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HYDROLOGICAL PROCESSESHydrol. Process. 24, 0–0 (2010)Published
online in Wiley Online Library(wileyonlinelibrary.com) DOI:
10.1002/hyp.7926
Large-scale modelling of channel flow and floodplaininundation
dynamics and its application
to the Pantanal (Brazil)
Adriano Rolim da Paz,1,2* Walter Collischonn,1 Carlos E. M.
Tucci1 and Carlos R. Padovani31 Instituto de Pesquisas
Hidráulicas, Universidade Federal do Rio Grande do Sul, Av. Bento
Gonçalves 9500, Agronomia, CEP 91501-970, Porto
Alegre-RS, Brazil2 EMBRAPA Monitoramento por Satélite, Av.
Soldado Passarinho 303, Fazenda Chapadão, CEP 13070-115,
Campinas-SP, Brazil
3 EMBRAPA Pantanal, Rua 21 de Setembro, 1880, Bairro Nossa
Senhora de Fátima, CEP 79320-900, Corumbá-MS, Brazil
Abstract:
For large-scale sites, difficulties for applying coupled
one-dimensional (1D)/2D models for simulating floodplain
inundationmay be encountered related to data scarcity, complexity
for establishing channel–floodplain connections, computationalcost,
long duration of floods and the need to represent precipitation and
evapotranspiration processes. This paper presentsa hydrologic
simulation system, named SIRIPLAN, developed to accomplish this
aim. This system is composed by a 1Dhydrodynamic model coupled to a
2D raster-based model, and by two modules to compute the vertical
water balance overfloodplain and the water exchanges between
channel and floodplain. Results are presented for the Upper
Paraguay River Basin(UPRB), including the Pantanal, one of the
world’s largest wetlands. A total of 3965 km of river channels and
140 000 km2 offloodplains are simulated for a period of 11 years.
Comparison of observed and calculated hydrographs at 15 gauging
stationsshowed that the model was capable to simulate distinct,
complex flow regimes along main channels, including
channel-floodplain interactions. The proposed system was also able
to reproduce the Pantanal seasonal flood pulse, with
estimatedinundated areas ranging from 35 000 km2 (dry period) to
more than 120 000 km2 (wet period). Floodplain inundation
mapsobtained with SIRIPLAN were consistent with previous knowledge
of Pantanal dynamics, but comparison with inundationextent provided
by a previous satellite-based study indicates that permanently
flooded areas may have been underestimated.The results obtained are
promising, and further work will focus on improving vertical
processes representation over floodplainsand analysing model
sensitivity to floodplain parameters, time step and precipitation
estimates uncertainty. Copyright 2010John Wiley & Sons,
Ltd.
KEY WORDS hydrologic modelling; hydrodynamic model; Pantanal;
lateral water exchange
Received 18 March 2010; Accepted 11 October 2010
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INTRODUCTION
Mathematical models have been developed and appliedfor
simulating the hydrologic regime of rivers since thenineteenth
century (Chow, 1959; Abbott, 1979; Cungeet al., 1981). The common
approach consists of assumingthat the flow is one-dimensional (1D)
along the longitu-dinal axis of the river and employing the Saint
Venant’sdynamic and continuity equations for flow routing.
Theseequations are used in their complete form (hydrodynamicmodel)
or disregarding some terms, which give rise tothe diffusive,
kinematic or storage models. The choiceof which model, approach and
discretization to use isdependent on several factors such as the
characteristicsof the study area, available data sets, purposes of
thestudy, available time, computational and human resources(Fread,
1992).
When dealing with rivers with floodplains, the twousual
approaches are to consider the 1D model with
* Correspondence to: Adriano Rolim da Paz, Instituto de
PesquisasHidráulicas, Universidade Federal do Rio Grande do Sul,
Av. BentoGonçalves 9500, Agronomia, CEP 91501-970, Porto
Alegre-RS, Brazil.E-mail: [email protected]
2425262728293031323334353637383940414243444546
extended cross sections representing both main channeland
floodplain or to consider explicitly storage areasconnected to the
1D model representing major wateraccumulation regions during
floods. These methods areable to reproduce the main channel flow
regime in asatisfactory way for most cases. Inundation maps maybe
further derived from the model results by interpolatingcross
sections of water levels and using a digital elevationmodel (DEM).
However, if the study aims at representingthe floodplain inundation
patterns, these methods maynot be suitable and a more recent
approach consists ofcoupling a 1D model for simulating the main
channelflow and a 2D model for simulating floodplain
inundation(Verwey, 2001; Gillan et al., 2005; Hunter et al.,
2007;Chatterjee et al., 2008).
Floodplain inundation plays a key role for severalecological
processes and phenomena, such as ecosystemproductivity, species
occurrence and distribution andnutrient and sediment dynamics (Junk
et al., 1989; Poffet al., 1997; Postel and Richter, 2003). Hence,
beingable to simulate the spatial inundation patterns
throughmathematical modelling provides a valuable tool to
watermanagement and prediction of climate change effects as
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well the effects of human interventions such as
waterwithdrawals, embankments, dykes and dredging projects.
In the 1D/2D coupled approach, the floodplain maybe modelled by
a full 2D hydrodynamic model (depth-averaged Navier–Stokes
equations) or by simpler meth-ods such as 2D diffusive and
kinematic approximations.Most of the latter are regular grid
models, which arecommonly referred as raster-based models.
Modelling floodplain with a 2D hydrodynamic codemay be
infeasible due to numerical instabilities related tosmall water
depths and the wetting and drying process aswell as excessive
computational costs. The use of raster-based models overcomes these
difficulties and providesa way to work with a large number of
floodplain gridelements. Additionally, this approach has the
advantagesof taking into account the spatial variability of
floodplainphysical characteristics (elevation and roughness) andof
being easily integrated into a geographic informationsystem (GIS).
Reasonable results have been obtained byseveral authors with this
modelling approach in terms ofreproducing floodplain spatial
inundation patterns (Horrittand Bates, 2001a; Bates et al., 2006;
Wilson et al., 2007).
The majority of literature examples of river-floodplainmodelling
using the 1D/2D coupled approach encom-passes relative small-scale
sites (single river reaches oflength less than 100 km), for which
there was largeamount of available data such as high-resolution
DEMand inundation maps for calibrating model results (Hor-ritt and
Bates, 2001a; Bradbrook et al., 2004; Bates et al.,2006; Tayefi et
al., 2007). The few exceptions include thestudy reported by
Biancamaria et al. (2009), which mod-elled a single reach of 900 km
length of the Ob river(Siberia), and the studies carried out by
Wilson et al.(2007) and Trigg et al. (2009), which modelled a 285
kmreach of the main stem of the Amazon (Solimões) riverand a 107
km reach of Purus tributary. If the study sitecomprises an even
larger and complex network of chan-nels, junctions and floodplains
(over hundreds of squarekilometers), difficulties may be
encountered related todata scarcity and complexity for establishing
main chan-nel and floodplain connections.
Additionally, the flood pulse may last for months longin
large-scale floodplains, which considerably increasethe
computational cost by necessitating more modelgrid elements and
model time steps. Moreover, forsimulating these long duration
floods the representationof the vertical water processes such
precipitation andevapotranspiration may be required (Wilson et al.,
2007).
In spite of the difficulties for modelling large-scalerivers and
floodplains, this is the major scale of interestfor assessing how
climate change and variability willaffect water resources. As an
increase in accuracy andreliability of flow and inundation
predictions is desirablefor better decisions concerning land use
and watermanagement in light of climate scenarios, it motivates
thedevelopment and improvement of methods for large-scalehydrologic
modelling.
This paper presents a hydrologic simulation system,named
SIRIPLAN, developed for large-scale river and
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floodplains drainage networks. This simulation systemis based on
coupling a 1D hydrodynamic model toa 2D raster model and
considering the precipitation,evapotranspiration and infiltration
processes over thefloodplain. Results are presented from the
applicationof the SIRIPLAN to the Upper Paraguay River Basin(UPRB),
including the Pantanal, one of the world’slargest wetlands. Results
are evaluated by comparingobserved and calculated hydrographs at
available gaugingstations and by comparing seasonal inundation
areas andinundation patterns provided by previous
satellite-basedstudies.
THE SIRIPLAN HYDROLOGIC SIMULATIONSYSTEM
Overview
The SIRIPLAN hydrologic simulation system is com-posed by a 1D
hydrodynamic model coupled to a 2Draster-based inundation model
(Figure 1). The 1D modelsimulates the flow routing along the river
drainage sys-tem, considering cross sections restricted to the
mainchannels. The raster-based model simulates the
wateraccumulation and the 2D propagation of inundation overthe
floodplains. A water exchange scheme is used to sim-ulate the
interactions between channel and floodplain. Ifthe water level in a
cross section of the main channel risesabove the levee, it spills
over and inundates the flood-plain. Analogously, if the inundation
propagation overfloodplain reaches the main channel pathway, water
istransferred to the channel.
