Modeling multidecadal surface water inundation dynamics and
key drivers on large river basin scale using multiple time series
of Earth-observation and river flow data
, M. G. Tulbure
, and M. Broich
School of Biological, Earth and Environmental Science, University of New South Wales, Sydney, New South Wales,
Abstract Periodically inundated ﬂoodplain areas are hot spots of biodiversity and provide a broad range
of ecosystem services but have suffered alarming declines in recent history. Despite their importance, their
long-term surface water (SW) dynamics and hydroclimatic drivers remain poorly quantiﬁed on continental
scales. In this study, we used a 26 year time series of Landsat-derived SW maps in combination with river
ﬂow data from 68 gauges and spatial time series of rainfall, evapotranspiration and soil moisture to statisti-
cally model SW dynamics as a function of key drivers across Australia’s Murray-Darling Basin (1 million
). We ﬁtted generalized additive models for 18,521 individual modeling units made up of 10 310 km
grid cells, each split into ﬂoodplain, ﬂoodplain-lake, and nonﬂoodplain area. Average goodness of ﬁt of
models was high across ﬂoodplains and ﬂoodplain-lakes (r
>0.65), which were primarily driven by river
ﬂow, and was lower for nonﬂoodplain areas (r
>0.24), which were primarily driven by rainfall. Local climate
conditions were more relevant for SW dynamics in the northern compared to the southern basin and had
the highest inﬂuence in the least regulated and most extended ﬂoodplains. We further applied the models
of two contrasting ﬂoodplain areas to predict SW extents of cloud-affected time steps in the Landsat series
during the large 2010 ﬂoods with high validated accuracy (r
>0.97). Our framework is applicable to other
complex river basins across the world and enables a more detailed quantiﬁcation of large ﬂoods and drivers
of SW dynamics compared to existing methods.
Wetlands and other periodically inundated surface water (SW) areas are hot spots of biodiversity [Gopal,
2009; Pearson et al., 2013; Arthington et al., 2014] play a crucial role in global climatic, hydrologic, and bio-
geochemical cycles [Hamilton, 2010] and provide numerous ecosystem services of value to people [Maltby
and Acreman, 2011; Costanza et al., 2014]. In recent history however, increasing development of water
resources, land use transformations, and agricultural intensiﬁcation have led to an alarming disappearance
and decline of ﬂoodplains and other types of wetlands around the world [Finlayson et al., 1999; Lemly et al.,
2000; Jones et al., 2009; Costanza et al., 2014], with the most comprehensive recent review estimating a loss
of 69–75% of inland wetland area present in 1900 AD [Davidson, 2014]. Our study area, the semi-arid Mur-
ray-Darling Basin (MDB) in Australia, is a good example of this global trend, where intensive agricultural and
water resources development in the second half of the 20th century has led to deterioration of many of the
basins ﬂoodplain wetlands [Kingsford and Thomas, 2004]. This problem was further intensiﬁed by the recent
Millennium Drought (from mid-1990s to 2009), the most severe recorded hydrological drought in the MDB,
which was followed by widespread extreme ﬂooding in 2010/2011 commonly referred to as the La Nina
Floods [Leblanc et al., 2012].
As a result of the dramatic global loss in wetland area and the paramount value of terrestrial SW resources
in the face of widely proclaimed global freshwater crisis [V€
osmarty et al., 2010; Bakker, 2012; World
Economic Forum, 2015], the usage of remote sensing for quantifying the distribution and temporal variabili-
ty of SW over large areas and periods of time has become an important ﬁeld of research in recent years
[Alsdorf and Lettenmaier, 2003; Alsdorf et al., 2007; Papa et al., 2010; Tulbure and Broich, 2013; Bates et al.,
2014; Bierkens, 2015; Hu et al., 2015; Kuenzer et al., 2015; Lettenmaier et al., 2015; Pekel et al., 2016; Tulbure
et al., 2016; World Bank, 2016]. A better understanding of SW extent dynamics, that is the distribution of
We quantiﬁed the role of river ﬂow
and hydroclimatic drivers in surface
water dynamics across a large and
complex river basin
Integrating a 26 year Landsat time
series of surface water with ﬂow data
produced accurate statistical ﬂood
models for 18,521 areas
The role of hydroclimatic drivers in
ﬂooding dynamics differed across the
system and between different types
of water bodies
Heimhuber, V., M. G. Tulbure, and
M. Broich (2017), Modeling
multidecadal surface water inundation
dynamics and key drivers on large
river basin scale using multiple time
series of Earth-observation and river
ﬂow data, Water Resour. Res.,53,
Received 28 SEP 2016
Accepted 10 JAN 2017
Accepted article online 13 JAN 2017
C2017. American Geophysical Union.
All Rights Reserved.
HEIMHUBER ET AL. MODELING SURFACE WATER 1
Water Resources Research
inland water bodies through time, at continental scale can enable better management of water resources
[Alsdorf et al., 2003; Overton, 2005; Powell et al., 2008; Viney et al., 2014], assist in managing ﬂoods and
droughts [Sakamoto et al., 2007; Chen et al., 2013; Huang et al., 2014; Hu et al., 2015] or help to identify prior-
ity conservation areas by studying ecological connectivity across space and time [Tulbure et al., 2014;
Bishop-taylor et al., 2015]. Remotely sensed series of SW maps have further been used to quantify reservoir
storage [Gao et al., 2012; Zhang et al., 2014b] and to estimate wetland area in the context of malaria man-
agement [Midekisa et al., 2014].
Large-scale ﬂooding events are a critical component of the terrestrial water cycle [Alsdorf et al., 2007] but
their propagation through river systems and the corresponding dynamics remain poorly quantiﬁed on con-
tinental or global scales [Bates et al., 2014]. The traditional approach for quantifying the extent of ﬂoods is
through hydrodynamic modeling and increases in computation power and data availability have led to
major advances in continental and even global scale ﬂood modeling in recent years [Yamazaki et al., 2011;
Getirana et al., 2012; Neal et al., 2012, 2015; Cauduro et al., 2013; Sampson et al., 2015]. Schumann et al.
(2016) applied a hydrodynamic model to simulate ﬂoodplain inundation across Australia continuously over
a 40 year period. Nevertheless, parameterization of these models remains difﬁcult and accurate representa-
tion of complex river and ﬂoodplain topographies and the corresponding storage effects is still limited by
the quality of suitable data sets such as digital elevation models (DEMs) with global coverage [Bates et al.,
2014]. A promising alternative for quantifying and monitoring SW dynamics over large areas and long peri-
ods of time is the use of time series of satellite data [Alsdorf et al., 2007; World Bank, 2016], with the Landsat
and MODIS satellites currently representing the most commonly used data source [Musa et al., 2015;
Karpatne et al., 2016]. The unique spectral signature of SW in the infrared and visible bands allows for sepa-
ration between water and dry land surface by applying a suitable classiﬁcation technique to satellite imag-
ery, enabling the generation of time series of SW maps on large river basin [Tulbure and Broich, 2013; Huang
et al., 2014; Kuenzer et al., 2015; Tulbure et al., 2016], continental [Klein et al., 2014; Mueller et al., 2016], and
even global scales [Papa et al., 2010; Klein et al., 2015; Pekel et al., 2016].
