Hydromorphological attributes such as flow width, water
extent, and gradient play an important role in river hydrological,
biogeochemical, and ecological processes and can help to predict river
conveyance capacity, discharge, and flow routing. While there are some river
width datasets at global or regional scales, they do not consider temporal
variation in river width and do not cover all Australian rivers. We combined
detailed mapping of 1.4 million river reaches across the Australian
continent with inundation frequency mapping from 27 years of Landsat
observations. From these, the average flow width at different recurrence
frequencies was calculated for all reaches, having a combined length of 3.3 million km. A parameter γ was proposed to describe the shape of the
frequency–width relationship and can be used to classify reaches by the
degree to which flow regime tends towards permanent, frequent, intermittent,
or ephemeral. Conventional scaling rules relating river width to gradient and
contributing catchment area and discharge were investigated, demonstrating
that such rules capture relatively little of the real-world variability.
Uncertainties mainly occur in multi-channel reaches and reaches with
unconnected water bodies. The calculated reach attributes are easily
combined with the river vector data in a GIS, which should be useful for
research and practical applications such as water resource management,
aquatic habitat enhancement, and river engineering and management. The
dataset is available at 10.25914/5c637a7449353 (Hou et al., 2019).
Introduction
Temporal and spatial information on river morphology is fundamental for
understanding CO2 and nutrient exchange, aquatic habitat distribution
and migration, fishery management, transportation, flooding hazards, and
hydrologic and hydrodynamic models (Miller et al., 2014).
Combining detailed data on river morphology with hydrological modelling, our
understanding of the flow and storage of water across spatio-temporal scales
can be improved further (Van Schaik et al., 2019). River morphology is
mainly controlled by eight variables: width, gradient, depth, velocity,
discharge, roughness, sediment size, and sediment load (Leopold et al.,
1964). In the platform dimension, river patterns have been categorized as
either single or anabranching channels. Laterally, active channels and
inactive channels can be further classified as straight, sinuous, meandering,
and braided forms (Nanson and Knighton, 1996). For a deeper
understanding of river morphology, Rosgen (1994) established a river
classification inventory system using delineation criteria or ranges in
different levels including the number of channels, entrenchment, width–depth
ratio, sinuosity, gradient, and channel material. Importantly, the
interaction between river channel and floodplain results in different river
morphology. For instance, the width of a channel changes in response to
formation and destruction of the floodplain, which subsequently alters bar
patterns as bar pattern is dominated by width–depth ratio (Kleinhans
and Van den Berg, 2011).
Among the variables affecting river morphology, river width is an essential
parameter to calculate river discharge, assess river conveyance capacity,
and improve river routing in models (Yamazaki et al., 2014). River
width at overbank flow level plays a significant role in delineating
inundated area, which affects water vapour fluxes and groundwater recharge
(Dadson et al., 2010; Pedinotti et al., 2012; Doble et al., 2014). From
flood inundation simulation with one-dimensional finite difference solutions
of the St. Venant equations to two-dimensional finite element and finite
difference models, the need for data on river morphology increases from
field survey measurements to continuous digital elevation models. Using a
low-resolution digital elevation model (DEM), details of flow channel features and connectivity cannot
be provided. Thus, river characteristics are not always represented well in
coarse-resolution grids used in large-scale river routing models
(Yamazaki et al., 2011). Although a high-resolution DEM can mitigate
this issue, there is an associated increase in computational cost and
sufficient computing resources are not always available. One way to deal
with this problem is to construct a simpler model structure (Bates
and De Roo, 2000). Another way is to parameterize sub-grid-scale topography
of river channels and floodplains in modelling (Neal et al., 2012;
Yamazaki et al., 2011). For example, Coe et al. (2008) considered
sub-grid-scale floodplain morphology (i.e. fractional flooding of grid
cells), which resulted in significant improvement in the simulations of
seasonal and inter-annual flooding.
The lack of detailed data on river characteristics often means that river
width is either ignored or left as a parameter for calibration, which may
increase model uncertainty and decrease accuracy (Andreadis et al.,
2013). River width may be set to a constant value without consideration of
spatial and temporal variations (Biancamaria et al., 2009).
Alternatively, river width may be estimated by empirical functions of
drainage area (Coe et al., 2008; Paiva et al., 2013) or river
discharge (Decharme et al., 2008; Getirana et al., 2012,
2013; Andreadis et al., 2013). However, river width estimates from empirical
functions cannot provide accurate representations of river reach morphology,
as relationships between river width and discharge or drainage area are
known to vary in different geomorphological and climate conditions
(Yamazaki et al., 2014). If river width is overestimated in
hydrological modelling, it may result in both overestimation of river
channel storage and underestimation of water storage on the floodplain and
vice versa (O'Loughlin et al., 2013). This will in turn cause
errors in the timing and location of flood wave and floodplain inundation
predictions.
