Using the results of a fully three‐dimensional model of variably saturated flow on a heterogeneous hillslope, the concept of equivalent homogeneous hillslopes is explored. By considering single realizations of random patterns of saturated hydraulic conductivity, attempts are made to determine single effective hydraulic conductivity values capable of reproducing both subsurface and surface flow hydrographs. For the case of high‐permeability soils, effective parameters were found to reasonably reproduce the hillslope hydrograph, although there was no consistent relationship between the effective values and the moments of the spatial distributions. For the case of low‐permeability soils, characterized by surface flow domination of the runoff hydrograph, single effective parameters were not found to be capable of reproducing both subsurface and surface flow responses. Furthermore, the event dependency of effective conductivity values under such conditions was demonstrated.

A fully three‐dimensional model of variably saturated flow on a hillslope has been used to explore the effects of different random patterns of saturated hydraulic conductivity on a 150 m by 100 m hillslope. Both surface and subsurface runoff production are simulated. The model's simulations suggest that peak discharges and runoff volumes are generally increased by the presence of heterogeneity, increasing with increasing variance and spatial dependence of the underlying random field. Simulations using different realizations with the same random field parameters show that for the case of soils showing no dependence, differences in runoff production between realizations are small at this scale of hillslope. The differences increase with increasing spatial dependence, particularly for the case of low‐conductivity soils producing surface runoff.

The identification of disease clusters in space or space-time is of vital importance for public health policy and action. In the case of meticillin-resistant Staphylococcus aureus (MRSA) it is particularly important to distinguish between community and healthcare-associated infections, and to identify reservoirs of infection. 832 cases of MRSA in the West Midlands (UK) were tested for clustering and evidence of community transmission, after being geo-located to the centroids of UK unit postcodes (postal areas roughly equivalent to Zip+4 zip code areas). An age-stratified analysis was also carried out at the coarser spatial resolution of UK Census Output Areas. Stochastic simulation and kernel density estimation were combined to identify significant local clusters of MRSA (p<0.025), which were supported by SaTScan spatial and spatio-temporal scan. In order to investigate local sampling effort, a spatial 'random labelling' approach was used, with MRSA as cases and MSSA (meticillin-sensitive S. aureus) as controls. Heavy sampling in general was a response to MRSA outbreaks, which in turn appeared to be associated with medical care environments. The significance of clusters identified by kernel estimation was independently supported by information on the locations and client groups of nursing homes, and by preliminary molecular typing of isolates. In the absence of occupational/lifestyle data on patients, the assumption was made that an individual's location and consequent risk is adequately represented by their residential postcode. The problems of this assumption are discussed, with recommendations for future data collection.

Perceived benefits of the best value strategy and the problems the lowest price strategy has caused in the construction industry; has led to the increase in the use of the best value strategy in selecting contractors. Whilst there is research that have tried to establish a direct relationship between a contractor selection strategy and the project outcomes, there are hardly any empirical research that tries to establish this relationship. This paper presents a quantifiable method of assessing the risk of selecting different contractor selection strategies using educational facilities projects in the UK. A Monte-Carlo simulation study was conducted to assess how the lowest priced contractor would have fared against the best value contractor had it been awarded the contract instead. It was concluded that selecting the best value contractor in educational facilities projects is not necessary in terms of cost. Furthermore, though the results are limited to educational facilities project, the method can be adapted to other sectors.

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