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United States Department of Agriculture

Agricultural Research Service

Research Project: PATHOGEN FATE AND TRANSPORT IN IRRIGATION WATERS

Location: Environmental Microbial and Food Safety Laboratory

Title: Ssda Code to Apply Data Assimilation in Soil Water Flow Modeling: documentation and User Manual

Authors
item Martinez, Gonzalo -
item Feng, Pan -
item Pachepsky, Yakov

Submitted to: Software and User Manual Public Release
Publication Type: Government Publication
Publication Acceptance Date: January 27, 2013
Publication Date: February 4, 2013
Citation: Martinez, G., Feng, P., Pachepsky, Y.A. 2013. SSDA code to apply data assimilation in soil water flow modeling: Documentation and user manual. Software and User Manual Public Release. Available: http://www.ars.usda.gov/research/docs.htm?docid=23018.

Interpretive Summary: Modeling of soil water time series has many applications in soil hydrology-related research and management. Models are based on simplifications of extremely complex real soil systems, and therefore are prone to errors. Using soil water monitoring creates the opportunity to periodically correct modeling results. Such correction is called data assimilation. Data assimilation experience from other knowledge fields shows that the correction depends on the uncertainty in data and uncertainty in modeling results. Earlier, we proposed to estimate the uncertainty in modeling results using ensembles of models built with pedoransfer functions, so we developed a library of pedotransfer functions for this purpose. In this work, we developed a flexible computer code for soil water monitoring data in soil water flow modeling. Application of this code demonstrated the efficiency of the proposed method and its implementation in the computer code. Results of this work will be useful in any modeling work involving soil water flow in that it provides a systematic way of improving modeling results using soil water content monitoring.

Technical Abstract: 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 purpose of this manual is to describe the application of DA with EnKF into soil water flow modeling to improve simulation results. The DA with EnKF code was developed to assimilate the data of soil water content measurements at one or more depths with the ensemble of models (e.g., pedotransfer functions (PTFs) for water retention function and saturated hydraulic conductivity (Ksat) in soil water flow modeling). The DA with EnKF code written in FORTRAN was coupled with HYDRUS-1D code as soil water flow modeling tools. The manual describes the DA with EnKF theory, the soil water flow model, and contains detailed instructions for input and output data, and sample problems.

Last Modified: 11/1/2014
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