Kansas Academy of Science
Practical and theoretical basis for mapping landscape sensitivity in the Southern Canadian Interior Plains1
David J. Sauchyn
Department of Geography, University of Regina, Regina, Saskatchewan, Canada S4S 0A2
This article is published in the Transactions of the Kansas
Academy of Science, vol. 100, no. 1/2, p. 61-72 (1997).
The Canadian Climate Centre's general circulation model predicts that, with increased CO2
concentrations, the largest rise in mean surface temperature in southern Canada will occur in the
Interior Plains. For five years the Palliser Triangle Global Change Project has focused on
geomorphic responses to climate in the driest part of this region. A major component of this
project is the GIS modeling and mapping of landscape sensitivity, the potential for change in rates
of surface processes. Soil landscapes, defined by superimposing 1:100,000 digital soil maps with
slope polygons derived from 1:50,000 digital topographic maps, are combined with digital maps
of climate, surficial geology, land cover and hydrography to evaluate the climatic and geopotential
energy for geomorphic work (e.g., rainfall erosivity, relief) and resistance to geomorphic activity
(e.g., soil texture, land cover). The spatial relations between resistance and potential disturbance
are the basis for determining the sensitivity of soil landscapes. Dimensionless, smoothed and
synthetic data are the basis for expressing the properties of slopes and streams as regional
landscape parameters. This methodology is better suited to a regional scale and a subhumid
poorly integrated plains landscape than are empirical soil loss models or the more demanding
physically based models.
1 Palliser Triangle Global Change Contribution no. 32
With the availability of digital geographic data, widespread use of GIS, and greater emphasis on
social relevance of scientific research, modeling of biophysical processes and landscape change is
increasingly spatially distributed over larger areas. In geomorphology, the traditional one-dimensional models
of slope profiles and stream channels have been extrapolated to two-dimensional landscape-scale models of
landforms and geomorphic processes (e.g., Moore et al. 1988; Dietrich et al. 1992). This generally is achieved
by coupling process models and relatively high-resolution digital geographic data, using a GIS (e.g., Desmet
and Govers, 1995; Mitasova et al. 1996).
This paper describes a conceptual framework for the GIS-modeling of landscape sensitivity to
climatic variability and change. The identification and mapping of metastable (sensitive) soil
landscapes at a regional scale (1:100,000) differs significantly from the prediction of soil erosion
at local scales over much smaller areas, the typical function of geomorphic models. Therefore
existing models of geomorphic processes and landscape change are not transferable to our study
without adjustments for scale. Furthermore the southern Canadian Interior Plains lack the fluvial
dissection, drainage development and integration of slopes and channels that most models assume.
The conceptual framework for our regional evaluation of landscape sensitivity has two parts: a
strategy to characterize regional soil landscapes with parameters measured at local scales, and a
model to convert these parameters into an expression of landscape sensitivity.
This research is a major component of the Palliser Triangle Global Change Project initiated by the
Geological Survey of Canada to examine geomorphic responses to climatic
change and variability in the driest part of the Interior Plains (Lemmen et al. 1993). According to the
Canadian Climate Centre's general circulation model (GCM), the largest CO2-induced rise in mean
surface temperature in southern Canada will occur in the Interior Plains (Boer et al. 1992). The impacts of
this climatic change will be greatest at the margins of land and
climate suitable for annual crop production, that is, the subhumid Palliser Triangle--see Fig.
1. The sustainability of dryland agriculture depends, to a large degree, on adjustment of land use and
production systems to climatic variability, the periodic fluctuation of atmospheric conditions (e.g.,
drought, early frosts, major storms) and to climatic change, a significant departure from previous
average conditions (Environment Canada, 1995). Historically prairie agriculture has succeeded in
adapting to climatic variability (Hill and Vaisey, 1995:52).
Various studies have examined the potential direct or 'first-order' impacts of global climatic
change on prairie agriculture (e.g., Schweger and Hooey, 1991). However little attention has
been paid to the links among land and water resources, climatic variability and change, and rates
of earth surface processes (e.g., Wheaton, 1984; Favis-Mortlock and Boardman, 1995). Any
attempt to anticipate the degree and distribution of landscape sensitivity must first consider the
distinctive physical and cultural landscapes of the southern Interior Plains. The unique character
of this region strongly influences the suitability of existing models of surface processes and the
application of geomorphic research to institutional land management.
