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首页> 外文期刊>Acta Geophysica >Analysis of deterministic and geostatistical interpolation techniques for mapping meteorological variables at large watershed scales
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Analysis of deterministic and geostatistical interpolation techniques for mapping meteorological variables at large watershed scales

机译：大流域尺度上气象变量测绘的确定性和地统计插值技术分析

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摘要

The widely scattered pattern of meteorological stations in large watersheds and remote locations, along with a need to estimate meteorological data for point sites or areas where little or no data have been recorded, has encouraged the development and implementation of spatial interpolation techniques. The various interpolation techniques featured in GIS software allow for the extraction of this new information from spatially distinct point data. Since no one interpolation method can be accurate in all regions, each method must be evaluated prior to each geographically distinct application. Many methods have been used for interpolating minimum temperature ((T_{ min })), maximum temperature ((T_{ max })) and precipitation data; however, only a few methods have been used in the Zayandeh-Rud River basin, Iran, and no comparison of methods has ever been carried out in the area. The accuracies of six spatial interpolation methods [Inverse Distance Weighting, Natural Neighbor (NN), Regularized Spline, Tension Spline, Ordinary Kriging, Universal Kriging] were compared in this study simultaneously, and the best method for mapping monthly precipitation and temperature extremes was determined in a large semi-arid watershed with high temperature and rainfall variation. A cross-validation technique and long-term (1970a??2014) average monthly (T_{ min }), (T_{ max }) and precipitation data from meteorological stations within the basin were used to identify the best interpolation method for each variable dataset. For (T_{ min }), Kriging (Gaussian) proved to be the most accurate interpolation method (MAEa??=a??1.827???°C), whereas, for (T_{ max }) and precipitation the NN method performed best (MAEa??=a??1.178???°C and 0.5241??mm, respectively). Accordingly, these variable-optimized interpolation methods were used to define spatial patterns of newly generated climatic maps.
机译：在大流域和偏远地区，气象站的分布广泛，并且需要估计很少或根本没有记录数据的点站点或地区的气象数据，这鼓励了空间插值技术的发展和实施。 GIS软件中的各种插值技术可以从空间上不同的点数据中提取新信息。由于没有一种插值方法在所有地区都可能是准确的，因此必须在每种地理上不同的应用程序之前评估每种方法。已经使用了许多方法来内插最低温度（（T_ { min} ）），最高温度（（T_ { max} ））和降水量数据;但是，伊朗的Zayandeh-Rud河流域仅使用了少数方法，该地区从未进行过方法的比较。本研究同时比较了六种空间插值方法的准确性[反距离加权，自然邻域（NN），正则样条，张力样条，普通克里格法，通用克里格法]，并确定了绘制月降水和极端温度的最佳方法在大型半干旱流域，高温和降雨变化很大。利用交叉验证技术和流域内气象台站的长期平均每月（T_ { min} ），（T_ { max} ）和降水数据来识别每个变量数据集的最佳插值方法。对于（T_ { min} ），克里格（高斯）被证明是最精确的插值方法（MAEa ?? = a ?? 1.827 ???°C），而对于（T_ { max} 沉淀法和NN法的沉淀效果最好（分别为MAEaδ＝ aδ1.178℃和0.5241δmm）。因此，这些变量优化的插值方法用于定义新生成的气候图的空间模式。

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