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Adriaan J. Teuling, Remko Uijlenhoet, Bart van den Hurk, and Sonia I. Seneviratne

point, porosity, saturated hydraulic conductivity) on soil moisture and the mean water budget components under stochastic forcing. Here, potential means that the soil parameters are isolated from their original model, and their effect is evaluated using a parsimonious framework of stochastic soil moisture models. Through this methodology, we only evaluate the effect of parameters from different LSMs, not the LSMs themselves. Also, model-dependent compensating effects as a result of parameter

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Damian J. Barrett and Luigi J. Renzullo

), particularly in situations where stream hydrographs are sparse or nonexistent ( Barrett et al. 2008 ). Studies have demonstrated that combining observations of soil moisture with water budget models using data assimilation techniques on time scales of 1–3 days improves the prediction of modeled flows ( Aubert et al. 2003 ; Pan et al. 2008 ). To capitalize on these benefits, it is necessary to develop and tailor methods for assimilating multiple types of observations into hydrological and

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