Multiple Rotations of Gaussian Quadratures: An Efficient Method for Uncertainty Analyses in Large-Scale Simulation Models

16845 11 92222 2 267291 document 92222 ENVSOFT_R3_LE_DS_clean.pdf application/pdf 8c2b815801b07a6ebc41827777029e39 MD5 2302707 2020-11-20 05:17:28 https://pure.iiasa.ac.at/id/eprint/16845/1/ENVSOFT_R3_LE_DS_clean.pdf 16845 1 1 application/pdf text en public cc_by_nc_nd_4

ENVSOFT_R3_LE_DS_clean.pdf

accepted 92223 2 267293 document 92223 lightbox.jpg image/png eb37747037b07af802d38d200a951382 MD5 28877 2020-11-20 05:18:00 https://pure.iiasa.ac.at/id/eprint/16845/2/lightbox.jpg 16845 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

lightbox.jpg

preview.jpg

medium.jpg

small.jpg

indexcodes.txt

http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/92222 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/92222 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/92222 archive 353 disk0/00/01/68/45 2020-11-16 12:38:48 2024-10-09 09:27:52 2020-11-16 12:38:48 article show Stepanyan D. Grethe H. Zimmermann G. Siddig K. Deppermann A. 2072 Feuerbacher A. Luckmann J. Valin H. 1923 0000-0002-0618-773X Nishizawa T. Ermolieva T. Havlik P. 1868 0000-0001-5551-5085 Multiple Rotations of Gaussian Quadratures: An Efficient Method for Uncertainty Analyses in Large-Scale Simulation Models pub BNR IBF prog_ysp Uncertainty analysis; systematic sensitivity analysis; stochastic modeling; multiple rotations of Gaussian quadratures; Monte Carlo sampling; computable general equilibrium model; spartial equilibrium models Concerns regarding the impact of climate change, food price volatility, and weather uncertainty have motivated users of simulation models to consider uncertainty in their simulations. One way to do this is to integrate uncertainty components in the model equations, thus turning the model into a problem of numerical integration. Most of these problems do not have analytical solutions, and researchers, therefore, apply numerical approximation methods. This article presents a novel approach to conducting an uncertainty analysis as an alternative to the computationally burdensome Monte Carlo-based (MC) methods. The developed method is based on the degree three Gaussian quadrature (GQ) formulae and is tested using three large-scale simulation models. While a standard single GQ method often produces low-quality approximations, the results of this study demonstrate that the proposed approach reduces the approximation errors by a factor of nine using only 3.4% of the computational effort required by the MC-based methods in the most computationally demanding model. 2021-02-14 published Elsevier 10.1016/j.envsoft.2020.104929 64 316 119 public Environmental Modelling & Software 281 e104929 TRUE 1873-6726 FALSE no info:eu-repo/semantics/article