All Issue

2018 Vol.51, Issue 11S Preview Page

Research Article

Uncertainty decomposition in water resources projection considering interaction effects

교호작용 효과를 고려한 수자원 전망의 불확실성 분해

Ilsang Ohna
,
Yongdai Kima*
,
Young-Oh Kimb
온 일상a
,
김 용대a*
,
김 영오b
aDepartment of Statistics, Seoul National University
bDepartment of Civil & Environmental Engineering, Seoul National University
a서울대학교 자연과학대학 통계학과
b서울대학교 공과대학 건설환경공학부
* Corresponding Author
30 November 2018. pp. 1067-1078
PDF XML Citation
Abstract

Water resources projection typically consists of several stages including emission scenarios, global circulation models (GCMs), downscaling techniques, and hydrological models, and each stage is a source of total uncertainty in water resources projection. Several studies proposed methods to quantify the relative contribution of each stage to total uncertainty, and we call such analysis uncertainty decomposition. Uncertainty decomposition enables us to investigate the stages yielding large uncertainties and to establish the uncertainty reduction plan that reflects them. Interactions between stages is one of the important issues to be considered in uncertainty decomposition. This study suggests a new uncertainty decomposition method considering interaction effect. The proposed method has an advantage of decomposing the total uncertainty to the uncertainty from each stage considering both the main and interactions effects. We apply the proposed method to streamflow projection for Chungju Dam basin. The results show that the uncertainties from the main effects are larger than the uncertainties from interaction effects in both summer and winter. Using the proposed uncertainty decomposition method, we show that the GCM stage is the largest source of the total uncertainty in summer and the downscaling technique stage is the one in winter among the following four stages: emission scenarios, GCMs, downscaling techniques, and hydrological models.

Keywords
Water resources projection
Total uncertainty
Uncertainty decomposition
Interaction

기후변화로 인한 수자원 전망은 배출 시나리오, 전지구적 순환모형, 상세화 기법, 수문 모형 등 여러 전망 단계를 거쳐 이루어지며, 각 단계는 수자원 전망의 총 불확실성의 원천이 된다. 몇몇 연구를 통해 개별 전망 단계의 총 불확실성에 대한 상대적인 기여를 계량화하는 방법이 제안되었으며며, 이러한 분석을 불확실성 분해라고 한다. 불확실성 분해 분석은 큰 불확실성을 발생시키는 단계를 진단하고, 이를 반영한 불확실성 저감 계획을 수립할 수 있게 한다. 전망 단계 간의 교호작용은 불확실성 분해 시 고려되어야 하는 중요한 문제 중 하나이다. 본 연구는 교호작용 효과로 인한 불확실성을 계량화하고 이를 불확실성 분해에 반영하는 새로운 방법을 제안한다. 제안한 방법은 전망 단계별 불확실성을 주효과와 교호작용 효과를 모두 고려하여 계량화함과 동시에 총 불확실성에서 개별 전망 단계가 차지하는 상대적인 비중을 제시할 수 있다는 장점이 있다. 제안한 방법을 충주댐 유량 전망의 불확실성 분석에 적용하였다. 충주댐 유역의 불확실성 분석 결과 여름과 겨울 두 계절 모두에서 교호작용 효과의 불확실성은 주효과의 불확실성에 비해 그 크기가 작은 것으로 나타났다. 교호작용 효과를 고려하여 불확실성을 분해한 결과 배출 시나리오, 전지구적 순환모형, 상세화 기법, 수문 모형의 네 단계 중 여름철은 전지구적 순환모형의 불확실성이, 겨울철은 상세화 기법의 불확실성이 가장 큰 것으로 분석되었다.

