Exe8_space_time_vers2 - School of Natural Resources

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Time Series Display of Soil Moisture Data and Spatial Interpolation
GIS in Water Resources
Exercise 8
2014
By Ayse Kilic, Univ. Nebraska-Lincoln
In this exercise we will learn how to tie time series of measurements made at a number of spatially
distributed measurement sites to the site locations in ArcMap and then produce two types of
animations of those time series in space. The animations help us to visualize the dynamic nature of the
data in time and in space.
Data Set:
The data set is comprised of soil water content measured at 12 sites in a rainfed grass field in Central
Nebraska. The data were measured on an hourly basis during the summer of 2014 and have been
provided by Dr. Suat Irmak of BSE at UNL. A Bowen ratio energy balance system (BREBS) was setup in
the center of field to determine evapotranspiration. The water content data were measured at 12 sites
distributed in the field as shown in the figure below, where sites were labeled as BRES1, BREBS 2, and so
on.
1
For this exercise, we will only analyze the soil moisture data collected at the shallowest depth (10 cm
below the ground surface). With this exercise, you are going to learn:
1) How to create a query table that joins a spatial feature to a time series data table (a one to
many join operation)
2) How to create an time enabled layer
3) How to create animation of data at specific points inside ArcMap
4) How to create time-based surfaces of a measurement using Python scripting. We will modify an
existing code to create the spatial patterns of soil moisture over time using an inverse distance
weighting (IDW) interpolation method.
5) We will use the time slider to produce animations of the spatial maps of soil moisture
Data layers
1. watermark_sites.xls. This excel file stores location information for the 12 Watermark sites.
2. timeseries.xls. This is a time series of measurements of water content at the 10 cm depth, only
for the period May 29 to August 7, 2014. The watermark sensor measures soil water potential
(this is how ‘tightly’ water is held in the soil. The soil water potential (WM) needs to be
converted into soil water content using a ‘characteristic curve’ for the specific soil. Units for
WM are in kPa. The following equation was used to convert from KPa to an equivalent
volumetric soil water content in percent (Eq. from Dr. Suat Irmak):
SM (%Vol) =65.519*WM^-0.292 where WM is the soil water potential (KPa)
The following steps will bring the data into ArcMap and then setup for the animations.
Adding Data Layers in ArcMap
Go to ArcCatalog and create a geodatabase and name it as “soilmoisture.gdb”
2
Back in ArcMap, create a map document and save your mxd as Exe8.
Also go to FileMap Document Properties and make sure that you check ”store relative paths” . This
will allow you to take your mxd setup to other computers and run it:
Add The Watermark_sites.xls to ArcMap and import “sheet1.” The added feature should look like the
following (12 rows with latitude and longitude data and site names):
3
Display X,Y data and select “Sensor ID” as the z field. On the same menu, select edit, and select
Geographic Coordinate System of NAD 1983.
The following display should appear for the sites feature:
4
Add imagery as a basemap:
The result should look like the following (green symbols are used via the symbology menu):
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Note that the soil sampling sites are laid out in a ‘cross’ pattern for representative sampling around the
BREB equipment. The BREB equipment are visible just below the soil site that is the third one in from
the right. Note also the variable pattern of vegetation. This is why it is important to sample a number of
sites to get a representative soil water content for the area.
In Arcatalog, create a feature dataset and call it projected_dataset. We are doing this so that all other
data layers can be stored to the same projection. We will choose Projected Coordinate System WGS
1984 Zone 14N (since we are in Nebraska)
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7
Save the sheet1 layer as a permanent feature named “sites” under soilmoisture.gdb The field type is
“file and personal Geodatabase feature classes.”
Now we will load the time series data.
Add the timeseries.xls table that has soil water content for the top 10 cm for the 12 sites that were
introduced as earlier as sites data. The data are in the “sheet1” sheet. The resulting table should have
20617 rows of data and look like the following:
8
There will be 12 sets of time series data placed “back-to-back” in this table. The sensor site number is
labeled “Sensor_ID”.
You need to save this table under the same geodatabase in which “sites” are located.
You can remove all the files other than the ones located under soilmoisture.gdb. Keep the world
imagery as your background image.