Return to Table of Contents
STUDY AREA AND THE GIS DATABASE
The driest part of the southern Interior Plains is the brown soil zone of southwestern
Saskatchewan and southeastern Alberta--see Fig. 1, also known as the Palliser Triangle. It is the
only major region of Canada where seasonal water deficits characterize the physical environment
and limit economic activity. Most landforms and surficial deposits are the product of late-Pleistocene deglaciation. Due to the dry climate and recent geomorphic history, the landscape is
poorly integrated, there are few permanent streams, and large areas are internally drained. In
general the region lacks the order and characteristic landforms of a well-developed fluvial
landscape and rather consists of largely disconnected soil landscapes with various geomorphic
histories. Persistence of Tertiary and Pleistocene morphology is juxtaposed with landforms of
recent origin (Sauchyn, 1993a).
The surficial geology is dominated by glacial drift derived primarily from underlying argillaceous,
poorly consolidated Cretaceous sediments. The glacial soils support a vast area of cropland and
pasture, but are susceptible to erosion where plant cover is lacking. Rates of erosion on cropland
are 2-3 times higher than sediment yields in small watersheds (Carson & Associates, 1990),
because wind and water erosion mostly redistribute soil within local landscapes, especially in the
hummocky moraine (Martz and de Jong, 1991; Pennock and de Jong, 1990). Fields (single crops)
are typically a quarter section (65 ha) and the average farm is about 800 ha (almost 2000 acres).
Cattle ranches encompass many sections (1000s ha) and represent the only land use over large
areas. The rural population density is less than one person per km². Five Canadian census
divisions, representing 104,043 km² of the brown soil zone, have a population of 133,116, but
44% of this total is in the cities of Swift Current and Medicine Hat--see Fig. 1 (Statistics Canada,
1992a, 1992b). In this context, detailed geomorphic studies have little immediate socio-economic
Table 1 lists the digital geographic data available for the study area. The basic components are
1:100,000 soil maps and 1:50,000 topographic maps. The basic spatial units are the soil map
polygons, as these are defined in terms of soil and landform. Digital topographic data are
preferred to the categorical (slope class) and descriptive (landform type) data provided with a soil
survey. Assigned to each soil landscape polygon are the inherent physical characteristics that
govern geomorphic response to climatic variability and change. Over 100s of km² and 100s of years
(steady time), the soil landscape boundaries and attributes are considered independent of climate
Table 1. Contents of the Palliser Triangle digital geographic database.
||Region or Map Sheet|
||Alberta and Saskatchewan|
||Klassen (1991, 1992)
||72F, 72G |
||72J, 72L, 72H|
||U of C
||RMs 138 and 139|
||Cypress Hills, Sask.|
* Scale of hardcopy map from which data were derived or
scale at which data
are output, expressed as a representative fraction with denominator in 1000's.
Abbreviated phrases: Saskatchewan Institute of Pedology (SIP), Central Survey and
Mapping Agency (CSMA), University of Calgary (U of C), digital elevation model
(DEM), Geological Survey of Canada (GSC), Centre for Land and Biological Resource
Research (CLBBR), Alberta Bureau of Surveys and Mapping (ABSM), Atmospheric
Environment Service (AES), Rural Municipality (RM).
The digital land cover data are classified Landsat TM data from 1991-92. They give the
distribution of crop, pasture, treed areas, bare ground and water. Whereas the interaction
between vegetation, climate and geomorphic processes could be modeled (Kirkby, 1995), land
cover in this region is mostly a function of human activities. It changes annually and seasonally.
Soil conservation practices (e.g., minimum tillage) reflect the continuous adjustment of land use
and management to climatic variability. Since human responses to climate are beyond the scope
of this investigation, there are only three land cover scenarios: 1991-92 (applicable over a longer
period bounded by major land use adjustment in response to institutional and economic factors),
pre-settlement (native) cover, and native cover for a given climatic change scenario.
Return to Table of Contents
The central issue of spatial scale in geomorphology (Schumm, and Lichty, 1965; de Boer, 1992)
includes the dichotomy between the slope- and channel-scale of geomorphic processes and the
regional scale of landscapes and institutional land and water management. Conventional
geomorphic modeling has been profile-based, or at the scale of small catchments, where individual
hillslopes and channels can be represented (e.g., Kirkby, 1989). Despite the use of GIS in the earth
sciences (Bonham-Carter, 1994; Vitek et al. 1996) and the application of
geomorphology to environmental issues (Eybergen and Imeson, 1989; Patrono, 1995), the spatial
dimensions of geomorphic modeling have remained relatively small. For example, Mitasova et al.
(1996:630) developed "methods for computation of topographic factors ...
suitable for complex terrain and applicable to large areas." Their methods were applied to a
"small region" (500 m by 500 m) and a second area, 3.6 km by 4 km. Similarly, Desmet and
Govers (1995) simulated patterns of erosion and deposition over an area of 1.23 km². With
objectives closely related to ours, Kerenyi and Csorba (1991) examined "the sensitivity of the
landscape to climatic conditions" in a 9 km² area. By comparison, our study area is nine
1:250,000 map sheets (138,600 km²), an area larger than England and about the size of Arkansas.
