This title appears in the Scientific Report : 2015 

Data assimilation for improved predictions of integrated terrestrial systems
Hendricks-Franssen, Harrie-Jan (Corresponding author)
Neuweiler, Insa
Agrosphäre; IBG-3
Advances in water resources, 86 (2015) S. 257 - 259
Amsterdam [u.a.] Elsevier Science 2015
10.1016/j.advwatres.2015.10.010
Journal Article
Terrestrial Systems: From Observation to Prediction
Please use the identifier: http://dx.doi.org/10.1016/j.advwatres.2015.10.010 in citations.
Predicting states or fluxes in a terrestrial system, such as, for example, a river discharge, groundwater recharge or air temperature is done with terrestrial system models, which describe the processes in an approximate way. Terrestrial system model predictions are affected by uncertainty. Important sources of uncertainty are related to model forcings, initial conditions and boundary conditions, model parameters and the model itself. The relative importance of the different uncertainty sources varies according to the specific terrestrial compartment for which the model is built. For example, for weather prediction with atmospheric models it is believed that a dominant source of uncertainty is the initial model condition [12]. For groundwater models on the other hand, a general assumption is that parameter uncertainty dominates the total model prediction uncertainty.Sequential data assimilation techniques allow improving model predictions and reducing their uncertainty by correcting the predictions with measurement data. This can be done on-line with real-time measurement data. It can also be done off-line by updating model predictions with time series of historical data. Off-line data assimilation is especially interesting for estimating parameters in combination with model states, or for a reanalysis of past states. The most applied sequential data assimilation techniques for terrestrial system model predictions are the Ensemble Kalman Filter (EnKF) [8] and the Particle Filter (PF) [2]. EnKF provides an optimal solution for Gaussian distributed parameters, states and measurement data, whereas the PF is more flexible but computationally more expensive and provides in theory an optimal solution independent of the distribution type