¶óÆæÆ®¦¢Ä«Æä¦¢ºí·Î±×¦¢´õº¸±â
¾ÆÄ«µ¥¹Ì Ȩ ¸í»çƯ°­ ´ëÇבּ¸½Ç޹æ Á¶°æ½Ç¹« µ¿¿µ»ó°­ÀÇ Çѱ¹ÀÇ ÀüÅëÁ¤¿ø ÇÐȸº° ³í¹®
ÇÐȸº° ³í¹®

Çѱ¹°Ç¼³°ü¸®ÇÐȸ
Çѱ¹°ÇÃà½Ã°øÇÐȸ
Çѱ¹µµ·ÎÇÐȸ
Çѱ¹»ý¹°È¯°æÁ¶ÀýÇÐȸ
Çѱ¹»ýÅÂÇÐȸ
Çѱ¹¼öÀÚ¿øÇÐȸ
Çѱ¹½Ä¹°ÇÐȸ
Çѱ¹½Ç³»µðÀÚÀÎÇÐȸ
Çѱ¹ÀÚ¿ø½Ä¹°ÇÐȸ
Çѱ¹ÀܵðÇÐȸ
Çѱ¹Á¶°æÇÐȸ
Çѱ¹Áö¹Ý°øÇÐȸ
Çѱ¹ÇÏõȣ¼öÇÐȸ
Çѱ¹È¯°æ»ý¹°ÇÐȸ
Çѱ¹È¯°æ»ýÅÂÇÐȸ

Çѱ¹¼öÀÚ¿øÇÐȸ / v.43, no.8, 2010³â, pp.733-745
±ØÄ¡¼ö¹®ÀÚ·áÀÇ °èÀý¼º ºÐ¼® °³³ä ¹× ºñÁ¤»ó¼º ºóµµÇؼ®À» ÀÌ¿ëÇÑ È®·ü°­¼ö·® ÇØ¼®
( Concept of Seasonality Analysis of Hydrologic Extreme Variables and Design Rainfall Estimation Using Nonstationary Frequency Analysis )
ÀÌÁ¤ÁÖ;±ÇÇöÇÑ;Ȳ±Ô³²; ÀüºÏ´ëÇб³ °ø°ú´ëÇÐ Åä¸ñ°øÇаú;ÀüºÏ´ëÇб³ °ø°ú´ëÇÐ Åä¸ñ°øÇаú;ÀüºÏ´ëÇб³ °ø°ú´ëÇÐ Åä¸ñ°øÇаú;
 
