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2023 Vol.65, Issue 1
Comparative Evaluation of Reproducibility for Spatio-temporal Rainfall Distribution Downscaled Using Different Statistical Methods

통계적 공간상세화 기법의 시공간적 강우분포 재현성 비교평가

Imgook Junga
,
Syewoon Hwangb†
,
Jaepil Choc
정 임국a
,
황 세운b†
,
조 재필c
aResearcher, Department of Climate Prediction, APEC Climate Center, Busan, Republic of Korea
bAssociate Professor, Department of Agricultural Engineering (Institute of Agriculture and Life Science), Gyeongsang National University, Jinju, Republic of Korea
cDirector, Convergence Laboratory for Watershed Management, Integrated Watershed Management Institute
a대한민국 부산 APEC기후센터 기후예측부 연구원
b대한민국 금주 경상국립대학교 농업공학과(농업과 생명과학연구소) 부교수
c유역종합관리연구소 유역관리융합실험실 주임
† Corresponding author Tel.: +82-55-772-1934, Fax: +82-55-722-1930 E-mail: swhwang@gnu.ac.kr
2023. pp. 1-13
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Abstract

Various techniques for bias correction and statistical downscaling have been developed to overcome the limitations related to the spatial and temporal resolution and error of climate change scenario data required in various applied research fields including agriculture and water resources. In this study, the characteristics of three different statistical dowscaling methods (i.e., SQM, SDQDM, and BCSA) provided by AIMS were summarized, and climate change scenarios produced by applying each method were comparatively evaluated. In order to compare the average rainfall characteristics of the past period, an index representing the average rainfall characteristics was used, and the reproducibility of extreme weather conditions was evaluated through the abnormal climate-related index. The reproducibility comparison of spatial distribution and variability was compared through variogram and pattern identification of spatial distribution using the average value of the index of the past period. For temporal reproducibility comparison, the raw data and each detailing technique were compared using the transition probability. The results of the study are presented by quantitatively evaluating the strengths and weaknesses of each method. Through comparison of statistical techniques, we expect that the strengths and weaknesses of each detailing technique can be represented, and the most appropriate statistical detailing technique can be advised for the relevant research.

Keywords
AIMS
BCSA
SQM
SDQDM
downscaling method
References

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Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 65
  • No :1
  • Pages :1-13
  • Received Date : 2022-08-30
  • Revised Date : 2022-10-21
  • Accepted Date : 2022-10-25
  • DOI :https://doi.org/10.5389/KSAE.2023.65.1.001
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