Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis

Sen, Subhamoy; He, Jianxun; Kasiviswanathan, K. S.

doi:10.1016/j.jhydrol.2020.124666

Cited by 23 publications

(32 citation statements)

References 43 publications

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“…The parameters of these distributions mainly define the characteristics such as scale, shape and location of the data being fitted using the EVDs. The tail behaviour of the distribution is described by the shape parameter which is estimated from higher order moments, and precise estimation of shape parameter is often computationally difficult [19, 20] and requires suitable moment estimation approaches. Among several methods (i.e.…”

Section: Methodsmentioning

confidence: 99%

A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions

Aadhityaa

Kasiviswanathan

Ilampooranan

et al. 2020

Preprint

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4The COVID-19 pandemic has created a global crisis and the governments are fighting rigorously 1 5 to control the spread by imposing intervention measures and increasing the medical facilities. In 1 6 order to tackle the crisis effectively we need to know the trajectories of number of the people 1 7 infected (i.e. confirmed cases). Such information is crucial to government agencies for 1 8 developing effective preparedness plans and strategies. We used a statistical modeling approach 1 9 4 1 number of confirmed, recovered, and mortality cases were 1,595,350, 353,975 and 95,455 4 2 respectively [3]. The reported cases globally are drastically increasing from 4,219/day in 4 3 February 2020 to 50,784/day in early April 2020. The trajectory of the infection spread during 4 4the coming days is important information in order to plan, prepare, and scale up the intervention 4 5

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Section: Methodsmentioning

confidence: 99%

A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions

Aadhityaa

Kasiviswanathan

Ilampooranan

et al. 2020

Preprint

Self Cite

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“…However, despite the numerous studies in NFFA, there is neither a noncontroversial method for performing NFFA nor well accepted governmental guidelines for updating current design flood strategies (Serago & Vogel 2018). One of the concerns is the tremendous uncertainties in NFFA induced by model structure and parameter estimation of NS models (Hu et al 2015;Serinaldi & Kilsby 2015;Luke et al 2017;Serago & Vogel 2018;Sen et al 2020). Typically, the model structure of NS model is more complex than that of ST model, since additional parameters are introduced to capture the historical trend of extreme flood events.…”

Section: Introductionmentioning

confidence: 99%

Design flood estimation with varying record lengths in Norway under stationarity and nonstationarity scenarios

et al. 2021

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In traditional flood frequency analysis, a minimum of 30 observations is required to guarantee the accuracy of design results with an allowable uncertainty; however, there has not been a recommendation for the requirement on the length of data in NFFA (nonstationary flood frequency analysis). Therefore, this study has been carried out with three aims: (i) to evaluate the predictive capabilities of nonstationary (NS) and stationary (ST) models with varying flood record lengths; (ii) to examine the impacts of flood record lengths on the NS and ST design floods and associated uncertainties; and (iii) to recommend the probable requirements of flood record length in NFFA. To achieve these objectives, 20 stations with record length longer than 100 years in Norway were selected and investigated by using both GEV (generalized extreme value)-ST and GEV-NS models with linearly varying location parameter (denoted by GEV-NS0). The results indicate that the fitting quality and predictive capabilities of GEV-NS0 outperform those of GEV-ST models when record length is approximately larger than 60 years for most stations, and the stability of the GEV-ST and GEV-NS0 is improved as record lengths increase. Therefore, a minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway.

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“…The non-stationarity of time series of rainfall extremes is sometimes expressed explicitly as a function of time, but it can also be related to climate variables observed at the same time or the preceding time when rainfall extremes occurred [21]. According to several studies, it is reported that it is more reasonable to use climate variables than time as co-variates for expressing non-stationarity in a non-stationary frequency analysis [22,23]. Recently, studies of non-stationary frequency analysis using climate variables have been suggested for the annual maximum series [24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

et al. 2021

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Interest in future rainfall extremes is increasing, but the lack of consistency in the future rainfall extremes outputs simulated in climate models increases the difficulty of establishing climate change adaptation measures for floods. In this study, a methodology is proposed to investigate future rainfall extremes using future surface air temperature (SAT) or dew point temperature (DPT). The non-stationarity of rainfall extremes is reflected through non-stationary frequency analysis using SAT or DPT as a co-variate. Among the parameters of generalized extreme value (GEV) distribution, the scale parameter is applied as a function of co-variate. Future daily rainfall extremes are projected from 16 future SAT and DPT ensembles obtained from two global climate models, four regional climate models, and two representative concentration pathway climate change scenarios. Compared with using only future rainfall data, it turns out that the proposed method using future temperature data can reduce the uncertainty of future rainfall extremes outputs if the value of the reference co-variate is properly set. In addition, the confidence interval of the rate of change of future rainfall extremes is quantified using the posterior distribution of the parameters of the GEV distribution sampled using Bayesian inference.

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Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis

Cited by 23 publications

References 43 publications

A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions

A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions

Design flood estimation with varying record lengths in Norway under stationarity and nonstationarity scenarios

Uncertainty of Rate of Change in Korean Future Rainfall Extremes Using Non-Stationary GEV Model

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