Reconstructed River Water Temperature Dataset for Western Canada 1980–2018
Watershed Hydrology and Ecology Research Division, Environment and Climate Change Canada, University of Victoria, 2472 Arbutus Rd., Victoria, BC V8N 1V8, Canada
*
Author to whom correspondence should be addressed.
Data 2023, 8(3), 48; https://doi.org/10.3390/data8030048
Submission received: 2 February 2023
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Revised: 24 February 2023
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Accepted: 24 February 2023
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Published: 26 February 2023
(This article belongs to the Collection Modern Geophysical and Climate Data Analysis: Tools and Methods)
Abstract
:Continuous water temperature data are important for understanding historical variability and trends of river thermal regime, as well as impacts of warming climate on aquatic ecosystem health. We describe a reconstructed daily water temperature dataset that supplements sparse historical observations for 55 river stations across western Canada. We employed the air2stream model for reconstructing water temperature dataset over the period 1980–2018, with air temperature and discharge data used as model inputs. The model was calibrated and validated by comparing with observed water temperature records, and the results indicate a reasonable statistical performance. We also present historical trends over the ice-free summer months from June to September using the reconstructed dataset, which indicate- significantly increasing water temperature trends for most stations. Besides trend analysis, the dataset could be used for various applications, such as calculation of heat fluxes, calibration/validation of process-based water temperature models, establishment of baseline condition for future climate projections, and assessment of impacts on ecosystems health and water quality.
Dataset: https://catalogue.ec.gc.ca/geonetwork/srv/eng/catalog.search#/metadata/cc103402-ba59-43eb-820e-f319b6b5f9b4 (accessed on 25 February 2023)
Dataset License: Open Government License–Canada: https://open.canada.ca/en/open-government-licence-canada (accessed on 25 February 2023)
1. Summary
We describe a reconstructed daily river water temperature (°C) dataset for 55 stations across western Canada. The dataset was reconstructed using the semi-empirical air2stream model with inputs primarily consisting of extracted air temperature from ~10 km resolution 1980–2018 Regional Deterministic Reforecast System version 2.1, and streamflow data from the Water Survey of Canada hydrometric station network. Air2stream was calibrated/validated with observed water temperature records from various sources and the model provided a reasonable statistical performance. The dataset is designed to supplement sparse observation-based data, which is usually based on a few spot measurements in a year. The dataset could be used for various applications, such as analysis of trend, calculation of heat fluxes, calibration/validation of process-based water temperature models, establishment of baseline condition for future climate projections, analysis of climatic and basin drivers, and assessment of impacts on ecosystems health and water quality. An example application of the dataset, in terms of monthly trend analysis over open-water summer months, is provided.
2. Data Description
The dataset consists of reconstructed river water temperature values (°C) at a daily time step using the air2stream model [1] for 55 stations across western Canada (Figure 1). The dataset for all stations is provided in a single file in comma-separated-format (csv) spanning the period from 1 January 1980 to 31 December 2018 through the Government of Canada data portal. The water temperature station locations (Table A1) correspond to the Water Survey of Canada hydrometric stations (https://wateroffice.ec.gc.ca/ (accessed on 23 February 2023)) and a separate text file provides the station coordinates. Since air2stream simulation requires observed discharge data as an input, the days with no observed discharge data are specified as not available (NA) in the simulated water temperature dataset. Statistical performance of the simulated water temperature compared to observations are summarized in Table A2.
We also provide 1980–2018 trends over the ice-free summer months of June-September using the reconstructed water temperature data. The trend results are depicted spatially in maps (Figure A1) and summarized in Table A3. The results indicate significantly increasing water temperature trends for most months. Additionally, while most stations in the southern region indicate significant trends for all months, most northern stations show significant trends only for the month of June.
3. Methods
We selected 55 stations across western Canada based on the criteria of at least 50 water temperature observations and drainage basin area >1000 km2. Additional criteria included continuous streamflow observations and unregulated or relatively minor level of river flow regulation, e.g., Mackenzie and Fraser river stations.
