J.E.M. Baartman, A.J.A.M. Temme, J.M. Schoorl, L. Claessens, W. Viveen, W. van
Wageningen University, Land Dynamics Group, P.O. Box 47, 6700 AA Wageningen, The
Netherlands. E-mail: email@example.com
Landscape evolution modeling can make the consequences of landscape evolution
hypotheses explicit and theoretically allows for their falsification and improvement. Ideally,
landscape evolution models (LEMs) combine the results of all relevant landscape forming
processes into an ever-adapting digital landscape (e.g. DEM). These processes may act on
different spatial and temporal scales. LAPSUS is such a LEM. Processes that have in
different studies been included in LAPSUS are water erosion and deposition, landslide
activity, creep, solifluction, weathering, tectonics and tillage. Process descriptions are as
simple and generic as possible, ensuring wide applicability. Vegetation-effects can be
included. Interactions between processes are turn-based: volumes of one process are
calculated and used to update the DEM before another process starts. LAPSUS uses
multiple flow techniques to model flows of water and sediment over the landscape. Though
computationally costly, this gives a more natural result than steepest descent methods. In
addition, the combination of different processes may create sinks during modelling. Since
these sinks are not spurious, the model has been adapted to deal with them in natural ways.
This is crucial for several purposes, for instance when studying damming of valleys by
landslides, and subsequent infilling of the resulting lake with sediments from upstream.
Keywords: Landscape Evolution Modelling, LAPSUS, soil redistribution, erosion
This extended abstract is merely a review of the work undertaken and developments into the
model) that combines the results of multiple landscape forming processes into one dynamic
landscape. Spatial and temporal extent and resolution may vary from slope, catchment to
basins, grids from 1 to 1000 m2, timesteps of multiple events, seasons, years, decades and
simulation periods from years to millennial.
Interactions between processes are turn-based: volumes of one process are calculated and
used to update the DEM before another process starts. Processes that have been included
in LAPSUS are water erosion and deposition, landslide activity, creep, solifluction, physical
weathering, frost weathering, tectonics and tillage (See Figure 1).
Process descriptions are as simple and generic as possible, ensuring wide applicability.
Vegetation-effects are included to different degrees in different case studies. LAPSUS uses
multiple flow techniques to model the flow of water and sediment over the landscape. This is
computationally costly, but yields a more natural result than steepest descent methods,
especially when combining multiple processes over multiple timesteps.
The combination of different processes may create sinks during modelling. Since these sinks
are not spurious, the model has been adapted to deal with them in a natural way. This is
crucial when studying damming of valleys by landslides, and subsequent infilling of the
resulting lake with sediments from upstream.
TOPIC 1: PHYSICAL GEOGRAPHY MODELLING
LAPSUS has been used for erosion and landscape evolution studies in many landscapes in
calibration and validation of the LAPSUS model and applications concerning land use in
Spain and Ecuador (Schoorl et al., 2000, 2002, 2004, 2006; Schoorl and Veldkamp, 2001,
2006). Firstly, the model has been extended in order to cover the process of landsliding in
New Zealand and Taiwan (Claessens et al., 2005, 2006a, 2006b, 2007a, 2007b). Secondly,
issues of DEM resolution and the treatment of sinks and pits in the landscape have been
investigated (Temme et al., 2006, 2009) as well as stretching the models time scale to
landscape evolution time spans in South Africa (Temme and Veldkamp, 2009). Thirdly,
different applications with specific processes have been developed, for example, the model
has been used in regional nutrient balance studies in Africa (Haileslassie et al., 2005, 2006,
2007; Roy et al., 2004; Lesschen et al., 2005). aplying the model in desert environments of
Israel (Buis and Veldkamp, 2008), using LAPSUS in combination with geostatistical tools and
tillage in Canada (Heuvelink et al, 2006), investigating the faith of phosphor in the
landscapes of the Netherlands (Sonneveld et al., 2006) and new developments concerning
connectivity, agricultural terraces and land abandonment (Lesschen et al., 2007, 2009) and
the processing of feedbacks between land use and soil redistribution (Claessens et al., 2009)
Figure 1. Overview of processes incorporated within the Lapsus modelling framework (see
Landscape evolution modelling allows for falsification and improvement of landscape
processes into an ever-adapting digital landscape (e.g. DEM). These processes may act and
interact on different spatial and temporal scales.
Buis, E. and A. Veldkamp, 2008. Modelling dynamic water redistribution patterns in arid
Issue 1, p. 107-122.
Claessens, L., Lowe, D.J., Hayward, B.W., Schaap, B.F., Schoorl, J.M., and Veldkamp, A., 2006a,
Reconstructing high-magnitude/low-frequency landslide events based on soil redistribution
modelling and a Late-Holocene sediment record from New Zealand: Geomorphology, 74, p. 29-
topographical based landslide hazard Modelling to the analysis of the spatial distribution and
ecology of Kauri (Agathis australis): Landscape Ecology 21, p. 63 - 76.
Claessens, L., Heuvelink, G.B.M., Schoorl, J.M., and Veldkamp, A., 2005, DEM resolution effects
on shallow landslide hazard and soil redistribution modelling. Earth Surface Processes and
Landforms, volume 30, p. 461-477.
Claessens, L., A. Knapen, M.G. Kitutu, J. Poesen and J.A. Deckers. 2007. Modelling landslide
hazard, soil redistribution and sediment yield of landslides on the Ugandan footslopes of Mount
Elgon. Geomorphology Volume 90 (Issues 1-2), p 23 - 35.
