Bodo Bookhagen

Bodo Bookhagen

Research and Teaching in Remote Sensing and Earth Surface Process

South America is characterized by steep gradients in tectonism, topography, and climate. In this blog entry, we show how these gradients can be visualized documenting the changes in topography, earthquake character, vegetation cover (NDVI), and TRMM rainfall distribution.

South America is characterized by steep gradients in tectonism, topography, and climate. In this blog entry, we show how these gradients can be visualized documenting the changes in topography, earthquake character, vegetation cover (NDVI), and TRMM rainfall distribution. The data and scripts are available at GMT-SAM-EQ-NDVI-PRECIP.

Data sources and setup

Topographic data are needed. In this example, we rely on the 15s global topography obtained from ftp://topex.ucsd.edu/pub/srtm15_plus/. The file earth_relief_15s.nc is needed. Use wget ftp://topex.ucsd.edu/pub/srtm15_plus/earth_relief_15s.nc to obtain the data. This will come in handy for many other examples as well and it will be good to keep a copy on your local computer.

NOTE: These data are 2.6GB.

Alternatively, you can download the SRTM DEM at 30m or 90m spatial resolution. I suggest to look at the most recent NASADEM. Data have not been finalized yet (but will so soon) and can be accessed here. There is a neat github repository nasadem by David Shean that provides some scripts for downloading and preprocessing. TanDEM-X data are also available. In any case, for our purposes the 15s data will be just fine.

The data were processed and maps were created with GMT version 5.4.3.

gmt --version
5.4.3

Conda setup

Install and start the GMT environment with:

conda create -y -n gmt5 gmt=5* python=3.6 scipy pandas numpy matplotlib scikit-image gdal spyder imagemagick
source activate gmt5

Depending on your system, you may need to change the order of the channels with the following and then re-run the conda create command from above:

conda config --remove channels defaults
conda config --append channels conda-forge/label/dev

Clone the repository with:

git clone https://github.com/BodoBookhagen/GMT-SAM-EQ-NDVI-PRECIP`.

Prepare Data

Prepare grid / raster data

Prepare DEM data

NOTE: These data are not included on the github page, because they are too large to be stored on github.

We first need to prepare the DEM data for the region of interest. Define the region of interest:

REGION=-80/-60/-40/-15

Next, we clip this region from the global Topo15s file (see above). Make sure to properly set the path to the location of the earth_relief_15s.nc file.

TOPO15_GRD_NC=/PATH/TO/FILE/earth_relief_15s.nc
TOPO15_GRD_NC_CentralAndes=earth_relief_15s_CentralAndes.nc
gmt grdcut -R$REGION $TOPO15_GRD_NC -G$TOPO15_GRD_NC_CentralAndes

You can turn this into somewhat more fancy bash-style command lines that verifies if the file exist. This will ensure that you only create and recut the file if it doesn’t exist:

TOPO15_GRD_NC=/PATH/TO/FILE/earth_relief_15s.nc
TOPO15_GRD_NC_CentralAndes=earth_relief_15s_CentralAndes.nc
if [ ! -e $TOPO15_GRD_NC_CentralAndes ]
then
    echo "generate Topo15S Clip $TOPO15_GRD_NC_CentralAndes"
    gmt grdcut -R$REGION $TOPO15_GRD_NC -G$TOPO15_GRD_NC_CentralAndes
fi

Next, generate a hillshade image. Here, we use more sophisticated hillshade calculation and store in $TOPO15_GRD_HS_NC:

TOPO15_GRD_NC=$TOPO15_GRD_NC_CentralAndes
TOPO15_GRD_HS_NC=earth_relief_15s_CentralAndes_HS.nc
if [ ! -e $TOPO15_GRD_HS_NC ]
then
    echo "generate hillshade $TOPO15_GRD_HS_NC"
    gmt grdgradient $TOPO15_GRD_NC -Ne0.6 -Es75/55+a -G$TOPO15_GRD_HS_NC
fi

If you instead want to have a simpler hillshading with less relief, use something akin to the Peucker algorithm. We call this $TOPO15_GRD_HS2_NC.

TOPO15_GRD_HS2_NC=earth_relief_15s_CentralAndes_HS_peucker.nc
if [ ! -e $TOPO15_GRD_HS2_NC ]
then
    echo "generate hillshade $TOPO15_GRD_HS2_NC"
    gmt grdgradient $TOPO15_GRD_NC -Nt1 -Ep -G$TOPO15_GRD_HS2_NC
fi

Prepare MODIS EVI and NDVI raster data

These have been created from the MODIS product MOD13A3 and compiled to annual and monthly averages. Data-preprocessing is not shown here. We have generated a Geotiff file containing mean NDVI and EVI from 2001-2017 stored in the files MOD13A3_2001-2017NDVImn.tif and MOD13A3_2001-2017EVImn.tif.

We use gmt grdcut to extract the relevant region.

MOD13A3_NDVI=/raid-everest/data/MODIS/MOD13A3/MOD13A3_2001-2017NDVImn.tif
MOD13A3_NDVI_CentralAndes=MOD13A3_2001-2017NDVImn_CentralAndes.nc
if [ ! -e $MOD13A3_NDVI_CentralAndes ]
then
    echo "generate NDVI Mean Clip $MOD13A3_NDVI_CentralAndes"
    gmt grdcut -R$REGION $MOD13A3_NDVI -G$MOD13A3_NDVI_CentralAndes
fi

Next, we resample this file to the resolution of the topographic data to allow hillshading and more sophisticated visualization. Resampling will be forced with number of lines and rows that have been obtained with gdalinfo earth_relief_15s_CentralAndes.nc: 4801x6001.

MOD13A3_NDVI_CentralAndes_rTOPO15=MOD13A3_2001-2017NDVImn_CentralAndes_Topo15S.nc
if [ ! -e $MOD13A3_NDVI_CentralAndes_rTOPO15 ]
then
    echo "resample NDVI Mean Clip $MOD13A3_NDVI_CentralAndes_rTOPO15"
    gmt grdsample $MOD13A3_NDVI_CentralAndes -R$REGION -I4801+/6001+ -G$MOD13A3_NDVI_CentralAndes_rTOPO15
fi

We do the same for the EVI data:

