A21e Checkpoint
Import libraries¶
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import ee
import geemap
import ee
import geemap
Create an interactive map¶
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Map = geemap.Map(center=[40, -100], zoom=4)
Map = geemap.Map(center=[40, -100], zoom=4)
Add Earth Engine Python script¶
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# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")
#*** Start of imports. If edited, may not auto-convert in the playground. ***#
res_table = ee.FeatureCollection("projects/gee-book/assets/A2-1/SW/reservoir_anoms_km3"),
sm2003 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2003"),
sm2004 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2004"),
sm2005 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2005"),
sm2006 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2006"),
sm2007 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2007"),
sm2008 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2008"),
sm2009 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2009"),
sm2010 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2010"),
sm2011 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2011"),
sm2012 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2012"),
sm2013 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2013"),
sm2014 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2014"),
sm2015 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2015"),
sm2016 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2016"),
swe2003 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2003"),
swe2004 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2004"),
swe2005 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2005"),
swe2006 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2006"),
swe2007 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2007"),
swe2008 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2008"),
swe2009 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2009"),
swe2010 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2010"),
swe2011 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2011"),
swe2012 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2012"),
swe2013 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2013"),
swe2014 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2014"),
swe2015 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2015"),
swe2016 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2016")
#**** End of imports. If edited, may not auto-convert in the playground. ****#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Chapter: A2.1 Groundwater Monitoring with GRACE
# Checkpoint: A21e
# Authors: A.J. Purdy, J.S. Famiglietti
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ** Part 1 **
# Import Basins
basins = ee.FeatureCollection('USGS/WBD/2017/HUC04')
# Extract the 3 HUC 04 basins for the Central Valley.
codes = ['1802', '1803', '1804']
basin = basins.filter(ee.Filter.inList('huc4', codes))
# Add the basin to the map to show the extent of our analysis.
Map.centerObject(basin, 6)
Map.addLayer(basin, {
'color': 'green'
}, 'Central Valley Basins', True, 0.5)
res = ee.FeatureCollection(
'projects/gee-book/assets/A2-1/ca_reservoirs_index')
res_cv = res.filterBounds(basin)
Map.addLayer(res_cv, {
'color': 'blue'
}, 'Reservoirs')
# Import GRACE groundwater.
GRACE = ee.ImageCollection('NASA/GRACE/MASS_GRIDS/MASCON_CRI')
# Get Liquid Water Equivalent thickness.
basinTWSa = GRACE.select('lwe_thickness')
# Make plot of TWSa for Basin Boundary
TWSaChart = ui.Chart.image.series({
'imageCollection': basinTWSa.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
}) \
.setOptions({
'title': 'TWSa',
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'TWSa (cm)'
},
'lineWidth': 1,
})
print(TWSaChart)
# Set start and end years to annualize the data.
yrStart = 2003
yrEnd = 2016
years = ee.List.sequence(yrStart, yrEnd)
def func_lnn(y):
date = ee.Date.fromYMD(y, 1, 1)
return basinTWSa.filter(ee.Filter.calendarRange(y, y,
'year')) \
.mean() \
.set('system:time_start', date) \
.rename('TWSa')
GRACE_yr = ee.ImageCollection.fromImages(years.map(func_lnn
).flatten())
).flatten())
# Make plot of annualized TWSa for Basin Boundary.
TWSaChart = ui.Chart.image.series({
'imageCollection': GRACE_yr.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
'scale': 25000
}).setChartType('ScatterChart') \
.setOptions({
'title': 'Annualized Total Water Storage anomalies',
'trendlines': {
'0': {
'color': 'CC0000'
}
},
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'TWSa (cm)'
},
'lineWidth': 2,
'pointSize': 2
})
print(TWSaChart)
# Compute Trend for each pixel to map regions of most change.
def addVariables(image):
# Compute time in fractional years.
date = ee.Date(image.get('system:time_start'))
years = date.difference(ee.Date('2003-01-01'), 'year')
