How do brown Christmases affect the chance of drought next year

Comparing historical data on brown christmases and drought years to determine whether there is a relationship. This would allow provinces to plan for droughts.
Grade 6

Presentation

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Problem

Problem

 

  • Droughts cause:
    • Crop and plant damage
    • Fires 
    • Soil erosion
    • Livestock and animals die of starvation and lack of water
    • Less hydro power
  • Predicting droughts would:
    • Give farmers time to prepare 
    •  Allow governments to better manage water resources
  • Could brown Christmases provide an early warning system for drought?

Method

Method

 

Gathering Data:

Data source: Environment Canada

  1. Find out if precipitation/snow on the ground is 0 cm on December 25th for that year.
  2. Mark year as either brown or white in a google sheets table named Brown Christmases.
  3. Continue until collected data for each available year.

 

Data source: Environment Canada:

  1. Check precipitation by month in May, June, July. 
  2. Note precipitation in a google sheets table named Droughts Based Off Of Precipitation.
  3. Continue until data collected for each available year.

 

*I found average climate data from Environment Canada called Climate Normals measured from 1981-2010. They have not yet published 1991-2020.

 

Comparing Data:

Data source: Environment Canada

  1. From the data in Brown Christmases and Droughts Based Off Of Precipitation, create a new sheet (Brown and Droughts):
    1. If there was no snow on the ground on December 25 for a particular year, I marked Brown Christmas as “Yes”
    2. For the following year, for each month that had below average precipitation, I marked a “1” for that specific month
    3. If 2 months next to each other are marked as “1”, then mark the Drought Next Year Column as “Yes”

 

  1.  Check the 2 columns - Brown Christmas and Drought Next Year that show both of those two things.
  2. If they both say “Yes”, mark column Both as “Yes”
  3. If the Brown Christmas and Drought Next Year Columns both or 1 do not say “Yes” , do not write anything in the Both column
  4. Continue until done for each available year 
  5.  Count the total of Yes values in all columns and create a row called "Totals".

Research

Research

 

While I was working on my project several news articles came out about drought and what the government is doing to prepare and predict them. Another part of my research was figuring out where to get my environmental data. Some options I considered were the Weather Network or the Weather Channel. I decided to go with Environment Canada because they are the organization that measures climate data at sites all across Canada.

Post-School science fair


Once I passed my school fair I sent two emails, one to Environment Canada, one to WaterSMART a company I read about in the news who made a drought model for Alberta. Environment Canada responded and told me that widening my data collection period from one day to a full month will likely provide better results. I had a call with WaterSMART and they agreed with what Environment Canada said. Additionally they told me that there are 2 types of drought: Meteorological and Hydrological and for my drought data collection period I should add August. For other variables to predict drought they said I could use snow pack/snow pillow data and river flow data. 

I did expand my data to include all of December .

Data

     

Do brown Christmases predict droughts

          
                 
     

Next year's summer months below normal:

