Trends, Variability and Extremes of Summer Drought in the Midwest United States: the Role of Climate Variability and Snow Storage

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Wednesday, 20 January 2010: 5:15 PM
B216 (GWCC)
Vimal Mishra, Purdue University, West Lafayette, IN; and K. A. Cherkauer

Drought is an extreme event which is related to soil moisture patterns, precipitation and runoff. The trends, variability, severity, frequency and extremes of drought are related to regional climate variability. The common indicators used to express climate variability are: the El Niņo/Southern Oscillation (ENSO), North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO) which are related to anomalies in surface temperature and precipitation. In the present study, the effect of large scale oscillations and snow storage on summer (JJA) drought occurrence and its severity in the Midwest United States is examined using a reconstruction of long term data set of soil moisture, precipitation and daily temperature extremes. We seek to answer the key questions: (i) How has the region experienced the trends, variability and extremes associated with the summer drought? How do the trends and variability of summer drought link with large scale oscillations? and (iii) To what degree the winter and spring snow storage play a role in the summer drought occurrence ?: The VIC model was calibrated for monthly stream flow at five streamflow gauging stations and was then evaluated for soil moisture and its persistence, soil temperature and soil heat fluxes. After calibration and evaluation the VIC model was implemented for the full historic (1916-2007) period across the study domain. The non parametric Mann-Kendall method was used to estimate trends using the gridded climatology of precipitation and air temperature variables. Trends were also estimated for seasonal drought occurrences and their associated severity. Historic agricultural drought, meteorological drought and hydrological drought events were identified using soil moisture percentiles; the Standard Precipitation Index (SPI) and the Standard Runoff Index (SRI), respectively. The singular value decomposition (SVD) analysis was used to study the relationship between large scale oscillations with the summer drought occurrences. We used the total snow water equivalent in winter and spring seasons to estimate its correlation with spring soil moisture and precipitation. To analyze the sensitivity of categorical droughts (i.e. meteorological, agricultural and hydrologic) towards the snow storage, the SVD analysis was used to study the heterogeneous correlation. Results indicate that precipitation and air temperature have experienced upward trends while the wetness is increasing in the study domain. Furthermore, total snow storage is correlated with the summer precipitation and soil moisture. Results also demonstrated that historic drought events were successfully reconstructed using the VIC model. Finally, the effects of climate variability, as defined by large scale oscillations state, on the frequency of drought occurrence and on its magnitude were evaluated.