Apart from providing a promising data source for calibrating large-scale hydrodynamic models [Karim et al.,
2011; Ticehurst et al., 2013], such series of SW maps derived from satellite imagery have previously been
used for developing statistical inundation models by empirically linking remotely sensed SW extents to river
ﬂow or stage [Costelloe et al., 2003; Gumbricht et al., 2004; Powell et al., 2008; Westra and De Wulf, 2009;
Frazier and Page, 2009; Ren et al., 2010; Jung et al., 2011; Leauthaud et al., 2013; Sims et al., 2014; Huang et al.,
2014; Ogilvie et al., 2015; Azman et al., 2016]. In addition to their high cost-beneﬁt ratio, satellite-based inun-
dation models are useful for a variety of applications including the prediction of wetland inundation extent
[Gumbricht et al., 2004; Overton, 2005; Westra and De Wulf, 2009; Huang et al., 2014] or the improvement of
environmental ﬂow strategies [Shaikh et al., 2001; Powell et al., 2008], a water management technique for
recovering ﬂoodplain ecosystem health by releasing ﬂushes of water from reservoirs. Most existing models,
however, are based on a single cohesive ﬂoodplain site, used only a limited number of manually selected
satellite images and did not account for lag times between river gauges and the area of inundation
response. Heimhuber et al.  addressed these limitations by developing a prototype framework for
modeling SW extent dynamics, depicted in a Landsat-derived time series of SW maps (1986–2011) [Tulbure
et al., 2016], continuously as a function of river ﬂow (Q), local rainfall (P), soil moisture (SM), and evapotrans-
piration (ET), yet to be tested and applied on large river basin scale. In this study, we built on and adapted
this framework to model SW extent dynamics and drivers across the entire MDB (1 million km
Like many large river basins, the MDB contains a broad variety of smaller river systems with unique climate
and hydromorphological characteristics, ﬂow and ﬂooding regimes as well as highly variable levels of river
regulation and water abstraction for irrigated agriculture and other uses [Murray-Darling Basin Authority
(MDBA), 2010; Leblanc et al., 2012]. All these aspects impose difﬁculties for accurate quantiﬁcation of the dis-
tribution and movement of SW across river and ﬂoodplain systems [Rudorff et al., 2014], which is key knowl-
edge for managing these resources more sustainably [World Bank, 2016]. Satellite-based time series of SW
extent, which are rapidly becoming more available [Karpatne et al., 2016], hold good potential for overcom-
ing these difﬁculties, but their application for modeling large-scale inundation processes remains poorly
explored [Bates et al., 2014]. Our study addresses this gap by integrating multiple novel time series of Earth-
observation and ground data for quantifying and modeling SW extent dynamics and drivers within a large
and complex river system. Speciﬁc objectives were to (1) statistically model inundation extent dynamics
Water Resources Research 10.1002/2016WR019858
HEIMHUBER ET AL. MODELING SURFACE WATER 2
across the MDB, (2) quantify the role of river ﬂow and local climate drivers (i.e., P,ET, and SM) in SW dynam-
ics, and (3) apply the models developed in (1) to predict SW extents for highly cloud-affected Landsat obser-
vations during the 2010/2011 La Nina Floods for two ﬂoodplains with contrasting ﬂooding regimes.
2. Materials and Methods
2.1. Study Area
The study area of this research is one of Australia’s largest river systems and the country’s bread basket, the
predominantly arid MDB (Figure 1). The MDB is often divided into a northern part covering the entire Dar-
ling River catchment until its conﬂuence with the Murray River and a southern part covering the remaining
area to the south [Leblanc et al., 2012; Huang et al., 2014]. The basin hosts almost 30,000 wetlands [MDBA,
2010] with 16 listed as ‘‘wetlands of international importance’’ [Ramsar Convention Secretariat, 2014] and
around 200 speciﬁed in the ‘‘Directory of Important Wetlands in Australia’’ [Environment Australia, 2001].
Together, all wetlands cover around 6% of the total area of the MDB and 89% of all wetland areas are ﬂood-
plains [Kingsford et al., 2004]. The system’s long-term average annual rainfall is 469 mm of which around
90% is lost due to evapotranspiration [Leblanc et al., 2012]. The MDB has a pronounced climate gradient
with average annual rainfall decreasing and climate variability and evapotranspiration increasing from S-E
to N-W [MDBA, 2010]. Due to this climate gradient and the diverse terrain, ﬂooding regimes differ
Figure 1. Location, major rivers [BOM, 2012], ecohydrological-zonation [Huang et al., 2013], and topography [CSIRO and Geoscience Australia, 2011] of the Murray-Darling Basin along
with the spatial framework used for modeling SW dynamics. Colored 10 310 km grid cells contained SW areas that are hydraulically connected (i.e., ﬂoodplain and ﬂoodplain-lake cate-
gory) to one of the 68 model gauges (colors of grid cells correspond to the color of their respective modeling gauge). For better readability of Figure 1a, irrigation areas [BRS, 2008] and
the ﬂoodplain and ﬂoodplain-lake SW categories (derived from Kingsford et al. ) are only shown in Figures 1b and 1c. The nonﬂoodplain category comprised all remaining areas
outside the two ﬂoodplain categories (colored grid cells and uncolored areas). Blue areas outside of grid cells in Figure 1a illustrate the location of mapped water bodies within the non-
ﬂoodplain SW category [Kingsford et al., 2004].
Water Resources Research 10.1002/2016WR019858
HEIMHUBER ET AL. MODELING SURFACE WATER 3
substantially across the MDB, with many of the ephemeral subcatchments in the northern basin having a
dry-land river ﬂow regime characterized by extreme variability with long dry spells, punctuated by large
ﬂood events [Bunn et al., 2006]. In the rivers of the southern basin, ﬂoods typically occur in winter and
spring as a result of reliable rainfall and snowmelt in the runoff generating catchment areas and can last for
several months at a time in the lower Murray River [Penton and Overton, 2007].
The very different hydrology of the northern compared to the southern basin is reﬂected in the fact that
despite the large size of its catchment, the Darling River only contributes about 14% of the total Murray Riv-
er inﬂows under natural conditions [Leblanc et al., 2012]. The MDB is a hot spot for agricultural activity with
irrigated agriculture accounting for about 60% of Australia’s total agricultural water use and the overall SW
use is about half of the MDB’s average annual water availability [Leblanc et al., 2012]. This very high level of
water usage and related water resources development has led to the deterioration of many of the basin riv-
er and ﬂoodplain ecosystems [Kingsford, 2000; Kingsford and Thomas, 2004; Leblanc et al., 2012].
2.2. Model Development and Validation
The following paragraphs provide a detailed description of the input data, the spatial modeling framework,
and the statistical techniques used for modeling SW extent as a function of key driver variables across the
MDB. In this study, we built on and adapted a prototype modeling framework that was developed for three
local river and ﬂoodplain systems in the MDB and is explained in detail in Heimhuber et al. . This
modeling framework is based on a segmentation of the river basin into spatial units that are suitable for
establishing meaningful statistical variable relationships. This segmentation accounts for the hydrological
structure of the river basin, the type of water body that is being modeled as well as for the hydraulic con-
nection of water bodies to a river with available gauge data. For each spatial unit, a statistical model
between a numerical time series of SW extent as the dependent variable and Q,P,ET, and SM as potential
driver variables was then ﬁtted and used to analyze the variable relationships.
