[논문]지역적 민감도 분석을 이용하여 계절성을 고려한 수문 모형 보정 기법 개발

이예린; 유재웅; 김경탁; 권현한

doi:10.12652/ksce.2023.43.3.0337

지역적 민감도 분석을 이용하여 계절성을 고려한 수문 모형 보정 기법 개발
A Development of Hydrological Model Calibration Technique Considering Seasonality via Regional Sensitivity Analysis 원문보기

KSCE Journal of Civil and Environmental Engineering Research = 대한토목학회논문집, v.43 no.3, 2023년, pp.337 - 352

이예린 (세종대학교 공과대학 건설환경공학과) , 유재웅 (세종대학교 공과대학 건설환경공학과) , 김경탁 (한국건설기술연구원 수자원하천연구본부) , 권현한 (세종대학교 공과대학 건설환경공학과)

초록
AI-Helper

일반적으로 강우-유출모형의 매개변수 최적화는 가용 자료 전체를 대상으로 수행하여 고유의 매개변수 집합을 활용한다. 그러나, 계절에 따른 강수량의 편차가 큰 국내의 기후 특성과 더불어 기후변화로 인하여 계절성에 따른 편차 및 변동성이 증가할 것으로 전망되고 있어, 물 수요자들에 대한 안정적인 공급을 위한 장기간의 계획에서 계절성을 반영한 매개변수 추정은 효율적인 물배분에 중요한 요소라 할 수 있다. 본 연구에서는 기후특성에 따른 강우-유출모형의 변동성을 분석하기 위하여 소양강댐 유역을 대상으로 GR4J 강우-유출모형을 활용한 지역적 민감도 분석을 수행하였으며, 산출된 민감도 분석 결과와 기상자료를 결합하여 SOM을 활용해 군집화하였다. 이를 통해 계절 분리를 수행하고 각 계절의 특징을 분석하여 강우-유출모형의 보정 기법을 개발하였으며, 통계적 지표를 이용하여 성능을 평가하였다. 결과적으로 비교적 유량이 적은 Cold 기간의 모형 성능이 개선되는 것을 확인할 수 있었다. 이는 몬순기후 등 강수편차가 큰 지역을 대상으로 수문모형의 성능 및 예측도를 높일 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

In general, Rainfall-Runoff model parameter set is optimized using the entire data to calculate unique parameter set. However, Korea has a large precipitation deviation according to the season, and it is expected to even worsen due to climate change. Therefore, the need for hydrological data considering seasonal characteristics. In this study, we conducted regional sensitivity analysis(RSA) using the conceptual Rainfall-Runoff model, GR4J aimed at the Soyanggang dam basin, and clustered combining the RSA results with hydrometeorological data using Self-Organizing map(SOM). In order to consider the climate characteristics in parameter estimation, the data was divided based on clustering, and a calibration approach of the Rainfall-Runoff model was developed by comparing the objective functions of the Global Optimization method. The performance of calibration was evaluated by statistical techniques. As a result, it was confirmed that the model performance during the Cold period(November~April) with a relatively low flow rate was improved. This is expected to improve the performance and predictability of the hydrological model for areas that have a large precipitation deviation such as Monsoon climate.

주제어

표/그림 (19)

그림 Fig. 1. Framework of the GR4J Model (Perrin et al., 2003)
표 Table 1. Description and Ranges of Parameters in the GR4J Model
그림 Fig. 2. Flowchart of Regional Sensitivity Analysis
그림 Fig. 3. Location and Observation Stations of the Soyanggang Dam Watershed
표 Table 2. Performance Evaluation Criteria for Recommended Statistical Performance Measures for Watershed Models (Moriasi et al., 2015)
표 Table 3. Specification of ASOS Weather Stations and Hydrological Station over Soyanggang Dam Watershed
그림 Fig. 4. Results of Silhouette Score by Number of Clusters
그림 Fig. 5. Comparison of Boxplots of Hydrometeorological Variables, Sensitivity Index, and Histogram of Month for Each Cluster
그림 Fig. 6. Comparison of Observed and Simulated Flows Using the Overall (upper) and Warm Period Partial (lower) Data with NSE as an Objective Function
표 Table 4. Comparison of Statistical Results of Overall and Warm Period Partial Optimization Using NSE as Object Function
그림 Fig. 7. Comparison of Observed and Simulated Flows Using the Overall (upper) and Cold Period Partial (lower) Data with NSE as an Objective Function
표 Table 5. Comparison of Statistical Results of Overall and Cold Period Partial Optimization Using NSE as Object Function
그림 Fig. 8. Comparison of Observed and Simulated Flows Using the Overall (upper) and Warm Period Partial (lower) Data with KGE as an Objective Function
표 Table 6. Comparison of Statistical Results of Overall and Warm Period Partial Optimization Using KGE as Object Function
그림 Fig. 9. Comparison of Observed and Simulated Flows Using the Overall (upper) and Cold Period Partial (lower) Data with KGE as an Objective Function
표 Table 7. Comparison of Statistical Results of Overall and Cold Period Partial Optimization Using KGE as Object Function
그림 Fig. 10. Flow Duration Curve using Objective Function (left) NSE (right) KGE
표 Table 8. Comparisons of AIC and BIC for Calibration with NSE as an Objective Function by Flow Range
표 Table 9. Comparisons of AIC and BIC for Calibration with KGE as an Objective Function by Flow Range

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