한국습지학회지 (Journal of Wetlands Research)

  • 제16권1호
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  • Pages.19-32
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  • 2014
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  • 1229-6031(pISSN)
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  • 2384-0056(eISSN)
한국습지학회 (Korean Wetlands Society)
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바람의 영향을 고려한 지상강우의 보정방법 연구

Calibration of Gauge Rainfall Considering Wind Effect

  • Shin, Hyunseok (Department of Civil Engineering, Inha university) ;
  • Noh, Huiseong (Department of Civil Engineering, Inha university) ;
  • Kim, Yonsoo (Department of Civil Engineering, Inha university) ;
  • Ly, Sidoeun (Department of Civil Engineering, Inha university) ;
  • Kim, Duckhwan (Department of Civil Engineering, Inha university) ;
  • Kim, Hungsoo (Department of Civil Engineering, Inha university)
  • 투고 : 2013.10.31
  • 심사 : 2013.11.20
  • 발행 : 2014.02.28
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초록

본 연구에서는 우량계로 측정한 강우량이 실제 지표면에 떨어지는 강우 값에 근접하도록 보정함으로써, 유출해석 및 기타 수문분석에 적용할 경우 신뢰도 높은 결과를 얻는데 목적이 있다. 지상우량계로 관측한 강우량에 대한 바람의 영향을 분석하기 위하여, 표준기상관측소인 추풍령기상대에 설치된 바람막이의 유(有) 무(無)에 따른 우량계와 기준우량계의 자료를 획득하였다. 획득한 강우를 단순선형회귀 모형과 신경망 모형을 이용하여 지상강우를 보정하였으며, $Vflo^{TM}$모형을 이용한 유출모의를 통하여 자료의 신뢰도를 검증하였다. 단순선형회귀 모형을 사용한 보정 강우량은 실제 관측된 강우량보다 5%~18%가 큰 강우량을 나타냈으며, 강우획득에 있어 바람의 영향은 1.6~3.3m/s의 풍속구간에서 가장 큰 것을 확인하였다. 또한 회귀모형에서는 풍속구간 5.5m/s이상일 경우 자료의 개수가 전체자료의 0.7%로 매우 작고, 이상치가 획득됨으로써 회귀모형 적용의 어려움이 있었다. 반면에 신경망 기법을 이용한 지상강우의 보정은 전체적으로 관측 값보다 10~20% 가량 강우가 적게 추정되었다. 통계분석결과, 전체적으로 편차가 크고 평균 강우획득량이 클수록 신경망 모형의 적용성이 높게 나타났으며, 획득한 강우량의 극치값이 크게 나타날수록 선형회귀 모형의 적용성이 높게 나타나는 것을 확인하였다. 본 연구결과로 신뢰성 높은 강우보정을 위해서는 지역별 강우 특성에 따른 적합한 보정방법을 선택해야 할 것으로 판단되며, 앞으로의 수문해석에 있어 본 논문에서 제시하는 강우 보정방법을 적용함으로써 신뢰도 높은 수문해석 결과를 기대할 수 있을 것으로 사료된다.

The purpose of this paper is to obtain reliable rainfall data for runoff simulation and other hydrological analysis by the calibration of gauge rainfall. The calibrated gauge rainfall could be close to the actual value with rainfall on the ground. In order to analyze the wind effect of ground rain gauge, we selected the rain gauge sites with and without a windshield and standard rain gauge data from Chupungryeong weather station installed by standard of WMO. Simple linear regression model and artificial neural networks were used for the calibration of rainfalls, and we verified the reliability of the calibrated rainfalls through the runoff analysis using $Vflo^{TM}$. Rainfall calibrated by linear regression is higher amount of rainfall in 5%~18% than actual rainfall, and the wind remarkably affects the rainfall amount in the range of wind speed of 1.6~3.3m/s. It is hard to apply the linear regression model over 5.5m/s wind speed, because there is an insufficient wind speed data over 5.5m/s and there are also some outliers. On the other hand, rainfall calibrated by neural networks is estimated lower rainfall amount in 10~20% than actual rainfall. The results of the statistical evaluations are that neural networks model is more suitable for relatively big standard deviation and average rainfall. However, the linear regression model shows more suitable for extreme values. For getting more reliable rainfall data, we may need to select the suitable model for rainfall calibration. We expect the reliable hydrologic analysis could be performed by applying the calibration method suggested in this research.

키워드

참고문헌

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