The rainfall erosivity factor in the Czech Republic and its uncertainty

Abstract

Abstract. In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10 min data for 96 stations and corresponding R factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance weighting, standard, ordinary, and regression kriging with parameters estimated by the method of moments and restricted maximum likelihood, and a generalized least-squares (GLS) model. For the regression-based methods, various statistics of monthly precipitation as well as geographical indices are considered as covariates. In addition to the uncertainty originating from spatial interpolation, the uncertainty due to estimation of the rainfall kinetic energy (needed for calculation of the R factor) as well as the effect of record length and spatial coverage are also addressed. Finally, the contribution of each source of uncertainty is quantified. The average R factor for the area of the Czech Republic is 640 MJ ha−1 mm h−1, with values for the individual stations ranging between 320 and 1520 MJ ha−1 mm h−1. Among various spatial interpolation methods, the GLS model relating the R factor to the altitude, longitude, mean precipitation, and mean fraction of precipitation above the 95th percentile of monthly precipitation performed best. Application of the GLS model also reduced the uncertainty due to the record length, which is substantial when the R factor is estimated for individual sites. Our results revealed that reasonable estimates of the R factor can be obtained even from relatively short records (15–20 years), provided sufficient spatial coverage and covariates are available.

Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

“…The Morrison double-moment scheme involves the number concentrations and mixing ratios of multiple hydrometeors (Morrison et al, 2009). Moreover, the WDM6 scheme further considers a prognostic factor to estimate and predict the cloud condensation nuclei (CCN) number concentration (Hong et al, 2010;Lim and Hong, 2010). Finally, the Thompson aerosol-aware (TAA) scheme can predict both ice nuclei (IN) and CCN number concentrations (Thompson and Eidhammer, 2014).…”

“…Simulations were performed using three different bulk double-moment MPs: the Morrison (Morrison et al, 2009), WDM6 (Hong et al, 2010;Lim and Hong, 2010), and TAA (Thompson and Eidhammer, 2014) schemes. All three can predict the number concentration and hydrometeors mixing ratio for each time step.…”

Rainfall Erosivity: An Overview of Methodologies and Applications

“…The vast majority of the interpolations were performed through kriging (simple, ordinary, and universal, 24 studies) and deterministic interpolation methods such as inverse distance weighting (11 studies). Hanel et al (2016) compared the spatial interpolation models for rainfall erosivity in the Czech Republic that included inverse distance weighting, simple kriging, ordinary kriging, simple co‐kriging, ordinary co‐kriging, regression kriging, and generalized least squares and reported that the spatial interpolation models that included long‐term rainfall characteristics as the covariates (regression kriging and generalized least squares) performed considerably better than those based on local interpolation and/or geographical information only (inverse distance weighting, simple kriging, ordinary kriging, simple co‐kriging, and ordinary co‐kriging) in the study area. Some of the studies performed with the interpolation techniques of kriging and inverse distance weighting also presented monthly or seasonal rainfall erosivity maps as well as annual average erosivity maps (Lu and Yu, 2002; Shamshad et al, 2008; Sadeghi et al, 2011, 2017; Klik et al, 2015).…”

“…It is important to quantify the uncertainty and generate the uncertainty map as well as the R factor map. Five key sources of uncertainty in a rainfall erosivity map were evaluated including: (i) rainfall measurement limitations (instrumental errors), (ii) the efficiency of the KE‐ I equation used to compute rainfall kinetic energy from intensity, (iii) the effectiveness of the regressions used to compute rainfall erosivity from daily or coarser temporal resolution rainfall inputs, (iv) the interannual variability of annual rainfall erosivity values, and (v) the spatial variability of rainfall erosivity values (Catari et al, 2011; Hanel et al, 2016). The estimation of the spatial variability of rainfall erosivity values is mainly related to station density or the resolution of gridded data and the interpolation method used, including the effectiveness of the covariates (Table 4).…”

Crop-management factor calculation using weights of spatio-temporal distribution of rainfall erosivity

“…Brychta and Janeček (2017) compared all available R datastets for the CR including calculation methods, interpolation methods and variables. Other information about interpolation methods was described by Hanel et al (2016) using 96 stations (R dataset with the largest number of stations published for the CR so far). These authors used different methodologies of R calculation and also different interpolation methods with different variables.…”