Vulnerability in a Tropical Cyclone Risk Model: Philippines Case Study

Baldwin, Jane W.; Lee, Chia‐Ying; Walsh, B.; Camargo, Suzana J.; Sobel, Adam H.

doi:10.1175/wcas-d-22-0049.1

Cited by 12 publications

(12 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2018, 2020), respectively. The CHAZ model has been used for case studies in Texas (Hassanzadeh et al., 2020), New York (Lee et al., 2022), Mumbai, India (Sobel, Lee, et al., 2019) and the Philippines (Baldwin et al., 2023). Meiler et al.…”

Section: Data Experimental Design and Methodsmentioning

confidence: 99%

“…The simulated TC activity in CHAZ, at global and basin scales, in both current and projected future climates have been discussed in detail in Lee et al (2018Lee et al ( , 2020, respectively. The CHAZ model has been used for case studies in Texas (Hassanzadeh et al, 2020), New York (Lee et al, 2022), Mumbai, India (Sobel, Lee, et al, 2019) and the Philippines (Baldwin et al, 2023). Meiler et al (2022) found that losses estimated from CHAZ are comparable to those estimated using comparable TC hazard models from Emanuel (2013) and Bloemendaal et al (2020).…”

Section: Synthetic Events From the Chaz Modelmentioning

confidence: 99%

See 1 more Smart Citation

Climate Change Signal in Atlantic Tropical Cyclones Today and Near Future

Lee,

Sobel,

Tippett

et al. 2023

Earth's Future

Self Cite

View full text Add to dashboard Cite

This manuscript discusses the challenges in detecting and attributing recently observed trends in the Atlantic tropical cyclone (TC) and the epistemic uncertainty we face in assessing future risk. We use synthetic storms downscaled from five CMIP5 models by the Columbia HAZard model (CHAZ), and directly simulated storms from high‐resolution climate models. We examine three aspects of recent TC activity: the upward trend and multi‐decadal oscillation of the annual frequency, the increase in storm wind intensity, and the decrease in forward speed. Some data sets suggest that these trends and oscillation are forced while others suggest that they can be explained by natural variability. Projections under warming climate scenarios also show a wide range of possibilities, especially for the annual frequencies, which increase or decrease depending on the choice of moisture variable used in the CHAZ model and on the choice of climate model. The uncertainties in the annual frequency lead to epistemic uncertainties in TC risk assessment. Here, we investigate the potential for reduction of these epistemic uncertainties through a statistical practice, namely likelihood analysis. We find that historical observations are more consistent with the simulations with increasing frequency than those with decreasing frequency, but we are not able to rule out the latter. We argue that the most rational way to treat epistemic uncertainty is to consider all outcomes contained in the results. In the context of risk assessment, since the results contain possible outcomes in which TC risk is increasing, this view implies that the risk is increasing.

show abstract

Section: Data Experimental Design and Methodsmentioning

confidence: 99%

Section: Synthetic Events From the Chaz Modelmentioning

confidence: 99%

Climate Change Signal in Atlantic Tropical Cyclones Today and Near Future

Lee,

Sobel,

Tippett

et al. 2023

Earth's Future

Self Cite

View full text Add to dashboard Cite

show abstract

“…They reported that GLM and RF models gave overall unbiased damage predictions across all methods and rarity levels, while KNN consistently under-predicted damage. A vulnerability risk model for the Philippines was put forward by Baldwin et al (2023), in which they assess the vulnerability using the wind field data and total asset value and determine the expected asset loss. In a study, Walsh (2020) proposed a traditional expanded risk assessment using asset losses as the primary metric to measure the severity of a disaster.…”