We used the semi-empirical air2stream model [1] to reconstruct the daily water temperature dataset. The model uses simplified process-based equations to simulate river water temperature, with air temperature as the only meteorological forcing and the surface and sub-surface flow contributions considered in terms of lumped discharge. Daily air temperature inputs were extracted from ~10 km resolution Regional Deterministic Reforecast System, version 2.1 (RSRS_v2.1) reanalysis [2], except the high resolution (500 m) NRCanMet_500 data [3] for the Similkameen River near Headley and Nighthawk stations. Discharge inputs consisted of daily streamflow data from the Water Survey of Canada (WSC) hydrometric stations (https://wateroffice.ec.gc.ca/ (accessed on 23 February 2023)) that are nearest to the water temperature stations, except for the USGS streamflow data for the Yukon River at Eagle station AK (https://waterdata.usgs.gov/nwis/ (accessed on 23 February 2023)). We infilled missing observed discharge data with the hydrologic model simulated data for the available stations in the Liard [4] and Similkameen [5] watersheds. For all other stations, we infilled missing discharge values with averages before and after the missing period, and water temperature values for the missing discharge periods were specified as NA values in the uploaded dataset. We obtained observed water temperature records from a number of sources for calibrating/validating air2stream, as summarized in Shrestha and Pesklevits [6].
The air2stream model consists of 3 to 8 parameters (except 6 parameters) depending on the form of equation used. In this study, we calibrated air2stream models for all parameter combinations by using the particle swarm stochastic optimization procedure [1]. The model calibration was performed by comparing the simulated water temperature with observations for either the years 2010–2018 or 2000–2009, with the years with larger (smaller) number of observed water temperature records used for calibration (validation). We selected the best performing model parameter set by using the Nash–Sutcliffe coefficient of efficiency (NSE) as the performance criterion, supplemented by the mean absolute error (MAE), Kling–Gupta coefficient (KGE) and ratio of root mean square error to standard deviation of observation (RSR) as additional criteria. Higher NSE and KGE values (approaching 1) indicate better model performance, and lower MAE and RSR (approaching 0) indicate better model performance. We used the selected best performing models to simulate water temperature over the period of 1980–2018, and the model’s statistical performance over the period is summarized in Table A2. The results indicate good model performance in terms of NSE (minimum = 0.79, median = 0.93, maximum = 0.97), KGE (0.81, 0.94, 0.99), MAE (0.51, 0.99, 1.61 °C) and RSR (0.16, 0.27, 0.46) goodness-of-fit metrics.
We used the reconstructed water temperature dataset to calculate trends over the years 1980–2018 by employing the non-parametric Thiel–Sen method. Trend significance was determined by using the Mann–Kendall test with the effects of serial correlation removed by using the iterative pre-whitening method [7], as implemented in the R “zyp” package [8]. Two significance levels of p ≤ 0.05 and p ≤ 0.10 were used to consider statistically significant trends.
It is to be noted that the dataset presented is partially based on our previous study [6]. However, this dataset uses an expanded domain with a larger number of stations (55 vs. 18 in the previous study). Additionally, while two studies employed the same input data (RDRS_v2.1) for model calibration and validation, this study uses air temperature extracted from RDRS_v2.1 over the period of 1980–2018 compared to the 1945–2012 air temperature from the Pacific Northwest North-American meteorological (PNWNAmet) dataset [9] in the previous study. Hence, the presented water temperature data and trend analysis results are not expected to match with the previous study. In addition, note that the model reconstructed water temperature is affected by a number of sources of uncertainties arising out of data and model limitations, especially the sparsity of observation data and the lack of representation of physical processes and physiographic controls in the model. Readers are referred to our previous study [6] for a detailed discussion on uncertainties.
