Spatiotemporal variations in rainfall erosivity during the period of 1960–2011 in Guangdong Province, southern China

Zhao, Qinghe; Liu, Qian; Ma, Lijiao; Ding, Shengyan; Xu, Shoufang; Wu, Changcai; Liu, Pu

doi:10.1007/s00704-015-1694-5

Cited by 23 publications

(8 citation statements)

References 38 publications

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“…Most of the monthly R-factors (96 %) of the lowest 10 % of all monthly values are part of the period between November and April, whereas 97 % of the highest 10 % are monthly rainfall erosivity in the period from May to October. Seasonality of R mo on a continental scale is observed for Europe (Panagos et al, 2016a) and Africa (Vrieling et al, 2014), on a national scale for Brazil (da Silva, 2004), Cabo Verde (Mannaerts and Gabriels, 2000), Chile (Bonilla and Vidal, 2011), Denmark (Leek and Olsen, 2000), El Salvador (da Silva et al, 2011), Greece (Panagos et al, 2016b), Iran (Sadeghi et al, 2011;Sadeghi and Hazbavi, 2015;Sadeghi and Tavangar, 2015), Italy (Diodato, 2005;Borrelli et al, 2016), New Zealand , South Korea (Lee and Won, 2013), and inter alia for the regions of Australia , Belgium (Verstraeten et al, 2006), Brazil (da Silva et al, 2013), Cabo Verde (Sanchez-Moreno et al, 2014), China (Jing et al, 2009;Zhu et al, 2011;Wang et al, 2013;Zhao et al, 2015;Lai et al, 2016), England and Wales (Davison et al, 2005), Ethiopia (Meshesha et al, 2015), Japan (Laceby et al, 2016), the Himalayas (Ma et al, 2014), Italy (Terranova and Gariano, 2015), South Korea (Arnhold et al, 2014), Malaysia (Shamshad et al, 2008), Poland (Banasik and Górski, 1993;Banasik et al, 2001), Slovenia (Petkovšek and Mikoš, 2004;Mikoš et al, 2006), Spain (Renschler et al, 1999;AnguloMartínez and Beguería, 2009), Turkey (Özşahin, 2014), and the USA (Wilkes and Sawada...…”

Section: Monthly Erosivity Densitymentioning

confidence: 99%

Regionalization of monthly rainfall erosivity patterns in Switzerland

Schmidt

Alewell

Panagos

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. 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 (I 30 ). 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 Rfactors. 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 (JuneSeptember). 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.

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Section: Monthly Erosivity Densitymentioning

confidence: 99%

Regionalization of monthly rainfall erosivity patterns in Switzerland

Schmidt

Alewell

Panagos

et al. 2016

Hydrol. Earth Syst. Sci.

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show abstract

“…In computation of rainfall erosivity, most of the previous studies used either long-term rainfall data of few rain gauge stations 47,52,62 or short-term data with dense rain gauges 26,27,29 by using elsewhere KE-I relations rather than adopting region specific relations. Conversely, some researchers used modified Fournier index 30 in estimation of erosivity, if there were no records of high temporal resolution precipitation data, and only few studies adopted the region specific KE-I relation in the reckoning of erosivity 33 .…”

Section: Discussionmentioning

confidence: 99%

An assessment of tropical cyclones rainfall erosivity for Taiwan

Janapati

Seela

Lin

et al. 2019

Sci Rep

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Rainfall erosivity (or water erosion) has severe implications on agriculture, water, and land use management. Though, there were Rainfall erosivity studies on regional and global scale, tropical cyclones’ Rainfall erosivity is poorly assessed and have not been documented for one of the most cyclones affecting regions of the world like Taiwan. Here, using 15-years of raindrop size distributions (RSD) and 60-years of hourly rain gauges data, we estimated cyclones (also called typhoons) rainfall erosivity over Taiwan, and establish that typhoons’ mean rainfall erosivity is higher than the global mean rainfall erosivity. Moreover, regional variability of typhoons rainfall erosivity showed an increasing pattern from north to south (Taipei to Pingtung), with relatively higher values over eastern and southern parts of Taiwan. The annual mean erosivity of typhoons rainfall showed raising trends over eastern and southern Taiwan during 1958–2017. Our results provide an insight in assessing the land use and agricultural management for Taiwan.

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“…Seasonality of R mo on a continental scale is observed for Europe (Panagos et al, 2016a) and Africa (Vrieling et al, 2014), on a national scale for Brazil (da Silva, 2004), Cabo Verde (Mannaerts and Gabriels, 2000), Chile (Bonilla and Vidal, 2011), Denmark (Leek and Olsen, 2000), El Salvador (da Silva et al, 2011), Greece (Panagos et al, 2016b), Iran (Sadeghi et al, 2011;Sadeghi and Hazbavi, 2015;Sadeghi and Tavangar, 2015), Italy (Diodato, 2005;Borrelli et al, 2016), New Zealand (Klik et al, 2015), South Korea (Lee and Won, 2013), and inter alia for the regions of Australia , Belgium (Verstraeten et al, 2006), Brazil (da Silva et al, 2013), Cabo Verde (Sanchez-Moreno et al, 2014), China (Jing et al, 2009;Zhu et al, 2011;Wang et al, 2013;Zhao et al, 2015;Lai et al, 2016), England and Wales (Davison et al, 2005), Ethiopia (Meshesha et al, 2015), Japan (Laceby et al, 2016), the Himalayas (Ma et al, 2014), Italy (Terranova and Gariano, 2015), South Korea (Arnhold et al, 2014), Malaysia (Shamshad et al, 2008), Poland (Banasik and Górski, 1993;Banasik et al, 2001), Slovenia (Petkovšek and Mikoš, 2004;Mikoš et al, 2006), Spain (Renschler et al, 1999;Angulo-Martínez and Beguería, 2009), Turkey (Özşahin, 2014), and the USA…”

Section: Monthly Erosivity Densitymentioning

confidence: 99%

Seasonal Dynamics of Rainfall Erosivity in Switzerland

Schmidt

Alewell

Panagos

et al. 2016

Preprint

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Abstract. One major controlling factor of water erosion is rainfall erosivity, which is quantified by the kinetic energy of a rainfall event and its maximum 30-min intensity. 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 dynamic rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a characteristic regional and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modelling approach to assess simultaneously spatial and monthly pattern 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-minute temporal resolution to calculate long-term monthly mean R-factors. Stepwise regression and Leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal pattern of the R-factor for each month across Switzerland. The monthly R-factor is mapped by its specific regression equation and the ordinary kriging interpolation of its residuals (Regression-Kriging). As covariates, a variety of precipitation indicator data has been included like 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 highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (June to September). Highest erosion risk can be expected for July where not only rainfall erosivity but also erosivity density is high. Additionally 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.

show abstract

Spatiotemporal variations in rainfall erosivity during the period of 1960–2011 in Guangdong Province, southern China

Cited by 23 publications

References 38 publications

Regionalization of monthly rainfall erosivity patterns in Switzerland

Regionalization of monthly rainfall erosivity patterns in Switzerland

An assessment of tropical cyclones rainfall erosivity for Taiwan

Seasonal Dynamics of Rainfall Erosivity in Switzerland

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