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Temporal- and spatial-scale and positional effects on rain erosivity derived from point-scale and contiguous rain data

机译:时间和空间和空间级和位置效应来自点尺度和连续的雨量数据的雨水窒息性

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Up until now, erosivity required for soil loss predictions has been mainly estimated from rain gauge data at point scale and then spatially interpolated to erosivity maps. Contiguous rain data from weather radar measurements, satellites, cellular communication networks and other sources are now available, but they differ in measurement method and temporal and spatial scale from data at point scale. We determined how the intensity threshold of erosive rains has to be modified and which scaling factors have to be applied to account for the differences in method and scales. Furthermore, a positional effect quantifies heterogeneity of erosivity within 1 km(2), which presently is the highest resolution of freely available gauge-adjusted radar rain data. These effects were analysed using several large data sets with a total of approximately 2 x 10(6) erosive events (e.g. records of 115 rain gauges for 16 years distributed across Germany and radar rain data for the same locations and events). With decreasing temporal resolution, peak intensities decreased and the intensity threshold was met less often. This became especially pronounced when time increments became larger than 30 min. With decreasing spatial resolution, intensity peaks were also reduced because additionally large areas without erosive rain were included within one pixel. This was due to the steep spatial gradients in erosivity. Erosivity of single events could be zero or more than twice the mean annual sum within a distance of less than 1 km. We conclude that the resulting large positional effect requires use of contiguous rain data, even over distances of less than 1 km, but at the same time contiguously measured radar data cannot be resolved to point scale. The temporal scale is easier to consider, but with time increments larger than 30 min the loss of information increases considerably. We provide functions to account for temporal scale (from 1 to 120 min) and spatial scale (from rain gauge to pixels of
机译:到目前为止,在点尺度下,土壤损失预测所需的侵蚀性主要是从雨量计数据估算,然后在空间内插入腐蚀性图。来自天气雷达测量,卫星,蜂窝通信网络和其他来源的典型雨量数据现在可用,但它们在点比例下的数据和空间和空间比例不同。我们确定必须如何修改腐蚀雨的强度阈值,并且必须应用缩放因素来解释方法和尺度的差异。此外,位置效果量化了1km(2)以内的腐蚀性的异质性,目前是可自由可用的仪表调整的雷达雨量数据的最高分辨率。使用几种大数据集进行分析这些效果,总共约为2×10(6)次腐蚀事件(例如,在德国的德国和雷达雨数据中分布了16年的115雨仪表的记录)。随着时间分辨率的降低,峰强度降低,并且频繁地达到强度阈值。当时间增量大于30分钟时,这变得特别明显。随着空间分辨率的降低,强度峰也减少,因为在一个像素中包括额外大区域而没有腐蚀雨。这是由于侵蚀性的陡峭空间梯度。单一事件的侵蚀性可能为零或超过距离不到1公里的平均年度的两倍。我们得出结论,由此产生的大型位置效应需要使用连续的雨量数据,即使在不到1公里的距离上,而且同时,连续测量的雷达数据无法解析为点比例。时间尺度更容易考虑,但随着时间的增量大于30分钟,信息的丢失很大增加。我们提供函数以考虑时间尺度(从1到120分钟)和空间刻度(从雨量尺到像素)

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