The development of a more accurate and explicit river width dataset has been
approached in several ways. Pavelsky and Smith (2008) developed a
software tool, RivWidth, to automatically extract river width along a river
course, combining one channel mask distinguishing water pixels from
non-water pixels and another river mask describing areas within the river
boundary or outside it. Miller et al. (2014) and Allen and
Pavelsky (2015, 2018) successfully applied this pioneering approach to map
river width at mean discharge for the Mississippi River basin, North
American rivers, and the whole world, respectively. However, laborious
manual inspection and corrections are needed in pre-processing of the water
masks, which prohibit its automated application over large scales. For
example, undetected channels in water masks have to be drawn manually in
order to make the river network fully connected (Neal et al., 2012). To
address this issue, Yamazaki et al. (2014) applied an automated
algorithm to produce a global river width database for large rivers, GWD-LR,
using flow direction maps and water masks. Isikdogan et al. (2017)
also developed an automated analysis and mapping engine, RivMap, which is
able to delineate rivers and estimate river width, and used it to generate a
river width dataset for North America. However, none of these regional and
global datasets considers temporal variability of river width or river width
beyond overbank flow conditions.
Our aim was to develop a method for estimating temporal and spatial river
width dynamics and use these to find summary parameters to represent river
morphology characteristics that could support a classification of river type
over the Australian continent. River width dynamics at different recurrence
frequencies were estimated from 27-year time series of 25 m resolution
surface water extent maps from Landsat remote sensing (Mueller et
al., 2016) and a detailed Geographic Information System (GIS) database
containing all 1 410 404 river segments and 1 474 271 sub-catchments mapped
across Australia (Bureau of Meteorology, 2012a). The width estimates
were compared with the global river width dataset (Allen and Pavelsky,
2018) at average flow conditions in 218 river regions of Australia. We
analysed the relationships between river width and discharge, drainage area,
and gradient, and calculated the coefficient and exponent of a hydraulic
geometry equation for Australia. The river morphology parameters were
intended to provide a description of temporal river width dynamics relating
to the dominant flow regime (permanent, frequent, intermittent, or ephemeral).
We demonstrate the usefulness of our dataset by showing the longitudinal
profile of hydromorphological attributes for the main river channel of the
Murray River and other complex river systems in a dry, low-relief
environment.
Data and methodDataRiver and sub-catchment segments
The Australian Hydrological Geospatial Fabric (Geofabric) is a digital
database of spatial surface and groundwater features based on a GIS platform
that relates important hydrologic features such as catchments, rivers, lakes,
and aquifers (Bureau of Meteorology, 2012a). The Geofabric Surface
Network provides a consistent hydrological surface stream network which was
derived using the 9 s ANUDEM raster streams product (Bureau of
Meteorology, 2012b). The surface network has six attributes, namely network
stream, upstream network connectivity, downstream network connectivity,
network node, water body, and catchment area. Any information associated with
the surface network can be easily connected to features in other Geofabric
products, including surface cartography, surface catchment, groundwater
cartography, and hydrological reporting catchments and regions, for further
applications. The network stream is divided into major rivers and minor
rivers, and major rivers generally represent the main watercourses across
Australia (Fig. 1a and b). These rivers are further classified as flow
segments (natural rivers), water area segments (rivers passing through a
water body), and artificial segments (to keep the stream network connected).
The catchment refers to a sub-catchment corresponding to each river segment.
The network stream and catchment features are the main data we used to
extract information from surface water extent observations (Fig. 1d).
Illustration of the data used in the investigations: (a) true colour median-value composite of Landsat-8 data for a 15.7 km × 15.3 km
area centered on 31.62∘ S and 143.42∘ N; (b) river segments and corresponding
sub-catchment area from the Geofabric Surface Network; (c) WOfS water
summary showing the percentage of times surface water was
observed; and (d) overlay of the Geofabric onto WOfS (i.e. b and c).
