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DOI	10.3390/w12102813
	Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation
	Gonzalez Cruz, Marangely; Hernandez, E. Annette; Uddameri, Venkatesh
通讯作者	Uddameri, V
来源期刊	WATER
EISSN	2073-4441
出版年	2020
卷号	12 期号:10
英文摘要	A bivariate kernel density estimation (KDE) method was utilized to develop a stochastic framework to assess how agricultural droughts are related to unfavorable meteorological conditions. KDE allows direct estimation of the bivariate cumulative density function which can be used to extract the marginal distributions with minimal subjectivity. The approach provided excellent fits to bivariate relationships between the standardized soil moisture index (SSMI) computed at three- and six-month accumulations and standardized measures of precipitation (P), potential evapotranspiration (PET), and atmospheric water deficit (AWD = P - PET) at 187 stations in the High Plains region of the US overlying the Ogallala Aquifer. The likelihood of an agricultural drought given a precipitation deficit could be as high as 40-65% within the study area during summer months and between 20-55% during winter months. The relationship between agricultural drought risks and precipitation deficits is strongest in the agriculturally intensive central portions of the study area. The conditional risks of agricultural droughts given unfavorable PET conditions are higher in the eastern humid portions than the western arid portions. Unfavorable PET had a higher impact on the six-month standardized soil moisture index (SSMI6) but was also seen to influence three-month SSMI (SSMI3). Dry states as defined by AWD produced higher risks than either P or PET, suggesting that both of these variables influence agricultural droughts. Agricultural drought risks under favorable conditions of AWD were much lower than when AWD was unfavorable. The agricultural drought risks were higher during the winter when AWD was favorable and point to the role of soil characteristics on agricultural droughts. The information provides a drought atlas for an agriculturally important region in the US and, as such, is of practical use to decision makers. The methodology developed here is also generic and can be extended to other regions with considerable ease as the global datasets required are readily available.
英文关键词	bivariate joint distribution stochastic risk assessment Ogallala Aquifer High Plains Aquifer agricultural droughts meteorology water resources management
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000586152000001
WOS关键词	SOIL-MOISTURE ; UNITED-STATES ; GROUNDWATER ; SEVERITY ; INDEX ; YIELD
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/327326
作者单位	[Gonzalez Cruz, Marangely; Hernandez, E. Annette; Uddameri, Venkatesh] Texas Tech Univ, Dept Civil Environm & Construct Engn, Lubbock, TX 79409 USA
推荐引用方式 GB/T 7714	Gonzalez Cruz, Marangely,Hernandez, E. Annette,Uddameri, Venkatesh. Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation[J],2020,12(10).
APA	Gonzalez Cruz, Marangely,Hernandez, E. Annette,&Uddameri, Venkatesh.(2020).Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation.WATER,12(10).
MLA	Gonzalez Cruz, Marangely,et al."Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation".WATER 12.10(2020).