2.2.1. Time Series of Surface Water Extent
For modeling SW extent holistically across the study area, we used an existing, currently unprecedented
Landsat-based time series of SW extent for the MDB [Tulbure et al., 2016]. This time series was derived from
all available Landsat 5 and 7 scenes between 1986 and 2011 (25,000) of the USGS/EROS L1T standard
product with less than 50% overall cloud cover by using random forest classiﬁcation models for water and
clouds. For each satellite observation time step, the series consisted of a raster ﬁle in which each pixel was
classiﬁed into water, dry-land, cloud or no-data. A rigorous accuracy assessment revealed overall classiﬁca-
tion accuracy of 99%. The median number of classiﬁed Landsat scenes per year per path/row was 13 images
as opposed to the potentially possible number of 22 scenes as a result of cloud cover [Tulbure et al., 2016].
2.2.2. Spatial Modeling Framework
Modeling SW extent dynamics consistently across the MDB required the development of a spatial modeling
framework. Here we built on a previously developed and tested prototype framework [Heimhuber et al.,
2016] to model SW dynamics across the entire river basin (Figure 1). To account for the hydrological struc-
ture of the study area, we used an existing ecohydrological (EH) zonation of the MDB into 89 zones with
uniform ecological and hydrological characteristics [Chen et al., 2012; Huang et al., 2013] as the major units
for analysis and modeling. We then combined this zonation with a regular 10 310 km grid, which enabled
the application of a consistent modeling approach across the river system. The MDB contains a large variety
of SW areas and even though the majority of wetlands are ﬂoodplains, not all ﬂoodplains have a direct
hydraulic connection to a river with available gauge data. To account for the different dynamics of different
type of SW areas, we categorized them into ﬂoodplains, ﬂoodplain-lakes, and nonﬂoodplain areas (hereafter
referred to as SW categories) based on the type of wetland and its connection to gauged rivers and mod-
eled them separately using a tailored modeling approach (see Figures 1b and 1c). It is important to note
here that this categorization did not intend to accurately deﬁne ﬂoodplain areas across the MDB. Instead, it
is intended to differentiate between SW areas (ﬂoodplains and lakes) that are primarily driven by the
dynamics of larger rivers with available gauge data and all remaining SW areas which are driven by more
local rainfall runoff processes. Hence, the nonﬂoodplain SW category contained a large variety of SW areas
including isolated wetland systems, lakes and areas of irrigated agriculture.
For generating the SW categorization, we ﬁrst selected 68 river gauges with long-term records to establish
homogenous gauge coverage across the MDB (Figure 1). We then used the Australian Geofabric [BOM,
2012], a fully connected and directed river network, to perform network routing operations and deﬁne
Water Resources Research 10.1002/2016WR019858
HEIMHUBER ET AL. MODELING SURFACE WATER 4
hydraulic connectivity between river gauges and grid cells that contained ﬂoodplain or lake areas as
deﬁned by an existing static wetland layer for the MDB [Kingsford et al., 2004] (see colored grid-cells in Figure
1). If the river network indicated that there was a hydraulic connection between a grid cell and the river
gauge, all ﬂoodplain areas in that cell were assigned to the ﬂoodplain category, and all lakes to the
ﬂoodplain-lake category. The river network did not always represent the hydraulic connectivity that may occur
on vast ﬂoodplains during times of ﬂooding. To address this limitation, we used a Landsat-based relative inun-
dation frequency layer [Tulbure et al., 2016] as an additional indicator for hydraulic connectivity to assign those
SW areas to the ﬂoodplain category that are only connected to gauged rivers during major ﬂooding events. In
some areas, this methodology led to numerous modeling grid cells with hydraulic connectivity to a gauge
that did not have any wetlands according to the static wetland layer but showed signiﬁcant SW dynamics in
the Landsat-based inundation frequency layer. For these grid cells that predominantly occurred in the very
N-E of the MDB, we considered all areas that were inundated at least once during the analysis period as ﬂood-
plain area, in order to capture and model the corresponding SW extent dynamics accordingly.
For generating the ﬁnal modeling units, we applied a buffer of 300 m to both the ﬂoodplain and the
ﬂoodplain-lake areas to ensure full coverage of ﬂoodplain areas that might inundate during the largest
ﬂoods. The resulting areas were then clipped to individual modeling units based on the regular grid and
the EH-zonation so that each grid cell potentially consisted of a combination of ﬂoodplain, ﬂoodplain-lake,
and nonﬂoodplain area. The resulting framework comprised a total of 4120 individual ﬂoodplain and
447 ﬂoodplain-lake modeling units across the MDB which were each linked to one of the 68 river gauges
(Figure 1). In addition, there were a total of 13,954 grid cells that contained nonﬂoodplain area.
2.2.3. River Flow Data
The river gauges that were used to model SW dynamics across the MDB were selected based on the follow-
ing considerations. The ﬂow data should cover the entire analysis period from 1986 to 2011, should be with-
out major gaps and there should roughly be at least one gauge per EH-zone. Out of the 68 selected gauges,
50 had full data coverage for the analysis period, nine covered between 20 and 25 years and nine less than
20 years, with the two most problematic ones having less than 10 years of data. Since the majority of
gauges had full or nearly full data coverage, we did not interpolate any ﬂow data and instead, only used
data for the exact period of coverage of each gauge in the modeling process. All ﬂow data were down-
loaded from respective government repositories [NSW Government, 2014; Government of SA, 2016; QLD
Government, 2016; State Government VIC, 2016].
2.2.4. Driver Variables and Data Preparation
For each modeling unit within each of the three SW categories, we derived a numerical time series of SW
extent in the form of the fraction of the percentage of SW area of the total area of the cloud-free modeling
unit. We dropped SW extents of Landsat observations, where more than 40% of the area of a modeling unit
was classiﬁed as cloud. The resulting time series of SW extent served as the dependent variable for each SW
category and was modeled as a function of all driver variables, using a dynamic multivariate regression
framework. In order to analyze the role of local climate conditions in SW dynamics, we used spatially explicit
daily time series of P,ET, and SM as additional driver variables (hereafter referred to as local climate drivers)
besides river ﬂow, which are all known to inﬂuence the magnitude and duration of ﬂoods [S
et al., 2004; Lamontagne and Herczeg, 2009]. The selection criteria for the corresponding data sets were that
they had to be spatially explicit and cover the entire analysis period. The Ptime series was based on interpo-
lation of gauge records throughout Australia [BOM, 2015] and the ET time series was modeled output of the
landscape component of the Australian Water Resource Assessment System (AWRA-L 4.5) continental scale
water balance model [Vaze et al., 2013; Viney et al., 2014]. The SM time series was based on a combination
of active and passive microwave data from multiple satellites [Liu et al., 2012; Wagner et al., 2012]. All three
local climate drivers were converted into numerical daily time series by computing spatial averages on the
level of the 10 310 km grid cells.
2.2.5. Statistical Modeling Approach
During the development of the basin-wide SW extent model, the original linear regression approach [Heim-
huber et al., 2016] was found not to accurately capture the large variety of SW extent to Qrelationships that
occurred on modeling units across the MDB. Therefore, we used generalized additive models (GAM) [Wood,
2011], which allowed us to model the nonlinear relationships between SW extent and Qusing a smooth
function, while the remaining explanatory variables were modeled additively based on linear regression.
