Deep drainage estimates using multiple linear regression with percent clay content and rainfall
Deep drainage estimates are required for effective management of water resources. However, field measurements are time consuming and costly so simple empirical relationships are often used. Relationships developed between clay content of the surface soil and deep drainage have been used extensively...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2012-02-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/16/563/2012/hess-16-563-2012.pdf |
| Summary: | Deep drainage estimates are required for effective management of water resources. However, field measurements are time consuming and costly so simple empirical relationships are often used. Relationships developed between clay content of the surface soil and deep drainage have been used extensively in Australia to provide regional estimates of deep drainage but these relationships have been poorly justified and did not include rainfall in the relationships. Here we present a rigorous appraisal of clay content of soils and rainfall as predictors of deep drainage using an extensive database of field observations from across Australia. This study found that annual average rainfall and the average clay content of the top 2 m of the soil are statistically significant predictors of point scale deep drainage. Relationships have been defined for annual, perennial and tree type vegetation as a line of best fit along with 95% confidence intervals. This allows the uncertainty in these deep drainage estimates to be assessed for the first time. |
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| ISSN: | 1027-5606 1607-7938 |