Additionally, the vertical processes of
precipitation,evapotranspiration and infiltration are simulated by
a thirdmodule, coupled with the raster-based model. Water
con-tributions from upstream of the modelled river drainagesystem
are considered as boundary conditions set using
1D hydrodynamicmodel (IPH4)
Raster-basedinundation model
Vertical balanceover floodplain
1D flow routing along mainchannels
2D inundation modeling overfloodplain
Simulation of vertical hydrologicprocesses over floodplain
Connectionmodule
Updating of vertical input/ouput overfloodplain
Connectionmodule
Water exchanges between mainchannels and floodplains
Rainfall-runoff modelOR observed data
Precipitation andevapotranspiration data
Meteorologicalboundary conditions
SIRIPLAN
Figure 1. Conceptual overview of the SIRIPLAN hydrologic
simulationsystem
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observed discharge data or by off-line coupling of
arainfall-runoff hydrologic model.
Channel flow routing
Flow routing along main channels is simulated withthe 1D
hydrodynamic model called IPH4 (Tucci, 1978).This model solves the
full Saint Venant equations througha finite difference method, with
an implicit scheme basedon a modified version of the Gauss
elimination process:
∂h
∂tC 1
b
∂Q
∂xD q �1�
∂Q
∂tC ∂
∂t
(Q2
A
)C gA∂h
∂xC gA�Sf � S0� D 0 �2�
where h is the water level, t is time, Q is the discharge,x is
the longitudinal distance along the river, b and A arethe cross
section width and area, respectively, g is thelocal gravitational
celerity, q is the lateral contributionto discharge per unit of
distance, S0 is the channelbotton slope and Sf is the energy
friction slope, whichis parameterized through Manning resistance
equation.
Cross-section data represented in the IPH4 model isrestricted to
the level which characterizes the transitionbetween main channel
and floodplain (levees). For eachriver reach between two cross
sections, length and slopemust be specified. Manning coefficients
may assume dis-tinct values for each river reach, and may also be
consid-ered variable as a function of the water level in a
givencross section. The discharge exchanged between mainchannel and
floodplains is considered as lateral contribu-tion in the
continuity equation (term q in Equation (1)).
Floodplain inundation modelling
The floodplain model is a raster-based inundationmodel, which
was developed following the approach ofthe LISFLOOD-FP model (Bates
and De Roo, 2000;Horritt and Bates, 2001b), but with adaptations
mainlyconcerning the water exchange between channel andfloodplain,
flow among floodplain elements, water storage
434445464748495051525354555657585960616263646566676869707172737475767778798081828384
in soil reservoirs and water input/loss on floodplain dueto
vertical water balance.
Floodplain is discretized by a regular grid of intercon-nected
elements, which may change flow with neighbour-ing elements and
with the main channel, in the case ofelements directly connected to
the channel (Figure 2a).The volume variation along time in a given
element ofthe raster model is the following:
V
tplanD Qup C Qdown C Qleft C Qright C Qcf
C Qvert C Qres �3�
where V is the volume variation during time intervaltplan; Qup,
Qdown, Qleft and Qright are the dischargesbetween the element and
its up, down, left and rightneighbours, respectively; Qcf is the
discharge betweenchannel and floodplain element; Qvert is the
result of thevertical water balance and Qres represents the volume
ofwater flowing to the soil reservoir.
A numerical scheme explicit on time and progressiveon space is
used to solve Equation (3), considering thewater level represented
in the center of the element andthe exchanges in its interfaces
(Figure 2b). As a result, thewater level in the time instant t C
tplan in a floodplainelement (i, j) is determined by:
tCthi,j D thi,j C
�tQi�1,jx � tQi,jx C tQi,j�1y � tQi,jyC tQi,jcf� Ð tplan
x Ð yC thi,jvert C thi,jres �4�
where thi,j is the water level in time instant t, tQi,jx isthe
discharge in x direction between elements i, j andi C 1, j; tQi,jy
is the discharge in y direction betweenelements i, j and i, j C 1;
thi,jvert is the result of the verticalwater balance and thi,jres
is the available volume of soilreservoir, both expressed in water
depth; x and yare the element dimensions in the x and y
directions,respectively.
main channel
elements of the floodplain model
elements connected to the main channel
(a)
i
j
j-1
j+1
i+1i-1
hi,j
Qxi,j
Qyi,j
Qyi,j-1
Qxi-1,j
(b)
Bch
Lch
(c)
Zb1Zb2
Zw2Zw1
hflow
flow
1 2
Figure 2. (a) Floodplain elements of the raster-based model; (b)
numerical discretization of water level and discharges between
elements of thefloodplain, which are calculated through linkage
channels of width Bch and length Lch and (c) indication of hflow
between two elements (Zw and
Zb refer to water level and botton elevation, respectively),
where hflow D Max(Zw1,Zw2)-Max(Zb1,Zb2) (adapted from Bates et al.,
2005)
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In the soil reservoir scheme, a floodplain element isinundated,
i.e. with surface water accumulation, only afterthe soil reservoir
is full (Figure 3). The term hres is givenby:
hres D hsub � Hsmax �5�where hsub is the current water content
of the soilreservoir, which has a maximum capacity of Hsmax(model
parameter), both variables being expressed inwater depth; hres
always assumes non-positive values,varying from hres D �Hsmax when
the reservoir is emptyto hres D 0 when it is full.
If the result of the water balance in a floodplain
element(Equation (4)) is positive, the soil reservoir is filled
andthere is surface water in this element. On the contrary,a
negative result means that the element was dried (interms of
surface water). The available water content inthe soil reservoir is
updated as follows:
if tCthi,j > 0 ) tCthi,jres D 0 �6�if tCthi,j < 0 )
tCthi,jres D tCthi,j, if∣∣∣ tCthi,j∣∣∣ < Hsmax
tCthi,jres D �Hsmax, if∣∣∣ tCthi,j∣∣∣ > Hsmax
tCthi,j D 0�7�
The discharge between two neighbour floodplain ele-ments is
determined by Manning equation with a numericand spatial
discretization similar to the used by Batesand De Roo (2000).
However, we consider that the flowbetween each two elements occurs
along straight chan-nels of width Bch and length Lch (Figure 2c),
and thus thedischarge is given by:
tQi,jx D šth5/3fluxoni,j
∣∣∣ thi,j � thiC1,j∣∣∣Lch
1/2
Ð Bch �8�
38394041424344454647484950515253545556575859606162636465666768697071727374
where tQi,jx is the discharge in the x direction betweenelements
(i, j) and (i C 1, j) in time instant t; ni,j isManning roughness
of the channel linking these elementsand thflow is the water depth
available to the flowbetween these elements; flow in y direction is
determinedanalogously.
The water depth hflow is defined as the differencebetween the
highest water level and the highest bot-ton elevation between the
two floodplain elements(Figure 2c), following Horritt and Bates
(2001a) andBates et al. (2005).
When modelling large-scale floodplains, model dis-cretization
may result in elements with dimensions ofhundreds or thousands of
meters to reduce computa-tional cost. If discharge along the
floodplain is calculatedconsidering the flow spilling over the
whole elementwidth, small differences in the water level may
gener-ate huge and unrealistic volumes of water exchangedbetween
two elements, causing numerical instabilities andartificially
accelerating the inundation propagation. Theadoption of channels
with controlled dimensions to rep-resent the hydraulic linkage
between each two floodplainelements aims at overcoming this
problem. In the flowequation between elements of the floodplain,
there arethree parameters related to the linkage channel (Man-ning
roughness, width and channel), which may be com-bined into only
one, called hydraulic conductivity fac-tor (fhc) (Equation (9)).
Albeit indeed inundation overlarge, vegetated floodplains such as
Pantanal may prop-agate along preferential pathways, the
disadvantage ofthe proposed approach is the increase in the number
ofmodel parameters and the difficulty to parameterize
themphysically. This may cause parameter equifinality,
i.e.different parameter sets leading to same results (Bevenand
Freer, 2001). Further study may focus on evaluatingmodel
sensitivity to these parameters and the associated
Zf
(a) (b) (c) (d)
floodplain wetting process
Hsmax
element dry;reservoir dry
element dry;reservoirwith water
there is awater demandequals toHsmax;element hasno watercontent
tolose
water demandbetween0 and Hsmax;element maylose waterfrom the
soilreservoir (ET)but no horizon-tal flow occurs
element dry;reservoir filled
element wet;reservoir filled
floodplain drying process
Za
hahres
water demandhres = 0;element maylose waterfrom the soilreservoir
(ET)but no horizon-tal flow occurs
water demandhres = 0;element maylose surfacewater andgenerate
hori-zontal flow
elementsurface
botton ofsoil reservoir
hsub
Figure 3. Wetting [(a)–(d)] and drying [(d)–(a)] processes of a
floodplain element of the raster model (Zf is floodplain elevation;
Za is water level;ha is surface water depth over the element; hsub
is water depth of soil reservoir; hres is the available volume of
soil reservoir, which has a maximum
capacity equals to Hsmax)
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uncertainties.
fi,jhc DBi,jch
ni,j√
Li,jch
�9�
Vertical water balance on floodplain
The vertical water balance on each floodplain elementis
performed as a balance between precipitation andevapotranspiration.
