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Medium-Resolution Surface Meteorological Forcing Data for Hydrological Modelling and Climate Change Impact Analysis over most of North America
Federated Research Data Repository / dépôt fédéré de données de recherche
Asong, Zilefac Elvis; Elshamy, Mohamed; Princz, Daniel; Wheater, Howard; Pomeroy, John; Pietroniro, Alain; Cannon, Alex — 2024-03-27 This dataset provides an improved set of forcing data for large-scale hydrological modelling and climate change impacts assessment over a domain covering most of North America. The EU WATCH ERA-Interim reanalysis (WFDEI) has a long historical record (1979-2016) and global coverage. 30 years of WFDEI data (1979-2008) were used to bias-correct climate projections from 15 ensemble members of Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) simulations between 1951–2100 under Representative Concentration Pathway— RCP8.5. A multivariate bias correction algorithm (MBCn) was used to adjust the joint distribution of a set of seven meteorological variables, preserving their auto and cross correlations simultaneously. An analysis of the datasets shows the biases in the CanRCM4 during the historical period with respect to WFDEI have been removed and that the climate change signals in CanRCM4 are preserved. The resulting bias-corrected dataset (CanRCM4-WFDEI 3h*0.50ᵒ resolution) is a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. **Please note: This dataset is linked to an ESSD paper at https://doi.org/10.5194/essd-12-629-2020.  The authors kindly request that you reference this paper in addition to the dataset. https://creativecommons.org/licenses/by/4.0/
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A Bias-Corrected 3-hourly 0.125 Gridded Meteorological Forcing Data Set (1979 – 2016) for Land Surface Modeling in North America
Federated Research Data Repository / dépôt fédéré de données de recherche
Asong, Zilefac Elvis; Wheater, Howard; Pomeroy, John; Pietroniro, Alain; Elshamy, Mohamed — 2024-04-10 Cold regions hydrology is very sensitive to the impacts of climate warming. Future warming is expected to increase the proportion of winter precipitation falling as rainfall. Snowpacks are expected to undergo less sublimation, form later and melt earlier and possibly more slowly, leading to earlier spring peak streamflow. More physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrologic responses to climate change. However, hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly temperature and precipitation. Cold regions often have sparse surface observations, particularly at high elevations that generate the major amount of runoff. The effects of mountain topography and high latitudes are not well reflected in the observational record. The best available gridded data in these regions is from the high resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and the Canadian Precipitation Analysis (CaPA) reanalysis but this dataset has a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record, but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long record product (WFDEI-GEM-CaPA). First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3h x 0.125ᵒ resolution during the 2005-2016 period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. **Please note: This dataset is linked to an ESSD paper at https://doi.org/10.5194/essd-12-629-2020.  The authors kindly request that you reference this paper in addition to the dataset. https://creativecommons.org/licenses/by/4.0/
Global Water Futures (FRDR) Translation missing: fr.blacklight.search.logo
Aggregated gridded bedrock depth dataset for Mackenzie and Nelson-Churchill River Basin based on bedrock data by Shangguan et al. (2017)
Federated Research Data Repository / dépôt fédéré de données de recherche
Keshav, Kuljeet; Haghnegahdar, Amin; Elshamy, Mohamed; Gharari, Shervan; Razavi, Saman — 2024-03-27 The bedrock dataset created by Shangguan et al. (2017) is aggregated to a lower resolution (larger pixels) of 0.125 degree for Mackenzie and Nelson-Churchill River Basins for its applicability with Hydrology and Land Surface Models. This dataset is in particular generated for using with MESH (Modélisation Environnementale communautaire - Surface Hydrology), a distributed coupled hydrology-land surface model developed by Environment and Climate Change Canada, as applied to Mackenzie and Nelson-Churchill River Basins. The statistics calculated for each final larger pixel (0.125 degrees) include mean, median, maximum, minimum, and standard deviation of the original higher resolution pixels within it. Also, in order to provide an idea of the reliability of this transformation, a corresponding 0.125 degrees raster dataset is created containing the number of missing values (NA) from the Shangguan et al. (2017) dataset within each larger 0.125 degree pixels. https://creativecommons.org/licenses/by/4.0/
Global Water Futures (FRDR) Translation missing: fr.blacklight.search.logo
Aggregated gridded soil texture dataset for Mackenzie and Nelson-Churchill River Basins.
