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GloRESatE: A dataset for global rainfall erosivity derived from multi-source data.

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  • 1Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India.

Scientific Data
|August 27, 2024
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Summary
This summary is machine-generated.

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.

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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.