No Future in the Past? The role of initial topography on landform evolution model predictions

Friday, 19 December 2014
Greg R Hancock, University of Newcastle, Callaghan, NSW, Australia, Tom J Coulthard, University of Hull, Hull, United Kingdom and John Lowry, Hydrologic, Geomorphic and Chemical Processes Program, Environmental Research Institute of the Supervising Scientist, Darwin, Australia
Our understanding of earth surface processes is based on long-term empirical understandings, short-term field measurements as well as numerical models. In particular, numerical landscape evolution models (LEMs) have been developed which have the capability to capture a range of both surface (erosion and deposition), tectonics, as well as near surface or critical zone processes (i.e. pedogenesis). These models have a range of applications for understanding both surface and whole of landscape dynamics through to more applied situations such as degraded site rehabilitation. LEMs are now at the stage of development where if calibrated, can provide some level of reliability. However, these models are largely calibrated based on parameters determined from present surface conditions which are the product of much longer-term geology-soil-climate-vegetation interactions. Here, we assess the effect of the initial landscape dimensions and associated error as well as parameterisation for a potential post-mining landform design. The results demonstrate that subtle surface changes in the initial DEM as well as parameterisation can have a large impact on landscape behaviour, erosion depth and sediment discharge. For example, the predicted sediment output from LEM’s is shown to be highly variable even with very subtle changes in initial surface conditions. This has two important implications in that decadal time scale field data is needed to (a) better parameterise models and (b) evaluate their predictions. We question how a LEM using parameters derived from field plots can firstly be employed to examine long-term landscape evolution. Secondly, the potential range of outcomes is examined based on estimated temporal parameter change and thirdly, the need for more detailed and rigorous field data for calibration and validation of these models is discussed.