Classification of Models
2.4 Classification of Models
Many attempts have been made to classify hydrological models (or model codes). Refsgaard (1996) presented the classification shown in Fig. 6 that I have used in all papers of the present thesis. Deter- ministic models can be classified according to whether the model gives a lumped or a distributed de- scription of the considered area, and whether the description of the hydrological processes is empirical, conceptual, or more physically-based. A lumped model implies that the catchment is considered as one computational unit. A distributed model, on the other hand, provides a description of catchment proc- esses at geo-referenced computational grid points within the catchment. An intermediate approach is a semi-distributed model, which uses some kind of distribution, either in sub-catchments or in hydrologi- cal response units, where areas with the same key characteristics are aggregated to sub-units without considering their actual locations within the catchment. Examples of hydrological response units con- sidered in semi-distributed models are elevation zones, which are relevant for snow modelling, and combinations of soil and vegetation type, which may be relevant for simulation of root zone processes such as evapotranspiration and nitrate leaching.
As most conceptual models are also lumped, and as most physically-based models are also distributed, the three main classes emerge: x Empirical (black box) x Lumped conceptual models (grey box) x Distributed physically-based (white box)
The classification is discussed in some details in Refsgaard (1996). Here, the focus is on the two tradi- tional approaches in deterministic hydrological catchment modelling, namely the lumped conceptual and the distributed physically-based ones. The fundamental difference between these two types of models lies in their process descriptions and the way spatial variability is treated. The distributed physi- cally-based models contain equations which have originally been developed for point scales and which provide detailed descriptions of flows of water and solutes. The variability of catchment characteristics is accounted for explicitly through the variations of hydrological parameter values among the different computational grid points. This approach leaves the variability within a grid as un-accounted for, which in some cases is of minor importance but in other cases may pose a serious constraint. The lumped conceptual models uses empirical process descriptions, which have built-in accounting for the spatial variability of catchment characteristics.
Fig. 6 Classification of hydrological models according to process description (Refsgaard, 1996).
Typical examples of lumped conceptual model codes are the Stanford Watershed Model (Crawford and Linsley, 1966), the Sacramento (Burnash, 1995), the HBV (Bergström, 1995) and the NAM (Nielsen and Hansen, 1973). Typical examples of distributed physically-based model codes are the MIKE SHE (Abbott et al., 1986a, b; Refsgaard and Storm, 1995) and the Thales (Grayson et al., 1992a, b). Groundwater model codes like MODFLOW belong to the distributed physically-based class.
The classification has some shortcomings that should be noted. First of all, the use of the term ‘concep- tual model’ is unfortunate, because this is a different meaning of the term as compared to the definition given in Section 2.2 and used in the modelling protocols (Section 2.3). This can cause some confusion, but to introduce a new term completely different from what is used by almost all other scientists in the community of catchment modelling may cause even more confusion. Secondly, and more fundamental, the names of the classes should be considered as relative rather than absolute. For example Beven (1989) argued that in most applications physically-based models are used as lumped conceptual mod- els at the grid scale. As discussed in [4] I agree that some degree of lumping and conceptualisation will always need to take place, but that in spite of this there is a fundamental difference in the functioning and, as shall also be discussed later, of the applicability of the two model types.