Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution

Area of Science:

• Environmental Science
• Hydrology
• Soil Science

Background:

• Soil erosion is a significant environmental challenge driven by rainfall intensity.
• Existing rainfall erosivity data is often localized and lacks global coverage.
• Accurate rainfall erosivity data is crucial for effective soil conservation and land management.

Purpose of the Study:

• To introduce and release the Global Rainfall Erosivity Database (GloREDa), a comprehensive global dataset.
• To provide open access to rainfall erosivity (R-factor) data for researchers and policymakers worldwide.
• To generate global monthly erosivity datasets at 1 km resolution using machine learning.

Main Methods:

• Compilation of rainfall erosivity values from nearly 4000 stations across 65 countries.
• Utilizing hourly and sub-hourly rainfall records for R-factor calculation.
• Application of an ensemble machine learning approach (mlr package in R) to predict global monthly erosivity.

Main Results:

• The establishment of GloREDa, the first open-access global database of rainfall erosivity.
• Inclusion of annual and mean monthly erosivity data for a vast number of stations.
• Generation of global monthly erosivity raster datasets at 1 km resolution.

Conclusions:

• GloREDa provides a vital resource for global soil erosion studies and environmental modeling.
• The generated monthly erosivity datasets can support climate change impact assessments and disaster management.
• Open access to such data facilitates further research and the development of sustainable land management practices.