# Return the image with the added bands.
return image \
.addBands(ee.Image(years).rename('t').float()) \
.addBands(ee.Image.constant(1))
cvTWSa = GRACE_yr.filterBounds(basin).map(addVariables)
print(cvTWSa)
# List of the independent variable names
independents = ee.List(['constant', 't'])
# Name of the dependent variable.
dependent = ee.String('TWSa')
# Compute a linear trend. This will have two bands: 'residuals' and
# a 2x1 band called coefficients (columns are for dependent variables).
trend = cvTWSa.select(independents.add(dependent)) \
.reduce(ee.Reducer.linearRegression(independents.length(), 1))
# Flatten the coefficients into a 2-band image.
coefficients = trend.select('coefficients') \
.arrayProject([0]) \
.arrayFlatten([independents])
# Create a layer of the TWSa slope to add to the map.
slope = coefficients.select('t')
# Set visualization parameters to represent positive (blue)
# & negative (red) trends.
slopeParams = {
'min': -3.5,
'max': 3.5,
'palette': ['red', 'white', 'blue']
}
Map.addLayer(slope.clip(basin), slopeParams, 'TWSa Annualized Trend', True,
0.75)
# ** Part 2 **
# ** Section 3 **
# 3.1 Load GLDAS Soil Moisture images from an Asset to an ImageCollection.
gldas_sm_list = ee.List([sm2003, sm2004, sm2005, sm2006, sm2007,
sm2008, sm2009, sm2010, sm2011, sm2012, sm2013, sm2014,
sm2015, sm2016
])
sm_ic = ee.ImageCollection.fromImages(gldas_sm_list)
kgm2_to_cm = 0.10
def func_kmm(img):
date = ee.Date.fromYMD(img.get('year'), 1, 1)
return img.select('RootMoist_inst').multiply(kgm2_to_cm) \
.rename('SMa').set('system:time_start', date)
sm_ic_ts = sm_ic.map(func_kmm)
kgm2_to_cm = 0.10
def func_zvs(img):
date = ee.Date.fromYMD(img.get('year'), 1, 1)
return img.select('RootMoist_inst').multiply(kgm2_to_cm) \
.rename('SMa').set('system:time_start', date)
sm_ic_ts = sm_ic.map(func_zvs)
# Make plot of SMa for Basin Boundary
SMaChart = ui.Chart.image.series({
'imageCollection': sm_ic_ts.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
'scale': 25000
}) \
.setChartType('ScatterChart') \
.setOptions({
'title': 'Soil Moisture anomalies',
'trendlines': {
'0': {
'color': 'CC0000'
}
},
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'SMa (cm)'
},
'lineWidth': 2,
'pointSize': 2
})
print(SMaChart)
gldas_swe_list = ee.List([swe2003, swe2004, swe2005, swe2006,
swe2007, swe2008, swe2009, swe2010, swe2011, swe2012,
swe2013, swe2014, swe2015, swe2016
])
swe_ic = ee.ImageCollection.fromImages(gldas_swe_list)
# 3.2. Load GLDAS Snow Water Equivalent Images from an Asset to an Image Collection
gldas_swe_list = ee.List([swe2003, swe2004, swe2005, swe2006,
swe2007, swe2008, swe2009, swe2010, swe2011, swe2012,
swe2013, swe2014, swe2015, swe2016
])
swe_ic = ee.ImageCollection.fromImages(gldas_swe_list)
def func_oyl(img):
date = ee.Date.fromYMD(img.get('year'), 1, 1)
return img.select('SWE_inst').multiply(kgm2_to_cm).rename(
'SWEa').set('system:time_start', date)
swe_ic_ts = swe_ic.map(func_oyl)
# Make plot of SWEa for Basin Boundary
SWEaChart = ui.Chart.image.series({
'imageCollection': swe_ic_ts.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
'scale': 25000
}) \
.setChartType('ScatterChart') \
.setOptions({
'title': 'Snow Water Equivalent anomalies',
'trendlines': {
'0': {
'color': 'CC0000'
}
},
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'SWEa (cm)'
},
'lineWidth': 2,
'pointSize': 2
})
print(SWEaChart)
# -----------------------------------------------------------------------
# CHECKPOINT
# -----------------------------------------------------------------------
# Extract geometry to convert time series of anomalies in km3 to cm
area_km2 = basin.geometry().area().divide(1000 * 1000)
km_2_cm = 100000
# Convert csv to image collection
res_list = res_table.toList(res_table.size())
def func_wfd(ft):
return ee.Date.fromYMD(ee.Feature(ft).get('YEAR'), 1, 1)
yrs = res_list.map(func_wfd)
def func_awn(ft):
return ee.Image.constant(ee.Feature(ft).get('Anom_km3'))
