Articles | Volume 15, issue 11
https://doi.org/10.5194/hess-15-3399-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-15-3399-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation
C. M. DeChant
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
H. Moradkhani
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
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- Statistical downscaling of precipitation on a spatially dependent network using a regional climate model R. Erhardt et al. 10.1007/s00477-014-0988-y
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- Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts A. Gragne et al. 10.1016/j.jhydrol.2015.05.039
- A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. 10.1016/j.eswa.2024.126004
- Role of multimodel combination and data assimilation in improving streamflow prediction over multiple time scales W. Li et al. 10.1007/s00477-015-1158-6
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- A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models Y. Fan et al. 10.1016/j.jhydrol.2015.09.035
- Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management R. Arsenault & P. Côté 10.5194/hess-23-2735-2019
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- Verification of Ensemble Water Supply Forecasts for Sierra Nevada Watersheds M. He et al. 10.3390/hydrology3040035
- Generating Coherent Ensemble Forecasts After Hydrological Postprocessing: Adaptations of ECC‐Based Methods J. Bellier et al. 10.1029/2018WR022601
- An intercomparison of approaches for improving operational seasonal streamflow forecasts P. Mendoza et al. 10.5194/hess-21-3915-2017
- Estimation of Radiative Transfer Parameters from L‐Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation C. Montzka et al. 10.2136/vzj2012.0040
- Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method H. Moradkhani et al. 10.1029/2012WR012144
- A probabilistic drought forecasting framework: A combined dynamical and statistical approach H. Yan et al. 10.1016/j.jhydrol.2017.03.004
- Accounting for three sources of uncertainty in ensemble hydrological forecasting A. Thiboult et al. 10.5194/hess-20-1809-2016
- Hydrological ensemble forecasting using a multi-model framework P. Dion et al. 10.1016/j.jhydrol.2021.126537
- Estimation of nonfluctuating reservoir inflow from water level observations using methods based on flow continuity C. Deng et al. 10.1016/j.jhydrol.2015.09.037
- Estimation of initial conditions for surface suspended sediment simulations with the adjoint method: A case study in Hangzhou Bay Y. Du et al. 10.1016/j.csr.2021.104526
- Assimilation of near-real time data products into models of an urban basin J. Leach et al. 10.1016/j.jhydrol.2018.05.064
- Exploration of sequential streamflow assimilation in snow dominated watersheds M. Abaza et al. 10.1016/j.advwatres.2015.03.011
- Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo P. Abbaszadeh et al. 10.1016/j.advwatres.2017.11.011
- An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook M. Taheri & A. Mohammadian 10.3390/su141811395
- Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter Y. Sun et al. 10.1029/2022WR033580
- Recent advance in earth observation big data for hydrology L. Chen & L. Wang 10.1080/20964471.2018.1435072
- Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities Y. Liu et al. 10.5194/hess-16-3863-2012
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