Rainfall Lab Forest Water Resources Spring 20XX
Introduction The most simplistic way to understand rainfall in a particular area is to look at the area s average annual rainfall. That simple statistic provides a great deal of information about what a place might look like: predominate plant and animal life; where it might be situated in respect to water bodies; where it might be situated on the globe; overall, one number, average annual rainfall, can provide a wealth of information about an environment. But knowing annual rainfall doesn t tell a person when it rains, or how much it rains during rainfall events. Furthermore, knowing annual rainfall doesn t tell a farmer when droughts typically occur, or, how long a drought typically lasts. Therefore, while average annual rainfall is a very useful statistic, there are other useful ways of looking at rainfall data to help people understand how water is utilized in an environment, as well as the water needs of an environment. The primary purpose of this lab is to review raw rainfall data for the city of Gainesville, Florida, and to construct that data in various ways to better understand rainfall patterns. Various graphs will be used to display different rainfall and non-rainfall patterns, and at times, rainfall information from the city of Seattle will be used as a comparison to Gainesville. Methods I reviewed the Gainesville rainfall data from 1900-2012, and removed years that did not contain at least 365 days of data. Then, I arranged the data to show annual rainfall; I then created a graph to illustrate annual rainfall. I also computed the average annual rainfall and standard deviation. Next, I created a bar graph to illustrate the monthly average rainfall during the entire time period; therefore, I used every year, whether it contained 365 days of data or not. To create this graph, I arranged all of the data by month, and calculated the average and standard deviation for each month. I also created error bars on the graph to illustrate the stand deviation. Then, I created a histogram that displays the distribution of rainfall events of different sizes. I used a data analysis feature in excel to distribute rainfall events into various bins (ranges of rainfall events broken out by inches of rainfall). After creating the histogram, I created a graph to plot the maximum rain event, for each year, from 1900-1980. To create this graph, I ranked the maximum rain event of each year, and then removed the years after 1980. Next, I created a graph to plot the data, and used excel functions to produce a trendline, and a linear regression equation. Using this equation, I calculated the rainfall amount of a 10 and 25 year storm. Next, I created a graph to illustrate periods of time with no rainfall, and how often those periods have occurred over the entire time period. I first identified the days when rain occurred, then used this information to calculate the periods of time between rainfall events. I sorted this information, and then used it to create a frequency distribution. In the last step of the lab, I calculated the culvert size necessary to withstand a 25 year rainfall event in a hypothetical 80 acre watershed. To calculate the culvert size, I used the rational equation, with the corresponding rainfall intensity equation and duration graph to determine the cubic feet per second of peak flow. Then, I used the table provided to determine the diameter of the culvert. Finally, I used the Florida BMP look up table to determine an alternative culvert size.
Results FIGURE 1 HERE Figure 1. The graph shows the annual rainfall, for Gainesville, for each year from 1900 to 2006. There are some notable defects in figure 1. For instance, only XX inches of rain was recorded in YYYY. This does not mean that only XX inches of rain fell in Gainesville in YYYY, rather, rainfall data was not collected for the majority of YYYY. There was also a period of time, YYYY-ZZZZ, where no data was recorded. The average rainfall for the entire time period was XX inches, with a standard deviation of XX; however, the years with incomplete data, less than 365 days of data entry, were omitted from that average calculation. If the years with incomplete data are included, the average annual rainfall would be XX inches, with a standard deviation of XX. FIGURE 2 HERE Figure 2. The graph shows the annual monthly rainfall, for Gainesville, for each year from 1900 to 2006. The lines running through the middle of the bars indicate the standard deviations of the averages. FIGURE 3 HERE Figure 3. The graph shows the distribution of rainfall events, for Gainesville, from 1900 to 2006. Each number on the X axis represents the upper limits of that rainfall bin. Example: the rainfall bin of 0.2 inches has a reading of approximately 5,000 rainfall events; this means that there were approximately 5,000 days where it rained between 0 and 0.2 inches. FIGURE 4 HERE Figure 4. The graph shows the maximum rainfall event for each year during the entire time period. FIGURE 5 HERE Figure 5. The graph ranks the maximum rainfall events from 1900 to 1980, for Gainesville. It also represents the likelihood of rainfall events of various sizes occurring over periods of time. The equation in the graph can be used to calculate the inches of a rainfall event of any X number of years. This equation, and the graph itself, only represent rainfall data from 1900 to 1980. FIGURE 6 HERE Figure 6. The graph shows the frequency of various periods with no rainfall. Example: there was only 1 occasion of a 75 day period with no rain (see 1 on the Y axis and 75 on the X axis).
