Artificial intelligence learns continental hydrology

Date:
August 27, 2020
Source:
GFZ GeoForschungsZentrum Potsdam, Helmholtz Centre
Summary:
The data sets on the Earth's gravitational field which are required
for this, stem from the GRACE and GRACE-FO satellite missions. Using
the South American continent as an example, Earth system modellers
have developed a new Deep-Learning-Method, which quantifies small
as well as large-scale changes to the water storage with the help
of satellite data.



FULL STORY ========================================================================== Changes to water masses which are stored on the continents can be
detected with the help of satellites. The data sets on the Earth's gravitational field which are required for this, stem from the GRACE and GRACE-FO satellite missions. As these data sets only include the typical large-scale mass anomalies, no conclusions about small scale structures,
such as the actual distribution of water masses in rivers and river
branches, are possible. Using the South American continent as an example,
the Earth system modellers at the German Research Centre for Geosciences
GFZ, have developed a new Deep-Learning-Method, which quantifies small
as well as large-scale changes to the water storage with the help
of satellite data. This new method cleverly combines Deep-Learning, hydrological models and Earth observations from gravimetry and altimetry.


==========================================================================
So far it is not precisely known, how much water a continent really
stores. The continental water masses are also constantly changing, thus affecting the Earth's rotation and acting as a link in the water cycle
between atmosphere and ocean. Amazon tributaries in Peru, for example,
carry huge amounts of water in some years, but only a fraction of it
in others. In addition to the water masses of rivers and other bodies
of fresh water, considerable amounts of water are also found in soil,
snow and underground reservoirs, which are difficult to quantify directly.

Now the research team around primary author Christopher Irrgang developed
a new method in order to draw conclusions on the stored water quantities
of the South American continent from the coarsely-resolved satellite
data. "For the so called down-scaling, we are using a convolutional neural network, in short CNN, in connection with a newly developed training
method," Irrgang says. "CNNs are particularly well suited for processing spatial Earth observations, because they can reliably extract recurrent patterns such as lines, edges or more complex shapes and characteristics."
In order to learn the connection between continental water storage
and the respective satellite observations, the CNN was trained with
simulation data of a numerical hydrological model over the period from
2003 until 2018.

Additionally, data from the satellite altimetry in the Amazon region was
used for validation. What is extraordinary, is that this CNN continuously
self- corrects and self-validates in order to make the most accurate
statements possible about the distribution of the water storage. "This
CNN therefore combines the advantages of numerical modelling with high-precision Earth observation" according to Irrgang.

The researchers' study shows that the new Deep-Learning-Method is
particularly reliable for the tropical regions north of the -20DEG
latitude on the South American continent, where rain forests, vast
surface waters and also large groundwater basins are located. Same
as for the groundwater-rich, western part of South America's southern
tip. The down-scaling works less well in dry and desert regions. This
can be explained by the comparably low variability of the already low
water storage there, which therefore only have a marginal effect on
the training of the neural network. However, for the Amazon region,
the researchers were able to show that the forecast of the validated
CNN was more accurate than the numerical model used.

In future, large-scale as well as regional analysis and forecasts of
the global continental water storage will be urgently needed. Further development of numerical models and the combination with innovative Deep-Learning-Methods will take up a more important role in this, in
order to gain comprehensive insight into continental hydrology. Aside
from purely geophysical investigations, there are many other possible applications, such as studying the impact of climate change on continental hydrology, the identification of stress factors for ecosystems such as
droughts or floods, and the development of water management strategies
for agricultural and urban regions.


========================================================================== Story Source: Materials provided by GFZ_GeoForschungsZentrum_Potsdam,_Helmholtz_Centre. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Christopher Irrgang, Jan Saynisch‐Wagner, Robert Dill, Eva
Boergens, Maik Thomas. Self‐validating deep learning for
recovering terrestrial water storage from gravity and altimetry
measurements.

Geophysical Research Letters, 2020; DOI: 10.1029/2020GL089258 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200827122115.htm

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