Weather and Society: Post from February 20, 2014

Conditions Over the Day:

This report will be slightly different than any up to this point. With a very large winter storm looming to our west, the purpose of this blog will be to examine the change in conditions as the day progresses. I created a table that is being updated hourly that examines temperature, wind speed and direction, pressure, relative humidity, and outside conditions (overcast, light rain, heavy snow, etc). I also will be compiling maps as the day progresses that highlight the current conditions. This large storm is expected to bring upwards of 18 inches of precipitation in some areas. Another update in storm capacity has included thunder as a possibility during this storm.

3:00 PM: First signs of wintry precipitation falling.

3:30 PM: Snow starting to pick up significantly. 

4:00 PM: Experiencing moderate snowfall with some rain still present. Conditions are very wet and slushy.

4:30 PM: Heavy snowfall occurring, foggy conditions, very slushy.

5:30 PM: Heavy snowfall continues, after looking at radars, it appears as though the storm is beginning to swirl. The stagnancy of the current pressure systems and fronts would back this up. Some gusting has been observed currently. Snowflakes are very large and slushy.

6:30 PM: Heavy snow continues to fall. Storm seems more intense. Gusting winds not as apparent, however a steady wind is present from the north.

7:30 PM: Heavy snow still continuing. Pressure continuing to drop, due to the presence of a large, mid-latitude cyclone that is stationary over top of the region.

8:30 PM: Moderate snowfall currently. A bit of a light patch in the storm is currently over Eau Claire, judging by the radar. Current estimates of 7-11 inches are in place.

9:30 PM: Moderate snow continues to fall, as the storm is rotating over much of Wisconsin and Minnesota. 5-15 mph winds are persisting, as they have been since just prior to the storm.

10:30 PM: Moderate snow and winds out of the north continue affect the area.

11:30 PM: Heavy snow again and much more noticeable, stronger NW winds. Visibility is also very low.

Overnight: Over the course of Thursday night, into Friday morning, temperature should start to gradually decrease, heavy winds should be present, snow should continue to fall, and the pressure may start to rise very slowly.

Shown here, a low pressure system is fueling the large winter storm moving through the area. Our portion of the state will see blizzard-like conditions, while areas to the east should expect an icy mix. 

This map shows what form of precipitation will occur in the various areas to be impacted by the winter storm.

This map shows the expected snowfall totals through Thursday evening, with the largest totals accumulating in the northern part of the state and over Lake Superior.

An occluded front is moving through the area, pushing a large storm towards us. An occluded front is where a cold front intersects and takes over a warm front. A low pressure system is also moving through, dropping pressure and adding to a large mid-latitude cyclone moving through the area.

This update shows the previous occluded front right at the edge of Wisconsin, while a warm front is moving up from the south. Two low pressure systems seem to be fueling this winter storm.

The occluded front is no longer present to our north, as it has turned into a stationary front. However, another occluded front is pushing up from the southern portion of the state.

No change has occurred since the last map was update. The occluded and stationary fronts are still in place. 

The occluded front in the southern region of the state has moved northward, while the stationary front has transitioned into cold front and warm front. A very large mid-latitude cyclone is hovering over the Wisconsin, Iowa, Illinois border. Three low pressure systems are now noted, affecting the region currently.

The low pressure systems, occluded front, and  center of the mid-latitude cyclone are currently right over the center of Wisconsin.

As stated in the previous map, the center of the mid-latitude cyclone that is facilitating this snow storm is currently right over the center of Wisconsin. Heavy winds are cycling on the southwestern portion of the cyclone, causing it to rotate.

The center of the cyclone is still hovering over central Wisconsin right now. The strongest winds are currently blowing in a westerly and northwesterly direction. If we look at the edge of the cyclone, further to the west, we can see the significantly lower temperatures that will be making their way to us in the coming days.

The current fronts and pressures systems have not moved much according to either map.

GIS I: Application of Skills

Figure 1: This map shows the best suitable hunting land, located on publicly managed DNR land,
in Sauk County, Wisconsin. The green polygons denote land that matches the specified criteria. 

Goal

Background: The goal of this assignment was to develop a personal spatial question and carry out the correct means to answer that question. The spatial question that this lab seeks to answer is: Where are the best places in Sauk County, Wisconsin to hunt on land open to the public?

Purpose: The purpose of this assignment was to demonstrate knowledge of the skills learned from this course and apply them through various methods.

