Virginia Thompson
5/4/2012
CE 547
Independent Project Final Report
Objective:
To gain a better understanding of the contribution of precipitation to physical hydrology at our (NM EPSCoR
HGE Group) study site in the Valles Caldera National Preserve (VCNP) by using a simple visualization technique
utilizing real-time data we are currently collecting in as a possible tool for communicating our work to
stakeholders and the public.
Motivation:
My specialty is in aquatic ecology, but I also learning how to become an ecohydrologist, aided by working with
an interdisciplinary group called the NM EPSCoR HydroGeoEcology (HGE) group. While not looking to be an
expert in physical hydrology, I seek to have a basic understanding of physical hydrological principles and how
they can affect the biota and systems that I study in order to better understand the function of the system as a
whole. I am also interested in how we as scientists can better communicate science to the public and simple
visualization techniques could be a tool we can use to promote understanding, so I wanted to understand the
time series tool in better depth as a potential tool for science communication.
I was fascinated with time series data after doing the class activity regarding streamflow gauges earlier in the
semester and decided to try to apply the concept to our real-time data that we are collecting in the VCNP. As
there are many interested stakeholders involved with both our program and the VCNP in general, simple
visualization techniques could be a valuable tool for communicating with these stakeholders.
Methods: (detailed and technical instructions are in Appendix 1)
Knowing that we have real-time (as little as 15 min interval) at the HGE group site collecting a variety of
parameters and multiple upper watershed precipitation stations that collect real-time data as well, I wanted
to see if using the simple time series visualization could show patterns regarding the timing and magnitude of
precipitation events versus the flow rate in our river channel.
After cleaning up the raw data, I created point locations and events in a time series. I adjusted the symbology
to blue circles and created a LOT of size classes in order to make the step intervals distinctive. I then used the
time options on the layer to run the layers in series rather than all on top of each other.
Due to the scales/units of each being rather different, there isn’t an easy way to compare the precipitation
gauges and the flow gauge quantitatively, but by making many classification breaks for the visualization I was
able to show specific differences in magnitude relative to size. In this case, accurate technical quantification
takes a back seat to conveying the changes in a visually effective way.
Data Sources:
My data came from two specific sources— our collected data and data collected by the instrumentation and
staff of the Valles Caldera. (Appendix 2)
For the flow gauge at our site, the NM EPSCoR HydroGeoEcology (HGE) group maintains a sonde that collects
real-time data at our site. We then have a group site that has some of our data stored on it. Figure 1 is an
example of one of our data sheets that I took data from.
Figure 1. Example of data sheet with flow values.
From our data sets I was able to obtain 15 minute interval real time data as well as geographic coordinates
and elevation.
For the precipitation data, I accessed the banked real-time data from the VCNP that is held on the Sevilleta
LTER servers for all five of the upper watershed weather stations. The site and data can be accessed here:
http://sev.lternet.edu/research/climate/meteorology/VCNP/index.html
Figure 2. Example of a data sheet from the VCNP archives via the Sevilleta LTER servers.
From this site I was able to obtain hourly precipitation measures from all 5 stations as well as their
coordinates, elevations and photographs of each.
Raw data was then combined into multiple excel spreadsheets covering different time scales, organized by
location and date and/or date/hour.
Figure 3. Example data sheet
Figure 4. Example data sheet
Projections used:
The projection I used was: NAD_1983_HARN_StatePlane_New_Mexico_Central_FIPS_3002. I used this as an
on-the-fly projection since all of the GPS coordinates were in WGS84. I chose this particular projection as I am
working in a very small, region in the central part of New Mexico where the shape and area would be best
preserved.
Software environments:
Much of the data manipulation was first done in Excel doing imports and data manipulation. ArcGIS
compatible tables and databases were created in ArcCatalog and then imported into and projected and
graphed in ArcMap. Time series were created in and exported from ArcMap.
Results:
The resulting work was two sets of time series data videos (the 2nd was created after the first didn’t illustrate
the point that I wanted to very well). Unfortunately this does not translate well to a written report other than
a couple still screenshots. Both video files have been included on a CD attached to this report. Some programs
are having issues playing it properly (extremely fast playback) so I recommend using VLC media player or
another specialty player that allows you to easily manipulate the playback rate to get a playback at a speed
that allows for visualization.
Figure 5. A still shot of the animation in progress showing time slider and legend.
Both animations did show simple patterns of change over time, but given the simplicity and limited range of
the data it didn’t convey the relationship between the precipitation and streamflow as expected.
Conclusions:
This technique, although simple, could be used to show changes over time in simple forms and potentially be
used in public communication. It is limited, especially given the format and scaling. It does do the job of
visualization well, however.
It also doesn’t take into account some external factors. Groundwater, percolation, residence time—all can
create differences in movement of the precipitation to the channel. Also, each of the weather stations is an
individual point, and precipitation doesn’t all fall onto one single point, nor does it fall evenly. Although one
could argue that this point system is inherently flawed, they are the best we have right now and give us good
data to work with.
Future work:
I would like to see if I could apply some sort of actual modeling software to understand why the movement is
delayed and how groundwater is playing a role in the flows besides baseflow. It would be fantastic to have
data that syncs up exactly on the same timescale instead of having to manipulate it.