Claessens, L., Schoorl, J.M., and Veldkamp, A., 2007, Modelling the location of shallow landslides
and their effects on landscape dynamics in large watersheds: an application for Northern New
Zealand: Geomorphology, Volume 87, Issues 1-2, p 16 - 27.
Claessens, L., J.M. Schoorl, P.H. Verburg, L. Geraedts and A. Veldkamp 2009. Modelling
interactions and feedback mechanisms between land use change and landscape processes.
Haileslassie, A., Priess, J., Veldkamp, E., Teketay, D. and J.P. Lesschen, 2005. Assessment of
soil nutrient depletion and its spatial variability on smallholders’ mixed farming systems in Ethiopia
using partial versus full nutrient balances. Agriculture, Ecosystems and Environment 108: p. 1 –
Haileslassie, A., Priess, J.A., Veldkamp, E., and J.P. Lesschen. 2006. Smallholders’ soil fertility
sustainability of agroecosystems. Nutrient Cycling in Agroecosystems, 75: 135-146.
Haileslassie, A., Priess, J.A., Veldkamp, E. and J.P. Lesschen, 2007. Nutrient flows and balances
at the field and farm scale: Exploring effects of land-use strategies and access to resources.
Heuvelink, G.B.M., Schoorl, J.M., Veldkamp, A. and D.J. Pennock. 2006. Space-time Kalman
filtering of soil redistribution. Geoderma, 133. p. 124 - 137.
Lesschen, J.P.,, Asiamah, R.D., Gicheru, P., Kante, S., Stoorvogel, J.J. & Smaling, E.M.A. 2005.
Scaling Soil Nutrient Balances - Enabling mesoscale approaches for African realities. FAO
Fertilizer and Plant Nutrition Bulletin 15, FAO, Rome.
Lesschen, J.P.; Stoorvogel, J.J.; Smaling, E.M.A.; Heuvelink, G.B.M.; Veldkamp, A. , 2007. A
spatially explicit methodology to quantify soil nutrient balances and their uncertainties at the
national level Nutrient Cycling in Agroecosystems 78 (2). - p. 111 - 131.
Lesschen J.P., J.M. Schoorl, L.H. Cammeraat, 2009. Modelling runoff and erosion for a semi-arid
catchment using a multi-scale approach based on hydrological connectivity. Geomorphology, In
Press, Accepted Manuscript, Available online 12 March 2009
Roy, R.N., R.V. Misra, J.P. Lesschen,, E.M. Smaling, 2004. Assessment of soil nutrient balances:
Approaches and Methodologies. Fertilizer and Plant Nutrition Bulletin 14, FAO, Rome.
Schoorl, J.M., Boix Fayos, C., de Meijer, R.J., van der Graaf, E.R., and Veldkamp, A., 2004. The
137Cs technique on steep Mediterranean slopes (Part 2): landscape evolution and model
calibration: Catena, v. 57, p. 35-54.
Schoorl, J.M., Veldkamp, A., and Bouma, J., 2002, Modelling water and soil redistribution in a
dynamic landscape context: Soil.Sci.Soc.Am.J., v. 66, p. 1610-1619.
Schoorl, J.M., and Veldkamp, A., 2001, Linking land use and landscape process modelling: a case
study for the Alora region (South Spain): Agric.Ecosyst.Environ., v. 85, p. 281-292.
TOPIC 1: PHYSICAL GEOGRAPHY MODELLING
process modelling: the effect of DEM resolution: Earth Surf.Proc.Landforms, v. 25, p. 1025-1034.
Schoorl, J.M., L. Claessens, M. Lopez Ulloa, G.H.J. de Koning & A. Veldkamp, 2006.
Geomorphological Analysis and Scenario Modelling in the Noboa – Pajan Area, Manabi Province,
Ecuador. Zeitschrift Fur Geomorfologie, Suppl.-Vol. 145, p. 105 - 118.
Schoorl, J.M.,, and Veldkamp, A., 2006, Multi-Scale Soil-Landscape Process Modeling, in
Grunwald, S., ed., Environmental Soil-Landscape Modeling: Geographic Information Technologies
and Pedometrics: Boca Raton, FL, CRC press, Taylor and Francis Group, p. 417 – 435.
Sonneveld, M.P.W. Schoorl, J.M.,and A. Veldkamp, 2006. Evaluating The Fate Of Phosphorus In
Apparent Homogeneous Landscapes Using a High-Resolution DEM. Geoderma 133, p. 32 - 42.
Temme, A.J.A.M., Schoorl, J.M., and Veldkamp, A., 2006, Algorithm for dealing with depressions
in dynamic landscape evolution models: Comp.Geosci., 32, p. 452 - 461.
Temme, A.J.A.M., Heuvelink, G.B.M., Schoorl, J.M. and Claessens, L. 2009. Chapter 5:
Geostatistical simulation and error propagation in geomorphometry. In: Geomorphometry:
Concepts, Software, Applications. Eds: Hengl, T., Reuter, H.I., Elsevier, (Developments in Soil
Science 33) - p. 121 - 140.
Temme, A.J.A.M., Veldkamp, A. 2009. Multi-process Late Quaternary landscape evolution
modelling reveals lags in climate response over small spatial scales. Earth Surface Processes and
Landforms 34 (4), p. 573 - 589.