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2024 Vol.57, Issue 12 Preview Page

Research Article

Comparison of quantile mapping methods for daily precipitation correction and future projections under the SSP5-8.5

SSP5-8.5 기반 일 강수량 보정 및 미래 예측을 위한 분위사상법 비교

Young Hoon Songa
,
Eun-Sung Chungb*
송 영훈a
,
정 은성b*
aSenior Researcher, Department Civil Engineering, Seoul National University of Science and Technology, Seoul, Korea
bProfessor, Department Civil Engineering, Seoul National University of Science and Technology, Seoul, Korea
a서울과학기술대학교 건설시스템공학과 선임연구원
b서울과학기술대학교 건설시스템공학과 교수
*Corresponding Author
31 December 2024. pp. 1069-1083
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Abstract

This study applied four Quantile Mapping (QM) methods (Parametric Linear, PL; Parametric Scale, PS; Empirical Quantile Mapping, EQM; and Multivariate Bias Correction using N-dimensional Probability Density Function Transform, MBCn) to correct daily precipitation data from 61 stations in South Korea. The performances of these methods were evaluated using six precipitation indices (r10 mm, r20 mm, SDII, R95ptot, R99ptot, and Rx1day) and five evaluation metrics (Percent bias, Pbias; Mean absolute error, MAE; Kling-Gupta efficiency, KGE; Euclidean Distance, ED and Jensen-Shannon divergence, JSD) for 14 General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 6(CMIP6). Additionally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision-making method, was used to rank the GCMs based on performance. The entropy theory was applied to calculate the weights for each evaluation criterion. For future projections, the Shared Socioeconomic Pathway (SSP) 5-8.5 scenario was used to predict future daily precipitation for each GCM and QM method, and a Multi-Model Ensemble was constructed based on TOPSIS weights. Furthermore, the uncertainty of future precipitation projections for each QM method was quantified using Reliability Ensemble Averaging (REA). The results showed that MBCn and EQM performed the highest in historical reproducibility, particularly in correcting extreme precipitation events (MBCn-EVS:0.99, KGE:0.99, Pbias:0.42; EQM-EVS:0.94, KGE:0.97, Pbias:-1.2). In contrast, PL showed the lowest performance, with differences from observed values exceeding 500 mm, indicating significantly lower reproducibility and extreme precipitation correction compared to other methods. MBCn's future precipitation projections effectively reflected the greenhouse gas emission trends in the SSP5-8.5 scenario, while PL failed to account for extreme precipitation events. Additionally, MBCn exhibited the highest reliability in uncertainty analysis at 0.51, while PL showed the lowest at 0.016. This study enhances the reliability of precipitation projections under climate change through the comparison of bias correction methods and the quantification of uncertainties, providing valuable insights for policy-making and water resource management strategies.

Keywords
Quantile mapping
CMIP6
Uncertainty
TOPSIS
MME

본 연구는 우리나라 61개 관측소를 대상으로 4개(Parametric Linear, PL; Parametric Scale, PS; Empirical Quantile Mapping, EQM; Multivariate Bias Correction using N-dimensional Probability Density Function Transform, MBCn)의 Quantile Mapping (QM)방법을 사용하여 일 강수량을 보정하고 6개(r10 mm, r20 mm, SDII, R95ptot, R99ptot, Rx1day)의 강수 지수와 5개(Percent bias, Mean absolute error, Kling-Gupta efficiency, Euclidean Distance, Jensen-Shannon divergence)의 평가지표를 통해 14개의 Coupled Model Intercomparison Project Phase (CMIP)6 General Circulation Model (GCM)의 과거 재현성을 평가하였다. 또한, 다기준의사결정기법 중 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)를 사용하여 GCM의 성능 기반 우선순위를 선정하고, entropy 이론을 적용하여 평가 요소에 대한 가중치를 산정하였다. 미래 예측에서는 Shared Socioeconomic Pathway (SSP)5-8.5 시나리오를 사용하여 각 GCM과 QM 방법에 따른 미래 강수량을 예측하고 TOPSIS 가중치에 기반한 Muli-Model ensemble을 구축하였다. 더 나아가, Reliability ensemble averaging을 통해 각 QM 방법별로 예측된 미래 강수량의 불확실성을 정량화하였다. 연구 결과로는 MBCn과 EQM은 과거 재현성에서 가장 높은 성능을 보였으며, 특히 극한 강수 사상에 대한 보정 성능이 뛰어났다(MBCn-EVS:0.99, KGE:0.99, Pbias:0.42; EQM-EVS:0.94, KGE:0.97, Pbias:-1.2). 반면, PL은 재현성과 극한 강수량 보정 성능 모두 가장 낮았으며 관측값보다 최대 500 mm 이상 차이났다. MBCn의 미래 강수 예측은 SSP5-8.5의 온실가스 배출 추세를 잘 반영하였으나 PL은 극한 강수 사상에 대해서 전혀 반영하지 못했다. 또한, 신뢰도 분석에서 MBCn은 0.51로 가장 높은 신뢰도를 보인 반면, PL은 0.016으로 가장 낮은 신뢰도를 보였다. 본 연구는 편의보정 방법의 비교 및 불확실성 정량화를 통해 기후변화에 따른 강수 예측의 신뢰성을 높이고, 이를 바탕으로 정책 수립 및 수자원 관리 전략에 기여할 수 있을 것으로 기대된다.

키워드
Quantile Mapping
CMIP6
불확실성
TOPSIS
MME
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 57
  • No :12
  • Pages :1069-1083
  • Received Date : 2024-10-10
  • Revised Date : 2024-11-22
  • Accepted Date : 2024-11-26
  • DOI :https://doi.org/10.3741/JKWRA.2024.57.12.1069
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