Surface water extent observations
Water Observations from Space (WOfS) is a publicly accessible 25 m
resolution gridded dataset providing surface water persistence and
recurrence information for the Australian continent (Mueller et
al., 2016). This historical flood information product was developed using a
decision tree method on a combination of normalized difference indices and
corrected spectral band values from approximately 184 500 Landsat images
(Mueller et al., 2016). The Landsat images used to produce WOfS
were the Australian archives of Landsat-5 and Landsat-7 data, which were
derived from raw data using the USGS Landsat Product Generation System with
a spatial resolution of 0.00025∘ (approximately 25 m pixel
size) and cover 27 years from 1987 to 2014 (Mueller et al., 2016). Here we
used the water summary product from WOfS, which provides the recurrence
frequency of surface water occurring as a percentage of the number of times
the surface was clearly observed for each grid cell. For example, a
frequency of 5 % means surface water extent was detected on average once
in 20 clear-sky Landsat acquisitions for that given pixel (Fig. 1c). The
WOfS product reflects inundation extent for rivers at both in-channel and
overflow levels, but cannot relate inundated area to its associated
river regime directly (Fig. 1a and c). However, this can be achieved by the network
stream and catchment features from the Geofabric (Fig. 1d).
MethodRiver widths at different recurrence frequencies
We used Geofabric sub-catchment boundaries to divide the WOfS water summary
map into 1 474 271 segments (Fig. 1d). Of these, there is a total of
1 379 224 sub-catchments that have river segments. The strong agreement
between Geofabric water courses and WOfS surface water was demonstrated
by Mueller et al. (2016). The sub-catchment polygon is used to select the
area and extract information from the inundation frequency raster for its
corresponding river polyline. We calculated inundation extent at different
recurrence frequencies (Table 1) in each of these sub-catchments. This step
required considerable computational power, and so we used a high-performance
computer. Next, we estimated corresponding effective width for each
sub-catchment by dividing inundation extent at different recurrence
frequencies by the geodesic length of the river segment calculated from the
Geofabric using Vincenty's inverse method (Vincenty, 1975). In addition,
DEM information was extracted from the 1 s DEM, an elevation data
product developed by Geoscience Australia using the Shuttle Radar Topography
Mission (SRTM) data (Gallant et al., 2011), for the upstream
and downstream end points of each river segment. River gradient was
calculated by dividing the elevation difference between upstream and
downstream points by river length for each river segment. Finally, we
obtained estimated spatial and temporal river widths, and river gradients
for 1 379 224 river reaches across the Australian continent.
The categories of recurrence frequency for which
calculations were performed.
FrequencyDescriptionFrequencyDescription0 %Water not detected5 %Water detected 5 times in 100 observations0.1 %Water detected 1 time in 1000 observations8 %Water detected 8 times in 100 observations0.2 %Water detected 2 times in 1000 observations10 %Water detected 10 times in 100 observations0.3 %Water detected 3 times in 1000 observations20 %Water detected 20 times in 100 observations0.4 %Water detected 4 times in 1000 observations50 %Water detected 50 times in 100 observations0.5 %Water detected 5 times in 1000 observations80 %Water detected 80 times in 100 observations0.8 %Water detected 8 times in 1000 observations90 %Water detected 90 times in 100 observations1 %Water detected 1 time in 100 observations95 %Water detected 95 times in 100 observations2 %Water detected 2 times in 100 observations100 %Water detected always
We compared our spatial and temporal river width data to the Global River
Widths from Landsat (GRWL) dataset (Allen and Pavelsky, 2018) over
Australia. Allen and Pavelsky (2018) produced estimates of global river
width at approximately mean discharge, which is related to the month that
rivers are most likely to be at the mean discharge value from the nearest
gauging station for each Landsat tile. However, it is less likely to
represent the average conditions of ungauged river reaches as the distance
from the nearest gauging station increases; most river reaches around the
world are ungauged. Additionally, mean discharge does not correspond to
any particular recurrence frequency. Thus, to facilitate comparison between
the two sets of results, we calculated Spearman correlations between our
river width estimates at a frequency of 50 % and GRWL river widths
separately for the 218 river regions in Australia. High correlations mean
our river width data reflect the same relative width variations along the
river channel from upstream to downstream for a river region as the global
river width dataset.
In addition, upstream cumulative mean runoff of each river segment was
calculated based on the Australian Water Resources Assessment (AWRA)
landscape hydrology model (Van Dijk, 2010), which is used by
the Australian Bureau of Meteorology to operationally estimate daily water
balance components across Australia at a spatial resolution of
0.05 ∘× 0.05∘ (Frost et al., 2018). We
analysed the relationship between river width and upstream cumulative runoff
or upstream drainage area and also the correspondence between river width
and gradient for Australia. Furthermore, we established hydraulic geometry
power-law relationships that relate river width to upstream cumulative
runoff or upstream drainage area as follows:
1w=aQb,2w=cAd,
where w is river width (m), Q is cumulative upstream runoff (m3 s-1), and A is
upstream drainage area (km2). Coefficients a and c and exponents b and d
were fitted and compared to the results from published studies.
