A Canadian viewpoint on data, information and uncertainty in the context of prediction in ungauged basins

Abstract

The quality (i.e. the degree of uncertainty that results from the interpretation and analysis) of information dictates its value for decision making. There has been much progress towards improving information on the water budgets of ungauged basins by improving knowledge, tools and techniques during the Prediction in Ungauged Basins (PUB) initiative. These improvements, at least in Canada, have come through efforts in both hydrological process and statistical hydrology research. This paper is a review of some recent Canadian PUB efforts to use data to generate information and reduce uncertainty about the hydrological regimes of ungauged basins. The focus is on the Canadian context and the problems it presents, but the lessons learned are applicable to other countries with similar challenges. With a large land mass that is relatively poorly gauged, novel approaches have had to be developed to extract the most information from the available data. It can be difficult in Canada to find gauged or research basins sufficiently similar to ungauged sites of interest that contain the data required to force either statistical or deterministic models. Many statistical studies have improved information or at least an understanding of the quality of that information, of ungauged basin streamflow regimes using innovative regression-based approaches and pooled frequency analysis. Hydrological process research has reduced knowledge uncertainty, particularly in regard to cold regions processes, and this situation has led to the development of new algorithms that are reducing predictive uncertainty. There remains much to do. Current progress has created an opportunity to better integrate statistical and deterministic models via data assimilation of regionalization model estimates and those from coupled atmospheric-hydrological models. Aspects of such a modelling system could also provide more robust uncertainty analyses than traditional approaches.