Methods

Data Collection: To begin the process of determining the best possible public hunting land in Sauk County, data had to be brought in from for streams, bodies of water, Wisconsin Department of Natural Resources (DNR) managed lands, county borders, state border, roads, and cities. Through ArcMap, database connections were made to the Wisconsin DNR Database and the ESRI Database. Through the DNR database the following data was acquired: Streams, DNR Managed Lands, County Borders, State Border. Through the ESRI database the following data was acquired: Bodies of Water, Roads, and Urban Areas. This data was brought into a file geodatabase created for this assignment. Concerns with this data stem from having to use a variety of different feature classes to examine one overall feature, such as the classes for streams and waterbodies.

Data Preparation:
Sauk County: In order to start utilizing the data, Sauk County had to be located and made into its own feature class. This allowed the analyst to use Sauk County to clip the other features to get details within only the target area. To do this, an attribute query was performed to locate Sauk County. Once the county was located, the option to make it into a feature class was chosen. Then, Sauk was projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection to make the feature class more conducive to viewing solely the county.

Roads: To begin using the Roads data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. This data had many different classes within it, depending on the size of the road. To differentiate between the different classes of roads, they were each given a different symbol, as seen in Figure 1. This class difference led to the need to create buffers based on different criteria to create different sized buffers. [Note: This was not a skill learned in this class, however, I was able to teach myself how to do it and used it for this feature class and for the Streams feature class]. To do this, a new field labelled "Buffer_Field" was added to the attribute table in the form of a Text field. Different values were assigned to this field, for example "0.5 Kilometers". This was added to each different class in order to create different sized buffers based on the size of the road and the perceived distance that a hunter would like to be away from the road in order to reduce the chance of traffic interfering with their hunt. The roads were then buffered by Field, rather than buffered by Distance. A dissolve was also applied to get rid of overlapping lines created by buffering. The resulting feature class was then clipped using Sauk County to get rid of buffers falling outside of the target area. Labels were activated for this data and a mask was applied to the labels to make them more visible on the map.

Urban Areas: To begin using the Urban Areas data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. A dissolve was applied to remove boundaries between cities like Lake Delton and Wisconsin Dells, and Sauk City and Prairie du Sac, who share borders. Then a buffer was applied around the city to account for anthropogenic influence. The data was clipped again using the Sauk County polygon to remove buffers that extended outside of the county. Labels were activated for this data and a mask was applied to the labels to make them more visible on the map.

Bodies of Water: To begin using the Waterbodies data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. A buffer was applied to buffer the area around the various bodies of water. This buffer assumed that wildlife would travel a certain distance to drink from a lake, pond, or river. Once the buffer was created and a dissolve was applied, it was clipped with Sauk County again to remove buffers that extended outside of the county. The data was then compared to the buffered urban class and an erase was applied, then to the buffered roads class and an erase was applied. This left waterbodies that were located a certain distance away from anthropogenic influence.

Streams: To begin using the Streams data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. The data was then compared to the Waterbodies feature class and an "Erase" was applied to the data in order to remove data that was overlayed with lakes or rivers, as they were not properly displayed with a line feature class. This data had many different classes within it, depending on the size of the stream. This led to the need to create buffers based on different criteria to create different sized buffers. To do this, a new field labelled "Buffer_Field" was added to the attribute table in the form of a Text field. Different values were assigned to this field, for example "0.4 Kilometers". This was added to each different class in order to create different sized buffers based on the size of the stream and the perceived distance that animals would travel in order to drink from it. Smaller streams received smaller buffers because they would not supply as much water to the surrounding wildlife, or potentially because they only carried water during precipitation events (intermittent streams). The streams were then buffered by Field, rather than buffered by Distance. Once the buffer was created and a dissolve was applied, it was clipped with Sauk County again to remove buffers that extended outside of the county. The data was then compared to the buffered urban class and an erase was applied, then to the buffered roads class and an erase was applied. This left streams that were located a certain distance away from anthropogenic influence. Finally, a union was applied between the Waterbodies feature class and the Streams feature class in order to derive a new feature class labelled "Sauk Water". This feature class contained all units of water in Sauk County.

DNR Managed Lands: To begin using the DNR Managed Lands data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. An attribute query was conducted for lands that were fully owned by the Wisconsin DNR or lands that were acquired through easements that allowed for public hunting. The interpretation of this data was assisted by Ann Runyard, GIS Analyst for the Wisconsin DNR. The query left only land that was available for public use of hunting. The data was then compared to the buffered urban class and an erase was applied, then to the buffered roads class and an erase was applied.

Data Utilization:
After all the data was prepared and the necessary feature classes were discerned, the data was finally able to be narrowed down to show only suitable public hunting land that fell within the proximity of a waterbody. To do this, an intersect was applied to show only data that met both of the necessary criteria of falling within the Sauk Water buffer and DNR huntable land. This left the answer to the initial query of the most suitable places to hunt publicly in Sauk County.

