Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future.

Decline in snow mass threatens the regional economy that critically depends on meltwater. However, the economic scale of snow mass loss is hardly understood, and its role in the vulnerability of future economic development is unclear. We investigate the current reserves of snow cover and the value of its loss. The result showed that the total annual snow mass in western China declines at a rate of 3.3 x 10(9) Pg per decade (p < 0.05), which accounts for approximately 0.46% of the mean of annual snow mass (7.2 x 10(11) Pg). Snow mass loss over the past 40 years in western China turns into an average loss value of CN0.1 billion (in the present value) every year ($1 = CN7). If the trend continues at the current rate, the accumulated loss value would rise to CN63 billion by 2040. Furthermore, subject to the combinations of RCPs and SSPs scenario, the future economic value of snow mass loss in western China appears to accelerate driven by both declining snowmelt resources and socioeconomic development demand. RCP26-SSP1 is the pathway among all to have the least economic cost in replacing the snowmelt loss, and the cost would be quadrupled in RCP80-SSP3 scenario by 2100. At a basin scale, the declining snow mass would turn the regional economy to be more vulnerable except Junggar and Ili endorheic basin. The Ertis river and Qaidam endorheic basins display to be most vulnerable. It highlights that the snowvalue can be economically important in the regions ofwest China and should be considered more properly in water resources management. (C) 2020 The Author(s). Published by Elsevier B.V.

积雪深度的变化对地表水热平衡起着至关重要的作用。选用了国际耦合模式比较计划第六阶段（CMIP6）中目前情景比较齐全的五个全球气候模式，通过对比新疆地区1979—2014年积雪深度长时间序列数据集，评估了气候模式在新疆地区模拟积雪深度的模拟能力，接着预估了未来不同SSPs-RCPs情景下新疆地区在2021—2040年（近期）、2041—2060年（中期）、2081—2100年（末期）相对于基准期（1995—2014年）的积雪深度变化。气温和降水对积雪深度变化有着重要的影响，因此还分析了新疆地区到21世纪末期气温和降水的变化趋势。结果表明：订正后的气候模式模拟的积雪深度数据与观测数据的相关系数均达到0.8以上，其中1月至3月与观测数据的结果更为吻合。气候模式基本上能够反映积雪深度年内变化的基本特征，气候模式模拟的积雪深度空间分布和观测数据具有相似的特征。气温和降水在未来不同情景下均会波动上升，其中气温的增幅相对比较明显，达0.43℃·（10a）-1，而降水的增幅为0.63mm·（10a）-1，新疆未来的气候总体上呈现出变暖变湿的趋势。新疆地区的平均积雪深度在未来不同时...

积雪深度的变化对地表水热平衡起着至关重要的作用。选用了国际耦合模式比较计划第六阶段（CMIP6）中目前情景比较齐全的五个全球气候模式，通过对比新疆地区1979—2014年积雪深度长时间序列数据集，评估了气候模式在新疆地区模拟积雪深度的模拟能力，接着预估了未来不同SSPs-RCPs情景下新疆地区在2021—2040年（近期）、2041—2060年（中期）、2081—2100年（末期）相对于基准期（1995—2014年）的积雪深度变化。气温和降水对积雪深度变化有着重要的影响，因此还分析了新疆地区到21世纪末期气温和降水的变化趋势。结果表明：订正后的气候模式模拟的积雪深度数据与观测数据的相关系数均达到0.8以上，其中1月至3月与观测数据的结果更为吻合。气候模式基本上能够反映积雪深度年内变化的基本特征，气候模式模拟的积雪深度空间分布和观测数据具有相似的特征。气温和降水在未来不同情景下均会波动上升，其中气温的增幅相对比较明显，达0.43℃·（10a）-1，而降水的增幅为0.63mm·（10a）-1，新疆未来的气候总体上呈现出变暖变湿的趋势。新疆地区的平均积雪深度在未来不同时...