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2025 Vol.58, Issue 11S-1 Preview Page

Research Article

Stochastic simulation of future snow depth time series under climate change

기후변화에 따른 미래 적설심 시계열의 확률적 모의

Hyojeong Choia
,
Soohyun Kimbc
,
Dongkyun Kimd
,
Jeongha Parke*
최 효정a
,
김 수현bc
,
김 동균d
,
박 정하e*
aResearch Associate (Post-master’s), Department of Civil Engineering, Hongik University, Seoul, Korea
bPostdoctoral Researcher, Department of Civil Engineering, Hongik University, Seoul, Korea
cPostdoctoral Researcher, Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA
dProfessor, Department of Civil and Environmental Engineering, Hongik University, Seoul, Korea
eResearch Professor, Department of Civil Engineering, Hongik University, Seoul, Korea
a홍익대학교 토목공학과 석사후연구원
b홍익대학교 토목공학과 박사후연구원
c하와이 대학교 마노아 캠퍼스 토목환경공학과 및 수자원연구센터 박사후연구원
d홍익대학교 건설환경공학과 교수
e홍익대학교 토목공학과 연구교수
*Corresponding Author
30 November 2025. pp. 1153-1163
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Abstract

This study quantifies future changes in snow depth under climate change by coupling a Change-Factor-based stochastic weather generator with a snow-depth estimation model to synthesize continuous hourly snow-depth time series and evaluate their characteristics. The analysis covers four synoptic stations in the Yeongdong region of Gangwon (Sokcho, Daegwallyeong, Gangneung, and Donghae). For the current climate and three future periods (2021-2040, 2041-2070, 2071-2100) under SSP1-2.6 and SSP5-8.5, 200-year hourly synthetic series were generated for each station-period-scenario combination. Results show that mean snow depth and the number of snow days decrease overall, while the frequency of no-snow years increases. Changes are largest in late-century SSP5-8.5, with stronger sensitivity at low-elevation/coastal stations. Extreme-value analysis indicates that the 100-year return level of snow depth decreases by 44-58% across the four stations in 2071-2100(SSP5-8.5). Converting these return levels with a depth-unit-weight table yields design snow loads that decline by 65-72%. At Gangneung (station 105), however, a near-term increase in extremes is observed; combined with stepwise unit-weight classes, this produces a nonlinear response in design load. Limitations include a tendency to underestimate wet-day frequency, empirical statistical scaling, spatial substitution of solar radiation, and reliance on a single GCM chain. Nevertheless, the framework enables a consistent assessment of mean conditions, extremes, and persistence indicators from a single product, providing a basis for rational adjustment of design standards and for operational planning in snow removal, disaster management, and water-resources operations.

Keywords
Snow depth estimation algorithm
Stochastic weather generator
Climate change
Snow statistics
Design snow load

본 연구는 기후변화 하에서의 미래 적설 특성을 정량화하기 위해, 변화계수(Change Factor) 기반의 추계 기상생성기와 적설 추정 모형을 결합하여 연속 적설심 시계열을 합성하고 그 특성을 평가하였다. 대상은 강원 영동의 4개 종관기상관측소(속초·대관령·강릉·동해)이며, 현재와 SSP1-2.6, SSP5-8.5의 세 미래 기간(2021-2040, 2041-2070, 2071-2100)에 대해 각 조합별 시단위 200년의 합성 시계열을 생성하였다. 결과에 따르면 평균 적설심과 신적설일수는 전반적으로 감소하고, 무적설 연도의 출현 빈도는 증가하였다. 특히 고배출 시나리오의 후기(2071-2100)에서 변화가 가장 크게 나타났으며, 저고도·해안 지점의 민감도가 컸다. 극값 해석에서 100년 재현수준 적설심 은 2071-2100(SSP5-8.5) 기준으로 4개 지점에서 44-58% 감소하였고, 이를 단위하중-깊이 대응표로 환산한 설계 적설하중은 65-72% 감소하였다. 다만 강릉(105)에서는 근미래에 적설의 극한이 일시적 증가가 관찰되었으며, 깊이에 따른 단계형 단위하중의 영향으로 하중 변화는 비선형적으로 나타났다. 방법적 한계로는 강수 발생빈도의 과소추정 경향과 경험식 기반의 통계 환산, 일사 자료의 공간 대체, 단일 GCM 체계 사용 등이 존재한다. 그럼에도 본 연구는 하나의 산출물에서 평균·극값·지속성 지표를 일관된 틀로 평가할 수 있음을 보였으며, 향후 제설·방재 및 수자원 운영을 위한 합리적 설계 기준 조정과 운영전략 수립에 기초 자료로 활용될 수 있다.

키워드
적설심 추정 모형
추계 기상생성 모형
기후변화
적설 특성 통계
설계 적설 하중
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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 : 58
  • No :11
  • Pages :1153-1163
  • Received Date : 2025-09-03
  • Accepted Date : 2025-09-24
  • DOI :https://doi.org/10.3741/JKWRA.2025.58.S-1.1153
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