Disaggregating this large region into component slopes is technically feasible with the functions of
a GIS (Sauchyn, 1993b), however, this scale of analysis serves little purpose relative to the scale
of land use activity, as described. Even if there was a purpose, the data processing, storage and
analysis would require major computing resources. When 1:100,000 soil surveys and slope
polygons generated from 1:50,000 topographic data were overlaid for one of the nine 1:250,000
map sheet areas, slightly less than 100,000 unique soil landscapes were mapped (Ambrosi, 1995).
This was after small map units (< 1 ha) had been eliminated.
The evaluation of agricultural soil loss commonly has involved extrapolation of empirical soil loss
equations (e.g., Logan et al. 1982; Snell, 1985). This requires judicious interpretation of
soil loss predictions, because parameter values averaged over heterogeneous map units represent
a misuse of process models derived from plot-scale research (Wischmeier, 1976; Roels, 1985).
Anderson and Knapic (1984) mapped soil erosion risk for western Canada at a scale of
1:2,500,000. Whereas these maps provide a broad geographic overview, they are based
necessarily on a limited number of factors and data of variable quality, given the size and diversity
of the soil landscapes. Our objective and possible results are not maps of soil loss and gain, but
rather the identification of sensitive soil landscapes, those combinations of soil, landform and land
cover that may respond to disturbance of a particular magnitude. We aim to maintain the rigor of
a theoretical approach, while evaluating landscape sensitivity at regional scales (1:50,000 -
1:100,000). Applying geographic data from large scale maps to the evaluation of landscape
sensitivity at smaller map scales requires three strategies for linking spatial scales and the
corresponding use of ratio, smoothed and synthetic data (Thorn, 1988).
Ratio or dimensionless data are scale-independent. For example, the ratios of contributing area
(slope area per unit contour width) to slope length and local relief to drift thickness (or regional
relief) have no units to imply the scale of observation. Relations among morphometric parameters
can be maintained over a range of scales. The second strategy is to generalize or smooth the
spatial data by trend surface analysis or classification. Either approach leaves broad patterns,
although the resulting parameters are either statistical indices (e.g., eigenvectors) or classified
The third strategy for linking scales is to construct a synthetic spatial model based on the
statistical relationships among topography, soil and land cover in each soil landscape unit. The
dominant (modal) attributes captured by the synthetic landform have the dimensions and physical
meaning of the original data. Uncharacteristic slopes and soils (azonal) are excluded from the
evaluation of landscape sensitivity, much like the intuitive process of survey and mapping, where
map units are delimited and classified according to dominant features. The complexity of a soil
landscape, and in turn the storage and processing of its attributes, is reduced to the analysis of a
synthetic landform with the correlative soil and land cover. The GIS enables a two-dimensional
extension to the synthetic slope profile (Caine, 1979).
Return to Table of Contents
As understood and applied here, landscape sensitivity is "the likelihood that a given change in the
controls of a system will produce a sensible, recognizable and persistent response" (Brunsden and
Thornes, 1979:476). Figure 2 is a reproduction of Brunsden and Thornes' (1979:477)
graphic analogy of the unstable, metastable (sensitive) and stable states in a dynamic geomorphic system.
This view of sensitivity led to the concept of the landscape change safety factor: "the ratio of the
magnitude of barriers to change [resistance] to the magnitude of the disturbing forces [energy for
geomorphic work]" (Brunsden and Thornes, 1979:476). Therein lies the basis for evaluating
landscape sensitivity. In fact, Brunsden and Thornes (1979:478) recommended that "A project
for future studies will be to map these safety factor distributions as a predictive aid to landform
The landscape change safety factor is both a conceptual and mathematical model. It implies that
the distribution of landforms and geomorphic processes reflects the spatial covariation of
disturbing and resisting forces. As a mathematical expression, it requires that the barriers and
disturbing forces be identified and quantified. Table 2 is list of parameters which represent either
measures of disturbance by water, wind or gravity or barriers to these forces. It does not include
parameters, barriers in particular, that cannot be evaluated from commonly mapped data because,
for example, they reflect the geomorphic history of a soil landscape (Brunsden, 1993).
