All Issue

2024 Vol.57, Issue 12 Preview Page

Research Article

Correction and evaluation of snow depth spatial data for South Korea based on Multi-Layer Perceptron

Multi-Layer Perceptron 기반 남한 적설 깊이 공간 자료 보정 및 평가

Hyunho Jeona
,
Yuju Chunb
,
Yangwon Leec
,
Jinsoo Kimc
,
Minha Choidef*
전 현호a
,
전 유주b
,
이 양원c
,
김 진수c
,
최 민하def*
aPh.D Candidate, Department of Global Smart City, Sungkyunkwan University, Suwon, Korea
bUndergraduate Student, School of Civil, Architecture Engineering and Landscape Architecture, Sungkyunkwan University, Suwon, Korea
cProfessor, Department of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University, Busan, Korea
dProfessor, Department of Water Resources, Sungkyunkwan University, Suwon, Korea
eProfessor, School of Civil, Architecture Engineering and Landscape Architecture, Sungkyunkwan University, Suwon, Korea
fProfessor, Department of Global Smart City, Sungkyunkwan University, Suwon, Korea
a성균관대학교 글로벌스마트시티융합전공 박사수료
b성균관대학교 건설환경공학부 학사과정
c부경대학교 지구환경시스템과학부 공간정보공학전공 교수
d성균관대학교 수자원전문대학원 수자원학과 교수
e성균관대학교 건설환경공학부 교수
f성균관대학교 글로벌스마트시티융합전공 교수
*Corresponding Author
31 December 2024. pp. 1099-1108
PDF XML Citation
Abstract

Snow is a key component in hydrological cycles and climate change research, and has a significant impact on a multitude of industries and societal domains, including agriculture, energy production, and disaster preparedness. In this study, a Multi-Layer Perceptron (MLP) model was employed for the purpose of correcting Local Data Assimilation and Prediction System (LDAPS) snow depth data for South Korea and evaluating its utility. The MLP model was trained on a combination of LDAPS snow depth, surface temperature, net radiation, and NASA Digital Elevation Model (NASADEM) data. The statistical analysis between Automated Synoptic Observing System (ASOS) ground observation data and LDAPS reanalysis data demonstrated that the MLP model effectively corrected the error of LDAPS, with a correlation coefficient and root mean square error (RMSE) of 0.838 and 0.048 m, respectively. Furthermore, the MLP model effectively represented the spatial distribution of LDAPS snow depths, providing a more accurate reflection of the variations in snow depth resulting from climate change and extreme weather events. The MLP-based snow depth data produced in this study is anticipated to serve as a valuable resource for climate change analysis and disaster preparedness in South Korea. In the future, the incorporation of time series models such as Long Short-Term Memory (LSTM) is expected to enhance the predictive capabilities of the model.

Keywords
Snow depth
MLP
LDAPS
Climate change
Data corrections

눈은 물순환과 기후 변화 관련 연구의 핵심 요소로, 농업, 에너지 생산, 방재 등 여러 산업과 사회적 분야에서 중요한 역할을 한다. 본 연구에서는 남한을 대상으로 Multi-Layer Perceptron (MLP) 모델을 사용하여 Local Data Assimilation and Prediction System (LDAPS) 적설 깊이 자료를 보정하였으며, 학습 결과의 통계 분석 및 공간 분포 분석을 통해 자료의 활용 가능성을 평가하였다. MLP 모델의 입력 자료로 LDAPS 적설 깊이, 지표면 온도, 순복사량과 NASA Digital Elevation Model (NASADEM) 자료 간의 조합을 구성하였으며, 이를 통해 각 데이터와 ASOS 적설 깊이 자료 간의 관계성을 파악하고자 하였다. Automated Synoptic Observing System (ASOS) 지상 관측 자료와 LDAPS 재분석 자료 및 MLP 모델 자료 간의 통계 분석 결과, MLP 모델은 상관계수와 RMSE가 각각 0.838, 0.048 m로 LDAPS의 오차를 효과적으로 보정하였다. 또한, LDAPS와 MLP 모델 자료의 공간 분포를 비교하였을 때, 기후 변화 및 이상 기후에 따른 적설량 변동이 자료에 반영된 것을 확인할 수 있었다. 본 연구에서 생산된 MLP 기반 적설 깊이 자료는 기후변화에 대응하는 기초 방재 자료로 활용 가능할 것이며, 추후 Long Short-Term Memory (LSTM)과 같은 시계열 자료 기반 예측 모델을 도입하여 우수한 성능의 예측 자료를 생산할 수 있을 것으로 기대된다.