4. User Notes
The reconstructed water temperature dataset is designed to supplement sparse observation-based dataset, which is usually based on few spot measurements (typically 6–10 measurements per year), and whose spatial coverage and sampling frequency are low. The reconstructed data could be used for various applications related to analyzing historical variability and change in river thermal characteristics, as well as implications on aquatic ecosystem health. An example application is the presented historical trends, which could be expanded to heat fluxes analyses [10]. The dataset could also provide a useful basis for calibration/validation of process-based water temperature models, e.g., River Basin Model [11] and Dynamic Water Temperature Model [12], and as a baseline condition to compare future climate projections [13,14]. The dataset could also be used for analyzing climatic and basin drivers of water temperature variability and change [6,10,15]. Furthermore, the dataset could provide a useful basis for analyzing the effects of changing water temperature on water quality [16] and aquatic species habitat [17,18,19].
Author Contributions
Conceptualization, R.R.S.; data curation, analyses, J.C.P.; writing—original draft preparation, R.R.S.; writing—review and editing, R.R.S. and J.C.P.; visualization, J.C.P.; supervision, R.R.S.; project administration, R.R.S. All authors have read and agreed to the published version of the manuscript.
Funding
This study was conducted with internal funding from Environment and Climate Change Canada.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Reconstructed water temperature data are available through the Government of Canada data portal: https://catalogue.ec.gc.ca/geonetwork/srv/eng/catalog.search#/metadata/cc103402-ba59-43eb-820e-f319b6b5f9b4 (accessed on 23 February 2023). Gridded air temperature and observed discharge and water temperature datasets are available through the original sources provided in citations.
Acknowledgments
We thank Laurent de Rham (ECCC) for providing water temperature records from the river–ice database. We acknowledge Marco Toffolon (University of Trento) for making available the air2stream model code.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A. Figures and Tables
Figure A1.
Historical trends in monthly water temperature over 1980–2018. The results show significantly increasing trends at 5 and 10% levels; no significantly decreasing trends were detected. The station numbers correspond to the station identifiers listed in Table A1.
Figure A1.
Historical trends in monthly water temperature over 1980–2018. The results show significantly increasing trends at 5 and 10% levels; no significantly decreasing trends were detected. The station numbers correspond to the station identifiers listed in Table A1.
Table A1.
Summary of streamflow stations in the water temperature database. WSC_ID corresponds to Water Survey of Canada hydrometric station identifiers.
Table A1.
Summary of streamflow stations in the water temperature database. WSC_ID corresponds to Water Survey of Canada hydrometric station identifiers.
| Station # | WSC_ID | WSC Discharge Station Name | Latitude | Longitude | Drainage Area (km2) |
|---|---|---|---|---|---|
| 1 | 05CB001 | Little Red Deer River near the Mouth | 52.0282 | −114.1403 | 2578 |
| 2 | 05CC001 | Blindman River near Blackfalds | 52.3540 | −113.7947 | 1796 |
| 3 | 05CC007 | Medicine River near Eckville | 52.3196 | −114.3442 | 1916 |
| 4 | 07BE001 | Athabasca River at Athabasca | 54.7220 | −113.2880 | 74,602 |
| 5 | 07CD001 | Clearwater River at Draper | 56.6853 | −111.2554 | 30,799 |
| 6 | 07DA001 | Athabasca River below Fort Mcmurray | 56.7804 | −111.4022 | 132,588 |
| 7 | 07EA005 | Finlay River above Akie River | 57.0751 | −125.1499 | 15,600 |