This balance is updated at a specifictime step (tvert) (Figure 4),
which is commonly severaltimes greater than time steps used in 1D
and 2D models.At each tvert, this simple water balance is
calculated fora given floodplain element (i, j):
tCthi,jvert D tCtPi,j � tCtETi,jactual �10�
where P is precipitation, ETactual is the actual
evapotran-spiration and hvert is the resultant of this balance, all
ofthem expressed in terms of water depth.
If hvert > 0, it represents a source of water to the
waterbalance of the element in the 2D model (Equation (4)),while a
negative value means a sink (definite loss) ofwater from the
modelling system. As tvert >> tplan,the result of the
vertical balance is considered constantalong the following npv
number of floodplain timesteps, where npv D tvert/tplan, but after
convertingto corresponding units by hvert D hvert/npv.
Actual evapotranspiration is calculated according towet or dry
condition of the floodplain element in eachtvert. If the element
has surface water, actual evapotran-spiration occurs at the maximum
rate equal to potentialevapotranspiration (Equation (11)). If the
element is dry,actual evapotranspiration is less than the potential
rate,being linearly proportional to water content of the soil
Main channel simulation withthe1D hydrodynamic model
along 1∆tch
Floodplain simulationwith the raster modelalong
np.∆tfl(=1∆tch)
Updating time instantt = t + ∆tch
Determination of flowexchanges between channel
and floodplain (Qcf)
Models initializationt = 0
Update of the vertical waterbalance in the floodplain
Completed 1 ∆tvert?
YesNo
Figure 4. Scheme of coupled running of hydrodynamic and raster
inun-dation models and vertical water balance
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reservoir (Equation (12)).
if thi,j > 0 ) tCtETi,jactual D tCtETi,jpot �11�
if thi,j D 0 ) tCtETi,jactual D tCtETi,jpot
Ð1 �
∣∣∣ thi,jres∣∣∣Hsmax
�12�
Channel–floodplain water exchanges
Every floodplain element located under the mainchannel
longitudinal axis is connected with it. Waterexchanges between
channel and floodplain are deter-mined as a function of the
difference between waterlevels. For the points located between two
cross sectionsof the main channel, the water level is calculated by
alinear approximation.
Occurrence of flow between channel and floodplain ina given
location is triggered by the condition of waterlevel in floodplain
and/or main channel higher than thespill elevation (Zspill). This
elevation is the maximumvalue between channel levee height and
floodplain bottomelevation.
When the water level in the main channel or in thefloodplain
reaches Zspill, there is hydraulic connectionand flow occurs. This
flow is calculated using simpleor flooded weir-type equations.
Analogously to the dis-charge between floodplain elements, if the
weir width isconsidered equal to the element width, unrealistic
exag-gerated flow may be calculated for small water depthsover the
weir in case of elements with large dimensions.Therefore, the weir
width is considered a model parame-ter, usually taken in the range
10–100 m, which may beregarded as the typical width values over
which occurslateral flows in large natural rivers. As previously
statedregarding parameters related to channels linking flood-plain
elements, considering the weir width as a modelparameter may lead
to equifinality and increase the uncer-tainties. Further study will
evaluate this issue, investigat-ing model sensitivity to each
parameter.
A decoupled 1D/2D time-step approach is considered(Trigg et al.,
2009), in which different time steps are setto the 1D and 2D
models. The 1D time step (tchan)is usually several times greater
than the 2D time step(tplan), as the 1D model uses an implicit
numericscheme while the 2D model is explicitly solved. Thus,the 1D
model is run by 1tchan and then the 2D modelis run by np times
tplan, where np D tchan/tplan.After a time interval of tchan, the
water exchanges(Qcf) between channel (1D model) and floodplain
(2Dmodel) are calculated. For the channel, Qcf is convertedinto
lateral contribution to discharge per unit of distancefor
calculation of the continuity equation (Equation (1))at the next
tchan. For the floodplain, Qcf is directlyused into the water level
updating equation (Equation (4))throughout a time interval of
tchan, i.e. for the next nptplan.
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Code and parallelization
The SIRIPLAN hydrologic simulation system wasdeveloped using
FORTRAN 90 programming languageand OpenMP (Open specifications for
Multi-Processing)Application Programming Interface (API). The
OpenMPrepresents a collection of directives, library routines
andenvironment variables that enables programs to run inparallel on
shared memory processors (Hermanns, 2002;Chapman et al., 2008). The
main advantages of thisapproach relative to other parallel
techniques are theease of implementation and requirements of
minimalmodification to the code. Recently, Neal et al.
(2009)implemented a parallel version of the LISFLOOD-FPmodel using
OpenMP, achieving parallel efficiencies ofup to 0Ð75 on four and
eight processor cores.
Two loops of the raster inundation model were par-allelized
through OpenMP: the calculation of dischargebetween floodplain
elements and the calculation of waterdepth in each element (general
water balance). The 1Dhydrodynamic model has an implicit numerical
scheme,and tests for parallelizing its code with OpenMP hasproven
not to be advantageous in terms of run-time reduc-tion (Paiva,
2009).
INPUT DATA REQUIREMENTS ANDPREPARATION
Main channel data
For the hydraulic modelling of channel flow, datarequirements
includes channel vector lines, length andslope, cross section
profiles and boundary conditions.Among these data, the profiles are
the most difficult toobtain. To overcome this issue, a simple
linear schemeis adopted for cross-section profiles interpolation
whennecessary. Given an upstream and a downstream sectionwith
available profiles, for each intermediate cross sectionto be
created, the horizontal and vertical location of itsith point is
determined through linear interpolation of theith upstream and
downstream points.
Main channel georeferenced vector lines may beobtained from
available maps or by digitizing satelliteimages, while length and
slope of main channels arederived from cross-section data and
channel vector lines,taking into account a floodplain DEM as
auxiliary data.
Floodplain data
The raster-based model requires a floodplain mask anda DEM to
represent floodplain topography. The maskdefines the modelled
domain, which is established basedon the main channel network,
floodplain topography andcontributing drainage areas of the
boundary conditions ofthe channels. As a no flow boundary condition
is imposedto the floodplain in the raster model, the floodplain
maskmust comprise the full extent of the inundation area.Areas
which certainly are not flooded and which do notsignificantly
contribute to flooding may be excluded fromfloodplain domain to
reduce computational cost.
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Additionally, precipitation and potential evapotranspi-ration
data are required for the vertical water balance onfloodplain.
Point specific data such as rainfall gaugingstation observations or
data provided by other sourcessuch as precipitation estimates from
atmospheric mod-els are interpolated to the raster model grid using
theinverse distance square method. This procedure is carriedout
before simulation to reduce model run time. Thesedata are required
with a discretization on time equalto tvert. Alternatively,
seasonal monthly estimates ofpotential evapotranspiration may be
used if more detaileddata are not available.
Channel–floodplain connection
The largest effort on input data preparation
involvesestablishing the topological connections between channeland
floodplain discretization elements. This is not a trivialtask when
dealing with several tributaries, junctionsand hundreds of cross
sections, and where the largedimensions of the floodplain elements
contrast withrelative small channel meanders.
The main channel drainage network must be repre-sented in terms
of raster model grid elements, identifyingwhich floodplain elements
are connected to each chan-nel reach, and which cross section or
intermediate pointof the reach is connected to each element. A
four-stepprocedure was developed to accomplish this task.
The first step is the conversion of vector channelnetwork to
raster format with spatial resolution and extentequal to the
floodplain discretization (Figure 5a). Theresulting image is
composed by pixels representing ornot the channel network (Figure
5b).
(b)
(c)
(d)
(a)
Figure 5. (a) Main channel vector drainage (VD); (b) VD
converted toraster (grey pixels); (c) flow directions and (d)
raster drainage with aunique pixel-to-pixel flow path (dark pixels
were excluded from the
original raster drainage)
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Derivation of flow directions is the second step(Figure 5c).
Considering the set of non-zero pixels asa mask, the direction
water flows out of each pixelis determined based on floodplain DEM,
through thewell-known D8 (deterministic eight-neighbour)
algorithm(Mark, 1984; Burrough and McDonnel, 1998; Jenson
andDomingue, 1988). This algorithm approximates the localflow
direction by the direction of the steepest downhillslope within a 3
ð 3 window of pixels over a raster DEM.As this algorithm has a
tendency of generating paralleldrainage paths on flat areas, a
stochastic factor as pro-posed by Fairfield and Leymarie (1991) was
introducedto lessen this problem.
Thirdly, starting from the most upstream pixel of eachchannel
reach, trace the downstream path following flowdirections and mark
every pixel reached. These markedpixels form the main channel
network representation interms of a unique pixel-to-pixel flow
path. Pixels non-marked are eliminated from the raster
representation ofmain channels (Figure 5d).
Every floodplain element corresponding to the
rasterpixel-to-pixel channel network is connected with mainchannel,
while none of the other elements are connected.The fourth step is
the identification of to which crosssection each element is
associated.
The cross sections with available profile and geo-graphic
coordinate data are associated to the pixel corre-sponding to these
coordinates. For the interpolated crosssections, albeit their
longitudinal position along the mainchannels is known, a rescaling
procedure is performedbefore locating them, due to the tendency of
underesti-mating distances on a coarse-resolution raster
representa-tion of meandering channel networks (Fekete et al.,
2001;Paz et al., 2008).
