An application of Ensemble Kalman Filter in integral-balance subsurface modeling |
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Authors: | Qiang Shu Mariush W Kemblowski Mac McKee |
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Institution: | (1) Department of Hydrology, Utah Water Reserach Laboratory, UMC8200 UWRL, Utah State Univeristy, Logan, UT 84321, USA |
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Abstract: | Data assimilation method provides a framework to decrease the uncertainty of hydrological modeling by sequentially incorporating
observations into numerical model. Such a process involves estimating statistical moments of different order based on the
evolution of conditional probability distribution function. Because of the nonlinearity of many hydrological dynamics, explicit
and analytical solutions for moments of state distribution are often impossible. Evensen J Geophys Res 99(c5): 10143–10162
(1994)] introduced Ensemble Kalman Filtering (EnKF) method to address such problems. We test and evaluate the performance
of EnKF in fusing model predictions and observations for a saturated–unsaturated integral-balance subsurface model. We find
EnKF improve the model predictions, and also we conclude a good estimate of state variance is essential for the success of
EnKF. |
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Keywords: | Ensemble Kalman Filter Uncertainty Integral-balance Soil Moisture |
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