Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity

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 limited availability of high temporal resolution rainfall data needed estimate erosivity. As will most likely not increase future decades since monitoring networks have declining 1980s, suitability alternative approaches erosivity using satellite-based was explored study. For purpose, we used spatial precipitation estimates obtained with National Oceanic Atmospheric Administration (NOAA) Climate Data Record (CDR) Prediction Center MORPHing (CMORPH) technique. Such (30 min) yet for estimation on a scale. Alternatively, density (ED) concept also The were validated against pluviograph included Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that CMORPH marked tendency underestimate when compared GloREDa estimates. underestimations observed areas highest values. At continental level, best agreement between annual interpolated maps Europe, while worst detected Africa South America. Further analyses conducted at monthly scale Europe revealed seasonal misalignments, occurrence underestimation summer period overestimation winter GloREDa. two found fall, especially central eastern Europe. Conducted analysis suggested appear be suitable low-erosivity regions, high-erosivity regions (> 1000–2000 MJ mm ha?1 h?1 yr?1) seasons 150–250 month?1), from pluviographs (GloREDa) lower. Concerning ED estimates, second approach yielded better CMORPH, which could regarded as an expected result indirectly uses data. application simple-linear function correction applied provide fit correct systematic underestimation. This improved performance but rates, observed. A preliminary trend performed 1998–2019 investigate possible changes scale, high-frequency such CMORPH. According analysis, increasing statistically significant frequently than decreasing trend.