Sachinthaka, RajithaKalatehjari, RoohollahBrook, Martin S2025-10-062025-10-062025-10-03Natural Hazards, ISSN: 0921-030X (Print); 1573-0840 (Online), Springer Science and Business Media LLC. doi: 10.1007/s11069-025-07634-00921-030X1573-0840http://hdl.handle.net/10292/19905Physically-based models play a critical role in assessing rainfall-induced landslide susceptibility, offering valuable insights into landslide hazard prediction and risk mitigation. This study conducts a scientometric and systematic review of deterministic, physically-based, rainfall-induced landslide susceptibility mapping, synthesising research trends and advancements in this domain. A comprehensive literature search, conducted through the Scopus database following PRISMA guidelines, identifying 70 key studies for in-depth analysis. The findings reveal significant progress, including the integration of climate change projections, enhanced real-time monitoring systems, and advancements in high-resolution data processing. Despite these developments, challenges persist in achieving a balance between model complexity and practical applicability. This review highlights the need for standardised validation protocols, robust uncertainty analysis, and interdisciplinary approaches that merge physical modelling with machine learning techniques. By evaluating the evolution and current state of deterministic physically-based landslide modelling, this study provides a valuable reference for researchers and practitioners, contributing to the advancement of more reliable and accessible landslide susceptibility assessments.Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by/4.0/0401 Atmospheric Sciences0406 Physical Geography and Environmental Geoscience1701 PsychologyStrategic, Defence & Security Studies3709 Physical geography and environmental geoscienceJournal ArticleOpenAccess10.1007/s11069-025-07634-0