State Key Laboratory of Earth Surface Processes and Resource Ecology, Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR China.
State Key Laboratory of Earth Surface Processes and Resource Ecology, Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR China.
Sci Total Environ. 2018 Jun 1;625:87-95. doi: 10.1016/j.scitotenv.2017.12.230. Epub 2017 Dec 27.
Livestock snow disaster occurs widely in Central-to-Eastern Asian temperate and alpine grasslands. The effects of snow disaster on livestock involve a complex interaction between precipitation, vegetation, livestock, and herder communities. Quantifying the relationship among livestock mortality, snow hazard intensity, and seasonal environmental stressors is of great importance for snow disaster early warning, risk assessments, and adaptation strategies. Using a wide-spatial extent, long-time series, and event-based livestock snow disaster dataset, this study quantified those relationships and established a quantitative model of livestock mortality for prediction purpose for the Qinghai-Tibet Plateau region. Estimations using generalized additive models (GAMs) were shown to accurately predict livestock mortality and mortality rate due to snow disaster, with adjusted-R up to 0.794 and 0.666, respectively. These results showed that a longer snow disaster duration, lower temperatures during the disaster, and a drier summer with less vegetation all contribute significantly and non-linearly to higher mortality (rate), after controlling for elevation and socioeconomic conditions. These results can be readily applied to risk assessment and risk-based adaptation actions.
牲畜雪灾广泛发生于中亚到东亚的温带和高山草原地区。雪灾对牲畜的影响涉及降水、植被、牲畜和牧民社区之间的复杂相互作用。量化牲畜死亡率、雪灾强度和季节性环境胁迫因素之间的关系,对于雪灾预警、风险评估和适应策略至关重要。本研究利用广泛的空间范围、长时间序列和基于事件的牲畜雪灾数据集,量化了这些关系,并建立了一个针对青藏高原地区的牲畜死亡率定量预测模型。广义加性模型(GAMs)的估计结果表明,该模型能够准确预测牲畜死亡率和雪灾导致的死亡率,调整后的 R 分别高达 0.794 和 0.666。这些结果表明,在控制海拔和社会经济条件的情况下,较长的雪灾持续时间、雪灾期间较低的温度以及较干燥的夏季和较少的植被都会显著且非线性地导致更高的死亡率(率)。这些结果可以直接应用于风险评估和基于风险的适应行动。