Forecasting tropical cyclone-induced rainfall in coastal Australia: implications for effective flood management

Saha, Kamal Kumar; Hasan, Mahbub; Quazi, Ali

doi:10.1080/14486563.2015.1028109

Cited by 6 publications

(3 citation statements)

References 37 publications

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“…The parameter θ can be positive or negative while φ is always positive [31]. Tweedie distributions are those EDMs for which variances are proportional to some power (p, also called index parameter) of the mean and have been used for modelling rainfall totals of Australia, Malaysia and India [7,32,33]. The Tweedie distribution with mean µ, dispersion parameter φ and index parameter p is dehnoted as Tw p (µ, φ); p / ∈ (0, 1).…”

Section: Methodsmentioning

confidence: 99%

Spatial and Seasonal Variations and Inter-Relationship in Fitted Model Parameters for Rainfall Totals across Australia at Various Timescales

Hasan

Croke

Karim

2019

Climate

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Probabilistic models are useful tools in understanding rainfall characteristics, generating synthetic data and predicting future events. This study describes the results from an analysis on comparing the probabilistic nature of daily, monthly and seasonal rainfall totals using data from 1327 rainfall stations across Australia. The main objective of this research is to develop a relationship between parameters obtained from models fitted to daily, monthly and seasonal rainfall totals. The study also examined the possibility of estimating the parameters for daily data using fitted parameters to monthly rainfall. Three distributions within the Exponential Dispersion Model (EDM) family (Normal, Gamma and Poisson-Gamma) were found to be optimal for modelling the daily, monthly and seasonal rainfall total. Within the EDM family, Poisson-Gamma distributions were found optimal in most cases, whereas the normal distribution was rarely optimal except for the stations from the wet region. Results showed large differences between regional and seasonal ϕ-index values (dispersion parameter), indicating the necessity of fitting separate models for each season. However, strong correlations were found between the parameters of combined data and those derived from individual seasons (0.70–0.81). This indicates the possibility of estimating parameters of individual season from the parameters of combined data. Such relationship has also been noticed for the parameters obtained through monthly and daily models. Findings of this research could be useful in understanding the probabilistic features of daily, monthly and seasonal rainfall and generating daily rainfall from monthly data for rainfall stations elsewhere.

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Section: Methodsmentioning

confidence: 99%

Spatial and Seasonal Variations and Inter-Relationship in Fitted Model Parameters for Rainfall Totals across Australia at Various Timescales

Hasan

Croke

Karim

2019

Climate

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“…While there is broad understanding, largely through case study or regional analysis (Saha et al, 2015;Hernández Ayala and Matyas, 2016;Touma et al, 2019, Yu et al, 2017 File generated with AMS Word template 1.0 confirm these drivers and their relationships to downstream flood hazard is lacking. This study aims to address this gap and help to provide more specific information in support of efforts to raise awareness of fluvial flood risk from TCs, by undertaking a systematic global analysis of the key meteorological and hydrological factors that lead to an increased fluvial flood hazard from TCs.…”

Section: Introductionmentioning

confidence: 99%

Key factors influencing the severity of fluvial flood hazard from tropical cyclones

Titley

Cloke

Harrigan

et al. 2021

Journal of Hydrometeorology

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Knowledge of the key drivers of the severity of river flooding from tropical cyclones (TCs) is vital for emergency preparedness and disaster risk reduction activities. This global study examines landfalling TCs in the decade from 2010 to 2019, to identify those characteristics that influence whether a storm has an increased flood hazard. The highest positive correlations are found between flood severity and the total precipitation associated with the TC. Significant negative correlations are found between flood severity and the translation speed of the TC, indicating that slower moving storms, that rain over an area for longer, tend to have higher flood severity. Larger and more intense TCs increase the likelihood of having a larger area affected by severe flooding but not its duration or magnitude, and it is found that the fluvial flood hazard can be severe in all intensity categories of TC, including those of tropical storm strength. Catchment characteristics such as antecedent soil moisture and slope also play a role in modulating flood severity, and severe flooding is more likely in cases where multiple drivers are present. The improved knowledge of the key drivers of fluvial flooding in TCs can help to inform research priorities to help with flood early warning, such as increasing the focus on translation speed in model evaluation and impact-based forecasting.

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“…In contemporary literature, the Gamma, Exponential, Kappa, Wakeby, Generalized Extreme Value (GEV) and Weibull distributions have been used to model daily non-zero rainfall intensities [16][17][18]. Distributions within the Tweedie family were fitted to model both components (occurrence and amounts of rainfall) simultaneously [19,20]. Gamma for rainfall intensities (three hours) [21,22].…”

Section: Introductionmentioning

confidence: 99%

Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia

et al. 2020

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Probabilistic models for sub-daily rainfall predictions are important tools for understanding catchment hydrology and estimating essential rainfall inputs for agricultural and ecological studies. This research aimed at achieving theoretical probability distribution to non-zero, sub-daily rainfall using data from 1467 rain gauges across the Australian continent. A framework was developed for estimating rainfall data at ungauged locations using the fitted model parameters from neighbouring gauges. The Lognormal, Gamma and Weibull distributions, as well as their mixed distributions were fitted to non-zero six-minutes rainfall data. The root mean square error was used to evaluate the goodness of fit for each of these distributions. To generate data at ungauged locations, parameters of well-fit models were interpolated from the four closest neighbours using inverse weighting distance method. Results show that the Gamma and Weibull distributions underestimate and lognormal distributions overestimate the high rainfall events. In general, a mixed model of two distributions was found better compared to the results of an individual model. Among the five models studied, the mixed Gamma and Lognormal (G-L) distribution produced the minimum root mean square error. The G-L model produced the best match to observed data for high rainfall events (e.g., 90th, 95th, 99th, 99.9th and 99.99th percentiles).

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Forecasting tropical cyclone-induced rainfall in coastal Australia: implications for effective flood management

Cited by 6 publications

References 37 publications

Spatial and Seasonal Variations and Inter-Relationship in Fitted Model Parameters for Rainfall Totals across Australia at Various Timescales

Spatial and Seasonal Variations and Inter-Relationship in Fitted Model Parameters for Rainfall Totals across Australia at Various Timescales

Key factors influencing the severity of fluvial flood hazard from tropical cyclones

Using Mixed Probability Distribution Functions for Modelling Non-Zero Sub-Daily Rainfall in Australia

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