Abstract
A model for predicting shallow depth soil temperatures is important and effective to assess the changes in soil conditions related to global climate change and local disturbances. Shallow-depth soil temperature estimation model in cold region in Alaska is developed based on thermal response using air temperature and shallow-depth soil water content during active layer development period of 160 days from May to October. Among the seven soil temperature measurement sites, data from four sites were used for model development, and the remaining three sites were used for model validation. Near the middle of the seven measurement sites, air temperature is monitored at one location. The proposed model implemented concepts of thermal response and cumulative temperature. Temperatures and soil water contents were measured using automated remote sensing technology. Consequently, it was confirmed that the developed model enables fast and accurate assessment of shallow-depth soil temperature during active soil layer development period.
Highlights
The evidence for climate change in the high-latitude ecosystems of Arctic regions is increasing [1,2,3,4,5,6,7].It has been reported that soil warming has a greater impact on climatic changes than global atmospheric warming [8]
Soil temperature is predicted during the active soil layer generation period, which is important for capturing sensible and latent heat fluxes, the heat energy from the geothermal system, assessing sea ice and permafrost, determining CO2 and NH4 emissions patterns, microbial decomposition, and rates of organic matter decomposition, mineralization, and plant growth [20,21]
October of 2015 and the soil temperatures measured at locations of M1–M4 during the same period
Summary
The evidence for climate change in the high-latitude ecosystems of Arctic regions is increasing [1,2,3,4,5,6,7].It has been reported that soil warming has a greater impact on climatic changes than global atmospheric warming [8]. Soil temperature is predicted during the active soil layer generation period, which is important for capturing sensible and latent heat fluxes (for soil temperatures ≥ 0 ◦ C), the heat energy from the geothermal system, assessing sea ice and permafrost, determining CO2 and NH4 emissions patterns, microbial decomposition, and rates of organic matter decomposition, mineralization, and plant growth [20,21]. Several models have been proposed to simulate soil temperatures in various regions and environments [18,22,23,24]. Most of these models have been successful to some extent; their prediction algorithms are complex and contain many fitting parameters, requiring a large amount of time for predicting soil temperatures. A more practical and efficient model for estimating soil temperature is needed to rapidly capture the spatial changes in soil temperatures across
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