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An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters with Multimodal Distributions

Journal Article · · Water Resources Research
DOI:https://doi.org/10.1002/2017WR020906· OSTI ID:1700519
 [1];  [2];  [3];  [4];  [1]
  1. Zhejiang University
  2. PURDUE UNIVERSITY
  3. BATTELLE (PACIFIC NW LAB)
  4. University of California, Riverside
+ Show Author Affiliations
Ensemble smoother (ES) has been widely used in inverse modeling of hydrologic systems. However, for problems where the distribution of model parameters is multimodal, using ES directly would be problematic. One popular solution is to use a clustering algorithm to identify each mode and update the clusters with ES separately. However, this strategy may not be very efficient when the dimension of parameter space is high or the number of modes is large. Alternatively, we propose in this paper a very simple and efficient algorithm, i.e., the iterative local updating ensemble smoother (ILUES), to explore multimodal distributions of model parameters in nonlinear hydrologic systems. The ILUES algorithm works by updating local ensembles of each sample with ES to explore possible multimodal distributions. To achieve satisfactory data matches in nonlinear problems, we adopt an iterative form of ES to assimilate the measurements multiple times. Numerical cases involving nonlinearity and multimodality are tested to illustrate the performance of the proposed method. It is shown that overall the ILUES algorithm can well quantify the parametric uncertainties of complex hydrologic models, no matter whether the multimodal distribution exists.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-76RL01830
OSTI ID:
1700519
Report Number(s):
PNNL-SA-136324
Journal Information:
Water Resources Research, Journal Name: Water Resources Research Journal Issue: 3 Vol. 54
Country of Publication:
United States
Language:
English

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