Section: Related Literaturementioning

confidence: 99%

Towards a global impact-based forecasting model for tropical cyclones

Kooshki Forooshani,

van den Homberg,

Kalimeri

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Tropical cyclones (TCs) produce strong winds and heavy rains accompanied by consecutive events such as landslides and storm surges, resulting in losses of lives and livelihoods particularly in regions where socioeconomic vulnerability is high. To proactively mitigate the impacts of TCs, humanitarian actors implement anticipatory action. In this work, we build upon such an existing anticipatory action for the Philippines, which uses an impact-based forecasting model for housing damage based on XGBoost to release funding and trigger early action. We improve it in three ways. First, we perform a correlation and selection analysis, to understand if Philippines-specific features can be left out or replaced with features from open global data sources. Secondly, we transform the target variable (percentage of completely damaged houses) and not yet grid-based global features to a 0.1 degrees grid resolution by de-aggregation using Google Building Footprint data. Thirdly, we evaluate XGBoost regression models using different combinations of global and local features at both grid and municipality spatial level. We introduce a two-stage model to first predict if the damage is above 10 % and then use a regression model trained on either all or on only high damage data. All experiments use data from 39 typhoons that impacted the Philippines between 2006–2020. Due to the scarcity and skewness of the training data, specific attention is paid to data stratification, sampling and validation techniques. We demonstrate that employing only the global features does not significantly influence model performance. Despite excluding local data on physical vulnerability and storm surge susceptibility, the two-stage model improves upon the municipality-based model with local features. When applied for anticipatory action our two-stage model would show a higher True Positive rate, a lower False Negative rate and furthermore an improved False Positive rate, implying that fewer resources would be wasted in anticipatory action. We conclude that relying on globally available data sources and working at grid level holds the potential to render a machine learning-based impact model generalisable and transferable to locations outside of the Philippines impacted by TCs. Also, a grid-based model increases the resolution of the predictions, which may allow for a more targeted implementation of anticipatory action. However, it should be noted that an impact-based forecasting model can only be as good as the forecast skill of the TC forecast that goes into it. Future research will focus on replicating to and testing the approach in other TC-prone countries. Ultimately, a transferable model will facilitate the scaling up of anticipatory action for TCs.

show abstract

“…In China, the statistical explanatory power in an empirical model has been found to be significantly higher for TC-induced economic impacts and can increase up to 14-fold when considering wind, inland and coastal floods instead of only wind 3 . However, so far global empirical estimates of TC damages use wind-fields as the only hazard predictor and do not account for floods [4][5][6] . This is particularly problematic as the evidence for a relation between climate change and changes in TC wind speeds is rather weak and subject to large uncertainties 7,8 , while the TC-induced flooding is clearly expected to increase under climate change due to sea level rise amplifying coastal flooding and increasing heavy precipitation extending the pluvial and fluvial flooding 9 .…”

Section: Introductionmentioning

confidence: 99%

Accounting for surge dynamics is key to modeling tropical cyclone-induced coastal flooding

Vogt,

Treu,

Mengel

et al. 2024

Preprint

View full text Add to dashboard Cite

Tropical cyclone-induced storm surge is a major coastal risk which will be further amplified by rising sea levels under global warming. The surge-induced floodplains are commonly modeled using a two-step approach by first producing coastal water levels which are, secondly, translated into inundated areas. Here, we evaluate an alternative approach where ocean dynamics and coastal inundation are modeled in a single step using the open-source solver GeoClaw that accounts for the event-specific variation over time of flood drivers. We compute the surge-induced flood extents for a global set of 71 storms from 2000-2019. We show that GeoClaw better reproduces satellite observations and high water marks than two different common modeling approaches that lack a process-based representation of inundation dynamics: a state-of-the-art two-step approach combining a full-scale ocean dynamics with a static (bathtub-type) inundation model and a lightweight approach that directly translates parametric tropical cyclone wind fields into inundation maps using statistical relationships. GeoClaw does not reach the performance of the ocean dynamics model in reproducing coastal water levels, but this deficiency is overcompensated by the dynamic modeling of inundated areas. The computational efficiency of the one-step approach opens up new perspectives for global assessments of coastal risks from tropical cyclones.

show abstract

Vulnerability in a Tropical Cyclone Risk Model: Philippines Case Study

Cited by 12 publications

References 43 publications

Climate Change Signal in Atlantic Tropical Cyclones Today and Near Future

Climate Change Signal in Atlantic Tropical Cyclones Today and Near Future

Towards a global impact-based forecasting model for tropical cyclones

Accounting for surge dynamics is key to modeling tropical cyclone-induced coastal flooding

Contact Info

Product

Resources

About