키워드
적설 깊이
MLP
LDAPS
기후변화
데이터 보정
References
1

Baik, J., and Choi, M. (2018). "Multi-satellite-based water budget components in South Korea." Environmental Earth Sciences, Vol. 77, pp. 1-19.

10.1007/s12665-018-7271-3
2

Barnett, T.P., Adam, J.C., and Lettenmaier, D.P. (2005). "Potential impacts of a warming climate on water availability in snow- dominated regions." Nature, Vol. 438, No. 7066, pp. 303-309.

10.1038/nature0414116292301
3

Beniston, M., and Stoffel, M. (2016). "Rain-on-snow events, floods and climate change in the Alps: Events may increase with warming up to 4 C and decrease thereafter." Science of the Total Environment, Vol. 571, pp. 228-236.

10.1016/j.scitotenv.2016.07.14627476005
4

Bottou, L. (2010). "Large-scale machine learning with stochastic gradient descent." In Proceedings of COMPSTAT'2010: 19th International Conference on Computational Statistics, Physica- Verlag HD, Paris, France, pp. 177-186.

10.1007/978-3-7908-2604-3_16
5

Changnon, S.A., Changnon, D., Fosse, E.R., Hoganson, D.C., Roth Sr, R.J., and Totsch, J.M. (1997). "Effects of recent weather extremes on the insurance industry: Major implications for the atmospheric sciences." Bulletin of the American Meteorological Society, Vol. 78, No. 3, pp. 425-436.

10.1175/1520-0477(1997)078<0425:EORWEO>2.0.CO;2
6

Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., and Bauer, P. et al. (2011). "The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system." Quarterly Journal of the Royal Meteorological Society, Vol. 137, No. 656, pp. 553-597.

10.1002/qj.828
7

Durand, Y., Laternser, M., Giraud, G., Etchevers, P., Lesaffre, B., and Mérindol, L. (2009). "Reanalysis of 44 yr of climate in the French Alps (1958-2002): Methodology, model validation, climatology, and trends for air temperature and precipitation." Journal of Applied Meteorology and Climatology, Vol. 48, No. 3, pp. 429-449.

10.1175/2008JAMC1808.1
8

Erxleben, J., Elder, K., and Davis, R. (2002). "Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains." Hydrological Processes, Vol. 16, No. 18, pp. 3627-3649.

10.1002/hyp.1239
9

Frei, A., and Robinson, D.A. (1999). "Northern Hemisphere snow extent: Regional variability 1972-1994." International Journal of Climatology: A Journal of the Royal Meteorological Society, Vol. 19, No. 14, pp. 1535-1560.

10.1002/(SICI)1097-0088(19991130)19:14<1535::AID-JOC438>3.0.CO;2-J
10

Groisman, P.Y., Karl, T.R., and Knight, R.W. (1994). "Observed impact of snow cover on the heat balance and the rise of continental spring temperatures." Science, Vol. 263, No. 5144, pp. 198-200.

10.1126/science.263.5144.19817839175
11

Ha, J., and Chung, G. (2019). "Snow vulnerability analysis for administrative districts in South Korea." Journal of the Korean Society of Hazard Mitigation, Vol. 19, No. 6, pp. 187-195.

10.9798/KOSHAM.2019.19.6.187
12

Hall, A. (2004). "The role of surface albedo feedback in climate." Journal of Climate, Vol. 17, No. 7, pp. 1550-1568.

10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2
13

Hatchett, B.J., Daudert, B., Garner, C.B., Oakley, N.S., Putnam, A.E., and White, A.B. (2017). "Winter snow level rise in the Northern Sierra Nevada from 2008 to 2017." Water, Vol. 9, No. 11, 899.

10.3390/w9110899
14

Jeon, H., Lee, S., Lee, Y., Kim, J., and Choi, M. (2023). "Evaluation of bias and uncertainty in snow depth reanalysis data over South Korea." Journal of Korea Water Resources Association, Vol. 56, No. 9, pp. 543-551.

