scholarly journals An Entropic Approach to Estimating the Instability Criterion of People in Floodwaters

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 74
Author(s):  
Zhongfan Zhu ◽  
Yongpeng Zhang ◽  
Lufeng Gou ◽  
Bo Pang
Keyword(s):  
Water Depth ◽  
Flood Hazard ◽  
Instability Criterion ◽  
Physical Parameters ◽  
Probability Method ◽  
Chinese Adults ◽  
Deterministic Models ◽  
Safety Criterion ◽  
Incipient Velocity ◽  
Adults And Children

People are always susceptible to a loss of stability in urban floodwaters that leads to serious casualties. Thus, the safety criterion for the instability of people in floodwaters must be determined. In this study, the hydrodynamic criterion of the instability of people in floodwaters in terms of the incipient velocity and water depth is derived using the probability method based on Shannon entropy theory. The derived model can characterize variations in the incipient velocity of people in floodwaters with respect to the inundating water depth. Furthermore, a comparison with seven experimental datasets available in the literature shows the validity of the proposed entropy-based model considering data scattering. A sensitivity analysis of the derived model to some of the incorporated parameters was performed, and the qualitative results are in accordance with our understanding of the physical mechanism of the instability of people in floodwaters. Taking the physical parameters (height and mass) of Chinese adults and children as a representative example, this study also showed the vulnerability degree of Chinese adults and children subject to floodwaters. These findings could provide a reference for administrators and stakeholders for flood hazard mitigation and flood strategy management. This study shows that an entropy-based method could be a valuable addition to existing deterministic models for characterizing the instability criterion of people in an urban flooding event.

scholarly journals Compounding effects of sea level rise and fluvial flooding

2017 ◽  
Vol 114 (37) ◽  
pp. 9785-9790 ◽  
Author(s):  
Hamed R. Moftakhari ◽  
Gianfausto Salvadori ◽  
Amir AghaKouchak ◽  
Brett F. Sanders ◽  
Richard A. Matthew
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Hazard Assessment ◽  
Failure Probability ◽  
River Flow ◽  
Flood Hazard ◽  
High Tide ◽  
Probability Method ◽  
Flood Hazards ◽  
Flood Hazard Assessment

Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.

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

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

<p><span xml:lang="EN-US" data-contrast="auto"><span>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 (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i.e.</span></span><span xml:lang="EN-US" data-contrast="auto"><span> 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</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-</span></span><span xml:lang="EN-US" data-contrast="auto"><span>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 “DEM-based methods”) 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 km</span></span><sup><span xml:lang="EN-US" data-contrast="auto"><span>2</span></span></sup><span xml:lang="EN-US" data-contrast="auto"><span>, 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 (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i</span></span><span xml:lang="EN-US" data-contrast="auto"><span>) 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.</span></span><span> </span></p>

scholarly journals A hydro-sedimentary modeling system for flash flood propagation and hazard estimation under different agricultural practices

2014 ◽  
Vol 14 (3) ◽  
pp. 625-634 ◽  
Author(s):  
N. N. Kourgialas ◽  
G. P. Karatzas
Keyword(s):  
Sediment Transport ◽  
Flow Velocity ◽  
Water Depth ◽  
Riparian Vegetation ◽  
Flash Flood ◽  
Flood Hazard ◽  
Hydraulic Modeling ◽  
Modeling Framework ◽  
Mike 11 ◽  
Discharge Velocity

Abstract. A modeling system for the estimation of flash flood flow velocity and sediment transport is developed in this study. The system comprises three components: (a) a modeling framework based on the hydrological model HSPF, (b) the hydrodynamic module of the hydraulic model MIKE 11 (quasi-2-D), and (c) the advection–dispersion module of MIKE 11 as a sediment transport model. An important parameter in hydraulic modeling is the Manning's coefficient, an indicator of the channel resistance which is directly dependent on riparian vegetation changes. Riparian vegetation's effect on flood propagation parameters such as water depth (inundation), discharge, flow velocity, and sediment transport load is investigated in this study. Based on the obtained results, when the weed-cutting percentage is increased, the flood wave depth decreases while flow discharge, velocity and sediment transport load increase. The proposed modeling system is used to evaluate and illustrate the flood hazard for different riparian vegetation cutting scenarios. For the estimation of flood hazard, a combination of the flood propagation characteristics of water depth, flow velocity and sediment load was used. Next, a well-balanced selection of the most appropriate agricultural cutting practices of riparian vegetation was performed. Ultimately, the model results obtained for different agricultural cutting practice scenarios can be employed to create flood protection measures for flood-prone areas. The proposed methodology was applied to the downstream part of a small Mediterranean river basin in Crete, Greece.

scholarly journals Effectiveness of Nature-Based Solutions in Mitigating Flood Hazard in a Mediterranean Peri-Urban Catchment

