- Volume 47 Issue 3
DOI QR Code
Efficient Uncertainty Analysis of TOPMODEL Using Particle Swarm Optimization
입자군집최적화 알고리듬을 이용한 효율적인 TOPMODEL의 불확실도 분석
- Cho, Huidae (Staff Water Resources Engineer, Dewberry) ;
- Kim, Dongkyun (Department of Civil Engineering, Hongik University) ;
- Lee, Kanghee (Department of Civil Engineering, Hongik University)
- Received : 2013.11.28
- Accepted : 2014.02.07
- Published : 2014.03.31
We applied the ISPSO-GLUE method, which integrates the Isolated-Speciation-based Particle Swarm Optimization (ISPSO) with the Generalized Likelihood Uncertainty Estimation (GLUE) method, to the uncertainty analysis of the Topography Model (TOPMODEL) and compared its performance with that of the GLUE method. When we performed the same number of model runs for the both methods, we were able to identify the point where the performance of ISPSO-GLUE exceeded that of GLUE, after which ISPSOGLUE kept improving its performance steadily while GLUE did not. When we compared the 95% uncertainty bounds of the two methods, their general shapes and trends were very similar, but those of ISPSO-GLUE enclosed about 5.4 times more observed values than those of GLUE did. What it means is that ISPSOGLUE requires much less number of parameter samples to generate better performing uncertainty bounds. When compared to ISPSO-GLUE, GLUE overestimated uncertainty in the recession limb following the maximum peak streamflow. For this recession period, GLUE requires to find more behavioral models to reduce the uncertainty. ISPSO-GLUE can be a promising alternative to GLUE because the uncertainty bounds of the method were quantitatively superior to those of GLUE and, especially, computationally expensive hydrologic models are expected to greatly take advantage of the feature.
Supported by : 한국연구재단
- Arnold, J.G., Srinivasan, R., Muttiah, R.S., and Williams, J.R. (1998). "Large Area Hydrologic Modelling and Assessment, Part I: Model Development." Journal of the American Water Resources Association, Vol. 34, No. 1, pp. 73-89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
- Beven, K.J., and Kirkby, M.J. (1979). "A Physically Based, Variable Contributing Area Model of Basin Hydrology." Hydrological Sciences Bulletin, Vol. 24, pp. 43-69. https://doi.org/10.1080/02626667909491834
- Beven, K., and Binley, A. (1992). "The Future of Distributed Models: Model Calibration and Uncertainty Prediction." Hydrological Processes, Vol. 6, pp. 279-298. https://doi.org/10.1002/hyp.3360060305
- Beven, K.J., Quinn, P., Romanowicz, R., Freer, J., Fisher, J., and Lanb, R. (1995). TOPMODEL and GRIDATB: A User's Guide to the Distribution Versions, Lancaster University, p. 31.
- Beven, K., Smith, P., and Freer, J. (2008). "So Just Why Would A Modeller Choose To Be Incoherent?" Journal of Hydrology, Vol. 354, pp. 15-32. https://doi.org/10.1016/j.jhydrol.2008.02.007
- Blasone, R.-S., Vrugt, J.A., Madsen, H., Rosbjerg, D., Robinson, B.A., and Zyvoloski, G.A. (2008). Generalized Likelihood Uncertainty Estimation (GLUE) Using Adaptive Markov Chain Monte Carlo Sampling. Advances in Water Resources, Vol. 31, pp. 630-648. https://doi.org/10.1016/j.advwatres.2007.12.003
- Cho, H. (2000). Development of a GIS Hydrologic Modeling System by Using the Programming Interface of GRASS GIS, Master's Thesis. Department of Civil Engineering, Kyungpook National University.
- Cho, H., Lee, D., Lee, K., Lee, J., and Kim, D. (2013). "Development and Application of a Storm Identification Algorithm that Conceptualizes Storms by Elliptical Shape." Journal of the Korean Society of Hazard Mitigation, Vol. 13, No. 5, pp. 325-335. https://doi.org/10.9798/KOSHAM.2013.13.5.325
- Cho, H., and Olivera, F. (2009). "Effect of the Spatial Variability of Land Use, Soil Type, and Precipitation on Streamflows in Small Watersheds." Journal of the American Water Resources Association, Vol. 45, No. 3, pp. 673-686. https://doi.org/10.1111/j.1752-1688.2009.00315.x
- Cho, H., Kim, D., Olivera, F., and Guikema, S.D. (2011). "Enhanced Speciation in Particle Swarm Optimization for Multi-Modal Problems." European Journal of Operational Research, Vol. 213, No. 1, pp. 15-23. https://doi.org/10.1016/j.ejor.2011.02.026
- Cho, H., and Olivera, F. (2014). "Application of Multimodal Optimization for Uncertainty Estimation of Computationally Expensive Hydrologic Models." Journal of Water Resources Planning and Management, Vol. 140, No. 3, pp. 313-321. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000330
- Conrad, O. (2003). System for Automated Geoscientific Analyses Module Library: sim_hydrology, topmodel.cpp. http://sourceforge.net/apps/trac/saga-gis/browser/trunk/saga-gis/src/modules_simulation/hydrology/sim_hydrology/topmodel.cpp, accessed in October 2013.
