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首页> 外文期刊>Hydrology and Earth System Sciences >Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types
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Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types

机译：高山区降水的空间分析：探索具有多尺度地形预测因子和环流类型的方法

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Statistical models of the relationship between precipitation and topography are key elements for the spatial interpolation of rain-gauge measurements in high-mountain regions. This study investigates several extensions of the classical precipitation–height model in a direct comparison and within two popular interpolation frameworks, namely linear regression and kriging with external drift. The models studied include predictors of topographic height and slope at several spatial scales, a stratification by types of a circulation classification, and a predictor for wind-aligned topographic gradients. The benefit of the modeling components is investigated for the interpolation of seasonal mean and daily precipitation using leave-one-out cross-validation. The study domain is a north–south cross section of the European Alps (154 km × 187 km) that is inclined towards dense rain-gauge measurements (approx. 440 stations, 1971–2008). brbr The significance of the topographic predictors was found to strongly depend on the interpolation framework. In linear regression, predictors of slope and at multiple scales reduce interpolation errors substantially. But with as many as nine predictors, the resulting interpolation still poorly replicates the across-ridge variation of climatological mean precipitation. Kriging with external drift (KED) leads to much smaller interpolation errors than linear regression, but this is achieved with a single predictor (local topographic height), whereas the incorporation of more extended predictor sets brings only marginal further improvement. Furthermore, the stratification by circulation types and the wind-aligned gradient predictor do not improve over the single predictor KED model. As for daily precipitation, interpolation accuracy improves considerably with KED and the use of a single predictor field (the distribution of seasonal mean precipitation) as compared to ordinary kriging (i.e., without any predictor). Nonetheless, information from circulation types did not improve interpolation accuracy. brbr Our results confirm that the consideration of topography effects is important for spatial interpolation of precipitation in high-mountain regions. But a single predictor may be sufficient and taking appropriate account of the spatial autocorrelation (by kriging) can be more effective than the development of elaborate predictor sets within a regression model. Our results also question a popular practice of using linear regression for predictor selection in spatial interpolation; however they support the common practice of using a climatological mean field as a background in the interpolation of daily precipitation.
机译：降水与地形之间关系的统计模型是高山区地区雨量计测量值空间插值的关键要素。本研究在直接比较和两个流行的插值框架内，即线性回归和外部漂移克里金法中，研究了经典降水高度模型的几种扩展。研究的模型包括在几个空间尺度上的地形高度和坡度的预测因子，按环流分类类型分类的分层以及风向地形梯度的预测因子。使用留一法交叉验证对季节平均值和每日降水量插值法研究了建模组件的好处。研究范围是欧洲阿尔卑斯山的南北横断面（154 km×187 km），倾向于密集的雨量计测量（1971-2008年约440个站点​​）。 发现地形预测变量的重要性在很大程度上取决于插值框架。在线性回归中，斜率和多个尺度的预测变量会显着降低插值误差。但是，由于有多达9个预测因子，因此所得的插值仍然不能很好地复制气候平均降水的跨岭变化。外部漂移的克里格（KED）导致的插值误差比线性回归小得多，但这是通过单个预测变量（局部地形高度）实现的，而合并更多扩展的预测变量集只会带来很小的进一步改善。此外，与单个预测变量KED模型相比，按循环类型和风向梯度预测变量进行的分层没有改善。至于日降水量，与普通克里格法（即没有任何预测变量）相比，使用KED和使用单个预测变量字段（季节性平均降水量分布）时，插值精度大大提高。但是，来自循环类型的信息并不能提高内插精度。 我们的结果证实，考虑地形因素对高山区降水的空间插值很重要。但是，单个预测变量可能就足够了，并且适当考虑空间自相关（通过克里金法）可能比在回归模型中开发详尽的预测变量集更为有效。我们的结果也质疑在空间插值中使用线性回归进行预测变量选择的流行做法。但是，他们支持在每天降水量插值中使用气候平均场作为背景的常规做法。

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