Statistical analysis of hydroclimatic time series: Uncertainty and insights

By Demetris Koutsoyiannis, Alberto Montanari. 2007

312 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/29ac9ef4875490be2cd622854d6eff4c7a5cae64

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

A review on the applications of wavelet transform in hydrology time series analysis (2013, background): “Therefore, accurate results of hydrologic time series analysis cannot be easily gained (Kuczera, 1992; Schreiber, 1993; Koutsoyiannis and Montanari, 2007).”
The importance of prewhitening in change point analysis under persistence (2016, result): “This overrejection (compared with the nominal rejection rate) is due to the information redundancy which makes the effective sample size smaller than the observed size, thus implying that the effective variance of the test statistics to be used in the testing procedure under serial dependence is lar”
Generic and parsimonious stochastic modelling for hydrology and beyond (2016, background): “, Earth’s temperature (Koutsoyiannis and Montanari, 2007; Koutsoyiannis 2013a, Markonis and Koutsoyiannis 2013).”
Factors influencing long range dependence in streamflow of European rivers (2014, background): “Koutsoyiannis and Montanari (2007) point out that ‘the statistical uncertainty is dramatically increased in the presence of dependence, especially if this dependence is long-term persistence’ and highlight the need of examining this phenomenon with respect to ‘other climatic hypothesis’.”
The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data (2011, methodology): “To illustrate the importance of applying the correct distribution of Kendall’s τ , we investigate the crosscorrelation between the global North Hemisphere (NH) temperature record and tree-ring width (TRW) as well as maximum latewood density (MXD) proxies, which were used in a study by Mann et al. (2”
Stationarity: Wanted Dead or Alive? 1 (2011, background): “Koutsoyiannis and Montanari (2007), considering the same question from a slightly different perspective, show that a time series consisting of 1,700 observations from an LTP stochastic process actually contains the information equivalent of only three independent observations.”
Revisiting long-range dependence in annual precipitation (2018, background): “strong dependence, even more than 1000) [Koutsoyiannis and Montanari, 2007].”
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, background): “Classification of salient non-stationary behaviors. certain region but variation in each winter for the past years can be regarded as stationary (Koutsoyiannis and Montanari, 2007).”
Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change (2014, background): “Many work towards answering this has been carried out by Lins and Cohn (2011), Koutsoyiannis (e.g. 2004, 2006, 2011) and Koutsoyiannis and Montanari (2007), who support the stationarity of climate.”
Simultaneous estimation of the parameters of the Hurst–Kolmogorov stochastic process (2011, background): “In addition, the classical statistical estimator of r encompasses strong bias if applied to a series with HK behaviour (Koutsoyiannis 2003a, b; Koutsoyiannis and Montanari 2007 ).”
A quick gap filling of missing hydrometeorological data (2014, background): “This dependence is theoretically justified in a few cases but appears to be physically implausible [Koutsoyiannis and Montanari, 2007; Koutsoyiannis, 2011].”