15

LeCun, Y., Bengio, Y., and Hinton, G. (2015). "Deep learning." Nature, Vol. 521, No. 7553, pp. 436-444.

10.1038/nature1453926017442
16

Li, H., Ke, C. Q., Zhu, Q., Li, M., and Shen, X. (2022). "A deep learning approach to retrieve cold-season snow depth over Arctic sea ice from AMSR2 measurements." Remote Sensing of Environment, Vol. 269, 112840.

10.1016/j.rse.2021.112840
17

Mott, R., Schirmer, M., Bavay, M., Grünewald, T., and Lehning, M. (2010). "Understanding snow-transport processes shaping the mountain snow-cover." The Cryosphere, Vol. 4, No. 4, pp. 545-559.

10.5194/tc-4-545-2010
18

Musselman, K.N., Lehner, F., Ikeda, K., Clark, M.P., Prein, A.F., Liu, C., Barlage, M., and Rasmussen, R. (2018). "Projected increases and shifts in rain-on-snow flood risk over western North America." Nature Climate Change, Vol. 8, No. 9, pp. 808-812.

10.1038/s41558-018-0236-4
19

Ntokas, K.F., Odry, J., Boucher, M.A., and Garnaud, C. (2021). "Investigating ANN architectures and training to estimate snow water equivalent from snow depth." Hydrology and Earth System Sciences, Vol. 25, No. 6, pp. 3017-3040.

10.5194/hess-25-3017-2021
20

O'Gorman, P.A. (2014). "Contrasting responses of mean and extreme snowfall to climate change." Nature, Vol. 512, No. 7515, pp. 416-418.

10.1038/nature1362525164753
21

Park, J., and Kim, D. (2023). "A stochastic approach to simulate realistic continuous snow depth time series." Journal of Hydrology, Vol. 617, 128980.

10.1016/j.jhydrol.2022.128980
22

Piani, C., Weedon, G.P., Best, M., Gomes, S.M., Viterbo, P., Hagemann, S., and Haerter, J.O. (2010). "Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models." Journal of Hydrology, Vol. 395, No. 3-4, pp. 199-215.

10.1016/j.jhydrol.2010.10.024
23

Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., and Smolander, T. et al. (2020). "Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018." Nature, Vol. 581, No. 7808, pp. 294-298.

10.1038/s41586-020-2258-032433620
24

Ruder, S. (2016). "An overview of gradient descent optimization algorithms." arXiv preprint, arXiv:1609.04747.

25

Song, H.J., Lim, B., and Joo, S. (2019). "Evaluation of rainfall forecasts with heavy rain types in the high-resolution unified model over South Korea." Weather and Forecasting, Vol. 34, No. 5, pp. 1277-1293.

10.1175/WAF-D-18-0140.1
26

Stoffelen, A. (1998). "Toward the true near‐surface wind speed: Error modeling and calibration using triple collocation." Journal of Geophysical Research: Oceans, Vol. 103, No. C4, pp. 7755- 7766.

10.1029/97JC03180
27

Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013). "On the importance of initialization and momentum in deep learning." In International Conference on Machine Learning, PMLR, Atlanta, GA, U.S., pp. 1139-1147.

28

Szczypta, C., Gascoin, S., Houet, T., Hagolle, O., Dejoux, J.F., Vigneau, C., and Fanise, P. (2015). "Impact of climate and land cover changes on snow cover in a small Pyrenean catchment." Journal of Hydrology, Vol. 521, pp. 84-99.

10.1016/j.jhydrol.2014.11.060
29

Terzago, S., von Hardenberg, J., Palazzi, E., and Provenzale, A. (2017). "Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models." The Cryosphere, Vol. 11, No. 4, pp. 1625-1645.

10.5194/tc-11-1625-2017
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 :1099-1108
  • Received Date : 2024-09-11
  • Revised Date : 2024-12-02
  • Accepted Date : 2024-12-05
  • DOI :https://doi.org/10.3741/JKWRA.2024.57.12.1099
Journal Informaiton Journal of Korea Water Resources Association Journal of Korea Water Resources Association
Online Submission
Archives
  • scopus
  • KCI-수자원
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close