Abstract
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.
Highlights
Agricultural droughts have resulted in billions of dollars of losses in recent years and have caused other socio-ecological impacts [1,2]
Understanding agricultural droughts is of paramount importance for sustaining rural economies and fostering food security and energy independence
SSMI6 with P, potential evapotranspiration (PET), and atmospheric water deficit (AWD) are presented in Supplementary Information in the interest of brevity
Summary
Agricultural droughts have resulted in billions of dollars of losses in recent years and have caused other socio-ecological impacts [1,2]. In addition to devastating fragile rural economies, agricultural droughts have the potential to disrupt global food security [3]. Droughts are known to decrease structural sugars and lignin content and affect the ethanol yields in biorefining operations [4]. Drought-related water deficits can impact renewable energy production. Understanding agricultural droughts is of paramount importance for sustaining rural economies and fostering food security and energy independence. Agricultural droughts arise when the soil moisture is affected by enhanced atmospheric dryness brought forth by precipitation deficits and associated temperature increases over an extended period
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