Erosion models: quality of spatial predictions

An overview is given on the predictive quality of spatially distributed runoff and erosion models. A summary is given of the results of model comparison workshops organized by the Global Change and Terrestrial Ecosystems Focus 3 programme, as well as other results obtained by individual researchers. The results concur with the generally held viewpoint in the literature that the predictive quality of distributed models is moderately good for total discharge at the outlet, and not very good for net soil loss. This is only true if extensive calibration is done: uncalibrated results are generally bad. The more simple lumped models seem to perform equally well as the more complex distributed models, although the latter produce more detailed spatially distributed results that can aid the researcher. All these results are outlet based: models are tested on lumped discharge and soil loss or on hydrographs and sedigraphs. Surprisingly few tests have been done on the comparison of simulated and modelled erosion patterns, although this may arguably be just as important in the sense of designing anti-erosion measures and determining source and sink areas. Two studies are shown in which the spatial performance of the erosion model LISEM (Limburg soil erosion model) is analysed. It seems that: (i) the model is very sensitive to the resolution (grid cell size); (ii) the spatial pattern prediction is not very good; (iii) the performance becomes better when the results are resampled to a lower resolution and (iv) the results are improved when certain processes in the model (in this case gully incision) are restricted to so called 'critical areas', selected from the digital elevation model with simple rules. The difficulties associated with calibrating and validating spatially distributed soil erosion models are, to a large extent, due to the large spatial and temporal variability of soil erosion phenomena and the uncertainty associated with the input parameter values used in models to predict these processes. They will, therefore, not be solved by constructing even more complete, and therefore more complex, models. However, the situation may be improved by using more spatial information for model calibration and validation rather than output data only and by using 'optimal' models, describing only the dominant processes operating in a given landscape. Copyright (C) 2003 John Wiley Sons, Ltd.