GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 82-25
Presentation Time: 9:00 AM-5:30 PM

DO ENSO EVENTS SIGNIFICANTLY AFFECT HYDROLOGY IN SOUTHWESTERN OHIO WATERSHEDS?


MAGEE, Lauren Elizabeth, Earth and Environmental Sciences, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45431, RITZI Jr., Robert W., Earth and Environmental Sciences, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435 and BOTTOMLEY, Michael, Mathematics and Statistics, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45431, magee.23@wright.edu

Regional-scale models in the literature suggest that winters in the Midwestern United States are warmer and drier during an El Niño event, and cooler and wetter during a La Niña event. Therefore, hydrologic time series in southwest Ohio watersheds should exhibit an ENSO influence. Our hypotheses are that average winter precipitation, stream discharge, and ground-water level will be lower during El Niño events and higher during La Niña events. Statistical methods are being applied to test the hypotheses, using data spanning 1900 to present. The first method quantifies the proportion of winters for which precipitation, steam flow and groundwater level are above or below the neutral-year average in La Nina and El Nino years respectively (departure analysis). The second method uses independent samples t-tests to determine if average El Niño winter values are statistically-significantly lower than the neutral-year average, or if La Niña winter values are significantly greater. The last method is regression to determine if there is a linear relationship between the winter monthly average and the ENSO index (the index is strongly positive in El Nino events and strongly negative in La Nina events). Preliminary results for the precipitation data are presented. The departures from mean and t-test results show that on average El Niño causes less precipitation in the years studied, as expected, but a La Niña year is statistically no different from a normal year. The linear regression analysis has shown that for every one unit increase in the ENSO index, precipitation decreases by 0.17 inches. However, the R-square value is 0.063 reflecting that a large amount of variability is not explained by ENSO.