Model inputs for prediction of runoff and soil erosion commonly require precipitation intensity information. Intensity is often estimated if precipitation data with high temporal resolution are unavailable. However, when intensity is time-averaged for fixed measurement intervals, estimates become increasingly underestimated with longer intervals due to the assumption that event durations begin and end at specified measurement intervals. In this study, adjustment factors were determined for downscaling the temporal resolution of intensity values derived from selected resolutions within the range of 10 to 1,440 min for Köppen-Geiger climate regions in the United States. In this case, monthly mean maximum 30 min intensity (MX.5P) was downscaled, which is a parameter used to generate stochastic meteorological inputs for models that include the Rangeland Hydrology and Erosion Model (RHEM) and the Water Erosion Prediction Project model (WEPP). The adjustment factors were given by regressions of reference MX.5P values derived from data with 5 min resolution against MX.5P values derived from data with lower temporal resolutions (≥10 min). In addition to using a slope coefficient for intensity in the regression equation, permutations of the equation included use of an elevation coefficient and constants, resulting in four total permutations. For the 143 stations and 17 climate regions analyzed, the four regression equations had roughly equal performance, and all gave statistically significant results. Regressions for adjusting hourly data using only an intensity coefficient in the equation had standard error of the estimate ranging from 1.01 to 2.96 mm h–1 with an average of 2.04 mm h–1. When downscaling daily values, the error range was 2.50 to 10.20 mm h–1 with an average of 5.63 mm h–1. Average time-to-peak intensity probability distributions for each climate region were also determined. Finally, a stochastic weather generator, CLIGEN, was used to test the effectiveness of applying the climate-based factors as an alternative to using subhourly data.

中文翻译：

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美国柯本气候区降水强度因子的时间降尺度

用于预测径流和土壤侵蚀的模型输入通常需要降水强度信息。如果无法获得具有高时间分辨率的降水数据，则通常会估计强度。然而，当强度对固定测量间隔进行时间平均时，由于假设事件持续时间在指定的测量间隔开始和结束，因此随着间隔的延长，估计值会越来越被低估。在这项研究中，确定了调整因子，以降低美国 Köppen-Geiger 气候区在 10 到 1,440 分钟范围内选定分辨率得出的强度值的时间分辨率。在这种情况下，每月平均最大 30 分钟强度 (MX.5P) 被缩小，这是用于为包括牧场水文和侵蚀模型 (RHEM) 和水侵蚀预测项目模型 (WEPP) 在内的模型生成随机气象输入的参数。调整因子由来自具有 5 分钟分辨率的数据的参考 MX.5P 值与来自具有较低时间分辨率（≥10 分钟）的数据的 MX.5P 值的回归给出。除了在回归方程中使用强度的斜率系数外，方程的排列还包括使用高程系数和常数，导致总共四个排列。对于分析的 143 个站点和 17 个气候区，四个回归方程的性能大致相同，并且都给出了统计显着的结果。仅使用方程中的强度系数调整每小时数据的回归具有估计值的标准误差，范围从 1.01 到 2.96 mm h-1，平均值为 2.04 mm h-1。当按比例缩小每日值时，误差范围为 2.50 到 10.20 mm h-1，平均为 5.63 mm h-1。还确定了每个气候区的平均到达峰值强度概率分布。最后，使用随机天气生成器 CLIGEN 来测试应用基于气候的因素作为使用亚小时数据的替代方法的有效性。还确定了每个气候区的平均到达峰值强度概率分布。最后，使用随机天气生成器 CLIGEN 来测试应用基于气候的因素作为使用亚小时数据的替代方法的有效性。还确定了每个气候区的平均到达峰值强度概率分布。最后，使用随机天气生成器 CLIGEN 来测试应用基于气候的因素作为使用亚小时数据的替代方法的有效性。