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Characterizing Times Between Storms in Mountainous Areas

机译:表征山区风暴之间的时间

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Storm simulation models are sometimes used to provide input to watershed models where there are no precipitation data. However, a basic understanding of the seasonal and spatial characteristics of parameters that comprise the inputs to the models is needed, and then methods for estimating the parameters must be developed. This study is an investigation of the behavior and estimation of two parameters for storm-occurrence modeling, average time between storms (ATBS) and minimum time between storms (MDPD), and how they vary with average annual precipitation (P ann ), average monthly precipitation (P mo ), and elevation (E) in mountainous terrain where snow is a major component of precipitation. Data used came from the dual-rain-gauge network in the USDA-ARS Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho. Median ATBS and MDPD data show consistent trends over the RCEW with narrow variability. The exponential distribution adequately models times between storms in snowfall-predominant mountainous areas except for very long dry periods. The power equation form generally fitted the data best for regressions where there was a statistically significant regression compared with a linear equation. For four of the regressions found in this article (P mo vs. E, ATBS vs. P mo , ATBS vs. E, and MDPD vs. ATBS), parameters were correlated, suggesting parameter mapping may be useful in their estimation. ATBS can be estimated by using the power equation for ATBS vs. P mo and ATBS vs. E (both inversely correlated). MDPD vs. ATBS correlations were positive, showing that as the average time between storms increases, so does the minimum time between storms. Guidance is given for estimating ATBS and MDPD for a specific location. Magnitudes of often-used fixed values of MDPD (e.g., 6 h) are too small to be representative of times between storms, and are not representative of the seasonal variability of times between storms. The results in this article for characterizing ATBS and MDPD in the mountainous 239-km 2 RCEW area follow closely those found for the 225,000-km 2 Colorado plains area in another study. Thus, equation forms identified in this article can suggest those that might be expected in general and may be used as guidance for estimating ATBS and MDPD in ungauged areas, including areas with snowfall as a major component of precipitation.
机译:有时使用风暴模拟模型为没有降水数据的流域模型提供输入。但是,需要对构成模型输入的参数的季节和空间特征有基本的了解,然后必须开发用于估计参数的方法。这项研究是对风暴发生模型的两个参数的行为和估计进行调查,两个参数是风暴平均间隔时间(ATBS)和最小风暴间隔时间(MDPD),以及它们如何随年平均降水量(P ann)变化。降雨(P mo)和海拔(E)的山区,雪是降水的主要组成部分。所使用的数据来自爱达荷州西南部USDA-ARS雷诺兹河实验流域(RCEW)中的双雨量表网络。 ATBS和MDPD数据的中位数显示了RCEW上的一致趋势,且变化范围狭窄。指数分布可以很好地模拟以降雪为主的山区风暴之间的时间,除了非常长的干旱时期。幂方程形式通常最适合回归的数据,与线性方程相比,回归具有统计学上的显着性。对于本文中发现的四个回归(Pmo与E,ATBS与Pmo,ATBS与E以及MDPD与ATBS),参数是相关的,这表明参数映射可能在其估计中很有用。可以通过使用ATBS对Pmo和ATBS对E的功率方程(两者呈反相关)来估计ATBS。 MDPD与ATBS的相关性为正,表明随着风暴之间的平均时间增加,风暴之间的最小时间也增加。给出了用于估计特定位置的ATBS和MDPD的指南。经常使用的MDPD固定值(例如6小时)的幅度太小,无法代表暴风雨之间的时间,而不能代表暴风雨之间的时间的季节性变化。本文中表征239 km 2山区RCEW地区ATBS和MDPD的结果紧随另一项研究中225,000 km 2科罗拉多平原地区的结果。因此,本文确定的等式形式可以建议通常可以预期的等式形式,并且可以用作估算无泄漏地区(包括降雪是降水主要成分的地区)的ATBS和MDPD的指南。

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