- Draper, N.R., and Box, G.E. (1987). Empirical Model-Building and Response Surfaces. John Wiley and Sons Inc.
- Evans, M. (1991). Adaptive Importance Sampling and Chaining." Contemporary Mathematics, Vol. 115, pp. 137-143. https://doi.org/10.1090/conm/115/1117053
- GRASS Development Team. (2012). Geographic Resources Analysis Support System(GRASS GIS) Software. Open Source Geospatial Foundation Project. http://grass.osgeo.org.
- Hornik, K. (2008). Changes on CRAN. R News 8(2), 60-68. http://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf, accessed in October 2013.
- Kim, D., Olivera, F., Cho, H., and Socolofsky, S. (2013). "Regionalization of the Modified Bartlett-Lewis Rectangular Pulse Stochastic Rainfall Model." Terrestrial, Atmospheric and Oceanic Sciences, Vol. 24, No 3, pp. 421-436. https://doi.org/10.3319/TAO.2012.11.12.01(Hy)
- Mantovan, P., and Todini, E. (2006). Hydrological Forecasting Uncertainty Assessment: Incoherence of the GLUE Methodology. Journal of Hydrology, Vol. 330, pp. 368-381. https://doi.org/10.1016/j.jhydrol.2006.04.046
- McKay, M.D., Beckman, R.J., and Conover, W.J. (1979). "A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code." Technometrics Vol. 21, No. 2, pp. 239-245.
- Muleta, M.K., and Nicklow, J.W. (2005). "Sensitivity and Uncertainty Analysis Coupled with Automatic Calibration for a Distributed Watershed Model." Journal of Hydrology Vol. 306, pp. 127-145. https://doi.org/10.1016/j.jhydrol.2004.09.005
- Nash, J.E., and Sutcliffe, J.V. (1970). River Flow Forecasting Through Conceptual Models, Part I-A Discussion of Principles. Journal of Hydrology, Vol. 10, pp. 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
- NOAA-CPC. (2013). National Oceanic & Atmospheric Administration-Climate Prediction Center. Joint Agricultural Weather Facility, U.S. Evaporation Data. http://www.cpc.ncep.noaa.gov/products/GIS/GIS_DATA/JAWF, accessed in September 2013.
- NOAA-NCDC. (2013). National Oceanic & Atmospheric Administration-National Climatic Data Center. Surface Data, Daily US. http://www.ncdc.noaa.gov, accessed in September 2013.
- Olaya, V. (2004). A Gentle Introduction to SAGA GIS. http://downloads.sourceforge.net/saga-gis/SagaManual.pdf, Accessed on September 13, 2013.
- R Development Core Team. (2006). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0.
- Thiessen, A.H., and Alter, J.C. (1911). Climatological Data for July, 1911: District No. 10, Great Basin. Monthly Weather Review July 1911:1082-1089.
- USGS. (2013a). U.S. Geological Survey. National Elevation Dataset (NED). http://ned.usgs.gov, accessed in September 2013
- USGS. (2013b). U.S. Geological Survey. Surface-Water Daily Data for the Nation. http://waterdata.usgs.gov/nwis, accessed in September 2013.
- Vrugt, J.A., ter Braak, C.J.F., Diks, C.G.H., Robinson, B.A., Hyman, J.M., and Higdon, D. (2009). "Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling." International Journal of Nonlinear Sciences & Numerical Simulation, Vol. 10, No. 3, pp. 271-288.
- Zheng, Y., and Keller, A.A. (2007). Uncertainty Assessment in Watershed-Scale Water Quality Modeling and Management: 1. Framework and Application of Generalized Likelihood Uncertainty Estimation (GLUE) Approach. Water Resources Research 43, W08407.