SWanoms = res_list.map(func_awn)
sw_ic_ts = ee.ImageCollection.fromImages(
res_list.map(
def function(ft):
date = ee.Date.fromYMD(ee.Feature(ft).get('YEAR'),
1, 1)
return ee.Image.constant(ee.Feature(ft).get(
'Anom_km3')).divide(area_km2).multiply(
km_2_cm).rename('SWa').set(
'system:time_start', date)
)
)
# Create a time series of Surface Water Anomalies
SWaChart = ui.Chart.image.series({
'imageCollection': sw_ic_ts.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
'scale': 25000
}) \
.setChartType('ScatterChart') \
.setOptions({
'title': 'Surface Water anomalies',
'trendlines': {
'0': {
'color': 'CC0000'
}
},
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'SWa (cm)'
},
'lineWidth': 2,
'pointSize': 2
})
print(SWaChart)
# -----------------------------------------------------------------------
# CHECKPOINT
# -----------------------------------------------------------------------
# Add Earth Engine dataset
image = ee.Image("USGS/SRTMGL1_003")
#*** Start of imports. If edited, may not auto-convert in the playground. ***#
res_table = ee.FeatureCollection("projects/gee-book/assets/A2-1/SW/reservoir_anoms_km3"),
sm2003 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2003"),
sm2004 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2004"),
sm2005 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2005"),
sm2006 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2006"),
sm2007 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2007"),
sm2008 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2008"),
sm2009 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2009"),
sm2010 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2010"),
sm2011 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2011"),
sm2012 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2012"),
sm2013 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2013"),
sm2014 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2014"),
sm2015 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2015"),
sm2016 = ee.Image("projects/gee-book/assets/A2-1/SM/sm2016"),
swe2003 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2003"),
swe2004 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2004"),
swe2005 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2005"),
swe2006 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2006"),
swe2007 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2007"),
swe2008 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2008"),
swe2009 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2009"),
swe2010 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2010"),
swe2011 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2011"),
swe2012 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2012"),
swe2013 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2013"),
swe2014 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2014"),
swe2015 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2015"),
swe2016 = ee.Image("projects/gee-book/assets/A2-1/SWE/swe2016")
#**** End of imports. If edited, may not auto-convert in the playground. ****#
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Chapter: A2.1 Groundwater Monitoring with GRACE
# Checkpoint: A21e
# Authors: A.J. Purdy, J.S. Famiglietti
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ** Part 1 **
# Import Basins
basins = ee.FeatureCollection('USGS/WBD/2017/HUC04')
# Extract the 3 HUC 04 basins for the Central Valley.
codes = ['1802', '1803', '1804']
basin = basins.filter(ee.Filter.inList('huc4', codes))
# Add the basin to the map to show the extent of our analysis.
Map.centerObject(basin, 6)
Map.addLayer(basin, {
'color': 'green'
}, 'Central Valley Basins', True, 0.5)
res = ee.FeatureCollection(
'projects/gee-book/assets/A2-1/ca_reservoirs_index')
res_cv = res.filterBounds(basin)
Map.addLayer(res_cv, {
'color': 'blue'
}, 'Reservoirs')