The resulting model equation was:
Water Resources Research 10.1002/2016WR019858
HEIMHUBER ET AL. MODELING SURFACE WATER 5
is the SW extent (%) at time t,SWextent
is the SW extent of the previous available
Landsat observation (t21), sis the GAM smooth function, Lag(Q) is the discharge at the related gauge
lagged by the time it takes for a ﬂood to travel from the gauge to the respective modeling cell, Pis local
rainfall, ET is evapotranspiration, SM is soil moisture, and eis the error term. The equation used for modeling
SW extent dynamics on nonﬂoodplain areas is the same as equation (1) but without discharge as a predictor
variable, given that those areas are not hydraulically connected to a river. For each SW category, SW extent
was also modeled as a function of SW extent on the previous available satellite observation as a ﬁxed vari-
able, which is hereafter referred to as the lagged dependent variable. Lagged dependent variables are often
used for modeling time series data in economics [Keele and Kelly, 2006; Shumway and Stoffer, 2006] and
help to improve predictions when a process is known to depend on the state of the process as observed on
a previous time step, which is the case for SW extent dynamics. The smooth function in the GAM models is
essentially a combination of two or more polynomial curves, which are joined together at ‘‘knots,’’ with the
smoothness of the curve depending on a single smoothing parameter [Wood, 2011]. Due to the large num-
ber of modeling units, it was not feasible to deﬁne this smoothing parameter individually for each model.
Instead, we used a global smoothing parameter of four for all GAM models. Based on visual examination of
model ﬁts, this value led to good approximations of the variable relationship in a variety of different areas
of the MDB.
The local climate drivers were modeled linearly and in the form of sums for the 16 day time period before
each observation for Pand ET (mm/16 days) and a moving average for the same time period for SM (%).
While some variables (Qfor ﬂoodplain and ﬂoodplain-lakes and Pfor nonﬂoodplain models) and the lagged
dependent variable were always included in the models (hereafter referred to as ﬁxed variables), the local
climate drivers P,ET, and SM, respectively, were subject to an automated stepwise variable selection process
and only included in the models if adding them led to a reduction in root mean squared error (RMSE) in
ﬁvefold cross validation (CV). The variables were added to the model in the order given in equation (1). For
the nonﬂoodplain models, only ET and SM were subject to the automated variable selection process since P
was the ﬁxed driver for this model category, with Qdropped from these nonﬂoodplain models. To ensure
that only meaningful empirical relationships were modeled, we only used SW extent observations >0 and
set a minimum of 20 observations >0 as a threshold for ﬁtting a model. In addition, we also masked out res-
ervoirs with surface area of more than 5 km
for analysis. We quantiﬁed the performance of the statistical
models based on the adjusted r
and RMSE in ﬁvefold CV. The absolute and relative improvement in the
model’s RMSE in ﬁvefold CV after accounting for the selected local climate drivers was used to analyze the
role of these drivers in SW extent dynamics. The absolute improvement in RMSE was calculated as the dif-
ference in ﬁvefold CV RMSE before and after accounting for the local climate drivers and by dividing this dif-
ference by the initial model RMSE (before accounting for the additional drivers), we obtained the relative
improvement in RMSE. In addition, we also quantiﬁed the percentage of models in each zone, for which
each local climate driver was selected. The management of time series data and all statistical modeling was
implemented in R[R Development Core Team, 2008].
2.2.6. Quantifying Flood Travel Times
To account for the time that it takes for a ﬂood to travel from the gauge to the modeling unit, Qwas lagged
by a previously quantiﬁed lag time before including it into the models. This lag time for discharge (hereafter
referred to as Qlag) is the modeled timing between discharges recorded at the gauge and the correspond-
ing remotely sensed inundation response of a downstream or upstream ﬂoodplain or ﬂoodplain-lake
modeling unit. Qlags were quantiﬁed by ﬁtting GAM spline models between SW extent on the ﬂoodplain
and ﬂoodplain-lake units and Qlagged by each possible number of days between a lower limit of 220 and
an upper limit of 40 days, and selecting the number of days that led to the highest r
. For a detailed analysis
of the effect of Qlags as well as the suitability of GAM for capturing different Qto SW extent relationships,
we exempliﬁed four ﬂoodplain modeling units with different SW extent dynamics and distances to the
respective modeling gauges (see Ex-A to Ex-D in Figure 1).
2.2.7. Predicting Surface Water Extent
One promising application of statistical SW inundation models is that they can be used to predict SW extent
time steps that are not captured by the Landsat time series as a result of cloud cover. Here we used two of
the four previously selected example ﬂoodplain modeling units that have contrasting inundation regimes
Water Resources Research 10.1002/2016WR019858
HEIMHUBER ET AL. MODELING SURFACE WATER 6
(see Ex-A and Ex-B in Figure 1) and applied the statistical models to predict SW extents at a regular 8 day
time step during the 2010/2011 La Nina Floods. These predictions were implemented using the previously
ﬁtted GAM models and the predict function in R. We validated these predictions by calculating r
between predictions and Landsat observations of SW extent that were available during the selected time
3.1. Example Floodplain Modeling Units
In many areas across the basin, the automated quantiﬁcation of Qlags was a key step for establishing a
meaningful Qto SW extent relationship, to which the GAM models were then ﬁtted along with the other
driver variables. The relationship between Qand SW extent is highly dependent on the ﬂoodplain topog-
raphy [Frazier and Page, 2009] and was found to vary substantially across different ﬂoodplains in the
study area (Figure 2). The four example ﬂoodplain modeling units (Ex) were located 140 (Ex-A), 85 (Ex-B),
130 (Ex-C), and 40 km (Ex-D) from their respective modeling gauges and had Qlagsof11,5,12,and4
days, respectively, which equals to an average ﬂood propagation speed of 13 km/d. It can be seen that
the application of Qlags reduces the scatter in the data (comparing data points before (green) and after
(black) applying Qlags in Figure 2) and reveal distinctive curves, which indicate the ﬂows that are need-
ed to achieve a certain SW extent on the corresponding ﬂoodplain area. In comparison to the two Paroo
ﬂoodplain units (Ex-B and Ex-C), the Lower Murray ﬂoodplain unit (Ex-A) does not respond to increased
river ﬂows until a threshold of about 500 m
/s is reached and the ﬂoodplain starts to ﬁll up. In the Paroo
ﬂoodplain units, increasing river ﬂow immediately leads to increasing ﬂoodplain inundation until a satu-
ration point, above which only the highest peak ﬂows of the 26 year analysis period leads to further
increase in SW extent. It is also evident that the Qlag has a more pronounced effect on the variable rela-
tionship in the Paroo examples (Ex-B and Ex-C) as compared to the lower Murray (Ex-A) example, where
there is already little scatter in the relationship before applying a lag time. The Barwon River example
(Ex-D) shows a linear relationship for the entire range of recorded discharge values which was also cap-
tured well by the GAM model.
3.2. Basin-Wide Modeling
Only 2953 out of all 4120 individual ﬂoodplain modeling units exceeded the threshold of more than 20 >0
observations of SW extent that was set for ﬁtting statistical models. For the ﬂoodplain-lake category, 332
out of 447 units were modeled and 8926 out of all 13,954 nonﬂoodplain units (Table 1). For illustration pur-
poses, we averaged model metrics of individual modeling units spatially for the ﬂoodplain and ﬂoodplain-
lake categories based on zones that result from combining all grid cells that were modeled using the same
gauge (Figures 3 and 4). For the nonﬂoodplain models, we summarized the individual modeling results on
the basis of EH-zones (Figure 5). Despite using a single smoothing parameter for the GAM spline function,
the models were able to capture the large variety of Qto SW extent relationships occurring across the basin.
