theoretically derived probability distribution of the peak outflows from in-line detention basins. The model has been derived assuming a rectangular hydrograph shape with a fixed duration, and a random flood peak. In the present study, the undisturbed flood distribution is assumed to be Gumbel distributed, but the proposed mathematical formulation can be extended to any other flood-peak probability distribution. A sensitivity analysis of parameters highlighted the influence of detention basin capacity and rainfall event duration on flood mitigation on the probability distribution of the peak outflows. The mathematical framework has been tested using for comparison a Monte Carlo simulation where most of the simplified assumptions used to describe the dam behaviours are removed. This allowed to demonstrate that the proposed formulation is reliable for small river basins characterized by an impulsive response. The new approach for the quantification of flood peaks in river basins characterised by the presence of artificial detention basins can be used to improve existing flood mitigation practices, support the design of flood control systems and flood risk analyses.

Download Full-text