柴达木盆地既是响应青藏高原气候暖湿化的敏感区,又是生态环境脆弱带,评估该区域降水时空格局对水资源合理利用以及生态环境治理至关重要,然而盆地内部气象台站稀少且分布不均,为区域降水插值带来挑战。本文使用专业气象插值软件ANUSPLI...柴达木盆地既是响应青藏高原气候暖湿化的敏感区,又是生态环境脆弱带,评估该区域降水时空格局对水资源合理利用以及生态环境治理至关重要,然而盆地内部气象台站稀少且分布不均,为区域降水插值带来挑战。本文使用专业气象插值软件ANUSPLIN(Australian National University Spline)模型进行插值,以柴达木盆地及其周边气象台站2019年降水数据为基础,参与插值的气象台站数和9种薄盘光滑样条函数(独立变量、协变量和样条次数多种组合)为第三变量,筛选最优插值台站数和最优模型,并分析该区域2000-2019年降水时空格局。结果表明:(1)选择盆地内部及其周边共120个气象台站,三变量局部薄盘光滑样条函数(TVPTPS4)进行区域尺度降水插值精度最高,均方根误差(RTGCV)、期望真实均方误差(RTMSE)和信噪比(SNR)均达到最小值,分别小于0.6 mm、0.3 mm和0.25。(2)柴达木盆地降水量具有地域分布差异和季节性特征。年、季降水量东丰西少,具有明显的经向地带性特征;四季中夏季降水量最大,占全年总量的62.13%。(3)2000-2019年,柴达木盆地年均、季节平均降水量均呈上升趋势,其中夏季降水量显著增加,最大增速达5.85 mm·a^(-1)(p<0.05),显著增加区域约占盆地总面积的42.36%。本研究结果证明AUNSPLIN模型结果能更清晰地表达出柴达木盆地降水的分布状况,对于该区域水资源优化配置和管理等具有重要的理论和现实意义。展开更多
To achieve refined temperature grid data with high accuracy and high spatial resolution,hourly temperature grid dataset with spatial resolution of 1 km in Anhui Province from January to December in 2016 was establishe...To achieve refined temperature grid data with high accuracy and high spatial resolution,hourly temperature grid dataset with spatial resolution of 1 km in Anhui Province from January to December in 2016 was established using the ANUSPLIN thin plate spline algorithm,which meets the needs of climate change research and meteorological disaster risk assessment. And the interpolation error was analyzed. The results show that the interpolated values of hourly temperature by ANUSPLIN are close to the observed values in 2016. The error is generally below 1. 5 ℃,and the root mean square error is 0. 937 6 ℃. On monthly scale,the interpolated values of hourly temperature by ANUSPLIN are also close to the observed values.In October,November,June and May,the interpolation accuracy is the highest,and the proportion of absolute error of hourly temperature lower than 2 ℃ is up to 99%,97. 4%,98. 1% and 97. 4% respectively. In February,March,August and December,the interpolation accuracy is the lowest,and the proportion of absolute error higher than 2 ℃ is 8. 1%,5. 3%,4. 1% and 4. 2% respectively. Due to the effect of complex topography in Anhui,the interpolation accuracy is the lowest in the mountainous areas of southern and western Anhui,and the interpolation error in these regions even exceeds 1. 5 ℃ annually and 1. 8 ℃ monthly.展开更多
为探究成渝地区双城经济圈NPP时空变化及与气候的关系,基于MOD17A3产品NPP数据,采用一元线性回归模型模拟2001—2020年植被NPP演变趋势,分析植被NPP变化特征,结合ANUSPLIN插值的气象数据,采用相关性分析法定量分析气候变化对研究区植被...为探究成渝地区双城经济圈NPP时空变化及与气候的关系,基于MOD17A3产品NPP数据,采用一元线性回归模型模拟2001—2020年植被NPP演变趋势,分析植被NPP变化特征,结合ANUSPLIN插值的气象数据,采用相关性分析法定量分析气候变化对研究区植被NPP变化的影响。研究表明:(1)研究区内植被NPP整体呈现缓慢增长的趋势,增长率为7.53 g C·m^(-2)·a^(-1),同时植被NPP均值分布呈现四周高中间低的空间格局。(2)研究区内气候因子对植被NPP变化的影响存在空间异质性,在眉山市、乐山市、雅安市以及重庆市黔江区、彭水县部分地区气温与植被NPP负相关关系明显,其呈正相关关系的区域广泛分布在成渝城市群的中部和东部;降水与植被NPP呈正相关关系的区域面积占比达到92.46%。(3)研究区主要受非气候因子影响,面积占比高达86.87%,说明人类活动对植被NPP变化的影响愈来愈烈,研究人为影响应是成渝城市群生态修复的重点。展开更多
