Hybrid climate datasets from a climate data evaluation system and their impacts on hydrologic simulations for the Athabasca River basin in Canada

Abstract

A reliable climate dataset is the backbone for modelling the essential processes of the water cycle and predicting future conditions. Although a number of gridded climate datasets are available for the North American content which provide reasonable estimates of climatic conditions in the region, there are inherent inconsistencies in these available climate datasets (e.g., spatially and temporally varying data accuracies, meteorological parameters, lengths of records, spatial coverage, temporal resolution, etc.). These inconsistencies raise questions as to which datasets are the most suitable for the study area and how to systematically combine these datasets to produce a reliable climate dataset for climate studies and hydrological modelling. This study suggests a framework called the REFerence Reliability Evaluation System (REFRES) that systematically ranks multiple climate datasets to generate a hybrid climate dataset for a region. To demonstrate the usefulness of the proposed framework, REFRES was applied to produce a historical hybrid climate dataset for the Athabasca River basin (ARB) in Alberta, Canada. A proxy validation was also conducted to prove the applicability of the generated hybrid climate datasets to hydrologic simulations. This study evaluated five climate datasets, including the station-based gridded climate datasets ANUSPLIN (Australia National University Spline), Alberta Township, and the Pacific Climate Impacts Consortium's (PCIC) PNWNAmet (PCIC NorthWest North America meteorological dataset), a multi-source gridded dataset (Canadian Precipitation Analysis; CaPA), and a reanalysis-based dataset (North American Regional Reanalysis; NARR). The results showed that the gridded climate interpolated from station data performed better than multi-source- and reanalysis-based climate datasets. For the Athabasca River basin, Township and ANUSPLIN were ranked first for precipitation and temperature, respectively. The proxy validation also confirmed the utility of hybrid climate datasets in hydrologic simulations compared with the other five individual climate datasets investigated in this study. These results indicate that the hybrid climate dataset provides the best representation of historical climatic conditions and, thus, enhances the reliability of hydrologic simulations.