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Çѱ¹¼öÀÚ¿øÇÐȸ / v.42, no.10, 2009³â, pp.773-783
°­¿ìÀÚ·áÀÇ ºÒÈ®½Ç¼ºÀ» °í·ÁÇÑ °­¿ì À¯Ãâ ¸ðÇüÀÇ Àû¿ë
( Application of Rainfall Runoff Model with Rainfall Uncertainty )
ÀÌÈ¿»ó;Àü¹Î¿ì;¹ß¸° ´Ù´Ï¿¤¶ó;·Îµå ¹ÌÇÏ¿¤; ÃæºÏ´ëÇб³ °ø°ú´ëÇÐ Åä¸ñ°øÇаú;ÃæºÏ´ëÇб³ °ø°ú´ëÇÐ Åä¸ñ°øÇÐ;½ºÀ§½º ·ÎÀÜ ´ëÇб³ Áö¸®Çаú;µ¶ÀÏ Ç︧ȦÂê ȯ°æ¿¬±¸¼Ò-UFZ;
 
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°­¿ìÀ¯Ãâ¸ðÇüÀÇ ÀÔ·Â ÀÚ·á·Î »ç¿ëµÇ´Â °­¿ì °üÃø ÀÚ·áÀÇ ºÒÈ®½Ç¼ºÀÌ À¯·®¿¹Ãø¿¡ ¹ÌÄ¡´Â ¿µÇâÀ» ºÐ¼®Çϱâ À§ÇÏ¿© ¸ðÇüº¯¼ö °ËÁ¤ÀÇ ºÒÈ®½Ç¼º ¿¬±¸¿¡¼­ »ç¿ëÇÏ´Â GLUE (Generalized Likelihood Uncertainty Estimation)¹æ¹ýÀ» ÀÔ·Â ÀÚ·á ºÎºÐÀ¸·Î È®ÀåÇÏ¿© Àû¿ë ÇÏ¿´´Ù. µ¶ÀÏÀÇ Weida À¯¿ªÀÇ °­¿ì °üÃø ÀڷḦ ¹ÙÅÁÀ¸·Î ±¸Á¶Àû ¹× ºñ±¸Á¶ÀûÀÎ ºÒÈ®½Ç¼º ºÎºÐÀ» °¢°¢ ±¸Á¶ÀûÀÎ ¿ÀÂ÷ ¼öÁ¤ °úÁ¤°ú DUE (Data Uncertainty Engine)À» ÅëÇÏ¿© °­¿ìÀڷḦ ±¸¼ºÇÏ¿´´Ù. À̸¦ À¯¿ªÀÇ ¼ö¹®ÇÐÀû ÀÛ¿ëÀ» °í·ÁÇϱâ À§ÇØ ¼±Á¤ÇÑ ÁýÁßÇü °­¿ìÀ¯Ãâ¸ðÇü, PDM (Probability Distribution Model)¿¡ MC (Monte Carlo)¿Í GLUE ¹æ¹ýÀ» Ȱ¿ëÇÏ¿© Àû¿ëÇÏ¿´´Ù. MC°ËÁ¤º¯¼öµéÀÇ °ËÁ¤ ÈÄ ¹ÝÀÀ Ç¥¸é(Posterior response surface)À» °ËÅäÇϰí GLUE ÀÇ ¹ÝÀÀ°ËÁ¤ ¸ðÇüº¯¼ö(Behavioural model parameter set)¸¦ ¼±ÅÃ, °£·«ÇÑ GLUE À¯·®°î¼±µéÀ» °è»êÇÏ¿´´Ù. °è»êµÈ GLUE À¯·®°î¼±µéÀ» ¸ðµÎ ÇÕÇÏ¿© ¾Ó»óºí À¯·®À» »êÁ¤Çϰí, ÀÌ À¯·®ÀÇ 90 ºÐÀ§¸¦ °­¿ì·®ÀÚ·á ¹× ¸ðÇüº¯¼ö °ËÁ¤ÀÇ ºÒÈ®½Ç¼ºÀ» °í·ÁÇÑ ½Å·Ú±¸°£À¸·Î Á¦½ÃÇÏ¿´´Ù. PDM ¸ðÇüÀÇ °á°ú´Â À¯·®°î¼±ÀÇ Àü±¸°£¿¡¼­ ¾ÈÁ¤ÀûÀÎ ¸ðÀÇ ´É·ÂÀ» º¸¿©ÁÖ°í ÀÖÀ¸³ª, ÷µÎÀ¯·® ºÎºÐÀÌ Àû°Ô »êÁ¤µÇ´Â ¹®Á¦Á¡À» º¸À̰í ÀÖ´Ù. º» ¿¬±¸¿¡¼­ »ó´ëÀûÀ¸·Î ÀûÀº ¼öÀÇ °­¿ì ½Ã³ª¸®¿À ¹× ¹ÝÀÀ°ËÁ¤ ¸ðÇüº¯¼öÀÇ Àû¿ëÀ̶ó´Â ÇѰ迡µµ ºÒ±¸Çϰí, GLUE ¹æ¹ýÀ» °­¿ì°üÃøÀÚ·áÀÇ ºÒÈ®½Ç¼º ºÎºÐÀ¸·Î È®ÀåÇÏ¿© °­¿ìÀÚ·á ¹× º¯¼ö °ËÁ¤ÀÇ ºÒÈ®½Ç¼ºÀ» °í·ÁÇÑ ¸ðÀÇµÈ À¯·®¿¹ÃøÀÇ ½Å·Ú±¸°£ÀÇ Àû¿ë°¡´É¼ºÀ» º¸¿©ÁÖ°í ÀÖ´Ù.
The effects of rainfall input uncertainty on predictions of stream flow are studied based extended GLUE (Generalized Likelihood Uncertainty Estimation) approach. The uncertainty in the rainfall data is implemented by systematic/non-systematic rainfall measurement analysis in Weida catchment, Germany. PDM (Probability Distribution Model) rainfall runoff model is selected for hydrological representation of the catchment. Using general correction procedure and DUE(Data Uncertainty Engine), feasible rainfall time series are generated. These series are applied to PDM in MC(Monte Carlo) and GLUE method; Posterior distributions of the model parameters are examined and behavioural model parameters are selected for simplified GLUE prediction of stream flow. All predictions are combined to develop ensemble prediction and 90 percentile of ensemble prediction, which are used to show the effects of uncertainty sources of input data and model parameters. The results show acceptable performances in all flow regime, except underestimation of the peak flows. These results are not definite proof of the effects of rainfall uncertainty on parameter estimation; however, extended GLUE approach in this study is a potential method which can include major uncertainty in the rainfall-runoff modelling.
 
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°­¿ì À¯Ãâ ¸ðÇü;ºÒÈ®½Ç¼º;À¯·®¿¹Ãø;Rainfall Runoff modelling;Uncertainty analysis;Prediction of streamflow;GLUE;
 
Çѱ¹¼öÀÚ¿øÇÐȸ³í¹®Áý / v.42, no.10, 2009³â, pp.773-783
Çѱ¹¼öÀÚ¿øÇÐȸ
ISSN : 1226-6280
UCI : G100:I100-KOI(KISTI1.1003/JNL.JAKO200932848675448)
¾ð¾î : Çѱ¹¾î
³í¹® Á¦°ø : KISTI Çѱ¹°úÇбâ¼úÁ¤º¸¿¬±¸¿ø
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