摘要 :
When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a
hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water ...
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When hydrology model parameters are determined, a traditional data assimilation method (such as Kalman filter) and a
hydrology model can estimate the root zone soil water with uncertain state variables (such as initial soil water content). The
simulated result can be quite good. However, when a key soil hydraulic property, such as the saturated hydraulic conductivity, is
overestimated or underestimated, the traditional soil water assimilation process will produce a persistent bias in its predictions.
In this paper, we present and demonstrate a new multi-scale assimilation method by combining the direct insertion assimilation
method, particle swarm optimisation (PSO) algorithm and Richards equation. We study the possibility of estimating root zone
soil water with a multi-scale assimilation method by using observed in situ data from the Wudaogou experiment station,
Huaihe River Basin, China. The results indicate there is a persistent bias between simulated and observed values when the
direct insertion assimilation surface soil water content is used to estimate root zone soil water contents. Using a multi-scale
assimilation method (PSO algorithm and direct insertion assimilation) and an assumed bottom boundary condition, the results
show some obvious improvement, but the root mean square error is still relatively large. When the bottom boundary condition
is similar to the actual situation, the multi-scale assimilation method can well represent the root zone soil water content. The
results indicate that the method is useful in estimating root zone soil water when available soil water data are limited to the
surface layer and the initial soil water content even when the soil hydraulic conductivities are uncertain. Copyright 2011
John Wiley & Sons, Ltd.
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