Microclimate

Microclimate Data Collection and Analysis

 

Introduction:

            The field activity this week was a second part to activity last week. The issues from the activity last week were reconciled to make a working and fully functioning database provided by the activity facilitator and the use of the Trimble Juno GPS was abandoned. The Juno software no longer could link to the new version of Arcscene and so a different data collection method was employed using smartphones. ArcCollector was downloaded onto phones and used in the same way as the Juno to collect microclimate data from the Kestrel portable weather station. With the phones however the data could be transferred using ArcOnline accounts to transfer data wirelessly, this allowed the issue of Arcscene to be ignored.

Methods:

            Using ArcCollector, each person in the field had access to the database and could readily see the domains and ranges set up for data collection. This allowed everyone to be using the same amount of significant digits, and the same forms of measurement such as temperature in Fahrenheit and wind speed in miles per hour. The ArcCollector method proved to be much more efficient than that of the Juno the week before. Data in different zones as seen in Figure 2, as outlined by the red lines, was collected by different groups. The collected data was temperature (F), dew point, wind chill, wind speed, and wind direction. Was subtracted from the displayed data because the data could not be accurate because no compasses were used to collect the data and so direction could only be guessed.

            The University of Wisconsin Eau Claire is an interesting place to collect microclimate data because of the general layout of the campus. A large portion, called “Upper Campus” is located on top of a large hill with lower campus below it, then there is the campus portions located across the Chippewa River. These locations can be seen in Figure 2 below. Groups were given an hour to set up ArcCollector and collect data points in the designated zones. Once the data was collected by each group it was transferred by group members into a temporary file that gave the entire class access to the data tables from each group and could be taken into the individual file for analysis in ArcMap. In order to simplify the data tables, to have one file instead of nine different false with different displays, all of the microclimate data from all of the different groups was merged into one table (Figure 1). The merged table put all of the data in one table so display was consistent and any changes in display or collection would not appear and damage data integrity.

            The data collected showed trends in the area for a microclimate yet there were only a finite amount of points collected and so only so much can be seen and assumed about the area. The best microclimate map would have points on every area of the area of interest, this is impossible to do, the lack of infinite point can be compensated for interpolation of the data. In this case an Inverse Distance Weighted (IDW) interpolation method was used to fill in the spaces between the points and make a microclimate map for the entire campus. The points in figure one contained all of the needed attributes, interpolation needed to be applied to only the desired field and the display would show the interpolated data for the desired field such as temperature in figure 2.

Conclusion:

            A comparison of the interpolated data shows trends across campus. The effects of the river are seen clearly in the temperature, dew point, and wind chill maps. The large cold thermal mass as well as the open flat river resulted in a large amount of cold wind coming off of it especially compared with the warm ground surrounding the river. This method of data display certainly shows trends in data that could not be seen with just points and allows for data inferences to be drawn. For example in the temperature display there is an area that is just as cold as next to the river and in the wind, this area is in the southeast of the area of interest.

            These few points which stand out as being colder than surrounding points allow for conclusions to be drawn on what that area is. It would be helpful however, in the future, to collect land cover data and suddenly these few points would make sense. The cold points were collected in an area than never gets the sun, it is at the bottom of a steep north facing hill, and is under dense tree coverage as well as near a swamp and a large source of cold thermal mass.

            As stated before the wind direction attribute could not be used for lack of a field compass and accurate data collection method but this field, if filled in would allow for more interpretation as the wind direction influences wind chill and temperature and it is likely that the river’s affect would again be visible. The time domain is another field that would be helpful to have in the data. The time field would not be displayed but the fact that data was collected in the afternoon hours as opposed to any other time of the day has certain implications for every attribute collected.

            Clearly there are some things that could be added to a later test to make it more accurate and telling of the area but for what was collected and useable this activity was useful for understanding the microclimate of the University of Wisconsin Eau Claire on March 8^th 2016 in the afternoon hours.

  

(Figure 1: A table showing the merge of the microclimate data fields)

(Figure 2: The collected data points from the groups. Each point contains microclimate attributes.)

(Figure 3: An interpolated map of campus temperatures)

 

 

(Figure 4: An IDW interpolation of campus dew point.)

(Figure 5: An interpolated map representation of wind chill.) 

(Figure 6: An interpolated map of campus wind speed.)