Cartographic Preparation: A proper map was created with a map to show location of the county within Wisconsin. A data flow model was also created to show workflow for the project.

Figure 2: Data flow model for the assignment. This shows the work flow carried out in order to achieve the desired output data.

Discussion
While I felt that most of this data provided a solid view of the potential lands to hunt in Sauk County, I also feel that more data would have better suited this query. It should be noted that the main focus of this project had deer in mind when the analysis was conducted. I feel that data regarding deer density would have helped show the most suitable land with the most potential for an encounter. There was data regarding deer management zones, but as they are not bound by county boundaries the data regarding density would have been skewed. I also feel that I ran into some issues with the hydrology aspect of this project. There are many different feature classes that deal with streams, rivers, ponds, and lakes, but I could not find one that contained a majority of those, so I had to settle for two feature classes and perform an erase followed by a union. With the streams feature class, lines were shown where lakes were present between an input stream to the lake and a drainage stream. The line between the two is what was erased, leaving only the input stream until the boundary of the lake and the drainage stream from the boundary of the lake. If I had the ability to input real world data gathering measures into my data I would attempt to get an overall idea on how many deer are registered per deer registration station. This would give me a general idea about the number of deer in the area, instead of a large scale classification based on spatially large zones.

Results
This project showed my aptitude to solve a question using applied GIS. It was a comprehensive project showing all that I had learned and gathered from previous coursework. As this is a potential field that I would like to go in to, understanding how to apply various tools and what those tools do is a crucial component to furthering my career.

Sources
Ann Runyard - GIS Analyst, Wisconsin Department of Natural Resources

ESRI Geodatabase

Wisconsin DNR Geodatabase

Remote Sensing: Photogrammetry

Goal

Background: The goal of this lab was to develop skills to perform photogrammetric tasks on aerial photographs and satellite images.

Methods

Scales, Measurements, and Relief Displacement:

Section 1: Determining scale is a very essential part of interpreting distance on maps. For the first portion of this assignment, real world points and their distances were provided and the scale had to be determined from them. The distance from point-to-point was measured using a ruler and then the was converted to a relative scale number by multiplying the real world distance, by 12 in order to convert it to inches, then it was divided by the number of inches measured by the ruler. The resulting number provides a relative number, or the scale, of the map (Figure 1). 

Figure 1: Fore this method, the distance from Point A to
Point B was measured wit ha ruler and, using the given
real world distance, a scale was determined for this image.

Scale can also be determined by using the focal length lens of the camera, the altitude of the lens, and the elevation of the object above sea level.

Section 2: Another way to determine the area of a given feature on Erdas is to use the "Measure" tool. This tool is found under the "Home" tab and allows the analyst to measure various aspects of area. The polygon tool was selected for use and a lagoon to the west-southwest of Eau Claire was digitized. This was used to determine the area in hectares and acres, as well as the perimeter in meters and miles (Figure 2). 

Figure 2: This shows the lagoon, denoted by the X, that
was digitized in order to determine area in hectare and
acre, and perimeter in meters and miles.

Section 3: Determining relief displacement allows the analyst to determine how far an object is from their true planimetric location on the ground through use of aerial photographs. The scale of the image was given as was the camera height above the datum. The height of the smokestack was measured and the scale was used to convert it to the smokestack's approximate height. Then, the distance from the top of the smokestack and the principal point was measured and converted to an approximate real-world distance. The numbers were plugged into the relief displacement equation and the approximate height was determined (Figure 3).

Figure 3: The smokestack and Point X was used to determine relief
displacement of the image. The principal point is located in the upper
left corner. The line of flight is denoted by the blue line.

Stereoscopy:

In order to allow for three-dimensional viewing on an image, which is what stereoscopy allows, multiple images were brought in for this exercise. The "Terrain" tab was selected, followed by "Anaglyph" in order to open the "Anaglyph Generation" window. The correct input and output images were determined, the vertical exaggeration of the image was increased to (2), and the model was run (Figure 4). 

Figure 4: In order to properly see the resulting image, polaroid glasses were
worn, which allowed the analyst to see the image in a three-dimensional way.

Orthorectification:

Section 1: The final portion of this lab allows the simultaneous rectification of positional and elevation errors of multiple images. To begin the process of orthorectification, Lecia Photogrammetric Suite (LPS) was opened through Erdas. This program is used for a variety of purposes, one being the orthorectification of images collected by various sensors. A new block file was created and the "Model Setup" window was opened. The correct geometric model category was selected, the correct projection was applied, and the model was prepared for orthorectification. 

