Multivariate Hazard Assessment for Nonstationary Seasonal Flood Extremes Considering Climate Change

2020 ◽  
Vol 125 (18) ◽  
Author(s):  
Pengcheng Xu ◽  
Dong Wang ◽  
Vijay P. Singh ◽  
Huayu Lu ◽  
Yuankun Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Hazard Assessment ◽  
Seasonal Flood

scholarly journals Large-scale flood hazard assessment under climate change: A case study

2020 ◽  
Vol 147 ◽  
pp. 105765 ◽  
Author(s):  
Amirhossein Shadmehri Toosi ◽  
Shahab Doulabian ◽  
Erfan Ghasemi Tousi ◽  
Giancarlo Humberto Calbimonte ◽  
Sina Alaghmand
Keyword(s):  
Climate Change ◽  
Hazard Assessment ◽  
Large Scale ◽  
Flood Hazard ◽  
Flood Hazard Assessment

scholarly journals Multi-hazard assessment in Europe under climate change

2016 ◽  
Vol 137 (1-2) ◽  
pp. 105-119 ◽  
Author(s):  
Giovanni Forzieri ◽  
Luc Feyen ◽  
Simone Russo ◽  
Michalis Vousdoukas ◽  
Lorenzo Alfieri ◽  
...  
Keyword(s):  
Climate Change ◽  
Hazard Assessment

scholarly journals Rainfall-intensity effect on landslide hazard assessment due to climate change in north-western Colombian Andes

2020 ◽  
Author(s):  
Edier Vicente Aristizábal Giraldo ◽  
Edwin García Aristizábal ◽  
Roberto Marín Sánchez ◽  
Federico Gómez Cardona ◽  
Juan Carlos Guzmán Martínez
Keyword(s):  
Climate Change ◽  
Hazard Assessment ◽  
Rainfall Intensity ◽  
Landslide Hazard ◽  
Climate Change Scenarios ◽  
Infiltration Process ◽  
Landslide Hazard Assessment ◽  
Landslide Occurrence ◽  
Colombian Andes ◽  
North Western

Landslides triggered by rainfall are one of the most frequent causes of disasters in tropical countries and mountainous terrains. Recent studies show an upsurge in landslide occurrence as an expected impact of human-induced climate change. This paper presents the analysis and implementation of two different physically-based models, SHALSTAB and TRIGRS, to evaluate the effect of rainfall on landslide hazard assessment in the north-western Colombian Andes. Intensity-Duration-Frequency curves were used in climate change scenarios for different return periods. According to the results, although higher rainfall intensities increase, landslide occurrence does not escalate in a direct or proportional relationship. Considering a steady infiltration process (SHALSTAB), the results show an expansion of d unstable areas, compared with a transient infiltration process (TRIGRS). A greater influence of rainfall duration instead of rainfall intensity was observed. The results highlight the need for studies that incorporate the scenarios of variability and climate change in the hazard assessment and land planning in the long term.

Machine learning approaches to parameter calibration and uncertainty propagation for seasonal flood risk prediction 

2021 ◽  
Author(s):  
Oliver Bent ◽  
Julian Kuehnert ◽  
Sekou Remy ◽  
Anne Jones ◽  
Blair Edwards
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Uncertainty Quantification ◽  
Supervised Learning ◽  
Risk Prediction ◽  
Flood Risk ◽  
Parameter Calibration ◽  
Model Parameter ◽  
Seasonal Flood ◽  
Node Type

