西北内陆河地区地形地貌复杂,气象台站较少,为提高该地区降水量空间化的精确度,支撑区域水文模拟及水资源管理,以西北内陆河黑河流域为研究区,利用19个气象台站与26个水文站点数据,基于ANUSPLIN软件构建局部薄盘光滑样条插值模型,分析...西北内陆河地区地形地貌复杂,气象台站较少,为提高该地区降水量空间化的精确度,支撑区域水文模拟及水资源管理,以西北内陆河黑河流域为研究区,利用19个气象台站与26个水文站点数据,基于ANUSPLIN软件构建局部薄盘光滑样条插值模型,分析模型参数、站点数量与数字高程模型(Digital Elevation Model,DEM)分辨率对降水量空间插值精度的影响。结果表明:1)独立变量为经度与纬度,协变量为海拔,样条次数为3,变量转换方式为平方根时,V2C1S3_RT模型的插值精度最优,其广义交叉验证的平方根(Square Roots of Generalized Cross Validation,RTGCV)和期望真实均方根误差(Square Roots of Mean Square Error,RTMSE)分别为6.10和4.82 mm;2)站点数量影响插值精度,当站点数增加至40个,RTGCV和RTMSE均达到最小值,进一步增加站点数对精度提升有限;3)不同分辨率DEM对降水量插值结果影响不大;4)2019年黑河流域平均降水量为211.39 mm,呈现西南向东北递减趋势,上游西南地区及下游边界地区降水量标准误差较相邻地区大,主要原因是该区域站点数量较少。展开更多
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
文摘西北内陆河地区地形地貌复杂,气象台站较少,为提高该地区降水量空间化的精确度,支撑区域水文模拟及水资源管理,以西北内陆河黑河流域为研究区,利用19个气象台站与26个水文站点数据,基于ANUSPLIN软件构建局部薄盘光滑样条插值模型,分析模型参数、站点数量与数字高程模型(Digital Elevation Model,DEM)分辨率对降水量空间插值精度的影响。结果表明:1)独立变量为经度与纬度,协变量为海拔,样条次数为3,变量转换方式为平方根时,V2C1S3_RT模型的插值精度最优,其广义交叉验证的平方根(Square Roots of Generalized Cross Validation,RTGCV)和期望真实均方根误差(Square Roots of Mean Square Error,RTMSE)分别为6.10和4.82 mm;2)站点数量影响插值精度,当站点数增加至40个,RTGCV和RTMSE均达到最小值,进一步增加站点数对精度提升有限;3)不同分辨率DEM对降水量插值结果影响不大;4)2019年黑河流域平均降水量为211.39 mm,呈现西南向东北递减趋势,上游西南地区及下游边界地区降水量标准误差较相邻地区大,主要原因是该区域站点数量较少。
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.
