The UW-Eau Claire Campus Microclimate

Introduction

This exercise was the second half of the microclimate geodatabase creation for deployment to arc pad exercise. The goals were to go out in to the field and collect micro climate data using a Kestral unit, a compass and a GPS unit, with the hope of creating micro climate maps in ArcMap. From our own groups data as well as the datum collected by the other groups in the class we would be able to get an overall impression of the climatic patterns across our campus.

Study Area

The whole study area was as the title stated the University of Wisconsin- Eau Claire Campus, as shown in figure 1 below. Which comprises of an upper an d lower campus separated by a hill and lies along the Chippewa river. But, the data that my group collected specified in the area just before the hill behind a residence hall and along the river, as can be sen in figure 2.

Figure 1: Aerial Image of the University of Wisconsin-Eau Claire campus, used for a micro climate study. 

Figure 2: The area of the University of Wisconsin- Eau Claire's campus that my group surveyed to collect micro climate data. 

Methods

Our first task was to move the database we had created in the previous exercise to the GPS unit we would be using outside, so that the data points we collected would be recorded correctly and transferred back to the computer easily. In ArcMap we had to make sure that the feature points we collected collected could be seen easily, so we chose a bright pink hue, in order to contrast with the aerial imagery base map we then added to the document. We also had to put the feature classes we previously created in to the map document.

The ArcPad toolbar was found, by selecting the get data button we were walked through the process of deploying our information on t the GPS unit. Once ensuring that our folder was added and the GPS unit was connected to the computer and then our folder was copied and pasted on to the SD card of the unit.

The different climatic factors we were collecting were; temperature, dew point, wind speed, wind direction, relative humidity, the height of the snow and of course the unit was recording our location. The Kestral unit (figure 3) was used for the temperature, dew point, wind speed and relative humidity. A basic field compass was used for the wind direction data collection. A meter stick was also used to record the snow height. The aim was to collect 50 points in our area of campus.

Figure 3: Image of a Kestrel Weather unit, used to take and read measurements of climatic conditions to do with temperature, winds, pressure etc. 

Once we had as many points as the time limit of our class would allow, the data then had to be moved from the GPS unit back in to ArcMap. This was done the same way as the folder was moved on to the unit in the first place, by copying and pasting. All of the groups data then had to be combined in one feature class to examine the whole of campus.

We then had to individually create a series of maps, that would portray different aspects of climate. This was done using ArcMap and techniques used in previous exercises to create maps, and had to be presented including the basic elements of map design. However, we were to put all of the different groups' study areas together so we could have a map for the whole of campus. This involved using the merge tool in ArcMap to put all the feature classes together in one geodatabase, within this coma every groups data was standardised under the parameter's of what the first group used. Some groups were missing descriptions and times in their data, so their part of the columns were left blank but the main data for temperature, wind speed etc, were all filled in.

Discussion

Below in figure 4 you can see all the data points that were collected when you put each group's fieldwork together, it covers on top of and below the hill as well as on and across the footbridge that lies over the Chippewa river.

Figure 4: A map of the points where microclimate data was collected. 

In figure 5 below we can see a map that I created of the wind speed data that was collected. It can be seen that on the day that the fieldwork was carried out wind speeds were fairly calm at round about 3-5mph on average. However there are three noticeable points in the centre of campus that are 9-11mph. This could be due to the fact that these points are between buildings and so the wind would be channeling and concentrated through the space. However the points around it are lower, which would tend to suggest that is not the case and maybe they just picked up a gust of wind in that moment in time. 

Figure 5: Map containing the wind speed in areas of the university campus. 

Some of the measurements may not be as accurate as possible as the readings on the Kestrel were fluctuating so much that we would not have been able to collect all our points if we had waited to get a definitive answer, or it may have not settled on a figure at all. As it was a very cold day, and we perhaps were not quite as appropriately dressed as we should have been, we went inside a building for a few minutes to warm up before continuing. This meant that the sensors were put in to the warmth before going back out in to the cold again and it took some time for them to readjust.

The snow depth and wind direction measurements were perhaps quite controversial also as, the snow depth can var greatly even withing the point you are standing at so it depends very much on where you choose to place the measuring stick.  Some of these areas of snow may also have been there because they were piles of cleared snow, and so would not be accurately representing the real world snow fall measurements. The wind direction was taken from a compass and so was not showing a precise location, but more of a general, direction.

Conclusion

The collection of information of data about an areas microclimate can be fairly simple to collect once you get the use of the equipment and in to a flow. There can be more general readings for points in cases where the Kestrel unit is fluctuating greatly. Physical conditions like snowfall, may have been influenced by man and would not represent the real world phenomenon one hundred percent.