Impacts of green infrastructure on flood risk perceptions in Hong Kong

2020 ◽  
Vol 162 (4) ◽  
pp. 2277-2299
Author(s):  
Seung Kyum Kim ◽  
Paul Joosse ◽  
Mia M. Bennett ◽  
Terry van Gevelt
Keyword(s):  
Hong Kong ◽  
Green Infrastructure ◽  
Flood Risk ◽  
Risk Perceptions

scholarly journals Flood Risk and Adaptation Strategies for Soybean Production Systems on the Flood-Prone Pampas under Climate Change

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1187
Author(s):  
Wouter Julius Smolenaars ◽  
Spyridon Paparrizos ◽  
Saskia Werners ◽  
Fulco Ludwig
Keyword(s):  
Climate Change ◽  
Flood Risk ◽  
Risk Perceptions ◽  
Production Systems ◽  
Adaptation Strategies ◽  
Flood Events ◽  
Climate Data ◽  
Soybean Production ◽  
Drying Trend ◽  
Sowing Season

In recent decades, multiple flood events have had a devastating impact on soybean production in Argentina. Recent advances suggest that the frequency and intensity of destructive flood events on the Argentinian Pampas will increase under pressure from climate change. This paper provides bottom-up insight into the flood risk for soybean production systems under climate change and the suitability of adaptation strategies in two of the most flood-prone areas of the Pampas region. The flood risk perceptions of soybean producers were explored through interviews, translated into climatic indicators and then studied using a multi-model climate data analysis. Soybean producers perceived the present flood risk for rural accessibility to be of the highest concern, especially during the harvest and sowing seasons when heavy machinery needs to reach soybean lots. An analysis of climatic change projections found a rising trend in annual and harvest precipitation and a slight drying trend during the sowing season. This indicates that the flood risk for harvest accessibility may increase under climate change. Several adaptation strategies were identified that can systemically address flood risks, but these require collaborative action and cannot be undertaken by individual producers. The results suggest that if cooperative adaptation efforts are not made in the short term, the continued increase in flood risk may force soybean producers in the case study locations to shift away from soybean towards more robust land uses.

Novel use of climate model data for deriving future flood risk underwriting and risk selection data – A Hong Kong Case Study

2021 ◽  
Author(s):  
Rebecca Alexandre ◽  
Iain Willis
Keyword(s):  
Climate Change ◽  
Hong Kong ◽  
Flood Risk ◽  
Climate Model ◽  
Risk Mitigation ◽  
Flood Damage ◽  
Flood Frequency ◽  
Commercial Building ◽  
Model Data ◽  
Change Factors

<p>The re/insurance, banking and mortgage sectors play an essential role in facilitating economic stability. As climate change-related financial risks increase, there has long been a need for tools that contribute to the global industry’s current and future flood risk resiliency. Recognising this gap, JBA Risk Management has pioneered use of climate model data for rapidly deriving future flood risk metrics to support risk-reflective pricing strategies and mortgage analysis for Hong Kong.</p><p>JBA’s established method uses daily temporal resolution precipitation and surface air temperature Regional Climate Model (RCM) data from the Earth System Grid Federation’s CORDEX experiment. Historical and future period RCM data were processed for Representative Concentration Pathways (RCPs) 2.6 and 8.6, and time horizons 2046-2050 and 2070-2080 and used to develop fluvial and pluvial hydrological model change factors for Hong Kong. These change factors were applied to baseline fluvial and pluvial flood depths and extents, extracted from JBA’s high resolution 30m Hong Kong Flood Map. From these, potential changes in flood event frequency and severity for each RCP and time horizon combination were estimated.</p><p>The unique flood frequency and severity profiles for each flood type were then analysed with customised vulnerability functions, linking water depth to expected damage over time for residential and commercial building risks. This resulted in quantitative fluvial and pluvial flood risk metrics for Hong Kong.</p><p>Newly released, Hong Kong Climate Change Pricing Data is already in use by financial institutions. When combined with property total sum insured data, this dataset provides the annualised cost of flood damage for a range of future climate scenarios. For the first time, our industry has a tool to quantify baseline and future flood risk and set risk-reflective pricing for Hong Kong portfolios.</p><p>JBA’s method is adaptable for global use and underwriting tools are already available for the UK and Australia with the aim of improving future financial flood risk mitigation and management. This presentation will outline the method, provide a comparison of baseline and climate change flood impacts for Hong Kong and discuss the wider implications for our scientific and financial industries.</p>

scholarly journals Link between Land Use and Flood Risk Assessment in Urban Areas