MOD13A3_EVI=/raid-everest/data/MODIS/MOD13A3/MOD13A3_2001-2017EVImn.tif
MOD13A3_EVI_CentralAndes=MOD13A3_2001-2017EVImn_CentralAndes.nc
MOD13A3_EVI_CentralAndes_rTOPO15=MOD13A3_2001-2017EVImn_CentralAndes_Topo15S.nc
if [ ! -e $MOD13A3_EVI_CentralAndes ]
then
    echo "generate EVI Mean Clip $MOD13A3_EVI_CentralAndes"
    gmt grdcut -R$REGION $MOD13A3_EVI -G$MOD13A3_EVI_CentralAndes
fi

if [ ! -e $MOD13A3_EVI_CentralAndes_rTOPO15 ]
then
    echo "resample EVI Mean Clip $MOD13A3_EVI_CentralAndes_rTOPO15"
    gmt grdsample $MOD13A3_EVI_CentralAndes -R$REGION -I4801+/6001+ -G$MOD13A3_EVI_CentralAndes_rTOPO15
fi

Prepare vector data / shapefiles

Convert gridded output data from HyDROSHED to vector data

We have used various software to generate polygon files outlining the hydrologic basins of South America. An easy way to start is to look at Hydroshed. Here, we provide several shapefiles in the subdirectory GMT_vector_data. The shapefiles have been converted to GMT files using ogr2ogr and the projection has been changed from UTM-19S to GEOGRAPHIC projection (EPSG 4326), where necessary. In the subdirectory GMT_vector_data, run:

ogr2ogr -f GMT -t_srs epsg:4326 AltiplanoPuna_1basin_EPSG4326.gmt AltiplanoPuna_1basin_UTM19S_WGS84.shp
ogr2ogr -f GMT -t_srs epsg:4326 SA_basins_15s_puna_EPSG4326.gmt SA_basins_15s_puna_UTM19S_WGS84.shp

NOTE: These files have already been generated.

Now, these gmt files can be plotted with psxy.

If you have exceptionally large GMT files, you can compress them with gzip or bzip2 (see next section).

Prepare OSM vector data

The OpenStreetMap (OSM) dataset is very useful for showing high-resolution shorelines, anthropogenic infrastructure and a variety of other data that are not contained in the GMT vector files. OSM data come in a different format and will need to be converted before usage. OSM Wiki provides some insights, but there are additional steps necessary. this is a multi-step process.

First, get the data from geofabrik. They provide pbf files for every continent (or country) that can be converted into GMT or shapefile format:

wget https://download.geofabrik.de/south-america-latest.osm.pbf

Next, the OSM-pbf file needs to be converted to GMT files. This will need to be done for every attribute separately to keep your database nice and clean. We usually use the following. NOTE: gdal/ogr will need to have been compiled with GEOS, SQL and pbf support!

NOTE: In order to generate the railway database, you have to change /usr/share/gdal/2.2/osmconf.ini and allow the tag railway to be created. See here. You first need to identify the relevant osmconf.ini file using locate osmconf.ini and then edit that to include additional attributes and fields.

NOTE: In order to generate the volcano database, you have to change /usr/share/gdal/2.2/osmconf.ini and allow the tag natural to be created.

ogr2ogr -f "GMT" south-america_roads01.gmt south-america-latest.osm.pbf -progress -sql "select highway from lines where highway in ('primary', 'secondary', 'tertiary', 'unclassified', 'residential')"
ogr2ogr -f "GMT" south-america_roads02.gmt south-america-latest.osm.pbf -progress -sql "select highway from lines where highway not in ('primary', 'secondary', 'tertiary', 'unclassified', 'residential')"
ogr2ogr -f "GMT" south-america_lakes.gmt south-america-latest.osm.pbf -progress -sql "select natural from multipolygons where natural in ('water')"
ogr2ogr -f "GMT" south-america_wetlands.gmt south-america-latest.osm.pbf -progress -sql "select natural from multipolygons where natural in ('wetland')"
ogr2ogr -f "GMT" south-america_rivers.gmt south-america-latest.osm.pbf -progress -sql "select waterway from lines where waterway in ('river')"
ogr2ogr -f "GMT" south-america_railway.gmt south-america-latest.osm.pbf -progress -sql "select railway from lines where railway in ('rail')"
ogr2ogr -f "GMT" south-america_volcano.gmt south-america-latest.osm.pbf -progress -sql "select natural from points where natural in ('volcano')"

If you replace -f "GMT" with -f "ESRI Shapefile", you create ESRI shapefiles as output.

These are large files and processing them with GMT for map making will take some time. I suggest to clip this to the area of interest (will make map making a little faster). Here, we use the coordinates from the $REGION variable (see above). -clipsrs expects xmin ymin xmax ymax coordinates.

ogr2ogr -f "GMT" CentralAndesroads01.gmt south-america_roads01.gmt -clipsrc -80 -40 -60 -15
ogr2ogr -f "GMT" CentralAndesroads02.gmt south-america_roads02.gmt -clipsrc -80 -40 -60 -15
ogr2ogr -f "GMT" CentralAndeslakes.gmt south-america_lakes.gmt -clipsrc -80 -40 -60 -15
ogr2ogr -f "GMT" CentralAndeswetlands.gmt south-america_wetlands.gmt -clipsrc -80 -40 -60 -15
ogr2ogr -f "GMT" CentralAndesrivers.gmt south-america_rivers.gmt -clipsrc -80 -40 -60 -15
ogr2ogr -f "GMT" CentralAndesrailway.gmt south-america_railway.gmt -clipsrc -80 -40 -60 -15
ogr2ogr -f "GMT" CentralAndesvolcano.gmt south-america_volcano.gmt -clipsrc -80 -40 -60 -15

The files CentralAndeslakes.gmt can be plotted with gmt psxy.

Prepare city dataset from OSM

First, edit osmconf.ini and add city to the list of attributes in the section [points]. The line attributes in the [points] section should read:

attributes=name,barrier,highway,ref,address,is_in,place,man_made,city,population

Next, extract the cities with ogr2ogr and store name and population select city,name,population and select only cities with a stored name where name is not null:

ogr2ogr -f "GMT" south-america_cities_with_names.gmt south-america-latest.osm.pbf -progress -sql "select city,name,population from points where name is not null"

We create a second dataset containing only cities with a stored population:

ogr2ogr -f "GMT" south-america_cities_with_population.gmt south-america-latest.osm.pbf -progress -sql "select city,name,population from points where population is not null"

Alternatively, create a dataset containing any city (also without names):

ogr2ogr -f "GMT" south-america_cities.gmt south-america-latest.osm.pbf -progress -sql "select city,name,population from points"

Prepare simplified city dataset

City locations are also included in the OSM dataset, but a simpler CSV file will do it as well. There are many datasets available, here we are using a free city CSV version currently hosted at maxmind. The CSV contains: Country,City,AccentCity,Region,Population,Latitude,Longitude. The file contains a global database of cities - we are only interested in showing a few for South America and we preprocess the data to select only Santiago de Chile, Salta, Mendoza and La Paz.

