DOI: 10.5194/hess-20-4359-2016
Scopus记录号: 2-s2.0-84993949848
论文题名: Regionalization of monthly rainfall erosivity patternsin Switzerland
作者: Schmidt S ; , Alewell C ; , Panagos P ; , Meusburger K
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 10 起始页码: 4359
结束页码: 4373
语种: 英语
Scopus关键词: Erosion
; Interpolation
; Precipitation (meteorology)
; Rain
; Regression analysis
; Risk assessment
; Sediment transport
; Statistical methods
; Combination products
; Explanatory variables
; Intra-annual variability
; Leave-one-out cross-validation (LOOCV)
; Precipitation measurement
; Regional characteristics
; Spatial and temporal patterns
; Universal soil loss equation
; Floods
; annual variation
; erosivity
; kriging
; map
; model validation
; precipitation intensity
; radar
; raingauge
; regression analysis
; Revised Universal Soil Loss Equation
; soil erosion
; spatial analysis
; Universal Soil Loss Equation
; water erosion
; Switzerland
英文摘要: One major controlling factor of water erosion is rainfall erosivity, which is quantified as the product of total storm energy and a maximum 30 min intensity (I30). Rainfall erosivity is often expressed as R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). As rainfall erosivity is closely correlated with rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a regional characteristic and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity. So far only national seasonal means and regional annual means exist for Switzerland. We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to calculate long-term monthly mean R- factors. Stepwise generalized linear regression (GLM) and leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal patterns of the R-factor for each month across Switzerland. The monthly R-factor is mapped by summarizing the predicted R-factor of the regression equation and the corresponding residues of the regression, which are interpolated by ordinary kriging (regression-kriging). As spatial covariates, a variety of precipitation indicator data has been included such as snow depths, a combination product of hourly precipitation measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), and monthly precipitation sums (RhiresM). Topographic parameters (elevation, slope) were also significant explanatory variables for single months. The comparison of the 12 monthly rainfall erosivity maps showed a distinct seasonality with the highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events.Winter months have the lowest rainfall erosivity. A proportion of 62% of the total annual rainfall erosivity is identified within four months only (June- September). The highest erosion risk can be expected in July, where not only rainfall erosivity but also erosivity density is high. In addition to the intra-annual temporal regime, a spatial variability of this seasonality was detectable between different regions of Switzerland. The assessment of the dynamic behavior of the R-factor is valuable for the identification of susceptible seasons and regions. © 2016 Author(s). CC Attribution 3.0 License. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78699
Appears in Collections: 气候变化事实与影响
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作者单位: Environmental Geosciences, University of Basel, Bernoullistrasse 30, Basel, Switzerland; European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, Ispra, Italy
Recommended Citation:
Schmidt S,, Alewell C,, Panagos P,et al. Regionalization of monthly rainfall erosivity patternsin Switzerland[J]. Hydrology and Earth System Sciences,2016-01-01,20(10)