Snow Disaster Early Warning in Pastoral Areas of Qinghai Province, China

Abstract

Abstract:It is important to predict snow disasters to prevent and reduce hazards in pastoral areas. In this study, we build a potential risk assessment model based on a logistic regression of 33 snow disaster events that occurred in Qinghai Province. A simulation model of the snow disaster early warning is established using a back propagation artificial neural network (BP-ANN) method and is then validated. The results show: (1) the potential risk of a snow disaster in the Qinghai Province is mainly determined by five factors. Three factors are positively associated, the maximum snow depth, snow-covered days (SCDs), and slope, and two are negative factors, annual mean temperature and per capita gross domestic product (GDP); (2) the key factors that contribute to the prediction of a snow disaster are (from the largest to smallest contribution): the mean temperature, probability of a spring snow disaster, potential risk of a snow disaster, continual days of a mean daily temperature below −5 • C, and fractional snow-covered area; and (3) the BP-ANN model for an early warning of snow disaster is a practicable predictive method with an overall accuracy of 80%. This model has quite a few advantages over previously published models, such as it is raster-based, has a high resolution, and has an ideal capacity of generalization and prediction. The model output not only tells which county has a disaster (published models can) but also tells where and the degree of damage at a 500 m pixel scale resolution (published models cannot).

Spatiotemporal Dynamics of the Carbon Budget and the Response to Grazing in Qinghai Grasslands

“…The ecological environment in this region is sensitive to climate change and human activities. Grassland degradation was previously widespread in Qinghai because of unrestricted grazing activities (Figure 1; Gao et al, 2017;Zhao et al, 2020).…”

“…Qinghai is located in the northeastern part of the Qinghai-Tibet Plateau, which is known as "the roof of the world." It is the site of origin of the Yellow River, Yangtze River, and Lancang River, and it has high ecological significance (Schaller et al, 1988;Gao et al, 2017;Dong Y. et al, 2020). Grassland is the most widely distributed ecosystem in this region.…”

A dynamic snow accumulation simulation approach for forecasting snow distribution over regional‐scale terrain

“…With rapid economic development and steady population growth in pastoral areas in recent decades, the risk associated with snow‐related disasters has increased in China (Ding et al., 2015; Su et al., 2011), particularly in pastoral areas such as Xinjiang and Tibet. Disasters caused by heavy snow (e.g., snow accumulation and refreezing of melted snow in pastoral areas) often cause mass debilitation, starvation, and the death of livestock (Gao et al., 2017). To mitigate related losses, reliable disaster management techniques (Qiu, Du, Zhu, & Fan, 2017), such as snow accumulation simulation approaches, are necessary to generate snow scenarios to predict snow depth distributions, which can help the government and the public examine future events, and stimulate consideration of appropriate policies and techniques for snow risk mitigation (Liston et al., 2018).…”

Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning

“…Meanwhile, snow water equivalent (SWE) is an important driving factor for meteorological, climate and hydrological models, which can enhance the understanding of regional water resources environment and improve the accuracy of a weather forecast. From a negative perspective, heavy snow, sustained low temperatures and prolonged SC will result in snow disasters that affect animal husbandry, and sudden excessive snow melting will also cause soil erosion and flood risk [5].…”