Table 2. Parameters for the evaluation of the landscape change sensitivity factor
|soil water storage
||de Ploey, Kirkby and
||fluvial, mass wasting
||Muhs and Maat (1993)|
|precipitation/ potential evapotranspiration
||Wolfe, in press|
Even though the parameters in Table 2 correspond to data in our GIS database, the landscape
safety factor cannot be solved numerically, because for some parameters, especially barriers such
as land use and soil texture, the data are categorical. Considerable field data would be required to
convert soil and vegetation types to quantitative measures of resistance (e.g., shear strength,
boundary layer roughness). There are empirical expressions of soil erodibility and land cover, in
particular, the K and C factors in the Universal Soil Loss Equation. However these factors, and
the USLE in general, have been applied to the Canadian plains with limited success, given the
different soil, climate and topography from western Kansas where the USLE was derived
(Pennock and de Jong, 1990).
Return to Table of Contents
The quantifiable parameters are almost exclusively climatic and topographic. Thus the disturbing
forces can be expressed as numerical indices. Climatic erosion potential (de Ploey, Kirkby, and
Ahnert, 1991; Kirkby, and Cox., 1995) and an index of wind magnitude-frequency (Muhs and
Maat, 1993; Wolfe, in press) can be computed for meteorological stations and assigned to
corresponding elevation-weighted Theissen polygons. The synthetic landforms for each soil
landscape have dimensionless gradients, contributing areas and local relief. Some barriers also are
related to topography and climate: landscape disorder, the ratio precipitation to potential
evapotranspiration, and persistent water deficit (drought) or surplus which reduce resistance to
surface processes and landsliding, respectively.
The methodology must at least differentiate between unstable, stable and metastable landscapes.
The unstable landscapes are active sand dunes, badlands and
unstable slopes that are the focus of field studies in process geomorphology (Campbell,
1982; Wolfe et al. 1995; Sauchyn and Lemmen, 1996). Differentiating between the stable and metastable (sensitive)
soil landscapes then is the crucial function of the model. Our present understanding of the plains
glacial landscape is not sufficient to define the topographic, soil and land cover thresholds that
separate stability and instability, although the many studies of agricultural soil loss in this region
(e.g., Pennock et al. 1995; Mermut et al. 1983; Martz and de
Jong, 1991; Pennock and de Jong, 1990) provide data for the verification of predicted potential
for change. The thresholds fall somewhere on the continuum between the most metastable and
most stable soil landscapes. We can at least establish this continuum and place soil landscapes on
In lieu of a numerical value for the landscape change safety factor for each soil landscape, the
various disturbance and resistance parameters are individually classified and mapped, using
appropriate scales and units. Landscape sensitivity is then determined and mapped by
superimposing the relevant parameter maps for fluvial, eolian and mass wasting processes. A
classification of relative sensitivity emerges from the spatial covariation in barriers and
disturbances, from the unstable and most metastable soil landscapes to those that exhibit the
greatest resistance to change.
Return to Table of Contents
This attempt to model landscape sensitivity differs from the typical geomorphic modeling of soil
loss by overland flow on individual slopes in small fluvially dissected catchments. The study area
is 138,600 km² of subhumid glaciated plains with poorly integrated drainage and weakly linked
slopes and channels. The basic spatial units are aggregations of landforms and soils. The
objective is to identify soil landscapes where there is potential for change in the rates of fluvial,
eolian and mass wasting processes. Ratio (dimensionless) data, synthetic (characteristic)
landforms and the landscape change safety factor are the basis for linking slope and landscape
scales and recognizing sensitive (metastable) soil landscapes.
Maps of landscape sensitivity are not reproduced here, because the product of this research is
digital spatial information, rather than a specific map series or paper maps. The results will be
distributed on CD-ROM, displaying various scenarios of landscape sensitivity. Of greatest
interest is the sensitivity of soil landscapes to weather conditions of a certain probability (e.g.,
annual or 10-year rainfall) or, that is, various scenarios of climatic variability, particularly in
relation to climatic change (Changnon and Changnon, 1992). Another option is to identify
sensitivity to simulated changes in land use. Over most of the study area, human activities
determine surface resistance to disturbance by wind and water. Thus the nature and rate of
adjustment of land use and management practices are the most significant factors determining the
response of agricultural landscapes to climate (Jones, 1993). However it is not the only factor. A
knowledge of the physical characteristics of the soil landscapes, their geographic distribution and
relation to disturbance will guide the adjustment of human activities to climatic change and
Return to Table of Contents
This research is a component of the Geological Survey of Canada's Palliser Triangle Global
Change Project. I gratefully acknowledgment the role of Project Coordinator D.S. Lemmen in
this research. J.S. Aber, G.A. Ambrosi, M. Black, I.A. Campbell, M.J. Kirkby, D.J. Pennock, and
S.A. Wolfe also provided much useful advice. Computing facilities were provided by the
University of Regina.
Return to Table of Contents
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