Microclimate

Introduction:

For this activity the class was split up into groups and assigned a zone to take data points from, each group was equipped with a Kestral unit which is shown in figure 1 below and a base plate compass to determine where the wind is coming from. Also each student was instructed to download Arc Collector onto his/her mobile device, because this was where we were going to log in all of the attribute data for each data point. My group was assigned zone 5 to take data points from, below is figure 2, which represents each of the 5 zones that data was taken from. All members of the class needed to record, their group number, temperature, dew point, wind speed, and wind direction. Each phone contained a live feed of all other group, allowing us to keep track of where the other data points were being taken during the activity.

Figure 1: Kestral unit used to collect attribute data for data points

Figure 2: Each zone where data was collected from

Methods:

While collecting data points the tools used were Arc collector on each students mobile device, a kestrel unit, and a base plate compass. The kestrel unit was used to obtain data such as temperature, wind speed, wind direction and dew point. A different measurement of each of these attributes were taken at every data point. Once all of those were collected they were then entered into Arc collector, easy to use in ArcMap because the arc collector was directly attached to a geodatabase on ArcGIS online. This made it easy to transfer all of the data points and attribute data for each point over to ArcMap. Once in ArcMap each group was required to make an interpolation of each field in the attribute data. I decided to do an Inverse Distance Weighted Interpolation or an IDW, This will generate a map that shows the different cell values of each area by averaging the sample data points in each area closest to the cell.


Results:

Below are figures representing each of the maps created for this field activity, each representing a different field in the attribute data.

Figure 3: Showing the temperature difference between areas around UWEC campus

Overall most areas in the figure above are about the same temperature wise except for the area in the far bottom right corner. It seems that area is much cooler or had a much smaller average temperature compared to other areas on the map. Below is figure 4, representing the dew point around the UWEC campus.

Figure 4: Showing the difference between Dew Point values in the area surround the UWEC campus

After looking at the figure above, once again most of the area contains the same dew point except for the area in the bottom right corner. Dew point is the measure of moisture in the air, since in the bottom right corner its a lower dew point than other areas. This signifies that there is less moisture in the air, meaning that area should also be colder than most areas, as seen in figure 3. Below is figure 5, representing the wind speed at the location of each data point.

Figure 5: Showing the wind speed collected at each data point location around the UWEC campus area

The areas that have the highest average wind speed are the areas with the largest elevation and the areas near the river. This is to be expected because wind travels faster over flatter surfaces such as water. Since wind moves from high to low pressure areas, that means at higher altitudes there is greater pressure so there would be faster moving winds at higher elevation. The final figure represents the direction in which the wind is moving, below is figure 6. This figure shows the wind direction of each data point at the time it was collected, and the areas are divided up into sections for what degree the wind is moving. In order to collect wind direction data properly, one must point in the direction the wind is coming from not where it is moving towards.

Figure 6: Represents the wind direction with arrows of each data point collected around the UWEC campus area

Conclusion:

This activity was a great experience for all students that were included, it shows them how easy it is to collect data and create maps representing the data that was collected. It taught me more then expected, proving that there are programs out there that are specifically built to make our lives simpler when it comes to data collection methods.