Evaluation of discrepancies in spatial distribution of rainfall erosivity in the Czech Republic caused by different approaches using GIS and geostatistical tools

Abstract

Brychta J., Janeček M. (2017): Evaluation of discrepancies in spatial distribution of rainfall erosivity in the Czech Republic caused by different approaches using GIS and geostatistical tools. Soil & Water Res., 12: 117−127.The study presents all approaches of rainfall erosivity factor (R) computation and estimation used in the Czech Republic (CR). A lot of distortions stem from the difference in erosive rainfall criteria, time period, tipping rain gauges errors, low temporal resolution of rainfall data, the type of interpolation method, and inappropriate covariates. Differences in resulting R values and their spatial distribution caused by the described approaches were analyzed using the geostatistical method of Empirical Bayesian Kriging and the tools of the geographic information system (GIS). Similarity with the highest temporal resolution approach using 1-min rainfall data was analyzed. Different types of covariates were tested for incorporation to the cokriging method. Only longitude exhibits high correlation with R and can be recommended for the CR conditions. By incorporating covariates such as elevation, with no or weak correlation with R, the results can be distorted even by 81%. Because of significant yearly variation of R factor values and not clearly confirmed methodology of R values calculation and their estimation at unmeasured places we recommend the R factor for agricultural land in the Czech Republic R = 40 MJ/ha·cm/h +/-10% depends on geographic location.

Operational USLE-Based Modelling of Soil Erosion in Czech Republic, Austria, and Bavaria—Differences in Model Adaptation, Parametrization, and Data Availability

“…To derive the rainfall erosivity map, high resolution rainfall (1 min) of recent 10 years of official Czech Hydro-meteorological Institute digital gauge stations is used. The rainfall erosivity maps in different areas of applications in the Czech Republic differ [23]. For the official USLE soil loss tolerance map used in the context of cross compliance, the calculated mean R factor of the 10 year period is reduced by 40%, based on the comparison of R factors of original pluviometer data and digital rain gauges in an overlapping training period (decision of Ministry of Agriculture/Ministry of Environment of Czech Republic).…”

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

“…The following authors used different erosive rainfall parameters of rainfall total and intensity which have to be fulfilled simultaneously (AND) or at least one of these parameters (OR). Angulo-Martinez et al (2009) used rainfall total > 12.7 mm OR intensity > 6.35 mm/15 min according to Renard et al (1997) in Spain, Janeček et al (2006Janeček et al ( , 2012aJaneček et al ( , 2013b Janeček et al (2012aJaneček et al ( , 2013b and Brychta and Janeček (2017). Using these parameters we calculated R factor values for all ombrographic stations and R maps were interpolated using an Ordinary kriging method.…”

“…Monitored time periods of stations vary between 19 and 48 years with an average https://doi.org/10.17221/100/2017-SWR of 35.3 years. R for each rainfall event that fulfilled the total amount > 12.5 mm and simultaneously the intensity > 6.25 mm/15 min and was separated from other rainfall at least by a 6-hour interval (or less if the section was considered as one erosive rainfall) was calculated according to Janeček et al (2013b) and Brychta and Janeček (2017) using equations (1-3):…”

“…With awareness of the weaknesses of our R dataset, in particular a small number of stations, we created two versions of STD C factor GIS model -with integrated R data and with optional R data (see the user interface in Figure 3). For more information about advantages and disadvantages of all published R datasets available for the CR see Brychta and Janeček (2017). Among optional R data (annual and monthly R maps) the tool requires only two inputs -land parcel polygons (shapefile format) and a table with information about crop rotations (Excel table).…”

“…Development of geoinformation systems (GIS), remote sensing (RS) and distance monitoring technologies bring a lot of possibilities for integration with these equations as the USLE/ RUSLE-GIS method. The greatest benefits include very accurate height data reached by LiDAR technology, on the other hand disproportion with accuracy of soil and vegetation data and discrepancies in rainfall erosivity calculation (Brychta & Janeček 2017) may cause large errors. The inappropriate integration with GIS and RS data may result in many simplifications and distortions.…”