In plains areas with semi-arid climates, shallow groundwater is one of the important factors affecting soil thermal properties. In this study, soil temperature and water content were measured when groundwater tables r...In plains areas with semi-arid climates, shallow groundwater is one of the important factors affecting soil thermal properties. In this study, soil temperature and water content were measured when groundwater tables reached 10 cm, 30 cm, and 60 cm depths （Experiment I, II, and III） by using sensors embedded at depths of 5 cm, 10 cm, 20 cm, and 30 cm for 5 days. Soil thermal properties were analyzed based on the experimental data using the simplified de Vries model. Results show that soil water content and temperature have fluctuations that coincide with the 24 h diurnal cycle, and the amplitude of these fluctuations decreased with the increase in groundwater table depth. The amplitude of soil water content at 5 cm depth decreased from 0.025 m^3·m^-3 in Experiment II to 0.01 m^3·m^-3 in Experiment III. Moreover, it should be noted that the soil temperature in Experiment III gradually went up with the lowest value increasing from 26.0℃ to 28.8℃. By contrast, the trends were not evident in Experiments I and II. Results indicate that shallow groundwater has a ＂cooling＂ effect on soil in the capillary zone. In addition, calculated values of thermal conductivity and heat capacity declined with the increasing depth of the groundwater table, which is consistent with experimental results. The thermal conductivity was stable at a value of 2.3 W.cm^-1·K^-1 in Experiment I. The average values of thermal conductivity at different soil depths in Experiment II were 1.82 W.cm^-1·K^-1, 2.15 W.cm^-1·K^-1, and 2.21 W. cm^-1·K^-1, which were always higher than that in Experiment III.展开更多
An ever-increasing number of sensor resources are being exposed via the World Wide Web to become part of the Digital Earth.Discovery,selection and use of these sensors and their observations require a robust sensor in...An ever-increasing number of sensor resources are being exposed via the World Wide Web to become part of the Digital Earth.Discovery,selection and use of these sensors and their observations require a robust sensor information model,but the consistent description of sensor metadata is a complex and difficult task.Currently,the only available robust model is SensorML,which is intentionally designed in a very generic way.Due to this genericness,interoperability can hardly be achieved without the definition of application profiles that further constrain the use and expressiveness of the root language.So far,such SensorML profiles have only been developed up to a limited extent.This work describes a new approach for defining sensor metadata,the Starfish Fungus Language(StarFL)model.This language follows a more restrictive approach and incorporates concepts from the recently published Semantic Sensor Network Ontology to overcome the key issues users are experiencing with SensorML.StarFL defines a restricted vocabulary and model for sensor metadata to achieve a high level of interoperability and a straightforward reusability of sensor descriptions.展开更多
文摘In plains areas with semi-arid climates, shallow groundwater is one of the important factors affecting soil thermal properties. In this study, soil temperature and water content were measured when groundwater tables reached 10 cm, 30 cm, and 60 cm depths （Experiment I, II, and III） by using sensors embedded at depths of 5 cm, 10 cm, 20 cm, and 30 cm for 5 days. Soil thermal properties were analyzed based on the experimental data using the simplified de Vries model. Results show that soil water content and temperature have fluctuations that coincide with the 24 h diurnal cycle, and the amplitude of these fluctuations decreased with the increase in groundwater table depth. The amplitude of soil water content at 5 cm depth decreased from 0.025 m^3·m^-3 in Experiment II to 0.01 m^3·m^-3 in Experiment III. Moreover, it should be noted that the soil temperature in Experiment III gradually went up with the lowest value increasing from 26.0℃ to 28.8℃. By contrast, the trends were not evident in Experiments I and II. Results indicate that shallow groundwater has a ＂cooling＂ effect on soil in the capillary zone. In addition, calculated values of thermal conductivity and heat capacity declined with the increasing depth of the groundwater table, which is consistent with experimental results. The thermal conductivity was stable at a value of 2.3 W.cm^-1·K^-1 in Experiment I. The average values of thermal conductivity at different soil depths in Experiment II were 1.82 W.cm^-1·K^-1, 2.15 W.cm^-1·K^-1, and 2.21 W. cm^-1·K^-1, which were always higher than that in Experiment III.
文摘An ever-increasing number of sensor resources are being exposed via the World Wide Web to become part of the Digital Earth.Discovery,selection and use of these sensors and their observations require a robust sensor information model,but the consistent description of sensor metadata is a complex and difficult task.Currently,the only available robust model is SensorML,which is intentionally designed in a very generic way.Due to this genericness,interoperability can hardly be achieved without the definition of application profiles that further constrain the use and expressiveness of the root language.So far,such SensorML profiles have only been developed up to a limited extent.This work describes a new approach for defining sensor metadata,the Starfish Fungus Language(StarFL)model.This language follows a more restrictive approach and incorporates concepts from the recently published Semantic Sensor Network Ontology to overcome the key issues users are experiencing with SensorML.StarFL defines a restricted vocabulary and model for sensor metadata to achieve a high level of interoperability and a straightforward reusability of sensor descriptions.