River morphology characteristics
The pixels in WOfS do not have equal numbers of clear observations, mainly
due to overlapping Landsat scenes, with minor influence of cloud and shadow
frequency. The number of clear observations in the overlapping scene areas
could be more than twice that in most of the rest areas. The estimated river
width is supposed to increase much more sharply in the overlapping areas
than the rest areas as recurrence changes towards very low ranges, even for
the same river reach, as a larger number of clear observations provide more
chances to catch the extreme conditions. As a result, the surface water
extent map shows oblique strips across the Australian continent for very low
recurrence frequencies. To standardize the width and analyse width dynamics
of rivers across Australia as a whole, a more homogeneous mapping was
desirable. To avoid these artefacts, we generated river width maps by
varying recurrence frequency from low to high until the majority of
artefacts disappeared. We regarded the resulting lowest-frequency river
width map without artefacts as representing the maximum river width. Next,
we standardized river widths at different recurrence frequencies by dividing
them by maximum river width, producing a width fraction representing the
ratio of river width at different recurrence frequencies to maximum width.
Fi=Wi/Wmax,
where Fi is width fraction at the ith frequency (Table 1), Wi is river
width (m) at the ith frequency, and Wmax is the maximum river width (m). The
relationship between width fraction and its corresponding frequency was
generally well-described by a Weibull distribution, and therefore we used
the Weibull inverse survival function (NIST/SEMATECH e-Handbook of Statistical Methods, 2018) to
fit the relationship for each river reach as follows:
Fi=(-ln(R))1/γ,
where R is the width fraction corresponding frequency i and γ is a parameter.
We used differential evolutions (Storn and Price, 1997) to estimate the
parameter for each river reach. The parameter γ reflects different
curve shapes for relationships between width fraction and frequency (Fig. 2) and was used to characterize river morphology for 1 379 224 river
reaches across the continent. The larger γ is, the more time the river
width is close to maximum width, and the closer to 0, the more time the
river width is close to minimum width. The characteristic curves for rivers
of invariable width show a horizontal line, which leads to infinitely large
estimates of γ. We empirically limited γ between 0 and 5,
which was suitable for the large majority of rivers in Australia.
Different river width distributions for different values
of the gamma parameter.
After γ was estimated for each river reach, we classified river
reaches into 10 categories according to their corresponding γ values
(Table 2). We selected median values of width fraction at certain recurrence
frequencies (Table 1), respectively, for each category, and used Eq. () to fit the relationship between width fraction and its corresponding
frequency for estimating γ values. Lastly, we used Eq. () and
estimated γ values to predict width fraction for each category, and
compared them with observed width fractions. The standard differences were
calculated for each category to evaluate their biases by the equation as
follows:
SD=∑(O-M)2n,
where O is the observation matrix, M is the estimate matrix, n is the number
of elements, and SD is the standard difference. This process was intended to
test whether the parameter γ represented recurrence–width
relationships for different river reaches well.
Ten categories of river reaches based on γ values.
Flow regimeEphemeral Intermittent Frequent Permanentγ0–0.050.05–0.10.1–0.20.2–0.40.4–0.60.6–0.80.8–11–1.51.5–33–5ResultsReach width–frequency distribution
Temporal observation frequency artefacts largely disappeared at 0.5 %
frequency. Hence, we selected river width at this frequency as the maximum
river width (Fig. 3a). We chose inundation at 80 % frequency to
represent minimum river extent (Fig. 3b). Large differences existed
between maximum and minimum widths (Fig. 3). Although there are many river
reaches across Australia, most of them are ephemeral. Most of the (semi-)permanent rivers are located along the northern and eastern coasts. There are
many ephemeral river reaches with very broad maximum widths in the tributary
catchments of the Murray–Darling Basin and in the interior Lake Eyre
catchment, as well as along the Gulf of Carpentaria. River reaches flowing
through a large water body, e.g. reservoir or lake, also have very large
calculated widths. We did not exclude these river reaches, because they keep
the river network connected and because such reaches are identified in the
Geofabric mapping and hence can be selected as required. Some regions showed
no meaningful river network (Fig. 3a). Most of these regions are arid
catchments with a sandy alluvial substrate. Presumably, this leads to an
infiltration capacity that is sufficient to prevent significant surface
runoff accumulation, whereas several of the catchments also contain recent
dune systems that have interrupted a pre-existing drainage network (Fig. 3a).
