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http://dx.doi.org/10.3741/JKWRA.2018.51.S-1.1067

Uncertainty decomposition in water resources projection considering interaction effects  

Ohn, Ilsang (Department of Statistics, Seoul National University)
Kim, Yongdai (Department of Statistics, Seoul National University)
Kim, Young-Oh (Department of Civil & Environmental Engineering, Seoul National University)
Publication Information
Journal of Korea Water Resources Association / v.51, no.spc, 2018 , pp. 1067-1078 More about this Journal
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;
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1 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.   DOI
2 Dobler, C., Hagemann, S., Wilby, R., and Stotter, 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.   DOI
3 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.   DOI
4 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.   DOI
5 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.   DOI
6 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.   DOI
7 Kim, Y., Ohn, I., Lee, J.-K., and Kim, Y. O. (2017). "Generalizing uncertainty decomposition theory in climate change impact assessments." Unpublished manuscript.
8 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.   DOI
9 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.   DOI
10 Lee J. K., (2013). Scenario Selection and Uncertainty Quantifcation for Climate Change Impact Assessments in Water Resources. PhD thesis, Seoul National University, Korea.
11 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.   DOI
12 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.   DOI
13 Jones, R. N. (2000). "Managing uncertainty in climate change projections: issues for impact assessment." Climatic change, Vol. 45, No. 3-4, pp. 403-419.   DOI
14 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.   DOI
15 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.   DOI
16 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.   DOI
17 Moser, W. R. (1996). Linear models: a mean model approach. Academic Press, California, USA.
18 Nobrega, 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.   DOI
19 Raisanen, 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.   DOI
20 von Storch H., and Zwiers, F. W. (1999) Statistical analysis in climate research. Cambridge University Press, UK.
21 Wilcox, R. R. (2011). Introduction to robust estimation and hypothesis testing. Academic press.
22 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.   DOI
23 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.   DOI
24 Deque, M., Rowell, D. P., Luthi, D., Giorgi, F., Christensen, J. H., Rockel, B., Jacob, D., Kjellstrom, 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.   DOI
25 Bosshard, T., Carambia, M., Goergen, K., Kotlarski, S., Krahe, P., Zappa, M., and Schar, C. (2013). "Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections." Water Resources Research, Vol. 49, No. 3, pp. 1523-1536.   DOI
26 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.