The rainfall amount for a 10 year storm is YY inches, and YY inches for a 25 year storm. The probability of a 3 inch storm occurring at any given year would be YY. From 1980 to 2006, there were B years that recorded storms over the 10 year storm event threshold of XX inches. There were no years from 1980 to 2006 that recorded storms events over the 25 year storm event threshold of XX inches, but one in 2004 that was very close. Culvert Size Using the rational equation, a peak flow of XX cubic feet per second was calculated. Using the lookup table provided in the lab handout, a culvert size of XX inches in diameter would be appropriate to handle that peak flow amount. Using the BMP lookup table, a culvert size of XX inches in diameter would be adequate. Discussion The average rainfall of Seattle is only 38.2 inches, considerably lower than the Gainesville average of YY inches. Seattle, an area known for its rainy weather, receives approximately ZZ% less rain than Gainesville, an area not regarded for its rainy weather. If the years with incomplete data are included, Gainesville s average rainfall is only ZZ inches less than the average computed with complete data. Therefore, omitting the data does not substantially affect the overall average rainfall, especially when using the data as a comparison to another city in a different region of the United States. It s not only the annual rainfall average that differs between Gainesville and Seattle; the monthly averages differ substantially as well. As seen in figure 2, Gainesville receives substantially more rain in the summer than it does in other seasons. In contrast, Seattle receives its least amount of rain in the summer months. A similar graph showing Seattle s monthly rainfall averages, would be U shaped, showing a large dip in rainfall during the summer months. Unsurprisingly, most of the rainfall events in Gainesville during the 20 th century were between 0 to 0.2 inches. Referring to figure 3, as the rainfall events get bigger, they become less frequent; this is similar to Seattle, although Seattle is even more likely to experience smaller rainfall events. Just as large rain events are rarer than small rain events; long periods of time with no rain are much rarer than short periods with no rain. Referring to figure 6, periods with no rain between 1 and 3 days occurred approximately 4,000 times, whereas periods with no rain between 10 and 12 days occurred approximately 350 times. Therefore, it s safe to say that Floridians, at least those in the Gainesville area, are accustomed to seeing rain, at least once, every 10 days. Even more importantly, farmers, plants, animals, and really everyone in the Gainesville area is dependent on frequent rainfall. Considering how hot it gets in Florida, if it didn t rain frequently, things would not only look different, but life, for everything and everybody, would be different. Seattle s distribution of non-rainfall periods is similar to Gainesville s distribution of non-rainfall periods. Seattle receives more of its rain in smaller portions than Gainesville. Correspondingly, Seattle has more
days, with some type of rainfall, than Gainesville; however, their monthly averages show that they receive very little rain in the summer months compared to the rest of the year. Therefore, even though Seattle receives more rainy days than Gainesville, they do have relatively dry periods during the summer, where rain could not fall for several days. Gainesville also has these relatively dry periods but they occur in the spring and fall, not in the summer. The equation from figure 5 was used to determine the rainfall amounts of a 10 and 25 year storm, using the maximum rainfall events from 1900-1980; those amounts were XX and YY inches respectively. This equation appears to be a useful tool at predicting the probability of rainfall events; during the 26 years between 1980-2006 there were only 3 years which received storms over ZZ inches. However, we cannot assume that there were only three 10 years storms during the 26 year time period. The data was ranked to illustrate the maximum storm event in each year, not to illustrate every storm that broke a 10 year rainfall event threshold. Therefore, it s possible that during a year that had at least one 10 year storm, there were multiple 10 year storms during that same year. None the less, the equation seems to be a fairly accurate predictor of the probabilities of large rainfall events. A bigger culvert size was calculated using the rational equation than the BMP lookup table: XX inches in diameter using the rational equation, and XX inches in diameter using the BMP lookup table. The rational equation is more accurate because it assesses more factors associated with rainfall and the watershed. The rational equation is used to calculate peak flow, which takes into account time of concentration, elevation, watershed acreage, and the watershed s furthest point from the outlet. The BMP manual only accounts for the size of the water shed, the slop, and the soil type. Conclusion While the city of Gainesville receives a lot of rain compared to other parts of the United States, that rainfall is not distributed evenly throughout the year; this information is extremely useful to many who live in the Gainesville area. According to the data analysis, about every 10 years it will rain approximately 6 inches in one day. This approximate rainfall amount, used in conjunction with other analysis is helpful to designing infrastructures, such as culverts, that are capable of handling high rainfall events. Another useful way of observing rainfall data is to better understand periods of drought. Understanding the lack of rainfall is almost as important as understanding rainfall itself. Decision makers rely on rainfall data to make important judgements on how rainfall is allocated to consumers. Overall, taking raw rainfall data and constructing it in different ways to better interpret rainfall patterns and cycles, produces very useful information.