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Ensemble Daily Streamflow Forecast Using Two-step Daily Precipitation Interpolation

일강우 내삽을 이용한 일유량 시뮬레이션 및 앙상블 유량 발생

  • 황연상 (아칸소 주립대학교) ;
  • 허준행 (연세대학교 사회환경시스템공학부) ;
  • 정영훈 (연세대학교 사회환경시스템공학부)
  • Received : 2011.01.05
  • Accepted : 2011.03.04
  • Published : 2011.03.31

Abstract

Input uncertainty is one of the major sources of uncertainty in hydrologic modeling. In this paper, first, three alternate rainfall inputs generated by different interpolation schemes were used to see the impact on a distributed watershed model. Later, the residuals of precipitation interpolations were tested as a source of ensemble streamflow generation in two river basins in the U.S. Using the Monte Carlo parameter search, the relationship between input and parameter uncertainty was also categorized to see sensitivity of the parameters to input differences. This analysis is useful not only to find the parameters that need more attention but also to transfer parameters calibrated for station measurement to the simulation using different inputs such as downscaled data from weather generator outputs. Input ensembles that preserves local statistical characteristics are used to generate streamflow ensembles hindcast, and showed that the ensemble sets are capturing the observed steamflow properly. This procedure is especially important to consider input uncertainties in the simulation of streamflow forecast.

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