Methodology for risk assessment of flash flood events due to climate and land-use changes: application to the Llobregat basin

Velasco, Marc; Cabello, À.; Escaler, I.; Barredo, José I.; Barrera-Escoda, A.

doi:10.2166/wcc.2014.027

Cited by 7 publications

(4 citation statements)

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“…The scope of study includes watersheds (Nuanchan et al 2015;, deserts (Duan et al 2012), wetlands (Gong et al 2011), etc., delimited by ecological scope, as well as provinces and municipalities delimited by administrative or territorial scope, and countries (Zhao et al 2007;Cirelli & Vineri 2014;Burgalassi & Luzzati 2015;Li & Qi 2019). The research content extends to urban structure (Yang et al 2017), urban flooding (Velasco et al 2014;Philip et al 2021), low-carbon cities (Piao et al 2009;Li & Zhou 2021), and urban climate (Suder & Szymanowski 2014). Urban expansion, a process of human-driven disturbance, has an increasingly significant impact on regional landscape patterns and ecological functions and processes (Zeng & Jiang 2000).…”

Section: Introductionmentioning

confidence: 99%

Evolution of landscape dynamics in the Yangtze River Delta from 2000 to 2020

Zhou

Wang

2022

Journal of Water and Climate Change

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Based on the 2000–2020 land cover data, the landscape dynamics and landscape pattern index are used to study the landscape pattern changes of the Yangtze River Delta. The results show that with the growth of built-in land area, the dominance of natural landscape in the Yangtze River Delta is gradually weakened. From the perspective of the overall landscape pattern, the degree of landscape fragmentation in this area is increasing, and the degree of landscape connectivity and aggregation are decreasing in varying degrees. The regional landscape is developing toward homogeneous distribution and increasing complexity. At the same time, from the perspective of classified landscape, the spatial distribution of various landscapes shows a strong correlation between climate and landform. Through comparative analysis, this study puts forward that the development of the Yangtze River Delta needs to pay attention to maintaining the integrity of the regional dominant landscape and paying attention to the diversity and connectivity of the natural landscape with high ecological service value.

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Section: Introductionmentioning

confidence: 99%

Evolution of landscape dynamics in the Yangtze River Delta from 2000 to 2020

Zhou

Wang

2022

Journal of Water and Climate Change

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“…The conventional approaches for modeling flood risk are mostly dependent on the flood water depth to estimate the associated damage (Aerts et al 2014, Velasco et al 2014. Several recent studies have shown that considering multivariate data will improve the damage estimates (Wagenaar et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Leveraging machine learning for predicting flash flood damage in the Southeast US

Alipour

Ahmadalipour

Abbaszadeh

et al. 2020

Environ. Res. Lett.

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Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framework that considers a variety of features explaining different components of risk (i.e. hazard, vulnerability, and exposure), and examine multiple machine learning methods to predict flash flood damage. A large database of flash flood events consisting of more than 14 000 events are assessed for training and testing the methodology, while a multitude of data sources are utilized to acquire reliable information related to each event. A variable selection approach was employed to alleviate the complexity of the dataset and facilitate the model development process. The random forest (RF) method was then used to map the identified input covariates to a target variable (i.e. property damage). The RF model was implemented in two modes: first, as a binary classifier to estimate if a region of interest was damaged in any particular flood event, and then as a regression model to predict the amount of property damage associated with each event. The results indicate that the proposed approach is successful not only for classifying damaging events (with an accuracy of 81%), but also for predicting flash flood damage with a good agreement with the observed property damage. This study is among the few efforts for predicting flash flood damage across a large domain using mesoscale input variables, and the findings demonstrate the effectiveness of the proposed methodology.

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“…Essential for the long-term sustainable management of watersheds is the need to compare the influences of climate and landuse management factors in concert (Jones, 2011). Because of this recognition, more researchers are now undertaking studies that specifically focus on the combined impacts of future climate and landuse changes on water resources (Velasco et al, 2014;Tong et al, 2016). And some of this work is being undertaken in Canada (Novotna et al, 2014;El-Khoury et al, 2015), as well as the north-east bioregion of New England states and the Atlantic Canadian provinces (Talib & Randhir, 2017;.…”

Section: Introductionmentioning

confidence: 99%

Climate Change Predictions of Increased Watershed Flow in Atlantic Canada: Implications for Surface Water Vulnerability and Ameliorative Land Use Planning and Management

France¹,

Aitchison²

2019

GEP

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Essential for comprehensive and sustainable watershed management is the need to understand interactions between climate change predictions and landuse modifications in concert on ecohydrology. The Atlantic Canada region is expected to experience elevated rainfall due to climate change over the next century. We undertook a predictive modeling study of a watershed in rural Nova Scotia, Thomas Brook, to investigate the potential of riparian reforestation to mitigate the deleterious environmental effects projected to occur from future climate change. A Watershed Analysis Risk Management Framework (WARMF) model was used to predict increased watershed flows using data from projections of the Canadian Regional Climate Change Model. The cold climate-validated WARMF model, which has been used previously to simulate surface flow hydrology in many agricultural and mixed-use landscapes, was found to predict increases of 9% to 25% in flow for the Thomas Brook watershed throughout the rest of the century. A spatial, exposure-based model, used previously in several studies, was adopted for assessing changes in surface water vulnerability based on GIS land-use and landscape topography estimates of nutrient loading, sedimentation, runoff, wetland loss, and stream geomorphology. This model indicated that increases in drainage intensity and drainage sensitivity expected through the climate change WARMF model resulted in greater proportions (from 5% to 27%) of the Thomas Brook watershed area being classified as "High vulnerability" for impacting surface water quality. In terms of land use planning, implementation of runoff and How to cite this paper: France, R. L., & Aitchison, P. W.

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Methodology for risk assessment of flash flood events due to climate and land-use changes: application to the Llobregat basin

Cited by 7 publications

References 0 publications

Evolution of landscape dynamics in the Yangtze River Delta from 2000 to 2020

Evolution of landscape dynamics in the Yangtze River Delta from 2000 to 2020

Leveraging machine learning for predicting flash flood damage in the Southeast US

Climate Change Predictions of Increased Watershed Flow in Atlantic Canada: Implications for Surface Water Vulnerability and Ameliorative Land Use Planning and Management

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