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Now we are going to join these two tables using one to many join operation. The sites layer represents
”space” and the timeseries represents a “time” based data. We will be using the “Make Query Table”
tool. For background on these following steps, you can refer to Arc Help at
http://support.esri.com/en/knowledgebase/techarticles/detail/37544
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We need to use the drop down icon in the input tables box twice: First, for the sites layer, and secondly
for the time series layer, in that order. We are using “sites” as the spatial basis and we will be joining to
it. Therefore, it must be first as shown above.
The same tool menu is shown again below, where we are now checking the sensor_ID field from both
tables because the join will be based on sensor_ID. Each sensor ID is a location in the field that is being
monitored. Notice that each of the sensor ID names are preceded by the table name.
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Click “OK” and take two sips of coffee while you wait for the tables to join.
This will create a layer called “query table” with about 247,404 records (!!).
We have one problem in this ‘joined’ query table. Each of the 12 time series of water contents have
been joined to each of the sites. Therefore, we have 12 times too many entries. The 247,404 records
need to be reduced to only 20,124 records that represent one time series per site. We will do this by
using the Select By Attributes tool. Select the QueryTable and then Select by Attributes from the top
menu.
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Type the following equation that will select, in the table, only those entries where the sites_sensor_ID is
equal to the
timeseries_sensor_ID. This
will associate the correct time
series with the correct site.
This will select only the
combinations in the table
that we want.
Now, let’s export these
20,000 selected records to a
new feature. Let’s call it
“sites_with_time_series” and
save it under the
geodatabase.
13
This will create the new feature. Open it and make sure that it has the 20,124 records. As a check, the
site 1 data should end at row 1693:
Preparation for animation
Now we are going to prepare the feature for animation.
We need to add a new field to the table that represents the time at the end of each period.
We need this for continuity in the data series so that we can do a time animation.
Call this new field “endtime”.
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Now, Search for “End Time” tool. We will use that tool to calculate the ending time for each time
period.
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The illustration below is from ArcGIS help file and shows how an End_time field has been
populated.
Enter the name of input table as “Sites_with_time_series”. And specify the start time field as the
“timeseries_DateTime” or whatever name you may have used. You should point to these names using
dropdown boxes, rather than typing them.
Also point to the “EndTIme” field as the End Time Field. This is the field that we just created and
currently it is null.
It is also important that you select the “sites_ObjectID” field so that the tool identifies each row as a
unique entry.
16
The resulting table should now have the EndTime field populated with a date/time that is one hour later
than the starting time, and is the same as the starting time of the next row:
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Animation
We will first do a simple animation of each soil sampling site by varying the ‘size’ of the symbol at each
site according to the magnitude of the soil water content. Go to Properties for the
Sites_with_time_series table and go to Symbology. Under ‘show’ select Quantities  Graduated
symbols. Select the WSM_10cm time series. The other settings can be left as the default values:
We need to set the time properties of the sites_with_time_series feature as shown below so that a)
time is ‘enabled’ and b) each feature has a start and end time field, c) the names of the start time and
stop time; and d) the time step interval is 1 hour. You can set this to “calculate” to optimize the timing
of animation.
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Save this as a layer file so that we can read it using python.
The result should look as follows:
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Go open the time slider tool from the menu bar so that you can set up the animation.
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Press the red circled icon on the right to begin the animation.
The sizes of the symbols at each of the 12 sites will change as the water content varies.
The animation may progress too slow. In that case, click the ‘options’ tool on the slider:
And select a longer time interval, for example, 4 hours. You can also select the ‘playback’ tab and
increase or decrease the speed:
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You can see the impact of rain events adding moisture to the soil sites. You can also see some
differences in the rehydration (response) of the individual sensors. The soil water dries out near the end
of the measuring period, so that the symbols become very small.
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Using Python to create time series of ‘surfaces’ of water content data in preparation of animation
Our next step is to do a similar animation, but with 2-D surfaces of water content. We will create these
as a series of raster images produced using the inverse distance weighting (IDW) interpolation method.
We will use some Python code to produce the rasters. The python code creates the rasters in
ArcCatalog. The code we are using was provided by Dr. David Tarboton of Utah State University.
Additional documentation was added by Ayse Kilic and Babu Kamble of UNL.
To operate the code, the names of folder paths and file names, as well as names of the parameter to be
animated, need to be changed in the code.
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