Classification of River Networks for Prediction in Ungauged Basins

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.

Autorentext

Anne F. J. Reungoat, Process Engineer - Université de Technologie de Compiègne (UTC), France ; M.Sc. in Water Resources Engineering Management - Heriot-Watt University, United Kingdom ; Ph.D.in Civil Engineering Hydrology, University of Glasgow, United Kingdom.

Klappentext
The majority of the world's river basins remain ungauged and, therefore, empirical techniques for predicting floods and droughts cannot be applied. An alternative approach is to develop continuous simulation models whose parameters pertain to physical or hydrological properties of the river basins. However, difficulties related to scale, heterogeneity and complexity of real river basins have made a priori estimation of such parameters impossible: their estimation has always required calibration using river flow data. Therefore, estimating hydrological model parameters in ungauged river basins is one of the greatest challenges currently facing hydrologists. In this work, a novel method for classifying river basins according to their physical properties is proposed. The study focuses on the surface flow component, applying the methodology to identify the best classifiers for surface flow through river networks. This required simulating river flow through a large number of Scottish river basins, developing a flow routing modelling system that extracts river network detail from digital databases and numerically solves a distributed flow routing model.