A decision tree approach to identify predictors of extreme rainfall events – A case study for the Fiji Islands

Sharma, Krishneel K.; Verdon‐Kidd, Danielle C.; Magee, Andrew D.

doi:10.1016/j.wace.2021.100405

Cited by 3 publications

(6 citation statements)

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“…For example, the 20CR‐derived annual TC numbers in the entire South Pacific Ocean basin, as well as in the two sub‐regions east and west of the 170° E longitude, have underestimation biases in representing observed TC numbers from the SPEArTC dataset (Figure 1). Note that we have divided the South Pacific basin into two sub‐regions from ~170° E as they have opposite response to effects of ENSO variability (Chand & Walsh, 2009, 2011, 2012; Chand et al, 2010, 2013, 2020; Dowdy et al, 2012; Magee et al, 2017; Magee & Kiem, 2020; Maru et al, 2018; Ramsay et al, 2012; Sharma, Magee, & Verdon‐Kidd, 2021; Sharma, Verdon‐Kidd, & Magee, 2021).…”

Section: Resultsmentioning

confidence: 99%

“…The Graphical Interpretation of Tracks (GrIT) technique (Diamond et al, 2012) was used to establish some novel quality control and duplicate track detection processes in the development of SPEArTC. Hence, it considered more appropriate for TC frequency studies from around 1950 onwards for the South Pacific (e.g., Magee et al, 2016, 2017; Sharma, Verdon‐Kidd, Magee, 2021) compared with other observational data sources.…”

Section: Data Definitions and Methodsmentioning

confidence: 99%

“…Several previous studies have used a TC track clustering technique developed by Gaffney (2004) to group cyclones into similar track regimes or ‘clusters’, including those conducted in the South Pacific (e.g., Chand & Walsh, 2009; Ramsay et al, 2012; Sharma, Verdon‐Kidd, & Magee, 2021). In this method, a variety of polynomial regression models are used to roughly classify TC tracks into groups determined by the location of TC genesis and the track's overall shape.…”

Section: Data Definitions and Methodsmentioning

confidence: 99%

“…That is, more TCs are formed in the Coral Sea region during La Niña years than El Niño years, whereas the opposite is true for the region east of 170° E. Similarly, Kuleshov et al (2008) found a statistically significant relationship between TC numbers and various ENSO indices in the South Pacific basin. In addition to TC frequency, spatial locations of TC genesis positions and tracks can also be substantially modulated by ENSO (e.g., Chand & Walsh, 2009, 2011, 2012; Chand et al, 2010, 2013, 2020; Dowdy et al, 2012; Magee et al, 2017; Magee & Kiem, 2020; Maru et al, 2018; Ramsay et al, 2012; Sharma, Magee, & Verdon‐Kidd, 2021; Sharma, Verdon‐Kidd, & Magee, 2021). In El Niño years, TC genesis systematically shifts northeastward right across to the Cook Islands and French Polynesia with the greatest formation around the dateline, extending east‐southeast of the Fiji Islands.…”

Section: Introductionmentioning

confidence: 99%

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Tropical cyclones and depressions over the South Pacific Ocean since the late 19th century: Assessing synergistic relationship between the El Niño Southern Oscillation and Interdecadal Pacific Oscillation

Yeasmin

Chand

Sultanova

2023

Intl Journal of Climatology

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Tropical cyclones (TCs) and tropical depressions (TDs), hereafter collectively referred to as tropical storms, often exhibit large year‐to‐year variability in the South Pacific Ocean basin. Many past studies have examined this variability in relation to the El Niño Southern Oscillation (ENSO) phenomenon, particularly using observational data from the post‐satellite era (i.e., after the 1970s when TC observations became more consistent). However, less emphasis is placed on how tropical storms are modulated at interdecadal and decadal time scales such as due to Interdecadal Pacific Oscillation (IPO). This is because post‐satellite data are available for relatively short time period (i.e., post‐1970s), limiting our understanding of the IPO–TC relationship in the South Pacific. Here, using NOAA‐CIRES 20th Century Reanalysis (20CR) dataset, we reconstruct historical records (1871–2014) of TC and depression proxies for the South Pacific Ocean basin, and then utilize these reconstructed proxies to first understand the connections between TC–ENSO and TC–IPO over the 20th century, and then investigate the combined effects of ENSO–IPO effects on TCs and depressions. Results show that La Niña (El Niño) is more dominant on TC activity than El Niño (La Niña) over the western subregion 140–170° E (eastern sub‐region, 170–220° E) as expected. We also show that TC numbers are strongly modulated by the IPO phenomenon with, on average, more TCs occurring during the positive phase than during the negative phase of the IPO in both western and eastern sub‐regions. We show for the first time (using a long‐term reconstructed TC dataset) that the combined phases of El Niño and + IPO account for increased TC activity, as opposed to the combined phase of La Niña and −IPO, in the eastern sub‐region. Similarly, the combined phase of La Niña and + IPO, as opposed to the combined phase of El Niño and −IPO, account for increased TC activity in the western sub‐region. However, unlike TCs, the patterns of ENSO variability seem to be reversed for TDs. Changes in large‐scale environmental conditions, such as environmental vertical wind shear, low‐level cyclonic relative vorticity, mid‐tropospheric relative humidity and sea surface temperature are linked to the various modes of variability patterns and their synergistic relationships.