文摘柴达木盆地既是响应青藏高原气候暖湿化的敏感区,又是生态环境脆弱带,评估该区域降水时空格局对水资源合理利用以及生态环境治理至关重要,然而盆地内部气象台站稀少且分布不均,为区域降水插值带来挑战。本文使用专业气象插值软件ANUSPLIN(Australian National University Spline)模型进行插值,以柴达木盆地及其周边气象台站2019年降水数据为基础,参与插值的气象台站数和9种薄盘光滑样条函数(独立变量、协变量和样条次数多种组合)为第三变量,筛选最优插值台站数和最优模型,并分析该区域2000-2019年降水时空格局。结果表明:(1)选择盆地内部及其周边共120个气象台站,三变量局部薄盘光滑样条函数(TVPTPS4)进行区域尺度降水插值精度最高,均方根误差(RTGCV)、期望真实均方误差(RTMSE)和信噪比(SNR)均达到最小值,分别小于0.6 mm、0.3 mm和0.25。(2)柴达木盆地降水量具有地域分布差异和季节性特征。年、季降水量东丰西少,具有明显的经向地带性特征;四季中夏季降水量最大,占全年总量的62.13%。(3)2000-2019年,柴达木盆地年均、季节平均降水量均呈上升趋势,其中夏季降水量显著增加,最大增速达5.85 mm·a^(-1)(p<0.05),显著增加区域约占盆地总面积的42.36%。本研究结果证明AUNSPLIN模型结果能更清晰地表达出柴达木盆地降水的分布状况,对于该区域水资源优化配置和管理等具有重要的理论和现实意义。
基金Support by New Technology Integration Project of Anhui Meteorological Bureau(AHXJ201704)
文摘To achieve refined temperature grid data with high accuracy and high spatial resolution,hourly temperature grid dataset with spatial resolution of 1 km in Anhui Province from January to December in 2016 was established using the ANUSPLIN thin plate spline algorithm,which meets the needs of climate change research and meteorological disaster risk assessment. And the interpolation error was analyzed. The results show that the interpolated values of hourly temperature by ANUSPLIN are close to the observed values in 2016. The error is generally below 1. 5 ℃,and the root mean square error is 0. 937 6 ℃. On monthly scale,the interpolated values of hourly temperature by ANUSPLIN are also close to the observed values.In October,November,June and May,the interpolation accuracy is the highest,and the proportion of absolute error of hourly temperature lower than 2 ℃ is up to 99%,97. 4%,98. 1% and 97. 4% respectively. In February,March,August and December,the interpolation accuracy is the lowest,and the proportion of absolute error higher than 2 ℃ is 8. 1%,5. 3%,4. 1% and 4. 2% respectively. Due to the effect of complex topography in Anhui,the interpolation accuracy is the lowest in the mountainous areas of southern and western Anhui,and the interpolation error in these regions even exceeds 1. 5 ℃ annually and 1. 8 ℃ monthly.
文摘为探究成渝地区双城经济圈NPP时空变化及与气候的关系,基于MOD17A3产品NPP数据,采用一元线性回归模型模拟2001—2020年植被NPP演变趋势,分析植被NPP变化特征,结合ANUSPLIN插值的气象数据,采用相关性分析法定量分析气候变化对研究区植被NPP变化的影响。研究表明:(1)研究区内植被NPP整体呈现缓慢增长的趋势,增长率为7.53 g C·m^(-2)·a^(-1),同时植被NPP均值分布呈现四周高中间低的空间格局。(2)研究区内气候因子对植被NPP变化的影响存在空间异质性,在眉山市、乐山市、雅安市以及重庆市黔江区、彭水县部分地区气温与植被NPP负相关关系明显,其呈正相关关系的区域广泛分布在成渝城市群的中部和东部;降水与植被NPP呈正相关关系的区域面积占比达到92.46%。(3)研究区主要受非气候因子影响,面积占比高达86.87%,说明人类活动对植被NPP变化的影响愈来愈烈,研究人为影响应是成渝城市群生态修复的重点。
