Large scale hydrologic and hydrodynamic modeling using limited data.pdf

8/10/2019 Large scale hydrologic and hydrodynamic modeling using limited data.pdf

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et al., 2002), in the transport of dissolved and particulate materials(e.g.Bourgoin et al., 2007), in biogeochemical processes and in theglobal carbon cycle (Richey et al., 2002;Zhang et al., 2002). Flood-plains may also influence feedback effects between land surfaceand atmosphere (Mohamed et al., 2005; Collini et al., 2008; Bettset al., 1996; Silva Dias et al., 2004). The explicit simulation of floodinundation could provide the basis for the representation of all

these processes.Hydrodynamic flow routing in large-scale hydrological modelscould also provide results of other flow related variables, such asriver stage, velocity and slope. These models could also be usedas a basis for transport models by solving the advectiondiffusionequation and sediment transport equations.

One dimensional (1D) hydrodynamic river models are welldeveloped since the late 1970s (Cunge et al., 1980), and there areeven well known packages for 1D hydrodynamic modeling (USACE,2002; DHI, 2003). These models have been applied in relativelylarge-scale problems (Paz et al., 2010; Remo and Pinter, 2007;Biancamaria et al., 2009; Lian et al., 2007), however their use with-in large-scale distributed hydrological models, which also repre-sent rainfall-runoff processes, is uncommon. There are alsoapproaches for flood inundation simulation ranging from simpli-fied models such as a composite cross section (Cunge et al.,1980) to 2D models (Galland et al., 1991; Bates and De Roo, 2000).

The reason why simple flow routing methods are still preferredwithin large-scale hydrological models is probably related to thelower input data needs and the lower computational burden theyimpose, while still providing reasonable results in several cases.Hydrodynamic river models need river cross section data that areusually surveyed only for relatively short reaches. Hydrodynamicmodels are also more computational intensive than simplified flowrouting methods, and require more detailed information on bound-ary and initial conditions.

However, computing capacity keeps growing, and also new andhigher accuracy remote sensing data such as the Digital ElevationModel (DEM) from the Shuttle Radar Topography Mission (SRTM)

(Rabus et al., 2003; Farr et al., 2007) can be used to provide someof the information that is needed to compute parameters for largescale hydrodynamic models. In a recent example, Trigg et al. (2009)showed that the use of a simplified bathymetry, i.e. a wide rectan-gular channel approximation and a simple bed slope, does notintroduce significant errors in the hydrodynamic simulation re-sults in part of the central Amazon.

In the present paper, we describe the development of a modelfor large scale hydrologic and hydrodynamic modeling. The modeluses limited data that can be usually found in global scale for mostriver basins, mainly SRTM DEM. GIS methods are used to extractthe needed information from SRTM DEM. The proposed model isbasically an improvement of the MGB-IPH large scale hydrologicalmodel (Collischonn et al., 2007). First, a brief description of the

MGB-IPH hydrological model is presented (Section2). Later, detailsconcerning the hydrodynamic model equations, then a representa-tion of the floodplains and the numerical scheme are shown (Sec-tion3). We also present the GIS based algorithms developed toextract the hydrodynamic model parameters from the DEM (Sec-tion4) and a simple approach for 2D flood inundation simulation(Section5). The model is tested in a case study of the river Purus,one of the major tributaries of the Amazon (Section 6). Other re-sults for an even larger region, and comparisons with simplifiedflow routing methods are presented byPaiva et al. (in review).

2. The hydrological model

The MGB-IPH model (Modelo Hidrolgico de Grandes Bacias)is a large scale distributed hydrological model. It is similar to other

large scale hydrological models such as LARSIM (Ludwig andBremicker, 2006) and VIC (Liang et al., 1994; Nijssem et al.,1997). It is a process based model that uses physical and concep-tual equations to simulate the terrestrial hydrological cycle: soilwater budget, energy budget and evapotranspiration, interception,superficial, sub-superficial and groundwater flow generation androuting and river flow routing. It uses a daily or hourly simulation