Maximum (a) and minimum (b) river width across Australia;
218 river regions delineated in grey in (b).
We divided river reaches into three categories based on their maximum width:
small (<25 m), medium (25–250 m), and large (>250 m).
It is noted that small rivers will not be narrower than 25 m along their
entire width; a non-zero width is calculated because some of the 25 m pixels
along the reach were mapped as inundated at 0.5 % maximum extent.
Therefore, we only list summary statistics here for medium and large rivers
(Table 3). The total length of reaches in different maximum and minimum
width classes is listed in Table 3, showing the same distribution pattern
regardless of whether segments flowing through water bodies are included or
excluded. The total length decreases as maximum and minimum river width
increases from 25 m up to more than 10 km. The total reach length in
different width ranges and at different recurrence frequencies (Fig. 4)
decreases by a factor of 21 as frequency increases from 0.5 % to 80 %. The
majority of river reaches are 25–250 m broad irrespective of frequency range
(Fig. 4).
The length percentages in different width ranges of maximum
(a) and minimum (b) river widths for Australia (all river segments include
flow segments and segments flowing through water bodies).
We compared river width at different recurrence frequencies with river
gradient for all river reaches, as well as with upstream drainage area and
cumulative runoff. For comparison with drainage area and runoff, we included
only major rivers because the contribution of upstream flow to minor rivers
can be uncertain, for example for anabranches and distributaries. Upstream
drainage area and total runoff best predicted maximum reach width, with
Pearson correlations of 0.52 and 0.43, respectively, while gradient showed
the expected negative relationship with maximum river width (Fig. 5).
Reflecting the most common method of river width estimation, we fitted
Eq. () to our data. We excluded river widths with small upstream
cumulative runoff (<104.5 m3 or 0.37 m3 s-1) due to
the influence of their sparse distribution on the overall relationship. This
resulted in a relationship with a coefficient a=13.17 and exponent
b=0.49. We found that reach gradient affects a significantly, with
decreasing width as gradient increases, but it did not appear to affect b,
except perhaps for the highest gradient class (>0.01, Fig. 6).
River width at 50 % frequency (i.e. median width) was compared with river
widths considered representative of “average” flows contained in the GRWL.
Among the 218 Australian river regions, only 111 regions had any inundated
river channel under “average” conditions in the GRWL river width dataset,
whereas all 218 river regions contained detected rivers in our analysis
results. The Spearman rank correlation between median river width and
river widths from the global dataset exceeded 0.6 for 68 % (75) of 111
river regions and exceeded 0.4 for 86 % (95) of them, suggesting that the two
datasets show reasonably similar relative width variations (Fig. 7).
The length percentages in different width ranges at
different recurrence frequencies for Australia.
The relationships between maximum river width and
cumulative upstream runoff (a), upstream drainage area (b), and river
gradient (c) (the colour coding is related to data counts with the highest in
dark blue and lowest in light blue, which corresponds to the histogram on
the axes; red dash line: the threshold (data below this threshold are
excluded due to the influence of their sparse distribution on the overall
relationship when we fitted Eq. to our data)).
The coefficients a and exponents b of the power scaling
relationship predicting maximum river width from upstream cumulative runoff
(Eq. 1) calculated for different reach gradients.
The Spearman rank correlations between our dataset and
the GRWL dataset in 111 river regions across Australia.
River hydromorphology classification
The parameter γ represents the degree to which rivers tend towards
permanent or ephemeral flow regimes. Most rivers, particularly the larger
rivers along the coast, flow through estuaries or other (quasi) permanent
water bodies, and hence are classified as permanent or frequent (γ>0.8, Fig. 8). Calculating the combined length of medium and
large river reaches in different γ ranges (Fig. 9) shows that the
majority fall within γ=0.1–0.6, indicating that the dominant
flow regime for Australian rivers is ephemeral or intermittent.
River morphology classification γ parameter values
for medium (a) and large (b) rivers for Australia.
The average frequency–width data and fitted curves for different γ
classes are shown in Fig. 10. The standard differences between observed
and fitted width fractions range from 0.0075 to 0.0725 (Table 4). Relative
width was overestimated for frequencies between 20 % and 50 % for γ0.6–1.5 and for frequencies between 5 % and 20 % for γ0.2–0.8.
Despite these biases, the curves generally capture the relationship between
width fraction and frequency well.
The length percentages of medium and large rivers for
different γ value ranges across Australia.
Curve characteristics for different river morphology
categories (line: prediction; dot: observation).
