40-7 Space-Time Variability of Agricultural, Meteorological, and Hydrologic Drought in Kansas.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: Global Climate Change General Oral (includes student competition)

Monday, November 7, 2016: 10:30 AM
Phoenix Convention Center North, Room 232 B

Zachary Zambreski, Department of Agronomy, Manhattan, KS and Xiaomao Lin, Kansas State University, Kansas State University, Manhattan, KS
Abstract:
In this study agricultural, meteorological, and hydrologic drought variability were investigated in Kansas using the instrumental record 1900-2014.  The drought indices used were the PDSI, SPEI, and SPI, and the -3,-6,-12, and -24 month time steps were individually assessed. Empirical Orthogonal Function techniques and Varimax rotation were applied to the drought datasets. Differences between drought types were small for both unrotated and rotated patterns.  Large scale synoptic patterns generally dominate the Kansas climate, especially during long duration wet and dry periods in central and eastern Kansas. The first principal components explain approximately 70% of the drought variability across the state and demonstrate strong wetting trends for the state over the last century, operating on a dominant period of 14 years for all drought indices. The strongest evidence of drying is shown in the second unrotated principal components for western Kansas with hydrologic drought the most significant. This underlying drying signal dampens the dominant wet signal for western Kansas, putting the Ogallala aquifer at risk of losing its ability to recharge in the long-term. The third principal component, which explains less than 10% drought variability, shows increasing intensity of drought and flooding after 1980.  Rotation applied to the original EOFs emphasized strong wetting patterns in southeast, northeast, and a small subsection of southwest Kansas.  The 99° W meridian acts as the dominant transitional boundary demarcating the rotated climate variability zones. The PDSI principal components demonstrate the highest cross-correlation with El Nino, underscoring its effect on soil moisture status rather than on long-term drought impacts in Kansas.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: Global Climate Change General Oral (includes student competition)