Makine Öğrenmesi ile Uydu Yağış Erozyonluğu Tahminleri İyileştirildi

Özet (abstract)

Rainfall erosivity is a key driver of soil erosion and hydrologic processes, yet its estimation remains limited by sparse gauge observations and uncertainties in satellite precipitation products. This study presents a machine learning–based framework to improve global rainfall erosivity estimates derived from the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data. Rather than directly estimating erosivity from satellite precipitation, we focus on modeling and correcting the errors between IMERG-derived and gauge-based erosivity using a suite of hydroclimatic and land surface predictors. Global evaluation using 5935 gauge stations shows that erosivity derived from IMERG V06 and V07 systematically underestimates ground-based values by 56% and 62%, respectively, although both products capture spatial variability with moderate-to-high correlation ( R = 0.70–0.73). The proposed machine learning correction substantially reduces bias and error magnitude, yielding near-unbiased estimates (relative bias <1%) and improved agreement with observations ( R = 0.93–0.94). Feature attribution analysis indicates that errors are primarily associated with atmospheric moisture, energy fluxes, and precipitation extremes, highlighting the physical controls on satellite retrieval limitations. We further show that differences in residual structure between IMERG versions influence model learnability, and that spatial scale plays a critical role in shaping erosivity estimates, with grid-based products smoothing extreme rainfall signals. The resulting open-source framework enables reproducible, point-scale estimation of rainfall erosivity from globally available data and provides an accessible workflow for applying machine-learning-based error correction to satellite-derived erosivity estimates. This open and reproducible implementation supports broader applications in soil erosion modeling, land management, and hydrologic assessments, particularly in regions with limited ground observations.

Yazarların özeti; kaynağından alınmıştır. CATENA, 2026 · DOI ↗