Abstract
Data assimilation is the model performance improvement methodology that relies in correcting (updating) modeling results based on observations, and using updated values as new initial conditions that will be used in modeling until the next measurement will be available and the update will be in order. The central problem of data assimilation is the accounting for the uncertainty in both monitoring data and modeling results. Such accounting for soil water sensor networks is described in this book chapter. With soil system specifics in mind, two most promising data assimilation methods are characterized, the statistical model of the uncertainty in soil data is presented, and a methodology to assess uncertainty in modeling results is proposed. Research needs and outlook are offered.
Published Version
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