ÃÊ ·Ï
¼ö¹®ÀÚ·áÀÇ °èÀý¼ºÀº ¼öÀÚ¿ø°ü¸®ÀÇ °üÁ¡¿¡¼­ ¸Å¿ì Áß¿äÇÑ ¿ä¼Ò·Î¼­ °èÀý¼ºÀÇ º¯µ¿Àº ´ïÀÇ ¿î¿µ, È«¼öÁ¶Àý, °ü°³¿ë¼ö °ü¸® µî ´Ù¾çÇÑ ºÐ¾ß¿Í ¹ÐÁ¢ÇÑ °ü°è¸¦ °¡Áö°í ÀÖ´Ù. ¼ö¹®ºóµµÇؼ®À» À§ÇØ POT ÀÚ·á¿Í °°Àº ºÎºÐ±â°£Ä¡°è¿­À» »ç¿ëÇÔÀ¸·Î½á ÀÚ·áÀÇ È®Ãæ, °èÀý¼º È®º¸, ¹ß»ýºóµµ¸ðÇüÀÇ ±¸Ãà µîÀÌ °¡´ÉÇÏ´Ù. º» ¿¬±¸¿¡¼­´Â POT ÀÚ·áÀÇ ÀåÁ¡À» È¿°úÀûÀ¸·Î ºóµµÇؼ®¿¡ ¿¬°è½ÃŰ´Â ¹æ¹ý·ÐÀ¸·Î¼­ POT ÀÚ·á·ÎºÎÅÍ °èÀý¼ºÀ» ÃßÃâÇϰí À̸¦ ºóµµÇؼ®°ú ¿¬°è½ÃÄÑ Bayesian ±â¹ýÀ» ±â¹ÝÀ¸·Î ÇÏ´Â ºñÁ¤»ó¼º ºóµµÇؼ® ¸ðÇüÀ» ±¸ÃàÇÏ¿´´Ù. ¼­¿ïÁöÁ¡ÀÇ °üÃø ÀÚ·á·ÎºÎÅÍ 98% Threshold¸¦ Àû¿ëÇÏ¿© POT ÀڷḦ ÃßÃâÇÏ¿´À¸¸ç, GEV ºÐÆ÷¿¡ ´ëÇÑÀûÇÕ¼ºÀ» °ËÅäÇÏ¿´´Ù. À§Ä¡ ¹× ±Ô¸ð¸Å°³º¯¼öÀÇ °èÀýÀûº¯µ¿¼ºÀ» Fourier ±Þ¼ö·Î Ç¥ÇöÇϰí, Bayesian Markov Chain Monte Carlo ¸ðÀǸ¦ ÅëÇØ ¸Å°³º¯¼öµéÀÇ »çÈÄºÐÆ÷¸¦ ÃßÁ¤ÇÏ¿´À¸¸ç, »çÈÄºÐÆ÷¿Í Quantile ÇÔ¼ö¸¦ ÀÌ¿ëÇÏ¿© ÀçÇö±â°£¿¡ µû¸¥ È®·ü°­¼ö·®À» ÃßÁ¤ÇÏ¿´´Ù. °èÀý¼ºÀ» °í·ÁÇÑ ºñÁ¤»ó¼ººóµµÇؼ® °á°ú 7~8¿ùÀÇ ºñÁ¤»ó¼º È®·ü°­¼ö·®°ú ±âÁ¸ Á¤»ó¼ººóµµÇؼ®ÀÇ °á°ú°¡ À¯»çÇÑ °ªÀ» ³ªÅ¸³»°í ÀÖÀ¸¸ç µ¿½Ã¿¡ °èÀý¼ºÀ» ¹Ý¿µÇÑ È®·ü°­¼ö·®ÀÇ °Åµ¿À» È¿°úÀûÀ¸·Î ¸ðÀǰ¡ °¡´ÉÇÏ¿´´Ù.
Seasonality of hydrologic extreme variable is a significant element from a water resources managemental point of view. It is closely related with various fields such as dam operation, flood control, irrigation water management, and so on. Hydrological frequency analysis conjunction with partial duration series rather than block maxima, offers benefits that include data expansion, analysis of seasonality and occurrence. In this study, nonstationary frequency analysis based on the Bayesian model has been suggested which effectively linked with advantage of POT (peaks over threshold) analysis that contains seasonality information. A selected threshold that the value of upper 98% among the 24 hours duration rainfall was applied to extract POT series at Seoul station, and goodness-fit-test of selected GEV distribution has been examined through graphical representation. Seasonal variation of location and scale parameter ($mu$ and $sigma$) of GEV distribution were represented by Fourier series, and the posterior distributions were estimated by Bayesian Markov Chain Monte Carlo simulation. The design rainfall estimated by GEV quantile function and derived posterior distribution for the Fourier coefficients, were illustrated with a wide range of return periods. The nonstationary frequency analysis considering seasonality can reasonably reproduce underlying extreme distribution and simultaneously provide a full annual cycle of the design rainfall as well.
 
Ű¿öµå
±ØÄ¡¼ö¹®·®;ºÎºÐ±â°£Ä¡°è¿­;°èÀý¼º;ºñÁ¤»ó¼º;Bayesian ÇØ¼®;Fourier ÇØ¼®;hydrologic extreme variable;peaks over threshold;seasonality;nonstationary;bayesian model;fourier analysis;
 
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.43, no.8, 2010³â, pp.733-745
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO201025240675833)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
¸ñ·Ïº¸±â
ȸ»ç¼Ò°³ ±¤°í¾È³» ÀÌ¿ë¾à°ü °³ÀÎÁ¤º¸Ãë±Þ¹æÄ§ Ã¥ÀÓÀÇ ÇѰè¿Í ¹ýÀû°íÁö À̸ÞÀÏÁÖ¼Ò ¹«´Ü¼öÁý °ÅºÎ °í°´¼¾ÅÍ
   

ÇÏÀ§¹è³ÊÀ̵¿