74 citations as recorded by crossref.

1. Water quality in New Zealand rivers: current state and trends S. Larned et al. 10.1080/00288330.2016.1150309
2. Modeling surface water-groundwater interaction in New Zealand: Model development and application J. Yang et al. 10.1002/hyp.11075
3. Methods for regional calibration - a case study using the TopNet hydrological model for the Bay of Plenty region, New Zealand S. Singh et al. 10.1080/13241583.2020.1821487
4. On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments A. Thiboult & F. Anctil 10.1016/j.jhydrol.2015.09.036
5. Hydrological post-processing of streamflow forecasts issued from multimodel ensemble prediction systems J. Xu et al. 10.1016/j.jhydrol.2019.124002
6. Moving horizon estimation for assimilating H-SAF remote sensing data into the HBV hydrological model R. Montero et al. 10.1016/j.advwatres.2016.04.011
7. Nitrogen loads to New Zealand aquatic receiving environments: comparison with regulatory criteria T. Snelder et al. 10.1080/00288330.2020.1758168
8. Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping A. Annis et al. 10.5194/hess-26-1019-2022
9. Assimilation of Discharge Data into Semidistributed Catchment Models for Short-Term Flow Forecasting: Case Study of the Seine River Basin S. Munier et al. 10.1061/(ASCE)HE.1943-5584.0001054
10. Utilizing satellite precipitation estimates for streamflow forecasting via adjustment of mean field bias in precipitation data and assimilation of streamflow observations H. Lee et al. 10.1016/j.jhydrol.2015.08.057
11. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
12. Sequential streamflow assimilation for short-term hydrological ensemble forecasting M. Abaza et al. 10.1016/j.jhydrol.2014.08.038
13. Operational aspects of asynchronous filtering for flood forecasting O. Rakovec et al. 10.5194/hess-19-2911-2015
14. Observation and integrated Earth-system science: A roadmap for 2016–2025 A. Simmons et al. 10.1016/j.asr.2016.03.008
15. Efficient treatment of climate data uncertainty in ensemble Kalman filter (EnKF) based on an existing historical climate ensemble dataset H. Liu et al. 10.1016/j.jhydrol.2018.11.047
16. Impact of assimilating dam outflow measurements to update distributed hydrological model states: Localization for improving ensemble Kalman filter performance M. Khaniya et al. 10.1016/j.jhydrol.2022.127651
17. Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping A. Annis & F. Nardi 10.1080/10095020.2019.1626135
18. The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models M. Seyam & F. Othman 10.1007/s11269-014-0628-9
19. An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting Y. Li et al. 10.1016/j.jhydrol.2014.08.009
20. Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme Y. Li et al. 10.1002/2014WR016667
21. Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon R. Paiva et al. 10.5194/hess-17-2929-2013
22. Data Assimilation for Streamflow Forecasting: State–Parameter Assimilation versus Output Assimilation L. Sun et al. 10.1061/(ASCE)HE.1943-5584.0001475
23. Anthropogenic increases of catchment nitrogen and phosphorus loads in New Zealand T. Snelder et al. 10.1080/00288330.2017.1393758
24. Simulation and forecasting of streamflows using machine learning models coupled with base flow separation H. Tongal & M. Booij 10.1016/j.jhydrol.2018.07.004
25. Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
26. Nonparametric Data Assimilation Scheme for Land Hydrological Applications M. Khaki et al. 10.1029/2018WR022854
27. Influence of spatial distribution of sensors and observation accuracy on the assimilation of distributed streamflow data in hydrological modelling M. Mazzoleni et al. 10.1080/02626667.2016.1247211
28. Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy X. Xie et al. 10.5194/hess-18-3923-2014
29. Catchment tomography - An approach for spatial parameter estimation D. Baatz et al. 10.1016/j.advwatres.2017.06.006
30. Constraining the ensemble Kalman filter for improved streamflow forecasting D. Maxwell et al. 10.1016/j.jhydrol.2018.03.015
31. Data assimilation for flow forecasting in urban drainage systems by updating a hydrodynamic model of Damhusåen Catchment, Copenhagen M. Babel et al. 10.1080/1573062X.2020.1828938
32. Exploring hydrologic post-processing of ensemble streamflow forecasts based on affine kernel dressing and non-dominated sorting genetic algorithm II J. Xu et al. 10.5194/hess-26-1001-2022
33. A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom H. Lewis & S. Dadson 10.1002/hyp.14438
34. The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models N. Wanders et al. 10.1002/2013WR014639
35. Long-term ensemble forecast of snowmelt inflow into the Cheboksary Reservoir under two different weather scenarios A. Gelfan et al. 10.5194/hess-22-2073-2018