time step.The early version of the MGB-IPH model was based on a squarecell discretization of the river basin (Collischonn et al., 2007), whilethe version described in the present paper uses a division of the ba-sin in small catchments using the ArcHydro methods (Maidment,2002). Each catchment is subdivided into Hydrological ResponseUnits (HRUs) which are areas with similar hydrological behaviorand defined by a combination of soil and land cover maps (Beven,2001; Kouwen et al., 1993). Vertical water and energy budgets arecomputed independently for each HRU in each catchment. Canopyinterception is represented by a reservoir with maximum storageas a function of vegetation leaf area index. Soil water budget iscomputed simulating the soil as a single water reservoir. Soil infil-tration and runoff are computed based on the variable contributingarea concept of the ARNO model (Todini, 1996), which is also usedin the PDM (Moore and Clarke, 1981), VIC2L and LARSIM models.Energy budget is computed using basic surface meteorologicalvariables and evapotranspiration from soil, vegetation and canopyinto the atmosphere is estimated based on the Penman Monteithapproach (Monteith, 1965; Shuttleworth, 1993; Allen et al.,1998). Subsurface flow is computed using an equation similar tothe Brooks and Corey unsaturated hydraulic conductivity equation(Rawls et al., 1993). Percolation from soil layer to groundwater iscalculated according to a simple linear relation between soil waterstorage and maximum soil water storage. Then, the flow generatedwithin each catchment is routed to the stream network using threelinear reservoirs (base flow, subsurface flow and surface flow). Adetailed description of the model is given by Collischonn et al.(2007), and recent applications of the model are presented byGet-

irana et al. (2010), Collischonn et al. (2008), andCollischonn et al.(2005).

Until now, river flow routing within the MGB-IPH model used tobe calculated using the Muskingum Cunge method, but in this pa-per we describe a mixed method, which employs a hydrodynamicmodel in flat reaches of the main rivers and the MuskingumCungemethod in the headwater parts of the drainage network, whereslope is usually higher.

3. The hydrodynamic model

The hydrodynamic model is based on the IPH-IV model, firstdeveloped byTucci (1978). The model solves the full Saint Venantequations (Cunge et al., 1980):

@Q

@x b

@h

@tqcatqfl 1

@Q

@t 2v

@Q

@x gA v2b

@h

@x v2

@A

@x

hcte

gAS0Sf 2

where the first and second equations are the 1D channel mass andmomentum conservation laws,Q(m3 s1) is river discharge,t(s) istime,x(m) is river longitudinal space coordinate,b(m) is river crosssection width at free surface elevation, qcat(m

2 s1) is local catch-ment lateral inflow (the sum of the surface, subsurface and baseflow from the catchment), qfl(m

2 s1) is the river-floodplain flowexchange, h (m) is water depth, v(m s1) is flow velocity averagedover the cross section, g (m s2) is acceleration due to gravity, A

(m2

) is the cross sectional flow area perpendicular to the flow direc-tion and S0(m m1) and Sf(m m

1) are the bed slope and friction

R.C.D. Paiva et al. / Journal of Hydrology 406 (2011) 170181 171


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slope in thex-direction. Friction slope is estimated using Manningsequation. Flow at river confluences is modeled using a simple masscontinuity equation and the energy equation discarding energylosses and the kinetic term (Cunge et al., 1980). It is also possibleto model hydraulic river singularities such as dams or other hydrau-lic structures using internal boundary conditions.

The river reaches are discretized into several river cross sections(Fig. 1) where the hydraulic variables are computed. The modelalso divides the catchments into floodplain units (Fig. 1). Theseare areas between two river cross sections where the river-floodplain flow exchange and floodplain water storage arecomputed.

Flood inundation is simulated using a simple storage model(Cunge et al., 1980), which assumes that (i) the flow velocity par-allel to the river direction is null on floodplains, (ii) the floodplainunits act only as storage areas and (iii) the floodplain water levelequals the water level at the main channel. Considering the modelbasic assumptions and the mass conservation law, theriver-floodplain flow exchange qflequals:

qflAflz

dx

@h

@tLz

@h

@t 3

whereAfl(m2) is the flooded area andL(m) is the floodplain equiv-alent width, measured for each floodplain unit, as described inSection4.5.

