Abstract
Despite recent developments in modelling 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 rainstorms rainfall erosivity. As this data scarcity is likely to characterize the upcoming years, the suitability of alternative approaches to estimate global rainfall erosivity using satellite-based rainfall data was explored. For this purpose, the high spatial and temporal resolution global precipitation estimates obtained with the NOAA CDR Climate Prediction Center MORPHing technique (CMORPH) were used. Alternatively, the erosivity density (ED) concept was used to estimate global rainfall erosivity as well. 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 continental level, the best agreement between annual CMORPH and interpolated GloREDa rainfall erosivity map was observed in Europe. Worse agreement was detected for Africa and South America. Further analyses conducted at 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 autumn, 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 and seasons (> 1,000–2,000 MJ mm ha−1 h−1 yr−1), the agreement with estimates obtained from pluviograph data such as GloREDa is lower. Concerning the ED estimates, this second approach to estimate rainfall erosivity yielded better agreement with GloREDa estimates compared to CMORPH. The application of a simple-linear function correction of the CMORPH data was applied to provide better fit to the GloREDa and correct systematic underestimation. This correction improved the performance of the 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. According to this trend analysis, increasing and statistically significant trend was more frequently observed than decreasing trend.
Highlights
Rainfall erosivity is among the main drivers of soil erosion, which can be characterized by large spatial and temporal variability (Angulo-Martínez and Beguería, 2012; Ballabio et al, 2017; Bezak et al, 2021; Cui et al, 2020; Panagos et al, 2017; Verstraeten et al, 2006)
Concerning inequality, the largest value of rainfall erosivity inequality obtained using the Gini coefficient was detected for Asia, followed by Africa and Oceania, while the smallest value was observed for Europe (Table 1). 190 Table 1: Mean, standard deviation and Gini coefficient of the global rainfall erosivity maps derived using the CMORPH and erosivity density (ED)
The results demonstrated that the Pearson correlation between the mean annual rainfall erosivity at the sub-catchment level between the CMORPH and Global Rainfall Erosivity Database (GloREDa) was 0.81 (R2 = 0.66 with p-value < 0.01)
Summary
Rainfall erosivity is among the main drivers of soil erosion, which can be characterized by large spatial and temporal variability (Angulo-Martínez and Beguería, 2012; Ballabio et al, 2017; Bezak et al, 2021; Cui et al, 2020; Panagos et al, 2017; Verstraeten et al, 2006). Due to the data scarcity most rainfall erosivity assessments based on long-term estimates including a period of at least 10-years are limited to few regions (Angulo-Martínez and Beguería, 2012; Nearing et al, 2015; Panagos et al, 2015, 2017), leaving large sectors of the world under-researched. In this regard, a step forward is needed to enable the generation of year-by-year and sub-annual rainfall erosivity assessments for under researched national or larger scale study areas. A temporal trend analysis of global rainfall erosivity is presented together with corrections 75 between data based on the CMORPH and GloREDa databases
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