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International Journal of River Basin Management· 2026Q2

Evolution of soil erosion and sediment delivery modelling over six decades: paradigms, limitations and new solutions

Melissa Latella, Monia Santini, Pierfranco Costabile, Roberta Padulano

Short summary

A review of 1,075 studies reveals that while soil erosion and sediment delivery modelling has evolved from lumped to distributed approaches over six decades, persistent data and model errors remain. Artificial intelligence (AI) and remote sensing (RS) show promise for addressing these limitations, though their transformative adoption is still pending.

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Key points

  • Analysis of 1,075 publications traces the evolution of sediment delivery modelling over six decades.
  • Modelling has shifted from lumped to spatially distributed approaches, but no single model type dominates.
  • Persistent data and model-related errors remain significant challenges.
  • Artificial intelligence (AI) and remote sensing (RS) are identified as key solutions to address current limitations.
  • The transformative adoption of AI and full leveraging of RS in sediment dynamics modelling are still developing.

AI-generated from the title and abstract; the full text is not read.

Abstract

Modelling sediment delivery, namely the linkage between hillslope erosion and yield in river systems, is still a challenge. In this study, 1,075 publications from Elsevier's Scopus were analysed to trace the evolution from lumped to spatially distributed approaches. Results confirm that no single model type dominates, while several studies rely on simplified approaches, and data- and model-related errors remain unresolved. Artificial intelligence (AI) and remote sensing (RS) can address such limitations. However, although AI has emerged as either a complementary tool for generating new climate or land-use scenarios or a standalone framework, its adoption has not been transformative so far. Similarly, RS is increasingly used to characterise topography and land cover, but is poorly leveraged to determine other factors influencing sediment dynamics. This overview of existing and evolving methods provides useful insights to support river basin management and model selection. Overall, this study underscores the need for further integration of diverse data sources and processing methods, while future research should prioritise the trade-offs among model complexity, accuracy, and scalability.

The authors' abstract, as published at the source. International Journal of River Basin Management, 2026 · DOI ↗

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Field: Soil Science

Soil ScienceAgricultural and Biological Sciences