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Hillslope and catchment scale landform evolution – Predicting catchment form and surface properties.

Authors :
Welivitiya, W.D.Dimuth P.
Hancock, Greg R.
Source :
Environmental Modelling & Software. Aug2023, Vol. 166, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

Computer-based coupled soilscape-landform evolution models provide an avenue for estimating the erosion potential of a landform over geological timescales. They also provide tools to understand soil-landform relationships in the context of both field and modelled pedogenesis processes. In this study, we used the State Space Soil Production and Assessment Model (SSSPAM) and CAESAR-Lisflood landform evolution model to simulate the evolution of a natural catchment where soil erosion rates have been closely monitored over the last 20 years. Both models predict comparable erosion rates to that of field measurements and produce similar catchment scale geomorphological patterns. Rock content data measured in the field along two transects were compared with SSSPAM simulations at the same representative points. The results showed that SSSPAM produced similar rock content variation along the respective transects. The findings demonstrate 'SSSPAM's ability to predict catchment scale erosion and surface soil distribution. The insights demonstrated here provide confidence in the SSSPAM model for Soilscape predictions for natural, agricultural and post-mining landform assessment. [Display omitted] • SSSPAM and CAESAR-Lisflood models were used to simulate the evolution of a natural catchment. • Both models produced catchment erosion rates comparable with field observations. • SSSPAM reproduced surface rock content variation on 2 transects observed in the field. • SSSPAM also revealed a possible relationship between area, slope and soil grading. • This study demonstrates SSSPAM's ability to predict erosion and surface soil distribution. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13648152
Volume :
166
Database :
Academic Search Index
Journal :
Environmental Modelling & Software
Publication Type :
Academic Journal
Accession number :
164155579
Full Text :
https://doi.org/10.1016/j.envsoft.2023.105725