Enhancement of Extremely Heavy Precipitation Induced by Typhoon Hagibis (2019) due to Historical Warming

Kawase, Hiroaki; Yamaguchi, Munehiko; Imada, Yukiko; Hayashi, Syugo; Murata, Akihiko; Nakaegawa, Tosiyuki; Miyasaka, Takafumi; Takayabu, Izuru

doi:10.2151/sola.17a-002

Cited by 34 publications

(30 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies (Takemi and Unuma 2020) have assessed the immediate meteorological factors causing the heavy precipitation during the passage of Typhoon Hagibis and found that a nearly moist-adiabatic lapse rate, moist absolute instability, abundant moisture content, and high relative humidity throughout the troposphere jointly contributed to generating the heavy precipitation. Kawase et al (2021) took a step further and conducted an anatomy of the Typhoon (Hoerling et al 2013) and found that the historical warming intensified the strength of Typhoon Hagibis as well as the extreme heavy precipitation associated with the passing of the typhoon and that the topography of the area also contributed to the enhancement in total precipitation. However, this still leaves the question to what extent human-induced climate change has altered the likelihood of an extreme heavy precipitation event like the one associated with Hagibis from an impact perspective.…”

Section: Introductionmentioning

confidence: 99%

“…Following Kawase et al (2021), we focus on precipitation, total column precipitable water (pwat) as a measure of moisture content throughout the atmospheric column, and sea level pressure (SLP)-to track minimum SLP as the central position of the typhoon. We use pwat and SLP to describe the environmental conditions during the passing of Typhoon Hagibis (shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The role of human-induced climate change in heavy rainfall events such as the one associated with Typhoon Hagibis

Otto

2022

Climatic Change

View full text Add to dashboard Cite

Around October 12, 2019, torrential rainfall from Typhoon Hagibis caused large-scale flooding in a large area around the metropole region of Tokyo leading to large-scale destruction including losses of lives, livelihoods, and economic losses of well over $10 bn US dollars. In this paper we use a multi-method probabilistic event attribution framework to assess the role of human-induced climate change in the heavy rainfall event responsible for a large proportion of the damages. Combining different observational datasets and various climate model simulations, we find an increase in the likelihood of such an event to occur of 15–150%. We use this assessment and the calculated fraction of attributable risk (FAR) to further estimate the economic costs attributable to anthropogenic climate change based on the insured economic losses. Our conservative estimate is that ~$4bn of the damages due to the extreme heavy rainfall associated with Typhoon Hagibis are due to human-induced climate change.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The role of human-induced climate change in heavy rainfall events such as the one associated with Typhoon Hagibis

Otto

2022

Climatic Change

View full text Add to dashboard Cite

show abstract

“…Consequently, previous studies have explored alternative methods for studying climate change impacts on convective events. There are commonly two approaches: event‐driven (Armon et al., 2022; Hibino et al., 2018; Kawase et al., 2020, 2021; Lackmann, 2013; Takayabu et al., 2015) and climatological (Kawase et al., 2019; Lenderink et al., 2021; Liu et al., 2017; Prein, Rasmussen, et al., 2017). The former focuses on the study of a specific event under different conditions related to climate change, while the latter focuses on the general changes of this type of event on a climatological time scale.…”

Section: Introductionmentioning

confidence: 99%

On the Potentials and Limitations of Attributing a Small‐Scale Climate Event

Matte

Christensen

Feddersen

et al. 2022

Geophysical Research Letters

View full text Add to dashboard Cite

A cloudburst is the result of strong convective mechanisms. Due to its intensity, suddenness and brief duration, it is particularly difficult to adequately drain the resulting surface water, which accordingly may lead to severe flooding and consequently large economic losses. Such flash floods are not uncommon across the world and are devastating when occurring in a densely populated city such as Copenhagen, Denmark-even without additional complicating runoff effects from surrounding more elevated areas (Khajehei et al., 2020;Ricchi et al., 2021). Predicting the precise location, timing and intensity of weakly forced convective events (e.g., initiated by local surface conditions) is notoriously difficult, even for a state-of-the-art, convection permitting Numerical Weather Prediction (NWP) models due to a combination of (a) model resolution not fully resolving either the deep convection mechanism or local interactions related to orography or land/sea contrasts, (b) imprecise depiction of the initial conditions (IC) due to a lack of high-resolution observations (in particular of humidity) and (c) the chaotic behavior and associated sensitivity to IC of the atmosphere in general and convection in particular (Coppola

show abstract

“…In Japan, the water vapor at about 1500 m asl is increasing, as is the number of days with precipitation exceeding 200 mm [2]. One study argued that the precipitation from Tropical Cyclone Hagibis, which fell over the Kanto area in October 2019, was higher by 10.9% due to the increase of ocean temperature [3]. Furthermore, record-breaking heavy rainfall events occur in Japan more frequently than before due to stagnant squall lines and other precipitation systems.…”

Section: Introductionmentioning

confidence: 99%

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

Wakatsuki

Nakane

Hashino

2022

Water

View full text Add to dashboard Cite

The increasing frequency of devastating floods from heavy rainfall—associated with climate change—has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep-learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is a long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with RMSE less than 67 cm for about 10 m of stage peaks, as well as accurately simulating stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision, we found that the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well-simulated, the physical concepts of runoff appear to be soundly embedded in the MLP.

show abstract

Enhancement of Extremely Heavy Precipitation Induced by Typhoon Hagibis (2019) due to Historical Warming

Cited by 34 publications

References 32 publications

The role of human-induced climate change in heavy rainfall events such as the one associated with Typhoon Hagibis

The role of human-induced climate change in heavy rainfall events such as the one associated with Typhoon Hagibis

On the Potentials and Limitations of Attributing a Small‐Scale Climate Event

River Stage Modeling with a Deep Neural Network Using Long-Term Rainfall Time Series as Input Data: Application to the Shimanto-River Watershed

Contact Info

Product

Resources

About