The distances along the raster channel representationare
measured between each of the cross sections alreadylocated. The
flow path is followed pixel by pixel,summing a distance equal to
pixel side for an orthogonalstep and equal to 1Ð414 times pixel
side for a diagonalstep. For each reach defined by two of these
crosssections, the ratio between the distances measured onthe
raster and on the vector drainages is calculated. Thisratio is
applied to convert the longitudinal position alongthe main channel
of the interpolated cross sections intodistances along the raster
channel representation, definingthe location of these sections.
EXAMPLE OF APPLICATION: UPRB
Site description and simulation period
The study site comprises the Pantanal area of theUPRB that has
an estimated drainage area of600 000 km2, extending over three
South American coun-tries (Figure 6): Brazil, Paraguay and Bolivia.
The UPRBis part of the La Plata basin and has three
distinctregions: Planalto (260 000 km2), Pantanal (140 000 km2)and
Chaco (200 000 km2). The Planalto region encom-passes the uplands
of the basin mainly in the North and
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East portions. Located in the West part of the UPRB, theChaco is
a region characterized by low annual rainfalland an endorheic and
undefined drainage system.
The Pantanal region is located in the central portion ofthe UPRB
and presents very low and flat relief, with acomplex drainage
system. Rivers seasonally inundate thefloodplains and flood waters
create an intricate drainagesystem, including vast lakes, divergent
and endorheicdrainage networks. Annual rainfall is less than
thepotential evaporation and drainage is very slow becauseof
shallow gradients (Bordas, 1996; Tucci et al., 1999).
The Pantanal region was modelled with the SIRIPLANhydrologic
simulation system, considering the contribu-tion of the Planalto
area as boundary condition, as flood-plain inundation is negligible
in this part of the basin. TheChaco region was not modelled due to
data scarcity andbecause its contribution to Paraguay River is
consideredinsignificant (Tucci et al., 2005). A period of 11
yearsand 4 months from 1 September 1995 to 31 December2006 was
selected for simulation, as this is a more recentperiod with
reliable available data (žTable I). AQ2
The Pantanal is considered one of the largest wet-lands of the
world, with extraordinary biodiversity (Harriset al., 2005) and of
great global ecologic value (Pottand Pott, 2004; Junk et al.,
2006). Modelling its hydro-logic regime and floodplain dynamics is
imperative forunderstanding, predicting and mitigating possible
effectsof anthropogenic activities that currently threaten
itsintegrity, such as dam building, agriculture and cattle rais-ing
(Tucci and Clarke, 1998; Hamilton, 1999; Hamiltonet al., 2002; Da
Silva and Girard, 2004; Junk and Cunha,2005).
1D hydrodynamic model application
The river drainage system modelled with the 1Dhydrodynamic model
covers 3965 km of river channels:1250 km of the Paraguay River and
2715 km of its maintributaries. The flow path of each channel was
obtainedby manually digitizing Landsat7 ETMC satellite images.
For the Paraguay River, a total of 288 detailed cross-section
profiles was available, with distances betweenconsecutive profiles
varying from 0Ð5 to 10 km. On thecontrary, only 19 profiles were
available for all the trib-utaries together and a linear
interpolation procedure wasperformed to generate profiles at about
5 km intervals.Further information concerning river morphology
andslopes available in former studies (DNOS, 1974; Brasil,1997;
Tucci et al., 2005) as well as elevation valuesextracted from
SRTM-90m DEM were used as auxil-iary data for the vertical
positioning of cross sections.Detailed description of data
preparation for cross sectionsis presented in Paz et al.
(2010).
Streamflow gauging stations with available observeddischarge
time series were defined as the upstreamboundary conditions of the
1D hydrodynamic model.Missing data were replaced by values
calculated withthe distributed hydrologic model MGB-IPH
(Collischonnet al., 2007). This model was previously applied
and
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-55° W
-55° W
-60° W
-60° W
-15°
S-2
0° S
0 200 400km100
Argentina
Paraguay
Brazil
Bolivia
Para
na ri
ver
a
b
c
f
g
hi
de
j
123 5467
8
11
1213
1415
16
1718
19
910
Planalto
Pantanal
Chaco
Country border
Modeled channel network
Modeled floodplain
Control point
Boundary condition
Piquiri r.
Cuiab
á r.
S.Lourenço r.
Aquid. r.
Miranda r.
Taquari r.
Negro
r.
Par
agua
y r.
Jauru r.
Boundary conditionsa Cuiabáb A. C. Grandec S. Jerônimod P.
Esperidiãoe Cáceresf Coximg P. Bocaínah Aquidauanai Mirandaj
Upstream of Apa River
Control points1 B. Melgaço2 P. Cercado3 S. João4 I. Camargo5 S.
J. Borireu6 S. J. Piquiri7 P. Taiamã8 P. Alegre9 S. Gonçalo10 P.
Rolom11 F. R. Negro12 P. Ciríaco13 T. Fogo14 Descalvados15 P.
Conceição16 Amolar17 S. Francisco18 P. Manga19 P. Murtinho
Figure 6. Location of Upper Paraguay River Basin and indication
of modelled channel network and floodplain, and of streamflow
gauging stationsused as control points or boundary conditions
Table I. List of boundary conditions with drainage area and
observed daily discharge data availability during the simulation
period(1 September 1995–31 December 2006)
Streamflow gauging station defining the boundarycondition
(reference to Figure 6)
River Drainage area(km2)
Observed discharge data availability(% of simulation
period)a
a Cuiabá Cuiabá 24 668 100b A. C. Grande S. Lourenço 23 327
94c S. Jerô nimo Piquiri 9215 99Ð7d P. Espiridião Jauru 6221
96Ð5e Cáceres Paraguay 32 574 96Ð4f Coxim Taquari 28 688 99Ð5g P.
Bocaı́na Negro 2807 0h Aquidauana Aquidauana 15 350 97Ð1i Miranda
Miranda 15 502 99Ð7j ž Upstream of Apa Riverb Paraguay 594 092
bAQ1
a Data available from the Brazilian Water Agency (ANA).b
Downstream boundary condition defined by the Paraguay River section
upstream of the affluence of Apa river, considering the energy
slope parallelto average bed slope.
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adjusted for all the sub-basins of the Planalto region ofthe
UPRB in the study reported by Tucci et al. (2005). Avery reasonable
fit of the MGB-IPH model was achievedby these authors, with
Nash–Suttcliffe (NS) coefficientsranging from 0Ð56 to 0Ð88.
The Paraguay River section upstream of the affluenceof Apa
River, about 60 km downstream from Porto Murt-inho, was taken as
the downstream boundary conditionof the modelled network,
considering the energy slope
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parallel to average bed slope. The time step of chan-nel flow
modelling (tchan) was adopted as 1 h, andthe initial conditions
were determined considering steadybackwater flow approximation.
2D raster-based model application
The floodplain modelled area was defined accordingto earlier
studies that delimited the Pantanal and theSRTM-90m DEM, but also
taking into account that a no
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flow boundary condition is imposed to the raster model.For this
reason, the modelled area was traced overesti-mating the area
subject to inundation, which is roughlyabout 140 000 km2. The
raster model domain comprises219 514 km2 (Figure 6), discretized
into 46 741 elementson a 0Ð02° ð 0Ð02° grid. In planar units, each
element isapproximately 2 km wide, with surface area ranging
from4Ð58 to 4Ð78 km2 depending on its latitude.
Floodplain topography was represented by the SRTM-90m DEM
resampled to the raster-based model dis-cretization, using the
nearest neighbour interpolationmethod. Following the data
preparation procedures, atotal of 1081 floodplain elements were
identified asdirectly connected to the main channels.
The inundation model was run with a 120-s time step,which was
selected after testing different values and ver-ifying that this
value avoided numerical instabilities. A1-day time step was
selected for the vertical water bal-ance, due to precipitation and
potential evapotranspirationdata availability on a daily basis and
also because this isadequate to represent the modelled processes in
this studyarea. Observed precipitation data available from 105
rain-fall gauging stations were interpolated to the 0Ð02°
gridresolution of the floodplain model using the inverse dis-tance
squared method. Although this rain gauge networkis sparse, for
instance it is sufficient to provide precip-itation estimates for
testing the proposed model. Futurework will try to investigate
model sensitivity to precipi-tation estimates and also the
combination of pluviometermeasures with satellite-based estimates,
such as thosegenerated by the Tropical Rainfall Measuring
Mission(TRMM; Kummerow et al., 2000).
The estimates of potential evapotranspiration producedby the
MGB-IPH distributed hydrological model appliedto the entire UPRB in
a earlier study (Tucci et al., 2005)were used as input data. The
MGB-IPH model calculatespotential evapotranspiration through
Penman–Monteithmethod as presented by Shuttleworth (1993) and
follow-ing the approach proposed by Wigmosta et al. (1994).Distinct
combinations of land cover and soil type arerepresented inside each
model cell through patches withspecific parameter values. This
model was applied tothe UPRB considering a 0Ð1° ð 0Ð1° regular grid
and a1-day time step. The simulation period was from 1968 to2006,
and the estimates of potential evapotranspirationused as input data
for the floodplain model correspond tothe patch representing
surface water, which were interpo-lated to the 0Ð02° floodplain
model grid using the inversedistance squared method.
