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1.Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

作者：Gift Dumedah ; Paulin Coulibaly

关键词：Streamflow forecasting ; Data assimilation ; Evolutionary algorithms ; The ensemble Kalman filter ; The particle filter

刊名：Advances in Water Resources

年：2013

2.Two-point or multiple-point statistics? A comparison between the ensemble Kalman filtering and the ensemble pattern matching inverse methods

作者：Liangping Li ; a ; b ; liangpingli@utexas.edu" class="auth_mail" title="E-mail the corresponding author ; Sanjay Srinivasana ; Haiyan Zhoua ; J. Jaime Gomez-Hernandezc

关键词：Multiple-point statistics ; Rejection sampling ; Conditional simulation ; Ensemble Kalman filter ; Inverse method

刊名：Advances in Water Resources

年：2015

3.An Experimental Study on Evapotranspiration Data Assimilation Based on the Hydrological Model

作者：Jian Yin ; Chesheng Zhan ; Wen Ye

关键词：Evapotranspiration ; Remote sensing ; Hydrological model ; Ensemble Kalman filter ; Data assimilation

刊名：Water Resources Management

出版者：Springer Netherlands

年：2016

4.Characterization of Non-Gaussian Geologic Facies Distribution Using Ensemble Kalman Filter with Probability Weighted Re-Sampling

作者：Siavash Nejadi (1) Juliana Leung (1) Japan Trivedi (1) 1. School of Mining and Petroleum Engineering ; University of Alberta ; Markin/CNRL Natural Resources Engineering Facility ; Edmonton ; AB ; T6G 2W2 ; Canada

关键词：Non ; Gaussian distributions ; Facies characterization ; Dynamic data integration ; History matching ; Ensemble methods ; Re ; sampling techniques

刊名：Mathematical Geosciences

出版者：Springer Berlin Heidelberg

年：2015

5.Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting

作者：He Chen ; Dawen Yang ; Yang Hong ; Jonathan J. Gourley ; Yu Zhang

关键词：Data assimilation ; Ensemble Kalman filter ; Ensemble Square-Root-Filter ; Flash flood forecast ; Rainfall&ndash ; runoff model

刊名：Advances in Water Resources

年：2013

6.Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation

作者：S. Pathirajaa ; s.pathiraja@unsw.edu.au" class="auth_mail" title="E-mail the corresponding author ; L. Marshalla ; A. Sharmaa ; H. Moradkhanib

关键词：Data assimilation ; Ensemble Kalman filter ; Non-stationarity ; Land use change ; Time varying parameters

刊名：Advances in Water Resources

年：2016

7.Comparison of Ensemble Kalman Filter groundwater-data assimilation methods based on stochastic moment equations and Monte Carlo simulation

作者：M. Panzeria ; marco.panzeri@polimi.it" class="auth_mail ; M. Rivaa ; b ; A. Guadagninia ; b ; S.P. Neumanb

关键词：Ensemble Kalman Filter ; Random hydraulic conductivity field ; Moment equations ; Data assimilation ; Transient groundwater flow ; Filter inbreeding

刊名：Advances in Water Resources

年：April, 2014

8.Assimilating passive microwave remote sensing data into a land surface model to improve the estimation of snow depth

作者：Tao Che ; Xin Li ; Rui Jin ; Chunlin Huang

关键词：Snow depth ; Data assimilation ; Passive microwave ; Remote sensing ; MEMLS ; CoLM ; Ensemble Kalman filter

刊名：Remote Sensing of Environment

年：5 March, 2014

9.State estimation of tidal hydrodynamics using ensemble Kalman filter

作者：Hitoshi Tamura ; Peter Bacopoulos ; Dingbao Wang ; Scott C. Hagen ; Ethan J. Kubatko

关键词：DG ADCIRC-2DDI ; Ensemble Kalman filter ; State estimation ; Parallelized simulation ; Tides ; Hydrodynamics

刊名：Advances in Water Resources

年：January, 2014

10.Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model

作者：Donghua Zhanga ; donghua.zhang@ign.ku.dk" class="auth_mail" title="E-mail the corresponding author ; Henrik Madsenb ; Marc E. Ridlerb ; Jens C. Refsgaardc ; Karsten H. Jensena

关键词：Uncertainty ; Data assimilation ; Ensemble Kalman filter ; Hydrological modelling ; MIKE SHE

刊名：Advances in Water Resources

年：2015

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