| 8 | 07EC002 | Omineca River above Osilinka River | 55.9169 | −124.5676 | 5560 |
| 9 | 07FC001 | Beatton River near Fort St. John | 56.2784 | −120.6999 | 15,600 |
| 10 | 07GE001 | Wapiti River near Grande Prairie | 55.0713 | −118.8029 | 11,300 |
| 11 | 07GJ001 | Smoky River at Watino | 55.7146 | −117.6231 | 50,300 |
| 12 | 07HC001 | Notikewin River at Manning | 56.9200 | −117.6184 | 4679 |
| 13 | 07JD002 | Wabasca River at Highway No. 88 | 57.8746 | −115.3891 | 35,800 |
| 14 | 07OB001 | Hay River Near Hay River | 60.7430 | −115.8596 | 51,700 |
| 15 | 07OC001 | Chinchaga River near High Level | 58.5971 | −118.3341 | 10,370 |
| 16 | 07RD001 | Lockhart River at Outlet of Artillery Lake | 62.8941 | −108.4660 | 26,600 |
| 17 | 08EC013 | Babine River at Outlet of Nilkitkwa Lake | 55.4265 | −126.6976 | 6760 |
| 18 | 08EE004 | Bulkley River at Quick | 54.6186 | −126.9000 | 7340 |
| 19 | 08EF001 | Skeena River at Usk | 54.6319 | −128.4306 | 42,300 |
| 20 | 08FB006 | Atnarko River near the Mouth | 52.3601 | −126.0059 | 2550 |
| 21 | 08KA004 | Fraser River at Hansard | 54.0787 | −121.8504 | 18,000 |
| 22 | 08KA007 | Fraser River at Red Pass | 52.9863 | −119.0067 | 1710 |
| 23 | 08KB001 | Fraser River at Shelley | 54.0037 | −122.6247 | 32,400 |
| 24 | 08KH006 | Quesnel River near Quesnel | 52.8431 | −122.2253 | 11,500 |
| 25 | 08LF051 | Thompson River near Spences Bridge | 50.3546 | −121.3936 | 55,400 |
| 26 | 08MC018 | Fraser River near Marguerite | 52.5303 | −122.4443 | 114,000 |
| 27 | 08MF005 | Fraser River at Hope | 49.3860 | −121.4542 | 217,000 |
| 28 | 08MF040 | Fraser River above Texas Creek | 50.6137 | −121.8534 | 154,000 |
| 29 | 08NA002 | Columbia River at Nicholson | 51.2436 | −116.9129 | 6660 |
| 30 | 08NB005 | Columbia River at Donald | 51.4833 | −117.1804 | 9700 |
| 31 | 08NL007 | Similkameen River at Princeton | 49.4597 | −120.5035 | 1810 |
| 32 | 08NL022 | Similkameen River near Nighthawk | 48.9847 | −119.6172 | 9190 |
| 33 | 08NL038 | Similkameen River near Hedley | 49.3770 | −120.1523 | 5580 |
| 34 | 09AB001 | Yukon River at Whitehorse | 60.7445 | −135.0640 | 19,600 |
| 35 | 09BC001 | Pelly River at Pelly Crossing | 62.8297 | −136.5806 | 48,900 |
| 36 | 09BC004 | Pelly River below Vangorda Creek | 62.2208 | −133.3778 | 21,900 |
| 37 | 09CD001 | Yukon River above White River | 63.0825 | −139.4969 | 149,000 |
| 38 | 09DD003 | Stewart River at The Mouth | 63.2822 | −139.2544 | 51,000 |
| 39 | 09EA003 | Klondike River above Bonanza Creek | 64.0428 | −139.4078 | 7810 |
| 40 | 09ED001 | Yukon River at Eagle | 64.7894 | −141.1978 | 288,000 |
| 41 | 10AA001 | Liard River at Upper Crossing | 60.0508 | −128.9069 | 32,600 |
| 42 | 10AA004 | Rancheria River near the Mouth | 60.2042 | −129.5500 | 5100 |
| 43 | 10AB001 | Frances River near Watson Lake | 60.4739 | −129.1189 | 12,800 |
| 44 | 10BE001 | Liard River at Lower Crossing | 59.4125 | −126.0972 | 104,000 |
| 45 | 10BE004 | Toad River above Nonda Creek | 58.8550 | −125.3826 | 2540 |
| 46 | 10BE013 | Smith River near the Mouth | 59.5533 | −126.4806 | 3740 |
| 47 | 10CB001 | Sikanni Chief River near Fort Nelson | 57.2382 | −122.6915 | 2180 |
| 48 | 10CD001 | Muskwa River near Fort Nelson | 58.7881 | −122.6616 | 20,300 |
| 49 | 10EA003 | Flat River near the Mouth | 61.5300 | −125.4108 | 8560 |
| 50 | 10EB001 | South Nahanni River above Virginia Falls | 61.6361 | −125.7970 | 14,500 |
| 51 | 10ED001 | Liard River at Fort Liard | 60.2416 | −123.4754 | 222,000 |
| 52 | 10ED002 | Liard River near the Mouth | 61.7427 | −121.2280 | 275,000 |
| 53 | 10GC001 | Mackenzie River at Fort Simpson | 61.8684 | −121.3589 | 1,301,440 |
| 54 | 10LC002 | Mackenzie River (East Channel) at Inuvik | 68.3742 | −133.7648 | 1,703,387 |
| 55 | 10LC014 | Mackenzie River at Arctic Red River | 67.4560 | −133.7533 | 1,680,000 |
Table A2.