          
Year Brown Christmas Next Year May June July   Drought next year Both
1956 No 1956 30.2 130.6 38.6   No No
1957 Yes 1957 21.1 64 41.7   Yes Yes
1958 Yes 1958 15.5 97 60.7   No No
1959 No 1959 46.2 116.6 58.4   No No
1960 No 1960 52.8 86.1 42.7   Yes No
1961 No 1961 42.7 9.9 153.7   Yes No
1962 No 1962 56.9 45.2 33.3   Yes No
1963 No 1963 19.6 146.3 90.4   No No
1964 No 1964 63.8 101.6 72.1   No No
1965 No 1965 44.2 169.9 117.6   No No
1966 No 1966 58.4 79.2 113.3   No No
1967 No 1967 61 54.1 7.6   Yes No
1968 No 1968 49.8 54.9 66.5   Yes No
1969 No 1969 29.5 126.5 75.2   No No
1970 No 1970 19.1 159 57.4   No No
1971 No 1971 18 95 73.4   No No
1972 No 1972 31 140.5 71.4   No No
1973 No 1973 28.2 86.1 38.1   Yes No
1974 Yes 1974 71.1 18.8 38.4   Yes Yes
1975 No 1975 68.1 70.9 63   Yes No
1976 No 1976 55.9 60.5 69.6   Yes No
1977 No 1977 97 29.3 63.3   Yes No
1978 No 1978 75.8 59.6 55.8   Yes No
1979 No 1979 41.4 47.5 46.3   Yes No
1980 No 1980 95.1 103.6 50   No No
1981 Yes 1981 142.1 68.9 127   No No
1982 No 1982 81.8 86.8 75.1   No No
1983 No 1983 9.6 47.8 59   Yes No
1984 No 1984 65.8 73 24.6   Yes No
1985 Yes 1985 21.9 40.9 53.2   Yes Yes
1986 Yes 1986 67.5 81.1 93.7   No No
1987 No 1987 12.7 21.8 126.3   Yes No
1988 No 1988 16 84.6 46.8   Yes No
1989 No 1989 41.2 80.7 50.6   Yes No
1990 No 1990 100.2 61.3 83.7   No No
1991 Yes 1991 96.1 113.2 29.6   No No
1992 N/A 1992 46.2 177.2 76.2   No No
1993 Yes 1993 61.9 118.4 87   No No
1994 Yes 1994 62.5 68.4 38   Yes Yes
1995 No 1995 71.9 43.4 133.4   No No
1996 No 1996 51.5 59.2 41.9   Yes No
1997 No 1997 100.7 138.4 16.9   No No
1998 No 1998 86.4 110.4 132.2   No No
1999 Yes 1999 52.8 95.4 103.8   No No
2000 No 2000 28.8 109.8 66.8   No No
2001 No 2001 30.5 121.4 58.8   No No
2002 Yes 2002 34 58.6 34.6   Yes Yes
2003 No 2003 34.5 104.8 42.2   No No
2004 Yes 2004 55.6 98.2 54.2   No No
2005 Yes 2005 18.8 247.6 19.8   No No
2006 Yes 2006 37 122.8 51.4   No No
2007 No 2007 90.8 165.8 25.2   No No
2008 No 2008 102.2 113.3 77.1   No No
2009 No 2009 14.2 42.6 70.6   Yes No
2010 No 2010 63.8 63.8 66   No No
2011 Yes 2011 87.6 78.8 107.8   No No
2012 N/A 2012 72.2 146.8 38.6   No No
2013 No 2013 104.8 146.6 47   No No
2014 Yes 2014 62.3 82.9 25.5   Yes Yes
2015 No 2015 33.9 58.1 56.3   Yes No
2016 No 2016 68.3 61.6 206.1   No No
2017 No 2017 38.1 41.5 55.5   Yes No
2018 No 2018 32.5 67.7 34.8   Yes No
2019 No 2019 45.5 134.2 83.5   No No
2020 No 2020 110.6 171.8 81.5   No No
2021 No 2021 35 30.3 61.9   Yes No
2022 N/A 2022 12.1 137.9 64.1   No No
2023 No 2023 30.4 70.1 42.9   Yes No
Total 16 65 39 39 40   30 6

Note: The drought collection months were highlited if they were beneath average but does not show in the platform.

Conclusion

  Conclusions

 

  • Only in 37.50% of years did brown Christmases predict a drought (as 16 out of 65 years had no snow, and only 6 of those 16 years were followed by drought the next summer)
  • Based on my definition of drought and my data, brown Christmases are not good predictors of droughts 
  • Some questions that I have now:
    • What other factors predict drought?
    • Why is there no data on precipitation from Environment Canada for parts of 1992, 2012, 2022
  • After expanding the data range to all of December I had better results at 44.44%

Citations

Citations

“Environment Canada.” 

https://climate.weather.gc.ca/historical_data/search_historic_data_e.html

 

“Understanding Droughts.” National Geographic Society, 19 October 2023, https://education.nationalgeographic.org/resource/understanding-droughts

 

Zapata, Karina, and Joan Donaldson. “Province plans ahead to mitigate severe drought this year — using a familiar modelling tool.” CBC, 16 January 2024, https://www.cbc.ca/news/canada/calgary/watersmart-solutions-drought-modelling-1.7084876.


Dryden, Joel. “Alberta to launch 'unprecedented' water-sharing negotiations Thursday amid drought fears.” CBC, 31 January 2024, https://www.cbc.ca/news/canada/calgary/alberta-water-sharing-negotiations-rebecca-schulz-old-man-river-1.7100450.

Acknowledgement

Mark with the Meteorological Service of Canada, Alison Regan and Shannon Smithwick from WaterSMART,Marie Claire Arrieta,Jan Owac,Jennifer Hodgins,Ajit George Mathew

Attachments

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