Spatial–temporal dynamics of rainfall erosivity in the state of Espírito Santo (Brazil) from remote sensing data

Abstract

PurposeThe most common methodology to estimate erosivity is using rainfall data obtained from rain monitoring stations. However, the quality of this estimation may be compromised due to low density, operational problems and maintenance cost of rainfall monitoring stations, common problem encountered in developing countries such as Brazil. The objective of this study was to evaluate the applicability of pluviometric data obtained by TRMM satellite images for the spatiotemporal characterization of erosivity in the state of Espírito Santo (Brazil).Design/methodology/approachFor this, rainfall data and annual and monthly erosivities of 71 rainfall stations were statistically compared with those from TRMM images.FindingsFor this, rainfall data and annual and monthly erosivities of 71 rainfall stations were statistically compared with those from TRMM images. The estimate proved that TRMM is efficient since the NSE values were higher than 0.70 and the coefficient of determination was higher than 0.77 for monthly and annual erosivities, but in most months and yearly, erosivity was overestimated.Practical implicationsThe use of satellite images to estimate rainfall allowed the spatial representation over time (months) of the oscillating degree of erosivity in the state of Espírito Santo (Brazil). The spatialization may provide an identification of areas and periods in which are essential for the implementation of land use management in order to minimize environmental problems related to soil loss.Originality/valueThe technique applied may be an alternative to overcome common problems on rainfall monitoring station, such as low density, low data reliability, high manutention and maintenance cost and operational problems.

Rainfall erosivity estimation: Comparison and statistical assessment among methods using data from Southeastern Brazil

“…This is in accordance with the results of Oğuz (2019) 2009) may be due to an embedded underestimation of the kinetic energy due to RUSLE methodology. Although there was still no test of the effectiveness of the modified Fournier index -until this study-, the MF method has been used for Brazilian conditions as a rainfall erosivity estimator in several parts of the country, as the states of Santa Catarina (Back et al, 2018), Espírito Santo (Moreira et al, 2020), Tocantins (Avanzi et al, 2019), Mato Grosso (Di Raimo et al, 2018), São Paulo (Lombardi Neto, 1977, and Amazonas (Silva et al, 2020), along with large areas for regional studies (Mello et al, 2013(Mello et al, , 2015Oliveira et al, 2013).…”

“…Other methodologies, such as rainfall disaggregation (Silveira, 2000) -considering annual or monthly totals for hourly or shorter periods-, or techniques based on orbital remote sensing to estimate rainfall depth (Duarte and Silva Filho, 2019;Li et al, 2020;Moreira et al, 2020), can be used at locations with low density of rain gauge data points. Rainfall erosivity mapping for Africa utilizing Tropical Rainfall Measurement Mission (TRMM), based on TMPA 3B43 satellite data (precipitation data) coupled with the modified Fournier index, proved to be a reliable methodology (Vrieling et al, 2010).…”

Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework

“…Although other input data were given at high spatial resolutions ranging from 90 to 250 m (e.g., elevation, land cover, soil, and vegetation), rainfall data was used at a lower spatial resolution. The Tropical Rainfall Measuring Mission (TRMM) dataset was used as the source of rainfall data in the estimation of rainfall erosivity (Li et al, 2020;Moreira et al, 2020;Vrieling et al, 2010) and soil loss mapping (Dutta et al, 2015;Haregeweyn et al, 2017). However, the coarse spatial resolution of TRMM (0.25°) may fail to capture the detailed precipitation spatial patterns, particularly on the basin and local scales.…”

“…Also, the López López et al (2018) study showed that discharge model estimates closely matched observations when the model was forced with the downscaled precipitation. In contrast, soil erosion studies focused on the impact of spatiotemporal precipitation changes on rainfall erosivity (e.g., Moreira et al, 2020) or improving rainfall erosivity by integrating TRMM and rain gauge data (e.g., Teng et al, 2017). Therefore, we attempted to investigate the impact of downscaling precipitation on rainfall erosivity and the resulting soil loss rate.…”