Classification of River Networks for Prediction in Ungauged Basins
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The majority of the world's river basins remainungauged and, therefore, empirical techniques forpredicting floods and droughts cannot be applied. Analternative approach is to develop continuoussimulation models whose parameters pertain tophysical or hydrological properties of the riverbasins. However, difficulties related to scale,heterogeneity and complexity of real river basinshave made a priori estimation of such parametersimpossible: their estimation has always requiredcalibration using river flow data. Therefore,estimating hydrological model parameters in ungaugedriver basins is one of th...
The majority of the world's river basins remainungauged and, therefore, empirical techniques forpredicting floods and droughts cannot be applied. Analternative approach is to develop continuoussimulation models whose parameters pertain tophysical or hydrological properties of the riverbasins. However, difficulties related to scale,heterogeneity and complexity of real river basinshave made a priori estimation of such parametersimpossible: their estimation has always requiredcalibration using river flow data. Therefore,estimating hydrological model parameters in ungaugedriver basins is one of the greatest challengescurrently facing hydrologists. In this work, a novelmethod for classifying river basins according totheir physical properties is proposed. The studyfocuses on the surface flow component, applying themethodology to identify the best classifiers forsurface flow through river networks. This requiredsimulating river flow through a large number ofScottish river basins, developing a flow routingmodelling system that extracts river network detailfrom digital databases and numerically solves adistributed flow routing model.
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