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ARS Home » Research » Publications at this Location » Publication #86722


item Moran, Mary
item Keefer, Timothy

Submitted to: American Meteorological Society of the Conference on Hydrology Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 11/1/1997
Publication Date: N/A
Citation: N/A

Interpretive Summary: Soil moisture plays an important part in many environmental processes. Unfortunately, extensive soil moisture data sets used to analyze these processes are limited because traditional instruments for measuring soil moisture are labor intensive and sometimes inaccurate. In this study, we used a network of Electrical Resistance Sensors (ERS) to monitor soil moisture changes over time and space. These sensors require little maintenance and supervision because they automatically collect nearly continuous data. To ensure that the ERS readings were accurate, we corrected them to match reliable soils moisture measurements taken at the same time and location. Ultimately, we were able to compile a 16 month, hourly soil moisture data set for three different depths and six different locations. With this data set, we plan to study the use of surface soil moisture measurements in simulation models to predict water flow pattern during and between storms. The spatial and temporal characteristics of this data set will be useful in many other long-term studies.

Technical Abstract: Soil moisture is a critical component of many regional and global climate studies. Unfortunately, long-term soil moisture data sets are rare because conventional soil moisture instruments have historically been procedurally and labor intensive. Electrical Resistance Sensors (ERS), however, are capable of collecting nearly continuous data with little maintenance using data-loggers. In this experiment, hourly ERS and intermittent Time Domain Reflectometer (TDR) measurements taken concurrently in 1990 and 1991 were used to develop a soil moisture data set for sensors located in the Walnut Gulch Experimental Watershed. Ultimately, these calibrations yielded a 16 month, hourly data set for sensors buried in six different trenches across the Lucky Hills subwatershed at 5, 15 and 30 cm depths. These data will be used to validate the one-dimensional Simultaneous Heat and Water (SHAW) model which simulates heat and water movement through plant cover, snow, residue and soil. The long-term, temporally continuous characteristics of these data should provide important insights into rangeland management issues.