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2012 - 11th Annual NC State Summer Undergraduate Research Symposium
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Session Time :
8/1/12 1:30 PM - 8/1/12 2:45 PM
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Content Area : NC State Independent Researchers
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Lead Student Presenters : Joseph Tokeshi Taylor
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Abstract Title : Predicting Observed Soil Moisture Using Statistical Modeling
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Abstract :
Soil moisture (SM) is the amount of water contained in a volume of soil. SM is important to agriculture, coastal ecosystems, and environmental engineering e.g. the amount of water holding capacity for a particular soil type is essential for irrigation, drought and flooding potential in coastal ecosystems. However, SM is measured by few stations in the U.S. including the North Carolina Environment and Climate Observing Network (ECONet) and U.S. Climate Reference Network (USCRN). The purpose of this study is to evaluate the accuracy of a SM estimation technique utilizing these observations. This estimate will assist with quality control of ECONet data and prediction of missing data. Observed hourly SM from each station and observed minute data from every ECONet station are used to create statistical models to predict SM. This model has a time series component to account for change in SM with time. The model predicts up to 24 hours in advance for hourly data and up to an hour in advance for minute data. With every new observed SM value, the model will adjust itself to account for new information. The model captures the observed SM with an error of less than ±0.07 m3m-3 at every ECONet and USCRN station. Also at USCRN stations, SM values at different depths are used to test sensitivity of the model to soil depth. One model limitation is the station of interest must measure SM for at least a year to have minimal error.
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Mentor and/or Co-Author : Ryan P Boyles
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