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GloRESatE: A dataset for global rainfall erosivity derived from multi-source data
Subhankar Das1, Manoj Kumar Jain2, Vivek Gupta3
1Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India.
A new global rainfall erosivity dataset was created by combining satellite and reanalysis data with ground observations. This high-resolution product improves soil erosion and hydrological assessments.
Area of Science:
- Hydrology
- Environmental Science
- Geoscience
Background:
- Accurate rainfall erosivity data is crucial for hydrological applications like soil erosion estimation and water resource management.
- Global assessment of rainfall erosivity is limited by sparse gauge data and uncertainties in satellite/reanalysis products.
Purpose of the Study:
- To develop a novel, high-resolution global rainfall erosivity dataset.
- To integrate multi-source rainfall erosivity estimates and ground observations.
- To overcome limitations of existing datasets for large-scale applications.
Main Methods:
- Machine learning-based Gaussian Process Regression (GPR) model was employed.
- Assimilation of satellite (CMORPH, IMERG) and reanalysis (ERA5-Land) rainfall erosivity estimates.
- Integration of approximately 6,200 global gauge rainfall erosivity observations and geoclimatic covariates.
Main Results:
- A unified, high-resolution (0.1° × 0.1°) mean annual rainfall erosivity product was generated.
- The dataset demonstrated strong performance during cross-validation with gauge records.
- Inter-comparison confirmed the reliability of the new dataset against existing global products.
Conclusions:
- The new dataset provides an improved global perspective on rainfall erosivity.
- It addresses critical limitations of current rainfall erosivity data availability and accuracy.
- Facilitates enhanced large-scale hydrological modeling and soil erosion assessments.
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