ALBERT

Beschreibung: Hydrological classification has emerged as a suitable procedure to disentangle the inherent hydrological complexity of river networks. This practice has contributed to determine key biophysical relations in fluvial ecosystems and the effects of flow modification. Thus, a plethora of classification approaches, which agreed in general concepts and methods but differed largely in specific procedures, have emerged in the last decades. However, few studies have compared the implication of applying contrasting approaches over the same hydrological data. In this work, using cluster analysis and modelling approaches, we classify the entire river network covering the northern third of the Iberian Peninsula. Specifically, we developed classifications of increasing level of detail, ranging from 2 to 20-class levels, either based on raw and normalized daily flow series and using two contrasting approaches to determine class membership: Classify-Then-Predict (ClasF) and Predict-Then-Classify (PredF). Classifications were compared in terms of their statistical strength, the hydrological interpretation, the ability to reduce the bias associated to the underrepresented parts of the hydrological space and the spatial correspondence. The results highlighted that both the data processing and the classification strategy largely influenced the classification outcomes and properties, although differences among procedures were not always statistically significant. The normalization of flow data removed the effect of flow size and generated more complex classifications in which a wider range of hydrologic characteristics were considered. The application of the PredF strategy produced, in most of the cases, classifications with higher discrimination ability, greater ability to address the bias associated with the presence of distinctive gauges and classifications in which classes were more evenly distributed than using the ClasF strategy.