分类： 物理学 >> 普通物理:统计和量子力学,量子信息等 提交时间： 2018-04-03 合作期刊: 《干旱区科学》
摘要： Forests and grasslands in arid and semi-arid regions receive high-intensity ultraviolet (UV) radiation year-round. However, how the UV radiation affects the litter decomposition on the forest floor remains unclear. Here, we conducted a field-based experiment in 2011 in the southeastern Horqin Sandy Land, Northeast China, to investigate the effects of UV radiation, litter layer thickness, and their interaction on the mass loss and chemical properties of decomposing litter from Xiaozhuan poplar (Populus × xiaozhuanica) and Mongolian pine (Pinus sylvestris var. mongolica) plantation trees. We found that UV radiation accelerated the decomposition rates of both the Xiaozhuan poplar litter and Mongolian pine litter. For both species, the thick-layered litter had a lower mass loss than the thin-layered litter. The interaction between UV radiation and litter layer thickness significantly affected the litter mass loss of both tree species. However, the effects of UV radiation on the chemical properties of decomposing litter differed between the two species, which may be attributed to the contrasting initial leaf litter chemical properties and morphology. UV radiation mostly had positive effects on the lignin concentration and lignin/N ratio of Xiaozhuan poplar litter, while it had negative effects on the N concentration of Mongolian pine litter. Moreover, litter layer thickness and its interaction with UV radiation showed mostly positive effects on the N concentration and lignin/N ratio of Xiaozhuan poplar litter and the ratios of C/N and lignin/N of Mongolian pine litter, and mostly negative effects on the C/N ratio of Xiaozhuan poplar litter and the N concentration of Mongolian pine litter. Together, these results reveal the important roles played by UV radiation and litter layer thickness in the process of litter decomposition in this semi-arid region, and highlight how changes in the litter layer thickness can exert strong influences on the photodegradation of litter in tree plantations.
分类： 地球科学 >> 地理学 提交时间： 2023-02-07 合作期刊: 《干旱区科学》
摘要： Soil water content is a key controlling factor for vegetation restoration in sand dunes. The deep seepage and lateral migration of water in dunes affect the recharge process of deep soil water and groundwater in sand dune ecosystems. To determine the influence of vegetation on the hydrological regulation function of sand dunes, we examined the deep seepage and lateral migration of dune water with different vegetation coverages during the growing season in the Horqin Sandy Land, China. The results showed that the deep seepage and lateral migration of water decreased with the increase in vegetation coverage on the dunes. The accumulated deep seepage water of mobile dunes (vegetation coverage13.75%) should be retained as a water resource reservoir to maintain the water balance of artificial fixed dune ecosystems. These findings provide reliable evidence for the accurate assessment of water resources within the sand dune ecosystem and guide the construction of desertification control projects.
分类： 生物学 >> 生态学 提交时间： 2023-06-13 合作期刊: 《干旱区科学》
摘要：The dead fuel moisture content (DFMC) is the key driver leading to fire occurrence. Accurately estimating the DFMC could help identify locations facing fire risks, prioritise areas for fire monitoring, and facilitate timely deployment of fire-suppression resources. In this study, the DFMC and environmental variables, including air temperature, relative humidity, wind speed, solar radiation, rainfall, atmospheric pressure, soil temperature, and soil humidity, were simultaneously measured in a grassland of Ergun City, Inner Mongolia Autonomous Region of China in 2021. We chose three regression models, i.e., random forest (RF) model, extreme gradient boosting (XGB) model, and boosted regression tree (BRT) model, to model the seasonal DFMC according to the data collected. To ensure accuracy, we added time-lag variables of 3 d to the models. The results showed that the RF model had the best fitting effect with an R2 value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764% among the three models. The accuracies of the models in spring and autumn were higher than those in the other two seasons. In addition, different seasons had different key influencing factors, and the degree of influence of these factors on the DFMC changed with time lags. Moreover, time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy, indicating that environmental conditions within approximately 48 h greatly influence the DFMC. This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.
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