Statistical performance of the best performing air2stream model compared to observed water temperature records over the data reconstruction period 1980–2018. the number of air2stream model parameters and number of water temperature observations are also included.
Table A2.
Statistical performance of the best performing air2stream model compared to observed water temperature records over the data reconstruction period 1980–2018. the number of air2stream model parameters and number of water temperature observations are also included.
| Station # | WSE_ID | MAE (°C) | NSE | KGE | RSR | Best Performing No. Parameters | No. Non-Zero Observed Water Temperature Records |
|---|---|---|---|---|---|---|---|
| 1 | 05CB001 | 1.15 | 0.92 | 0.96 | 0.28 | 5 | 249 |
| 2 | 05CC001 | 1.06 | 0.94 | 0.96 | 0.25 | 5 | 252 |
| 3 | 05CC007 | 1.04 | 0.94 | 0.95 | 0.25 | 5 | 251 |
| 4 | 07BE001 | 0.85 | 0.96 | 0.94 | 0.19 | 7 | 368 |
| 5 | 07CD001 | 0.72 | 0.96 | 0.97 | 0.19 | 5 | 88 |
| 6 | 07DA001 | 0.96 | 0.96 | 0.97 | 0.21 | 5 | 264 |
| 7 | 07EA005 | 0.66 | 0.96 | 0.97 | 0.20 | 5 | 4777 |
| 8 | 07EC002 | 0.97 | 0.92 | 0.91 | 0.28 | 7 | 4457 |
| 9 | 07FC001 | 1.57 | 0.86 | 0.89 | 0.37 | 7 | 227 |
| 10 | 07GE001 | 0.82 | 0.93 | 0.95 | 0.26 | 5 | 99 |
| 11 | 07GJ001 | 0.96 | 0.93 | 0.95 | 0.27 | 5 | 111 |
| 12 | 07HC001 | 0.93 | 0.93 | 0.93 | 0.26 | 5 | 186 |
| 13 | 07JD002 | 0.90 | 0.93 | 0.91 | 0.26 | 5 | 111 |
| 14 | 07OB001 | 1.30 | 0.91 | 0.90 | 0.30 | 5 | 132 |
| 15 | 07OC001 | 1.06 | 0.93 | 0.95 | 0.26 | 5 | 156 |
| 16 | 07RD001 | 1.44 | 0.79 | 0.85 | 0.46 | 4 | 92 |
| 17 | 08EC013 | 1.17 | 0.94 | 0.94 | 0.25 | 3 | 1264 |
| 18 | 08EE004 | 0.67 | 0.97 | 0.98 | 0.17 | 8 | 1102 |
| 19 | 08EF001 | 1.12 | 0.88 | 0.88 | 0.34 | 7 | 785 |
| 20 | 08FB006 | 0.69 | 0.97 | 0.97 | 0.17 | 5 | 1370 |
| 21 | 08KA004 | 1.46 | 0.85 | 0.89 | 0.39 | 7 | 421 |
