Classification of River Networks for Prediction in Ungauged Basins

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.