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Çѱ¹¼öÀÚ¿øÇÐȸ / v.43, no.2, 2010³â, pp.187-199
ºñÁ¤»ó¼º °­¿ìºóµµÇؼ®¹ý¿¡ ÀÇÇÑ È®·ü°­¿ì·®ÀÇ Æò°¡
( Evaluation of Probability Rainfalls Estimated from Non-Stationary Rainfall Frequency Analysis )
ÀÌâȯ;¾ÈÀçÇö;±èÅ¿õ; ÇѾç´ëÇб³ ´ëÇпø °Ç¼³È¯°æ°øÇÐ;¼­°æ´ëÇб³ Åä¸ñ°øÇаú;ÇѾç´ëÇб³ °Ç¼³È¯°æ½Ã½ºÅÛ°øÇÐ;
 
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This study evaluated applicability and confidence of probability rainfalls estimated by the non-stationary rainfall frequency analysis which was recently developed. Using rainfall data at 4 sites which have an obvious increasing trend in observations, we estimated 3 type probability rainfalls; probability rainfalls from stationary rainfall frequency analysis using data from 1973-1997, probability rainfalls from stationary rainfall frequency analysis using data from 1973-2006, probability rainfalls from non-stationary rainfall frequency analysis assuming that the current year is 1997 and the target year is 2006. Based on the comparison of residuals from 3 probability rainfalls, the non-stationary rainfall frequency analysis provided more effective and well-directed estimates of probability rainfalls in the target year. Using Bootstrap resampling, this study also evaluated the parameter estimation methods for the non-stationary rainfall frequency analysis based on confidence intervals. The confidence interval length estimated by the maximum likelihood estimation (MLE) is narrower than the probability weighted moments (PWM). The results indicated that MLE provides more proper confidence than PWM for non-stationary probability rainfalls.
 
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ºñÁ¤»ó¼º °­¿ìºóµµÇؼ®¹ý;È®·ü°­¿ì·®;½Å·Ú±¸°£;Non-stationary rainfall frequency analysis;Probability rainfall;Confidence interval;
 
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.43, no.2, 2010³â, pp.187-199
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO201007633730108)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
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