1. Modelling Temperature in South-
West Nova Scotia for 2013
Stephanie Clarke – May 30th, 2014
Semester Long Project

2. Overview
1. Introduction
 Previous Project
 Reason for the Project
 Weather Stations & Deployment
 Growing Degree Days
 Methods used
2. Methodology
 Input Data
 Arc Method
 Regression Method
 Scripting
3. Results
 Outputs
 Comparisons
 Validation
4. Conclusions
 Limitations
 Conclusions

3. 1. Introduction
Previous Project
 Last year a similar project was completed by another student
 9 different methods in Arc and 3 different regression methods
 This student concluded that one method from Arc and one
regression method were the best methods when modelling
temperature
 This years project differs because only the two best methods were
examined and comparisons between 2012 & 2013 were made

4. 1. Introduction
Reasons for the Project
 In 2011, the Community Business Development Corporation(CBDC)
commissioned the AGRG
 Shelburne, Queens, Lunenburg, Yarmouth and Digby counties
 The goal for the CBDCs is to find the best locations for agricultural potential
based on the temperature throughout each year of the project.

5. 1. Introduction
Weather Stations & Deployment
 73 stations were deployed
throughout South-West Nova
Scotia
 59 Onset Stations (records
every 10 seconds)
 14 Campbell Scientific Stations
(records every 5 seconds)
 1 Environment Canada Station
(Greenwood)
 2 HOBO Stations
 Deployed in 12 transects
 Averaged monthly for 2013

6. 1. Introduction
Growing Degree Days (GDD)
 Measure of heat accumulation and is useful when assessing the
suitability of a region for a crop production.
 GDD is calculated using the following equation:
𝐺𝐷𝐷 =
𝑇𝑚𝑎𝑥 − 𝑇𝑚𝑖𝑛
2 − 𝑇𝑏𝑎𝑠𝑒
 Maximum temperature minus minimum temperature divided by 2
minus a base temperature (i.e. 10˚C for grapes)

7. 1. Introduction
Methods Used
 Two methods were used to create temperature maps:
 Geostatistical Interpolation
 The values between the point data are a prediction
 Regression Techniques
 Takes into account other variables and how they change

8. 2. Methodology
Input Data
 Supplied Data
 Locations of stations with
monthly temp averages
 DEM layer
 Coastline/Roads/Hillshade
 Solar GOES data
 Esri solar potential data
 Validation data
 Predictors
 Easting
 Northing
 Aspect
 Elevation
 Distance from the coast
 Solar radiation

9. 2. Methodology
Arc Method
 Empirical Bayesian Kriging (EBK)
 Geostatistical Interpolation
Method
 Presets the most difficult aspects of
a normal kriging model
 Estimates the underlying
semivariogram
 Advantages
 Little interactive modelling
 More accurate for smaller datasets
 Disadvantages
 Slow processing time
 Limits the ability to customize the
parameters of the semivariogram
 What is a Semivariogram?
 It depicts the spatial autocorrelation
of the measured points.
 Range: The distance where the model
first flattens out
 Sill: the values on the y-axis
 Nugget: The y intercept
 Partial Sill: The sill minus the nugget

10. 2. Methodology
Arc Method
 Semivariogram Estimation:
1. A semivariogram is estimated from the data
2. Uses the semivariogram as a model with new data being simulated
at each location of the data
3. A new semivariogram is estimated from the simulated data
4. Steps 2 & 3 are repeated numerous times creating new
semivariograms

11. 2. Methodology
Regression Method
 Generalized Additive Model
(GAM)
 Purpose:
 Maximize the quality of the
prediction of a dependent
variable (i.e. Temperature)
 Estimates the smoothing
functions of the predictor
variables which are connected
to the dependent variable. (i.e.
solar radiation, elevation,
location, aspect and distance
from the coast)
 Completed completely in the stats
package R
 Rasters were created and brought
into Arc for manipulation

12. 2. Methodology
Scripting
 In total 4 scripts were used to produce both EBK and GAM outputs
 2 Python
 2 R
 These scripts could produce EBK and GAM outputs for each month,
extracted differences between the two methods, and measured the
error
 These scripts were written by the student last year that completed
the project
 Made alterations to fit my data

13. 3. Results
Comparison between 2012 & 2013 GAM

14. 3. Results
Comparison between 2012 & 2013 GAM

15. 3. Results
Comparison between 2012 & 2013 GAM

16. 3. Results
Averages
 This map was produced
within ArcMap using the
Raster Calculator and map
algebra.
 The map shows the areas
with the highest heat
accumulation
 Wolfville area
 interior of SWNS near
Bridgewater.

17. 3. Results
Averages
 Created using Raster
Calculator and map algebra
 This map shows a similar
pattern to the locations of
the GDDs. The warmer
temperatures are found near
Wolfville and in the interior
of SWNS
 The coldest temperatures
are located along the coast
and the North and South
Mountain

18. 3. Results
Validation
 Validation was done using
Environment Canada data and
data from the HOBO stations
 Mean Squared Error (MSE) was
calculated for each month
 EBK is in red and GAM is in
green. Shows the MSE for each
station for every month
 GAM is the “better” method, with
the lowest MSE

19. 4. Conclusion
Limitations
 Time
 Completing other class work
 Trying to understand R scripts
 Better validation data
 One Environment Canada station is not enough
 Had to incorporate other stations

20. 4. Conclusion
 GAM is the superior method
 Having better validation data can greatly increase in the accuracy of
the models.
 There are locations that can sustain agricultural growth in SWNS
 2013 was generally cooler compared to 2012

21. References
Ambrose, S. (2013). Modeling Temperature Data throughout South West Nova Scotia.
Colville, D. & Regier, W. (2012). South West Nova Scotia (SWNS) Temperature and Solar
Radiation Study, 2011 Project Summary. Retrieved February 1, 2012 from http://southshore
opportunities.com/south-west-nova-scotia-temperature-and-solar-radiation-study- 2011-project-summary-2/
Esri 2014. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.
Wood, S. N. (2006). Generalized Additive Models: An Introduction with R. Boca Raton, FL:
Chapman & Hall/CRC/

22. Thank You!
Questions?