The average r
for all ﬂoodplain models within gauge connected cells was 0.65, with better average good-
ness of ﬁt in the northern (0.68) as compared to the southern (0.58) MDB (Figure 3a). Floodplain-lakes
showed a similar pattern in model performance with an overall average r
of 0.66 for the entire MDB and r
of 0.7 and 0.59 for the northern and southern basin, respectively (Figure 4a). In contrast to this, the non-
ﬂoodplain models had a low total-basin r
of 0.25 and slightly better goodness of ﬁt in the southern (0.28)
than in the northern (0.24) basin (Figure 5a). In this category, there was a noticeable pattern with the high-
predominantly occurring in the S-W part of the basin, where 27 adjacent EH-zones achieved an aver-
of 0.33 (see highlighted EH-zones in Figure 5a).
The relative improvement in model RMSE (ﬁvefold CV) resulting from the lagged dependent variable was
by far the highest in the ﬂoodplain-lake category, with 47% improvement as compared to 22% for ﬂood-
plains and 27% for nonﬂoodplain areas. The lagged dependent variable also led to the highest absolute
RMSE improvement in the ﬂoodplain-lake category with 0.15 compared to 0.05 for ﬂoodplains and 0.005 for
nonﬂoodplains and there was no noticeable difference between the northern and southern basin. In com-
parison to the improvements resulting from the lagged dependent variable, the improvements in RMSE
after adding the local climate drivers were small for all three model categories. The average absolute RMSE
reduction of the ﬂoodplain models across the entire basin was 0.002, with 0.0005 in the southern and
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HEIMHUBER ET AL. MODELING SURFACE WATER 7
0.0026 in the northern basin (compare Figure 3b). In this category, there was also a distinctive patch of 14
adjacent zones in the N-W of the basin with a particularly high average RMSE improvement of 0.0036 for
1160 individual ﬂoodplain units (see highlighted areas in Figure 3b). It is important to note here that RMSEs
of ﬂoodplain models also differed across the basin before accounting for the local climate drivers (initial
RMSE), with an average initial RMSE of 0.048 in the northern compared to 0.025 in the southern basin
To account for the different magnitude in RMSE of models prior to the variable selection process, we also
calculated the relative improvement in RMSE that was achieved through the local climate drivers (Figures
3c, 4c, and 5c). We found that the local climate drivers helped to improve predictive performance of ﬂood-
plain models by 5.5% in the northern compared to 2.0% in the southern basin with an overall average
improvement of 4.9% (Table 1). Furthermore, there were 280 individual ﬂoodplain models where the local
climate drivers led to RMSE improvement of more than 10% of which 251 were in the northern and 29 were
in the southern basin. The average initial RMSE of these 280 ﬂoodplain models prior to accounting for the
Figure 2. River ﬂow to SW extent relationship (black dots) and GAM model ﬁts (blue lines) for four example ﬂoodplain modeling units (Ex)
located within the Lower Murray (Ex-A), Paroo (Ex-B, Ex-C), and Barwon River (Ex-D) (location is given in Figure 1). Green dots illustrate the
same relationship before applying Qlag times.
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HEIMHUBER ET AL. MODELING SURFACE WATER 8
Table 1. Summary Table of the Statistical Modeling Results
and Part of Basin
Units Modeled r
Variable RMSE Without
(abs.) (rel.) (abs.) (rel.)
Floodplains All 4120 2953 0.65 0.0419 22% 0.0413 0.0393 0.0020 4.86% 280 9% 58% 36% 40%
Floodplains South 1242 874 0.58 0.0260 25% 0.0247 0.0243 0.0005 1.97% 29 3% 48% 32% 34%
Floodplains North 2878 2079 0.68 0.0510 21% 0.0483 0.0456 0.0026 5.48% 251 12% 63% 37% 43%
Floodplain-Lakes All 447 332 0.66 0.1467 47% 0.0809 0.0784 0.0025 3.04% 29 9% 54% 30% 41%
Floodplain-Lakes S 190 136 0.59 0.1166 41% 0.0734 0.0716 0.0019 2.54% 4 3% 53% 34% 32%
Floodplain-Lakes N 257 196 0.70 0.1675 50% 0.0888 0.0853 0.0035 3.89% 25 13% 54% 28% 48%
Nonﬂoodplains All 13954 8926 0.25 0.0053 27% 0.0752 0.0750 0.00015 0.20% 935 10% 48% 51%
Nonﬂoodplains S 5779 3281 0.28 0.0048 36% 0.0802 0.0801 0.00007 0.09% 251 8% 41% 41%
Nonﬂoodplains N 8175 5345 0.24 0.0056 22% 0.0541 0.0539 0.00016 0.29% 684 13% 49% 56%
Results are given separately for the three model categories (i.e., ﬂoodplains, ﬂoodplain-lakes, and nonﬂoodplain areas) as well as for the entire, northern and southern
Figure 3. Model statistics for the ﬂoodplain category across the Murray-Darling Basin, averaged over grid cells that used the same modeling gauge. Shown are zonal averages of (a) r
(b) the absolute, and (c) relative improvement in RMSE after adding the local climate driver variables and the percentage of models, for which adding (d) P, (e) ET, and (f) SM led to RMSE
improvement. For better illustration of patterns in the modeling results, the symbology was adapted individually for each result metric.
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HEIMHUBER ET AL. MODELING SURFACE WATER 9
local climate drivers was high with a RMSE of 0.070 compared to an overall average initial RMSE of 0.041 for
all models. This illustrates that large improvement in RMSE after accounting for the local climate drivers
may partially be linked to high initial model RMSEs. To understand this connection better, we regressed the
absolute RMSE improvement resulting from the local climate drivers against the initial RMSEs for all individ-
ual models. These regressions revealed an average r
of 0.2 for the ﬂoodplain and 0.3 for the nonﬂoodplain
category which indicates that despite a limited connection between these two metrics, high absolute
improvements after accounting for the local climate drivers can be achieved for low initial model RMSEs
and vice versa.
For ﬂoodplain-lakes, the average improvement in RMSE after accounting for the local climate drivers was
0.0025 across the basin and twice as high in the northern (0.0035) than in the southern (0.0019) basin. For
nonﬂoodplains, where Pwas a ﬁxed variable and only ET and SM were accounted for in the variable selec-
tion process, the average improvement was much lower as compared to the other two model categories
with a reduction in RMSE of 0.00015 across the basin and 0.00007 and 0.00016 for the southern and north-
ern basin respectively. While the distribution of r
-based model performance between the northern and
southern basin was the opposite between the ﬂoodplain and ﬂoodplain-lake categories and the nonﬂood-
plain category, the RMSE improvement after accounting for the local climate drivers was higher in the
northern basin for all three model categories.
Figure 4. Model statistics for the ﬂoodplain-lake category across the Murray-Darling Basin, averaged over grid cells that used the same modeling gauge. Shown are zonal averages of
, (b) the absolute, and (c) relative improvement in RMSE after adding the local climate driver variables and the percentage of models, for which adding (d) P, (e) ET, and (f) SM led to
RMSE improvement. For better illustration of patterns in the modeling results, the symbology was adapted individually for each result metric.
