Our initial purpose is to compare the methodology of a man-made and read rain gauge to that of a manufactured, electronically read rain gauge. Because "small elevation differences can cause considerable changes in amounts of rainfall" (Campbell 5), we plan to place eight hand-made gauges in four different locations on the Northeast side of the Art Museum Building (two on the top of a hill, two in the middle of a hill, two at the bottom of a hill, and two in an open field). This arrangement will give us the best chance at accuracy, therefore, enabling a true comparison. Each gauge will be subject to experimental error because of varying exposure to wind and evaporation. By placing these gauges at different elevations and different wind exposures, we will be able to calculate an approximate daily value by averaging the eight numbers together to account for any error caused by wind. Evaporation, however, is inevitable. We will then compare these results to that of Hayes Cummins' electronic device. Each location on the hill will be void of any coverage or foliage obstruction to guarantee more accurate and precise data collection.
We hypothesize that Hayes Cummins' electronic rain gauge and our man-made rain gauges will yield similar results. In other words, we expect that the electronic rain gauge will be equally representative. This hypothesis is similar to the clover lab in that we will use the t test to prove or disprove our hypothesis.
We decided on this project by process of elimination. Our initial idea was to deal with the chlorophyll levels in various tree leaves, where we soon found multiple obstacles in our process and experimental design. With the leaves already starting to change color, we ran out of time and were skeptical about the accuracy our results would yield. Our second experimental idea was going to involve observation of gender dominance through handholding. This was going to be a human behavioral study. The difficulty we met in this case was lack of resources and the anticipation of insufficient data. We came upon our final and current idea by returning to our original interest in nature within our surrounding environment. Our last minute change of plans turned out to accommodate a more independent student based lab. This idea reduces the dependency we had previously placed on the science lab and equipment. Therefore, we decided to study the methods of rainfall collection. After making this decision, we encountered problems that hindered our rainfall collection. The rainfall collectors were too small for the amount of rain we received, and there were too few rainfall collectors to make our sample representative. Therefore, we doubled the amount of rainfall collectors and equipped them all with larger containers for collecting rainfall.
We hope to apply the ideas and statistical methods that we learned in the clover lab to a more useful and relevant topic, accomplishing a better understanding of statistical methods for our class and ourselves. This will allow our class and us to become more familiar with the practical uses of stat view, p values, and t tests.
Rain has been an important factor of life for human beings since Ancient Egyptian times. They relied on it much like we do for the growth of their food. They portrayed rain as having originated from a deity. Aristotle, a philosopher of Greco-roman times, was the first to separate rain from astrology. He was the first to construct theories as to why it rains. Isaac Newton added to the theories on rainfall and weather. Hundreds of years later people are studying the same question as past philosophers and scientists (Middleton 57).
The study and recording of rainfall is beneficial to all mankind; you can see it on weather channels or the news. Such recordings influence the daily lives of humans through effects on crops, allergies, and traffic. Everyday, we trust the weathermen to tell us what the daily rainfall is, but we are analyzing it ourselves in our experiment. These all-knowing weathermen use practically the same devices and techniques as we will but more technically advanced, like Hayes Cummins' electronic rain gauge. Just as many forecasters and airports, we will use a rain gauge type that was invented a hundred years ago. It will consist of a collecting funnel that drains the water into a tall measuring container. We will then record the daily rainfall amount (USAToday.com). Another possible method of rainfall collection can be taken from the tipping bucket rain gauge. This contraption consists of two buckets that will tip when approximately 0.1 inches of rain is collected. This will signal an attached recorder and calculate the rainfall over a period of time (USAToday.com). Another type of rain gauge is the optical rain gauge, which "estimates rainfall from the number density of the rain drops" (Encyclopedia of Weather and Climate 443). It accomplishes this by emitting an infrared laser about a meter long, which fluctuates as drops pass. This estimates the amount of rain by how fast the beam is moved. There are many ways to measure rainfall electronically: "weighing the amount of accumulated water, using float valve to measure the height of the water column, or measuring the electrical capacitance of the water column" (Encyclopedia of Weather and Climate 442). This experiment will lead us to our conclusion--that a man-made rain gauge is equivalent in reading rainfall amount to that of an electronic rain gauge.
· (8) 1 meter long stakes
· (1) graduated cylinder
· (8) uniform rainfall containers (jars)
· (8) empty 2-liter, plastic, Coke bottles
· (8) plastic funnels with mouths approximately 20 centimeters in diameter
· Duct Tape
· Hayes Cummins' electronic rain gauge
· Construct the four rain gauges by placing a rainfall container into each of the four 2-liter bottles, attaching a funnel to the mouth of each container with Duck tape, and attaching the above collecting device with Duck tape to stakes.
· Place entire device into the soil so that each are level and equidistant to the ground.
· Place two stakes in each of four planned locations that are void of any obstruction due to trees and buildings.
· Record daily amount of rainfall at the same time, 7pm, everyday, keeping in mind necessary conversions from milliliters to inches.
· Empty recorded rainfall by removing the tape strip from the bottom and pouring rain out of collecting containers.
· Visit Hayes Cummins web site to record the daily readings of his rain gauge
· Continue this procedure for approximately five to six weeks.
