杭州市复杂地形下月平均气温分布式模拟
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摘要
杭州位于中国东南沿海北部,地形复杂多样,本文利用复杂地形下月平均气温分布式模拟的方法对杭州市2010-2014年的月平均气温进行了空间化模拟,生成了100 m×100 m分辨率的月平均气温的空间分布图。模拟结果通过与使用较多的反距离加权法和普通克里格法进行了对比。结果表明:复杂地形下月平均气温分布式模拟在杭州的应用中,模型拟合的平均绝对误差在0. 17℃～0. 35℃之间,平均相对误差在于1%～6%之间,拟合结果略好于反距离加权法和普通克里格法。坡度、坡向和海拔高度对月平均气温的影响较大,复杂地形下月平均气温的空间分布具有明显的局地地域分布特征,模型拟合结果能很好的体现出杭州市月平均气温的空间分布信息。考虑了地形,太阳辐射等因素的杭州市复杂地形下月平均气温分布式模拟的结果包含了各种地形信息,具有更好的适用性。
Hangzhou is located in the southeastern coast of China north,complex and diverse terrain,In this paper,the spatial simulation of monthly mean temperature in Hangzhou during the past 2010-2014 years has been carried out by distributed simulation of monthly mean temperature over complex terrains,the spatial distribution map of monthly mean temperature of 100 m × 100 m resolution was generated. The simulation results are compared with those using the inverse distance weighting method and the Ordinary Kriging method. The results show that Application of Distributed Simulation of Monthly Mean Temperature in Complex Terrains in Hangzhou,the mean absolute error of the model fitting is between 0. 17 and-0. 35 degrees centigrade,the average relative error is between 1% ～ 6%,the fitting results are slightly betterthan the inverse distance weighted method and ordinary kriging method. The effect of slope,aspect and altitude on monthly average temperature is significant, the spatial distribution of monthly mean temperature in complex terrain has obvious regional distribution characteristics,the results of model fitting can well reflect the spatial distribution of monthly mean temperature in Hangzhou,Considering the terrain,solar radiation and other factors,the results of the distributed simulation of the monthly mean temperature of complex terrain in Hangzhou include a variety of terrain information,which has better applicability.
Hangzhou is located in the southeastern coast of China north,complex and diverse terrain,In this paper,the spatial simulation of monthly mean temperature in Hangzhou during the past 2010-2014 years has been carried out by distributed simulation of monthly mean temperature over complex terrains,the spatial distribution map of monthly mean temperature of 100 m × 100 m resolution was generated. The simulation results are compared with those using the inverse distance weighting method and the Ordinary Kriging method. The results show that Application of Distributed Simulation of Monthly Mean Temperature in Complex Terrains in Hangzhou,the mean absolute error of the model fitting is between 0. 17 and-0. 35 degrees centigrade,the average relative error is between 1% ～ 6%,the fitting results are slightly betterthan the inverse distance weighted method and ordinary kriging method. The effect of slope,aspect and altitude on monthly average temperature is significant, the spatial distribution of monthly mean temperature in complex terrain has obvious regional distribution characteristics,the results of model fitting can well reflect the spatial distribution of monthly mean temperature in Hangzhou,Considering the terrain,solar radiation and other factors,the results of the distributed simulation of the monthly mean temperature of complex terrain in Hangzhou include a variety of terrain information,which has better applicability.
引文
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[15]邱新法,仇月萍,曾燕.重庆山地月平均气温空间分布模拟研究[J].地球科学进展,2009,24(6):621-628.
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[17]游松财,李军.海拔误差影响气温空间插值误差的研究[J].自然资源学报,2005,20(1):140-144.
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[19]于飞,郑小波,谷晓平,等.复杂山地环境下气候要素空间插值精度比较研究[J].贵州气象,2008,32(3):3-6.
[20] Mcbratney A B,Webster R. Choosing functions for semivariograms of soil properties and fitting them to sampling estimates[J]. European Journal of Soil Science,1986,37(4):617-639.
[2]李军,黄敬峰.山区气温空间分布推算方法评述[J].山地学报,2004,22(1):126-132.
[3]袁爱民,王建源.气候要素栅格化技术方法研究[J].气象,2006,32(6):111-115.
[4] Bartier P M,Keller C P. Multivariate interpolation to incorporate thematic surface data using inverse distance weighting(IDW)[J]. Computers&Geosciences,1996,22(7):795-799.
[5] Price D T,Mckenney D W,Nalder I A,et al. A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data[J]. Agricultural&Forest Meteorology,2000,101(2–3):81-94.
[6]关宏强,蔡福,王阳,等.短时间序列气温要素空间插值方法精度的比较研究[J].气象与环境学报,2007,23(5):13-16.
[7]阎洪.薄板光顺样条插值与中国气候空间模拟[J].地理科学,2004,24(2):163-169.
[8]张洪亮,倪绍祥,邓自旺,等.基于DEM的山区气温空间模拟方法[J].山地学报,2002,20(3):360-364.
[9]杨昕,汤国安,王春,等.基于DEM的山区气温地形修正模型——以陕西省耀县为例[J]. Journal of Geographical Sciences(地理学报(英文版),2007,27(4):525-530.
[10] Running SW. Thornton PE. Generating daily surfaces of temperature and precipitation over complex topography[M]//Good child M F,Sryaert L T,Parks B O. GIS and Environmental Model:Progress and Research Issues. Collins:GIS World Inc,1996.
[11] Bellasio R,Maffeis G,Scire J S,et al. Algorithms to Account for Topographic Shading Effects and Surface Temperature Dependence on Terrain Elevation in Diagnostic Meteorological Models[J]. Boundary-Layer Meteorology,2005,114(3):595-614.
[12]曾燕,邱新法,刘绍民.起伏地形下天文辐射分布式估算模型[J].地球物理学报,2005,48(5):60-65.
[13]曾燕,邱新法,刘昌明,等.起伏地形下黄河流域太阳直接辐射分布式模式[J].地理学报,2005,60(4):680-688.
[14]曾燕,邱新法,何永健,等.起伏地形下黄河流域太阳散射辐射分布式模拟研究[J].地球物理学报,2008,51(4):991-998.
[15]邱新法,仇月萍,曾燕.重庆山地月平均气温空间分布模拟研究[J].地球科学进展,2009,24(6):621-628.
[16]袁淑杰,谷晓平,缪启龙,等.基于DEM的复杂地形下平均气温分布式模拟研究——以贵州高原为例[J].自然资源学报,2010,25(5):859-867.
[17]游松财,李军.海拔误差影响气温空间插值误差的研究[J].自然资源学报,2005,20(1):140-144.
[18]李军龙,张剑,张丛,等.气象要素空间插值方法的比较分析[J].草业科学,2006,23(8):6-11.
[19]于飞,郑小波,谷晓平,等.复杂山地环境下气候要素空间插值精度比较研究[J].贵州气象,2008,32(3):3-6.
[20] Mcbratney A B,Webster R. Choosing functions for semivariograms of soil properties and fitting them to sampling estimates[J]. European Journal of Soil Science,1986,37(4):617-639.