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Abstract
This paper investigates methods to analyze and forecast soil moisture time series. We extend an existing Antecedent Water Index (AWI) model, which expresses soil moisture as a function of time and rainfall. Unfortunately, the existing AWI model does not forecast effectively for time periods beyond a few hours. To overcome this limitation, we develop a novel AWI-based model. Our model accumulates rainfall over a time interval and can fit a diverse range of wetting and drying curves. In addition, parameters in our model reflect hydrologic redistribution processes of gravity and suction.We validate our models using experimental soil moisture and rainfall time series data collected from steep gradient post-wildfire sites in Southern California, where rapid landscape change was observed in response to small to moderate rain storms. We found that our novel model fits the data for three distinct soil textures, occurring at different depths below the ground surface (5, 15, and 30 cm). Our model also successfully forecasts soil moisture trends, such as drying and wetting rate.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Wetting and drying of soil in response to precipitation: Data analysis, modeling, and forecasting |
| Year Published | 2016 |
| Language | English |
| Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
| Contributing office(s) | Geology, Minerals, Energy, and Geophysics Science Center |
| Conference Title | 13th Conference of the Association for the Advancement of Artificial Intelligence |
| Conference Location | Phoenix, Arizona |
| Conference Date | February 12–17, 2016 |
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