A strategy for controlling error of distributed environmental models by aggregation

In this paper a strategy for controlling error when calculating model parameter values from low-resolution low-accuracy data (LRLA-data) is presented. The basic principle of the proposed strategy is that the error in the model output can be reduced by output aggregation. By comparing the output from LRLA input data and high-resolution, high-accuracy input data (HRHA-data) at various aggregation levels, the error due to a decrease in accuracy and resolution of the input data can be assessed. An aggregation level can then be chosen so that an output is generated with the desired level of error and sufficient spatial detail. The method may also be used to evaluate the information loss when simplified models are used. The calculation of the topographic factors in an erosion model is taken here as an example application.