Precipitation maps
Percent of normal precipitation is calculated by dividing measured precipitation by the 'normal' precipitation for a given time period and multiplying by 100.
Departure from average precipitation expresses the difference between measured precipitation and average precipitation (based on a 30-year period from 1981 to 2010).
Precipitation percentile values provide a method to define conditions based on the frequency of their occurrence. The ‘Precipitation Compared to Historical Distribution' maps use percentiles to report precipitation by relating how the current conditions compare to historical accumulated values.
Temperature Maps
Growing degree days (GDD) are used to estimate the growth and development of plants and insects during the growing season. Insect and plant development are very dependent on temperature and the daily accumulation of heat. The amount of heat required moving a plant or pest to the next development stage remains constant from year to year. However, the actual amount of time (number of days) required can vary considerably from year to year because of weather conditions.
Daily GDD values are computed by subtracting a base value temperature from the mean daily temperature and are assigned a value of zero if negative:
[(Maximum Temperature + Minimum Temperature) ÷ 2] − Base Value
Where the result is >= 0.
The basic concept is that development will only occur if the temperature exceeds some minimum development threshold, or base temperature. The base temperatures are determined experimentally and are different for each organism.
| Crop | Base Temperature |
|---|---|
| Grasses | 0.0 |
| Alfalfa | 5.0 |
| Clover | 5.0 |
| Crop | Base Temperature |
|---|---|
| Wheat | 0.0 |
| Barley | 0.0 |
| Oats | 0.0 |
| Rye | 0.0 |
| Peas | 5.5 |
| Dry Beans | 10.0 |
| Corn | 10.0 |
| Crop | Base Temperature |
|---|---|
| Mustard | 0.0 |
| Hemp | 2.0 |
| Flaxseed | 4.4 |
| Canola | 5.0 |
| Safflower | 5.0 |
| Sunflower | 7.0 |
| Soybeans | 10.0 |
| Crop | Base Temperature |
|---|---|
| Spinach | 2.2 |
| Lettuce | 4.4 |
| Asparagus | 5.5 |
| Potatoes | 7.0 |
| Pumpkins | 13.0 |
| Tomatoes | 13.0 |
| Insect/Pest | Base Temperature |
|---|---|
| Cabbage maggot | 6.0 |
| Variegated cutworm | 7.0 |
| Grasshoppers, corn borers | 10.0 |
| General insect development | 15.0 |
Adapted from:
[1] S. Edey, 1977. Growing Degree-Days and Crop production in Canada,
[2] Cao and Moss, 1989. Temperature Effect on Leaf Emergence and Phyllochron in Wheat and Barley,
[3] Ash et al., 1999. Agricultural Climate of Manitoba
[4] Sadras and Hal, 1988. Quantification of temperature, photoperiod and population effects on plant leaf area in sunflower crops.
Drought Watch maps include monthly GDD accumulations for base values of 0°C, 5°C, 10°C, and 15°C.
Monthly GDD values are computed by adding up all of the daily values for the month.
For more information on GDD bases and their applications, please contact your local provincial agriculture representative.
Crop heat units (CHU, sometimes referred to as corn heat units) are based on a similar principle as growing degree days. CHUs are calculated on a daily basis using the maximum and minimum temperatures; however, the equation that is used is quite different. The CHU model uses separate calculations for maximum and minimum temperatures.
The formula used to calculate the CHU value for a day is:
(1.8 × (Minimum Temperature − 4.4) + 3.33 × (Maximum Temperature − 10) − 0.084 × (Maximum Temperature − 10)2) ÷ 2.0
Drought Indices
Various drought indices are used to measure drought conditions; each index measures drought in a different way, depending on the design of the index. Some indices have been designed specifically for agriculture, forestry or water management, while others are purely meteorological. Some measure long term drought, while others are concerned with short term drought. No single index works under all circumstances.
The Palmer Drought Index (PDI) estimates the departure of the moisture supply from average conditions by solving a water balance equation. The index is standardized so comparisons can be made between locations and time periods.
The PDI for a particular month consists of two components:
- the moisture state of the current month, which contributes 10 per cent to the index; and
- the moisture state of previous months, which contributes 90 per cent to the index.
Since PDI is heavily weighted to previous months, the index is said to have a "memory". For this reason, PDI, although often referred to as a meteorological index, is more appropriately considered as a measure of long-term drought or moisture surplus. A continuous record is required to calculate the PDI.
The Moisture Anomaly Index (PDI-Z) is an estimate of the moisture difference from normal (a 30-year mean). It compares conditions for the current month against the long-term average.
The Standardized Precipitation Index (SPI) is the number of standard deviations that observed cumulative precipitation deviates from the climatological average for a specific range of time.
The Standardized Precipitation Evapotranspiration Index (SPEI) is computed in much the same manner as the SPI. The main difference is that SPI assesses precipitation variance, while SPEI assesses moisture surplus/deficit (water demand) by subtracting potential evapotranspiration from the accumulated precipitation, and then assesses long term deviations.
Time frames
An agricultural year starts on September 1st and ends on August 31st the following year.
The growing season starts on April 1st and ends on October 31st of the same year.
The winter season starts on November 1st and ends on March 31st the following year.
Unlike seasonal or monthly time frames which have pre-defined start and end dates, the start and end date for a rolling map will depend on the selected time frame. For example, a 30-day rolling accumulated precipitation map with an end date of January 31st, 2013 will show the accumulated precipitation between January 2nd, 2013 and January 31st, 2013.
Legend classifications
Static class maps use pre-defined class breaks that do not change.
Dynamic class maps use class breaks that change based on the range of values.
Where the data come from
The Near Real Time (NRT) data system is the source of in-situ weather observation data used in the climate maps. NRT data are an amalgamation of climate datasets from federal, provincial and private networks which are assembled within 12 hours of capture.
Analyzed weather variables include daily precipitation (mm) and daily maximum and minimum temperature (°C). Incoming weather observations are compared with historical values and against neighbouring stations for inconsistencies through an automated and manual quality assurance and control procedure.