Assimilation of surface soil moisture to estimate profile soil water content

• Published in: Journal of hydrology (Amsterdam) Vol. 279; no. 1; pp. 1 - 17
• Main Authors: Heathman, Gary C, Starks, Patrick J, Ahuja, Lajpat R, Jackson, Thomas J
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 25.08.2003; Elsevier Science
• Subjects: Applied geophysics; Data assimilation; Earth sciences; Earth, ocean, space; Exact sciences and technology; Hydrology; Hydrology. Hydrogeology; Internal geophysics; Profile soil moisture; Remote sensing; Soil water modeling; United States; Oklahoma; Great Plains; Profile soil moisture; Soil water modeling; Remote sensing; Data assimilation; digital simulation; evapotranspiration; North America; drainage basins; hydraulics; fluctuations; soil profiles; water content; soils; boundary conditions; rivers; models; rainfall; soil moisture; remote sensing; assimilation; surface water; depth; daily average; water quality; statistical analysis; errors; methodology
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/S0022-1694(03)00088-X

The use of surface soil water content data as additional input for the Root Zone Water Quality Model in modeling profile soil water content was investigated at four field sites in the Little Washita River Experimental Watershed in south central Oklahoma, coincident with the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Modeled soil water profile estimates were compared to field measurements made periodically during the same time period using a field calibrated time-domain reflectometry (TDR) system. The model was first run in the normal mode with inputs of initial conditions and upper boundary conditions of measured rainfall intensities and daily mean meteorological variables that determined evapotranspiration (ET). Soil hydraulic properties used in the model were estimated from limited soils data information, since in practical terms this is usually the case. Moreover, in our earlier study even the complete description of hydraulic properties based on laboratory and field measurements did not improve the results over average profile estimates using only limited input data. The model runs were then repeated with the daily simulated soil water content in the surface 0–5 cm layer being replaced by 0–5 cm measured soil water content. This process of forcing measured surface water content as additional model input is called direct insertion data assimilation. The simulated profile soil water contents, with and without data assimilation, were compared with TDR-measured profiles to a depth of 60 cm. Gravimetric surface soil water content was measured during SGP97 from June 18 to July 16, 1997 and used as a surrogate for remotely sensed surface moisture data. Data assimilation of surface soil moisture improved model estimates to a depth of 30 cm at all sites. Of particular significance, with data assimilation, model estimates more closely matched the measured dynamic fluctuations of soil moisture in the top 30 cm in response to rainfall events. There was no significant improvement in soil water estimates below the 30 cm depth. This may indicate that data assimilation of surface soil moisture tends to compensate for any errors in model simulations emanating from: (1) errors in the measurement of rainfall intensities or in using 5-min averaged rainfall intensities as done here; (2) errors in using daily average values of meteorological variables that determine ET in a daily ET model; (3) errors in determining hydraulic properties of the surface soil by either laboratory methods or more simple techniques; (4) errors due to the spatial variability of soil hydraulic properties not properly represented in the model.