This is a simplistic approach that cannot fully represent all as-pects of floodplain hydrodynamics such as floodplain water levelsthat are not equal to the adjacent main channel, floodplain flowssubparallel to the main river channel, and bidirectional floodplainflows, that where observed byAlsdorf et al. (2007)using remotesensing data in the Amazon. Additionally, as observed byAlsdorfet al. (2003), water level changes in the main channel may not in-stantly propagate across the entire floodplain, as is implicitly con-sidered in the model described here. However, the proposedmodel, which is relatively complex in terms of river hydraulics,but somewhat simplified in terms of floodplain simulation, maybe appropriate because (i) this model is oriented to large scaleapplications and aimed at providing discharge, water level andflood extent results, by simulating translation and diffusion offlood waves, backwater effects and the influence of floodplain stor-age on flood waves, (ii) it is not oriented for studying smaller scalefloodplain hydraulics, (iii) it is compatible with large scale applica-

tions, different from complex floodplain models which requirehigher computational resources and detailed floodplain bathyme-try data, that cannot be provided by current global scale DEMsand (iv) it is in accordance withTrigg et al. (2009), who stated thatit is important to get the hydraulics of the main channel right be-fore tackling the more complex interactions with the floodplain.

The partial differential equations of the model are solved using

a linear and implicit finite difference numerical scheme developedby Chen (1973), similar to the Preissman scheme (Cunge et al.,1980). Since the model simulates a river network with lots of con-fluences, the set of discretized equations forms a non symmetricsparse linear system. Then, for better computational efficiency,the matrix solver uses a modified Gauss elimination procedurebased on a skyline storage method, avoiding the storage of null ele-ments of the linear system of equations. This method was devel-oped byTucci (1978)and is similar to methods used by HEC-RAS(USACE, 2002). In addition, a modification to the method devel-oped byTucci (1978)is proposed. An additional pointer is set inthe beginning of the simulation to store only algebraic operationsthat are actually needed for the Gauss elimination algorithm, i.e.it avoids operations with null elements, and then computes onlythose during the rest of the simulation. This improvement in-creased the speed of calculations but it will not be fully describedhere.

4. GIS based algorithms

Any hydrodynamic model should be applied using data fromdetailed surveys of river cross sections and, when necessary, flood-plain extent. This kind of data is usually available only at smallscale, for relatively short river reaches. For large scale hydrologicmodeling the necessary information has to be derived from glob-ally available data sets.

In our case, which is focused on applications of the model inlarge river basins, most of the necessary data is extracted from Dig-ital Elevation Models (DEM) (e.g. the SRTM DEM described inRa-bus et al., 2003; Farr et al., 2007) and some GIS algorithms. Thealgorithms use a raster representation of topography and all otherspatial variables. The spatial variables are described through matri-ces ofrrows and ccolumns, which store gridded values in r cpositions defined as pixels. The pixel hi,jicorresponds to the ele-ment of a matrix located at row i and columnj. The Digital Eleva-tion Model is denoted by Zhi,jiand provides the terrain elevationin each point hi,ji. The algorithms for model parameter extractioninclude, as presented inFig. 2, (1) DEM preprocessing, (2) river net-work and catchment discretization, (3) river cross section discret-ization, location and topology, (4) river cross section geometry, (5)river cross section bottom level, (6) floodplain geometry. Theseprocedures are presented in the following sections. Examples of re-sults from these algorithms are presented in Section6.3.

4.1. DEM preprocessing and catchment discretization

DEM preprocessing and the river and catchment discretizationalgorithms presented in this section are computed using wellknown algorithms as presented inJenson and Domingue (1988)and available in several GIS packages such as the ArcHydro tools(Maidment, 2002).

