Zobrazeno 1 - 10
of 1 574
pro vyhledávání: '"L-Moments"'
Publikováno v:
Discover Environment, Vol 2, Iss 1, Pp 1-21 (2024)
Abstract Monthly precipitation data from 58 synoptic stations throughout Botswana, spanning 1981–2016, were used in this study. The data were examined using multivariate analysis to determine regions exhibiting distinct precipitation variability pa
Externí odkaz:
https://doaj.org/article/ebc7bd4ef419472bb43b8ed29ef4f6a3
Knowable moments for high-order stochastic characterization and modelling of hydrological processes.
Autor:
Koutsoyiannis, Demetris1 dk@itia.ntua.gr
Publikováno v:
Hydrological Sciences Journal/Journal des Sciences Hydrologiques. Jan2019, Vol. 64 Issue 1, p19-33. 15p.
Publikováno v:
Environmental & Social Management Journal / Revista de Gestão Social e Ambiental; 2024, Vol. 18 Issue 3, p1-21, 21p
Publikováno v:
Weather and Climate Extremes, Vol 44, Iss , Pp 100688- (2024)
This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rain
Externí odkaz:
https://doaj.org/article/ec7225fa38e3463a97edf93b8b48a8b8
Publikováno v:
Trends in Ecological and Indoor Environmental Engineering, Vol 1, Iss 1, Pp 24-34 (2023)
Extreme rainfall events are occasional, and understanding their intensity and frequency is important for long-term planning and for public safety. The current study aims to investigate the stability of extreme precipitation events in different region
Externí odkaz:
https://doaj.org/article/be15625dd7dc412c81d144b35fcc8e74
Autor:
Manero, Ana1,2 (AUTHOR) ana.manero@anu.edu.au
Publikováno v:
Applied Economics Letters. 2017, Vol. 24 Issue 14, p977-981. 5p. 2 Charts.
Publikováno v:
Theoretical & Applied Climatology. Aug2018, Vol. 133 Issue 3-4, p1219-1233. 15p. 1 Diagram, 3 Charts, 7 Graphs, 2 Maps.
Publikováno v:
Hydrology, Vol 11, Iss 9, p 154 (2024)
Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potenti
Externí odkaz:
https://doaj.org/article/82f2c4f99c7b417db719e226fac755b5