Implícitas Cuestiones

In this study and following the definitions proposed by Blöschl and Sivapalan (1995), the term ‗scale‘ refers to a characteristics length or time, and the term ‗scaling‘

denotes a change in scale. Moreover, upscaling means transferring information from smaller to larger scales (i.e. aggregating) whereas downscaling refers to the opposite transference of information, where the information is disaggregated from large to small scales.

In general the scale at which the data is collected is different from the scale at which predictions are needed. Measurements are made to get information about the

of the processes mainly due to instrument error and the spatial dimensions of the instruments. Hence, patterns of the data will differ from the true natural patterns.

Precipitation, for example, is measured at widely space points typically with fine resolution. Even at an experimental watershed, spacing between gauges may be on the order of 5 to 10 km. In order to capture the diurnal pattern of heating and cooling on the surface temperature, which can be a strongly nonlinear process, climate information is needed on time scales of at least one to a few hours. Interpolation of monthly precipitation appears reasonable in some studies, but hourly or even daily precipitation cannot be reasonably interpolated from widely spaced precipitation gages (Johnson and Handson, 1995). Similar problems almost certainly exist for temperature and longwave radiation. Wind data, so critical to blowing snow and turbulent heat transfers, is even rarer than precipitation data.

Typically, the modelling or working scale is a compromise between the process representation and the model application. Since more often than not the modelling scale is different from the process scale (i.e. scale that the natural phenomena exhibit) and much larger than the observation scale (i.e. scale at which observations are sampled), scaling techniques are needed to bridge this gap (Blöschl and Sivapalan, 1995). Thus, interpolation and aggregation/disaggregation techniques are the more common methods used. Interpolation techniques estimate patterns from points (i.e. changes of scale in terms of spacing) whereas aggregation methods involve the combination of a number of point values in space to form one average value (i.e. change of scale in terms of support) which correspond to an increase in support scale. Disagregation methods on the other hand, are the opposite transformation and estimate patterns from spatial average values.

Hydrological models are sensitive to scaling issues (Klemeš, 1983; Gupta et al., 1986; Beven, 2001a). At small scales, basin response is dominated by specific features (e.g. macropore distribution, variation of saturated hydraulic conductivity with depth, canopy influences in snowmelt) whereas at larger scales basin response is largely

controlled by the spatial distribution of meteorological and hydrologic inputs (e.g.

precipitation, SWE), topography and landcover types. As a result, application of models at large scales should include some transference of information or adjustment of model parameters to account for the difference in scales between the process description and the model application. The typical modelling approach is to apply the same model structure in several basins whereas the parameters, empirical or not, are varied in the calibration process. This means that the model structure is general but not the parameters. Therefore, a change in scale might involve a change in the parameter values, in particular if these parameters are related to local conditions such as climate and physiography (Bergströn and Graham, 1998). Moreover, due to the data is combined (i.e. aggregated and/or disaggregated) in the models, predictions will, in general, be different from the apparent variance of the data (Blöschl, 1999).

A particular scaling issue is the coupling between atmospheric and hydrological models using the LSS as the common link. In general, LSSs, also known as soil-vegetation atmospheric transfer schemes (SVATS), are meant to provide the lower boundary conditions to Global Climate Models (GCM). Because the grid scale of GCMs is typically 2.5 x 2.5 or 15 km x 15 km like in the regional configuration of the Global Environmental Multiscale (GEM) model of Canada, landcover will be strongly heterogeneous within a model grid (De Boer 2001). Reliable representation of the landscape heterogeneity requires the application of upscaling techniques to transfer information from small to larger scales. This had been performed by assuming that the landcover of the model grid is represented by the dominant landcover type within the grid. This approach has serious limitations since it can not capture the natural variability and the different spatial scales that hydrological processes exhibit. An alternative method for representing the landscape heterogeneity is the mosaic approach, in which each grid cell is subdivided into a number of tiles and the LSS is run on each

tile independently to calculate the energy and water fluxes to the atmosphere. Soulis et at. (2000) combined the Canadian Land Surface Scheme (CLASS; Verseghy, 1991;

Verseghy et al., 1993) with a hydrological streamflow model (WATFLOOD; Kouwen, 1988; Kouwen et al., 1993) to provide a stand-alone land surface hydrological (LSH) model known as WATCLASS. This model evolved later into the MESH modelling system (Pietroniro et al., 2007) which allows running the LSH coupled to the atmospheric model (online version) or as a stand-alone (offline) version. This coupling system has the flexibility to run the LSH model at different time and space scales relative to the atmospheric model, but more appropriate for hydrological simulation, while still providing two-way water and energy feedback between the atmosphere and the land surface (Soulis et al., 2005).