2.7 Monitoring Aral Sea level:

Using altimeter data for enclosed sea level monitoring.

The Aral Sea level has been shrinking since the 60s, due to irrigation. The recession is estimated as nearly half of its volume over the last fifteen years. In 1989, the Sea split into two basins, the Great and the Small Aral. The small Aral is now stabilized, due to a dam, and its level may even be rising, but the Great Aral is still drying up. Altimetry satellites measure sea level continuously over the long term and thus enable us to monitor variations in the Aral Sea.

Data used

We will take advantage of the more than 10 years continuous Topex/Poseidon dataset, and use the GDR-M 1Hz data, since the Aral Sea is (still) a big enough body of water that high-resolution data are not absolutely mandatory to study its variations.

Methodology

We will use the Basic Radar Altimetry Toolbox to have a loook at the Aral sea level variations along the years.

Geographic extraction

The exact coordinates of the actual Aral Sea have changed along the years, and the land mask provided within the data is no longer correct in that case (places flagged as "water" are now dry). Roughly, the Big (South) Aral is between 59°N to 61°N, and 44°E to 46°E

Data selection and BRAT Datasets definition

We have to select the track(s) we wish to work on (we do not need the whole cycles of T/P data).
To select the right track, you can have a look at the ground track maps that may be provided with the data handbook. You can also use a pass locator using Google Earth available through Aviso.
The "best" Topex/Poseidon track that passes over the Aral Sea is the #107 (up till cycle 365, when the satellite orbit was changed). We took all available pass 107 data file from cycle 010 to cycle 364.
For Envisat, you could use pass #126 or 253.

T/P (left) and Envisat (right) tracks over the Aral Sea

Name the dedicated BRAT workspace you are using for this job. Within this workspace, name your datasets; We will need 3 datasets: one for a cycle in 1992-1993 (e.g. cycle 010), one for a cycle in 2002 (e.g. 360), and one with the whole series of the pass #107.

BRAT Operations definition

In the 'Operations' menu, name your operation, then select your dataset and data. In 'Data mode' keep Y=F(X) for a plot and 'MEAN' selected.
Enter your data expression: you will be computing the sea level with respect to the geoid, as for the "Amazon" data use case. The expression using T/P GDR field names is:
sat_alt - h_alt - h_geo - h_set - h_pol - iono_cor - dry_corr - wet_h_rad
(satellite altitude - altimetric range - corrections)
name it, e.g., LLH (for "Lake Level Height").
For the X field the variable is 'Lon_Tra' when looking at one cycle, tim_moy_1 when working with the whole series.

Data editing

As we will see, data editing is in order, to remove some less accurate data. The formula we will be using is:
geo_bad_1.water_land_distribution == 0 && is_bounded(-130,(sat_alt-h_alt),100) && nval_h_alt >= 5 && is_bounded(0,rms_h_alt,0.1) && is_bounded(-2.5,dry_corr,-1.9) && is_bounded(-0.500,wet_h_rad,-0.001) && is_bounded(-0.400,iono_cor,0.040) && is_bounded(7,sigma0_k, 30) && is_bounded(-1,h_set,1) && is_bounded(-0.150,h_pol,0.150) && is_bounded(0,att_wvf,0.4)
( )
It is an adaptation of the Ocean data editing (provided within BRAT), with some fields removed.