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Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality
Author(s) -
Keating Elizabeth H.,
Doherty John,
Vrugt Jasper A.,
Kang Qinjun
Publication year - 2010
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2009wr008584
Subject(s) - curse of dimensionality , surrogate model , computer science , mathematical optimization , parameterized complexity , uncertainty quantification , uncertainty analysis , groundwater model , monte carlo method , estimation theory , context (archaeology) , nonlinear system , machine learning , groundwater flow , algorithm , mathematics , aquifer , groundwater , statistics , simulation , engineering , paleontology , geotechnical engineering , biology , physics , quantum mechanics
Highly parameterized and CPU‐intensive groundwater models are increasingly being used to understand and predict flow and transport through aquifers. Despite their frequent use, these models pose significant challenges for parameter estimation and predictive uncertainty analysis algorithms, particularly global methods which usually require very large numbers of forward runs. Here we present a general methodology for parameter estimation and uncertainty analysis that can be utilized in these situations. Our proposed method includes extraction of a surrogate model that mimics key characteristics of a full process model, followed by testing and implementation of a pragmatic uncertainty analysis technique, called null‐space Monte Carlo (NSMC), that merges the strengths of gradient‐based search and parameter dimensionality reduction. As part of the surrogate model analysis, the results of NSMC are compared with a formal Bayesian approach using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Such a comparison has never been accomplished before, especially in the context of high parameter dimensionality. Despite the highly nonlinear nature of the inverse problem, the existence of multiple local minima, and the relatively large parameter dimensionality, both methods performed well and results compare favorably with each other. Experiences gained from the surrogate model analysis are then transferred to calibrate the full highly parameterized and CPU intensive groundwater model and to explore predictive uncertainty of predictions made by that model. The methodology presented here is generally applicable to any highly parameterized and CPU‐intensive environmental model, where efficient methods such as NSMC provide the only practical means for conducting predictive uncertainty analysis.