Analysis of aggregation and disaggregation effects for grid-based hydrological models and the development of improved precipitation disaggregation procedures for GCMs

Appropriate representation of hydrological processes within atmospheric Gen eral Circulation Models (CGMs) is important with respect to internal model dynamics (e.g. surface feedback effects on atmospheric fluxes, continental runoff production) and to simulation of terrestrial impacts of climate chan ge. However, at the scale of a GCM grid-square, several methodological prob lems arise. Spatial disaggregation of grid-square average climatological pa rameters is required in particular to produce appropriate point intensities from average precipitation. Conversely, aggregation of land surface hetero geneity is necessary for grid-scale or catchment scale application. The performance of grid-based hydrological models is evaluated for two larg e (10(4)km(2)) UK catchments. Simple schemes, using sub-grid average of ind ividual land use at 40 km scale and with no calibration, perform well at th e annual time-scale and, with the addition of a (calibrated) routing compon ent, at the daily and monthly time-scale. Decoupling of hillslope and chann el routing does not necessarily improve performance or identifiability Scal e dependence is investigated through application of distribution functions for rainfall and soil moisture at 100 km scale. The results depend on clima te, but show interdependence of the representation of sub-grid rainfall and soil moisture distribution. Rainfall distribution is analysed directly using radar rainfall data from t he UK and the Arkansas Red River, USA. Among other properties, the scale de pendence of spatial coverage upon radar pixel resolution and GCM grid-scale , as well as the serial correlation of coverages are investigated. This lea ds to a revised methodology for GCM application, as a simple extension of c urrent procedures. A new location-based approach using an image processing technique is then p resented, to allow for the preservation of the spatial memory of the proces s.