Calibration procedure and model skill assessment
To evaluate the performance of the 1D hydrodynamicmodel, 15
streamflow gauging stations with available datawere used as control
points for comparing calculatedand observed discharges along the
main channel network(Figure 6). Floodplain inundation dynamics
simulated bythe raster model was compared with estimates of
totalinundated area provided by Hamilton et al. (1996) and
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with estimates of inundation extent produced by
Padovani(2007).
Hamilton et al. (1996) estimated the total of floodedareas of
Pantanal in the period 1979–1987 throughanalysis of data obtained
by the scanning multichannelmicrowave radiometer (SMMR) sensor of
the Nimbus-7satellite. Despite the related uncertainties mostly due
tocoarse resolution of satellite images (27 km), vegetationcover
heterogeneity, and of being relative to a time perioddistinct from
the one simulated in this article, the studyof Hamilton et al.
(1996) presented to date the mostcomplete time series of seasonal
floods in the Pantanal.
Padovani (2007) classified images of the sensor wide-field
imager (WFI) on board of the CBERS-2 satellite(China–Brazil Earth
Resources Satellite) to distinguishbetween flooded and non-flooded
areas of Pantanal forthe dates 6 October 2004 (dry period) and 13
February2005 (wet period). These images have a spatial resolutionof
260 m and, as the WFI has a ground swath of890 km, a unique scene
covering the entire area ofinterest for each date was used (path
165, row 116).These images were classified by an unsupervised
method,the Iterative Self-Ordering Data Analysis
(ISODATA)algorithm, as implemented in the ERDAS Imagine
8Ð5software. The resulting classes were grouped into floodedor
non-flooded areas, taking the RGB color compositeof Landsat 7 ETMC
images for the year 2000 anddigital aerial photographs of the
region as ancillary data.Undoubtedly these estimates have
uncertainties, mostlyassociated to inundated areas covered by
vegetationand areas with wet saturated soil, which may lead
tounder- and overestimation of flooded extent,
respectively.However, this is the only readily available
inundationextent mapping of the entire Pantanal area for
comparisonwith our results.
A simplified approach was adopted for adjusting modelparameters,
as the calibration process of coupled 1D/2Dmodels is not
straightforward. For instance, some stud-ies indicate that it is
not possible to find a unique setof parameters of the raster model
that provide acceptableadjustments for both channel flow and
floodplain inun-dated area (Horritt and Bates, 2001b). žAnother
ques- AQ3tion concerns whether using constant or spatially
varyingparameters on 2D floodplain models (Werner et al.,
2005;Hunter et al., 2007). Albeit several efforts have been
con-ducted to estimate friction parameters based on remotesensing
data (Bates et al., 2004), in the case of sim-plified models, such
as the proposed in this article, theparameters are related to
aggregated hydraulic processdescriptions (Hunter et al., 2007),
weakening the rela-tion of them with floodplain physical
characteristics. Inlight of this discussion and due to the large
extent of thestudy case and scarce available data sets, in this
studythe calibration process focused primarily on reproducingmain
channel flow, but also trying to reproduce generalaspects of
floodplain dynamics. Further study may focusparticularly on
adjusting model parameters for reproduc-ing inundation
patterns.
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Initially, a constant Manning coefficient was adoptedfor all
main channel reaches in the 1D hydrodynamicmodel, and several runs
of the hydrologic simulationsystem were performed with varying
floodplain modelparameter values. The Manning channel roughness
wasselected as 0Ð035 following a recommendation for largenatural
rivers (Chow, 1959, 1964). The parameters fhcand Hsmax were varied
in each simulation run, butassuming constant values along the
floodplain.
This rough sensitivity analysis of floodplain param-eters lead
to the selection of the values fhc D 50 andHsmax D 1Ð0 m, based on
channel hydrograph compar-isons and the modelled general inundation
patterns, bothin terms of total inundated area and inundation
extent.Adopting these values for the floodplain parameters, anew
set of simulation runs was carried out for adjust-ing main channel
roughness. This was done in a trialand error process, by manually
varying the Manningcoefficient values and comparing calculated and
recordedhydrographs through visual inspection and using as
statis-tical measures the NS model efficiency coefficient (NS),the
NS coefficient for logarithms of discharge values(NSlog), the
relative streamflow volume error (V) andthe root mean square error
(RMSE). The calibration pro-cedure was realized first for the
tributaries and then forthe Paraguay River, from upstream to
downstream alongeach river.
Finally, assessment of floodplain inundation dynam-ics, through
comparison with results of Hamilton et al.(1996) and Padovani
(2007), was carried out consideringthe simulation run using the
adjusted main channel Man-ning coefficients and the selected values
for floodplainparameters. It is worth noting that those authors
con-sidered distinct delimitations for defining the Pantanalarea in
their studies, albeit in general these delimita-tions are very
similar between them. The Pantanal’s areafollowing the outline of
Hamilton et al. (1996) is about138 139 km2, while the one used in
the study of Padovani(2007) has 138 437 km2. The major difference
betweenthem regards to the west portion, where the delimitationused
by Padovani (2007) follows the Brazilian countryborder, as this
sketch defines the Pantanal region offi-cially adopted by Brazilian
Government.
Simulated total inundated area was converted into aver-age
seasonal values for comparison with the results ofHamilton et al.
(1996), considering the Pantanal delimi-tation adopted by those
authors and adopting the depththreshold of 2 cm to distinguish
between dry and inun-dated condition of each element of the
raster-basedmodel.
The comparison between simulated and Padovani’sestimates of
inundation extent was carried out through apixel-to-pixel basis,
and considering the Pantanal delim-itation used by that author. We
aggregated the 260 minundation maps of Padovani (2007) to the
spatial reso-lution of the raster-based model (2 km). Each pixel of
thePantanal area was compared whether wet or dry on bothsimulated
and estimated inundation maps. A 2 ð 2 con-tingency table was built
as shown in Figure 7, where ‘a’
a
Satellite-basedestimate
Mod
elsi
mul
atio
n
b
c d
Wet
Dry
Dry
Wet a + b + c + d
a + dPC =
a + b + ca
CSI =
a + ca
POD =
a + bb
FAR =
Figure 7. Contingency table (2 ð 2) for comparison between
inundationmaps resultant from satellite-based estimates and
floodplain modelsimulation, where ‘a’ and ‘b’ are the number of
pixels which were wet onboth maps, ‘c’ is the number of pixels
which were wet on estimated mapbut dry on simulated map and ‘d’ is
the number of pixels which weredry on estimated map but wet on
simulated map; and four derived skillscores: proportion correct
(PC, critical success index (CSI, probability of
detection (POD and false alarm ratio (FAR)
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and ‘d’ correspond to the number of wet and dry
pixels,respectively, simultaneously on both simulated and
esti-mated maps. The number of pixels which were estimatedas wet
but simulated as dry are summed in ‘c’, while‘d’ is the number of
pixels that were wrongly simulatedas wet (they were estimated as
dry). Four skill scoreswere then derived: proportion correct (PC),
critical suc-cess index (CSI), probability of detection (POD) and
falsealarm ratio (FAR) (Figure 7). Each of these measures offit
suggests distinct analysis of the results (Wilks, 2006).
The index PC is simply the fraction of the total amountof pixels
in agreement between model simulation andPadovani’s estimate,
indistinctly whether wet or dry. Itranges from 0 (no agreement) to
1 (perfect agreement),and means the area correctly predicted by the
model.For instance, the PC was used as a measure of fit
ofinundation models by Bates and De Roo (2000) andPearson et al.
(2001).
The CSI is similar to PC, but accounting for onlythe agreement
of wet pixels and disregarding the correctsimulation of dry pixels,
under the assumption that it isrelatively easier to correctly
predict non-flooded areas.The CSI may also be interpreted as the
ratio betweenthe intersection of simulated and estimated flooded
areasand the combination of them. It ranges from 0, when nooverlap
occurs between flooded areas of simulated andestimated inundation
maps, to 1, when there is exactly acoincidence. This is by far the
most widely used measureof fit for evaluating simulated inundation
extent againstestimates from others sources (Bates and De Roo,
2000;Horritt and Bates, 2001a; Bates et al., 2005; Tayefi et
al.,2007; Wilson et al., 2007).
The POD skill score, also known as hit rate, meansthe fraction
of the pixels estimated as wet which werecorrectly simulated as so,
ranging from 0 to 1 (the higherthe value the better the
performance). The FAR meansthe fraction of the pixels estimated as
dry which werewrongly simulated as wet, also ranging from 0 to 1,
butthe smaller the value the better the performance. Theseindices
are mostly used for comparing spatial fields ofprecipitation and
other meteorological variables (Wilks,2006), but also provide
interesting analysis for floodplaininundation assessment.
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RESULTS AND DISCUSSION
Computation time and performance
To evaluate the gain of introducing the parallelizationscheme
via OpenMP for part of the floodplain model, theSIRIPLAN was run
for the UPRB in a sequential modeand further considering two and
four processor cores inthe parallelization. The three runs were
performed in aquad core Intel processor 3 GHz with 4 GB RAM.