# Import GRACE groundwater.
GRACE = ee.ImageCollection('NASA/GRACE/MASS_GRIDS/MASCON_CRI')
# Get Liquid Water Equivalent thickness.
basinTWSa = GRACE.select('lwe_thickness')
# Make plot of TWSa for Basin Boundary
TWSaChart = ui.Chart.image.series({
'imageCollection': basinTWSa.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
}) \
.setOptions({
'title': 'TWSa',
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'TWSa (cm)'
},
'lineWidth': 1,
})
print(TWSaChart)
# Set start and end years to annualize the data.
yrStart = 2003
yrEnd = 2016
years = ee.List.sequence(yrStart, yrEnd)
def func_lnn(y):
date = ee.Date.fromYMD(y, 1, 1)
return basinTWSa.filter(ee.Filter.calendarRange(y, y,
'year')) \
.mean() \
.set('system:time_start', date) \
.rename('TWSa')
GRACE_yr = ee.ImageCollection.fromImages(years.map(func_lnn
).flatten())
).flatten())
# Make plot of annualized TWSa for Basin Boundary.
TWSaChart = ui.Chart.image.series({
'imageCollection': GRACE_yr.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
'scale': 25000
}).setChartType('ScatterChart') \
.setOptions({
'title': 'Annualized Total Water Storage anomalies',
'trendlines': {
'0': {
'color': 'CC0000'
}
},
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'TWSa (cm)'
},
'lineWidth': 2,
'pointSize': 2
})
print(TWSaChart)
# Compute Trend for each pixel to map regions of most change.
def addVariables(image):
# Compute time in fractional years.
date = ee.Date(image.get('system:time_start'))
years = date.difference(ee.Date('2003-01-01'), 'year')
# Return the image with the added bands.
return image \
.addBands(ee.Image(years).rename('t').float()) \
.addBands(ee.Image.constant(1))
cvTWSa = GRACE_yr.filterBounds(basin).map(addVariables)
print(cvTWSa)
# List of the independent variable names
independents = ee.List(['constant', 't'])
# Name of the dependent variable.
dependent = ee.String('TWSa')
# Compute a linear trend. This will have two bands: 'residuals' and
# a 2x1 band called coefficients (columns are for dependent variables).
trend = cvTWSa.select(independents.add(dependent)) \
.reduce(ee.Reducer.linearRegression(independents.length(), 1))
# Flatten the coefficients into a 2-band image.
coefficients = trend.select('coefficients') \
.arrayProject([0]) \
.arrayFlatten([independents])
# Create a layer of the TWSa slope to add to the map.
slope = coefficients.select('t')
# Set visualization parameters to represent positive (blue)
# & negative (red) trends.
slopeParams = {
'min': -3.5,
'max': 3.5,
'palette': ['red', 'white', 'blue']
}
Map.addLayer(slope.clip(basin), slopeParams, 'TWSa Annualized Trend', True,
0.75)