NOTE: We have converted the gzip-compressed file to a bzip2 compressed file

First we search for the country ar (Argentina) and city Salta and we only use the longitude, latitude columns. In the second command, we add the label Salta to the end of the first row:

bzip2 -dc worldcitiespop.txt.bz2 | grep -w ar | grep -w Salta | gmt select -i6,5 >salta.txt
sed -i "1 s|$| Salta|" salta.txt

This can be done in the same fashion for the other cities:

bzip2 -dc worldcitiespop.txt.bz2 | grep -w ar | grep ",Mendoza" | gmt select -i6,5 >mendoza.txt
sed -i "1 s|$| Mendoza|" mendoza.txt

bzip2 -dc worldcitiespop.txt.bz2 | grep -w bo | grep ",La Paz,04" | gmt select -i8,7 >lapaz.txt
sed -i "1 s|$| LaPaz|" lapaz.txt

bzip2 -dc worldcitiespop.txt.bz2 | grep -w cl | grep ",Santiago" | gmt select -i6,5 >SdC.txt
sed -i "1 s|$| SdC|" SdC.txt

The txt files can be plotted with gmt psxy.

Prepare Earthquake catalog from USGS

There exists different EQ catalogs. Here, we download data from the USGS by selecting a range of magnitude, time, and geographic region and write to a CSV file. If you use the entire geographic region of SAM (or the region we have been working with), you will need to select the magnitude in steps, to produce less than 20k searches (max. allowed number of records to export). Because we will plot the magnitudes with different sizes/colors, this will be useful in any case. We have generated several files with the following names (should be self explanatory): USGS_EQ_CentralAndes_1970_2018_mag3.5_to_4.csv, USGS_EQ_CentralAndes_1970_2018_mag4_to_4.5.csv, USGS_EQ_CentralAndes_1970_2018_mag4.5_to_5.csv, USGS_EQ_CentralAndes_1970_2018_mag5_to_5.5.csv, USGS_EQ_CentralAndes_1970_2018_mag5.5_to_6.csv, USGS_EQ_CentralAndes_1970_2018_mag6_to_9.csv. We compress them with bzip2. bzip2 is much more efficient in compressing ASCII text files than gzip. A quick test shows that file USGS_EQ_CentralAndes_1970_2018_mag3.5_to_4.csv compressed to 291293 bytes (291 kb) with bzip2 and to 410052 bytes (410 kb) with gzip (using best compression option -9 for both algorithms). We combine magnitude ranges 4 to 5 and 5 to 6:

bzip2 -9 USGS_EQ_CentralAndes_1970_2018_mag3.5_to_4.csv

cp USGS_EQ_CentralAndes_1970_2018_mag4_to_4.5.csv USGS_EQ_CentralAndes_1970_2018_mag4_to_5.csv
cat USGS_EQ_CentralAndes_1970_2018_mag4.5_to_5.csv >> USGS_EQ_CentralAndes_1970_2018_mag4_to_5.csv
bzip2 -9 USGS_EQ_CentralAndes_1970_2018_mag4_to_5.csv

cp USGS_EQ_CentralAndes_1970_2018_mag5_to_5.5.csv USGS_EQ_CentralAndes_1970_2018_mag5_to_6.csv
cat USGS_EQ_CentralAndes_1970_2018_mag5.5_to_6.csv >>USGS_EQ_CentralAndes_1970_2018_mag5_to_6.csv
bzip2 -9 USGS_EQ_CentralAndes_1970_2018_mag5_to_6.csv

bzip2 -9 USGS_EQ_CentralAndes_1970_2018_mag6_to_9.csv

These files are available in the GMT_vector_data directory.

Generating a topographic map

After compiling all the various data source, we can use GMT to create a topographic map of SAM.

As a reminder, we have done the following preparatory steps. This excerpt is taken from plot_CentralAndes_TOPO_EQ.sh:

#!/bin/bash
gmt gmtset MAP_FRAME_PEN    0.5p,black
gmt gmtset MAP_FRAME_WIDTH    0.1
gmt gmtset MAP_FRAME_TYPE     plain
gmt gmtset FONT_TITLE    18p,Helvetica-Bold,black
gmt gmtset FONT_LABEL    12p,Helvetica-Bold,black
gmt gmtset PS_PAGE_ORIENTATION    portrait
gmt gmtset PS_MEDIA    A4
gmt gmtset FORMAT_GEO_MAP    D
gmt gmtset MAP_DEGREE_SYMBOL degree
gmt gmtset PROJ_LENGTH_UNIT cm

REGION=-80/-60/-40/-15

# DEM/Topography Data
#clip area and generate hillshade
TOPO15_GRD_NC=/home/bodo/Downloads/earth_relief_15s.nc
TOPO15_GRD_NC_CentralAndes=earth_relief_15s_CentralAndes.nc
if [ ! -e $TOPO15_GRD_NC_CentralAndes ]
then
    echo "generate Topo15S Clip $TOPO15_GRD_NC_CentralAndes"
    gmt grdcut -R$REGION $TOPO15_GRD_NC -G$TOPO15_GRD_NC_CentralAndes
fi

TOPO15_GRD_NC=$TOPO15_GRD_NC_CentralAndes
TOPO15_GRD_HS_NC=earth_relief_15s_CentralAndes_HS.nc
if [ ! -e $TOPO15_GRD_HS_NC ]
then
    echo "generate hillshade $TOPO15_GRD_HS_NC"
    gmt grdgradient $TOPO15_GRD_NC -Ne0.6 -Es75/55+a -G$TOPO15_GRD_HS_NC
fi

#Simpler Peucker algorithm
TOPO15_GRD_HS2_NC=earth_relief_15s_CentralAndes_HS_peucker.nc
if [ ! -e $TOPO15_GRD_HS2_NC ]
then
    echo "generate hillshade $TOPO15_GRD_HS2_NC"
    gmt grdgradient $TOPO15_GRD_NC -Nt1 -Ep -G$TOPO15_GRD_HS2_NC
fi

# Vector data
AltiplanoPuna_1bas=GMT_vector_data/AltiplanoPuna_1basin_UTM19S_WGS84.gmt
OSM_CentralAndeslakes=GMT_vector_data/CentralAndeslakes.gmt
OSM_CentralAndesvolcano=GMT_vector_data/CentralAndesvolcano.gmt

At the end of the script, we also set the filenames to Bash variables that we are going to plot (AltiplanoPuna_1bas, OSM_CentralAndeslakes, OSM_CentralAndesvolcano). Make sure you set the proper path if you store the data elsewhere.