The DEM preprocessing algorithms include first the computa-tion of the flow direction dhi,jiraster. Flow direction is a functionof DEM that returns the direction of the largest slope (N, NE, E,SE, S, SW, W, NW or null). Given the flow direction, the coordinatesof the downstream pixel hydrologically connected tohi,jiare givenby the function F:

Fhi;ji hi;ji Ddhi;ji 4Fig. 1. Model discretization into catchments (grey lines), river reaches (black lines),river cross sections (grey dots) and floodplain units (grey thin lines).

172 R.C.D. Paiva et al. / Journal of Hydrology 406 (2011) 170181


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considering D(dhi,ji) a function that returns the relative positionhDi, Dji of the downstream pixel (e.g. D(N) =h+1, +1i orD(NE) =h+1,1i). The flow direction algorithm also includes the re-moval of pits from the DEM to ensure hydrological continuity (Jen-son and Domingue, 1988). The next step is the computation of theflow accumulation raster A hi,ji, that returns for each pixel h i,ji,the sum of the surface area of all upstream pixels (Burrough andMcDonnell, 1998).

Later, using the flow direction and flow accumulation results,the river and catchment discretization is performed. The drainageD hi,jimaps the river network on pixels with flow accumulation

A hi,ji larger than a threshold Amin (Burrough and McDonnel,1998). Then, the river network is segmented into several riverreaches, defined by river segments between upstream and down-stream confluences (or between a confluence and a river source)and each river reach gets a different code. Finally, catchment dis-cretization is performed by identifying pixels located upstream ofeach river reach and the result is the catchment raster B hi,ji. Foreach pixel hi,ji, the algorithms follow downstream the flow pathdefined by the flow direction using the function Funtil it finds ariver reach and assigns B hi,jiequal to the river reach code (Maid-ment, 2002).

4.2. Cross section discretization

A river reach within a catchment is then subdivided in severalshorter sub-reaches, as shown inFig. 1. These sub-reaches are lim-ited by the positioning of model river cross-sections, wherehydraulic variables Qand h are computed. For a given river reach

p, the number of cross sections is defined as np=Lp/Dxp+ 1, whereDxpis the distance between two cross sections and L is the lengthof the reach p. A predefined value of the distance Dxbetween thecross sections is defined considering the spatial scale of interestand the performance of the numerical scheme of the hydrody-namic model (Cunge et al., 1980) and can be estimated a prioriconsidering recommendations such as Dx


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z0

zDEM HHveg if B 6 a res

zDEM HHveg 1 Bares

bres

if B 6 a b res

zMDEH if B>a b res

8>>>:

7

whereH(m),B(m) are the cross section depth and width estimatedas described in Section4.3and a and b are parameters.

Finally, we use a heuristic approach to eliminate random noise(e) from the longitudinal profile to avoid possible numerical insta-bilities on the numerical scheme of the hydrodynamic model. Aniterative moving average filter is applied on z0data:

zi;t1 hzi1;t 1 2h zi;t hzi1;t 8

wherezi,t(m) is the bottom level of the pixeliin the tth iteration, his

a parameter (h= 1/3, which makes the filter a simple moving aver-age), i 1 and i+ 1 are the indexes related to the upstream anddownstream pixels and t is the iteration. Eq. (8) is computeditmax times. Bottom level discontinuities near the confluences are

avoided by solving Eq.(8)in all reaches at the same iteration. A par-ticular form of the filter is applied in pixels located at confluencesand the parameter h is weighted by the upstream drainage area ofeach pixel to avoid small tributaries to generate noise in larger

rivers. At the extreme of the reaches, the filter also takes a specialform: zi,t+ 1= 0.95zi,t+ 0.05zi+ 1,t. The main parameter of the filteris the maximum number of iterations itmax. Its choice is subjectiveand a verification of the results is needed. A large (or small) valueofitmax gives smooth (or noisy) longitudinal profiles. This iterativemoving average filter approach was preferred under a simpler poly-nomial fit for each river reach (e.g. LeFavour and Alsdorf, 2005) be-cause the latter creates discontinuities in the confluences andbetween neighbor river reaches.