· Enter data in Statview program and analyze the t-test results and p-values.
q In-Class Rainfall Collection
· For our class lab, we plan to have the students take the daily reading of all nine gauges. We will display our data and results regarding our findings, allowing the class to see if our results support the null hypothesis.
Averaging the eight daily rain gauge measurements, will ensure statistically sound results by eliminating as much human error as possible. Our lab is a simple and productive way of conducting an experiment that is comprehensible to the average student. The class will not be expected to work the recorded data because we plan to give an in depth explanation of our procedures and findings. They will collect data for one day that may or may not be added into our findings. We plan to start collecting daily data October 17th and continue for the next five to six weeks.
Here is our data that we collected for the month of November. There were only six days of rainfall in November and none in our collecting period from October.
Date Average RainfallMan-Made Gauges (in.) RainfallHays Rain Gauge (in.)
11/2/00 .43 .05
11/6/00 .11 .18
11/7/00 .05 .08
11/13/00 .08 .11
11/25/00 1.07 .60
11/26/00 .06 .01
(See other Data sheets attachments at end of Lab Packet)
Through our data analysis we have concluded that our initial hypothesis of Hays electronic rain gauge being more representative is incorrect according to the result of our t-test. Our p-value is greater than .5 so that we accept the null hypothesis and conclude that our data could be representative of the same collection sight. Our standard deviation of .404 from the mean of .300 is different from Hays' .218 and his mean of .172. This does show the possibility of our gauges being less accurate because the variance of data should not be so great the mean. The line chart follows this idea of showing how much our data deviates compared to that of Hays.
Also, our pie chart shows that the major percentage of rain was less than or equal to .116in. This is because of the season we recorded rainfall amount. Our data would have been more representative if we had recorded for a complete year instead of late Fall and early Winter.
q Discussion and Conclusions
There were several problems that we dealt with in the process of our experiment. The first of these was the number of rain gauges used to collect the rain that fell on our site. We started with four, but later doubled that for a total of eight in order to increase our chances of a more representative data set. Throughout the course of our experiment we learned that using a greater number than eight would have been beneficial. These disturbances effected the amount of rain collected each time. Some of these disturbances included wind and Frisbee golfers knocking down the rain gauges.
Another problem that occurred included the containers we used to collect rain. They did not have the capacity to hold all the rain that fell in the course of 24 hours. Because of this we lost some crucial data due to overflowing containers. To overcome this problem we replaced the original graduated cylinders with 500mL beakers. When collecting our data each day we still used the graduated cylinder to measure the rainfall by pouring it from the beaker to the graduated cylinder.
Our use of duct tape to hold the beakers in place was another problem. Over time it lost its hold and beakers often fell down causing the water to spill out and causing more lost data.
Finally, we found that accessing Hays' data for the month of November was difficult. This is because his daily calculations included the rainfall every fifteen minutes and not the complete twenty-four hours of rain collected. Unfortunately he was unable to post the November data until December. Therefore our first t-tests had to be compared to the rainfall data supplied by Butler County. This caused us to accept the alternative hypothesis because the rainfall may have in fact differed between our site in Oxford and the site of the Butler county's gauge in Hamilton. Since that time we were able to obtain Hayes data and found that the p-value concluded that we should accept the null hypotheses.
These problems were all part of the learning process with our experiment and overcoming and dealing with them lead us to produce a stronger, more accurate experiment.
For the most part, the class portion of our lab went very well. We portrayed the intentions and methods of our lab went as planned. People began to understand our exact methods when we went out to the site and took our measurements. The class was then able to see what we do daily and how we take our readings. One point we made that did not seem to be clear was what exactly our hypothesis consisted of. Our hypothesis was: the two gauges will represent two significantly different areas whereas it seems, from the class responses, that they thought that we were trying to prove which method of measurement is more "accurate" which we realize would be impossible without some undisputable amount of rainfall. Some people brought up in their peer reviews how we will account for human error. This is all part of our lab. After listening to our peers we realized that we have had far more human error than what was originally expected. We hoped to prove that without the effects of human error we would get two significantly different sets of data through our rainfall collection, much to our dismay this did not occur. That's all a part of science. Another issue in the peer reviews was the elevation differences in our gauges. We explained this briefly in our class presentation. We explained that the reason for this elevation difference was to try and make our gauges a more accurate measurement of the actual rainfall, since we are averaging all eight gauges. We thought factoring in the different elevations could take out one more factor in skewing our data. A final issue that was brought up in the majority of the peer reviews was the notion that the members of our class sympathized with our situation because of the amount of uncontrollable outside sources that affected our data. Other than these issues, all of the peer reviews seemed to have a positive reaction to our lab and the information we had gathered thus far.
As a group, we learned a lot from our student-generated rainfall collection lab. We learned more about methods of experimentation than anything else. As far as rainfall collection goes, we learned a bit but in encountering the many problems we did, we gained the most. We learned what extensive work must be put into even the preliminary stages of an experiment. Our idea to test the chlorophyll in leaves had to be rejected because of the difficultly in accessing the resources. Our next proposal to measure rainfall in different areas fell through because we didn't have a smooth plan for that and we eventually settled on measuring different methods of rainfall collection. This process in itself, before even starting the lab gave us just a taste of what we were in for. I think we took so much from this experiment in the problems we had and learning in how to deal with those. Every member of this group is coming out of this project with a newfound appreciation of experimental design and consideration for materials and methods.
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