Partitioning Spatial Model Uncertainty Based on Joint Spatial Simulation

In this study, an uncertainty analysis procedure for joint sequential simulation of multiple attributes of spatially explicit models used in geographical informational systems was developed based on regression analysis. This procedure utilizes information obtained from joint sequential simulation to establish the relationship between model uncertainty and variation of model inputs. Using this procedure, model variance can be partitioned by model input parameters on a cell by cell basis. In the partitioning, the correlation of neighboring cells is accounted for. With traditional uncertainty analysis methods, this is not possible. In a case study, spatial variation of soil erodibility from a joint sequential simulation of soil properties was analyzed. The results showed that the regression approach is a very effective method in the analysis of the relationship between variation of the model output and model input parameters. It was also shown for the case study that: (1) the uncertainty of soil erodibility of a cell is mainly propagated from its own soil properties; (2) the interactions of soil properties of neighboring cells could reduce uncertainty of soil erodibility; (3) it is sufficient for uncertainty analysis to include the nearest three neighboring cell groups; and (4) the largest uncertainty contributors vary by soil properties and location.