Organization | • | Illinois State Water Survey | [X] |
| | 1: | | Title: | | | | | Volume/Number: | 2002 | | | | Issuing Agency: | | | | | Description: | Soil erosion and nonpoint source pollution runoff rates are estimated using output from the Revised Universal Soil Loss Equation (RUSLE). The underlying influence of climate on surface transport processes as represented in the RUSLE is carried within one constant, the R-factor. It has been assumed that the R-factor is temporally stationary; that is, it does not change with time. The purpose of this study was to process climate information from the most recent decades to update the R-factor, to examine the nature of precipitation variation and change and their impacts on the R-factor over space and time, and, specifically, to test the hypothesis that storm erosivity and the R-factor are temporally stationary. This was addressed by developing a database of precipitation data and related information needed to calculate single-storm erosivity and cumulative R-factor for each half-month of the year and for the total year. In addition the 10-year, single-storm erosive index for each station is provided. The R-factor, a nonlinear, cumulative measure of the erosive energy contained in storm precipitation, was calculated directly from 15-minute rainfall data. However, because of some undocumented quality difficulties with the 15-minute data, single-storm erosivity index statistics for accumulation into R-factors were calculated from more reliable daily data through the use of a power law transfer function. These new R-factors were tested for spatial covariation, which was found to be minimal in even terrain, and related to the limited amount of station R-factor data from past studies. Comparison with past R-factor studies indicated strongly that the methodologies used adequately duplicated old R-factors based on data from the 1930s to the 1950s. General increases observed in R-factors in this study were related to increasing amounts of precipitation and storms with rainfall greater than 12.7 millimeters, especially in the western United States. Mean seasonal patterns of storm precipitation total, duration, intensity, 30-minute and 15-minute maximum intensity, kinetic energy, erosivity, and the numbers of storms also were mapped for the conterminous United States. These analyses showed distinct patterns of precipitation change with seasons and identified regions of strong gradients where climate change first may be noticed. Trend analyses of storm precipitation variables over the 1971-1999 period indicated the lack of temporal stationarity of storm characteristics. Storm duration changes were especially an important cause of the observed changes in storm precipitation totals. However, storm trends in 30-minute maximum intensity seemed to be more important in changing the patterns of storm erosivity. Examination of storm characteristic response to interannual and interdecadal variations also indicated that storm characteristics were responding at these time scales to large-scale climate system forcings. In the winter season, atmospheric teleconnections such as the Pacific/North American Pattern and the North Atlantic Oscillation were shown to influence not only storm track positions and the number of storms at a location, but also the characteristics of individual storms. El Nio and La Nia events of the Southern Oscillation (ENSO events) had distinctive impacts on storm variables in every season of the year. Even the Pacific Decadal Oscillation showed a clear effect on storm characteristics, especially in the western United States. The results of R-factors derived from modern data compared to previous R-factors combined with storm characteristic trend and variability studies indicate conclusively that storm precipitation characteristics change sufficiently over time to warrant an evaluation of the necessity to recalculate R-factors on a regular basis. | | | | Date Created: | 9 24 2004 | | | | Agency ID: | CR-2002-08 | | | | ISL ID: | 000000000878 Original UID: 999999994349 FIRST WORD: Spatial | |
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