State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter

By Dingbao Wang, Yuguo Chen, Ximing Cai. 2009

92 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/497cd35f3c532929ad222af816ade7b98919eee1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Valentijn R. N. Pauwels
2. G. De Lannoy
3. Laura Giustarini
4. Patrick Matgen
5. Robain de Keyser
6. D. Plaza
7. William A. Lahoz
8. Philipp Schneider

In Global and Planetary Change

1. Valentijn R. N. Pauwels
2. G. De Lannoy
3. Patrick Matgen

In Water Science and Technology

1. Valentijn R. N. Pauwels
2. G. De Lannoy
3. Patrick Matgen

Citations in the corpus

• The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter (2011, methodology): “Dual or joint estimation has been widely studied using either the Kalman filter (Moradkhani et al., 2005a; Franssen and Kinzelbach, 2008; Wang et al., 2009) or recently, the particle filter (Moradkhani et al., 2005b; Nagarajan et al., 2010; Montzka et al., 2011).”