A25c 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")

#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#  Chapter:      A2.5 Water Balance and Drought
#  Checkpoint:   A25c
#  Authors:      Ate Poortinga, Quyen Nguyen, Nyein Soe Thwal, Andréa Puzzi Nicolau
#  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Import the Lower Mekong boundary.
mekongBasin = ee.FeatureCollection(
    'projects/gee-book/assets/A2-5/lowerMekongBasin')

# Center the map.
Map.centerObject(mekongBasin, 5)

# Add the Lower Mekong Basin boundary to the map.
Map.addLayer(mekongBasin, {}, 'Lower Mekong basin')

# Set start and end years.
startYear = 2010
endYear = 2020

# Create two date objects for start and end years.
startDate = ee.Date.fromYMD(startYear, 1, 1)
endDate = ee.Date.fromYMD(endYear + 1, 1, 1)

# Make a list with years.
years = ee.List.sequence(startYear, endYear)

# Make a list with months.
months = ee.List.sequence(1, 12)

# Import the CHIRPS dataset.
CHIRPS = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD')

# Filter for relevant time period.
CHIRPS = CHIRPS.filterDate(startDate, endDate)

# Import the MOD16 dataset.
mod16 = ee.ImageCollection('MODIS/006/MOD16A2').select('ET')

# Filter for relevant time period.
mod16 = mod16.filterDate(startDate, endDate)

# We apply a nested loop where we first iterate over
# the relevant years and then iterate over the relevant
# months. The function returns an image with P - ET
# for each month. A flatten is applied to convert an
# collection of collections into a single collection.
waterBalance = ee.ImageCollection.fromImages(

def func_egz(y):
        return months.map(function(m) {

            P = CHIRPS.filter(ee.Filter \
                    .calendarRange(y, y, 'year')) \
                .filter(ee.Filter.calendarRange(m, m,
                    'month')) \
                .sum()

            ET = mod16.filter(ee.Filter \
                    .calendarRange(y, y, 'year')) \
                .filter(ee.Filter.calendarRange(m, m,
                    'month')) \
                .sum() \
                .multiply(0.1)

            wb = P.subtract(ET).rename('wb')

            return wb.set('year', y) \
                .set('month', m) \
                .set('system:time_start', ee.Date \
                    .fromYMD(y, m, 1))

        })

    years.map(func_egz
).flatten()























).flatten()
)

# Add layer with monthly mean. note that we clip for the Mekong river basin.
balanceVis = {
    'min': -50,
    'max': 200,
    'palette': 'red, orange, yellow, blue, darkblue, purple'
}

Map.addLayer(waterBalance.mean().clip(mekongBasin),
    balanceVis,
    'Mean monthly water balance')

# Set the title and axis labels for the chart.
title = {
    'title': 'Monthly water balance',
    'hAxis': {
        'title': 'Time'
    },
    'vAxis': {
        'title': 'Evapotranspiration (mm)'
    },
    'colors': ['green']
}

# Plot the chart using the Mekong boundary.
chartMonthly = ui.Chart.image.seriesByRegion({
        'imageCollection': waterBalance,
        'regions': mekongBasin.geometry(),
        'reducer': ee.Reducer.mean(),
        'band': 'wb',
        'scale': 500,
        'xProperty': 'system:time_start'
    }).setSeriesNames(['WB']) \
    .setOptions(title) \
    .setChartType('ColumnChart')

# Print the chart.
print(chartMonthly)

# -----------------------------------------------------------------------------------
# CHECKPOINT
# -----------------------------------------------------------------------------------

# Add Earth Engine dataset image = ee.Image("USGS/SRTMGL1_003") # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Chapter: A2.5 Water Balance and Drought # Checkpoint: A25c # Authors: Ate Poortinga, Quyen Nguyen, Nyein Soe Thwal, Andréa Puzzi Nicolau # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Import the Lower Mekong boundary. mekongBasin = ee.FeatureCollection( 'projects/gee-book/assets/A2-5/lowerMekongBasin') # Center the map. Map.centerObject(mekongBasin, 5) # Add the Lower Mekong Basin boundary to the map. Map.addLayer(mekongBasin, {}, 'Lower Mekong basin') # Set start and end years. startYear = 2010 endYear = 2020 # Create two date objects for start and end years. startDate = ee.Date.fromYMD(startYear, 1, 1) endDate = ee.Date.fromYMD(endYear + 1, 1, 1) # Make a list with years. years = ee.List.sequence(startYear, endYear) # Make a list with months. months = ee.List.sequence(1, 12) # Import the CHIRPS dataset. CHIRPS = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD') # Filter for relevant time period. CHIRPS = CHIRPS.filterDate(startDate, endDate) # Import the MOD16 dataset. mod16 = ee.ImageCollection('MODIS/006/MOD16A2').select('ET') # Filter for relevant time period. mod16 = mod16.filterDate(startDate, endDate) # We apply a nested loop where we first iterate over # the relevant years and then iterate over the relevant # months. The function returns an image with P - ET # for each month. A flatten is applied to convert an # collection of collections into a single collection. waterBalance = ee.ImageCollection.fromImages( def func_egz(y): return months.map(function(m) { P = CHIRPS.filter(ee.Filter \ .calendarRange(y, y, 'year')) \ .filter(ee.Filter.calendarRange(m, m, 'month')) \ .sum() ET = mod16.filter(ee.Filter \ .calendarRange(y, y, 'year')) \ .filter(ee.Filter.calendarRange(m, m, 'month')) \ .sum() \ .multiply(0.1) wb = P.subtract(ET).rename('wb') return wb.set('year', y) \ .set('month', m) \ .set('system:time_start', ee.Date \ .fromYMD(y, m, 1)) }) years.map(func_egz ).flatten() ).flatten() ) # Add layer with monthly mean. note that we clip for the Mekong river basin. balanceVis = { 'min': -50, 'max': 200, 'palette': 'red, orange, yellow, blue, darkblue, purple' } Map.addLayer(waterBalance.mean().clip(mekongBasin), balanceVis, 'Mean monthly water balance') # Set the title and axis labels for the chart. title = { 'title': 'Monthly water balance', 'hAxis': { 'title': 'Time' }, 'vAxis': { 'title': 'Evapotranspiration (mm)' }, 'colors': ['green'] } # Plot the chart using the Mekong boundary. chartMonthly = ui.Chart.image.seriesByRegion({ 'imageCollection': waterBalance, 'regions': mekongBasin.geometry(), 'reducer': ee.Reducer.mean(), 'band': 'wb', 'scale': 500, 'xProperty': 'system:time_start' }).setSeriesNames(['WB']) \ .setOptions(title) \ .setChartType('ColumnChart') # Print the chart. print(chartMonthly) # ----------------------------------------------------------------------------------- # CHECKPOINT # -----------------------------------------------------------------------------------

Display the interactive map¶

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Map

Map