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Section: Resultsmentioning

confidence: 99%

Section: Data Definitions and Methodsmentioning

confidence: 99%

Section: Data Definitions and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tropical cyclones and depressions over the South Pacific Ocean since the late 19th century: Assessing synergistic relationship between the El Niño Southern Oscillation and Interdecadal Pacific Oscillation

Yeasmin

Chand

Sultanova

2023

Intl Journal of Climatology

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“…One common metric used to quantify extreme precipitation is the 95th percentile of the cumulative daily precipitation distribution [47], which was the definition used in this study. The 95th percentile metric has been used extensively in analysis of extreme events (e.g., [48][49][50][51]).…”

Section: Methodsmentioning

confidence: 99%

Characterizing Long Island’s Extreme Precipitation and Its Relationship to Tropical Cyclones

2022

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Since extreme precipitation impacts society on small scales (i.e., a few kilometers and smaller, it is worthwhile to explore extreme precipitation trends in localized regions, such as Long Island (LI), New York. Its coastal location makes it vulnerable to various extreme events, such as tropical cyclones (TCs). This work aimed to quantify the extreme precipitation events on LI that are caused by TCs, as well as the percentage of TCs passing close to LI that cause extreme precipitation events. Both gauge-based and satellite-based precipitation datasets of varying resolutions (DAYMET, IMERG, and CPC) were used to understand the impact of dataset selection. Results are shown for the common time period of 2001–2020, as well as the full time periods of each dataset. DAYMET shows the highest percentage of extreme precipitation events linked to TCs for 2001–2020 (a maximum of 7.2%) and the highest number of TCs that caused extreme precipitation events (36.5%), with IMERG showing similar results. For the full and common time periods, the highest percentage of extreme precipitation events caused by TCs was found in eastern LI. TC-related extreme precipitation averaged over LI varied year to year, and amounts were dependent on the resolution of the observational dataset, but most datasets showed an increasing trend in the last 19 years that is larger than the trend in mean precipitation. Current infrastructure in the region is likely inadequately prepared for future impacts from TC-related extreme precipitation events in such a population-dense region.

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Copula-Probabilistic Flood Risk Analysis with an Hourly Flood Monitoring Index

Chand,

Nguyen-Huy,

Deo

et al. 2024

Water

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Floods are a common natural disaster whose severity in terms of duration, water resource volume, peak, and accumulated rainfall-based damage is likely to differ significantly for different geographical regions. In this paper, we first propose a novel hourly flood index (SWRI24−hr−S) derived from normalising the existing 24-hourly water resources index (WRI24−hr−S) in the literature to monitor flood risk on an hourly scale. The proposed SWRI24−hr−S is adopted to identify a flood situation and derive its characteristics, such as the duration (D), volume (V), and peak (Q). The comprehensive result analysis establishes the practical utility of SWRI24−hr−S in identifying flood situations at seven study sites in Fiji between 2014 and 2018 and deriving their characteristics (i.e., D, V, and Q). Secondly, this study develops a vine copula-probabilistic risk analysis system that models the joint distribution of flood characteristics (i.e., D, V, and Q) to extract their joint exceedance probability for the seven study sites in Fiji, enabling probabilistic flood risk assessment. The vine copula approach, particularly suited to Fiji’s study sites, introduces a novel probabilistic framework for flood risk assessment. The results show moderate differences in the spatial patterns of joint exceedance probability of flood characteristics in different combination scenarios generated by the proposed vine copula approach. In the worst-case scenario, the probability of any flood event occurring where the flood volume, peak, and duration are likely to exceed the 95th-quantile value (representing an extreme flood event) is found to be less than 5% for all study sites. The proposed hourly flood index and the vine copula approach can be feasible and cost-effective tools for flood risk monitoring and assessment. The methodologies proposed in this study can be applied to other data-scarce regions where only rainfall data are available, offering crucial information for flood risk monitoring and assessment and for the development of effective mitigation strategies.

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A decision tree approach to identify predictors of extreme rainfall events – A case study for the Fiji Islands

Cited by 3 publications

References 57 publications

Tropical cyclones and depressions over the South Pacific Ocean since the late 19th century: Assessing synergistic relationship between the El Niño Southern Oscillation and Interdecadal Pacific Oscillation

Tropical cyclones and depressions over the South Pacific Ocean since the late 19th century: Assessing synergistic relationship between the El Niño Southern Oscillation and Interdecadal Pacific Oscillation

Characterizing Long Island’s Extreme Precipitation and Its Relationship to Tropical Cyclones

Copula-Probabilistic Flood Risk Analysis with an Hourly Flood Monitoring Index

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