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HEIMHUBER ET AL. MODELING SURFACE WATER 10
Based on the agreement between absolute and relative improvements in RMSE, we found the north-
ern and N-W part of the MDB to represent a hot spot for the role of the local climate drivers in
explaining SW dynamics in both the ﬂoodplain (see Figures 3b and 3c) and ﬂoodplain-lake (see
Figures 4b and 4c) category. In comparison, many of the nonﬂoodplain areas that had the highest
absolute RMSE improvements showed the lowest relative improvements after accounting for the local
climate drivers (see Figures 5b and 5c). The corresponding zones contain large areas of irrigated agri-
culture, which inhibit the establishment of meaningful variable relationships due to the high level of
artiﬁcial SW areas that are subject to irrigation management strategies. The artiﬁcially increased inun-
dation frequency of the corresponding areas (see irrigation areas in Figures 5b and 5c) can be seen
when looking at the number of >0 observations in the nonﬂoodplain areas (Figure 5f), which is higher
in these areas compared to the surrounding areas. The resulting dynamics of these SW areas led to
poor model performance and consequently high model RMSEs, which make larger absolute improve-
mentsinRMSEmorelikelywhenaccounting for the local climate drivers. At the same time, the very
low relative improvements in RMSE indicate that the local climate drivers did not play signiﬁcant roles
in the dynamics of these artiﬁcial SW bodies. Instead, the relative RMSE improvements indicate that
similar to the other two model categories, the local climate drivers were most important for explaining
SW dynamics on nonﬂoodplain areas in the N-W part of the MDB.
Figure 5. Model statistics for the nonﬂoodplain SW category across the Murray-Darling Basin, averaged over the ecohydrological zones [Huang et al., 2013]. Shown are zonal averages of
, (b) the absolute, and (c) relative improvement in RMSE after adding the local climate driver variables along with irrigation areas [BRS, 2008] and the percentage of models, for
which adding (d) ET and (e) SM led to RMSE improvement. (f) The number of SW extent observations >0 of each modeling grid cell for the 26 year analysis period. For bett er illustration
of patterns in the modeling results, the symbology was adapted individually for each result metric.
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HEIMHUBER ET AL. MODELING SURFACE WATER 11
Both the absolute and relative improvements in the model’s predictive performance (RMSE improvement)
were based on each model’s individual combination of local climate drivers as determined in the variable
selection process. To get a better understanding of the role of each local climate driver, we also analyzed
their individual importance in general and across different zones in the MDB (d, e, f in Figures 3 and 4 and
d, e, in Figure 5). For both ﬂoodplain categories, Pwas the most important local climate driver and was
selected for 58% of all individual ﬂoodplain models as compared to 36% for ET and 40% for SM. There was a
noticeable difference in the role of Pfor explaining SW extent dynamics across the basin with 63% of all
ﬂoodplain model units in the northern basin accounting for Pcompared to 48% in the southern basin. P
helped to improve the predictive power of more than 70% of all ﬂoodplain models in many of the zones in
the N-W of the MDB (Figure 3d) with a maximum percentage of 77% within a zone with more than 50 indi-
vidual models in the zone that contains example cell Ex-A in Figure 1. The highest percentage within a zone
with more than 50 individual models where SM helped to explain SW extent dynamics on ﬂoodplains was
67% which was also achieved in the same zone.
Pwas selected for 54% of all ﬂoodplain-lake models and ET and SM for 30% and 41%, respectively. Similar
to the ﬂoodplain category, the highest percentages of models accounting for these drivers occurred in the
N-W of the MDB, which represented a hot spot for these local climate drivers. For the nonﬂoodplain catego-
ry, ET and SM were selected for about half of all models across the basin with a noticeable difference of
49% compared to 41% (ET) and 56% compared to 41% (SM) of models accounting for these drivers in the
northern and southern part of the basin, respectively. These ﬁndings indicate that Pplayed the most impor-
tant role for explaining SW dynamics on ﬂoodplains and ﬂoodplain-lakes followed by SM and ET. Additional-
ly, Pwas the ﬁxed driver variable for the nonﬂoodplain models and the RMSE improvements resulting from
the local climate drivers (ET and SM) were by far the smallest in this category which indicates that Palso
played the most important role in explaining SW dynamics across nonﬂoodplain areas. In summary, our
results suggest that the local climate drivers played a more important role in SW dynamics in the northern
compared to the southern basin for all three model categories, with the N-W of the MDB representing a hot
3.3. Modeling the 2010/2011 La Nina Floods
The extreme dynamics during the large 2010/2011 La Nina Floods revealed the strengths and limitations of
Landsat data and the statistical inundation models for quantifying SW extent dynamics during major ﬂood-
ing events. The very different ﬂooding dynamics of the two example ﬂoodplain modeling units can be seen
clearly with several short but intense ﬂood bursts in the Paroo site (Ex-C in Figure 6), which is located close
to the runoff generating catchment, as compared to the Lower Murray site (Ex-A in Figure 6), where the
large ﬂood lasted for over half a year (see Figure 1, for location of Ex-A and Ex-C). The Paroo example illus-
trates the limitation of Landsat for capturing the short and intense ﬂooding as a result of the comparatively
Figure 6. Comparison of the ability of statistical inundation models for quantifying SW extent dynamics during the 2010/2011 La Nina
ﬂoods based on two example ﬂoodplain modeling units with contrasting ﬂooding regimes (see Ex-A and Ex-C in Figure 1). Shown are the
hydrographs of the modeling gauge along with observed (green bars) and statistically modeled (black bars) SW extents. For each example
ﬂoodplain unit, r
and RMSE of the validation against observed Landsat-based SW extents during the analysis period are given.
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HEIMHUBER ET AL. MODELING SURFACE WATER 12
long satellite revisit interval and missing observations resulting from cloud cover. Even though this area in
the Paroo is located within overlapping satellite paths resulting in a potential time step of 8 days, there was
not a single observation with less than 50% cloud cover which would allow to quantify the ﬂooding dynam-
ics during the 1 month period of the largest ﬂood peak in March 2010. In both examples, the statistical inun-
dation models were able to overcome this limitation by predicting SW extents of the missing time steps
Validation of predicted SW extents (black bars in Figure 6) against the observed Landsat-based SW extents
(green bars in Figure 6) revealed an r
of 0.97 and a RMSE of 2% for the Paroo ﬂoodplain unit and an r
0.97 and RMSE of 4% for the Murray example. It can be seen that in the Murray example, the prediction of
the maximum SW extent during the 9 month period of the ﬂood (see 2011-03 in Figure 6a) is slightly bigger
than the highest available Landsat observation in the same period. This means that there was a SW extent
observation in a similar range during a different ﬂood before the illustrated time period to which the model
was trained as no predictions above the maximum satellite-observed SW extent were made. The statistical
inundation models captured the contrasting ﬂooding dynamics of the two example cells well and provided
predictions of SW extent at a regular interval of 8 days. Based on the error statistics of the validation time
steps, these predictions were a good approximation of the corresponding temporal ﬂooding dynamics for
both examples. Both of the example ﬂoodplain modeling units had high r
for their respective GAM model
ﬁts with an r
of 0.9 in the Paroo and 0.88 in the Murray example, which partly explains the high accuracy of
the predicted validation time steps. Considering the differences in goodness of ﬁt across the basin for all
three model categories (see Figures 3a, 4a, and 5a), the accuracy of predicting missing time steps is likely to
vary correspondingly for different modeling units and would likely lead to the least accurate predictions for
the nonﬂoodplain category.
4.1. Modeling Framework
Despite the growing need for better understanding the role of periodically inundated SW areas in the ter-
restrial water cycle and the Earth system in general, the propagation of large ﬂoods through river systems
and the corresponding SW dynamics remain poorly quantiﬁed on continental or global scales [Bates et al.,
2014]. Here we used an unprecedented Landsat time series of SW maps to quantify inundation dynamics
and the role of local climate drivers across a large and highly regulated river system. We quantiﬁed these
complex dynamics by building on a transferable grid-based framework [Heimhuber et al., 2016] and devel-
oped a model that comprised 68 river gauges and 18,521 individual spatial modeling units among three
SW categories (i.e., ﬂoodplains, ﬂoodplain-lakes, and nonﬂoodplains), out of which 12,211 exceeded the
threshold of 20 >0 observations of SW extent. The development of this framework required a variety of
ambiguous decisions in some areas such as selecting the most suitable gauge out of multiple candidates or
deﬁning hydraulic connectivity between model gauges and grid cells on the edge of large and scattered
ﬂoodplains (i.e., top right corner in Figure 1b). Additionally, despite using comparatively small modeling
units, the framework was still a simpliﬁed approximation of the complex hydrological and hydraulic struc-
ture of the river system. Nevertheless, it enabled us to capture and model the spatiotemporal dynamics of
large ﬂoods and their propagation through the river system at an unprecedented level of detail.
