Predictability in dice motion: how does it differ from hydro-meteorological processes?

Dimitriadis, Panayiotis; Koutsoyiannis, Demetris; Tzouka, Katerina

doi:10.1080/02626667.2015.1034128

Cited by 28 publications

(28 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The paper by Dimitriadis et al (2016a) compares the predictability of a die's motion with that of hydrometeorological processes, and challenges the false dichotomy between determinism and randomness, showing how deterministic approaches can be combined with uncertainty estimation.…”

Section: Preface-special Issue: Facets Of Uncertaintymentioning

confidence: 98%

Preface—Special Issue: Facets of Uncertainty

Cârsteanu

Eriş

Volpi

et al. 2016

Hydrological Sciences Journal

View full text Add to dashboard Cite

Section: Preface-special Issue: Facets Of Uncertaintymentioning

confidence: 98%

Preface—Special Issue: Facets of Uncertainty

Cârsteanu

Eriş

Volpi

et al. 2016

Hydrological Sciences Journal

View full text Add to dashboard Cite

“…PCA has useful descriptive properties of the underlying structure of the data. These properties can be efficiently visualized in the biplot (Gabriel, 1971), which is the combined plot of the scores of the data for the first two principal components along with the relative position of the p variables as vectors in the two-dimensional space. Herein, the distance biplot type (Gower and Hand, 1995), which approximates the Euclidean distances between the observations, is used.…”

Section: Principal Component Analysismentioning

confidence: 99%

A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

Iliopoulou¹,

Aguilar²,

Arheimer³

et al. 2019

Hydrol. Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes.

show abstract

“…However, we acknowledge that streamflow records may exhibit more complex patterns (e.g. Hall and Tajvidi, 2000;Ramesh and Davison, 2002;Villarini et al, 2009b) and that there is ongoing discussion about the limitations of a nonstationary description of hydrological processes (Montanari and Koutsoyiannis, 2014;Koutsoyiannis and Montanari, 2015;Read and Vogel, 2015;Serinaldi, 2015;Serinaldi and Kilsby, 2015;Dimitriadis et al, 2016). One last issue that should also be taken into account is the period of record.…”

Section: Generalized Extreme Valuementioning

confidence: 99%

On the impact of gaps on trend detection in extreme streamflow time series

Slater

Villarini

2016

Intl Journal of Climatology

View full text Add to dashboard Cite

Streamflow time series often contain gaps of varying length and location. However, the influence of these gaps on trend detection is poorly understood and cannot be estimated a priori in trend detection studies. We simulated the effects of varying gap size (1, 2, 5, and 10 years) and location (one quarter, one third, and half of the way) on the detection rate of significant monotonic trends in annual maxima and peaks‐over‐threshold, based on the most commonly‐used trend tests in time series of varying length (from 15 to 150 years) and trend magnitude (β1). Results show that, in comparison with the complete time series, the loss in trend detection rate tends to grow with (1) increasing gap size, (2) increasing gap distance from the middle of the time series, (3) decreasing β1 slope, and (4) decreasing time series length. Based on these findings, we provide objective recommendations and cautionary remarks for maximal gap allowance in trend detection in extreme streamflow time series.

show abstract

Predictability in dice motion: how does it differ from hydro-meteorological processes?

Cited by 28 publications

References 21 publications

Preface—Special Issue: Facets of Uncertainty

Preface—Special Issue: Facets of Uncertainty

A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

On the impact of gaps on trend detection in extreme streamflow time series

Contact Info

Product

Resources

About