Plain language summary
Estimates of water flow in rivers are needed to manage water resources
and flood risk. However, many of the world’s rivers are not gauged,
limiting hydrological understanding of river response to changing
environmental conditions and storm events. Here, we demonstrate the use
of satellite-acquired video to compute river discharges by mapping
velocity based on the movement of surface water features from one video
frame to the next. Using a video of a flood on the River Tilpa,
Australia, our results agree to 0.3 - 15% of ground-based measurements.
Micro- and nano- satellite configurations that are light, cheap and can
be deployed to acquire video anywhere globally will contribute to
measuring discharge on ungauged rivers.
Introduction
Globally, 29% of the world’s population are exposed to flood risk and
insecure water supplies, yet knowledge of the river discharges upon
which flood risk and water resource management are based remains
inadequate (Rentschler and Salhab, 2020). Global monitoring networks for
quantifying river discharge are in decline and remain logistically and
politically difficult to access (King et al ., 2018; Lins, 2008;
Zakharova et al ., 2020). Satellite-based remote sensing
approaches to monitoring discharge are helping to alleviate these issues
and expanding the availability of discharge data globally.
Previous approaches to satellite-based river discharge monitoring
typically rely upon various statistical and hydraulic approximations to
make indirect estimates of river discharge from space. Popular amongst
these methods, satellite radar altimetry measures water elevations at
virtual river cross-sections (Revel et al ., 2023; Tarpanelliet al ., 2013; Zakharova et al ., 2020) and
near-simultaneous optical imagery infers water surface flow velocity
from space (Kääb et al ., 2019). Other satellite approaches have
relied on remote sensing of discharge (RSQ) algorithms, which retrieve
hydraulic variables such as stage from remotely sensed data and then
relate these quantities to river discharge (Q ) to supplement
gauge networks using hydrological models and data assimilation
techniques (e.g., Gleason and Durand, 2020; Riggs et al ., 2022).
These techniques are limited by relatively coarse spatial resolution and
the requirement for near-simultaneous satellite swath overlaps, which
limits global coverage. Sichangi et al . (2016) provide a detailed
review of indirect techniques to estimate river discharge including:
correlations of satellite-derived water surface area with discharge
rating curves (e.g., Pan et al ., 2016; Pavelsky, 2014), hydraulic
estimations (e.g. Bjerklie et al ., 2018; Durga Rao et al .,
2020), and the use of remote sensing data and scaling laws based on
at-many-stations hydraulic geometry (AMHG) (e.g., Barber and Gleason,
2018; Brinkerhoff et al ., 2019).
Optical satellite video sensors can record dynamic phenomena on the
earth’s surface at temporal and spatial resolutions unmatched by
traditional remote sensing sensors. Optical flow measurement algorithms
can estimate velocity by tracking the movement of visible features
between frames (e.g., Eltner et al ., 2019; Perks et al .,
2020), which supplemented with pre-existing channel bathymetry data,
could enable estimation of river discharge. Currently, satellite video
acquired by low earth orbiting sensors offer spatial resolutions (pixel
sizes) ranging from 0.9 – 1.2 m at frame rates up to 30 Hz (e.g. SkySat
(Bhushan et al ., 2021) and Jilin-1 (European Space Agency, 2022)
constellations). Optical satellite video sensors offer unmatched
temporal resolution for the observation of dynamic phenomena such as
floods. Inference of flow velocities using satellite video has
previously been demonstrated by Legleiter and Kinzel (2021), who
utilized up to 17 frames of cloud-free satellite video acquired by
Planet Labs SkySat constellation to estimate surface flow velocities on
the Tanana River in central Alaska. Surface flow velocities matched
measurements from a radar gage to within 8.65% and were further
assessed using asynchronously acquired acoustic Doppler current profiler
(aDcp) velocity data.
Here, we extend the estimation of space-borne velocities to introduce an
application of satellite video-based velocities for estimation of
discharge. We couple freely available, high-resolution topographic data
with velocity estimates derived from satellite video Large-Scale Image
Velocimetry (LSPIV) and some critical assumptions regarding channel
hydraulics to estimate flood discharge following monsoonal rainfall in
Darling River at Tilpa, Australia. Satellite video-derived velocity
estimates are assessed using hydraulic model simulations while discharge
estimates are compared with in-situ gauging station observations for
validation and accuracy assessment, respectively.
Study Area
The River Darling at Tilpa is located within the Murray-Darling basin,
with a 502,500 km2 drainage area (Matheson and Thoms,
2018; Murray-Darling Basin Authority, 2010) (Figure 1). The river basin
has a strongly episodic climate, with large floods followed by lengthy
dry spells due to the influence of the El-Niño-Southern Oscillation
(Grimaldi et al ., 2019). Prolonged rainfall across south-eastern
Australia from late February to early April 2022 led to a flood event
with a 5-year return period (Q = 722 m3s-1). This location is ideally suited to testing our
ability to measure river discharge using non-contact, image-based
velocity calculation techniques due to the availability of: (i)
cloud-free satellite video sensor overpass; (ii) high-resolution LiDAR
digital elevation model (DEM) that was acquired when the river bed was
dry and; (iii) gauged in-situ discharge observations at Tilpa (Station
number 425900; Water New South Wales,
https://realtimedata.waternsw.com.au/, last access: 6 March 2023)
.