There is growing interest in a data integration approach to survey sampling, particularly where population registers are linked for sampling and subsequent analysis. The reason for doing this is simple: it is only by linking the same individuals in the different sources that it becomes possible to create a data set suitable for analysis. But data linkage is not error free. Many linkages are nondeterministic, based on how likely a linking decision corresponds to a correct match, that is, it brings together the same individual in all sources. High quality linking will ensure that the probability of this happening is high. Analysis of the linked data should take account of this additional source of error when this is not the case. This is especially true for secondary analysis carried out without access to the linking information, that is, the often confidential data that agencies use in their record matching. We describe an inferential framework that allows for linkage errors when sampli
Ongoing changing climate has raised the attention towards weather driven natural hazards, such as rain-induced flash floods. Flooding model provides an efficient tool in flash flood warning and hazard management. More and more evidence showed significant impacts from sediment on hydrodynamic and flooding hazard of flash flood. But little information is provided regarding flooding hazard sensitivity to sediment characteristics, which hampers the inclusion of sediment characteristics into flash flood warning system and hazard management. This study used 1D model to simulate flood hazards. After calibrating and validating the hydrodynamic model, we carried out simulations to test the sensitivity of flood hazard to sediment characteristics of inflow point, size distribution and concentration. Our results showed that sediment from highly erosive slopes affects the flooding hazard more than sedimentsfrom watershed. This is particularly true when sediments are fine particles with medium size