Qualitative evaluation
A longitudinal profile of river width, gradient, and flow regime parameter
γ for the main channel of the Murray River (Fig. 11) illustrates
several features in the data. Strong width variations between 0.5 % and
80 % frequency can occur, indicating that the river channel contains water
for most of the time (>50 % frequency) but contained within a
relatively narrow channel. River width increases significantly between
5 %–8 % and 1 %–2 % frequency, which coincides with the threshold for
overbank flows. The greatest widths are in reaches flowing through large
water bodies, such as Lake Hume (C; storage reservoir), Yarrawonga Weir (D;
impoundment), Lake Alexandrina (K; the terminal lake system), and other
wetlands (H and I). Where river flows are confined, widths do not change
much (e.g. G; Mildura). Where flow splits into multiple channels before
converging again, minimum channel width reduces (e.g. E and F), whereas
maximum width increases, corresponding to a broader floodplain. Between A and
B, river reaches have water for 5 %–8 % of the time in our data, but not
in the GRWL dataset (Allen and Pavelsky, 2018) (Fig. 11b). The GRWL
widths under “average” conditions are contained within the distribution in
our data, but for rather widely varying frequencies. A narrowing relative
difference between the 0.5 % and 80 % frequency widths corresponds to
higher γ values and vice versa (Fig. 11b). This shows that the
river morphology parameter γ can capture the river flow regime. In line
with Fig. 5c, river width increases as gradient decreases from upstream to
downstream overall (Fig. 11b and c).
Figure 12 illustrates some of the data characteristics and challenges in
complex river systems common in Australia's dry and low-relief environments.
During floods, all river channels are inundated and rivers are broad
(Fig. 12a). Only the main Darling River channel contains water frequently
(Fig. 12b), but is still very narrow during low flows (Fig. 12c). All
reaches have an ephemeral or intermittent flow regime (Fig. 12f) with
γ<0.8, with the southern Talyawalka Creek showing a more
ephemeral regime than the Darling River.
Validation of curves fitting for different river morphology
categories.
Longitudinal profile of the Murray River showing (a) location of river and selected locations; (b) profile of width at different
frequencies and river morphology parameter γ; and (c) profile of
river gradient. Values are averaged for 30 km sections along the river
channel. Letters indicate (A) source according to our data; (B) source
according to GRWL data (Allen and Pavelsky, 2018); (C) Lake Hume; (D)
Yarrawonga Weir; (E–F) anabranching locations; (G) Mildura; (H–I)
Lower Murray wetlands; and (J–K) Lake Alexandrina.
Illustration of data characteristics for an area
including the Darling River and adjoining Talyawalka Creek near Wilcannia
(NSW) (15.7 by 15.3 km centred on 31.62∘ S,
143.42∘ E). Shown are (a) maximum river width (0.5 %
frequency); (b) median width (50 %); (c) minimum river width (80 %); (d) least detected river width and (e) corresponding frequency; and (f) hydromorphology parameter γ.
Discussion
The Geofabric contains nearly 1.4 million river segments across the
Australian continent and provides valuable information on river path,
length, and contributing sub-catchment area. We were able to assign summary
metrics derived from spatial and temporal surface water extent information
contained in the satellite-derived WOfS to the river reaches and calculate
river widths at different recurrence frequencies. The Geofabric Surface
Network had a total length of 3.3 million km. Compared to HydroSHEDS
(Lehner et al., 2008), probably the most commonly used global
hydrological dataset, the Geofabric delineates a finer-resolution stream
network and describes the natural variation in drainage density and complex
distributary and anabranching drainage patterns better (Stein et
al., 2014). Nonetheless, the 9 s DEM resolution is still insufficient to
delineate all floodplains and river flow paths accurately. A new version of
the Geofabric at 1 s resolution has been produced for part of Australia, and
once available nationally could be used to improve derived river
characteristics. Similarly, the WOfS data will continue to receive updates.
Errors in the calculated river characteristics also derive from
uncertainties in WOfS inundation mapping. Narrow rivers, small water bodies,
and wetlands with vegetation cover may be missed in mapping, and conversely
topographical shadows in steep terrain or in high-rise cityscapes can be
misclassified as water. Noise in very clear water can also result in
misclassification (Mueller et al., 2016). There are also data gaps
in WOfS and occasional linear artefacts caused by the
Scan-Line-Corrector-Off (SLC-Off) problem in Landsat-7 (Mueller et
al., 2016). Nevertheless, the overall accuracy of the water classifier used
in WOfS was 97 %, which gives confidence in our derived metrics.
Our dataset has some advantages over existing datasets such as GRWL.
