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United States Department of Agriculture

Agricultural Research Service

Research Project: AGRICULTURAL LAND MANAGEMENT TO OPTIMIZE PRODUCTIVITY AND NATURAL RESOURCE CONSERVATION AT FARM AND WATERSHED SCALES

Location: Great Plains Agroclimate and Natural Resources Research Unit

Title: Upper Washita River Experimental Watersheds: Data Screening Procedure for Data Quality Assurance

Authors
item Guzman Jaimes, Jorge
item Chu, Ma. -
item Starks, Patrick
item Moriasi, Daniel
item Steiner, Jean
item Fiebrich, Christopher -
item Mccombs, Alexandria -

Submitted to: Journal of Environmental Quality
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 25, 2014
Publication Date: July 14, 2014
Citation: Guzman Jaimes, J.A., Chu, M.L., Starks, P.J., Moriasi, D.N., Steiner, J.L., Fiebrich, C.A., Mccombs, A.G. 2014. Upper Washita River Experimental Watersheds: Data Screening Procedure for Data Quality Assurance. Journal of Environmental Quality. 43:1250-1261.

Interpretive Summary: Changes in the stationary conditions of hydrologic time series are associated with natural or man-made activities, or network operation problems. Detection and identification of network operation drivers is fundamental in hydrologic investigation as changes in random and systematic errors can exacerbate modeling results or bias research conclusions. In this paper, a data screening procedure was applied to weather and soil climate time series data from the USDA-ARS Micronet dataset. Detection of statistically significant monotonic trends and changes in mean and variance were used to investigate non-stationary conditions with network operation drivers. Detection of spurious data, filling in missing data, and statistical tests were applied to over 1,000 time series, and then confronted with Micronet network operation reports (metadata). Processed data were made publicly available at the ARS Grazinglands Research Laboratory data repository. A Grazinglands Research laboratory newly developed application, SPELLmap was used to perform data data manipulation and statistical tests on watershed segregated datasets, and temporally aggregated data. Tests for independency (e.g., Anderson test), normality, monotonic trend (e.g., Spearman test), detection of change point (Pettitt test), and split test (e.g., F-test, and T-test) were used to assess non-stationary conditions. Statistically significant (95% confident level) monotonic trends and changes in mean and variance were detected on annual maximum air temperature, rainfall, relative humidity, and solar radiation, and in maximum and minimum soil temperature time series. Network operation procedures such as change in calibration protocols and sensor upgrades as well as natural regional weather trends were suspected to drive the detection of statistically significant trends and changes in mean and variance. It is concluded that a data screening procedure is fundamental to identify potential sources of uncertainty in hydrologic modeling and assess potential transfer functions.

Technical Abstract: The presence of non-stationary condition in long term hydrologic observation networks are associated with natural and anthropogenic stressors or network operation problems. Detection and identification of network operation drivers is fundamental in hydrologic investigation due to changes in systematic errors that can exacerbate modeling results or bias research conclusions. In this paper, a data screening procedure was applied to the Micronet USDA-ARS dataset. Detection of statistically significant monotonic trends and changes in mean and variance were used to investigate non-stationary conditions with network operation drivers. Detection of spurious data, filling in missing data, and data screening procedures were applied to over 1,000 time series, and processed data made publicly available. The SPELLmap application was used for data processing and statistical tests on watershed segregated datasets, and temporally aggregated data. Test for independency (Anderson test), normality, monotonic trend (Spearman test), detection of change point (Pettitt test), and split test (F- and T-test) were used to assess non-stationary conditions. Statistically significant (95% CL) monotonic trends and changes in mean and variance were detected on annual maximum air temperature, rainfall, relative humidity, and solar radiation, and in maximum and minimum soil temperature time series. Network operation procedures such as change in calibration protocols and sensor upgrades as well as natural regional weather trends were suspected to drive the detection of statistically significant trends and changes in mean and variance. It is concluded that a data screening procedure is fundamental to identify potential sources of uncertainty in hydrologic modeling and assess potential transfer functions.

Last Modified: 12/20/2014
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