Next, we define the parameters for the plot:

## CREATE Topographic map
#Set Parameters for Plot
POSTSCRIPT_BASENAME=CentralAndes_Topo15S
#xmin/xmax/ymin/ymax
WIDTH=10
XSTEP=10
YSTEP=10
DPI=300
CITY_STAR_SIZE=0.4c

This is the file prefix POSTSCRIPT_BASENAME, the width of the plot in cm, and the longitude (XSTEP) and latitude (YSTEP) grid spacing. We also set the output DPI to 300 - this generate the figures slightly faster, but you nay want to set this too 600 if you produce your final figure for publication. We also set the size in cm for the stars indicating the cities (CITY_STAR_SIZE).

Next, we set title of the plot and the outputfilename that includes the prefix POSTSCRIPT_BASENAME with a postfix and stores this in POSTSCRIPT1:

TITLE="Topography C. Andes"
POSTSCRIPT1=${POSTSCRIPT_BASENAME}_relieftopo.ps

As a next step, we create the colorscale for the topography. Here, we use the colormap mby.cpt that can be obtain from cpt-city (or the direct download link for mpy.cpt). Note that this colorscale stretches from -8000m for ocean depth to 5000m elevation height on land. Different colorscales have diffirent ranges - here we inidcate the ranges for the arctic and relief (built in) colorscales as well, but these are commented out.

#Make colorscale
DEM_CPT=relief_color.cpt
#gmt makecpt -T-5000/4000/250 -D -Carctic >$DEM_CPT
#gmt makecpt -T-6000/6000/250 -D -Crelief >$DEM_CPT
gmt makecpt -T-8000/5000/250 -D -Cmby >$DEM_CPT

We can now start with creating the topographic map:

echo " "
echo "Creating file $POSTSCRIPT1"
echo " "
gmt grdimage $TOPO15_GRD_NC -I$TOPO15_GRD_HS_NC -JM$WIDTH -C$DEM_CPT -R$TOPO15_GRD_NC -Q  -Xc -Yc -E$DPI -K -P > $POSTSCRIPT1

And we may want to add an A in the upper right corner:

echo A | gmt pstext -R -J -D0.1c/-0.1c -W0.5p,black -Gwhite -C0.2c -F+cTL+f24p,Helvetica-Bold -P -O -K >> $POSTSCRIPT1

We plot the coastal outline and international border:

gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne -W1/thin,black -R -J -N1/thin,black -O -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p -P -K >> $POSTSCRIPT1

NOTE: If you want to generate a title header for your figure add replace -BWSne with -BWSne+t"$TITLE": gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne -W1/thin,black -R -J -N1/thin,black -O -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p -P -K >> $POSTSCRIPT1

We can plot volcanos and lakes from the OSM database and define their outline colors with -W and fill colors with -L:

gmt psxy $OSM_CentralAndeslakes -R -J -L -Wfaint,lightblue -Glightblue -K -O -P >> $POSTSCRIPT1
gmt psxy $OSM_CentralAndesvolcano -R -J -L -St0.15c -Gred -K -O -P >> $POSTSCRIPT1

Similiarily, add the outline of the Central Anden Plateau (Altiplana-Puna Plateau):

gmt psxy $AltiplanoPuna_1bas -R -J -L -Wthick,darkblue -K -O -P >> $POSTSCRIPT1

Next, add the cities with a star and label:

gmt psxy SdC.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt pstext SdC.txt -D-0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy lapaz.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt pstext lapaz.txt -D+0.9c/0.2c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy salta.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt pstext salta.txt -D+0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy mendoza.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt pstext mendoza.txt -D+1.0c/-0.5c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1

We also want a topographic colorscale below the figure. We set the minor tick spacing to 1000m -Baf1000 and add label and unit -Baf1000:"Bathymetry and Elevation":/:"[m]":. We also set the font to 12p

gmt psscale -R -J -DjBC+h+o-0.0c/-3.0c/+w7c/0.3c+ml -C$DEM_CPT -F+c0.1c/0.5c+gwhite+r1p+pthin,black -Baf1000:"Bathymetry and Elevation":/:"[m]": --FONT=12p --FONT_ANNOT_PRIMARY=12p --MAP_FRAME_PEN=0.5p --MAP_FRAME_WIDTH=0.1 -O -P >> $POSTSCRIPT1

Last, convert the ps file to a PNG file with the option -Tg and convert to a jpg:

gmt psconvert $POSTSCRIPT1 -A -P -Tg
convert -alpha off -quality 100 -density 150 -trim $POSTSCRIPT1 ${POSTSCRIPT1::-3}.jpg

The resulting map display the bathymetry and topography of SAM with volcanoes in red triangles and major lakes in light blue colors, and cities in black stars with labels.

Bathymetry and Topography of the Central Andes: Elevation and bathymetry from a merged data (see text), lakes (lightblue) and volcanoes (red) are from the OSM database, and major city are shown in black stars.

Generating a grayscale topographic map with USGS earthquake location and depths

We use the approach described in the previous section and add a second figure (B) showing gray-scale topography and USGS earthquake location.

First, let’s define additional vector data (the USGS EQ data that have been prepared above):

# Vector data
AltiplanoPuna_1bas=GMT_vector_data/AltiplanoPuna_1basin_UTM19S_WGS84.gmt
OSM_CentralAndeslakes=/raid-cachi/bodo/Dropbox/Argentina/GIS_Data/OSM/CentralAndeslakes.gmt
OSM_CentralAndesvolcano=/raid-cachi/bodo/Dropbox/Argentina/GIS_Data/OSM/CentralAndesvolcano.gmt
USGS_EQ_LT4=GMT_vector_data/USGS_EQ_CentralAndes_1970_2018_mag3.5_to_4.csv.bz2
USGS_EQ_GT4_LT5=GMT_vector_data/USGS_EQ_CentralAndes_1970_2018_mag4_to_5.csv.bz2
USGS_EQ_GT5_LT6=GMT_vector_data/USGS_EQ_CentralAndes_1970_2018_mag5_to_6.csv.bz2
USGS_EQ_GT6=GMT_vector_data/USGS_EQ_CentralAndes_1970_2018_mag6_to_9.csv.bz2

Next, we plot and color the earthquakes by their depth using the colorscale viridis:

