Monitoring and forecasting drought through the assimilation of satellite water observations
Date
2018
Authors
Tian, Siyuan
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Abstract
Drought poses the greatest threat to freshwater availability and
food security, affecting larger areas for longer periods than any
other natural hazards. In many regions, droughts increase in
frequency and severity due to climate change. As a slow
developing natural disaster, better estimates of water
availability can be valuable for forecasting droughts and their
impacts on ecosystem, agriculture and food security. With
accurate knowledge of root-zone soil water and groundwater
dynamics, effective planning of water resources and agriculture
can be made months in advance. However, the simulated root-zone
soil moisture and groundwater are often highly uncertain due to
the unpredictable nature of soil water and groundwater dynamics
caused by human activities such as water extraction and
irrigation. Ground-based and remotely sensed measurements of
water content are often limited in both spatial coverage and
temporal resolution. Therefore, quantifying the change of water
availability and its impacts on vegetation conditions at large
scales remains largely unexplored.
In my study, contrasting satellite observations of water presence
over different vertical domains were assimilated into a global
water balance model, providing unprecedented accuracy of soil
moisture profile and groundwater storage estimates. The water
availability at different depths observed from soil moisture
(SMOS) and space gravity (GRACE) missions provides an opportunity
to separate total water storage vertically into different layers
through data assimilation. However, combining these two data sets
is challenging due to the disparity in temporal and spatial
resolution at both vertical and horizontal scales. SMOS provides
global high spatial and temporal resolution (i.e. 40 km2, 3-day)
near-surface (0-5cm) soil moisture estimates from microwave
brightness temperature observations. In contrast, the GRACE
mission provides accurate measurements of the entire vertically
integrated terrestrial water storage column, but it is
characterized by low spatial and temporal resolutions (i.e. 300km
x 300km, monthly). An ensemble Kalman smoother based global data
assimilation system was developed to resolve the discrepancy
between model and observations in space and time.
The use of data assimilation integrates these two measurements to
effectively constrain model simulations and to accurately
characterize the vertical distribution of water storage. Compared
with model estimates without the assimilation or single-variant
assimilation, joint assimilation typically led to more accurate
soil moisture profile and groundwater estimates with improved
consistency with in situ measurements. The improved water storage
estimates integrated over different depths were used to determine
the vegetation-accessible storage in association with vegetation
growth and surface greenness. Accessible storage reflects a
combination of vertical root distribution and soil properties,
and its spatial distribution correlates with aridity and
vegetation type. Skillful forecasts of vegetation conditions are
achievable several months in advance for most of the world's
drylands, which offers exciting new prospects for the improvement
of drought early warning systems to help reduce human suffering
and economical and environmental damage.
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Keywords
data assimilation, water balance model, drought forecasting, GRACE, SMOS
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