36. Accounting for three sources of uncertainty in ensemble hydrological forecasting A. Thiboult et al. 10.5194/hess-20-1809-2016
37. A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology B. Ait-El-Fquih et al. 10.5194/hess-20-3289-2016
38. The role of rating curve uncertainty in real-time flood forecasting D. Ocio et al. 10.1002/2016WR020225
39. Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale G. Piazzi et al. 10.1029/2020WR028390
40. 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
41. Assessment of SWE data assimilation for ensemble streamflow predictions K. Franz et al. 10.1016/j.jhydrol.2014.07.008
42. A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events C. Young et al. 10.1016/j.asoc.2016.12.052
43. 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
44. Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions J. Bergeron et al. 10.5194/hess-20-4375-2016
45. Variational assimilation of streamflow data in distributed flood forecasting G. Ercolani & F. Castelli 10.1002/2016WR019208
46. Improving flood forecasting using an input correction method in urban models in poorly gauged areas M. Fava et al. 10.1080/02626667.2020.1729984
47. Analysis of the hydrological response of a distributed physically-based model using post-assimilation (EnKF) diagnostics of streamflow and in situ soil moisture observations M. Trudel et al. 10.1016/j.jhydrol.2014.03.072
48. A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation Z. Musa et al. 10.5194/hess-19-3755-2015
49. Nonparametric catchment clustering using the data depth function S. Singh et al. 10.1080/02626667.2016.1168927
50. Validation of a national hydrological model H. McMillan et al. 10.1016/j.jhydrol.2016.07.043
51. Review of the Kalman-type hydrological data assimilation L. Sun et al. 10.1080/02626667.2015.1127376
52. Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter S. Wang et al. 10.1016/j.jhydrol.2017.09.052
53. RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers M. Seyam et al. 10.1007/s11269-016-1518-0
54. The Met Office Operational Soil Moisture Analysis System B. Gómez et al. 10.3390/rs12223691
55. Discharge Estimation via Assimilation of Multisatellite-Based Discharge Products: Case Study Over the Amazon Basin C. Emery et al. 10.1109/LGRS.2020.3020285
56. Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models M. Mazzoleni et al. 10.1016/j.advwatres.2015.07.004
57. Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags S. Meng et al. 10.1016/j.jhydrol.2017.05.024
58. Underlying Fundamentals of Kalman Filtering for River Network Modeling C. Emery et al. 10.1175/JHM-D-19-0084.1
59. On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models S. Noh et al. 10.1016/j.jhydrol.2014.07.049
60. Review of assimilating GRACE terrestrial water storage data into hydrological models: Advances, challenges and opportunities S. Soltani et al. 10.1016/j.earscirev.2020.103487
61. Assessing model state and forecasts variation in hydrologic data assimilation J. Samuel et al. 10.1016/j.jhydrol.2014.03.048
62. Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding M. El Gharamti et al. 10.5194/hess-25-5315-2021
63. Remote sensing data assimilation A. Nair et al. 10.1080/02626667.2020.1761021
64. Improving water quality forecasting via data assimilation – Application of maximum likelihood ensemble filter to HSPF S. Kim et al. 10.1016/j.jhydrol.2014.09.051
65. Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method Y. Fan et al. 10.1016/j.envsoft.2016.09.012
66. Assimilating flow and level data into an urban drainage surrogate model for forecasting flows and overflows N. S.V. Lund et al. 10.1016/j.jenvman.2019.05.110
67. Two decades of ensemble flood forecasting: a state-of-the-art on past developments, present applications and future opportunities J. Das et al. 10.1080/02626667.2021.2023157
68. Comparing and combining physically-based and empirically-based approaches for estimating the hydrology of ungauged catchments D. Booker & R. Woods 10.1016/j.jhydrol.2013.11.007
69. Comparison of Sequential and Variational Streamflow Assimilation Techniques for Short-Term Hydrological Forecasting M. Abaza et al. 10.1061/(ASCE)HE.1943-5584.0001013
70. Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations R. Crumley et al. 10.5194/hess-25-4651-2021
71. Analysis of rainfall intensity impact on the lag time estimation in tropical humid rivers e. Seyam 10.21833/ijaas.2017.010.003
72. The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method H. Lü et al. 10.1016/j.jhydrol.2012.12.011
73. Impact of improved meteorological forcing, profile of soil hydraulic conductivity and data assimilation on an operational Hydrological Ensemble Forecast System over France M. Coustau et al. 10.1016/j.jhydrol.2015.04.022
74. Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags S. Meng et al. 10.1016/j.jhydrol.2017.05.024