Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Development of methodology for daily rainfall simulation considering distribution of rainfall events in each duration

강우사상의 지속기간별 분포 특성을 고려한 일강우 모의 기법 개발

  • Jung, Jaewon (Department of Civil Engineering, Inha University) ;
  • Kim, Soojun (Department of Civil Engineering, Inha University) ;
  • Kim, Hung Soo (Department of Civil Engineering, Inha University)
  • 정재원 (인하대학교 공과대학 사회인프라공학과) ;
  • 김수전 (인하대학교 공과대학 사회인프라공학과) ;
  • 김형수 (인하대학교 공학대학 사회인프라공학과)
  • Received : 2018.11.20
  • Accepted : 2019.01.04
  • Published : 2019.02.28
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Abstract

When simulating the daily rainfall amount by existing Markov Chain model, it is general to simulate the rainfall occurrence and to estimate the rainfall amount randomly from the distribution which is similar to the daily rainfall distribution characteristic using Monte Carlo simulation. At this time, there is a limitation that the characteristics of rainfall intensity and distribution by time according to the rainfall duration are not reflected in the results. In this study, 1-day, 2-day, 3-day, 4-day rainfall event are classified, and the rainfall amount is estimated by rainfall duration. In other words, the distributions of the total amount of rainfall event by the duration are set using the Kernel Density Estimation (KDE), the daily rainfall in each day are estimated from the distribution of each duration. Total rainfall amount determined for each event are divided into each daily rainfall considering the type of daily distribution of the rainfall event which has most similar rainfall amount of the observed rainfall using the k-Nearest Neighbor algorithm (KNN). This study is to develop the limitation of the existing rainfall estimation method, and it is expected that this results can use for the future rainfall estimation and as the primary data in water resource design.

기존의 Markov Chain 모형으로 일강우량 모의시에 강우의 발생여부를 모의하고 강우일의 강우량은 Monte Carlo 시뮬레이션을 통해 일강우 분포 특성에 맞는 분포형에서 랜덤으로 강우량을 추정하는 것이 일반적이다. 이때 강우 지속기간에 따른 강도 및 강우의 시간별 분포 등의 강우 사상의 특성을 반영할 수 없다는 한계가 있다. 본 연구에서는 이를 개선하기 위해 강우 사상을 1일 지속강우, 2일 지속강우, 3일 지속강우, 4일이상 지속강우로 구분하여 강우의 지속기간에 따라 강우량을 추정하였다. 즉 강우 사상의 강우 지속일별로 총강우량의 분포형을 비매개변수 추정이 가능한 핵밀도추정(Kernel Density Estimation, KDE)를 적용하여 각각 추정하였고, 강우가 지속될 경우에 지속일별로 해당하는 분포형에서 강우량을 구하였다. 각 강우사상에 대해 추정된 총 강우량은 k-최근접 이웃 알고리즘(k-Nearest Neighbor algorithm, KNN)을 통해 관측 강우자료에서 가장 유사한 강우량을 가지는 강우사상의 강우량 일분포 형태에 따라 각 일강우량으로 분배하였다. 본 연구는 기존의 강우량 추정 방법의 한계점을 개선하고자 하였으며, 연구 결과는 미래 강우에 대한 예측에도 활용될 수 있으며 수자원 설계에 있어서 기초자료로 활용될 수 있을 것으로 기대된다.

Keywords

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Fig. 1. Kernel density function with different bandwidths

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Fig. 2. Concept diagram of rainfall amount distribution

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Fig. 3. Flow chart

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Fig. 4. Transition probability and concept diagram

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Fig. 5. Distribution function by rainfall event duration

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Fig. 6. Rainfall distribution of total period and rainy days [unit : mm]

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Fig. 7. Rainfall distribution of rainfall events in each duration [unit : mm]

Table 1. Rainfall distribution of total period and rainy days

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Table 2. Rainfall distribution of rainfall events in each duration

SJOHCI_2019_v52n2_141_t0002.png 이미지

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