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2893
Author(s):  
Carla S.Ferreira ◽  
Sandra Mourato ◽  
Milica Kasanin-Grubin ◽  
António J.D. Ferreira ◽  
Georgia Destouni ◽  
...  
Keyword(s):  
Water Depth ◽  
Model Simulation ◽  
Flood Hazard ◽  
Coupled Model ◽  
Urban Catchment ◽  
Rainfall Events ◽  
Flood Depth ◽  
Mitigation And Adaptation ◽  
Flood Magnitude ◽  
Central Portugal

Urbanization alters natural hydrological processes and enhances runoff, which affects flood hazard. Interest in nature-based solutions (NBS) for sustainable mitigation and adaptation to urban floods is growing, but the magnitudes of NBS effects are still poorly investigated. This study explores the potential of NBS for flood hazard mitigation in a small peri-urban catchment in central Portugal, prone to flash floods driven by urbanization and short but intense rainfall events typical of the Mediterranean region. Flood extent and flood depth are assessed by manually coupling the hydrologic HEC-HMS and hydraulic HEC-RAS models. The coupled model was run for single rainfall events with recurrence periods of 10–, 20–, 50–, and 100–years, considering four simulation scenarios: current conditions (without NBS), and with an upslope NBS, a downslope NBS, and a combination of both. The model-simulation approach provides good estimates of flood magnitude (NSE = 0.91, RMSE = 0.08, MAE = 0.07, R2 = 0.93), and shows that diverting streamflow into abandoned fields has positive impacts in mitigating downslope flood hazard. The implementation of an upslope NBS can decrease the water depth at the catchment outlet by 0.02 m, whereas a downslope NBS can reduce it from 0.10 m to 0.23 m for increasing return periods. Combined upslope and downslope NBS have a marginal additional impact in reducing water depth, ranging from 0.11 m to 0.24 m for 10– and 100–year floods. Decreases in water depth provided by NBS are useful in flood mitigation and adaptation within the peri-urban catchment. A network of NBS, rather than small isolated strategies, needs to be created for efficient flood-risk management at a larger scale.

Magnetic Resonance Imaging Assessment of the Velopharyngeal Mechanism at Rest and During Speech in Chinese Adults and Children

2010 ◽  
Vol 53 (6) ◽  
pp. 1595-1615 ◽  
Author(s):  
Wei Tian ◽  
Heng Yin ◽  
Richard J. Redett ◽  
Bing Shi ◽  
Jin Shi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Chinese Adults ◽  
Magnetic Resonance Imaging Assessment ◽  
Adults And Children

Exposure, Social Vulnerability and Recovery Disparities in New Jersey after Hurricane Sandy

2014 ◽  
Vol 01 (01) ◽  
pp. 1450002 ◽  
Author(s):  
Susan L. Cutter ◽  
Ronald L. Schumann ◽  
Christopher T. Emrich
Keyword(s):  
New York ◽  
New Jersey ◽  
Water Depth ◽  
Social Vulnerability ◽  
Flood Hazard ◽  
Damage Zone ◽  
Hurricane Sandy ◽  
Housing Tenure ◽  
Government Support ◽  
Small Business Administration

Hurricane Sandy's 80 mph wind speeds did not achieve major hurricane status on the Saffir-Simpson scale, yet the storm had extreme consequences for the New York metropolitan area. Post-event recovery has been quite variable across the region, especially in New Jersey. This paper examines the progression of recovery at two time intervals — 6 months and 1 year after the storm made landfall. Based on in situ observations of reconstruction and repair within the surge damage zone along New Jersey's coast (N = 765 locations or points), we hypothesize that the timing and distribution of recovery is based on exposure (water depth, prior flood experience, building heights), vulnerability (social vulnerability, seasonal occupancy), and government support (FEMA assistance, Small Business Administration loans). Predictions of recovery (recovered, not recovered) based on these input measures is quite good at 6 months (80 percent correctly predicted) with slight improvements at 1 year (82 percent correctly predicted). The model is much better at predicting recovered points than recovering. In terms of the individual factors predicting recovery the level of exposure is the most significant, statewide. Within the two most affect counties — Monmouth and Ocean — only water depth and building height were significant predictors. Housing tenure (seasonal occupancy) was an important predictor of the location and timing of recovery for the first 6 months, especially in Monmouth County, but became insignificant thereafter. Government support in the form of FEMA individual assistance grants was a significant predictor statewide in both time periods. Clearly other factors are influencing the pace and location of recovery including confusion surrounding flood hazard zones and insurance. Those factors influencing the lack of recovery for the roughly 20 percent of our observations awaits further, more detailed analyses.

scholarly journals Objective Analysis of Corrosion Pits in Offshore Wind Structures Using Image Processing

