Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity
Authors: Bezak Nejc , Borrelli Pasquale , Panagos Panos 
Published: April 2022

Abstract. Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towards
more dynamic inter- and intra-annual assessments. In this regard, the main
challenge is still represented by the limited availability of high temporal
resolution rainfall data needed to estimate rainfall erosivity. As the
availability of high temporal resolution rainfall data will most likely not
increase in future decades since the monitoring networks have been declining
since the 1980s, the suitability of alternative approaches to estimate
global rainfall erosivity using satellite-based rainfall data was explored
in this study. For this purpose, we used the high spatial and temporal
resolution global precipitation estimates obtained with the National
Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the
estimation of rainfall erosivity on a global scale. Alternatively, the
erosivity density (ED) concept was also used to estimate global rainfall
erosivity. The obtained global estimates of rainfall erosivity were
validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH
estimates have a marked tendency to underestimate rainfall erosivity when
compared to the GloREDa estimates. The most substantial underestimations
were observed in areas with the highest rainfall erosivity values. At the
continental level, the best agreement between annual CMORPH and interpolated
GloREDa rainfall erosivity maps was observed in Europe, while the worst agreement was detected in Africa and South America. Further analyses
conducted at the monthly scale for Europe revealed seasonal misalignments,
with the occurrence of underestimation of the CMORPH estimates in the summer
period and overestimation in the winter period compared to GloREDa. The best agreement between the two approaches to estimate rainfall erosivity was
found for fall, especially in central and eastern Europe. Conducted analysis suggested that satellite-based approaches for estimation of rainfall erosivity appear to be more suitable for low-erosivity regions,
while in high-erosivity regions (> 1000–2000 MJ mm ha−1 h−1 yr−1) and seasons (> 150–250 MJ mm ha−1 h−1 month−1), the agreement with estimates obtained from pluviographs (GloREDa) is lower. Concerning the ED estimates, this second approach to
estimate rainfall erosivity yielded better agreement with GloREDa estimates
compared to CMORPH, which could be regarded as an expected result since this
approach indirectly uses the GloREDa data. The application of a
simple-linear function correction of the CMORPH data was applied to provide
a better fit to GloREDa and correct systematic underestimation. This correction improved the performance of CMORPH, but in areas with the highest rainfall erosivity rates, the underestimation was still observed. A
preliminary trend analysis of the CMORPH rainfall erosivity estimates was
also performed for the 1998–2019 period to investigate possible changes in
the rainfall erosivity at a global scale, which has not yet been conducted
using high-frequency data such as CMORPH. According to this trend analysis,
an increasing and statistically significant trend was more frequently
observed than a decreasing trend.