Saturated hydraulic conductivity and steady-state infiltration rate database for brazilian soils

Abstract

Soil saturated hydraulic conductivity (Ksat) and steady-state infiltration rate (SSIR) are essential and necessary soil properties for different geoscience applications. Values of these hydraulic properties for the Brazilian territory are difficult to access and are dispersed in research efforts carried out around the country. This study developed an easy-to-manipulate, freely accessible database of soil saturated hydraulic conductivity, comprising field and laboratory analyses, and steady-state infiltration rates for Brazilian soils. This database was named Ksat-SSIR-DB. One analysis of the Ksat-SSIR-DB aimed to evaluate its coverage in Brazilian territory and in different soil groups. Average values of these hydraulic properties were also presented for textural classes, with values compared to those reported in international literature, and for other groupings, such as soil class, land use class, and porosity class. The variability of Ksat data in these groupings and in their combinations were also analyzed. The Ksat-SSIR-DB showed broad national coverage, comprising a total of 2,579 records, corresponding to 409 sampling sites, with Ksat and/or SSIR data and other associated soil information. A significant difference was observed between Ksat values for the vast majority of Brazilian clayey and very clayey soils compared to soils from the same textural groups from temperate regions. The two groupings that presented the lowest variability in terms of Ksat standard deviation values were the combination of textural classes with soil classes at the second category level of SiBCS (Brazilian Soil Classification System), and porosity classes with soil classes at the second category level of SiBCS. The Ksat-SSIR-DB has enormous potential for developing and testing Ksat pedotransfer functions in Brazilian soils, serving as a reference source for different geoenvironmental applications and, in particular, for modeling land surface processes. It is open access and can be accessed at , which also includes the python script for data analysis.