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

Abstract

The quality (i.e. the degree of unceitainty 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 if 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.

Modelling river flow in cold and ungauged regions: a review of the purposes, methods, and challenges

“…Recent PUB efforts in Canada using process-based models have focused on small basins, which represent a challenge for practicing hydrologists using empirical models (Spence et al 2013;Chapman et al 2018). At this scale, small changes in topo-geomorphological features and predominant hydrological processes can have large impacts on catchment response.…”

“…Although this practice has seen more application for process-based methods, the transferring of parameters can be also executed from empirical models. The topology of HRUs, or their relative location and distribution, has implications for the runoff response across many Canadian landscapes such as the Boreal Plains, Canadian Shield, and Prairies (Spence et al 2013). High spatial resolution data are often not available in Canada, and this impacts the amount of information that can be derived for ungauged basins with regional physiographic data.…”

“…With relatively sparse monitoring data and many complicated and diverse hydrological processes, it is often difficult to produce sound information on certain Canadian hydrological regimes. There is recognition in the scientific community that processbased and empirical approaches need not be mutually exclusive (Spence et al 2013;Young et al 2015). Although currently rare in practice, hydrological ensemble prediction systems (H-EPS) and data assimilation together represent an opportunity to fully couple empirical and process-based approaches.…”

Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

“…This method classifies basins or regions based on geomorphological (e.g., terrain characteristics, land cover, hydrologic response) and climatology information by similarity, through a set of parameters. This method could become challenging when attempting to transfer numerous explanatory variables, often associated with process-driven models, to a new area of interest [16,17]. This is especially important in cases where the area of interest has little or no data available for specific explanatory inputs (i.e., groundwater levels and SCD) to use in the model calibration phase.…”