This tutorial presents a simple yet effective method for analyzing water budget dynamics in areas with limited ground data by using Earth observation data in Google Earth Engine.
Link to Google Earth Engine code: https://code.earthengine.google.com/13c8776e7d2370cbe8a5ff953c89fe75
1. Importing Mosul river basin boundary
The Mosul Dam is a large water reservoir located on the Tigris River, which originates in eastern Turkey and flows through Iraq. The water flow to the dam reservoir is supplied by the Tigris River, which discharges between 60 and 5000 m³/s of water. Since its construction in 1985, the outflow has ranged between 100 and 1000 m³/s. The Mosul dam reservoir is a shared river basin between Iraq and Turkey, with a drainage area of 52,000 km², of which 8% is in Iraq and 92% outside Iraq.
var
basin_boundary
=
ee
.
FeatureCollection
(
'projects/ee-nasagsfcsoils/assets/mosul_dissolve'
);
// Add basin boundary in the map.
Map
.
addLayer
(
basin_boundary
,
{},
'Area of interest'
);
Map
.
centerObject
(
basin_boundary
,
7
);
2. Importing soil moisture and precipitation datasets
The NASA-USDA Enhanced SMAP dataset integrates SMAP Level 2 soil moisture observations into a modified Palmer model using a 1-D Ensemble Kalman Filter, improving model-based soil moisture estimation, especially in areas with a lack of quality precipitation instruments. In this demonstration, we used surface and subsurface soil moisture, which are available from April, 2015 to August, 2022.
Global Precipitation Mission (GPM) is a satellite mission that observes rain and snow every three hours. The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the algorithm that provides rainfall estimates using data from all GPM instruments. In this demonstration, we used monthly GPM IMERG Final product , which is available from June, 2001 to September, 2021.
// Enhanced soil moisture datasets.
var
nasa_usda_smap
=
ee
.
ImageCollection
(
'NASA_USDA/HSL/SMAP10KM_soil_moisture'
);
// Precipitation datasets.
var
gpm_imerg
=
ee
.
ImageCollection
(
'NASA/GPM_L3/IMERG_MONTHLY_V06'
);
3. Define study period and functions
// Define study period.
var
startYear
=
2001
;
var
endYear
=
2022
;
var
startMonth
=
1
;
var
endMonth
=
12
;
var
startDate
=
ee
.
Date
.
fromYMD
(
startYear
,
startMonth
,
1
);
var
endDate
=
ee
.
Date
.
fromYMD
(
endYear
,
endMonth
,
31
);
var
years
=
ee
.
List
.
sequence
(
startYear
,
endYear
);
var
months
=
ee
.
List
.
sequence
(
1
,
12
);
// Define a function to convert GPM IMERG from mm/hr to mm/day.
var
gpmScale
=
function
(
image
)
{
return
image
.
multiply
(
24
)
.
copyProperties
(
image
,
[
'system:time_start'
]);
};
// Define a function to compute the anomaly for a given month.
var
computeAnomalyPrecipitation
=
function
(
image
)
{
// Get the month of the image.
var
year
=
image
.
date
().
get
(
'year'
);
var
month
=
image
.
date
().
get
(
'month'
);
// Get the corresponding reference image for the month.
var
referenceImage
=
meanMonthlyPrecipitation
.
filter
(
ee
.
Filter
.
eq
(
'month'
,
month
)).
first
();
// Check if the images have bands
var
hasBands
=
image
.
bandNames
().
size
().
gt
(
0
);
// Compute the anomaly by subtracting reference image from input image.
var
anomalyImage
=
ee
.
Algorithms
.
If
(
hasBands
,
image
.
subtract
(
referenceImage
),
image
);
return
ee
.
Image
(
anomalyImage
).
set
(
'system:time_start'
,
ee
.
Date
.
fromYMD
(
year
,
month
,
1
).
millis
());
};
4. Processing soil moisture datasets
We directly use anomalies soil moisture products from NASA-USDA Enhanced SMAP soil moisture.
// Anomalies surface and subsurface soil moisture (mm).
var
ssSusMa
=
nasa_usda_smap
.
filterDate
(
startDate
,
endDate
)
.
sort
(
'system:time_start'
,
true
)
// sort a collection in ascending order
.
select
([
'ssma'
,
'susma'
]);
// surface and subsurface soil moisture bands
// Compute monthly anomalies surface and subsurface soil moisture.
var
monthlySsSusMa
=
ee
.