POSTSCRIPT1=${POSTSCRIPT_BASENAME}_reliefgray_EQ.ps
#Make colorscale
DEM_CPT=relief_gray.cpt
gmt makecpt -T-6000/6000/250 -D -Cgray >$DEM_CPT
EQ_DEPTH_CPT=EQ_color.cpt
gmt makecpt -Ic -T0/400/25 -D -Cviridis >$EQ_DEPTH_CPT
TITLE="USGS EQ 1970-2019"
echo " "
echo "Creating file $POSTSCRIPT1"
echo " "
gmt grdimage $TOPO15_GRD_NC -I$TOPO15_GRD_HS_NC -JM$WIDTH -C$DEM_CPT -R$TOPO15_GRD_NC -Q -Xc -Yc -E$DPI -K -P > $POSTSCRIPT1
echo B | gmt pstext -R -J -D0.1c/-0.1c -W0.5p,black -Gwhite -C0.2c -F+cTL+f24p,Helvetica-Bold -P -O -K >> $POSTSCRIPT1
#gmt psxy $AltiplanoPuna_1bas -R -J -L -Wthick,brown -K -O -P >> $POSTSCRIPT1
bzip2 -dc $USGS_EQ_GT4_LT5 | gmt select -i2,1,3 | gmt psxy -R -J -Wfaint,black -Sc0.07c -C$EQ_DEPTH_CPT -O -P -K >> $POSTSCRIPT1
bzip2 -dc $USGS_EQ_GT6 | gmt select -i2,1,3 | gmt psxy -R -J -Wfaint,black -Sc0.25c -C$EQ_DEPTH_CPT -O -P -K >> $POSTSCRIPT1
bzip2 -dc $USGS_EQ_GT5_LT6 | gmt select -i2,1,3 | gmt psxy -R -J -Wfaint,black -Sc0.12c -C$EQ_DEPTH_CPT -O -P -K >> $POSTSCRIPT1
gmt psxy SdC.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt psxy lapaz.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt psxy salta.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt psxy mendoza.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#-BWSne+t"$TITLE"
gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne -W1/thin,black -R -J -N1/thin,black -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p -O -P -K >> $POSTSCRIPT1
gmt psscale -R -J -DjBC+h+o-0.5c/-3.0c/+w7c/0.3c+ml -C$EQ_DEPTH_CPT -F+c0.1c/0.3c+gwhite+r1p+pthin,black -Baf100:"EQ Depth":/:"[km]": --FONT=12p --FONT_ANNOT_PRIMARY=12p --MAP_FRAME_PEN=0.5p --MAP_FRAME_WIDTH=0.1 -O -P >> $POSTSCRIPT1
gmt psconvert $POSTSCRIPT1 -A -P -Tg
convert -alpha off -quality 100 -density 150 -trim $POSTSCRIPT1 ${POSTSCRIPT1::-3}.jpg

The resulting figure shows EQ depth for the Central Andes:

Topography in grayscale for the Central Andes overlain by USGS earthquakes depths in color

Combining color topography and EQ depth into one plot imagemagick

We can now combine plot (A) and (B) into on figure using convert:

convert -quality 50 -density 150 ${POSTSCRIPT_BASENAME}_relieftopo.png ${POSTSCRIPT_BASENAME}_reliefgray_EQ.png -fuzz 1% -trim -bordercolor white -border 10x0 +repage +append ${POSTSCRIPT_BASENAME}_relief_topo_EQ_combined.jpg

convert -quality 100 -density 300 ${POSTSCRIPT_BASENAME}_relieftopo.png ${POSTSCRIPT_BASENAME}_reliefgray_EQ.png -fuzz 1% -trim -bordercolor white -border 10x0 +repage +append ${POSTSCRIPT_BASENAME}_relief_topo_EQ_combined.png
A: Color topography and B: USGS EQ depth

Comparing colorscales

If you are interested in using a different colorscale, here is a comparison of the different colorscales mentioned above. The steps to create this figure are in the GMT Bash script plot_CentralAndes_colorbar_comparison.sh.

Color scale comparison for the Central Andes including bathymetry and topography

Create a NDVI and EVI vegetation cover map for the Central Andes

Similar to the previous approaches, we set the topographic parameters and variables:

#!/bin/bash
gmt gmtset MAP_FRAME_PEN    0.5p,black
gmt gmtset MAP_FRAME_WIDTH    0.1
gmt gmtset MAP_FRAME_TYPE     plain
gmt gmtset FONT_TITLE    18p,Helvetica-Bold,black
gmt gmtset FONT_LABEL    12p,Helvetica-Bold,black
gmt gmtset PS_PAGE_ORIENTATION    portrait
gmt gmtset PS_MEDIA    A4
gmt gmtset FORMAT_GEO_MAP    D
gmt gmtset MAP_DEGREE_SYMBOL degree
gmt gmtset PROJ_LENGTH_UNIT cm

REGION=-80/-60/-40/-15

# DEM/Topography Data
#clip area and generate hillshade
TOPO15_GRD_NC=/home/bodo/Downloads/earth_relief_15s.nc
TOPO15_GRD_NC_CentralAndes=earth_relief_15s_CentralAndes.nc
if [ ! -e $TOPO15_GRD_NC_CentralAndes ]
then
    echo "generate Topo15S Clip $TOPO15_GRD_NC_CentralAndes"
    gmt grdcut -R$REGION $TOPO15_GRD_NC -G$TOPO15_GRD_NC_CentralAndes
fi

TOPO15_GRD_NC=$TOPO15_GRD_NC_CentralAndes
TOPO15_GRD_HS_NC=earth_relief_15s_CentralAndes_HS.nc
if [ ! -e $TOPO15_GRD_HS_NC ]
then
    echo "generate hillshade $TOPO15_GRD_HS_NC"
    gmt grdgradient $TOPO15_GRD_NC -Ne0.6 -Es75/55+a -G$TOPO15_GRD_HS_NC
fi

#Simpler Peucker algorithm
TOPO15_GRD_HS2_NC=earth_relief_15s_CentralAndes_HS_peucker.nc
if [ ! -e $TOPO15_GRD_HS2_NC ]
then
    echo "generate hillshade $TOPO15_GRD_HS2_NC"
    gmt grdgradient $TOPO15_GRD_NC -Nt1 -Ep -G$TOPO15_GRD_HS2_NC
fi

# Vector data
AltiplanoPuna_1bas=GMT_vector_data/AltiplanoPuna_1basin_UTM19S_WGS84.gmt
OSM_CentralAndeslakes=/raid-cachi/bodo/Dropbox/Argentina/GIS_Data/OSM/CentralAndeslakes.gmt
OSM_CentralAndesvolcano=/raid-cachi/bodo/Dropbox/Argentina/GIS_Data/OSM/CentralAndesvolcano.gmt

# Grid data
MOD13A3_NDVI_CentralAndes_rTOPO15=MOD13A3_2001-2017NDVImn_CentralAndes_Topo15S.nc
MOD13A3_EVI_CentralAndes_rTOPO15=MOD13A3_2001-2017EVImn_CentralAndes_Topo15S.nc

## CREATE Topographic map
#Set Parameters for Plot
POSTSCRIPT_BASENAME=CentralAndes
#xmin/xmax/ymin/ymax
WIDTH=10
XSTEP=10
YSTEP=10
DPI=300
CITY_STAR_SIZE=0.4c

And then we plot the NDVI data with colorscale precip4_diff_19lev.

