DOI: 10.5194/hess-22-287-2018
Scopus记录号: 2-s2.0-85040563376
论文题名: Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables
作者: Mortensen E ; , Wu S ; , Notaro M ; , Vavrus S ; , Montgomery R ; , De Piérola J ; , Sánchez C ; , Block P
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2018
卷: 22, 期: 1 起始页码: 287
结束页码: 303
语种: 英语
Scopus关键词: Arid regions
; Atmospheric pressure
; Forecasting
; Nickel
; Oceanography
; Principal component analysis
; Surface waters
; Ensemble forecasts
; Meteorological drought
; Precipitation predictions
; Principal component regression
; Ranked probability skill scores
; Sea surface temperature (SST)
; Southern oscillation index
; Spatiotemporal variability
; Drought
; air-sea interaction
; climate change
; drought
; El Nino
; El Nino-Southern Oscillation
; ensemble forecasting
; hydrological cycle
; index method
; precipitation (climatology)
; prediction
; regression analysis
; sea surface salinity
; sea surface temperature
; semiarid region
; spatiotemporal analysis
; stakeholder
; Peru
英文摘要: Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought. © 2018 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79427
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, United States; Nelson Institute Center for Climatic Research, University of Wisconsin-Madison, Madison, United States; Montgomery Assoc. Resource Solutions LLC, Cottage Grove, United States; Southern Peru Copper Corporation, Santiago-de Surco-Lima, Peru
Recommended Citation:
Mortensen E,, Wu S,, Notaro M,et al. Regression-based season-ahead drought prediction for southern Peru conditioned on large-scale climate variables[J]. Hydrology and Earth System Sciences,2018-01-01,22(1)