Data Assimilation with Soil Water Content Sensors and Pedotransfer Functions in Soil Water Flow Modeling
- Feng Pan,
- Yakov Pachepsky *,
- Diederik Jacques,
- Andrey Guber and
- Robert L. Hill
- D ep. of Environmental Science and Policy,Univ. of Maryland,College Park, MD 20742.Current address:Dep. of Civil & Environmental Engineering,Energy & Geoscience Institute,The Univ. of Utah,Salt Lake City, UT 84112
U SDA-ARS,Environmental Microbial and Food Safety Lab.Beltsville, MD 20705
I nstitute for Environment, Health, and Safety.Belgian Nuclear Research Centre (SCKCEN),BE-2400 Mol Belgium
U SDA-ARS Environmental Microbial and Food Safety Lab. Beltsville, MD 20705.Current address: Dep. of Crop and Soil Sciences,Michigan State Univ.East Lansing, MI 48824
D ep. of Environmental Science and Policy,Univ. of Maryland,College Park, MD 20742
Soil water flow models are based on simplified assumptions about the mechanisms, processes, and parameters of water retention and flow. That causes errors in soil water flow model predictions. Data assimilation (DA) with the ensemble Kalman filter (EnKF) corrects modeling results based on measured state variables, information on uncertainty in measurement results and uncertainty in modeling results. The objectives of this work were (i) to evaluate pedotransfer functions (PTFs) as a source of data to generate an ensemble of Richards equation-based models for the EnKF application to the assimilation of soil water content data and (ii) to research how effective assimilation of soil moisture sensor data can be in correcting simulated soil water content profiles in field soil. Data from a field experiment were used in which 60 two-rod time domain reflectometry (TDR) probes were installed in a loamy soil at five depths to monitor the soil water content. The ensemble of models was developed with six PTFs for water retention and four PTFs for the saturated hydraulic conductivity (Ksat). Measurements at all five depths and at one or two depths were assimilated. Accounting for the temporal stability of water contents substantially decreased the estimated noise in data. Applicability of the Richards equation was confirmed by the satisfactory calibration results. In absence of calibration and data assimilation, simulations developed a strong bias caused by the overestimation of Ksat from PTFs. Assimilating measurements from a single depth of 15 cm or of 35 cm provided substantial improvements at all other observation depths. An increase in data assimilation frequency improved model performance between the assimilation times. Overall, bringing together developments in pedotransfer functions, temporal stability of soil water patterns, and soil water content sensors can create a new source of data to improve modeling results in soil hydrology and related fields.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
Copyright © 2012. . Copyright © by the Soil Science Society of America, Inc.