Section 2: An image was brought in and "Show and Edit Frame Properties" was selected, followed by "Edit", then "OK" twice. This process specifies the correct sensor that the image is using.

Section 3: Now the main process of recording ground control points was carried out. The GCP icon was selected and "Classic Point Measurement Tool" was chosen as the method. "Reset Horizontal Reference Source" was selected, which opens the GCP Reference Source dialog. "Image Layer" was checked, "OK" was selected, and "Use Viewer As Reference" was selected. The GCPs were then collected in much the same manner as they had in the previous labs. Points were added, the GCP was selected, and the corresponding GCP was selected on the referencing image. After the second point, "Automatic (x,y) Drive" was selected, which allows LPS to approximate where the GCP is on the other image to allow for quicker GCP collection. This was done for nine ground control points. The points were then saved and the last two points were ready to be added. Again, the "Reset Horizontal Reference Source" icon was selected, a new image was brought in, and the last two points were collected. 

Now that the Horizontal Reference Source was set, the Vertical Reference Source needed to be set as well. The "Reset Vertical Reference Source" icon was selected and a Digital Elevation Model image was brought in to supply elevation data. The "Update Z Values on Selected Points" icon was selected and all the Z values, (elevation data) was updated. 

Section 4: The "Type" column was selected and "Formula" was opened in order to set the "Type". In this case the type was set to "Full" and the change was applied. This process was carried out for "Usage" and the usage was set to "Control". The data was saved and the point measurement tool was closed. Next, a second image was brought in. The same work flow was carried out to prepare this image as had been done for the previous image. The GCPs for this image were added as instructed by the guidelines, as portions collected on the previous image were not present for this image (Figure 5). 

Figure 5: This shows the status of orthorectification, thus far. The image is still somewhat tilted, as it has not been fully
rectified yet.

Section 5: "Automatic Tie Point Generation Properties" was selected, opening up a dialog. The necessary characteristics were input, the "Intended Number of Points/Images" was set to forty, and the process was run. Given the data previously collected, this step accurately places even more GCPs and pins the image down even more accurately. This step allows for triangulation of the various components. "Edit" then "Triangulation Properties" was selected to open a dialog. The necessary characteristics were input and a report for the data was generated.

This finally prepared the data to be resampled.  "Start Ortho Resampling Process" was selected and the correct characteristics were input. Bilinear Interpolation was the resampling method used for this process. After all the settings were specified, the model was run and the orthorectified image was produced. 

Section 6: After orthorectification, the images match up properly and are able to be compared (Figure 6). The images are brought into the same viewer and examined to ensure that the various features match (Figure 7 and Figure 8).

Figure 6: After the orthorectification process was complete, the image is rectified to the image above. This shows that the image is properly rectified and is not off improperly referenced.

Figure 7: This image is the result of the orthorectification process. After properly analyzing and assigning values to the images, they blend together very accurately.

Figure 8: This zoomed in view of the orthorectified image
shows that, while there are very small details that may not
match up perfectly, the orthorectification process leads to
a very accurate image mosaic.

Results

This lab provided an extremely in depth look at photogrammetric processes. An understanding was gained that focused on understanding how scales were formulated for aerial imagery, how stereoscopy and stereograms were created in order to show three-dimensional models, and how multiple photographs can be seamlessly mosaicked through orthorectification. 

Fluvial Geomorphology: Flood Frequency Analysis of Peak Discharge Data

Goal

Background:

In order to better understand how to read peak discharge data, processes were conducted to shed light on how to graph, calculate, and analyze discharge data. The National Atlas, United States Geological Survey (USGS) website, and Microsoft Excel were used to acquire and analyze all of the data from the Little Blackfoot River in Montana, and the La Crosse River in Wisconsin. Microsoft Excel was used to model peak discharge figures and determine exceedence probability and recurrence interval figures. Then, the recurrence interval figures were graphed on Gumbel and log-Gumbel graph paper by hand to determine which graph fit the data to a straight-line trend more accurately. Finally, a hypothesis was developed, attempting to determine what conditions caused the peak discharge events to be higher in the La Crosse River. 

