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DOI	10.1016/j.atmosres.2020.105116
	Hydrological hotspots of climatic influence in Brazil: A two-step regularization approach
	Ndehedehe C.E.; Haile G.G.; Agutu N.O.; Ferreira V.G.; Getirana A.; Okwuashi O.
发表日期	2020
ISSN	0169-8095
卷号	246
英文摘要	Interest in region-specific assessments of droughts and the need to optimise water resources planning and allocation on a local scale via additional investments in water infrastructures is emerging as novel management initiatives to build drought resilience. In this study, a novel two-step regularization procedure that combines statistical rotation with support vector machine regression (SVMR) is employed to assess and identify hydrological regions in Brazil associated with global climate teleconnection patterns (e.g., ENSO, PDO, etc.). To enhance realistic drought impact assessments, region-specific attributes of drought and the climate modes associated with its variability and characteristics are studied using standardised precipitation index (SPI) and reanalysis data (MERRA). Compared to other regions, results show that drought variability and its occurrence are relatively higher in the extreme north, north-east, and south of Brazil. The predominance of extreme drought events shows that more than 50% of Brazil was affected by the 1998/1999 drought while areas under droughts in recent times fluctuated between 25% in 2012 and 70% in 2015. Results also show significant association of ENSO (e.g., R2 = 28%) and PDO (e.g., R2 = 18%) with drought indicators in several climatic hotspots. The synthesis of climate modes as predictors of droughts in the SVMR scheme highlights the influence and importance of the Pacific and Atlantic oceans on drought evolutions in Brazil. The MERRA-derived drought indicator extracted this influence better (e.g., r = 0.72) than the SPI and appears to be a more suitable drought metric to understand the impacts of global climate on extreme events in the region. © 2020 Elsevier B.V.
英文关键词	Brazil; Drought; ENSO; Rainfall; SPI; Support vector machine
语种	英语
scopus关键词	Climatology; Investments; Support vector machines; Support vector regression; Water resources; Impact assessments; Precipitation indices; Regularization approach; Regularization procedure; Support vector machine regressions; Teleconnection patterns; Water infrastructure; Water resources planning; Drought; climate effect; drought; global climate; hydrometeorology; precipitation assessment; regression analysis; support vector machine; teleconnection; Brazil
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141789
作者单位	Australian Rivers Institute and Griffith School of Environment & Science, Griffith University, Nathan, Queensland 4111, Australia; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Department of Geomatic Engineering and Geospatial Information systems JKUAT, Nairobi, Kenya; School of Earth Sciences and Engineering, Hohai University, Nanjing, China; Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States; Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States; Department of Geoinformatics and Surveying, University of Uyo, P.M.B, Uyo, 1017, Nigeria
推荐引用方式 GB/T 7714	Ndehedehe C.E.,Haile G.G.,Agutu N.O.,et al. Hydrological hotspots of climatic influence in Brazil: A two-step regularization approach[J],2020,246.
APA	Ndehedehe C.E.,Haile G.G.,Agutu N.O.,Ferreira V.G.,Getirana A.,&Okwuashi O..(2020).Hydrological hotspots of climatic influence in Brazil: A two-step regularization approach.Atmospheric Research,246.
MLA	Ndehedehe C.E.,et al."Hydrological hotspots of climatic influence in Brazil: A two-step regularization approach".Atmospheric Research 246(2020).