키워드
수자원 전망
총 불확실성
불확실성 분해
교호작용
References
1
Bosshard, T., Carambia, M., Goergen, K., Kotlarski, S., Krahe, P., Zappa, M., and Sch¨ar, C. (2013). "Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections." Water Resources Research, Vol. 49, No. 3, pp. 1523-1536.
10.1029/2011WR011533
2
Chen, J., Brissette, F. P., Poulin, A., and Leconte, R. (2011). "Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed." Water Resources Research, Vol. 47, No. 12.
10.1029/2011WR010602
3
Déqué, M., Rowell, D. P., Lüthi, D., Giorgi, F., Christensen, J. H., Rockel, B., Jacob, D., Kjellström, E., de Castro, M., and van den Hurk, B. (2007). "An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections." Climatic Change, Vol. 81, No. 1, pp. 53-70.
10.1007/s10584-006-9228-x
4
Dessai, S., and Hulme, M. (2007), "Assessing the robustness of adaptation decisions to climate change uncertainties: A case study on water resources management in the east of England." Global Environmental Change, Vol. 17, No. 1, pp. 59-72.
10.1016/j.gloenvcha.2006.11.005
5
Dobler, C., Hagemann, S., Wilby, R., and St¨otter, J. (2012). "Quantifying different sources of uncertainty in hydrological projections in an alpine watershed." Hydrology and Earth System Sciences, Vol. 16, No. 11, pp. 4343-4360.
10.5194/hess-16-4343-2012
6
Gardner, M. W., and Dorling, S. (1998). "Artificial neural networks (the multilayer perceptron): a review of applications in the atmospheric sciences." Atmospheric Environment, Vol. 32, No. 14. pp. 2627-2636.
10.1016/S1352-2310(97)00447-0
7
Gay, C., and Estrada, F. (2010). "Objective probabilities about future climate are a matter of opinion." Climatic Change, Vol. 99, No. 1-2, pp. 27-46.
10.1007/s10584-009-9681-4
8
Jones, R. N. (2000). "Managing uncertainty in climate change projections: issues for impact assessment." Climatic change, Vol. 45, No. 3-4, pp. 403-419.
10.1023/A:1005551626280
9
Kay, A., Davies, H., Bell, V., and Jones, R. (2009). "Comparison of uncertainty sources for climate change impacts: flood frequency in England." Climatic Change, Vol. 92, No. 1, pp. 41-63.
10.1007/s10584-008-9471-4
10
Kilsby, C., Cowpertwait, P., O'connell, P., and Jones, P. (1998). "Predicting rainfall statistics in England and Wales using atmospheric circulation variables." International Journal of Climatology, Vol. 18, No. 5 pp. 523-539.
10.1002/(SICI)1097-0088(199804)18:5<523::AID-JOC268>3.0.CO;2-X
11
Kim, Y., Ohn, I., Lee, J.-K., and Kim, Y. O. (2017). "Generalizing uncertainty decomposition theory in climate change impact assessments." Unpublished manuscript.
12
Lee J. K. (2018). "Uncertainty analysis of quantitative rainfall estimation process based on hydrological and meteorological radars." Journal of Korea Water Resources Association, Vol. 51, No. 5, pp. 439-449.
13
Lee J. K., (2013). Scenario Selection and Uncertainty Quantifcation for Climate Change Impact Assessments in Water Resources. PhD thesis, Seoul National University, Korea.
14
Lee, J. K., Kim, Y. O., and Kim, Y. (2017). "A new uncertainty analysis in the climate change impact assessment." International Journal of Climatology, Vol. 37, No. 10, pp. 3837-3846.
10.1002/joc.4957
15
Lee, M. H., and Bae, D. H. (2016). "Future projection and uncertainty analysis of low flow on climate change in dam basins." Journal of Climate Change Research, Vol. 7, No. 4, pp. 407-419.
10.15531/ksccr.2016.7.4.407
16
Mandal, S., and Simonovic, S. P. (2017). "Quantification of uncertainty in the assessment of future streamflow under changing climate conditions.", Hydrological Processes. Vol. 31, pp. 2076-2097.
10.1002/hyp.11174
17
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J. (2008). "Stationarity is dead: Whither water management?" Science, Vol. 319, No. 5863, pp. 573-574.
10.1126/science.115191518239110
18
Minville, M., Brissette, F., and Leconte, R. (2008). "Uncertainty of the impact of climate change on the hydrology of a Nordic watershed." Journal of hydrology, Vol. 358, No. 1, pp. 70-83.
10.1016/j.jhydrol.2008.05.033
19
Moser, W. R. (1996). Linear models: a mean model approach. Academic Press, California, USA.
20
N´obrega, M., Collischonn, W., Tucci, C., and Paz, A. (2011). "Uncertainty in climate change impacts on water resources in the Rio Grande basin, Brazil." Hydrology and Earth System Sciences, Vol. 15, No. 2, pp. 585-595.
10.5194/hess-15-585-2011
21
Räisänen, J. (2001). "CO2-induced climate change in CMIP2 experiments: Quantification of agreement and role of internal variability." Journal of Climate, Vol. 14, No. 9, pp. 2088-2104.
10.1175/1520-0442(2001)014<2088:CICCIC>2.0.CO;2
22
Sansom, P. G., Stephenson, D. B., Ferro, C. A., Zappa, G., & Shaffrey, L. (2013). "Simple uncertainty frameworks for selecting weighting schemes and interpreting multimodel ensemble climate change experiments." Journal of Climate, Vol. 26, No. 12, pp. 4017-4037.
10.1175/JCLI-D-12-00462.1
23
von Storch H., and Zwiers, F. W. (1999) Statistical analysis in climate research. Cambridge University Press, UK.
10.1017/CBO9780511612336
24
Wilcox, R. R. (2011). Introduction to robust estimation and hypothesis testing. Academic press.
25
Wolock, D. M,. and McCabe, G. J. (1999). "Estimates of runoff using waterbalance and atmospheric general circulation models." Journal of the American Water Resources Association, Vol. 35, No. 6, pp. 1341-1350.
10.1111/j.1752-1688.1999.tb04219.x
26
Yip, S., Ferro, C. A., Stephenson, D. B., and Hawkins, E. (2011). "A simple, coherent framework for partitioning uncertainty in climate predictions." Journal of Climate, Vol. 24, No. 17, pp. 4634-4643.
10.1175/2011JCLI4085.1
Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 51
  • No :11
  • Pages :1067-1078
  • Received Date : 2018-07-30
  • Revised Date : 2018-11-06
  • Accepted Date : 2018-11-13
  • DOI :https://doi.org/10.3741/JKWRA.2018.51.S-1.1067
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