Federated Research Data Repository / dépôt fédéré de données de recherche
Keshav, Kuljeet; Haghnegahdar, Amin; Elshamy, Mohamed; Gharari, Shervan; Razavi, Saman — 2024-03-27 The dataset contains 0.125 resolution gridded soil texture data for Mackenzie and Nelson-Churchill River Basins. The data has been aggregated from two source datasets, namely Soil Landscapes of Canada 2.2 dataset and STATSGO2 dataset for USA. The final texture data has the minimum, maximum and average percent for sand, clay and organic components of soil per grid cell of the two basins. This data set is particularly created for the use with MESH (Modélisation Environnementale communautaire - Surface Hydrology), a distributed coupled hydrology-land surface model developed by Environment and Climate Change Canada, as applied to Mackenzie and Nelson-Churchill River Basins. It can be used for similar applications of land surface, hydrology, or environmental models as appropriate. https://creativecommons.org/licenses/by/4.0/
Global Water Futures (FRDR) Translation missing: fr.blacklight.search.logo
High-Resolution Meteorological Forcing Data for Hydrological Modelling and Climate Change Impact Analysis in Mackenzie River Basin
Federated Research Data Repository / dépôt fédéré de données de recherche
Asong, Zilefac Elvis; Wheater, Howard; Pomeroy, John; Pietroniro, Alain; Elshamy, Mohamed; Princz, Daniel; Cannon, Alex — 2024-03-27 This dataset provides an improved set of forcing data for large scale hydrological models for climate change impacts assessment in Mackenzie River Basin (MRB). The best available gridded data in the MRB is from the high resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA), but these datasets have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record, but has often been found to be biased relative to observations over Canada. The strengths of both datasets (GEM-CaPA and WFDEI) were blended to produce a less-biased long record product (WFDEI-GEM-CaPA http://doi.org/10.20383/101.0111) for hydrological modelling and climate change impacts assessment over the MRB. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1951–2100 under Representative Concentration Pathway— RCP8.5, and an analysis of the datasets shows the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected data (CanRCM4-WFDEI-GEM-CaPA 3h*0.125ᵒ resolution) are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. **Please note: This dataset is linked to an ESSD paper at https://doi.org/10.5194/essd-12-629-2020.  The authors kindly request that you reference this paper in addition to the dataset. https://creativecommons.org/licenses/by/4.0/
Global Water Futures (FRDR) Translation missing: fr.blacklight.search.logo
High-Resolution Meteorological Forcing Data for Hydrological Modelling and Climate Change Impact Analysis over most of North America, 1951-2100
Federated Research Data Repository / dépôt fédéré de données de recherche
Asong, Zilefac Elvis; Wheater, Howard; Pomeroy, John; Pietroniro, Alain; Elshamy, Mohamed; Princz, Daniel; Cannon, Alex — 2024-03-27 This dataset provides an improved set of forcing data for large scale hydrological models for climate change impacts assessment over most of North America. The gridded data from the high-resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA), have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record at a lower resolution (0.5°) and has often been found to be biased relative to observations over Canada. The strengths of both datasets (GEM-CaPA and WFDEI) were blended to produce a less-biased long record product (WFDEI-GEM-CaPA - http://doi.org/10.20383/101.0111) for hydrological modelling and climate change impacts assessment. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1951–2100 under Representative Concentration Pathway— RCP8.5, and an analysis of the datasets shows the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected data (CanRCM4-WFDEI-GEM-CaPA 3h*0.125ᵒ resolution) are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. Data reflects the meteorology at 40m height above ground for wind speed, temperature, and specific humidity. https://creativecommons.org/licenses/by/4.0/
Global Water Futures (FRDR) Translation missing: fr.blacklight.search.logo
Parameterization of LSMs for Permafrost - A case study of the Mackenzie River Basin, Canada
Federated Research Data Repository / dépôt fédéré de données de recherche
Abdelhamed, Mohamed S.; Elshamy, Mohamed; Razavi, Saman; Wheater, Howard S. — 2024-03-27 This dataset contains several outputs of the MESH/CLASS permafrost simulation in three sites located along the main stem of the Mackenzie River, Northwest Territories, Canada. These sites are Havikpak Creek (HPC), Bosworth Creek (BWC), and Jean Marie Creek (JMC). The shared data are based on extensive uncertainty, sensitivity, and identifiability analyses. https://creativecommons.org/licenses/by/4.0/
Global Water Futures (FRDR) Translation missing: fr.blacklight.search.logo
Meteorological and spatial datasets for Saint John River Basin
Federated Research Data Repository / dépôt fédéré de données de recherche
Budhathoki, Sujata; Rokaya, Prabin; Lindenschmidt, Karl-Erich; Elshamy, Mohamed — 2024-03-27 The Saint John River Basin (SJRB) is an important transboundary coastal river basin in northeastern North America. Meteorological forcing data play a pivotal role in model performance and therefore can introduce a large degree of uncertainty in hydrological modelling. The data archived here comprises the processed meteorological and spatial dataset used for the modelling purpose for SJRB as reported in Budhathoki et al., 2021.

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(source : Global Water Futures (FRDR)) AND (auteur : Elshamy, Mohamed)

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