The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system.Thus,a long-time snow water equivalent(SWE)dataset is necessary.This work presents a daily SWE produ...The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system.Thus,a long-time snow water equivalent(SWE)dataset is necessary.This work presents a daily SWE product of 1980-2020 with a linear unmixing method through passive microwave data including SMMR,SSM/I and SSMIS over China after cross-calibration and bias-correction.The unbiased root-mean-square error of snow depth is about 5-7 cm,corresponding to 10-15 mm for SWE,when compared with stations measurements and field snow course data.The spatial patterns and trends of SWE over China present significant regional differences.The overall slope trend presented an insignificant decreasing pattern during 1980-2020 over China;however,there is an obvious fluctuation,i.e.a significant decrease trend during the period 1980-1990,an upward trend from 2005 to 2009,a significant downward trend from 2009 to 2018.The increase of SWE occurred in the Northeast Plain,with an increase trend of 0.2 mm per year.Whereas in the Hengduan Mountains,it presented a downward trend of SWE,up to−0.3 mm per year.In the North Xinjiang,SWE has an increasing trend in the Junggar Basin,while it shows a decreasing trend in the Tianshan and Altai Mountains.展开更多
积雪物候是指季节性积雪随季节周期的变化趋势和变化规律,对融雪径流、土壤冻融、植被物候和动物迁徙等过程有着重要影响,是积雪区能量平衡、水文、生态及气象模型的重要输入因子,也是积雪变化研究主要内容之一。中国是中低纬度主要的...积雪物候是指季节性积雪随季节周期的变化趋势和变化规律,对融雪径流、土壤冻融、植被物候和动物迁徙等过程有着重要影响,是积雪区能量平衡、水文、生态及气象模型的重要输入因子,也是积雪变化研究主要内容之一。中国是中低纬度主要的季节性积雪区,积雪物候研究具有重要的意义。本文基于1980–2020年5 km AVHRR逐日无云积雪面积产品,制备了中国长时间序列积雪物候数据集,该数据集包含积雪日数、积雪初日、积雪终日三个数据子集。利用地面气象台站实测雪深数据对产品进行验证,验证结果表明:积雪日数、积雪初日和积雪终日验证相关系数R2分别为0.80,0.76和0.94,均方根误差RMSE分别为22.78天,17.87天和16.39天,平均绝对误差MAE分别为13.26天,7.51天和7.76天,精度可靠。本数据集可服务于中国积雪时空变化分析,为气候变化,水文水资源,生态环境,人文经济等科学研究、工程建设以及社会服务提供基础数据资料。展开更多
基金supported by the Science and Technology Basic Resources Investigation Program of China(2017FY100502)the National Natural Science Foundation of China(42090014,42171317).
文摘The reliable knowledge of seasonal snow volume and its trend is very important to understand Earth’s climate system.Thus,a long-time snow water equivalent(SWE)dataset is necessary.This work presents a daily SWE product of 1980-2020 with a linear unmixing method through passive microwave data including SMMR,SSM/I and SSMIS over China after cross-calibration and bias-correction.The unbiased root-mean-square error of snow depth is about 5-7 cm,corresponding to 10-15 mm for SWE,when compared with stations measurements and field snow course data.The spatial patterns and trends of SWE over China present significant regional differences.The overall slope trend presented an insignificant decreasing pattern during 1980-2020 over China;however,there is an obvious fluctuation,i.e.a significant decrease trend during the period 1980-1990,an upward trend from 2005 to 2009,a significant downward trend from 2009 to 2018.The increase of SWE occurred in the Northeast Plain,with an increase trend of 0.2 mm per year.Whereas in the Hengduan Mountains,it presented a downward trend of SWE,up to−0.3 mm per year.In the North Xinjiang,SWE has an increasing trend in the Junggar Basin,while it shows a decreasing trend in the Tianshan and Altai Mountains.
文摘积雪物候是指季节性积雪随季节周期的变化趋势和变化规律,对融雪径流、土壤冻融、植被物候和动物迁徙等过程有着重要影响,是积雪区能量平衡、水文、生态及气象模型的重要输入因子,也是积雪变化研究主要内容之一。中国是中低纬度主要的季节性积雪区,积雪物候研究具有重要的意义。本文基于1980–2020年5 km AVHRR逐日无云积雪面积产品,制备了中国长时间序列积雪物候数据集,该数据集包含积雪日数、积雪初日、积雪终日三个数据子集。利用地面气象台站实测雪深数据对产品进行验证,验证结果表明:积雪日数、积雪初日和积雪终日验证相关系数R2分别为0.80,0.76和0.94,均方根误差RMSE分别为22.78天,17.87天和16.39天,平均绝对误差MAE分别为13.26天,7.51天和7.76天,精度可靠。本数据集可服务于中国积雪时空变化分析,为气候变化,水文水资源,生态环境,人文经济等科学研究、工程建设以及社会服务提供基础数据资料。
