Abstract:
Catchment-scale runoff generation involves a complex interaction of physical and chemical processes operating over a wide distribution of spatial and temporal scales. Understanding runoff generation is challenged by this inherent complexity ? the more uncertain step of predicting the hydrologic response of catchments is that much more challenging. Many different hypotheses have been implemented in hydrological models to capture runoff generation processes and provide hydrologic predictions. These concepts have been developed based on extended field observations. Here we propose inferring water flux understanding and catchment exploring through the application of a variety of available hydrological models as a mechanism to build upon and extend models that have been developed to capture particular hydrological processes. We view this ensemble modeling strategy as particularly appropriate in ungauged catchments. The study is carried out in a tropical montane rainforest catchment in Southern Ecuador. The catchment is 75 km2 and is covered by forest in the south, while the northern slopes have been partly deforested for grazing. Annual rainfall is highly variable, reaching up to 5700 mm per year in the upper parts of the catchment. To explore the dominating runoff processes, an ensemble of 6 hydrological models with different structures applied over different levels of both spatial and temporal detail was developed. The ensemble includes spatially lumped (HBV-light), semi-distributed (HEC-HMS, CHIMP, SWAT, LASCAM) and a fully distributed model (HBV-N-D). The hydro-statistical toolkit WETSPRO was used to characterize simulated and observed hydrographs. Estimated baseflow indices, flow minima and maxima, flow duration curves and cumulative errors were generated and compared among the ensemble of models. This process facilitated the exploration of processes controlling runoff generation, enabled an evaluation of the applicability of the screened models to tropical montane rainforests, and provided the capacity to evaluate and explain where different models failed.