The computation time required in each run is shownin Table II.
When running sequentially, the run time wasgreater than 4 h. This
run time was reduced by 45% whenadopting a two cores
parallelization and by 67% whenparallelizing with four cores.
Parallel speedup (run timeof parallel execution divided by run time
of sequentialexecution) equal to 1Ð82 and 3Ð07 was obtained for
twoand four cores parallelization, respectively. In terms
ofparallel efficiency (speedup divided by the number ofprocessor
cores), running in parallel with two and fourcores resulted in
values of 0Ð91 and 0Ð77, respectively.
The values of parallel speedup and efficiency obtainedwith
SIRIPLAN in this study were similar to the bestresults presented by
Neal et al. (2009), who ran theLISFLOOD-FP model applied to several
different studycases considering the OpenMP parallelization
technique.
Flow regime along main channels
A very reasonable model fit was obtained in terms ofreproducing
main channel flow along the Paraguay River
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and its tributaries, as indicated by the performance mea-sures
comparing observed and calculated hydrographsshown in Table III,
relative to the period from 1 Decem-ber 1997 to 31 December 2006
(the antecedent periodwas disregarded due to initial conditions
influence).
For the gauging stations located at the tributaries, theadjusted
Manning coefficients ranged from 0Ð02 to 0Ð055,and were obtained NS
and NSlog coefficients rangingfrom 0Ð75 to 0Ð94 and from 0Ð80 to
0Ð97, respectively.The volume error for these stations was less
than 10%in absolute value, except for the Ilha Camargo station(V D
�13Ð5%), while the RMSE ranged from lessthan 20 m3/s at P. Cirı́aco
(Aquidauana River) to near100 m3/s at P. Taiamã (Cuiabá
River).
The model was capable to reproduce the general shapeof observed
hydrographs at the tributaries, as illustratedby visually comparing
observed and calculated hydro-graphs at P. Cercado, P. Taiamã and
P. Cirı́aco gaugingstations (Figure 8a–c, respectively). For
instance, thesethree cases exemplify the complexity of flow regime
ofrivers flowing along Pantanal. There is a small over-estimation
trend on calculated seasonal peak flows atP. Cercado station, of
about 10% for the wettest years,while at P. Taiamã and P. Cirı́aco
there is an underesti-mation trend of up to 15% and 5% on
calculated seasonalpeak flows, respectively. For these three
gauging stations,there are insignificant differences between
observed andcalculated recession flows.
Table II. Run time and performance of the SIRIPLAN hydrologic
system applied to the Upper Paraguay River Basin
Run type Run time Performance relative to single core
Run-time reduction Speedup Efficiency
Sequentially 4 h 23 min 47 s — — —Parallel two cores 2 h 25 min
10 s 45% 1Ð82 0Ð91Parallel four cores 1 h 26 min 25 s 67% 3Ð07
0Ð77
Table III. Performance measures of SIRIPLAN hydrologic system in
simulating main channel flow along Paraguay River and
itstributaries
Reference to Figure 6 Station names River Drainage area (km2)
Statisticsa
RMSE (m3/s) NS NSlog V (%)
1 B. Melgaço Cuiabá 27 050 70Ð2 0Ð94 0Ð97 �5Ð82 P. Cercado
Cuiabá 38 720 46Ð1 0Ð91 0Ð92 �4Ð63 S. João Cuiabá 39 908 50Ð2
0Ð82 0Ð84 �8Ð84 I. Camargo Cuiabá 40 426 85Ð3 0Ð78 0Ð80 �13Ð55 S.
J. Borireu S. Lourenço 24 989 26Ð6 0Ð92 0Ð94 4Ð96 S. J. Piquiri
Piquiri 28 871 89Ð2 0Ð75 0Ð82 8Ð97 P. Taiamã Cuiabá 96 492 98Ð5
0Ð90 0Ð92 �2Ð18 P. Alegre Cuiabá 104 408 79Ð8 0Ð82 0Ð85 8Ð39 P.
Cirı́aco Aquidauana 19 204 18Ð0 0Ð76 0Ð83 �3Ð510 Descalvados
Paraguay 48 360 79Ð3 0Ð91 0Ð92 �5Ð011 P. Conceição Paraguay 65
221 80Ð1 0Ð63 0Ð62 7Ð612 Amolar Paraguay 246 720 180Ð7 0Ð67 0Ð72
6Ð313 P. S. Francisco Paraguay 251 311 258Ð7 0Ð70 0Ð73 �2Ð014 P.
Manga Paraguay 331 114 191Ð3 0Ð82 0Ð76 2Ð515 P. Murtinho Paraguay
581 667 343Ð5 0Ð61 0Ð65 �6Ð1a To exclude the effect of initial
conditions, statistics were calculated for the period from 1
December 1997 to 31 December 2006.
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12 A. R. PAZ ET AL.
-800
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0
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800
98 99 00 01 02 03 04 05 06 07
Qobs Qcalc Qlat(a)
(b)
(c) (f)
P. Cercado (Cuiabá River)
P. Ciríaco (Aquidauana River)
P. Conceição (Paraguay River)
P. Manga (Paraguay River)
Dai
ly fl
ow (
m3 /
s)D
aily
flow
(m
3 /s)
P. Taiamã (Cuiabá River)
(d)
(e)Amolar (Paraguay River)
Dai
ly fl
ow (
m3 /
s)
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Qobs Qcalc Qlat
0
500
1000
1500
2000
2500
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98 99 00 01 02 03 04 05 06 07
Qobs Qcalc Qlat
-600
-500
-400
-300
-200
-100
0
100
200
98 99 00 01 02 03 04 05 06 07
Qobs Qcalc Qlat
Figure 8. Comparison of calculated (Qcalc) and observed (Qobs)
hydrographs at three gauging stations located at tributaries and
three stations ofParaguay river; Qlat is the lateral flow exchanged
between main channel and floodplain along the following river
reaches: (a) from B. Melgaçoto P. Cercado; (b) from the confluence
of Piquiri and Cuiabá Rivers to P. Taiamã; (c) from Aquidauana to
P. Cirı́aco; (d) from Descalvados to P.Conceição; (e) from the
confluence of Cuiabá and Paraguay Rivers to Amolar and (f) from P.
S. Francisco to P. Manga; Qlat 0 means flow in the opposite
direction
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In the graphs of Figure 8, Qlat means the calculatedlateral flow
exchanged between main channel and flood-plain along the upstream
river reach specified on the cap-tion of the figure for each case,
being negative if flowingfrom the channel to floodplain and
positive if flowing inthe opposite direction. Along the 107 km
reach of CuiabáRiver upstream of P. Cercado, was simulated a huge
lossof water from channel to floodplain during rising limb offlood
hydrograph, with Qlat achieving up to �600 m3/s(around 8% greater
than flood peak along main chan-nel), and a gain of water after
flood peak flow of up
1213141516171819202122
to 180 m3/s. Meanwhile, no water exchanges betweenchannel and
floodplain were simulated for the river reachupstream of P. Taiamã
station.
At P. Cirı́aco station, located on the AquidauanaRiver 230 km
downstream from Aquidauana station(boundary condition), the
observed hydrograph presentsa marked maximum value of 150 m3/s. At
Aquidauanastation, observed peak flow reaches up to 700 m3/s.
Thisenormous reduction of channel flow in this river reachwas well
represented by the model, which simulatedlateral exchanges of water
from channel to floodplain of
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0 100 km50
A. C. Grande (boundary condition) S. J. Borireu
S. Jerônimo (boundary condition) S. J. Piquiri
Dai
ly fl
ow (
m3 /
s)D
aily
flow
(m
3 /s)
(a) (b)
(c) (d)
Figure 9. (a) and (c) Observed hydrographs at the boundary
conditions of S. Lourenço (A. C. Grande station) and Piquiri (S.
Jerônimo) rivers and(b) and (d) comparison between calculated
(Qcalc) and observed (Qobs) hydrographs at their downstream gauging
stations, also showing lateral flow
exchanged between main channel and floodplain along each river
reach between the boundary condition and the downstream station
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up to 500 m3/s during flood peaks. The maximum lateraldischarge
simulated corresponds to 3Ð3 times peak flowalong main channel at
P. Cirı́aco. During the dry period,no water drainage from the
floodplain was simulated andthe observed recession flow at this
station was also wellreproduced.
As at P. Cirı́aco, a marked maximum flow (of about400 m3/s) on
observed hydrograph is also seen at S. J.Borireu station, located
on the S. Lourenço River, whichwas well reproduced by the model
(difference less than5%) (Figure 9a and b). Along the 250 km long
reachbetween this station and the upstream boundary condition(A. C.
Grande station), the model simulated lateral flowsof up to 750 m3/s
from main channel to floodplain.
In the reach of the Piquiri River upstream of S. J.Piquiri
station (80 km downstream from S. Jerônimo,taken as boundary
condition), the exchanges of waterbetween floodplain and main
channel was simulated asoccurring in the opposite direction of that
reported tothe S. Lourenço River (Figure 9c and d). A gain of
waterfrom the floodplains to the main channel was simulated inthis
reach of Piquiri River, totalling up to 400 m3/s duringthe floods.