Introduction

This assignment focused on the peak discharge figures of two different streams at a single one of their gaging stations. The first stream was the La Crosse River, which is located in west-central Wisconsin, at Stream Gage Site 05383000. This gaging station was located near West Salem, Wisconsin (Figure 1) and its records span from 1914-1978. The second stream was the Little Blackfoot River, which is located in west-central Montana, at Stream Gage Site 12324590. The gaging station was located near Garrison, Montana (Figure 2) and its records span from 1973-2011. The United States Geological Survey (USGS) was used to acquire stream gage data for both of these rivers. After the data was acquired, the objective of the assignment was to gain a better understanding of how to interpret recurrence interval data and exceedence probability data. Then, a hypothesis was constructed to determine why two watersheds with similar drainage area could have such variance in their peak discharge events. This hypothesis states that the reason the average peak discharge events of the La Crosse River are so much higher is because 1) the La Crosse has a higher Mean Annual Precipitation than the Little Blackfoot and 2) the Little Blackfoot is on the leeward side of a large mountain in Montana.

Methods

Acquire Data From the USGS Website: 

In order to being this assignment, the peak discharge data was gathered from the USGS website. This was done by opening the USGS website and navigating to the National Water Information Systems Interface. From here, the Data Category of Surface Water, then Peak-Flow Data were selected. The parameters of Hydrologic Region, Drainage Area, and Number of Observations were selected to assist in narrowing down the target drainage size, location, and ensure the quality of data. For this personalized assignment, 386-410 square miles was input for drainage area, the hydrologic regions of the Pacific Northwest and the Upper Mississippi were input, and a minimum record of thirty years was designated to ensure that there was enough data to fully highlight peak discharge events. The results were selected to be displayed via a map interface on the USGS website. In order to view comparable data, the size of drainage area could have no more than a 10% difference between the two sizes.

Once the data was displayed through the map interface, a stream gage station was selected depending on personal preference. Figure 1 and Figure 2 are clipped images of the map interface displayed on the USGS website. Through a hyperlinked path of the site number, access was gained to data gathered by the stream gage. The description of each stream was saved for further reference and the peak streamflow data was exported as a “tab-separated file”. This process was repeated for two different stream gages and data was gathered for La Crosse River, Near West Salam, Wisconsin (05383000) and Little Blackfoot River, Near Garrison, Montana (12324590).

After this, Microsoft Excel was used for the rest of the assignment, in order to analyze peak discharge data. The tab-separate files were opened in Excel and any excess data was deleted, leaving only the name designation, and the peak streamflow data. A column was added to show water year, which runs from October 1 through September 30. This process was repeated for the other tab-separated file. 

Analyzing Flood Data With Microsoft Excel:

After acquiring all of the data off of the USGS website, it was ready to be analyzed using Microsoft Excel. The two tab-separated files were imported to Excel on two different worksheets and any unnecessary data was deleted. The correct water year was assigned to each collected peak discharge event. Water years start on October 1st and end on September 30th. These results can be seen in Appendix I for the La Crosse River data and Appendix II for the Little Blackfoot River data. Following this the data was added to a table. The x-axis for these tables contained the water year for every year on record while the y-axis contained peak discharge values. This was carried out for both sets of data, which can be seen in Chart 1 and Chart 2. These charts were in the form of bar graphs and showed the amount of peak discharge in order of when they occurred with the earliest years first.

Following this, the objective was to determine the exceedence probability and the recurrence interval for the peak discharge events of each separate dataset. Exceedence probability is the likelihood that a discharge event will be surpassed in a given year. This is denoted as EP = m / (n + 1) where (m) is magnitude in comparison to the other ranked events and (n) is the number of years on record. The recurrence interval is the average time, in number of years, in which a discharge event of a given size will occur. This is denoted as RI = (n + 1) / m. To gather the data to figure this out two new worksheets were opened and the peak discharge column was copied over. The peak discharges were sorted using the Sort Descending option in Excel and they were assigned a numerical rank, with 1 being the highest rank. From here, the equations listed above were formulated in Excel and the exceedence probability and recurrence intervals for all peak discharge events were calculated (Appendix III and Appendix IV). 

Creating Flood Frequency Curves:

Now that all the recurrence interval data was calculated, the data was ready to be plotted by hand on flood frequency graphs. The two types of graphs were arithmetic and logarithmic and were referred to respectively as Gumbel and log-Gumbel probability paper. This graph paper is used by the USGS and flood analysts to calculate the frequency that a discharge event will occur in a given year. Both rivers were plotted on the Gumbel graph and the La Crosse River was plotted on the log-Gumbel as well. The Gumbel differed from the log-Gumbel in that the y-axis on the Gumbel graph was spaced evenly, while the y-axis on the log-Gumbel was spaced logarithmically.