TITLE="NDVI - C. Andes"
POSTSCRIPT1=${POSTSCRIPT_BASENAME}_ndvi.ps
#Make colorscale
NDVI_CPT=ndvi_color.cpt
gmt makecpt -T0.0/0.8/0.042 -D -Cprecip4_diff_19lev >$NDVI_CPT
echo " "
echo "Creating file $POSTSCRIPT1"
echo " "
gmt grdimage $MOD13A3_NDVI_CentralAndes_rTOPO15 -I$TOPO15_GRD_HS_NC -JM$WIDTH -C$NDVI_CPT -R$TOPO15_GRD_NC -Q -Xc -Yc -E$DPI -K -P > $POSTSCRIPT1
gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne+t"$TITLE" -Swhite -W1/thin,black -R -J -N1/thin,black -O -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p,black --MAP_FRAME_WIDTH=0.1 -P -K >> $POSTSCRIPT1
gmt psxy $OSM_CentralAndeslakes -R -J -L -Wfaint,lightblue -Glightblue -K -O -P >> $POSTSCRIPT1
#gmt psxy $OSM_CentralAndesvolcano -R -J -L -St0.15c -Gred -K -O -P >> $POSTSCRIPT1
gmt psxy $AltiplanoPuna_1bas -R -J -L -Wthin,white -K -O -P >> $POSTSCRIPT1
gmt psxy SdC.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext SdC.txt -D-0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy lapaz.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext lapaz.txt -D+0.9c/0.2c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy salta.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext salta.txt -D+0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy mendoza.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext mendoza.txt -D+1.0c/-0.5c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
echo A | gmt pstext -R -J -D0.1c/-0.1c -W0.5p,black -C0.2c -F+cTL+f24p,Helvetica-Bold --MAP_FRAME_PEN=0.5p,black --MAP_FRAME_WIDTH=0.1 -P -O -K >> $POSTSCRIPT1
gmt psscale -R -J -DjBC+h+o-0.0c/-3.0c/+w7c/0.3c+ml -C$NDVI_CPT -F+c0.1c/0.3c+gwhite+r1p+pthin,black -Baf1000:"NDVI":/:"": --FONT=12p --FONT_ANNOT_PRIMARY=12p --MAP_FRAME_PEN=0.5p --MAP_FRAME_WIDTH=0.1 -O -P >> $POSTSCRIPT1
gmt psconvert $POSTSCRIPT1 -A -P -Tg
convert -quality 100 -density 150 -trim $POSTSCRIPT1 ${POSTSCRIPT1::-3}.jpg

The resulting image nicely show the steep vegetation-cover gradient in the Central Andes:

Mean annual NDVI (MOD13A2, 2001 to 2017) using color scale precip4_diff_19lev.

EVI

The NDVI plot can be combined with the EVI plot (also from MOD13A2 data, 2001-2017) using the same approach:

POSTSCRIPT1=${POSTSCRIPT_BASENAME}_evi.ps
#Make colorscale
EVI_CPT=evi_color.cpt
gmt makecpt -T0.0/0.6/0.05 -D -Cprecip_diff_12lev >$EVI_CPT
TITLE="EVI - C. Andes"
echo " "
echo "Creating file $POSTSCRIPT1"
echo " "
gmt grdimage $MOD13A3_EVI_CentralAndes_rTOPO15 -I$TOPO15_GRD_HS_NC -JM$WIDTH -C$EVI_CPT -R$TOPO15_GRD_NC -Q -Xc -Yc -E$DPI -K -P > $POSTSCRIPT1
gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne+t"$TITLE" -Swhite -W1/thin,black -R -J -N1/thin,black -O -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p,black --MAP_FRAME_WIDTH=0.1 -P -K >> $POSTSCRIPT1
gmt psxy $AltiplanoPuna_1bas -R -J -L -Wthin,white -K -O -P >> $POSTSCRIPT1
gmt psxy SdC.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext SdC.txt -D-0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy lapaz.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext lapaz.txt -D+0.9c/0.2c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy salta.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext salta.txt -D+0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy mendoza.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext mendoza.txt -D+1.0c/-0.5c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
echo B | gmt pstext -R -J -D0.1c/-0.1c -W0.5p,black -C0.2c -F+cTL+f24p,Helvetica-Bold -P -O -K >> $POSTSCRIPT1
gmt psscale -R -J -DjBC+h+o-0.0c/-3.0c/+w7c/0.3c+ml -C$NDVI_CPT -F+c0.1c/0.3c+gwhite+r1p+pthin,black -Baf1000:"EVI":/:"": --FONT=12p --FONT_ANNOT_PRIMARY=12p --MAP_FRAME_PEN=0.5p --MAP_FRAME_WIDTH=0.1 -O -P >> $POSTSCRIPT1
gmt psconvert $POSTSCRIPT1 -A -P -Tg
convert -quality 100 -density 150 -trim $POSTSCRIPT1 ${POSTSCRIPT1::-3}.jpg

Combine NDVI and EVI plots with imagemagick

We put both files into one plot using convert (see above). We generate a low-resolution version (JPG) and a higher-resolution version (PNG):

convert -quality 50 -density 150 ${POSTSCRIPT_BASENAME}_ndvi.png ${POSTSCRIPT_BASENAME}_evi.png -fuzz 1% -trim -bordercolor white -border 10x0 +repage +append ${POSTSCRIPT_BASENAME}_ndvi_evi.jpg

convert -quality 100 -density 300 ${POSTSCRIPT_BASENAME}_ndvi.png ${POSTSCRIPT_BASENAME}_evi.png -fuzz 1% -trim -bordercolor white -border 10x0 +repage +append ${POSTSCRIPT_BASENAME}_ndvi_evi.png
Mean annual NDVI (MOD13A2, 2001 to 2017) using color scale precip4_diff_19lev.