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5428
Author(s):  
Waseem Khodabux ◽  
Feargal Brennan
Keyword(s):  
Image Processing ◽  
North Sea ◽  
Water Depth ◽  
Pitting Corrosion ◽  
Offshore Wind ◽  
Real Field ◽  
Deterministic Models ◽  
The North ◽  
The North Sea ◽  
Pit Depth

Corrosion in the marine environment is a complex mechanism. One of the most damaging forms of corrosion is pitting corrosion, which is difficult to design and inspect against. In the North Sea, multiple offshore wind structures have been deployed that are corroding from the inside out. One of the most notable corrosion mechanisms observed is pitting corrosion. This study addresses the lack of information both in the literature and the industry standards on the pitting corrosion profile for water depth from coupons deployed in the North Sea. Image processing was therefore conducted to extract the characteristics of the pit, which were defined as pit major length, minor length, area, aspect ratio, and count. The pit depth was measured using a pit gauge and the maximum pit depth was found to be 1.05 mm over 111 days of exposure. The goal of this paper is to provide both deterministic models and a statistical model of pit characteristics for water depth that can be used by wind farm operators and researchers to inform and simulate pits on structures based on the results of a real field experiment. As such, these models highlight the importance of adequate corrosion protection.

scholarly journals ADVANCES AND ISSUES IN UNCERTAINTY QUANTIFICATION FOR COASTAL FLOOD HAZARDS

2018 ◽  
pp. 19
Author(s):  
Taylor G. Asher ◽  
Jennifer L. Irish ◽  
Donald T. Resio
Keyword(s):  
Tropical Cyclones ◽  
Historical Data ◽  
Flood Hazard ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Probability Method ◽  
Flood Hazards ◽  
Significant Cost ◽  
Flooding Risk ◽  
Flood Elevation

Probabilistic flood hazard assessments have advanced substantially, with modern methods for dealing with the risk from tropical cyclones utilizing either a variation of the joint probability method with optimal sampling (JPM-OS)2,3 or the statistical deterministic track method (SDTM)1,4. In the JPM-OS, tropical cyclones are reduced to a set of 5 to 9 parameters, whose characteristics are analyzed statistically to develop a joint probability distribution for tropical cyclones of given characteristics. In the SDTM, cyclogenesis of a large number of storms is seeded via a statistical model from historical data, then storms are propagated using one of several different methods, incorporating varying degrees of the physics of cyclone transformation as the storms propagate. Due to the significant cost of storm surge simulations, some form of optimization or selection is then performed to reduce the number of synthetic storms that must be simulated to determine the flood elevation corresponding to a given recurrence interval (e.g. the so-called 100-year flood). In both methods, substantial uncertainties exist, which have a tendency to increase the estimated flooding risk. Efforts to account for these uncertainties have varied, and there remains significant work to be done. Here, we demonstrate how these uncertainties tend to increase the flood risk and show that additional sources of uncertainty remain to be accounted for.

scholarly journals A hydro-sedimentary modelling system for flash flood propagation and hazard estimation under different agricultural practices

2013 ◽  
Vol 1 (5) ◽  
pp. 5855-5880 ◽  
Author(s):  
N. N. Kourgialas ◽  
G. P. Karatzas
Keyword(s):  
Sediment Transport ◽  
Flow Velocity ◽  
Water Depth ◽  
Riparian Vegetation ◽  
Transport Model ◽  
Flash Flood ◽  
Flood Hazard ◽  
Mike 11 ◽  
Discharge Velocity ◽  
Modelling System

Abstract. A modelling system for the estimation of flash flood flow characteristics and sediment transport is developed in this study. The system comprises of three components: (a) a modelling framework based on the hydrological model HSPF, (b) the hydrodynamic module of the hydraulic model MIKE 11 (quasi-2-D), and (c) the advection-dispersion module of MIKE 11 as a sediment transport model. An important parameter in hydraulic modelling is the Manning's coefficient, an indicator of the channel resistance which is directly depended on riparian vegetation changes. Riparian vegetation effect on flood propagation parameters such as water depth (inundation), discharge, flow velocity, and sediment transport load is investigated in this study. Based on the obtained results, when the weed cutting percentage is increased, the flood wave depth decreases while flow discharge, velocity and sediment transport load increase. The proposed modelling system is used to evaluate and illustrate the flood hazard for different cutting riparian vegetation scenarios. For the estimation of flood hazard, a combination of the flood propagation characteristics of water depth, flow velocity and sediment load was used. Next, an optimal selection of the most appropriate agricultural cutting practices of riparian vegetation was performed. Ultimately, the model results obtained for different agricultural cutting practice scenarios can be employed to create flood protection measures for flood prone areas. The proposed methodology was applied to the downstream part of a small mediterranean river basin in Crete, Greece.

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