Proceedings ◽  
2020 ◽  
Vol 30 (1) ◽  
pp. 62
Author(s):  
Zahra Kalantari ◽  
Johanna Sörensen
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Green Infrastructure ◽  
Flood Risk ◽  
Urban Areas ◽  
Land Use Changes ◽  
Drainage System ◽  
Property Owners ◽  
Pluvial Flooding ◽  
The Impact

The densification of urban areas has raised concerns over increased pluvial flooding. Flood risk in urban areas might increase under the impact of land use changes. Urbanisation involves the conversion of natural areas to impermeable areas, causing lower infiltration rates and increased runoff. When high-intensity rainfall exceeds the capacity of an urban drainage system, the runoff causes pluvial flooding in low-laying areas. In the present study, a long time series (i.e., 20 years) of geo-referenced flood claims from property owners has been collected and analysed in detail to assess flood risk as it relates to land use changes in urban areas. The flood claim data come from property owners with flood insurance that covers property loss from overland flooding, groundwater intrusion through basement walls, as well as flooding from drainage systems; these data serve as a proxy of flood severity. The spatial relationships between land use change and flood occurrences in different urban areas were analysed. Special emphasis was placed on examining how nature-based solutions and blue-green infrastructure relate to flood risk. The relationships are defined by a statistical method explaining the tendencies whereby land use change affects flood risk.

scholarly journals Modeling the Influence of Public Risk Perceptions on the Adoption of Green Stormwater Infrastructure: An Application of Bayesian Belief Networks Versus Logistic Regressions on a Statewide Survey of Households in Vermont

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2793
Author(s):  
Qing Ren ◽  
Asim Zia ◽  
Donna M. Rizzo ◽  
Nancy Mathews
Keyword(s):  
Logistic Regression ◽  
Green Infrastructure ◽  
Risk Perceptions ◽  
Bayesian Belief Networks ◽  
Point Sources ◽  
Supervised Machine Learning ◽  
Belief Networks ◽  
Rain Gardens ◽  
Environmental Behaviors ◽  
Public Risk

There is growing environmental psychology and behavior literature with mixed empirical evidence about the influence of public risk perceptions on the adoption of environmentally friendly “green behaviors”. Adoption of stormwater green infrastructure on residential properties, while costlier in the short term compared to conventional greywater infrastructure, plays an important role in the reduction of nutrient loading from non-point sources into freshwater rivers and lakes. In this study, we use Bayesian Belief Networks (BBNs) to analyze a 2015 survey dataset (sample size = 472 respondents) about the adoption of green infrastructure (GSI) in Vermont’s residential areas, most of which are located in either the Lake Champlain Basin or Connecticut River Basin. Eight categories of GSI were investigated: roof diversion, permeable pavement, infiltration trenches, green roofs, rain gardens, constructed wetlands, tree boxes, and others. Using both unsupervised and supervised machine learning algorithms, we used Bayesian Belief Networks to quantify the influence of public risk perceptions on GSI adoption while accounting for a range of demographic and spatial variables. We also compare the effectiveness of the Bayesian Belief Network approach and logistic regression in predicting the pro-environmental behaviors (adoption of GSI). The results show that influencing factors for current adoption differ by the type of GSI. Increased perception of risk from stormwater issues is associated with the adoption of rain gardens and infiltration trenches. Runoff issues are more likely to be considered the governments’ (town, state, and federal agencies) responsibility, whereas lawn erosion is more likely to be considered the residents’ responsibility. When using the same set of variables to predict pro-environmental behaviors (adoption of GSI), the BBN approach produces more accurate predictions compared to logistic regression. The results provide insights for further research on how to encourage residents to take measures for mitigating stormwater issues and stormwater management.

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