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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #342155
Research Project: Design and Implementation of Monitoring and Modeling Methods to Evaluate Microbial Quality of Surface Water Sources Used for Irrigation

Location: Environmental Microbial & Food Safety Laboratory

Title: Obtaining soil hydraulic parameters from data assimilation under different climatic/soil conditions

Author
item VALDES-ABELLAN, JAVIER - Universidad De Alicante
item Pachepsky, Yakov
item MARTINEZ, GONZALO - Universidad De Cordoba

Submitted to: Agronomy Society of America, Crop Science Society of America, Soil Science Society of America Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 8/10/2017
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Obtaining reliable soil hydraulic properties is essential to correctly simulating soil water content (SWC), which is a key component of countless applications such as agricultural management, soil remediation, aquifer protection, etc. Soil hydraulic properties can be measured in the laboratory; however, the procedures are laborious and costly, and may provide estimates different from those observed in the field. An alternative approach is to obtain soil hydraulic properties using soil water flow modelin conjunction with SWC monitoring data. The goal of the present study was to analyze the efficiency of obtaining soil hydraulic properties from the joint soil water state utilizing data assimilation (DA) based on the Ensemble Kalman filter method. Two soil textures and four climatic conditions were considered; observations of soil moisture were synthetically generated by the use of HYDRUS-1D and latter perturbed by the application of the conditional multivariate normal distribution .Comparison of parameter estimation with the data assimilation and with the frequently used Levenberg Marquardt algorithm (SC-LM) showed the substantial advantages of the former. When observed SWC did not show a broad range of values, both DA and SC-LM provided sets of soil hydraulic parameters that led to the good Richards model performance, with the RMSE below 0.01 cm3 cm-3 and/or r2 above 0.8 after a period of 100 days. Goodness-of-fit statistics from DA were clearly better than from SC LM more than 95 percent of the time. Based on the soil and climatic conditions used for simulations, oneyear was adequate to obtain reliable soil properties with data assimilation.