Spatial scaling has been one of the fundamental problems in eco-hydrological modeling over the past two decades. The input parameters for regional climate change impact research operating at a 1~50 km resolution cannot be directly derived from GCM (general circulation model) operating at a 100~500 km resolution. Conversely, large scale eco-hydrological models can only simulate the grid-based integrated response instead of directly parameterizing the small-scale earth surface processes. Therefore, upscaling methodology is required to scale up the information derived from the small-scale to the information required for the large-scale models; and downscaling methodology is required to scale down the information from large scale model, i.e. GCM, to the information required for regional eco-hydrological models, which operate at a much smaller scale than the one for a GCM. Several methodologies have been developed over the past two decades to undertake these non-trivial upscaling and downscaling tasks. In this paper, we discuss how the upscaling and downscaling schemes have been implemented in eco-hydrological modeling.
Two primary downscaling schemes are reviewed in this paper. The first is empirical statistical downscaling, which disaggregates the information through establishing the empirical statistical relationships that link the information between small scale and large scale by comparing the large-scale values with long-term historical observation. The second scheme is dynamic downscaling, which disaggregates information by downscaling the information generated from dynamically coupling a RCM(regional climate model) with a GCM. Two upscaling schemes, empirical statistical upscaling and mosaic upscaling, are examined in this paper. Empirical statistical upscaling is achieved by assuming that the sub-grid variability of environmental variables can be represented by a probability density function (PDF), such as the VIC (variable infiltration capacity) model and the gamma distribution model. Mosaic upscaling scheme subdivides a big grid into several patches and the environmental variables are evaluated separately for each patch, and then averaged. We suggest an approach that combines the mosaic and PDF scheme for upscaling the modeling outputs from catchment to global scales.