| 22 | 08KA007 | 1.08 | 0.88 | 0.94 | 0.35 | 5 | 656 |
| 23 | 08KB001 | 0.60 | 0.97 | 0.99 | 0.16 | 7 | 4050 |
| 24 | 08KH006 | 0.93 | 0.95 | 0.97 | 0.22 | 8 | 3480 |
| 25 | 08LF051 | 1.06 | 0.94 | 0.96 | 0.24 | 5 | 4841 |
| 26 | 08MC018 | 1.33 | 0.87 | 0.87 | 0.35 | 4 | 621 |
| 27 | 08MF005 | 0.97 | 0.95 | 0.97 | 0.23 | 7 | 3616 |
| 28 | 08MF040 | 0.84 | 0.97 | 0.98 | 0.16 | 5 | 4553 |
| 29 | 08NA002 | 1.61 | 0.84 | 0.90 | 0.40 | 5 | 228 |
| 30 | 08NB005 | 0.99 | 0.93 | 0.96 | 0.26 | 5 | 1969 |
| 31 | 08NL007 | 1.24 | 0.89 | 0.94 | 0.33 | 7 | 822 |
| 32 | 08NL022 | 1.29 | 0.91 | 0.91 | 0.30 | 7 | 833 |
| 33 | 08NL038 | 1.34 | 0.89 | 0.91 | 0.33 | 7 | 262 |
| 34 | 09AB001 | 1.02 | 0.92 | 0.91 | 0.27 | 7 | 639 |
| 35 | 09BC001 | 1.08 | 0.91 | 0.95 | 0.30 | 5 | 179 |
| 36 | 09BC004 | 1.05 | 0.91 | 0.93 | 0.31 | 7 | 200 |
| 37 | 09CD001 | 1.15 | 0.92 | 0.94 | 0.28 | 5 | 117 |
| 38 | 09DD003 | 1.12 | 0.92 | 0.88 | 0.28 | 3 | 89 |
| 39 | 09EA003 | 0.52 | 0.96 | 0.97 | 0.19 | 7 | 1232 |
| 40 | 09ED001 | 0.82 | 0.93 | 0.94 | 0.26 | 5 | 1321 |
| 41 | 10AA001 | 0.83 | 0.94 | 0.91 | 0.24 | 7 | 411 |
| 42 | 10AA004 | 0.94 | 0.92 | 0.92 | 0.28 | 7 | 115 |
| 43 | 10AB001 | 0.56 | 0.95 | 0.95 | 0.22 | 5 | 491 |
| 44 | 10BE001 | 1.25 | 0.87 | 0.87 | 0.36 | 5 | 218 |
| 45 | 10BE004 | 1.16 | 0.83 | 0.83 | 0.41 | 4 | 231 |
| 46 | 10BE013 | 1.30 | 0.88 | 0.88 | 0.34 | 3 | 180 |
| 47 | 10CB001 | 1.16 | 0.85 | 0.89 | 0.39 | 5 | 193 |
| 48 | 10CD001 | 1.21 | 0.88 | 0.94 | 0.35 | 5 | 199 |
| 49 | 10EA003 | 0.76 | 0.93 | 0.96 | 0.27 | 7 | 240 |
| 50 | 10EB001 | 0.92 | 0.92 | 0.93 | 0.29 | 8 | 3382 |
| 51 | 10ED001 | 0.96 | 0.93 | 0.89 | 0.26 | 8 | 195 |
| 52 | 10ED002 | 0.75 | 0.94 | 0.93 | 0.24 | 5 | 158 |
| 53 | 10GC001 | 0.51 | 0.97 | 0.95 | 0.16 | 5 | 79 |
| 54 | 10LC002 | 0.60 | 0.94 | 0.81 | 0.25 | 4 | 51 |
| 55 | 10LC014 | 0.93 | 0.93 | 0.87 | 0.27 | 3 | 199 |
Table A3.