The majority of all SW areas in the MDB are ﬂoodplains and our modeling framework achieved high average
goodness of ﬁt (r
0.65) for the ﬂoodplain and ﬂoodplain-lake model categories. A key step for achieving
good model performance across the large variety of river and ﬂoodplain locations was the quantiﬁcation of
Qlags. In many areas of the basin, meaningful variable relationships were revealed only after quantifying
and applying these Qlag times to the river ﬂow data prior to ﬁtting the models (i.e., Figure 2). Although this
automated quantiﬁcation can lead to implausible Qlags for some individual modeling units [Heimhuber
et al., 2016], the basin-wide estimation of ﬂow travel times that this study provides is an important step
toward quantifying the propagation of ﬂoods across large river systems. Furthermore, the application of Q
lags allowed us to provide statistical models of SW extent for all major river and ﬂoodplain systems across
the basin, which are essentially rating curves that relate ﬂow in the river to the corresponding inundation
extent response on the modeling unit (i.e., Figure 2). In an unprecedented effort to restore the health of the
MDB’s water-dependent ecosystems, the Australian Government is currently undertaking large investments
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HEIMHUBER ET AL. MODELING SURFACE WATER 13
in buying back water licenses and improving irrigation efﬁciencies to increase the share of water available
for environmental ﬂows in the MDB [Burke, 2007; Banks and Docker, 2014]. In the context of this effort, our
whole-of-basin inundation response models can provide useful knowledge for managing the distribution of
environmental ﬂow water across the MDB holistically and more effectively [Powell et al., 2008; Chen et al.,
2011; Sims et al., 2014].
4.2. Surface Water Categories
The unique dynamics of different type of SW bodies have previously been addressed by either focusing
study sites entirely on lakes [Gao et al., 2012; Wang et al., 2014; Zhang et al., 2014b; Hu et al., 2015]or ﬂood-
plains [Frazier and Page, 2009; Jung et al., 2011; Sims et al., 2014], or by using large spatial modeling units
and modeling all SW areas simultaneously [Huang et al., 2014]. We found that nonﬂoodplain areas had by
far the lowest model performance (average r
of 0.24), which means that modeling all SW areas within a giv-
en EH-zone simultaneously would have resulted in lower average goodness of ﬁt across the basin. The poor
performance of this category may partly be owed to the fact that the corresponding SW areas were mod-
eled as a function of Pover the grid cell as the ﬁxed driver variable instead of Qso that potentially occurring
lateral inﬂows of SW through the ungauged local drainage network into the modeling unit were not
accounted for. This category also covered all areas that were not part of the two ﬂoodplain categories and
hence, comprised a wide range of natural and artiﬁcial SW bodies such as farm dams, irrigation paddies,
and isolated wetland systems. Additionally, many nonﬂoodplain areas did not exhibit sufﬁcient SW dynam-
ics for establishing meaningful empirical relationships. In contrast to this, the S-W of the MDB contains
numerous isolated ephemeral river and wetland systems (see blue areas outside of colored grid cells in
Figure 1a) that seem to have a pronounced relationship with Pand had comparatively good model perfor-
mance (i.e., Figure 5a). Another major difference that we found between the three model categories was
that the lagged dependent variable was clearly most important for explaining SW extent dynamics in the
ﬂoodplain-lake category compared to the other two categories as a result of the slow changes in SW area
that occur on lakes. This implies that modeling applications focusing on more static SW areas such as lakes
and reservoirs can particularly beneﬁt from statistical methods designed for dynamic processes such as the
regression-based lagged dependent variable approach used in this study or Auto Regressive Integrated
Moving Average (ARIMA) models. All these differences between the three model categories illustrate that
future studies should take into account that SW areas of large and complex rivers systems can have highly
variable dynamics that may require tailored modeling approaches.
4.3. Local Climate Drivers
We found that Pwas the most important local climate driver for explaining SW extent dynamics in all three
model categories, followed by SM and ET, which may partly be the result of the different nature of the data
sets (i.e., observed versus modeled) that we used for representing these drivers [Heimhuber et al., 2016]. In
addition, Pis arguably the main input of water to a modeling unit despite river ﬂow, which might be anoth-
er reason for the higher importance of this variable. We also quantiﬁed the combined role of the local cli-
mate drivers for SW dynamics and found that they played a more relevant role in the northern as compared
to the southern MDB with a noticeable hot spot in the N-W. It is important to note that the improvements
in model RMSE achieved through the local climate drivers were generally small and hardly ever exceeded
10%. Nevertheless, the model results showed distinctive spatial patterns that were in line with key site char-
acteristics of the MDB, which are likely to have an effect on the role of local climate drivers in large-scale
The highest levels of river regulation are found in the mountainous areas along the S-E and eastern bound-
ary of the basin [CSIRO, 2008; MDBA, 2010] and the adjacent zones to the west have large areas of irrigated
agriculture (i.e., Figure 5b). These areas are affected by high levels of SW abstraction and diversion, which
highly alters river ﬂow and ﬂoodplain inundation regimes and may consequently inhibit the establishment
of meaningful statistical relationships between the model variables. In comparison, the Paroo and Warregoo
river systems in the very N-W of the MDB have little to no intervention to their natural ﬂow regimes, are of
ephemeral and, in the case of the Paroo, also of terminal nature [MDBA, 2010], which means that the drying
of inundated ﬂoodplains is solely the result of inﬁltration and evapotranspiration. Additionally, the ﬂood-
plains of these river systems are extensive, shallow, and sparsely vegetated as compared to many of the
southern basin’s ﬂoodplains, which are often more conﬁned [Tulbure et al., 2016] and covered with
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HEIMHUBER ET AL. MODELING SURFACE WATER 14
ﬂoodplain forest. It is likely that the increased importance of the local climate drivers in the N-W of the basin
is the result of a combination of these characteristics. However, we applied a simpliﬁed data-driven empiri-
cal modeling approach for quantifying complex land surface processes and further analyses are needed to
reinforce these ﬁndings on the role of hydroclimatic site characteristics in SW extent dynamics. It is well
known that the local climate characteristics before and during ﬂooding such as P,ET, and SM can inﬂuence
the extent and duration of large-scale inundation processes [Overton et al., 2006; World Bank, 2016] but to
our best knowledge, their role in long-term SW extent dynamics has not been quantiﬁed across a large river
basin to date. Future work on SW dynamics should take into account that local Pand other local climate
drivers can play an important role in the ﬂooding and drying dynamics of certain SW areas in addition to
lateral inﬂows and outﬂows of SW.
