A coupled soilscape–landform evolution model: model formulation and initial results

Abstract

Abstract. This paper describes the coupling of the State Space Soil Production and Assessment Model (SSSPAM) soilscape evolution model with a landform evolution model to integrate soil profile dynamics and landform evolution. SSSPAM is a computationally efficient soil evolution model which was formulated by generalising the mARM3D modelling framework to further explore the soil profile self-organisation in space and time, as well as its dynamic evolution. The landform evolution was integrated into SSSPAM by incorporating the processes of deposition and elevation changes resulting from erosion and deposition. The complexities of the physically based process equations were simplified by introducing a state-space matrix methodology that allows efficient simulation of mechanistically linked landscape and pedogenesis processes for catena spatial scales. SSSPAM explicitly describes the particle size grading of the entire soil profile at different soil depths, tracks the sediment grading of the flow, and calculates the elevation difference caused by erosion and deposition at every point in the soilscape at each time step. The landform evolution model allows the landform to change in response to (1) erosion and deposition and (2) spatial organisation of the co-evolving soils. This allows comprehensive analysis of soil landform interactions and soil self-organisation. SSSPAM simulates fluvial erosion, armouring, physical weathering, and sediment deposition. The modular nature of the SSSPAM framework allows the integration of other pedogenesis processes to be easily incorporated. This paper presents the initial results of soil profile evolution on a dynamic landform. These simulations were carried out on a simple linear hillslope to understand the relationships between soil characteristics and the geomorphic attributes (e.g. slope, area). Process interactions which lead to such relationships were also identified. The influence of the depth-dependent weathering function on soilscape and landform evolution was also explored. These simulations show that the balance between erosion rate and sediment load in the flow accounts for the variability in spatial soil characteristics while the depth-dependent weathering function has a major influence on soil formation and landform evolution. The results demonstrate the ability of SSSPAM to explore hillslope- and catchment-scale soil and landscape evolution in a coupled framework.

Evaluating a new landform evolution model: A case study using a proposed mine rehabilitation landform

“…The critical immersion depth is greater for larger particles and less for smaller particles which leads to size selective deposition. Further details of the formulation and implementation of the deposition module in SSSPAM can be found in Welivitiya (2017) and Welivitiya et al (2019).…”

“…Following is a brief introduction to the mathematical framework of SSSPAM modules. Detailed information on SSSPAM modules and its implementation can be found elsewhere (Welivitiya et al, 2016, 2019; Welivitiya, 2017). Figure 2 displays a process flowchart.…”

On the main components of landscape evolution modelling of river systems

“…In fact, the soil represents one of the most important substances found on the Earth, given that, covering its uppermost layer, it provides the support for all the terrestrial organisms and guarantees the terrestrial life (Lin 2011). In the last decade, many tentative were made for incorporating the soil behaviour into landscape evolution models, as well as in combining soilscape and landscape modelling (Ebel et al 2007;Welivitiya et al 2019). Indeed, on the one part, the soil controls the interaction between vegetation and water, as well as the atmosphere in terms of carbon and nutrient cycling.…”

Hillslope erosion in a grassland environment: Calibration and evaluation of the SIBERIA landscape evolution model

“…Recent developments include soil production, weathering and armour development on new (i.e. post‐mining) landscapes (Sharmeen & Willgoose, 2006; Sharmeen & Willgoose, 2007; Minasny et al, 2015; Welivitiya, Willgoose, & Hancock, 2019). Models such as LAPSUS (Schoorl, Sonneveld, & Veldkamp, 2000; Schoorl, Veldkamp, & Bouma, 2002), LORICA (Temme & Vanwalleghem, 2016), MILESD (Vanwalleghem et al, 2013), SoilGen2 (Opolot, Yu, & Finke, 2015) and LANDLAB (Hobley et al, 2017; Barnhart et al, 2020) provide huge potential for incorporation of these processes, with many of these models having a modular framework.…”