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Predicting degree day snow melt factors with crown closure in Arizona ponderosa pine.

Patrick Lee Jackson

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Abstract

The increasing demand for water in Arizona has caused man to evaluate possibilities for increasing surface runoff from high elevation snow accumulation areas. To date, a consistent method of predicting snow melt in field situations, for runoff estimation, has not been developed. In an attempt to alleviate this demand for water, snow melt indices based on median and maximum air temperatures were evaluated using Arizona ponderosa pine overstory crown closure as an inventory-prediction variable. Regression analysis was used to develop the prediction relationship, with degree day snow melt factors as the dependent variables and percent crown closure as the independent variable. The median temperature prediction relationship was non-significant. The maximum temperature prediction relationship was significant, although it explained only 12 percent of the variation in degree day snow melt factors. Validation of the maximum temperature relationship indicated it may not be adequate for snow melt prediction in field situations. On the basis of this study, crown closure (as an inventory-Prediction variable) did not appear to be an adequate predictor of median or maximum temperature degree day snow melt factors in Arizona ponderosa pine.

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What this paper is about

The increasing demand for water in Arizona has caused man to evaluate possibilities for increasing surface runoff from high elevation snow accumulation areas. To date, a consistent method of predicting snow melt in field situations, for runoff estimation, has not been developed. In an attempt to alleviate this demand for water, snow melt indices based on median and maximum air temperatures were evaluated using Arizona ponderosa pine overstory crown closure as an inventory-prediction variable. Regression analysis was used to develop the prediction relationship, with degree day snow melt factors as the dependent variables and percent crown closure as the independent variable. The median temperature prediction relationship was non-significant. The maximum temperature prediction relationship was significant, although it explained only 12 percent of the variation in degree day snow melt factors. Validation of the maximum temperature relationship indicated it may not be adequate for snow melt prediction in field situations. On the basis of this study, crown closure (as an inventory-Prediction variable) did not appear to be an adequate predictor of median or maximum temperature degree day snow melt factors in Arizona ponderosa pine.

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Available abstract

The increasing demand for water in Arizona has caused man to evaluate possibilities for increasing surface runoff from high elevation snow accumulation areas. To date, a consistent method of predicting snow melt in field situations, for runoff estimation, has not been developed. In an attempt to alleviate this demand for water, snow melt indices based on median and maximum air temperatures were evaluated using Arizona ponderosa pine overstory crown closure as an inventory-prediction variable. Regression analysis was used to develop the prediction relationship, with degree day snow melt factors as the dependent variables and percent crown closure as the independent variable. The median temperature prediction relationship was non-significant. The maximum temperature prediction relationship was significant, although it explained only 12 percent of the variation in degree day snow melt factors. Validation of the maximum temperature relationship indicated it may not be adequate for snow melt prediction in field situations. On the basis of this study, crown closure (as an inventory-Prediction variable) did not appear to be an adequate predictor of median or maximum temperature degree day snow melt factors in Arizona ponderosa pine.

Key concepts: Crown (dentistry), Snow, Degree (music), Degree day, Forestry, Closure (psychology), Environmental science, Geography

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