[논문]입자 필터를 이용한 월 물 수지 모형의 시간변화 매개변수 추정: 하천유량 자료의 동화

최정현; 김상단

doi:10.3741/jkwra.2021.54.6.365

입자 필터를 이용한 월 물 수지 모형의 시간변화 매개변수 추정: 하천유량 자료의 동화
Estimating time-varying parameters for monthly water balance model using particle filter: assimilation of stream flow data 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.6, 2021년, pp.365 - 379

최정현 (부경대학교 지구환경시스템과학부 (환경공학전공)) , 김상단 (부경대학교 환경공학과)

초록
AI-Helper

수문 모형 매개변수는 모형 모의에 필수적이며, 지형, 기후조건, 기후변화와 인간 활동으로 인해 시간에 따라 달라질 수 있다. 결과적으로 고정된 매개변수의 사용은 부정확한 하천유량 모의로 이어질 수 있다. 본 연구의 목표는 하천유량 관측자료를 이용하여 시간에 따라 변하는 매개변수를 추정하는 방법을 살펴보고, 하천유량 자료가 모형에 동화될 때 모의 효율성이 어떻게 변하는지 분석하는 것이다. 자료 동화 방법은 변화하는 다양한 환경에 적응하여 수문 모형의 매개변수를 자동으로 추정하기 위하여 사용될 수 있다. 입자 필터를 이용하여 하천유량 관측치를 2개 매개변수 월 물 수지 모형에 동화했다. 자료 동화 방법으로 시간변화 매개변수를 사용한 모의 결과는 SCEM 방법으로 고정 매개변수를 사용한 모의 결과와 비교되었다. 먼저 다양한 시나리오에 기반한 합성 실험을 수행하여 입자 필터 방법이 시간에 따라 변화하는 매개변수를 적절하게 추적할 수 있는지를 살펴보았다. 이후 실제 유역에 적용하여 시간에 따라 변화하는 매개변수와 고정된 매개변수를 사용하였을 때의 하천유량 예측성능과 비교하였다. 본 연구를 통해 얻은 결론은 다음과 같다. (1) 전체적인 월 하천유량 시계열의 예측성능은 입자 필터 방법과 SCEM 방법이 서로 비슷하였다. (2) 우기를 제외한 시기의 월 유출고 예측성능은 자료 동화 방법을 이용한 주기적으로 변화하는 매개변수에 의한 모의가 더 우수하였다. (3) 동화에 사용되는 하천유량 관측자료의 불확실성은 입자 필터의 하천유량 예측성능에 중요한 역할을 하였다.

Abstract ▼ AI-Helper

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.

주제어

표/그림 (10)

그림 Fig. 1. Study area and location of meteorological observation sites
표 Table 1. Annual mean values of precipitation, reference evapotran-spiration, and runoff depth, and areas for four watersheds
그림 Fig. 2. Model parameter assimilated by PF and estimated by SCEM, and its true value
표 Table 2. Scheme performance for parameter estimation and stream flow prediction in numerical experiments
그림 Fig. 3. Time evolution of posterior parameter distributions at three different time segments by assimilation of stream flow for Namgang Dam watershed
그림 Fig. 4. Uncertainty bound tracking of parameters in two parameter monthly water balance model for 19 years assimilation of historical stream flow at Namgang Dam watershed
표 Table 3. Performance metrics for monthly stream flow prediction
그림 Fig. 5. Monthly parameter ensemble average estimate
그림 Fig. 6. Monthly performance metrics for stream flow prediction
그림 Fig. 7. Sensitivity of Particle filter parameter to stream flow prediction performance

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저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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