It would also be nice to resolve why the exported .avi files were not playing back properly. I didn’t have time
to dig around in this project timeframe, but I’d specifically look at codec conversions and utilities to adjust the
frame rate.
It could be potentially interesting to look at the effects of the Las Conchas fire, including any possible land
cover and infiltration changes from the vegetation loss.
Appendix 1 -- Procedures:
These can be used for any particular time scale as desired: hourly, daily, etc.
First I obtained and organized the data as described in the data sources section, making sure to include
date/time, location and value (flow or precip) for each entry. I then verified there were no blanks and then
save the resulting file as an .xls (Office 97-2003 format) called PrecipData. I then obtained all coordinates and
elevations for each site and entered them into another into another excel file, making sure to have Location,
Name, X, Y, and Z (elevation) columns and named it GaugeLoc. I then opened ArcCatalog and created a new
geodatabase that I named Hydrology. I selected the GaugeLoc .xls file, right clicked on it and selected Export to
Geodatabase (Single) and then directed the output to the Hydrology gdb I just made. I repeated the same
procedure for the GaugeLoc .xls. I then closed ArcCatalog and opened up a new file in ArcMap and inserted
both newly created tables into the document. I then right clicked on the GaugeLoc table and selected “Display
XY data”, specified the X,Y, and z fields and made the GaugeLoc Events layer appear with a point for each of
my locations for the gauge and precip stations. I then right clicked this layer and selected Data - Export Data
and created a new shapefile with the points, which I saved to the gdb.
I then joined the PrecipData table to the GaugeLoc shapefile I made by right clicking on the table and selecting
Joins & Relates - Join. I joined the tables based off the Location designations in both and verified the join. I
then followed the same procedure as above to display the new joined table data using the “Display XY Data”
menu, created the points layers, and exported it to the new gdb. I then right clicked on the new PrecipData
point layer and, selected Properties, went to the Time tab, and clicked “Enable Time on this layer”. I then
selected the desired fields and parameters in the menu based on an interval of 1 day and adjusted time zone
values to MST. I then went to the Symbology tab and changed the symbols to graduated symbols, adjusted the
color and shape to solid blue circles, and went with the original Jenks breaks. I then went to the Labels tab and
selected “Label features in the layer” and had them using the Name field.
I then went back to the main map and brought up the time slider and hit play. When I found that the
animation was very patchy, I looked into the data to find that most of it was 0s—leaving the map essentially
‘blank’ for quite a while. I then trimmed down the data to a narrower, ‘more hydrologically interesting’ point
of time, and switched from daily to hourly time scale. I created a new data sheet named AugPrecip, and
followed the same steps as above to create a new time series. After satisfied this was a more interesting
animation, I then tweaked the symbology into many more classes to enable easy visualization of the different
events. I then added a basemap (momentarily) in order to add context, and then clicked the “export to video”
button on the time slider. Once exported, I then tried to play it on various utilities and found it wouldn’t play
back correctly, but it had exported. I’m still trying to figure this part out, but it’s somehow a compatibility
issue. Due to limited webspace for UNM, I will be posting it to a free host site. I am not sure if it will have the
same playback issues, but that’s the only choice I have right now.
Once I exported the video, I created a couple of maps (added scale bars, legend, title, etc) and images for still
visualization. It’s not quite the same but it gives you at least an idea of where the gauge locations are, etc.
Appendix 2 -- Data Dictionary:
Rather than kill multiple trees by providing the endless piles of numerical data I pulled from as well as the
many data points in the final tables, I will provide the links to the publicly available data that I used. Files of
the final tables used for the animation are available upon request.
EPSCoR East Fork Jemez River Gauge:
Data source: hosted on private server, (available via request) data collected by the NM EPSCoR HGE team via
real-time sonde collection from 2010-present
Coordinates: GPS coordinates recorded by team member(s), NAD83
VCNP Precipitation Gauge Stations:
VCNP precipitation gauge data: http://sev.lternet.edu/research/climate/meteorology/VCNP/index.html
VCNP gauge station imagery: (bottom of page)
http://sev.lternet.edu/research/climate/meteorology/VCNP/index.html
VCNP Precip gauge coordinates: http://sev.lternet.edu/research/climate/meteorology/VCNP/Coordinates
(WGS84)
Appendix 3—Acknowelgements
Acknowledgments:
Funding and support have been provided by: New Mexico EPSCoR, the National Science Foundation, the
University of New Mexico, the UNM Department of Biology, the LTER Network, and the Valles Caldera
National Preserve.
The people of the NM HydroGeoEcology (HGE) group and the others who have helped make our work
possible: Cliff Dahm, Becky Bixby, Laura Crossey, Dave VanHorn, Anna Hamilton, Betsy Shafer, Susan Kutvirt,
Lauren Sherson, Justin Reale, Becky Frus, Pavel Vahlkamov, Tyler VanRiper, Chad Bryant, Jennifer Clark, Lauren
Main, Miranda Bitsoi, Katrina Powell, Ann Stevenson, Diane Jimenez-Stinson, John Craig, Bob Parmenter and
Scott Compton.