Results

National Atlas Results:

Through the processes described above, data was acquired that allowed for analysis. The National Atlas allowed for Mean Annual Precipitation Data (MAP), which showed that the MAP of Garrison, MT was in the range of 10.1-15.0 inches per year from 1961-1990 (Figure 3). The MAP for the La Crosse River area was in the range of 30.1-35.0 inches per year from 1961-1990 (Figure 4). The MAP from 1961-1990 was used because this sufficiently encompassed a large amount of time from both of the studies. The Little Blackfoot River gage used was on record from 1973-2011, while the La Crosse River was on record from 1914-1978. 

Excel Results:

The data that was brought in and analyzed through Microsoft Excel yielded the exceedence probability and recurrence interval data as seen in Appendices III and IV. Through Excel, the ability to see the peak discharge event in order of when it occurred is also possible. For the La Crosse River, the largest recorded peak discharge event occurred in 1935 (Chart 1). The peak discharge for this event 8,200 cfs, with a recurrence interval of 59.00 years, and an exceedence probability of 1.7%. For the Little Blackfoot River, the largest recorded peak discharge event occurred in 1981 (Chart 2). The peak discharge for this event was 8,650 cfs, with a recurrence interval of 40.00 years, and an exceedence probability of 2.5%.

Probability Paper Results:

Gumbel probability paper is used to plot flood frequency data that is more or less linear in progression. This allows the vertical axis to be an arithmetic progression instead of a logarithmic progression. The Gumbel probability paper results showed that while the La Crosse River had a much higher average discharge, the Little Blackfoot River had the highest peak discharge overall. The ability to fit the plotted data to a linear fit line was very practical for both sets of data. The log-Gumbel probability plot is generally used to plot flood frequency data that is characterized by extremely high variation. The La Crosse River was plotted on the log-Gumbel probability paper and the result was a generally concave down, curved graph. This result was not conducive to plotting a linear fit line.

Discussion

Given all the results and the interpretation of the data, they hypothesis that the La Crosse River exhibits higher average discharge events because of a higher mean annual precipitation and the location of the gage on the Little Blackfoot on the leeward side of a mountain seem to have been upheld. When looking at the mean annual precipitation maps for both areas, it is very obvious that the La Crosse River has a higher mean annual precipitation. There are also no features that would impede a precipitation event from falling on the La Crosse. The Little Blackfoot River, however, has a number of large mountains directly to the west. These mountains have a much greater mean annual precipitation than the area to the east of the mountains. The leeward sides of mountains are much drier than the windward sides, so this could account for the noticeably drier climate found to the eastern side of the mountains. 

This leeward theory is supported when looking at the Gumbel probability paper. The La Crosse, on average, has a much higher and steeper fit line than the Little Blackfoot has. This means that the average discharge for the La Crosse is greater than that of the Little Blackfoot. The same would be said for the mean annual precipitation. That being said, the highest peak discharge event for the Little Blackfoot is 450 cfs higher than that of the La Crosse. This can most likely be accounted for by understanding that, because this area is much more arid than that of the La Crosse, when a large precipitation event does occur that effects the Little Blackfoot, the effects will be much greater. This could most likely be attributed to impermeable soil due to hardening with a lack of precipitation. This leads to greater runoff and higher peak discharges. This idea can be supported when examining the discharge events more carefully. There is a very large gap between the highest and second highest peak discharge events from the Little Blackfoot. The highest, as stated before, was 8650 cfs, while the second highest was only 3650 cfs, a difference of 5000 cfs.

Conclusion

Through the use of the National Atlas, United States Geological Society, and Microsoft Excel, the peak discharge events of the La Crosse River and the Little Blackfoot River were analyzed. Both of these rivers had a similar drainage area, though had very different reactions to discharge events. While the La Crosse had, on average, higher peak discharge events, the Little Blackfoot had the highest at 8650 cfs. The low discharge was hypothesized to be due to an almost 20 inch decrease in mean annual precipitation in the Little Blackfoot. The Little Blackfoot was on the leeward side of a mountain range that, according to maps gathered from the National Atlas, exhibit a much higher mean annual precipitation than the areas on its leeward side. This mountain was hypothesized to act as a windbreak that impeded precipitation from reaching the area of Garrison, MT in large amounts.

Figure 1: This is a map of Wisconsin taken from the USGS website. The gage that is highlighted in yellow is the gage used to record discharge for the La Crosse River, Gage Station # 05383000.

Figure 2: This is a map of Montana taken from the USGS website. The gage that is highlighted in yellow is the gage used to record discharge for the Little Blackfoot River, Gage Station # 12324590.

Figure 3: Mean Annual Precipitation Map for the Little Blackfoot River and surrounding area. The stream in the center of the picture represents the Little Blackfoot River. The mean annual precipitation here falls in the range of 10.1-15.0 inches per year.

