Improvement of watershed flood forecasting by typhoon rainfall climate model with an ANN-based southwest monsoon rainfall enhancement

This paper studies the dramatic decadal increase of Taiwan rainfall in the typhoon exit phase. The exit phase is defined as the time interval between a typhoon centre leaving the Taiwan coast to reaching 100 km away from the nearest coastline. Typhoons which move across Taiwan's northern (track N), central (track C), or southern (track S) areas are selected for a statistical study. Taiwan hourly rainfall data at 21 surface gauge stations from the past 57 years are divided into two periods: 1960–1989 (P1) and 1987–2016 (P2). From P1 to P2, there are decadal increases of rainfall (∼60%) and rainfall intensity (∼30%, mm h−1) in the exit phase. There is also a decadal increase of the track C typhoons. The southwest monsoon water vapour flux (SWF) in a local region southwest of Taiwan, as computed from the JRA‐55 dataset, is substantially larger in the track C typhoons than that in the track N typhoons. Our analysis indicates that the increase of SWF leads to the increase of rainfall intensity. Moreover, both the enhanced SWF and the prolonged duration time contribute to the increased rainfall in the exit phase. Typhoon Morakot (2009), a track C typhoon with extremely slow speed in the exit phase, produced the record‐breaking rainfall. Model experiments and potential vorticity tendency diagnosis of typhoon Morakot are used to understand the dynamics of the increased duration time. The slowdown of typhoon motion is shown to be due to the asymmetric convection in the Taiwan Strait, which is produced by the interaction between typhoon circulation and southwest monsoon flow. The enhanced SWF and the prolonged duration time may explain the observation that the decadal rainfall increase is much larger than that of rainfall intensity in the exit phase.

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“…The region to compute SWF is set to 16.25–22.5°N, 110–120°E following Pan et al . (), with N = 54 in Eq. .…”

Section: Methodsmentioning

confidence: 99%

“…where N is the number of grid points in the region, q i , u i , and v i are grid-point-specific humidity, zonal and meridional wind speed, respectively. The region to compute SWF is set to 16.25-22.5 • N, 110-120 • E following Pan et al (2013), with N = 54 in Eq. (1).…”

Section: Methodsmentioning

confidence: 99%

Influence of southwest monsoon flow and typhoon track on Taiwan rainfall during the exit phase: modelling study of typhoon Morakot (2009)

Chen

Kuo

Wang

et al. 2017

Quart J Royal Meteoro Soc

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“…Pan et al [185] proposed a monsoon rainfall enhancement (AME) based on ANNs, which was a hybrid form of linear regression and a state-space neural network (SSNN). The performance of the proposed model was benchmarked against the hybrid method of MLR-ANN.…”

Section: Svm-fr Hec-hms-ann Sas-mp Som-r-narx Wavelet-based Narmentioning

confidence: 99%

Flood Prediction Using Machine Learning, Literature Review

Mosavi¹,

Öztürk²,

Chau³

2018

Preprint

210

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Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

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“…The correlations with the duration of the exit phase are also shown. The indexes, computed from the Japanese 55-year Reanalysis data, are the southwest monsoon water vapor flux (SWF) and the total monsoon water vapor flux (TSWF) at 925 hPa in the region 16.25 • N-22.5 • N, 110 • E-120 • E, the domain used by as Pan et al (2013) to cover the upstream inflow of southwest monsoon adequately. The SWF is the average moisture flux over the region during the exit phase and the TSWF is the product of SWF and the exit phase duration.…”

Section: The Roles Of Terrain and Southwest Monsoonmentioning

confidence: 99%

Large Increasing Trend of Tropical Cyclone Rainfall in Taiwan and the Roles of Terrain and Southwest Monsoon

Kuo¹,

Chang²,

Yang³

et al. 2017

World Scientific Series on Asia-Pacific Weather and Climate

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Since the beginning of the 21st century Taiwan has experienced a dramatic increase in typhoonrelated rainfall. Some investigators suggested that they are the manifestation of global warming effects. However, an analysis of typhoon rainfall intensity with respect to typhoon tracks in different landfall phases relative to the Central Mountain Range indicates this is unlikely the cause. Rather, most of the recently observed large increase in typhoon rainfall in the pre-landing and overland phases is the result of slower moving tropical cyclones (TCs) and their tracks relative to the high mountains. A positive feedback mechanism in which the convective heating pattern forced by topography acts to slow down the TC motion, which is most efficient for the slowly-moving northern-track storms. Another factor contributing to increased TC-related rainfall is the interaction between the typhoon circulation and southwest monsoon wind surges. This factor is most important after the typhoon center exits Taiwan and led to the increase of both typhoon rainfall intensity and rainfall amount in the new century. Both the slower TC motion and the increased monsoon interaction are consistent with the recently observed multidecadal trend of intensifying subtropical monsoon and tropical circulations, which is contrary to some theoretical and model projections of global warming. There is also no evidence of a positive feedback between global warming-related water vapor supply and TC intensity in the typhoon rainfall increase, as the number of strong landfalling TCs has decreased significantly since 1960 and the recent heavy rainfall typhoons are all of weak to medium intensity.

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Improvement of watershed flood forecasting by typhoon rainfall climate model with an ANN-based southwest monsoon rainfall enhancement

Cited by 29 publications

References 33 publications

Influence of southwest monsoon flow and typhoon track on Taiwan rainfall during the exit phase: modelling study of typhoon Morakot (2009)

Influence of southwest monsoon flow and typhoon track on Taiwan rainfall during the exit phase: modelling study of typhoon Morakot (2009)

Flood Prediction Using Machine Learning, Literature Review

Large Increasing Trend of Tropical Cyclone Rainfall in Taiwan and the Roles of Terrain and Southwest Monsoon

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