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Inverse modelling and data assimilation techniques should help in that respect.
Variational data assimilation techniques are based on a combination of three very important components.
The sand wave migration model is based on physical principles and tuned with field data through data assimilation techniques.
This aim may be attained by using data assimilation techniques such as the popular Ensemble Kalman filter (EnKF).
Data assimilation techniques that combine complementary information from hydrological models and observed data can overcome some of the limitations.
In addition to the sequential data assimilation systems mentioned above, there are also efforts in developing GDAS systems based on variational data assimilation techniques.
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This paper proposes a catchment scale ETa data assimilation technique (termed ET-DA) that assimilates remotely sensed ETa data into a distributed time-variant gain hydrological model (DTVGM-ET) for improving hydrological model simulations.
A radar data assimilation technique, namely the three-dimensional variational data assimilation (3DVar) system, is used.
In addition, the SVM model simulated data without updating with data assimilation technique is discussed as well to evaluate the data assimilation technique.
The PF is a data assimilation technique based on a Bayesian approach.
The proposed data assimilation technique makes use of the TEC maps provided by GIM for the estimate of TECgps.
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