Zobrazeno 1 - 10
of 7 691
pro vyhledávání: '"GCM"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Due to spatial scarcity and uncertainties in sediment data, initial and boundary conditions in deep-time climate simulations are not well constrained. On the other hand, depending on these conditions, feedback mechanisms in the climate syste
Externí odkaz:
https://doaj.org/article/f737858396d14dcd87bce5244c054b3c
Publikováno v:
Business Process Management Journal, 2024, Vol. 30, Issue 4, pp. 1154-1184.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/BPMJ-06-2023-0456
Publikováno v:
Green Energy & Environment, Vol 9, Iss 12, Pp 1802-1811 (2024)
The melting points of ionic liquids (ILs) reported since 2020 were surveyed, collected, and reviewed, which were further combined with the previous data to provide a database with 3129 ILs ranging from 177.15 to 645.9 K in melting points. In addition
Externí odkaz:
https://doaj.org/article/a611d65faecb49af8382643e8cb7caef
Autor:
Mahmoud Roushdi
Publikováno v:
Water Science, Vol 38, Iss 1, Pp 77-91 (2024)
ABSTRACTEgypt is grappling with water shortage, with agriculture using about 80% of its water consumption. Climate change is only going to further complicate water availability and water consumption. This study investigates the potential impacts of c
Externí odkaz:
https://doaj.org/article/be496775437b47a7ae1d97baed3db244
Autor:
Hamid Anwar, Afed Ullah Khan, Basir Ullah, Abubakr Taha Bakheit Taha, Taoufik Najeh, Muhammad Usman Badshah, Abdulnoor A. J. Ghanim, Muhammad Irfan
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-26 (2024)
Abstract This research was carried out to predict daily streamflow for the Swat River Basin, Pakistan through four deep learning (DL) models: Feed Forward Artificial Neural Networks (FFANN), Seasonal Artificial Neural Networks (SANN), Time Lag Artifi
Externí odkaz:
https://doaj.org/article/bb1fbe848f6e42c4a65d7517f8d0a88a
Publikováno v:
Water Practice and Technology, Vol 19, Iss 6, Pp 2419-2441 (2024)
The present research endeavors to simulate daily stream flow by employing hydrologic and hydraulic modeling techniques to comprehensively assess the impact of climate change on flood risk. This investigation was conducted within the Shekhan basin, si
Externí odkaz:
https://doaj.org/article/da351881b2f343419cda896d8948f4e7
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 21, Pp n/a-n/a (2024)
Abstract Synoptic climatology, which connects atmospheric circulation with regional environmental conditions, is pivotal to understanding climate dynamics. While regional climate models (RCMs) can reproduce key mesoscale precipitation patterns, biase
Externí odkaz:
https://doaj.org/article/5689dba39ece4800978a08b618d12575
Publikováno v:
Journal of Flood Risk Management, Vol 17, Iss 3, Pp n/a-n/a (2024)
Abstract Estimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability mode
Externí odkaz:
https://doaj.org/article/eab63d689d4d4ca28ade7a8cc1816a77
Publikováno v:
Open Engineering, Vol 14, Iss 1, Pp 4205-18 (2024)
Regions characterized by an arid or semi-arid climate are highly susceptible to prospective climate change impacts worldwide. Therefore, evaluating the effects of global warming on water availability in such regions must be accurately addressed to id
Externí odkaz:
https://doaj.org/article/0ef0d98c8f55465482337f50843a6cbc
Autor:
Wenyu Yang, Ziyong Zhao, Liping Pan, Ruifei Li, Shixue Wu, Pei Hua, Haijun Wang, Britta Schmalz, Peter Krebs, Jin Zhang
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 103243- (2024)
Urban flooding poses significant threats to human lives and urban development worldwide, while the impact of climate change on urban flooding remains unclear. To systematically analyze the variabilities of hydrological patterns and urban flooding und
Externí odkaz:
https://doaj.org/article/f70075f3c53b40f28bc8f7ad04417363