Zobrazeno 1 - 10
of 71
pro vyhledávání: '"Jai Vaze"'
Autor:
David J. Penton, Jin Teng, Catherine Ticehurst, Steve Marvanek, Andrew Freebairn, Cherry Mateo, Jai Vaze, Ang Yang, Fathaha Khanam, Ashmita Sengupta, Carmel Pollino
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract With growing concerns over water management in rivers worldwide, researchers are seeking innovative solutions to monitor and understand changing flood patterns. In a noteworthy advancement, stakeholders interested in the changing flood patte
Externí odkaz:
https://doaj.org/article/df9094f06ac04e78a5316d41b226cd52
Publikováno v:
Journal of Hydroinformatics, Vol 24, Iss 5, Pp 1004-1019 (2022)
Efficient and accurate flood inundation predictions can provide useful information for flood risk mitigation and water resource management. In this paper, we propose a new modelling method, LoHy + , which can be applied to efficiently simulate the sp
Externí odkaz:
https://doaj.org/article/f3cd20c792304b4aa0e264066c59ce3d
Publikováno v:
Australasian Journal of Water Resources. :1-13
Publikováno v:
Journal of Hydrology. 622:129683
Autor:
Shaun S. H. Kim, Justin D. Hughes, Lucy A. Marshall, Cuan Petheram, Ashish Sharma, Jai Vaze, Russell S. Crosbie
Publikováno v:
Hydrological Processes. 36
Autor:
Jai Vaze, Michael R. Kuchka, John K. Leiser, Zachary Carroll, Layla Al-Shaer, Timothy Paciorek, Andrew Bloch, Michael A. McQuillan, Rachel Moyer, Murray Itzkowitz
Publikováno v:
Behaviour. 158:19-34
Operational sex ratio (OSR) is predicted to influence the direction and intensity of sexual selection. Thus, as the relative numbers of reproductively active males vs females change, the behavioural competition among males and their differences in re
Autor:
Morgane Terrier, Alban de Lavenne, Jai Vaze, Charles Perrin, Vazken Andréassian, Julien Lerat
Publikováno v:
Hydrological Sciences Journal
Hydrological Sciences Journal, Taylor & Francis, 2021, 66 (1), pp.12-36. ⟨10.1080/02626667.2020.1839080⟩
Hydrological Sciences Journal, Taylor & Francis, 2021, 66 (1), pp.12-36. ⟨10.1080/02626667.2020.1839080⟩
International audience; Over the past few decades, several naturalization methods have been developed for removing anthropogenic influences from streamflow time series, to the point that naturalized flows are often considered true natural flows in ma
Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales
Autor:
Bernardo Teufel, Fernanda Carmo, Laxmi Sushama, Lijun Sun, Naveed Khaliq, Stephane Belair, Asaad Yahia Shamseldin, Dasika Nagesh Kumar, Jai Vaze
The high computational cost of super-resolution (< 250 m) climate simulations is a major barrier for generating climate change information at such high spatial and temporal resolutions required by many sectors for planning local and asset-specific cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7419a98e1718b85e7194b1e617b5a6f7
https://doi.org/10.5194/egusphere-egu22-8649
https://doi.org/10.5194/egusphere-egu22-8649
Publikováno v:
Water Resources Research. 57
Publikováno v:
Water Resources Management. 33:831-845
The simple conceptual flood inundation model TVD (Teng-Vaze-Dutta) is more computationally efficient and cost-effective than traditional hydrodynamic models. It is especially useful for applications that do not require velocity output and have low de