Integrating InSAR-derived deformation into coupled RUSLE-TLSD modelling reshapes erosion-deposition and sediment export patterns in dryland watersheds

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Authors: Avrodeeep Paul, Tamir Caras, Tarin Paz-Kagan
Cited by: 0

Abstract

This dataset provides model outputs, summary tables, and reproducible scripts supporting an evaluation of baseline RUSLE (LS) versus deformation-aware LS′ derived from PSInSAR, and the downstream implications for routed sediment redistribution using TLSD in two dryland watersheds (Yarkon–Ayalon and HaBsor). Outputs are provided for (i) gross detachment estimates from RUSLE and (ii) routed net erosion/deposition and sediment export derived from TLSD, enabling scenario comparison between DEM-based LS and PSInSAR-conditioned LS′. Included materials comprise: (1) geospatial rasters (GeoTIFF) at the analysis resolution used in the study (e.g., LS and LS′ surfaces, gross erosion rate, TLSD deposition and net balance, sediment export and SDR outputs), (2) PSInSAR spatial-support masks and associated summaries used to document where LS′ reverts to LS outside PS-supported areas, (3) block-aggregated land-use statistics based on non-overlapping 10×10 pixel blocks (modal land-use class; ≥70% valid coverage) and non-parametric comparisons (Kruskal–Wallis with ε²; Mann–Whitney with Holm correction; Cliff’s δ), and (4) Python scripts (rasterio + matplotlib) used to generate the main-paper and supplementary figures and tables. All p-values are reported descriptively given spatial dependence; effect sizes are provided to support interpretation. PSInSAR deformation is used to condition LS′ and does not directly measure erosion. Where third-party input datasets cannot be redistributed, the repository includes derived products and scripts/instructions to reproduce results from the original sources.

Fields of Study & Subjects

Soil erosion and sediment transport Synthetic Aperture Radar (SAR) Applications and Techniques Hydrology and Sediment Transport Processes

Publication details

Journal / source
Mendeley Data
Publication date
15 February 2026
Language
EN
Document type
Dataset