Figure 4: Mean Annual Precipitation Map for the La Crosse River and surrounding area. The stream in the center of the picture represents the La Crosse River. The mean annual precipitation here falls in the range of 30.1-35.0 inches per year.

Figure 5: Legend for the Mean Annual Precipitation Maps for Figures 3 and 4.

Chart 1: Peak Discharge Per Water Year for the La Crosse River, Near West Salem, WI, Gaging Station # 05383000.

Chart 2: Peak Discharge Per Water Year of the Little Blackfoot River, Near Garrison, MT, Gaging Station # 12324590.

Chart 3: This is a plot of the recurrence intervals of the La Crosse River (the red line) and the Little Blackfoot River (the green line) on Gumbel probability paper.

Chart 4: This is a plot of the recurrence interval of the La Crosse River on log-Gumbel probability paper.

Appendices

Appendix 1:

# Sites in this file include:		#  USGS 05383000 LA CROSSE RIVER NEAR WEST SALEM, WI
Site #	QPk Date		Water Year	QPk
5383000	6/28/14		1914	1800
5383000	2/23/15		1915	1800
5383000	1/29/16		1916	1850
5383000	3/24/17		1917	2990
5383000	3/18/18		1918	3130
5383000	3/16/19		1919	3900
5383000	6/16/20		1920	2600
5383000	6/10/21		1921	1150
5383000	2/24/22		1922	2920
5383000	4/4/23		1923	2480
5383000	8/20/24		1924	2600
5383000	6/15/25		1925	2120
5383000	8/22/26		1926	1920
5383000	7/21/27		1927	1370
5383000	9/15/28		1928	5160
5383000	6/16/29		1929	1170
5383000	2/21/30		1930	3270
5383000	6/23/31		1931	635
5383000	6/8/32		1932	2380
5383000	3/31/33		1933	4310
5383000	4/4/34		1934	3890
5383000	8/6/35		1935	8200
5383000	3/18/36		1936	3020
5383000	3/8/37		1937	1100
5383000	9/11/38		1938	3490
5383000	3/20/39		1939	1510
5383000	6/24/40		1940	1140
5383000	9/16/41		1941	3020
5383000	6/30/42		1942	4170
5383000	5/31/43		1943	2790
5383000	3/13/44		1944	2150
5383000	5/23/45		1945	4590
5383000	1/7/46		1946	4170
5383000	6/30/47		1947	2900
5383000	2/29/48		1948	2300
5383000	3/23/49		1949	2020
5383000	3/7/50		1950	2900
5383000	3/29/51		1951	1630
5383000	7/20/52		1952	2470
5383000	3/19/53		1953	1320
5383000	7/5/54		1954	1730
5383000	6/3/55		1955	3650
5383000	4/3/56		1956	5720
5383000	2/26/57		1957	984
5383000	2/27/58		1958	1310
5383000	4/1/59		1959	3270
5383000	5/8/60		1960	1780
5383000	3/27/61		1961	4490
5383000	3/29/62		1962	2150
5383000	3/25/63		1963	2060
5383000	4/7/64		1964	1020
5383000	3/3/65		1965	2610
5383000	2/8/66		1966	5940
5383000	6/16/67		1967	3620
5383000	6/21/68		1968	2360
5383000	6/27/69		1969	1750
5383000	3/4/70		1970	1800
5383000	7/2/78		1978	7600

Appendix II:

# Sites in this file include:			#  USGS 12324590 Little Blackfoot River near Garrison MT
Site #	QPk Date	Water Year		QPk
12324590	5/21/73	1973		266
12324590	1/15/74	1974		2700
12324590	6/19/75	1975		3650
12324590	5/11/76	1976		1820
12324590	4/9/77	1977		319
12324590	5/19/78	1978		1200
12324590	5/24/79	1979		1120
12324590	5/25/80	1980		2920
12324590	5/22/81	1981		8650
12324590	2/21/82	1982		1440
12324590	5/27/83	1983		959
12324590	5/16/84	1984		1540
12324590	4/2/85	1985		1250
12324590	2/24/86	1986		1710
12324590	3/4/87	1987		536
12324590	5/14/88	1988		424
12324590	4/7/89	1989		2220
12324590	5/30/90	1990		1090
12324590	5/19/91	1991		906
12324590	4/30/92	1992		175
12324590	5/15/93	1993		816
12324590	4/25/94	1994		730
12324590	6/6/95	1995		1640
12324590	2/9/96	1996		2860
12324590	5/25/97	1997		1630
12324590	7/4/98	1998		1980
12324590	6/3/99	1999		881
12324590	5/31/00	2000		177
12324590	5/15/01	2001		687
12324590	4/6/02	2002		746
12324590	3/13/03	2003		1280
12324590	3/8/04	2004		711
12324590	6/4/05	2005		1500
12324590	6/10/06	2006		649
12324590	6/7/07	2007		901
12324590	6/5/08	2008		1130
12324590	4/13/09	2009		1530
12324590	6/17/10	2010		1450
12324590	6/9/11	2011		2810

