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

Hydrol. Earth Syst. Sci., 23, 5151–5173, 2019 https://doi.org/10.5194/hess-23-5151-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Hybrid climate datasets from a climate data evaluation system and their impacts on hydrologic simulations for the Athabasca River basin in Canada Hyung-Il Eum1 and Anil Gupta1,2 1 Alberta Environment and Parks, Environment Monitoring and Science Division, 3535 Research Road NW, Calgary, Alberta, T2L 2K8, Canada 2 Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada Correspondence: Hyung-Il Eum () Received: 25 April 2019 – Discussion started: 23 May 2019 Revised: 8 October 2019 – Accepted: 23 November 2019 – Published: 19 December 2019 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 reanalysisbased 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. 1 Introduction A reliable historical climate dataset is essential to understanding the climatic and hydrological characteristics of a watershed, as it is crucial forcing input data for simulating key processes of the water and energy cycles in impact models (Deacu et al., 2012; Essou et al., 2016; Wong et al., 2017). Although climate monitoring networks have advanced over the last decades, poor network density still exists, especially in western mountainous and northern parts of Canada. Moreover, climate observations are often spatially interpolated to cover ungauged regions, which may cause unexpected erroneous model predictions as a consequence of the sparse measurement network, especially for mountainous areas af- Published by Copernicus Publications on behalf of the European Geosciences Union. 5152 H.-I. Eum and A. Gupta: Hybrid climate datasets and their impacts on hydrologic simulations fected by orographic effects (Rinke et al., 2004; Wang and Lin, 2015). As advances in numerical hydrologic and hydrodynamic modelling have increased the capability and reliability in simulating complex natural processes to detect anthropogenic and natural climate changes, a need for temporally and spatially reliable climate data has also grown to accommodate the requirements of input data for numerical models (Shen et al., 2010; Shrestha et al., 2012; Islam and Déry, 2017). For instance, process-based distributed hydrologic models have a grid-based structure that requires input data for each grid cell. However, a simple spatial interpolation of observational station data to all model grid cells may not produce a reliable input forcing dataset for hydrologic models, particularly in a region with a sparse gauging network. A reliable historical climate dataset is also crucial in climate change studies when used for statistical downscaling techniques that employ the relationships between observations and outputs of global (or regional) climate models to produce climate forcing at regional or local scales. Since the resolution of products from a statistical downscaling technique usually corresponds to that of the historical climate dataset (Werner and Cannon, 2016; Eum and Cannon, 2017), the availability of temporally and spatially reliable historical climate data is essential for climate-related impact studies (Christensen and Lettenmaier, 2007; Kay et al., 2009; Gutmann et al., 2014; Eum et al., 2016). A number of high-resolution gridded climate datasets have been developed for various applications such as intercomparison studies (Eum et al., 2014a; Wong et al., 2017) and hydrologic modelling (Choi et al., 2009; Eum et al., 2016). There are various types of gridded climate datasets available for the North American region: (1) station-based interpolated, (2) station-based multi-source, and (3) reanalysisbased multi-source (Wong et al., 2017). By interpolation of observational station data, long-term gridded climate datasets have been produced over various domains defined by stations incorporated such as the Canada-wide Australia National University Spline (ANUSPLIN, Hutchison et al., 2009), the Alberta Township data (Shen et al., 2001), and the Pacific Climate Impacts Consortium (PCIC) NorthWest North America meteorological (PNWNAmet) datasets (Werner et al., 2019). The Canadian Precipitation Analysis (CaPA) system, a multi-source-based climate dataset, has been developed to produce near-real-time precipitation analyses (6 h accumulated precipitation) over North America at 15 km resolution which has been further improved to 10 km resolution (Lespinas et al., 2015). North American Regional Reanalysis (NARR), one of the reanalysis-based datasets derived from a regional climate model ( ∼ 32 km), has been tested as an alternative climate dataset (Choi et al., 2009; Praskievicz and Bartlein, 2014; Essou et al., 2016; (...truncated)