# ** Part 2 **
# ** Section 3 **
# 3.1 Load GLDAS Soil Moisture images from an Asset to an ImageCollection.
gldas_sm_list = ee.List([sm2003, sm2004, sm2005, sm2006, sm2007,
sm2008, sm2009, sm2010, sm2011, sm2012, sm2013, sm2014,
sm2015, sm2016
])
sm_ic = ee.ImageCollection.fromImages(gldas_sm_list)
kgm2_to_cm = 0.10
def func_kmm(img):
date = ee.Date.fromYMD(img.get('year'), 1, 1)
return img.select('RootMoist_inst').multiply(kgm2_to_cm) \
.rename('SMa').set('system:time_start', date)
sm_ic_ts = sm_ic.map(func_kmm)
kgm2_to_cm = 0.10
def func_zvs(img):
date = ee.Date.fromYMD(img.get('year'), 1, 1)
return img.select('RootMoist_inst').multiply(kgm2_to_cm) \
.rename('SMa').set('system:time_start', date)
sm_ic_ts = sm_ic.map(func_zvs)
# Make plot of SMa for Basin Boundary
SMaChart = ui.Chart.image.series({
'imageCollection': sm_ic_ts.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
'scale': 25000
}) \
.setChartType('ScatterChart') \
.setOptions({
'title': 'Soil Moisture anomalies',
'trendlines': {
'0': {
'color': 'CC0000'
}
},
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'SMa (cm)'
},
'lineWidth': 2,
'pointSize': 2
})
print(SMaChart)
gldas_swe_list = ee.List([swe2003, swe2004, swe2005, swe2006,
swe2007, swe2008, swe2009, swe2010, swe2011, swe2012,
swe2013, swe2014, swe2015, swe2016
])
swe_ic = ee.ImageCollection.fromImages(gldas_swe_list)
# 3.2. Load GLDAS Snow Water Equivalent Images from an Asset to an Image Collection
gldas_swe_list = ee.List([swe2003, swe2004, swe2005, swe2006,
swe2007, swe2008, swe2009, swe2010, swe2011, swe2012,
swe2013, swe2014, swe2015, swe2016
])
swe_ic = ee.ImageCollection.fromImages(gldas_swe_list)
def func_oyl(img):
date = ee.Date.fromYMD(img.get('year'), 1, 1)
return img.select('SWE_inst').multiply(kgm2_to_cm).rename(
'SWEa').set('system:time_start', date)
swe_ic_ts = swe_ic.map(func_oyl)
# Make plot of SWEa for Basin Boundary
SWEaChart = ui.Chart.image.series({
'imageCollection': swe_ic_ts.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
'scale': 25000
}) \
.setChartType('ScatterChart') \
.setOptions({
'title': 'Snow Water Equivalent anomalies',
'trendlines': {
'0': {
'color': 'CC0000'
}
},
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'SWEa (cm)'
},
'lineWidth': 2,
'pointSize': 2
})
print(SWEaChart)
# -----------------------------------------------------------------------
# CHECKPOINT
# -----------------------------------------------------------------------
# Extract geometry to convert time series of anomalies in km3 to cm
area_km2 = basin.geometry().area().divide(1000 * 1000)
km_2_cm = 100000
# Convert csv to image collection
res_list = res_table.toList(res_table.size())
def func_wfd(ft):
return ee.Date.fromYMD(ee.Feature(ft).get('YEAR'), 1, 1)
yrs = res_list.map(func_wfd)
def func_awn(ft):
return ee.Image.constant(ee.Feature(ft).get('Anom_km3'))
SWanoms = res_list.map(func_awn)
sw_ic_ts = ee.ImageCollection.fromImages(
res_list.map(
def function(ft):
date = ee.Date.fromYMD(ee.Feature(ft).get('YEAR'),
1, 1)
return ee.Image.constant(ee.Feature(ft).get(
'Anom_km3')).divide(area_km2).multiply(
km_2_cm).rename('SWa').set(
'system:time_start', date)
)
)
# Create a time series of Surface Water Anomalies
SWaChart = ui.Chart.image.series({
'imageCollection': sw_ic_ts.filter(ee.Filter.date(
'2003-01-01', '2016-12-31')),
'region': basin,
'reducer': ee.Reducer.mean(),
'scale': 25000
}) \
.setChartType('ScatterChart') \
.setOptions({
'title': 'Surface Water anomalies',
'trendlines': {
'0': {
'color': 'CC0000'
}
},
'hAxis': {
format: 'MM-yyyy'
},
'vAxis': {
'title': 'SWa (cm)'
},
'lineWidth': 2,
'pointSize': 2
})
print(SWaChart)
# -----------------------------------------------------------------------
# CHECKPOINT
# -----------------------------------------------------------------------
Display the interactive map¶
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Map
Map