Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada

Abstract

Abstract. A number of global and regional gridded climate products based on multiple data sources are available that can potentially provide reliable estimates of precipitation for climate and hydrological studies. However, research into the consistency of these products for various regions has been limited and in many cases non-existent. This study inter-compares several gridded precipitation products over 15 terrestrial ecozones in Canada for different seasons. The spatial and temporal variability of the errors (relative to station observations) was quantified over the period of 1979 to 2012 at a 0.5° and daily spatio-temporal resolution. These datasets were assessed in their ability to represent the daily variability of precipitation amounts by four performance measures: percentage of bias, root mean square error, correlation coefficient, and standard deviation ratio. Results showed that most of the datasets were relatively skilful in central Canada. However, they tended to overestimate precipitation amounts in the west and underestimate in the north and east, with the underestimation being particularly dominant in northern Canada (above 60° N). The global product by WATCH Forcing Data ERA-Interim (WFDEI) augmented by Global Precipitation Climatology Centre (GPCC) data (WFDEI [GPCC]) performed best with respect to different metrics. The Canadian Precipitation Analysis (CaPA) product performed comparably with WFDEI [GPCC]; however, it only provides data starting in 2002. All the datasets performed best in summer, followed by autumn, spring, and winter in order of decreasing quality. Findings from this study can provide guidance to potential users regarding the performance of different precipitation products for a range of geographical regions and time periods.

High-resolution meteorological forcing data for hydrological modelling and climate change impact analysis in the Mackenzie River Basin

“…Other gridded products such as the EU WATCH ERA-Interim reanalysis (WFDEI; Weedon et al, 2014) and Princeton (Sheffield et al, 2006) have a longer historical record but have been found to be biased relative to observations over Canada and the United States (Behnke et al, 2016;Sapiano and Arkin, 2009). However, the WFDEI reanalysis has been found to outperform other long-record gridded products (Chadburn et al, 2015;Park et al, 2016;Wong et al, 2017).…”

“…Flows in these high-elevation headwaters depend heavily on meltwater from snowpacks and glaciers. However, given that it is characterized by a highly varying cold region hydro-climate, studies indicate that it is in these highelevation regions where climate variability and change are expected to be most pronounced in terms of their impacts on water supply (Beniston, 2003;Kane et al, 1991;Prowse and Beltaos, 2002;Woo and Pomeroy, 2011). More physically realistic and sophisticated hydrological models driven by reliable climate forcing information can enhance our ability to assess short-and long-term regional hydrologic responses to increasing variability and uncertainty in hydro-climatic conditions in a changing climate.…”

“…Apart from the uncertainty due to the many empirical statistical techniques which have been developed to postprocess ESM outputs (Maraun, 2016), the quality and length of the reference observational dataset for bias correction remains a major issue (Reiter et al, 2016;Schoetter et al, 2012;Sippel et al, 2016). In Canada and other regions of North America, regional gridded datasets such as the combined Global Environmental Multiscale (GEM) atmospheric model forecasts (Yeh et al, 2002) and the Canadian Precipitation Analysis (CaPA; Mahfouf et al, 2007) have been found to perform comparably to ground observations, both statistically and hydrologically (Alavi et al, 2016;Boluwade et al, 2018;Eum et al, 2014;Fortin et al, 2015a;Gbambie et al, 2017;Wong et al, 2017). However, the duration of GEM-CaPA is too short to be used to directly correct ESM climate due to unsynchronized internal variability -the recommended minimum record length for bias correction is 30 years (Maraun, 2016;Maraun et al, 2017).…”

Evaluating the suitability of three gridded‐datasets and their impacts on hydrological simulation at Scotty Creek in the southern Northwest Territories, Canada

“…Several studies have evaluated gridded datasets across Canada; ERA‐Interim and Modern‐Era Retrospective Analysis for Research and Application (MERRA) were the most consistent with observed data (Lindsay, Wensnahan, Schweiger, & Zhang, ) and interannual variability (Rapaić, Brown, Markovic, & Chaumont, ). Summer biases in gridded precipitation were consistently the lowest in Australian National University Spline (ANUSPLIN) (Wong, Razavi, Bonsal, Wheater, & Asong, ). Before supplementing, or replacing, observations with gridded datasets, gridded datasets should be thoroughly verified against existing observations to understand their limits (Crout et al, ; Mo et al, ).…”

“…Smoothing of daily data to monthly and seasonal means can reduce the effect of biases and outliers (Zhang et al, ) at the cost of lost temporal resolution. Many studies have focussed on seasonal or annual means of climate variables (e.g., Wong et al, ; Zhang et al, ) but means alone may not reflect variability that is important hydrologically. Although identifying changes in mean climate is important, the entire distribution is known to have climatological and hydrological impacts (Cornes & Jones, ; Field et al, ).…”