Παγκόσμια διερεύνηση σχετικά με την εποχιακή διακύμανση της περιθώριας κατανομής της ημερήσιας κατακρήμνισης

S.M. Papalexiou, and D. Koutsoyiannis, A global survey on the seasonal variation of the marginal distribution of daily precipitation, Advances in Water Resources, 94, 131–145, doi:10.1016/j.advwatres.2016.05.005, 2016.

[Παγκόσμια διερεύνηση σχετικά με την εποχιακή διακύμανση της περιθώριας κατανομής της ημερήσιας κατακρήμνισης]

[doc_id=1617]

[Αγγλικά]

Βλέπε επίσης: http://dx.doi.org/10.1016/j.advwatres.2016.05.005

Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:

1.	S.M. Papalexiou, and D. Koutsoyiannis, Entropy based derivation of probability distributions: A case study to daily rainfall, Advances in Water Resources, 45, 51–57, doi:10.1016/j.advwatres.2011.11.007, 2012.
2.	S.M. Papalexiou, D. Koutsoyiannis, and C. Makropoulos, How extreme is extreme? An assessment of daily rainfall distribution tails, Hydrology and Earth System Sciences, 17, 851–862, doi:10.5194/hess-17-851-2013, 2013.
3.	S.M. Papalexiou, and D. Koutsoyiannis, Battle of extreme value distributions: A global survey on extreme daily rainfall, Water Resources Research, 49 (1), 187–201, doi:10.1029/2012WR012557, 2013.

Εργασίες μας που αναφέρονται σ' αυτή την εργασία:

1.	I. Tsoukalas, S.M. Papalexiou, A. Efstratiadis, and C. Makropoulos, A cautionary note on the reproduction of dependencies through linear stochastic models with non-Gaussian white noise, Water, 10 (6), 771, doi:10.3390/w10060771, 2018.
2.	I. Tsoukalas, C. Makropoulos, and D. Koutsoyiannis, Simulation of stochastic processes exhibiting any-range dependence and arbitrary marginal distributions, Water Resources Research, 54 (11), 9484–9513, doi:10.1029/2017WR022462, 2018.
3.	T. Iliopoulou, D. Koutsoyiannis, and A. Montanari, Characterizing and modeling seasonality in extreme rainfall, Water Resources Research, 54 (9), 6242–6258, doi:10.1029/2018WR023360, 2018.
4.	P. Kossieris, and C. Makropoulos, Exploring the statistical and distributional properties of residential water demand at fine time scales, Water, 10 (10), 1481, doi:10.3390/w10101481, 2018.
5.	P. Kossieris, I. Tsoukalas, C. Makropoulos, and D. Savic, Simulating marginal and dependence behaviour of water demand processes at any fine time scale, Water, 11 (5), 885, doi:10.3390/w11050885, 2019.
6.	G. Papacharalampous, H. Tyralis, A. Langousis, A. W. Jayawardena, B. Sivakumar, N. Mamassis, A. Montanari, and D. Koutsoyiannis, Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms, Water, doi:10.3390/w11102126, 2019.
7.	L. Katikas, P. Dimitriadis, D. Koutsoyiannis, T. Kontos, and P. Kyriakidis, A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series, Applied Energy, 295, 116873, doi:10.1016/j.apenergy.2021.116873, 2021.
8.	P. Kossieris, I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Generic framework for downscaling statistical quantities at fine time-scales and its perspectives towards cost-effective enrichment of water demand records, Water, 13 (23), 3429, doi:10.3390/w13233429, 2021.

Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία: Δείτε τις στο Google Scholar ή στο ResearchGate

Κατηγορίες: Μοντέλα βροχής