Data Assimilation for Estimating the Terrestrial Water Budget Using a Constrained Ensemble Kalman Filter

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Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results (2013, background): “A possible remedy for this problem was suggested by Pan and Wood (2006), and more investigations are”
Data assimilation: making sense of Earth Observation (2014, background): “Challenges particular to the land surface include: (i) need to explore advanced data assimilation methods such as the PF (see section Representation of Errors); and (ii) need to preserve water balance in the land system (Pan and Wood, 2006; Yilmaz et al., 2011).”
Reliable, robust and realistic: the three R's of next-generation land-surface modelling (2014, methodology): “Data assimilation has evolved from Newtonian “nudging” to more comprehensive approaches including various flavours of traditional, extended, ensemble Kalman filtering, variational data assimilation using the adjoint method, and the particle filtering method (e.g. Houser et al., 1998; Walker and Hous”
State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter (2009, background): “For example, the updated states and parameters after data assimilation may violate some equality or inequality constraints such as water balance or nonnegativity of parameters and states [Pan and Wood, 2006].”