Appendix III:

# Sites in this file include:		#  USGS 05383000 LA CROSSE RIVER NEAR WEST SALEM, WI
Sorted Peaks	Ranks	EP	RI
8200	1	1.7%	59.00
7600	2	3.4%	29.50
5940	3	5.1%	19.67
5720	4	6.8%	14.75
5160	5	8.5%	11.80
4590	6	10.2%	9.83
4490	7	11.9%	8.43
4310	8	13.6%	7.38
4170	9	15.3%	6.56
4170	10	16.9%	5.90
3900	11	18.6%	5.36
3890	12	20.3%	4.92
3650	13	22.0%	4.54
3620	14	23.7%	4.21
3490	15	25.4%	3.93
3270	16	27.1%	3.69
3270	17	28.8%	3.47
3130	18	30.5%	3.28
3020	19	32.2%	3.11
3020	20	33.9%	2.95
2990	21	35.6%	2.81
2920	22	37.3%	2.68
2900	23	39.0%	2.57
2900	24	40.7%	2.46
2790	25	42.4%	2.36
2610	26	44.1%	2.27
2600	27	45.8%	2.19
2600	28	47.5%	2.11
2480	29	49.2%	2.03
2470	30	50.8%	1.97
2380	31	52.5%	1.90
2360	32	54.2%	1.84
2300	33	55.9%	1.79
2150	34	57.6%	1.74
2150	35	59.3%	1.69
2120	36	61.0%	1.64
2060	37	62.7%	1.59
2020	38	64.4%	1.55
1920	39	66.1%	1.51
1850	40	67.8%	1.48
1800	41	69.5%	1.44
1800	42	71.2%	1.40
1800	43	72.9%	1.37
1780	44	74.6%	1.34
1750	45	76.3%	1.31
1730	46	78.0%	1.28
1630	47	79.7%	1.26
1510	48	81.4%	1.23
1370	49	83.1%	1.20
1320	50	84.7%	1.18
1310	51	86.4%	1.16
1170	52	88.1%	1.13
1150	53	89.8%	1.11
1140	54	91.5%	1.09
1100	55	93.2%	1.07
1020	56	94.9%	1.05
984	57	96.6%	1.04
635	58	98.3%	1.02

Appendix IV:

# Sites in this file include:		#  USGS 12324590 Little Blackfoot River near Garrison MT
Sorted Peaks	Ranks	EP	RI
8650	1	2.5%	40.00
3650	2	5.0%	20.00
2920	3	7.5%	13.33
2860	4	10.0%	10.00
2810	5	12.5%	8.00
2700	6	15.0%	6.67
2220	7	17.5%	5.71
1980	8	20.0%	5.00
1820	9	22.5%	4.44
1710	10	25.0%	4.00
1640	11	27.5%	3.64
1630	12	30.0%	3.33
1540	13	32.5%	3.08
1530	14	35.0%	2.86
1500	15	37.5%	2.67
1450	16	40.0%	2.50
1440	17	42.5%	2.35
1280	18	45.0%	2.22
1250	19	47.5%	2.11
1200	20	50.0%	2.00
1130	21	52.5%	1.90
1120	22	55.0%	1.82
1090	23	57.5%	1.74
959	24	60.0%	1.67
906	25	62.5%	1.60
901	26	65.0%	1.54
881	27	67.5%	1.48
816	28	70.0%	1.43
746	29	72.5%	1.38
730	30	75.0%	1.33
711	31	77.5%	1.29
687	32	80.0%	1.25
649	33	82.5%	1.21
536	34	85.0%	1.18
424	35	87.5%	1.14
319	36	90.0%	1.11
266	37	92.5%	1.08
177	38	95.0%	1.05
175	39	97.5%	1.03

Sources
Faulkner, Douglas J., Dr. "Lab 4: Moisture in the Atmosphere." Lecture. Print.

"Map Maker." National Atlas of the United States. N.p., n.d. Web. 08 Nov. 2013. <http://nationalatlas.gov/mapmaker>.

"USGS Water Data for the Nation." USGS Water Data for the Nation. United States Geological Society, n.d. Web. 08 Nov. 2013. <http://waterdata.usgs.gov/nwis>.