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Abstract:
Understanding the role of plants in soil water relations, and thus ecosystem functioning, requires information
about root water uptake. We evaluated four different complex
water balance methods to estimate sink term patterns
and evapotranspiration directly from soil moisture measurements.
We tested four methods. The first two take the difference
between two measurement intervals as evapotranspiration,
thus neglecting vertical flow. The third uses regression
on the soil water content time series and differences between
day and night to account for vertical flow. The fourth
accounts for vertical flow using a numerical model and iteratively
solves for the sink term. None of these methods requires
any a priori information of root distribution parameters
or evapotranspiration, which is an advantage compared
to common root water uptake models. To test the methods, a
synthetic experiment with numerical simulations for a grassland
ecosystem was conducted. Additionally, the time series
were perturbed to simulate common sensor errors, like those
due to measurement precision and inaccurate sensor calibration.
We tested each method for a range of measurement
frequencies and applied performance criteria to evaluate the
suitability of each method. In general, we show that methods
accounting for vertical flow predict evapotranspiration and
the sink term distribution more accurately than the simpler
approaches. Under consideration of possible measurement
uncertainties, the method based on regression and differentiating
between day and night cycles leads to the best and most
robust estimation of sink term patterns. It is thus an alternative
to more complex inverse numerical methods. This study
demonstrates that highly resolved (temporally and spatially)
soil water content measurements may be used to estimate the sink term profiles when the appropriate approach is used.