This gain of water represents almost 50% ofthe water flowing along
the main channel at S. J. Piquiristation. In fact, while at S.
Jerônimo observed peak flowranges between 400 and 700 m3/s, at S.
J. Piquiri thisrange is between 400 and 1100 m3/s. The increase
in
282930313233343536373839404142434445464748495051525354
observed peak flow from upstream to downstream isdue to lateral
floodplain contribution, which the modelwas capable to simulate.
The estimated hydrograph ofthis lateral gain of water to main
channel presents asmall time delay relative to channel flood peak.
Duringdry periods, this hydrograph reached null values,
whichallowed recession flow at S. J. Piquiri to be quite
wellreproduced. Most interestingly is that the major part ofthe
contribution of floodplain to main channel of PiquiriRiver at this
location during floods was resultant fromthe volume of water lost
by the main channel of theS. Lourenço River, 35 km to North, which
flowed alongfloodplains.
Owing to large drainage areas and complexity ofprocesses
involved, including contributions of tributariesthat may occur both
through main channel and floodplainflows, reproduction of flow
regimes along the ParaguayRiver is even more difficult than along
its tributaries.However, the model was able to reproduce the
seasonalflow regime along the Paraguay River, as illustratedby the
performance measures comparing observed andcalculated flows at six
gauging stations (Table III). TheNS and NSlog coefficients ranged
from 0Ð61 to 0Ð91 andfrom 0Ð62 to 0Ð92, respectively. RMSE were
obtainedbetween 80 and 343 m3/s, which seem to be largeerrors in
absolute terms, but correspond roughly to lessthan 13% of average
peak flow in each station: 7%
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98 99 00 01 02 03 04 05 06 07
98 99 00 01 02 03 04 05 06 07
98 99 00 01 02 03 04 05 06 07
Cáceres - Descalvados
P. S. Francisco - P. Manga
Descalvados - P. Conceição
P. Manga - P. Murtinho
Dai
ly fl
ow (
m3 /
s)D
aily
flow
(m
3 /s)
Dai
ly fl
ow (
m3 /
s)
P. Conceição - Amolar Amolar - P. S. Francisco-800
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0
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600
800
98 99 00 01 02 03 04 05 06 07
Qlat < 0: flow from channel to floodplain
Qlat > 0: flow from floodplain to channel
Figure 10. Lateral exchanges of water between main channel and
floodplain simulated by SIRIPLAN along the modelled reach of
Paraguay River,separated into six river reaches between each, two
consecutive gauging stations: Cáceres, Descalvados, P.
Conceição, Amolar, P. S. Francisco,
P. Manga and P. Murtinho
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at Descalvados, 12% at P. Conceição, 11% at Amolar,13% at P.
S. Francisco, 9% at P. Manga and 13%at P. Murtinho. In terms of
volume error, the resultsobtained ranged from �6Ð1% at P. Murtinho
to 7Ð6% atP. Conceição station. Manning coefficients ranged
from0Ð012 to 0Ð055.
Hydrographs along Paraguay River have marked sea-sonality, as
can be seen on Figure 8d (P. Conceiçãostation), Figure 8e
(Amolar) and Figure 8f (P. Manga),which were quite well reproduced
by the developedmodel, despite some discrepancies between observed
andestimated hydrographs, as the overestimation of recessionflows
and underestimation of peak flows in some years.
It is important to highlight the model ability
fordifferentiating the intensity of the seasonal flood amongyears.
For instance, at P. Manga station, which has a
17181920212223242526272829303132
drainage area greater than 330 000 km2, the SIRIPLANwas able to
estimate the reduced peak flows (less than1800 m3/s) of the floods
of the years 2001 and 2005 andthe large flood of 2002 (peak flow
around 2700 m3/s).
The simulated lateral flow in the Paraguay River reachfrom P. S.
Francisco to P. Manga (almost 200 km length)was negligible, while a
loss of water from main channelto floodplain achieving peak flows
up to 600 m3/s wasestimated for the reach between Descalvados and
P.Conceição (¾120 km). Along the 21-km long reachdownstream of
the confluence of Cuiabá River up toAmolar station, a gain of
water from floodplain to mainchannel was simulated. This gain
occurred throughoutthe entire year, with peak flows up to 330 m3/s
in theperiod June–July and flows up to 30 m3/s in the
othermonths.
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To better analyse the channel–floodplain waterexchanges along
the modelled reach of the ParaguayRiver, the estimates of lateral
flows for each reach delim-ited by two consecutive streamflow
gauging stations isshown in Figure 10. This figure shows distinct
patternsof lateral water exchanges along the upper, middle andlower
reaches of the Paraguay River. A loss of waterfrom channel to
floodplain prevails in the most upperpart of the Paraguay River,
from Cáceres (boundary con-dition) to Descalvados station.
Simulated lateral flowsfrom channel to floodplain achieved peaks of
up to650 m3/s in the reach between Cáceres and Descalvados,and up
to 590 m3/s in the reach between Descalvados andP. Conceição. In
the reach Cáceres–Descalvados, resultsshow that water flows from
channel to floodplain mostlyduring the period December–April and in
the oppositedirection during the period May–July, with null
flowsfrom August to November. In the downstream
reach(Descalvados–P.Conceição), null lateral flows were
sim-ulated from July to November, with a loss of water fromchannel
to floodplain over the rest of the year.
In the middle part of the Paraguay River, downstreamof P.
Conceição station and upstream of P. S. Fran-cisco, the simulated
lateral exchanges of water werepredominantly a gain from
floodplains to main chan-nel. Indeed, the model simulated that this
reach of theParaguay River receives contribution propagated from
itsupstream floodplains and also drained by the floodplainsof
Cuiabá River. The simulated lateral peak flows wereup to 800 m3/s
in the reach between P. Conceição andAmolar, and up to 620 m3/s
in the reach between Amolarand P. S. Francisco. In the former
reach, lateral water lossfrom channel to floodplain was simulated
in the periodDecember–March, with flows in the opposite
directionduring the following months. In the latter reach, a gainof
water from floodplain to channel was simulated asoccurring over the
entire year.
For the lower part of the Paraguay River, from P. S.Francisco to
P. Murtinho station, simulated lateral flowswere relatively small,
in comparison to the flows ofthe upstream reaches. Along the reach
between P. S.Francisco and P. Manga, these flows were
approximatelynull, while a gain of water less than 200 m3/s
wassimulated along the reach between P. Manga and P.Murtinho
stations.
Floodplain inundation
Typical inundation maps of a dry and wet periodare shown in
Figure 11, relative to the dates 6 October2004 and 13 February
2005, respectively. The estimatesof inundation extent produced by
Padovani (2007) forthese same dates are also shown in this figure.
Thecorrespondent measures of fit between simulated (ourresults) and
estimated (Padovani’s results) inundationmaps are given in Table
IV.
The model was capable to reproduce part of the majorpermanent
inundated areas during the dry period, whichare exclusively due to
water spilling from main chan-nels and flowing along floodplain.
These areas are located
Simulated
0 100 20050 km
6 October 2004
13 February 2005
Simulated
Estimated by Padovani (2007)
Estimated by Padovani (2007)
Figure 11. Inundation maps of Pantanal simulated and estimated
byPadovani (2007), for two dates: 6 October 2004 (dry period) and
13
February 2005 (wet period)
Table IV. Skill scores of the comparison between inundationmaps
estimated by Padovani (2007) and simulated with SIRI-
PLAN, at two dates
Accuracymeasure
Dry period(6 October 2004)
Wet period(13 February 2005)
PC 0Ð60 0Ð57CSI 0Ð24 0Ð51POD 0Ð37 0Ð59FAR 0Ð60 0Ð23
606162636465666768697071727374
along the north and central portions of Paraguay River,in the
reach between Descalvados and P. Manga gaug-ing stations, along the
floodplains of the lower reachof Cuiabá River and along both
margins of the TaquariRiver. Also, the inundation along Taquari
floodplains isconsistent with the expected pattern, as this region
com-prises the distributary fan lobe of the Taquari alluvialmegafan
(Assine, 2005). However, considering the esti-mates of Padovani
(2007) as correct, these major flooded
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areas were underestimated by the model, as is clear byvisual
comparison of both maps. This underestimationresulted in the low
CSI and POD skill scores. About 60%(PC D 0Ð60) of the pixels were
in agreement betweenthese two inundation maps, i.e. 60% of the area
was wetor dry simultaneously on both maps. However, disregard-ing
the coincident dry pixels on both maps, the agreementbetween them
reaches 24% (CSI D 0Ð24). From the areaestimated as flooded in
Padovani’s work, 37% was alsoflooded in the simulated map (POD D
0Ð37). On thecontrary, the obtained FAR score means that, from
thearea simulated as flooded, 60% was estimated as dryby Padovani
(2007), and this relatively high value ismostly due to dispersed
isolated pixels wrongly simu-lated as flooded by the model. In
terms of total area,the model simulated 40 491 km2 as flooded
areas, whichcorresponds to 29Ð2% of the Pantanal, while the
esti-mates of Padovani (2007) indicate an inundation extentof 45
135 km2 (32Ð6% of total) (Table V).
