Investigating soil moisture sensitivity to precipitation and evapotranspiration errors using SiB2 model and ensemble Kalman filter

Accurate soil moisture information is useful in agricultural practice, weather forecasting, and various hydrological applications. Although land surface modeling provides a viable approach to simulating soil moisture, many factors such as errors in the precipitation can affect the accuracy of soil moisture simulations. This paper examined how precipitation rate and evapotranspiration rate affect the accuracy of soil moisture simulation using simple biosphere model with and without data assimilation through ensemble Kalman filter (EnKF). For each of the two variables, seven levels of relative errors (−20, −10, −5, 0, 5, 10 and 20 %) were introduced independently, thus a total of 49 combined cases were investigated. Observations from Wudaogou Hydrology Experimental site in the Huaihe River basin, China, were used to drive and verify the simulations. Results indicate that when the error of precipitation rate is within 10 % of the observations, the resulting error in soil moisture simulations is less significant and manageable, thus the simulated precipitation can be used to drive hydrological models in poorly gauged catchments when observations are not available. When the error of evapotranspiration rate is within 20 % of the observations, which is partly caused by model structural and parameterization errors, its impact on soil moisture simulation is less significant and can be acceptable. This study also demonstrated that the EnKF can perform consistently well to improve soil moisture simulation with less sensitivity to precipitation errors.

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