Firstly, it provides spatial and temporal information on river dynamics at
both in-channel and overbank flows. Secondly, if one river reach has
multiple channels, we calculated river width for each channel rather than
considering them a single channel. Thirdly, our data provide more detailed
information due to the finer river network contained in the Geofabric.
Finally, our product can readily be related to any hydrological feature in the
Geofabric for further application.
The relationship between river width and contributing catchment area,
cumulative runoff, and reach gradient can be compared to literature values.
The positive relationships of discharge–width and catchment area–width, and
negative relationship of width–gradient have also been demonstrated by
Frasson et al. (2019). Empirically relating drainage area to river width
(i.e. Eq. ), Coe et al. (2008) obtained a coefficient c=0.42 and
exponent d=0.59, whereas Paiva et al. (2013) found c of 0.35–3.75
and d of 0.36–0.63 for different river basins (Table 5). We found
intermediate values of c=0.91 and d=0.43. A more common way to estimate
river width is from mean annual discharge (i.e. Eq. ). Decharme et
al. (2008), Getirana et al. (2012, 2013), and Andreadis et
al. (2013) all assumed an exponent b=0.5, as suggested by Leopold
and Maddock (1953) and Leopold et al. (1964) (Table 5). We found very
similar values for the exponent. By contrast, the value of the coefficient
a varies widely between studies (Table 5). Getirana et al. (2012,
2013) used a high value of a=18 for the Amazon basin, whereas
Andreadis et al. (2013) used a=7.2 for their global application. We
estimated a=13.1 for all Australian reaches combined, but also found
evidence that a correlates with the river reach gradient (Fig. 6), which may help explain differences between previous studies.
Comparison of coefficients and exponents for Eqs. ()
and () between different studies.
ResearchEquationabcdScaleCoe et al. (2008)w=cAd––0.420.59AmazonPaiva et al. (2013)w=cAd––0.35–3.750.36–0.63AmazonDecharme et al. (2008)w=max(25,aQb)a=(10-4Qm,mouth+6)∗10.5––South AmericaGetirana et al. (2012, 2013)w=max(10,aQb)180.5––AmazonAndreadis et al. (2013)∗2w=aQb7.20.5±0.02––GlobeThis studyw=aQb;w=cAd13.170.490.910.43Australia
1 Suggested by Arora and Boer (1999) (Qm,mouth is the mean
annual discharge at the mouth of the river).
2 Based on the regression relation developed by Moody and Troutman (2002).
Although the empirical scaling functions discussed here can be used to
estimate river width based on drainage area or modelled runoff with modest
skill, there are also clear limitations. Firstly, they are not able to
estimate river widths at different recurrence frequencies. Secondly,
individual river reach with the same upstream drainage area or cumulative
runoff can have widely different river widths. For example, as mentioned, we
found evidence that a reach with a more gentle gradient can be expected to
be wider in comparison (Fig. 6), consistent with the Manning equation.
Thirdly, scaling equations cannot be applied to multi-channel rivers.
Therefore, rather than empirical functions, the river width–frequency
relationships derived here should help to improve the description of river
morphology in hydrological modelling.
Some uncertainties are inherent to the approach followed here and would
benefit from further research. Firstly, we calculated river width based on
inundation extent within the entire designated sub-catchment boundary for
each river reach. Although this excluded most unrelated water bodies and
other river channels, the water mapping may still include unconnected water
bodies, such as off-channel storages. This would cause overestimation of
river width. However, for analysing width dynamics, normally unconnected water
bodies remaining in the sub-catchment can be assumed to be part of the river
channel conceptually, because they generally merge with channels at low
recurrence frequencies (i.e. high flows) and separate at high recurrence frequencies (i.e. low
flows). If these nearby water bodies are removed, it could fail to detect
the maximum river width; if they are retained at high flows and removed at
low flows, there would be abrupt changes in river width. Secondly, although
the data provide detailed information on the width of individual channels in
anabranching river systems, the multiple channels will often merge into a
single channel during overbank flow events. Our data do not reflect this
merging and separating of channels at different flow levels, and it is
challenging to find a way to conceptually address this in the Geofabric
framework.
Besides, there are some uncertainties from input variables, including DEM,
runoff, and length. The SRTM-derived 1 s (approximately 30 m) DEM has
a root mean square (rms) error of 3.868 m, and its uncertainties include residual stripes,
broad-scale stripes, steps in elevation, large offsets along the edge of the
valley floor, noise due to the nature of the radar acquisition and
processing, incomplete removal of vegetation offsets and urban and built
infrastructure, and vegetation height overestimated (Gallant et al., 2011).