During floods, the loss of water from main channels
tofloodplains is increased and the most important floodedareas
identified in the dry period become larger anddeeper. However, the
major difference between inunda-tion maps of dry and wet periods is
that in the wet periodthe flooded areas cover a much larger
extension along thewhole domain. Although with prevailing shallow
waterdepths, the simulated flooded area on 13 February 2005covers
almost twice the extent estimated at 6 October2004, i.e. a flooded
area of about 76 406 km2 or 55Ð2%of the entire Pantanal. The
estimates of Padovani (2007)show an even larger flooded area, of
about 100 393 km2
(72Ð5% of total), and indicate again an underestimationtrend on
model results, but weaker than that for the dryperiod. In terms of
skill scores, the general agreementbetween simulated and estimated
inundation maps wasincreased in comparison to the dry period.
Although thePC index was almost equal between the two periods,
theCSI and POD indices were quite improved at this time,with CSI D
0Ð51 and POD D 0Ð59. Also, the FAR hasdecreased (FAR D 0Ð23),
meaning that only 23% of thearea simulated as flooded was dry in
the inundation mapof Padovani (2007).
In comparison to others studies of floodplain inunda-tion
modelling, our CSI scores are relatively similar withthem. For
instance, the greater difficulty to reproduce theinundation extent
during the dry period is also pointed
47484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192
out by Wilson et al. (2007), which was the unique previ-ous
study žwe found that assessed inundation map during AQ4dry period.
Those authors used the LISFLOOD-FP modelto simulate part of the
Amazon River and Purus trib-utary, obtaining CSI D 0Ð23,
approximately the samescore we achieved. They state that their
model inabil-ity to simulate low water inundation extent is
mostlydue to not including floodplain vertical hydrological
pro-cesses and the SRTM DEM aggregation, which makesdifficult the
representation of complex, small-scale topog-raphy controlling part
of the floodplain drying out pro-cess. Although we have included
representation of evap-otranspiration and infiltration processes,
the simplicity ofadopted schemes together with the aggregation of
SRTMDEM to the 2 km resolution may have reduced modelcapability on
reproducing the full drainage of the flood-plain. The sparse
pluviometer network and uncertaintieson precipitation estimates may
also have contributed tothis model inability. For the wet period,
our CSI scoreof 0Ð51 is similar to the lower limit of the range
ofresults obtained by others authors varying model param-eters or
structure, such as Wilson et al. (2007), Tayefiet al. (2007),
Horritt and Bates (2001b) and Bates andDe Roo (2000).
As stated before, during the dry period, the inundationextent
was almost limited to the major permanent floodedareas resultant
from water spilling from main channel tofloodplains. During the wet
period, regions not directlyconnected to overbank flow from main
channels wereflooded due to delayed drainage of precipitation.
Thisinput of water to the floodplain gives origin to localwater
accumulation which drains slowly, or is evaporatedin the following
dry period, resulting in a markedseasonal variation in total
inundated area as illustratedin Figure 12. Peaks of total inundated
areas simulatedby the model ranged from 100 000 to 126 000 km2
alongthe simulation period, which are similar to the maximumvalues
of inundation estimated by Hamilton et al. (1996)for a different
period (1979–1987). The total inundatedareas during dry periods
simulated with SIRIPLANranged from 35 000 to 45 000 km2, while the
mentionedstudy estimated much smaller minimum inundated areas,of up
to 11 000 km2. This result could indicate anoverestimation of our
inundated area during dry period.However, given that the estimate
of inundation extentof Padovani (2007) for the date 6 October 2004
(dry
Table V. Flooded and dry total areas over Pantanal on two dates
simulated by SIRIPLAN and estimated by Padovani (2007)
Floodplain Dry period (6 October 2004) Wet period (13 February
2005)
Simulated Estimated byPadovani (2007)
Simulated Estimated byPadovani (2007)
Area(km2)
Percentage oftotal area
Area(km2)
Percentage oftotal area
Area(km2)
Percentage oftotal area
Area(km2)
Percentage oftotal area
Flooded 40 491 29Ð2 45 135 32Ð6 76 406 55Ð2 100 393 72Ð5Dry 97
946 70Ð8 93 302 67Ð4 62 032 44Ð8 38 044 27Ð5Total 138 437 100Ð0 138
437 100Ð0 138 437 100Ð0 138 437 100Ð0
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(a)D
aily
inun
date
d ar
ea (
× 10
00 k
m2 )
0
20
40
60
80
100
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140
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40
60
80
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N J M M J A
Hamilton et al. (1996) Simulated
(b)
Ave
rage
mon
thly
inun
date
dar
ea (
× 10
00 k
m2 )
D F A J OS
Figure 12. (a) Daily inundated areas simulated over Pantanal
[the horizontal grey lines represent the maximum and minimum values
estimated byHamilton et al. (1996) for the period 1979–1987] and
(b) average monthly inundated areas simulated along the period from
1 January 1998 to 31
December 2006 and estimated by Hamilton et al. (1996) for the
period 1979–1987
Flood occurrence during>5% of simulation period
0 100 20050 km
Flood occurrence during>25% of simulation period
Flood occurrence during>75% of simulation period
Figure 13. Maps showing areas subject to inundation during
frequencies greater than 5%, 25% or 75% of simulation period
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period) corresponds to an area of about 45 000 km2
and seems consistent to expected inundation patterns ofPantanal,
may be the results of Hamilton et al. (1996)are underestimated or
their period of analysis was muchmore drier than our area.
Comparison of average monthly estimates shows thatin our study
the peak of flooding occurred between 1 and2 months in advance
relative to the results of Hamiltonet al. (1996) (Figure 9b).
Again, it can be noted thedifference on inundated areas in the dry
period betweenthe two studies. Nevertheless, it is worth noting
theimportance of including the vertical water balance onfloodplain
modelling and the capability of SIRIPLAN tosimulate the Pantanal
seasonal flood pulse.
The model capability to simulate the major permanentflooded
areas are also highlighted by maps shown inFigure 13, which
provides an analysis of simulatedinundation frequency spatially
distributed over Pantanal.The maps in this figure show the areas
that wereinundated during time periods greater than 5%, 25% and75%
of the simulation period (considering the 9 years
23242526272829303132333435363738394041424344
from 1 January 1998 to 31 December 2006). Theseinundation
frequencies were calculated regardless ofbeing during consecutive
days or not. Approximately32% (43 624 km2) of the Pantanal was
flooded duringmore than 75% of the simulation period, while 58%(80
330 km2) of Pantanal was flooded during more than25% of the
simulation period. This area increases to115 033 km2 (83% of total)
when the 5% frequencythreshold is considered, and it goes to the
limit of100% of Pantanal area as the threshold approaches zero,i.e.
the entire Pantanal was flooded in at least 1 dayof the simulation
period. On the contrary, when thefrequency threshold approaches
100%, i.e. consideringsolely pixels which were strictly permanently
inundated,the area covers roughly 22% of entire Pantanal (¾30
000km2).
SUMMARY AND CONCLUSIONS
This paper presents the hydrologic simulation systemSIRIPLAN,
developed for simulating the flow regime
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and spatial inundation over large-scale networks of riversand
floodplains. The SIRIPLAN couples the 1D hydro-dynamic model IPH4
for simulating main channel flowto a 2D raster-based floodplain
model, which simulatesthe floodplain inundation dynamics. Auxiliary
modulessimulate the vertical water processes of
precipitation,infiltration and evapotranspiration over floodplains
andwater exchanges between channels and floodplains.
The application example of the SIRIPLAN to theUPRB, which
includes the Pantanal, one of the largestwetlands of the world,
showed the viability and adequacyof the proposed approach. A total
of 3965 km of mainchannels and 140 000 km2 of floodplains were
simulatedfor a time period of 11 years. The computational
routinesdeveloped for establishing the topological
connectionsbetween channel and floodplain discretization
elementsstrongly reduced the effort and time needed on inputdata
preparation. Additionally, the use of a parallelizationscheme
through OpenMP method for two loops of thefloodplain model has
proven to be a satisfactory wayto reduce run time, which may allow
higher level offloodplain spatial discretization.
The model was capable to reproduce the flow regimealong main
channels of Paraguay River and its tributaries.Distinct cases were
satisfactorily simulated, such as riversthat present enormous loss
of water from main channelto floodplain during the floods, rivers
where this lossoccurs during both the flood and dry periods, rivers
wherethere is a gain of water from floodplains to main channeland
rivers which do not exchange water laterally. Forinstance, it must
be emphasized that the ability of theproposed model to simulate the
complex behaviour ofchannel–floodplain interactions specifically in
the regionof the S. Lourenço and Piquiri Rivers, in which thewater
spills over the channel of the S. Lourenço River,inundates the
floodplain and propagates over it untilreaching and contributing to
the flow of the main channelof the Piquiri River.
The SIRIPLAN was also able to reproduce the Pantanalseasonal
flood pulse, with estimates of inundated areavarying from 35 000 to
45 000 km2 in the dry period andranging from 100 000 to 126 000 km2
in the wet period.These estimates were consistent with the results
obtainedby a earlier study, which was based on
coarse-resolutionsatellite images and analysed a distinct period of
time,but with