ImageCollection
.
fromImages
(
years
.
map
(
function
(
y
)
{
return
months
.
map
(
function
(
m
)
{
var
filtered
=
ssSusMa
.
filter
(
ee
.
Filter
.
calendarRange
(
y
,
y
,
'year'
))
.
filter
(
ee
.
Filter
.
calendarRange
(
m
,
m
,
'month'
))
.
mean
();
return
filtered
.
set
(
'system:time_start'
,
ee
.
Date
.
fromYMD
(
y
,
m
,
1
).
millis
());
});
}).
flatten
()
);
5. Processing precipitation datasets
GPM IMERG does not have anomalies product. To calculate anomalies monthly precipitation time series from a monthly precipitation time series, subtract the mean value of each month across all years from the original value of that month in each year.
// Precipitation from monthly GPM IMERG Final product (mm/day).
var
rawMonthlyPrecipitation
=
gpm_imerg
.
select
(
'precipitation'
)
.
filterDate
(
startDate
,
endDate
)
.
sort
(
'system:time_start'
,
true
)
.
map
(
gpmScale
);
// convert rainfall unit from mm/hr to mm/d
// Make sure monthly precipitation has same duration as soil moisture.
var
monthlyPrecipitation
=
ee
.
ImageCollection
.
fromImages
(
years
.
map
(
function
(
y
)
{
return
months
.
map
(
function
(
m
)
{
var
filtered
=
rawMonthlyPrecipitation
.
filter
(
ee
.
Filter
.
calendarRange
(
y
,
y
,
'year'
))
.
filter
(
ee
.
Filter
.
calendarRange
(
m
,
m
,
'month'
))
.
mean
();
return
filtered
.
set
({
'month'
:
m
,
'system:time_start'
:
ee
.
Date
.
fromYMD
(
y
,
m
,
1
).
millis
()
});
});
}).
flatten
()
);
// Compute climatological monthly precipitation.
var
meanMonthlyPrecipitation
=
ee
.
ImageCollection
.
fromImages
(
ee
.
List
.
sequence
(
1
,
12
).
map
(
function
(
m
)
{
var
filtered
=
monthlyPrecipitation
.
filter
(
ee
.
Filter
.
eq
(
'month'
,
m
)).
mean
();
return
filtered
.
set
(
'month'
,
m
);
})
);
// Map the function over the monthly precipitation collection to compute
// the anomaly precipitation for each month.
var
monthlyPrecipitationAnomalies
=
monthlyPrecipitation
.
map
(
computeAnomalyPrecipitation
);
6. Plot anomalies time series for both soil moisture and precipitation
Use the ui.Chart.image.series
function to extract the time series of
soil moisture or precipitation pixel values from the soil moisture or
precipitation image and display the chart.
Create a plot that displays three anomaly time series for surface and subsurface soil moisture and precipitation, with soil moisture values on the primary y-axis and precipitation values on the secondary y-axis.
This anomalies time series analysis presents the overlap period between these two datasets, which could span from April, 2015 to September, 2021.
// Combine two image collections into one collection.
var
smpreDatasets
=
monthlySsSusMa
.
combine
(
monthlyPrecipitationAnomalies
);
print
(
'soil moisture and precipitation'
,
smpreDatasets
);
var
chart
=
ui
.
Chart
.
image
.
series
({
imageCollection
:
smpreDatasets
,
region
:
basin_boundary
,
scale
:
10000
,
xProperty
:
'system:time_start'
})
.
setSeriesNames
([
'surface SM'
,
'subsurface SM'
,
'precipitation'
])
.
