Testing the realism of a topography-driven model (FLEX-Topo) in the nested catchments of the Upper Heihe, China
Although elevation data are globally available and used in many existing hydrological models, their information content is still underexploited. Topography is closely related to geology, soil, climate and land cover. As a result, it may reflect the dominant hydrological processes in a catchment. In this study, we evaluated this hypothesis through four progressively more complex conceptual rainfall-runoff models. The first model (FLEX L) is lumped, and it does not make use of elevation data. The second model (FLEX D) is semi-distributed with different parameter sets for different units. This model uses elevation data indirectly, taking spatially variable drivers into account. The third model (FLEX T0), also semi-distributed, makes explicit use of topography information. The structure of FLEX T0 consists of four parallel components representing the distinct hydrological function of different landscape elements. These elements were determined based on a topography-based landscape classification approach. The fourth model (FLEX T) has the same model structure and parameterization as FLEX T0 but uses realism constraints on parameters and fluxes. All models have been calibrated and validated at the catchment outlet. Additionally, the models were evaluated at two sub-catchments. It was found that FLEX T0 and FLEX T perform better than the other models in nested sub-catchment validation and they are therefore better spatially transferable. Among these two models, FLEX T performs better than FLEX T0 in transferability. This supports the following hypotheses: (1) topography can be used as an integrated indicator to distinguish between landscape elements with different hydrological functions; (2) FLEX T0 and FLEX T are much better equipped to represent the heterogeneity of hydrological functions than a lumped or semi-distributed model, and hence they have a more realistic model structure and parameterization; (3) the soft data used to constrain the model parameters and fluxes in FLEX T are useful for improving model transferability. Most of the precipitation on the forested hillslopes evaporates, thus generating relatively little runoff.