<div data-node-type="line"> <div data-node-type="line"><span>The increase in extreme weather associated with acute climate change is leading to more frequent and severe flood events. </span><span> In the window of months </span><span>and </span><span>years, climate change </span><span>adaption </span><span>is critical to </span><span>mitigate risk on socio-economic systems</span><span>. Mathematical and computational models have become widely used tool</span><span>s</span><span> to </span><span>quantify the impact of catastrophic flooding</span><span> and to predict future</span><span> flood</span> <span>risks</span><span>.</span><span> For decision makers to plan ahead and to select informed policies and interventions, it is </span><span>vital</span><span> that the uncertainties of these models are well estimated</span><span>.</span><span> Besides the inherent uncertainty of the mathematical model, uncertainties arise from parameter calibration and the driving observational climate data.</span></div> <div data-node-type="line"><span>Here we focus on the uncertainty of seasonal flood risk prediction for which we</span><span> treat u</span><span>ncertainty propagation</span><span> as a two step process. Firstly through calibration of model parameter distributions based on observational data. In order to propagate parameter uncertainties, the posed calibration framework is required to infer model parameter posterior distributions, as opposed to a single best-fit estimate. While secondly uncertainty is propagated by the </span><span>seasonal </span><span>weather </span><span>forecasts </span><span>driving the flood risk prediction models, such model drivers have their own inherent uncertainty as predictions. Through handling both sources of uncertainty and its propagation we investigate the impacts of combined</span><span> uncertainty</span><span> quantification methods</span><span> for flooding predictions. </span><span>The first step focussing on the flooding models own characterisation of uncertainty and the second characterising how uncertain model drivers impact our future predictions.</span></div> <div data-node-type="line"><span>In order to achieve the above features of a calibration framework for flood models we leverage concepts from machine learning. At the core we assume a minimisation of a loss function by the methods based on the supervised learning task in order to achieve calibration of the flood model. Uncertainty quantification is equally a growing field in machine learning or AI with regards the interpretability of parametric models. For this purpose we have adopted a Bayesian framework which contains natural descriptions of model expectation and variance. Through combining uncertainty quantification with the steps of supervised learning for parameter calibrations we propose a novel approach for seasonal flood risk prediction.</span></div> </div><div data-node-type="line"></div>

Drought hazard assessment in the context of climate change for South Korea

2015 ◽  
Vol 160 ◽  
pp. 106-117 ◽  
Author(s):  
Won-Ho Nam ◽  
Michael J. Hayes ◽  
Mark D. Svoboda ◽  
Tsegaye Tadesse ◽  
Donald A. Wilhite
Keyword(s):  
Climate Change ◽  
South Korea ◽  
Hazard Assessment ◽  
Drought Hazard

Framework for probabilistic tsunami hazard assessment considering the effects of sea-level rise due to climate change

2022 ◽  
Vol 94 ◽  
pp. 102152
Author(s):  
Abdul Kadir Alhamid ◽  
Mitsuyoshi Akiyama ◽  
Hiroki Ishibashi ◽  
Koki Aoki ◽  
Shunichi Koshimura ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Hazard Assessment ◽  
Tsunami Hazard ◽  
Tsunami Hazard Assessment ◽  
Probabilistic Tsunami Hazard Assessment

scholarly journals Effectiveness of water infrastructure for river flood management – Part 1: Flood hazard assessment using hydrological models in Bangladesh

2015 ◽  
Vol 370 ◽  
pp. 75-81 ◽  
Author(s):  
M. A. Gusyev ◽  
Y. Kwak ◽  
M. I. Khairul ◽  
M. B. Arifuzzaman ◽  
J. Magome ◽  
...  
Keyword(s):  
Climate Change ◽  
Risk Assessment ◽  
Hazard Assessment ◽  
Flood Risk ◽  
Flood Hazard ◽  
Climate Change Impacts ◽  
Flood Management ◽  
Flood Inundation ◽  
Flood Risk Assessment ◽  
Flood Hazard Assessment

Abstract. This study introduces a flood hazard assessment part of the global flood risk assessment (Part 2) conducted with a distributed hydrological Block-wise TOP (BTOP) model and a GIS-based Flood Inundation Depth (FID) model. In this study, the 20 km grid BTOP model was developed with globally available data on and applied for the Ganges, Brahmaputra and Meghna (GBM) river basin. The BTOP model was calibrated with observed river discharges in Bangladesh and was applied for climate change impact assessment to produce flood discharges at each BTOP cell under present and future climates. For Bangladesh, the cumulative flood inundation maps were produced using the FID model with the BTOP simulated flood discharges and allowed us to consider levee effectiveness for reduction of flood inundation. For the climate change impacts, the flood hazard increased both in flood discharge and inundation area for the 50- and 100-year floods. From these preliminary results, the proposed methodology can partly overcome the limitation of the data unavailability and produces flood~maps that can be used for the nationwide flood risk assessment, which is presented in Part 2 of this study.

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