USGS. 2016. USGS Surface-Water Data for Minnesota. Retrieved July 29, 2016 from, 5. Flood Inundation Tutorial Required External Python Dependencies: Click dotmap multiprocess Required Programs: Python 2.7x ArcGIS 10.x Required Input For Script: Cross-Section Shapefile River Centerline Shapefile Surface DEM Control Points Steps Start Here Make sure environment path variables are pointing to ArcGIS10.x Open ‘System properties’ in ‘Computer’ Click ‘Advanced system settings’ Click the ‘Environment Variables’ button In the ‘System variables’ list, click the row that contains ‘Path’ under the ‘Variable’ column and the click the ‘Edit’ Button. Copy the file paths located in the ‘Value’ column and paste them to a text document. This ensures that a copy of the system path variables will remain if the path variables get mixed up. At the end of the list add the following paths: ;C:\Python27\ArcGIS10.3\ ;C:\Python27\ArcGIS10.3\Lib ;C:\Python27\ArcGIS10.3\Lib\site-packages ;C:\Python27\ArcGIS10.3\DLLs ;C:\Python27\ArcGIS10.3\libs ;C:\Python27\ArcGIS10.3\Scripts Click ‘OK’ to finish Install pip by going to https://pip.pypa.io/en/stable/installing/ Right click ‘get-pip.py’ and save link as ‘get-pip.py’ in this folder ‘C:/Python27/ArcGIS10.3’ First open ‘PowerShell’. Click on the Start Menu and type in “powershell” and then open PowerShell.
(point A-B-C-D). For this reason, it is imperative to save on edit the trajectory graphics to recalculate correct measurements. Step 7: The graphics files discussed in step 4 need to be saved as a shapefile. This can be accomplished by right-clicking the layer and selecting “Convert Feature to Graphic”. By doing this, spatial data is created for the trajectory lines (lateral migration lines) which can be manipulated by editing and recalculating measurements if needed. Step 8: Once “Convert Feature to Graphic” is selected, a dialog box appears where the shapefile name and storage location is selected by the user. Note a check box for “Automatically delete graphics after conversion” must be selected to avoid ArcMap from crashing. Step 9: The newly created trajectory line shapefile is added to ArcMap, and three lines running the longitudinal length of the river need to be selected and deleted in an editing session so only the lateral migration trajectory lines remain. Step 10: Finally, all trajectory lines need to be inspected and edited to catch any errors made by the tool. This is most commonly seen where cut-offs occur (Figure 3). In this study, the lines were first edited by students from an upper-division fluvial geomorphology class and then inspected and further edited by myself. This methodology for running the NCED “Lateral Migration” tool and creating the trajectory lines is summarized in Figure 4 with images showing the step by step processes of the tool.
Figure 16. Displays the output for the “Color_Mig” shapefile from the Post MATLAB script in Reach 3 (Mankato to Henderson). Figure 17. Displays the output for the “Color_Mig” shapefile from the Post MATLAB script in Reach 2 (Henderson to Chaska). Figure 18. Displays the output for the “Color_Mig” shapefile from the Post MATLAB script in Reach 1 (Chaska to Mendota).
Reach 3 with migration displayed (Red = 1937-1951, Orange = 1951-1964, Yellow = 1964-1980, Green = 1980-1991, Blue = 1991-2013) Reach 3 annual channel migration from 1937-2013 Reach 3 width change from 1937-2013 Reach 3 sinuosity from 1937-2013 Reach 4 Reach 4 is ~3.6 km rural stretch of river that is abnormally straight in terms of the reach upstream and downstream of it, the study area overall, and naturally meandering rivers in general. There has been some migration observed in this reach but has been contained to the east side of the valley. Width has increased in this reach but not as dramatically as other reaches. Since the channel is so straight in this reach, a sinuosity just above one has been observed over the record of time. Despite the river being so straight over a 76 year period of time, the oxbow lake observed in the south-west portion of the reach shows this straight channel has not always been characteristic. Based on the LiDAR an initial hypothesis that this stretch of river is being pressed up against the east valley wall by a large alluvial fan from a large sediment laden tributary (Barney Fry Creek) entering from the west side of the valley. Reach 4 with migration displayed (Red = 1937-1951, Orange = 1951-1964, Yellow = 1964-1980, Green = 1980-1991, Blue = 1991-2013) Reach 4 annual channel migration from 1937-2013 Reach 4 width change from 1937-2013 Reach 4 sinuosity from 1937-2013 Reach 5 Reach 5 is approximately the same stream length as Reach 4 but juxtaposes the former reach in planform characteristics. This reach is highly active in terms of channel migration, with significant width increase, and a sinuosity between 1.5-1.7 and two cutoffs over the record of time. If the prior zone in being controlled by an alluvial fan this area could mark the transition away from that control allowing the river to react in a highly dynamic way much like Reach 2 from Reach 1.
details about locality in google earth. Occurrence records that could not be georeferenced they were deleted. Due to potential duplication in occurrence data, as multiple sources were used, replicated occurrence points were deleted. Occurrence data were then plotted in ArcMap using the display X, Y data tool and compared to a base map to verify the country listed matched the spatial location (Figures 2.1.A-D).
7.1. Grass Carp (C. idella) Grass carp had the highest percentage of moderately suitable habitat with an average of 62.3% of the accessible area (Figures 5.2.A-E). However, a majority of the remaining habitat, 34.8%, had low suitability. An average of 2.3% of the predicted grass carp habitat was highly suitable. This left only 1.3% of habitat as poorly suited, the lowest predicted average in the study. Grass carp experienced a decline in highly and moderately suitable habitat as flooded area increased while simultaneously experiencing an increase in low suitability. The percent of poorly suited habitat had a small decline, 0.1%. The grass carp model predicted occurrences better than at random with an AUC of 0.800.