Decadal trends (°/decade) obtained from the simulated water temperature records. The results summarize monthly trends for June to September together with June to September averages. The bold and underlined values indicate significant trends at p ≤ 0.05 and p ≤ 0.10, respectively.
Table A3.
Decadal trends (°/decade) obtained from the simulated water temperature records. The results summarize monthly trends for June to September together with June to September averages. The bold and underlined values indicate significant trends at p ≤ 0.05 and p ≤ 0.10, respectively.
| Station # | WSC_ID | June | July | August | September | June–September |
|---|---|---|---|---|---|---|
| 1 | 05CB001 | 0.08 | 0.21 | 0.20 | 0.23 | 0.18 |
| 2 | 05CC001 | 0.13 | 0.31 | 0.29 | 0.32 | 0.27 |
| 3 | 05CC007 | 0.15 | 0.35 | 0.21 | 0.27 | 0.21 |
| 4 | 07BE001 | −0.01 | 0.21 | 0.33 | 0.39 | 0.20 |
| 5 | 07CD001 | 0.18 | 0.25 | 0.16 | 0.26 | 0.22 |
| 6 | 07DA001 | 0.24 | 0.24 | 0.22 | 0.26 | 0.25 |
| 7 | 07EA005 | 0.32 | 0.08 | 0.14 | 0.05 | 0.12 |
| 8 | 07EC002 | 0.25 | 0.32 | 0.42 | 0.04 | 0.29 |
| 9 | 07FC001 | 0.11 | 0.10 | 0.25 | 0.37 | 0.16 |
| 10 | 07GE001 | −0.02 | 0.19 | 0.16 | 0.37 | 0.19 |
| 11 | 07GJ001 | 0.15 | 0.15 | 0.14 | 0.17 | 0.13 |
| 12 | 07HC001 | 0.10 | 0.04 | 0.07 | 0.37 | 0.17 |
| 13 | 07JD002 | 0.20 | 0.20 | 0.18 | 0.45 | 0.29 |
| 14 | 07OB001 | 0.10 | 0.12 | 0.06 | 0.15 | 0.08 |
| 15 | 07OC001 | 0.19 | 0.09 | 0.16 | 0.37 | 0.25 |
| 16 | 07RD001 | 0.89 | 0.88 | 0.34 | 0.39 | 0.65 |
| 17 | 08EC013 | 0.76 | 0.48 | 0.30 | 0.09 | 0.45 |
| 18 | 08EE004 | 0.12 | 0.19 | 0.20 | 0.05 | 0.16 |
| 19 | 08EF001 | 0.35 | 0.95 | 1.73 | 0.95 | 1.01 |
| 20 | 08FB006 | 0.22 | 1.22 | 0.42 | 0.08 | 0.56 |
| 21 | 08KA004 | 0.05 | 0.06 | 0.19 | 0.06 | 0.10 |
| 22 | 08KA007 | 0.29 | 0.22 | 0.12 | 0.07 | 0.17 |
| 23 | 08KB001 | 0.08 | 0.13 | 0.37 | 0.04 | 0.15 |
| 24 | 08KH006 | 0.01 | 0.12 | 0.22 | 0.10 | 0.12 |
| 25 | 08LF051 | 0.14 | 0.24 | 0.17 | 0.12 | 0.16 |
| 26 | 08MC018 | 0.10 | 0.46 | 0.44 | 0.17 | 0.28 |
| 27 | 08MF005 | 0.22 | 0.27 | 0.37 | 0.28 | 0.28 |
| 28 | 08MF040 | 0.13 | 0.41 | 0.30 | 0.14 | 0.25 |
| 29 | 08NA002 | 0.24 | 0.24 | 0.13 | 0.11 | 0.18 |
| 30 | 08NB005 | 0.27 | 0.21 | 0.10 | 0.10 | 0.16 |
| 31 | 08NL007 | 0.26 | 0.90 | 0.76 | 0.52 | 0.62 |
| 32 | 08NL022 | 0.15 | 0.62 | 0.49 | 0.42 | 0.39 |
| 33 | 08NL038 | 0.13 | 0.85 | 0.91 | 0.74 | 0.66 |
| 34 | 09AB001 | 0.23 | 0.00 | 0.07 | 0.15 | 0.13 |