Plot Topography, NDVI, and TRMM2B31 for the NW Argentinean Andes

In the next plot, we use TRMM 2B31 data and generate a plot for the NW Argentinean Andes. Note that the area displayed in the map has been reduced to match the latitudinal range of the TRMM 2B31 data. First, we setup the topographic data and clip the relevant data to a different REGION:

#!/bin/bash
gmt gmtset MAP_FRAME_PEN    0.5p,black
gmt gmtset MAP_FRAME_WIDTH    0.1
gmt gmtset MAP_FRAME_TYPE     plain
gmt gmtset FONT_TITLE    18p,Helvetica-Bold,black
gmt gmtset FONT_LABEL    12p,Helvetica-Bold,black
gmt gmtset PS_PAGE_ORIENTATION    portrait
gmt gmtset PS_MEDIA    A4
gmt gmtset FORMAT_GEO_MAP    D
gmt gmtset MAP_DEGREE_SYMBOL degree
gmt gmtset PROJ_LENGTH_UNIT cm

REGION=-75/-60/-35/-15

# DEM/Topography Data
#clip area and generate hillshade
TOPO15_GRD_NC=/home/bodo/Downloads/earth_relief_15s.nc
TOPO15_GRD_NC_NWArg=earth_relief_15s_NWArg.nc
if [ ! -e $TOPO15_GRD_NC_NWArg ]
then
    echo "generate Topo15S Clip $TOPO15_GRD_NC_NWArg"
    gmt grdcut -R$REGION $TOPO15_GRD_NC -G$TOPO15_GRD_NC_NWArg
fi

TOPO15_GRD_NC=$TOPO15_GRD_NC_NWArg
TOPO15_GRD_HS_NC=earth_relief_15s_NWArg_HS.nc
if [ ! -e $TOPO15_GRD_HS_NC ]
then
    echo "generate hillshade $TOPO15_GRD_HS_NC"
    gmt grdgradient $TOPO15_GRD_NC -Ne0.6 -Es75/55+a -G$TOPO15_GRD_HS_NC
fi

#Simpler Peucker algorithm
TOPO15_GRD_HS2_NC=earth_relief_15s_NWArg_HS_peucker.nc
if [ ! -e $TOPO15_GRD_HS2_NC ]
then
    echo "generate hillshade $TOPO15_GRD_HS2_NC"
    gmt grdgradient $TOPO15_GRD_NC -Nt1 -Ep -G$TOPO15_GRD_HS2_NC
fi

# Vector data
AltiplanoPuna_1bas=GMT_vector_data/AltiplanoPuna_1basin_UTM19S_WGS84.gmt
OSM_CentralAndeslakes=/raid-cachi/bodo/Dropbox/Argentina/GIS_Data/OSM/CentralAndeslakes.gmt
OSM_CentralAndesvolcano=/raid-cachi/bodo/Dropbox/Argentina/GIS_Data/OSM/CentralAndesvolcano.gmt

# Grid data
MOD13A3_NDVI_CentralAndes_rTOPO15=MOD13A3_2001-2017NDVImn_CentralAndes_Topo15S.nc
MOD13A3_EVI_CentralAndes_rTOPO15=MOD13A3_2001-2017EVImn_CentralAndes_Topo15S.nc

# Prepare TRMM data
TRMM2B31_1998_2009_MM_PER_YR=trmm2b31_annual_mm_per_year.tif
TRMM2B31_1998_2009_MM_PER_YR_NWArg=trmm2b31_annual_mm_per_year_NWArg.nc
TRMM2B31_1998_2009_MM_PER_YR_NWArg_rTOPO15=trmm2b31_annual_mm_per_year_NWArg_Topo15S.nc
if [ ! -e $TRMM2B31_1998_2009_MM_PER_YR_NWArg ]
then
    echo "generate TRMM2B31 Mean clip $TRMM2B31_1998_2009_MM_PER_YR_NWArg"
    gmt grdcut -R$REGION $TRMM2B31_1998_2009_MM_PER_YR -G$TRMM2B31_1998_2009_MM_PER_YR_NWArg
fi

if [ ! -e $TRMM2B31_1998_2009_MM_PER_YR_NWArg_rTOPO15 ]
then
    echo "resample TRMM2B31 Mean Clip $TRMM2B31_1998_2009_MM_PER_YR_NWArg_rTOPO15"
    gmt grdsample $TRMM2B31_1998_2009_MM_PER_YR_NWArg -R$REGION -I3601+/4801+ -G$TRMM2B31_1998_2009_MM_PER_YR_NWArg_rTOPO15
fi

TRMM2B31_1998_2009_EEVENT_NR=trmm2b31_eevent_nr.tif
TRMM2B31_1998_2009_EEVENT_NR_NWArg=trmm2b31_eevent_nr_NWArg.nc
TRMM2B31_1998_2009_EEVENT_NR_NWArg_rTOPO15=trmm2b31_eevent_nr_NWArg_Topo15S.nc
if [ ! -e $TRMM2B31_1998_2009_EEVENT_NR_NWArg ]
then
    echo "generate TRMM2B31 Mean clip $TRMM2B31_1998_2009_EEVENT_NR_NWArg"
    gmt grdcut -R$REGION $TRMM2B31_1998_2009_EEVENT_NR -G$TRMM2B31_1998_2009_EEVENT_NR_NWArg
fi

if [ ! -e $TRMM2B31_1998_2009_EEVENT_NR_NWArg_rTOPO15 ]
then
    echo "resample TRMM2B31 Mean Clip $TRMM2B31_1998_2009_EEVENT_NR_NWArg_rTOPO15"
    gmt grdsample $TRMM2B31_1998_2009_EEVENT_NR_NWArg -R$REGION -I3601+/4801+ -G$TRMM2B31_1998_2009_EEVENT_NR_NWArg_rTOPO15
fi

Next, we generate the topographic plot:

POSTSCRIPT_BASENAME=NWArg
#xmin/xmax/ymin/ymax
WIDTH=10
XSTEP=10
YSTEP=10
DPI=300
CITY_STAR_SIZE=0.4c

TITLE="Topo NW Arg"
POSTSCRIPT1=${POSTSCRIPT_BASENAME}_topo.ps
#Make colorscale
DEM_CPT=relief_color.cpt
gmt makecpt -T-8000/5000/250 -D -Cmby >$DEM_CPT
echo " "
echo "Creating file $POSTSCRIPT1"
echo " "
gmt grdimage $TOPO15_GRD_NC -I$TOPO15_GRD_HS_NC -JM$WIDTH -C$DEM_CPT -R$TOPO15_GRD_NC -Q  -Xc -Yc -E$DPI -K -P > $POSTSCRIPT1
#gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne+t"$TITLE" -W1/thin,black -R -J -N1/thin,black -O -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p -P -K >> $POSTSCRIPT1
gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne -W1/thin,black -R -J -N1/thin,black -O -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p -P -K >> $POSTSCRIPT1
gmt psxy $OSM_CentralAndeslakes -R -J -L -Wfaint,lightblue -Glightblue -K -O -P >> $POSTSCRIPT1
gmt psxy $OSM_CentralAndesvolcano -R -J -L -St0.2c -Gblack -K -O -P >> $POSTSCRIPT1
gmt psxy $AltiplanoPuna_1bas -R -J -L -Wthick,darkblue -K -O -P >> $POSTSCRIPT1
gmt psxy SdC.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt pstext SdC.txt -D-0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy lapaz.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt pstext lapaz.txt -D+0.9c/0.2c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy salta.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt pstext salta.txt -D+0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy mendoza.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,white -Gblack -K -O -P >> $POSTSCRIPT1
gmt pstext mendoza.txt -D+1.0c/-0.5c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psscale -R -J -DjBC+h+o-0.0c/-3.0c/+w7c/0.3c+ml -C$DEM_CPT -F+c0.1c/0.3c+gwhite+r1p+pthin,black -Baf1000:"Bathymetry and Elevation":/:"[m]": --FONT=12p --FONT_ANNOT_PRIMARY=12p --MAP_FRAME_PEN=0.5p --MAP_FRAME_WIDTH=0.1 -O -P >> $POSTSCRIPT1
gmt psconvert $POSTSCRIPT1 -A -P -Tg

