Alternative approaches to assessing soil erosion
There are a number of possible methodologies for creating a coarse scale erosion map (Gobin et al., 2004). Some of these are based on the collection of distributed field observations, others on an assessment of factors, and their combination, which influence erosion rates, and others primarily on a modelling approach. All of these methods require calibration and validation, although the type of validation needed is different for each category. There are also differences in the extent to which the assessment methods identify past erosion of an already degraded soil resource, as opposed to risks of future erosion, under present climate and land use, or under scenarios of global change.
Process models have the potential to respond explicitly and in accordance with experience to changes in climate or land use, and so have great promise for developing scenarios of change and 'what-if' analyses of policy or economic options. Set against this advantage, process models generally make no assessment of degradation up to the present time, and can only incorporate the impact of past erosion where this is recorded in other data, such as soil data bases. Models also generally simplify the set of processes operating, so that they may not be appropriate under particular local circumstances. Although the USLE and RUSLE are the models most widely applied in Europe (e.g. van der Knijff et al, 2000), the USLE-approach is now widely considered to be conceptually flawed in that it fails to properly distinguish between soil and climatic conditions in the infiltration process. The other models that are now emerging are based on runoff thresholds (e.g. Kirkby et al, 2000) or the MIR (Minimum Information Requirement) approach (Brazier et al, 2001) applied to the more complex USDA WEPP model (Nearing et al, 1989).
The application of a process model has been preferred here for three main reasons:
- It applies the same objective criteria to all areas, and so can be applied throughout a region, subject to the availability of suitable generic data;
- It provides a quantitative estimate of erosion rate which can be compared with long term averages for tolerable erosion; and
- The methodology can be re-applied with equal consistency as available data sources are improved, and for past and present scenarios of changed climate and land use.
Nevertheless a process model, and particularly a coarse-scale model such as PESERA, has a number of inherent disadvantages compared to simpler models, including:
- The need for input data which may not be freely or readily available. For example it has not been possible to access Europe-wide climate data at better than daily resolution, or better than 50km or 0.5 degree spatial resolution, even though such data could be purchased from national databases (but at prohibitive cost). It is well known (e.g. Wainwright and Parsons, 2002) that there is a inter-dependence between temporal resolution and erosion estimates;
- The need to rely on spatial soil data that have been collected nationally, using criteria that differ from country to country, combined into soil types that are not completely uniform, and only partially harmonised when compiled in the European Soil Map (King et al., 1994) and incorporated into the European Soil Database (King et al., 1995); and
- An inevitable concentration on the relevant dominant processes that are most widespread, in this case infiltration excess overland flow, so that erosion by saturation overland flow and snowmelt, for example, are less well estimated.
There are many pitfalls and alternative approaches to the issues of scale. Here we focus primarily on a single spatial scale, even though this scale is applied over a large spatial extent. Some of the particular problems identified by Zhang et al (2002) are thus minimized and the approaches used have been deliberately selected to reduce the impact of scale. For example the use of relief has been found empirically to be much less sensitive to DEM resolution than estimated gradients, provided that relief is measured within the same radius around each point. Similarly cover is generally defined, via land use, at the field scale rather than from the scale-dependent estimates derived from remote sensing.
It is recognised that the interdependence between infiltration parameters and the temporal resolution of rainfall discussed by Wainwright & Parsons (2002) remains a potential problem as spatial scale changes. In practice, this means that the effective storage capacities vary with spatial resolution. In the model as described here, however, spatial scale remains fixed at 1 km, so that the runoff thresholds used are implicitly linked to this scale, and might need to change only when the spatial scale is altered.