| 35 | 09BC001 | 0.20 | 0.00 | 0.18 | 0.16 | 0.11 |
| 36 | 09BC004 | 0.28 | 0.00 | 0.27 | 0.29 | 0.20 |
| 37 | 09CD001 | 0.11 | -0.01 | 0.12 | 0.15 | 0.10 |
| 38 | 09DD003 | 0.51 | -0.01 | 0.36 | 0.38 | 0.32 |
| 39 | 09EA003 | 0.11 | 0.04 | 0.07 | 0.15 | 0.09 |
| 40 | 09ED001 | 0.19 | 0.01 | 0.14 | 0.17 | 0.12 |
| 41 | 10AA001 | 0.06 | 0.02 | 0.19 | 0.13 | 0.09 |
| 42 | 10AA004 | 0.21 | 0.02 | 0.24 | 0.23 | 0.16 |
| 43 | 10AB001 | 0.19 | 0.02 | 0.13 | 0.11 | 0.11 |
| 44 | 10BE001 | 0.12 | 0.07 | 0.23 | 0.18 | 0.11 |
| 45 | 10BE004 | 0.36 | 0.13 | 0.31 | 0.18 | 0.22 |
| 46 | 10BE013 | 0.18 | 0.06 | 0.39 | 0.23 | 0.17 |
| 47 | 10CB001 | 0.15 | 0.14 | 0.20 | 0.14 | 0.14 |
| 48 | 10CD001 | 0.06 | 0.10 | 0.26 | 0.21 | 0.10 |
| 49 | 10EA003 | 0.04 | 0.08 | 0.22 | 0.30 | 0.20 |
| 50 | 10EB001 | 0.10 | 0.10 | 0.14 | 0.15 | 0.12 |
| 51 | 10ED001 | 0.01 | 0.08 | 0.19 | 0.14 | 0.14 |
| 52 | 10ED002 | 0.12 | 0.07 | 0.12 | 0.14 | 0.11 |
| 53 | 10GC001 | 0.19 | 0.07 | 0.12 | 0.21 | 0.08 |
| 54 | 10LC002 | 0.22 | 0.05 | 0.50 | 0.65 | 0.39 |
| 55 | 10LC014 | 0.69 | 0.23 | 0.36 | 0.43 | 0.40 |
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Figure 1.
Location map of the river water temperature stations across western Canada. The mean observed June–September water temperature over the period of 1980–2018 is also shown. The station numbers correspond to the station identifiers listed in Table A1.
Figure 1.
Location map of the river water temperature stations across western Canada. The mean observed June–September water temperature over the period of 1980–2018 is also shown. The station numbers correspond to the station identifiers listed in Table A1.
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Shrestha, R.R.; Pesklevits, J.C. Reconstructed River Water Temperature Dataset for Western Canada 1980–2018. Data 2023, 8, 48. https://doi.org/10.3390/data8030048
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Shrestha RR, Pesklevits JC. Reconstructed River Water Temperature Dataset for Western Canada 1980–2018. Data. 2023; 8(3):48. https://doi.org/10.3390/data8030048
Chicago/Turabian StyleShrestha, Rajesh R., and Jennifer C. Pesklevits. 2023. "Reconstructed River Water Temperature Dataset for Western Canada 1980–2018" Data 8, no. 3: 48. https://doi.org/10.3390/data8030048
APA StyleShrestha, R. R., & Pesklevits, J. C. (2023). Reconstructed River Water Temperature Dataset for Western Canada 1980–2018. Data, 8(3), 48. https://doi.org/10.3390/data8030048
