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Citation:

Seasonal Dynamics and Prediction of Forest Litter Moisture in Daxing’anling Region

  • Received Date: 2013-12-28
  • The dynamics of litter moisture under Scots pine, larch and birch stands in Pangu Forest Farm, Daxing'anling Region, Heilongjiang Province, were observed in the spring of 2010 and the autumns of 2010 and 2011. The responses of the litter moisture to weather variables were analyzed. The results showed that the litter moistures of the three stands were closely related to the relative humidity and rainfall, particularly to the humidity and rainfall two days ago, showing a obviously lagging but not related to the air temperature and wind velocity. The litter moistures under the three stands in spring showed an exponential relationship with the air humidity and an asymptotic relationship with rainfall while those for autumn were both linear. The prediction models established in nonlinear form using spring data are more accurate than those of linear form. These relationships are different from those commonly used in current fuel moisture prediction models and could be beneficial for improving the modeling accuracy in the future.
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Seasonal Dynamics and Prediction of Forest Litter Moisture in Daxing’anling Region

  • 1. College of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China

Abstract: The dynamics of litter moisture under Scots pine, larch and birch stands in Pangu Forest Farm, Daxing'anling Region, Heilongjiang Province, were observed in the spring of 2010 and the autumns of 2010 and 2011. The responses of the litter moisture to weather variables were analyzed. The results showed that the litter moistures of the three stands were closely related to the relative humidity and rainfall, particularly to the humidity and rainfall two days ago, showing a obviously lagging but not related to the air temperature and wind velocity. The litter moistures under the three stands in spring showed an exponential relationship with the air humidity and an asymptotic relationship with rainfall while those for autumn were both linear. The prediction models established in nonlinear form using spring data are more accurate than those of linear form. These relationships are different from those commonly used in current fuel moisture prediction models and could be beneficial for improving the modeling accuracy in the future.

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