4.4. Quantifying Surface Water Inundation Dynamics
Traditionally, the inundation characteristics of an area have been quantiﬁed based on the statistical return
period of ﬂoods. The corresponding maximum ﬂood extents are typically obtained through hydrodynamic
modeling for a range of relevant return periods (i.e., 5, 25, and 100 years) and incorporated into ﬂood haz-
ard maps, which provide useful and well known tools for water resources and disaster management [Heim-
huber et al., 2015]. This physically based approach has been upscaled to generate return period ﬂood
hazard maps at 90 m resolution for the whole terrestrial land surface between 568S and 608N[Sampson
et al., 2015]. There have been recent advances in improving the representation of complex ﬂoodplain top-
ographies and corresponding interactions between rivers and their adjacent ﬂoodplains in large-scale
hydrodynamic models [Yamazaki et al., 2011; Neal et al., 2012, 2015; Sampson et al., 2015]. In addition, a
recent study [Schumann et al., 2016] has applied a hydrodynamic model to generate a 40 year climatology
of ﬂoodplain inundation dynamics across Australia and found good agreement with total inundated areas
derived from a Landsat-based time series of SW extent. Nevertheless, these large-scale hydrodynamic mod-
els are still limited for accurately quantifying the complex and ﬁne-scaled dynamics of periodically inundat-
ed and potentially shifting SW areas across large regulated river basins and over long periods of time
[Alsdorf and Lettenmaier, 2003].
In comparison to hydrodynamic modeling, satellite-based approaches typically do not provide information
about the return periods of ﬂoods. Instead, SW dynamics are characterized based on composite ﬂooding
frequency maps which are derived directly from a series of classiﬁed images. Landsat-based studies have
expressed inundation frequency as the number of times a given pixel was ﬂagged as water, divided by the
total number of satellite observations during a given year or the entire analysis period [Mueller et al., 2016;
Tulbure et al., 2016]. One of the limitations with this approach is that ﬂooding may be underestimated, since
there are typically less cloud-free observations available during ﬂoods and peak ﬂood extents are often
missed as a result of the 16 day revisit cycle of the Landsat satellite. As an alternative, MODIS imagery has
been used to create daily series of SW maps which were combined into temporally consistent composite
maps, showing the number of days that every pixel was inundated during a given year [Klein et al., 2015;
Kuenzer et al., 2015]. CSIRO has combined MODIS-based SW maps with river ﬂow data, on which frequency
analysis was performed, to generate return period inundation maps for the MDB [Huang et al., 2014].
Despite their potentially achievable daily time step, MODIS-based SW time series are limited by their coarse
250 or 500 m resolution as well as their, in comparison to Landsat, lower SW classiﬁcation accuracies and
limitations for capturing ﬁne-scaled water bodies and inundation patterns [Chen et al., 2013]. It is evident,
that there is currently no validated method available for quantifying SW inundation dynamics continuously
at a high Landsat-like resolution on subcontinental or continental scale.
Our study partly addresses this limitation by using a Landsat-based time series of SW maps to provide mod-
els that statistically relate SW extent to river ﬂow and other hydrological key drivers, for which long-term
daily data is available. The resulting statistical models can predict SW extent for each individual ﬂoodplain
and ﬂoodplain-lake modeling unit (max. 10 310 km) at a regular 8 or 16 day time step, which can lead to
substantially improved quantiﬁcation of the temporal dynamics of large ﬂoods (i.e., Figure 6) as compared
to using only cloud-free satellite observations. One limitation of this approach was that estimation of predic-
tive accuracy was based on validation against a limited number of available SW extent observations. In
addition, the accuracy of predictions partly depends on the goodness of ﬁt of each individual model, which
was variable across the MDB but predominantly high for all gauge connected SW areas.
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HEIMHUBER ET AL. MODELING SURFACE WATER 15
Assuming that a certain numerical SW extent always corresponds to the same distribution of SW across the
modeling unit, statistical predictions of SW extent could be converted into SW maps, to generate a spatial
time series with regular time step. Our modeling units, however, are still large compared to a Landsat or
MODIS pixel so that conversion of numerical SW extents back into the spatial domain would be subject to a
variety of uncertainties. To address this limitation, future work should evaluate the suitability of image
fusion algorithms for quantifying SW dynamics [Gao et al., 2006; Zhu et al., 2010]. Image fusion takes advan-
tage of the complementary data characteristics of different satellite sensors such as Landsat and MODIS
and has previously been used to generate image time series with Landsat-like resolution and a regular time
step of MODIS composite images of eight days [Hilker et al., 2009; Walker et al., 2012; Emelyanova et al.,
2013; Knauer et al., 2016]. Although a few applications have applied image fusion to areas that are subject
to ﬂooding [Emelyanova et al., 2013; Zhang et al., 2014a], a comprehensive analysis of the suitability for
quantifying SW extent dynamics on large river basin scale is currently missing. Therefore, future research
should assess the potential of data-fusion for quantifying long-term and large-scale SW extent dynamics
and compare this approach with the statistical modeling approach presented here.
We integrated 26 year long time series of Landsat-derived SW maps, P,ET, and SM with ﬂow data from 68
river gauges to model SW extent dynamics and the role of drivers holistically across the MDB, and quanti-
ﬁed ﬂood propagation times for all of the basin’s key river and ﬂoodplain sites. GAM models captured the
large variety of nonlinear empirical Qto SW extent relationships across the basin well and the lagged
dependent variable led to large improvements in model performance, particularly for the ﬂoodplain-lake
category, which exhibits the slowest changes in SW extents. Categorizing SW areas was another crucial step
for achieving high goodness of ﬁt of statistical inundation models, which was much higher for gauge-
connected SW areas (i.e., ﬂoodplains and ﬂoodplain-lakes) than for the remaining SW areas (nonﬂoodplain
areas) and better in the northern compared to the southern MDB. The local climate drivers were also more
important for SW dynamics in the northern basin with the N-W representing a hot spot. These N-S gradients
that our models revealed were in good accordance with key basin characteristics such as lower levels of riv-
er regulation, vaster and shallower ﬂoodplains and higher climate variability in the northern as compared to
the southern MDB. Our study illustrates that integrating multidecadal time series of earth observation data
with statistical modeling techniques can provide cost-effective tools for improving the management of lim-
ited SW resources and better understanding of long-term inundation dynamics and respective drivers. The
data-driven modeling framework is applicable to other large and complex river basins across the world and
provides statistical models that can predict SW extent for cloud-affected Landsat observations or during the
peak of ﬂoods and hence, allows a more detailed quantiﬁcation of the spatiotemporal inundation dynamics
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The Landsat-based time series of SW
maps will be made freely available as
part of the Australian Research Data
Storage Infrastructure in accordance
with the rules of the funding agency
and embargo regulations of UNSW.
The Australian Geofabric river network
[BOM, 2012], the gridded Pdata, and
the AWRA-L ET data are available
through the Australian Bureau of
water/landscape/). The SM time series
is provided by the European Space
Agency’s CCI (Climate Change
Initiative) project. The static wetland
layer for the MDB can be requested
from the Murray-Darling Basin
Authority. All river ﬂow data used in
this analysis can be obtained from
Australian state government
repositories (see section 2.2.3). This
work was supported by the Australian
Research Council Linkage grant
(LP130100408) with co-funding from
the Murray-Darling Basin Authority
entitled ‘‘A novel approach for
assessing environmental ﬂows using
satellite data.’’ The ecohydrological
zonation of the MDB used in this study
was provided by Chang Huang. The
static wetland layer for the MDB was
provided by Richard Kingsford and the
ET data were provided by the Bureau
of Meteorology with support from
Albert Van Dijk and Andrew Frost.
Water Resources Research 10.1002/2016WR019858
HEIMHUBER ET AL. MODELING SURFACE WATER 16
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