Inter‐comparison of high‐resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed, Canada

Abstract

Abstract:Several different gridded climate data sets have recently been made available with the purpose of providing a consistent set of climatic data for many hydro-climatic studies. Recent advances in land-surface schemes and their implementation in fully distributed processes-based hydrologic models have demanded even higher-resolution gridded data. It remains, however, a challenge to identify the most reliable gridded climate data for hydrologic modelling, especially in mountainous headwater regions where there is significant spatial variability but few observing stations. Moreover, the accuracy of such climate forcing data applied to alpine headwaters directly affects the modelled hydrologic responses of the lower, downstream portions of river basins. This study evaluates the spatial and temporal differences in precipitation and temperature fields among three highresolution climate data sets available in Canada, namely, the North American Regional Reanalysis, the Canadian Precipitation Analysis and the thin-plate smoothing splines (ANUSPLIN). Inter-comparison of the quality of these data sets was undertaken for the Athabasca River basin in western Canada. The hydrologic responses of this watershed with respect to each of the three gridded climate data sets were also evaluated using the Variable Infiltration Capacity model. Results indicate that the data sets have systematic differences, which vary with regional characteristics -the largest differences being for mountainous regions. The hydrologic model simulations corresponding to those three forcing data sets also show significant differences and more with North American Regional Reanalysis than those between Canadian Precipitation Analysis and ANUSPLIN.

An Evaluation of Temperature and Precipitation Surface-Based and Reanalysis Datasets for the Canadian Arctic, 1950–2010

“…Dataset spread is also higher in the cold seasons, which is related to the challenges of accurate measurement of snowfall and the integration of unadjusted gauge data in some databases (e.g., UDEL and GPCC; Schneider et al, 2014). Eum et al (2014) also found the highest spread between precipitation analysis datasets during winter over higher elevation regions of the Athabasca watershed in northern Alberta. Other potential sources of difference are the inclusion of satellite retrievals, which are subject to sensor biases, in observational datasets (Serreze et al, 2005).…”

“…NARR 1979-present 32 km/ regional North America NCEP Mesinger et al (2006) Assimilates precipitation observations up to 2003 over Canada (Eum, Dibike, Prowse, & Bonsal, 2014). Several changes were induced in assimilation system in December 2002; PCP over Canada is not as good as hoped for because of small number of gauges available (Mesinger et al, 2006 Assimilates surface air temperature observations.…”

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

“…Most studies have focused on evaluating the performance of grid-based precipitation data in simulating hydrologic processes [15][16][17][18][19][20][21][22][23][24][25], while others have focused on evaluating the performances of different parameters in one data set in simulating hydrologic processes [26][27][28][29]. Some studies have evaluated the respective performances of different variables associated with multisource grid-based data in hydrologic modeling [30,31]. However, nearly 80% of water resources in the current region of interest are generated from snow and glacier melt.…”

Assimilation of radar quantitative precipitation estimations in the Canadian Precipitation Analysis (CaPA)

“…CaPA-RDPA products are used in Canada for various environmental prediction applications, and in particular for hydrological modelling and forecasting (Eum et al, 2014; configuration of CaPA analysis is also embedded within the Canadian land-data assimilation system (CaLDAS), and thus contributes to the estimation of soil moisture, soil temperature and snow depth in real-time over Canada (Carrera et al, 2015). These state variables are then used to initialize Environment Canada's national High-Resolution Deterministic Prediction System (HRDPS) which provides 48-h experimental weather forecasts at 2.5 km horizontal resolution (Mailhot et al, 2010).…”