Then, we generate the NDVI map:

TITLE="NDVI - NW Arg"
POSTSCRIPT1=${POSTSCRIPT_BASENAME}_ndvi.ps
#Make colorscale
NDVI_CPT=ndvi_color.cpt
#gmt makecpt -T0.0/0.8/0.042 -D -Cprecip4_diff_19lev >$NDVI_CPT
gmt makecpt -T0.0/0.8/0.067 -D -Cprecip_diff_12lev.cpt >$NDVI_CPT
echo " "
echo "Creating file $POSTSCRIPT1"
echo " "
gmt grdimage $MOD13A3_NDVI_CentralAndes_rTOPO15 -I$TOPO15_GRD_HS_NC -JM$WIDTH -C$NDVI_CPT -R$TOPO15_GRD_NC -Q -Xc -Yc -E$DPI -K -P > $POSTSCRIPT1
gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne -Swhite -W1/thin,black -R -J -N1/thin,black -O -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p,black --MAP_FRAME_WIDTH=0.1 -P -K >> $POSTSCRIPT1
gmt psxy $OSM_CentralAndeslakes -R -J -L -Wfaint,lightblue -Glightblue -K -O -P >> $POSTSCRIPT1
#gmt psxy $OSM_CentralAndesvolcano -R -J -L -St0.15c -Gred -K -O -P >> $POSTSCRIPT1
gmt psxy $AltiplanoPuna_1bas -R -J -L -Wthin,darkblue -K -O -P >> $POSTSCRIPT1
gmt psxy SdC.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,black -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext SdC.txt -D-0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy lapaz.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,black -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext lapaz.txt -D+0.9c/0.2c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy salta.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,black -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext salta.txt -D+0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy mendoza.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,black -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext mendoza.txt -D+1.0c/-0.5c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psscale -R -J -DjBC+h+o-0.0c/-3.0c/+w7c/0.3c+ml -C$NDVI_CPT -F+c0.1c/0.3c+gwhite+r1p+pthin,black -Baf1000:"NDVI":/:"": --FONT=12p --FONT_ANNOT_PRIMARY=12p --MAP_FRAME_PEN=0.5p --MAP_FRAME_WIDTH=0.1 -O -P >> $POSTSCRIPT1
gmt psconvert $POSTSCRIPT1 -A -P -Tg
#convert -quality 100 -density 150 $POSTSCRIPT1 ${POSTSCRIPT1::-3}.jpg

Last, we use the colorscale precip_11lev to plot TRMM 2B31 rainfall:

POSTSCRIPT1=${POSTSCRIPT_BASENAME}_trmm2b31.ps
#Make colorscale
RAINFALL_CPT=rainfall_color.cpt
#gmt makecpt -T0.0/2000/167 -D -Cprecip_diff_12lev >$RAINFALL_CPT
gmt makecpt -T0.0/2000/182 -D -Cprecip_11lev >$RAINFALL_CPT
TITLE="Rainfall - NW Arg"
echo " "
echo "Creating file $POSTSCRIPT1"
echo " "
gmt grdimage $TRMM2B31_1998_2009_MM_PER_YR_NWArg_rTOPO15 -I$TOPO15_GRD_HS_NC -JM$WIDTH -C$RAINFALL_CPT -R$TOPO15_GRD_NC -Q -Xc -Yc -E$DPI -K -P > $POSTSCRIPT1
gmt pscoast -Bx$XSTEP -By$YSTEP -BWSne -W1/thin,black -R -J -N1/thin,black -O -Df --FORMAT_GEO_MAP=ddd:mm:ssF --MAP_FRAME_PEN=0.5p,black --MAP_FRAME_WIDTH=0.1 -P -K >> $POSTSCRIPT1
gmt psxy $AltiplanoPuna_1bas -R -J -L -Wthin,darkblue -K -O -P >> $POSTSCRIPT1
gmt psxy SdC.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,black -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext SdC.txt -D-0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy lapaz.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,black -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext lapaz.txt -D+0.9c/0.2c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy salta.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,black -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext salta.txt -D+0.8c/0.0c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psxy mendoza.txt -Sa${CITY_STAR_SIZE} -R -J -L -Wthin,black -Gblack -K -O -P >> $POSTSCRIPT1
#gmt pstext mendoza.txt -D+1.0c/-0.5c -F+f12p,Helvetica -W0.1p,black -Gwhite -R -J -K -O -P >> $POSTSCRIPT1
gmt psscale -R -J -DjBC+h+o-0.0c/-3.0c/+w7c/0.3c+ml -C$RAINFALL_CPT -F+c0.1c/0.3c+gwhite+r1p+pthin,black -Baf1000:"Mean annual rainfall 1998-2014":/:"[mm/yr]": --FONT=12p --FONT_ANNOT_PRIMARY=12p --MAP_FRAME_PEN=0.5p --MAP_FRAME_WIDTH=0.1 -O -P >> $POSTSCRIPT1
gmt psconvert $POSTSCRIPT1 -A -P -Tg

Last, we combine the figures into one panel with convert:

convert -quality 50 -density 150 ${POSTSCRIPT_BASENAME}_topo.png ${POSTSCRIPT_BASENAME}_ndvi.png ${POSTSCRIPT_BASENAME}_trmm2b31.png -fuzz 1% -trim -bordercolor white -border 10x0 +repage +append ${POSTSCRIPT_BASENAME}_topo_ndvi_rainfall.jpg

The script is stored in the GMT Bash file plot_NWArgentina_Topo_NDVI_Precip.sh.

Bathymetry and Topography, mean annual NDVI (MOD13A2, 2001 to 2017) using color scale precip4_diff_19lev, and TRMM 2B31 mean annual rainfall (1998-2014) using colorscale precip_11lev.

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