setOptions
({
title
:
'Anomalies time series: surface soil moisture, sub-surface soil '
+
'moisture, and precipitation'
,
series
:
{
0
:
{
targetAxisIndex
:
0
,
type
:
'line'
,
lineWidth
:
3
,
pointSize
:
1
,
color
:
'#ffc61a'
},
1
:
{
targetAxisIndex
:
0
,
type
:
'line'
,
lineWidth
:
3
,
pointSize
:
1
,
lineDashStyle
:
[
2
,
2
],
color
:
'#330000'
},
2
:
{
targetAxisIndex
:
1
,
type
:
'line'
,
lineWidth
:
3
,
pointSize
:
1
,
lineDashStyle
:
[
4
,
4
],
color
:
'#1a1aff'
},
},
hAxis
:
{
title
:
'Date'
,
titleTextStyle
:
{
italic
:
false
,
bold
:
true
}
},
vAxes
:
{
0
:
{
title
:
'soil moisture (mm)'
,
baseline
:
0
,
titleTextStyle
:
{
bold
:
true
,
color
:
'#ffc61a'
},
viewWindow
:
{
min
:
-
4
,
max
:
4
}
},
1
:
{
title
:
'precipitation (mm)'
,
baseline
:
0
,
titleTextStyle
:
{
bold
:
true
,
color
:
'#1a1aff'
},
viewWindow
:
{
min
:
-
2.5
,
max
:
2.5
}
}
},
curveType
:
'function'
});
print
(
chart
);
7. Plot spatial distribution of soil moisture and precipitation anomalies
Based on the time series anomalies chart, we detected the most negative anomalies in soil moisture and precipitation in May 2021. The following code block displays spatial variations in these variables across the studied basin during that month. Pixels with more brown color have more negative values.
// Setup colors for soil moisture anomalies.
var
palette
=
[
'8c510a'
,
'bf812d'
,
'dfc27d'
,
'f6e8c3'
,
'ffffff'
,
'ffffff'
,
'c7eae5'
,
'80cdc1'
,
'35978f'
,
'01665e'
];
var
smVis
=
{
min
:
-
4
,
max
:
4
,
opacity
:
0.9
,
palette
:
palette
,
};
// Setup colors for precipitation anomalies.
var
preVis
=
{
min
:
-
3
,
max
:
3
,
opacity
:
0.9
,
palette
:
palette
,
};
// Filter soil moisture to May 2021, subset first image, and clip to AOI.
var
thisSsSusMa
=
monthlySsSusMa
.
filterDate
(
'2021-05-01'
,
'2021-05-31'
).
first
().
clip
(
basin_boundary
);
// Filter precipitation to May 2021, subset first image, and clip to AOI.
var
thisPre
=
monthlyPrecipitationAnomalies
.
filterDate
(
'2021-05-01'
,
'2021-05-31'
).
first
().
clip
(
basin_boundary
);
// Display the images on the map.
Map
.
addLayer
(
thisSsSusMa
.
select
(
'ssma'
),
smVis
,
'Surface Soil Moisture'
);
Map
.
addLayer
(
thisSsSusMa
.
select
(
'susma'
),
smVis
,
'Subsurface Soil Moisture'
);
Map
.
addLayer
(
thisPre
,
preVis
,
'Precipitation'
);
8. Remarks
In 2022, a 3,400-year-old city was discovered in Iraq after the water level in the Mosul reservoir dropped due to a severe drought. Using Earth observations, a prolonged drought was detected from mid-2020 to the end of 2021 by tracking the water balance flowing to the Mosul river reservoir. This basin is challenging to estimate the water budget with in-situ observations due to insufficient ground observations. Therefore, Earth observations are the only feasible way to have a completed picture of water availability throughout the basin.
This tutorial provides a practical approach for other regions which have similar limited-ground problems as the Mosul River does. There are, however, limited timeframes for NASA-USDA Enhanced SMAP and monthly GPM IMERG Final products. In lieu of real-time products, you can use NASA SMAP L3, SMAP L4, and GPM IMERG Late products instead.
9. References
A 3,400-year-old city in Iraq emerges from underwater after an extreme drought. https://www.cnn.com/2022/06/20/world/iraq-city-unearthed-drought-scn/index.html. Accessed Date: Mar 20, 2023
Albarakat, R., Le, M. H., & Lakshmi, V.,2022. Assessment of drought conditions over Iraqi transboundary rivers using FLDAS and satellite datasets. Journal of Hydrology: Regional Studies, 41, 101075. doi: 10.1016/j.ejrh.2022.101075
Sazib, N., Mladenova, I. and Bolten, J., 2018. Leveraging the google earth engine for drought assessment using global soil moisture data. Remote sensing, 10(8): 1265. doi:10.3390/rs10081265
Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC)