However, the majority of rivers flow on flat plains without vegetation cover,
and urban and built infrastructure and river gradients were only produced
for the main river reaches, presumably with wider channels, which reduce the
influence from uncertainties and limitations of DEM. Uncertainties from
input data, parameterization, and conceptual structure in the model could
affect runoff estimates, although the AWRA-L model has a strong documented
pedigree in runoff estimation in comparisons with gauge data (e.g. Van Dijk
and Warren, 2010; Frost et al., 2018). The raster–vector conversion
anomalies to produce the Geofabric lead to overestimation of the segment
length, which to some extent may counterbalance the overestimation of river width.
Looking beyond Australia, the method proposed here is applicable in any
region of the world where high-resolution inundation time series mapping is
feasible, and where good-quality DEM-derived river path, length, and
sub-catchment area data are available. Thus, it would seem feasible to use a
similar methodology to that employed here to develop a global river
hydromorphology dataset using global inundation time series Landsat mapping
produced by Donchyts et al. (2016), Pekel et al. (2016), or Jones (2019), for example.
Data availability
The river hydromorphology data are available at 10.25914/5c637a7449353 (Hou et al., 2019) and also can be
downloaded directly from http://wald.anu.edu.au/data/ (last access: 20 May 2019; ANU Centre for Water and Landscape Dynamics, 2019). The data
are in ASCII format, which can be directly joined to the Geofabric products,
including surface cartography, surface catchments, surface network,
groundwater cartography, and hydrological reporting catchments and regions.
The Geofabric Surface Network can be accessed from http://www.bom.gov.au/water/geofabric/ (last access: 20 May 2019; Bureau of Meteorology, 2012b). The instruction for using these
data can be found in the “readme” file. The data may be converted to any
format (e.g. shapefile or raster) and combined with other Geofabric data,
such as river name, length, feature type (nature, artificial, or water area
flow segment), hierarchy (major or minor rivers), flow direction, and
upstream drainage area.
Conclusions
We developed a river hydromorphology dataset for Australia by combining
surface water recurrence information from the WOfS Landsat-derived dynamic
water mapping product and GIS-based hydrological features from the
Australian Geofabric. Our data provide river widths at different recurrence
frequencies for 5.84×108 m river reaches across the
Australian continent. A river morphology parameter γ is proposed to
describe the shape of the width–frequency curve and can be interpreted as
the degree to which rivers tend towards permanent, frequent, intermittent, or
ephemeral. The majority of medium and large rivers in Australia have widths
between 25 and 250 m and show an ephemeral or intermittent flow regime. The
data show correlation between maximum river width and cumulative upstream
runoff, upstream drainage area, and gradient, in line with previously
published results. The hydromorphological dataset developed contains river
width dynamics, flow regime, and river gradient information for all
Australian river reaches. The data provide new opportunities to analyse
floodplain–river interactions at different scales and analyse the influence
of climate, hydrology, vegetation, and terrain on river morphology. Such an
understanding can help to predict future changes in landscape evolution in
response to e.g. climate change. The dataset developed here may also be
useful in providing fundamental information for understanding hydrological,
biogeochemical, and ecological processes in floodplain–river systems;
describing river width features in hydrological modelling; estimating river
depth and discharge; assessing river conveyance capacity; identifying
flooding-prone areas, and determining potential locations for
satellite-based river gauging (Hou et al., 2018).
Author contributions
JH and AIJMVD conceived the idea. AIJMVD, LJR, RAV, and NM guided the study. JH carried out the research and wrote the manuscript with contributions from all the co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Linking landscape organisation and hydrological functioning: from hypotheses and observations to concepts, models and understanding (HESS/ESSD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The authors acknowledge the Australian Bureau of Meteorology and Geoscience
Australia for developing the Australian Hydrological Geospatial Fabric
(Geofabric) and Water Observations from Space (WOfS). We are grateful to
Adam Lewis for his assistance in reviewing this paper. The first author
thanks the ANU-CSC (the Australian National University and the China
Scholarship Council) Scholarship for supporting his PhD study at the
Australian National University. Calculations were performed on the
high-performance computing system, Raijin, from the National Computational
Infrastructure (NCI), which is supported by the Australian Government. This
paper is published with the permission of the CEO, Geoscience Australia. We
also thank Loes van Schaik, George Allen, and the anonymous reviewer for
their helpful suggestions that improved the manuscript.
Review statement
This paper was edited by Loes van Schaik and reviewed by George Allen and one anonymous referee.
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