Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation
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 margi...
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MDPI AG
2020-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/10/2813 |
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| author | Marangely Gonzalez Cruz E. Annette Hernandez Venkatesh Uddameri |
| author_facet | Marangely Gonzalez Cruz E. Annette Hernandez Venkatesh Uddameri |
| author_sort | Marangely Gonzalez Cruz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:44:44Z |
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| id | doaj.art-c8244e6e81574ae284ddc7a30aa9bf58 |
| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-03-10T15:44:44Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-c8244e6e81574ae284ddc7a30aa9bf582023-11-20T16:35:35ZengMDPI AGWater2073-44412020-10-011210281310.3390/w12102813Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density EstimationMarangely Gonzalez Cruz0E. Annette Hernandez1Venkatesh Uddameri2Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409-1023, USADepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409-1023, USADepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409-1023, USAA 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.https://www.mdpi.com/2073-4441/12/10/2813bivariate joint distributionstochastic risk assessmentOgallala AquiferHigh Plains Aquiferagricultural droughtsmeteorology |
| spellingShingle | Marangely Gonzalez Cruz E. Annette Hernandez Venkatesh Uddameri Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation Water bivariate joint distribution stochastic risk assessment Ogallala Aquifer High Plains Aquifer agricultural droughts meteorology |
| title | Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation |
| title_full | Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation |
| title_fullStr | Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation |
| title_full_unstemmed | Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation |
| title_short | Climatic Influences on Agricultural Drought Risks Using Semiparametric Kernel Density Estimation |
| title_sort | climatic influences on agricultural drought risks using semiparametric kernel density estimation |
| topic | bivariate joint distribution stochastic risk assessment Ogallala Aquifer High Plains Aquifer agricultural droughts meteorology |
| url | https://www.mdpi.com/2073-4441/12/10/2813 |
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