10A.2
A Study of the Magnitude and Distribution of Daily and Monthly Heavy Rainfall in Kansas

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Wednesday, 5 February 2014: 4:15 PM
Room C102 (The Georgia World Congress Center )
Vahid Rahmani, Kansas State University, Manhattan, KS; and S. L. Hutchinson, J. A. Harrington, J. M. S. Hutchinson, and A. Anandhi

Heavy rainfall event magnitude and distribution are expected to change with the changing climate in Kansas. Investigating trends of intense rainfall events provides important information for improved water management decisions. The daily rainfall data from 1890 to 2012 were analyzed for 23 stations in Kansas for annual daily maximum, upper 1%, 5%, and 10% of daily rainfall, and annual monthly maximum. Increasing trends were found at the majority of stations for all parameters since 1890. The daily annual maximum ranged from 129 mm (Saint Francis) in the northwest to 356 mm (Fort Scott) in the southeast with the slowest increase of 0.05 mm/10yr (Elkhart) in the southwest and the sharpest increase of 4.2 mm/10yr (Independence) in the southeast. In general, eastern Kansas exhibited a higher increasing trend for the daily annual maximum rain. The monthly annual maximum ranged from 291 mm (Lakin) in the west to 626 mm (Horton) in the northeast, both in June. In addition to the entire period (18902012), four sequential periods, 18911920, 19211950, 19511980, and 19812010, were selected to analyze the variability of extremes over time. The trend and variability of heavy rainfall events over the subperiods did not match the entire period, further supporting the high variability in the region. The frequency of occurrence date of the annual maxima and the month of the annual monthly maxima were calculated over the entire data record. June experienced the highest frequency of maximum monthly rainfall in the majority of stations (18 out of 23) since 1890 following by May and July; however at only 9 of 23 stations did the annual daily maximum occur in June following by July (6 out of 23). With both the monthly and daily annual maximum rainfall occurring in May, June, and July, these three months have the highest risk for flooding and the important need of decision making and water management plans. The analysis showed that the daily and monthly heavy rainfall event patterns are changing, specially in the recent years (19802010). The results are useful for updating hydrologic structural designs, agricultural production, social and human effects and emergency managers to make appropriate decision considering the historical heavy rainfall distributions.