4.5. Floodplain geometry

Floodplains are characterized by a function Afl(z,m) that relateswater level zwith flooded area Afl for each sub-catchment con-nected to a sub-reach of the river. These sub-catchments, whichare called here floodplain units, are denoted by P hi,ji and relateeach pixelhi,ji to a cross section located immediately downstream.These are the areas hydrologically connected to the river segmentbetween two cross sections, i.e. a set of pixels that are immediatelyupstream of a cross section (Fig. 1). To compute Phi,ji, we use araster watershed algorithm similar to the one used for catchmentdiscretization (Burrough and McDonnel, 1998). The purpose ofdefining those floodplain units is only to derive local functionsrelating water stage and inundated area. Rainfall-runoff calcula-tions are done at the catchment level only.

The function Afl is defined using the DEM and floodplain unitsrasters. The total flooded area between the cross sections mandm 1 when the average water level equals zis computed as thesum of the surface area of the set of pixels inside the floodplainunitm that are lower thanz:

Aflz; m X

hi;ji2S

dAhi;ji 9

whereS= {hk,li|Phk,li =m, Z hk,li6z},dA(km2) is the pixel surfacearea (m2),Z hi,ji(m) is a corrected DEM. A simple approach to cor-

rect the DEM is to consider a constant value of vegetation heightHveg for the whole DEM and compute Z hi,ji=Zhi,ji Hveg, but

other approaches for DEM correction could also be used such as dif-ferentHvegvalues combined with land cover maps. The floodplain

Fig. 3. River longitudinal profile extracted from DEM, showing original data (red)filtered water line (grey) and estimated river bottom level (black). (For interpre-tation of the references to colour in this figure legend, the reader is referred to theweb version of this article.)

AA AA

A

H

z0

Hve

zDEM

zDEM

A

AA

Fig. 4. Example of two drainage rasters and real river limits considering different ratio between river width and raster resolution and the errors on SRTM DEM data due tovegetation and water level effects.



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equivalent width curve is computed asL(z,m) =Afl(z,m)/Dxmand acorrection is made L= max[0,L B) to remove the main river width.

5. Flood inundation model

In this section, we describe two procedures: (i) how the modelgenerates 2D water level results and (ii) how the model considersthe flood extent in the water and energy budget computations.

Considering the model hypothesis to simulate the floodplainhydraulics, it is possible to generate inundation results in termsof floodplain water level or depth. The floodplain water depth Ythi,ji in the time intervaltand pixel hi,ji is computed using previousresults of 1D water levels in each cross sectionz, of floodplain unitsrasterPhi,jiand of corrected DEM Z hi,ji:

Ythi;ji maxztPhi;jiZ

hi;ji;0 10

The flood extent is also considered in the surface water and en-ergy budget of the model. To consider this, we use a methodologycalled here as Dynamic HRU. Basically, the fraction area of theHydrological Response Units (HRUs) in each catchment is consid-

ered to be variable in time. The fraction area of the HRU represen-tative of water surface equals to the total flooded area inside thecatchment. Also, the areas of the other HRUs are adjusted to keepthe sum of all HRUs equal the catchment area. Since we use a sim-plistic model for simulating floodplains, the Dynamic HRU ap-proach may also have some limitations, although these might beless severe than not considering the flood extent time variabilityinto water and energy balance computations.

6. The Purus River basin case study

As previously mentioned, the main objective of this work is todevelop methods for large scale hydrodynamic modeling consider-ing limited data and GIS based algorithms for parameter extractionfrom DEMs. In this section, we show an evaluation of the devel-oped methods through an application of the model in the PurusRiver basin. First, we give a brief description of the Purus River ba-sin and a presentation of the main data used. Subsequently, theevaluation of the developed methods is presented focusing onGIS procedures, model operation and feasibility and also someanalyses on hydrological results. Hydrological results are not fullyexplored here, since further confirmation of the developed modeland discussion about advantages of a full hydrodynamic modelfor large scale hydrologic modeling is presented in Paiva et al. (inreview).

6.1. The Purus River basin

Purus River is one of the main tributaries of the Amazon River(Fig. 5), its drainage area equals 370,000 km2 and its average dis-charge is 11,000 m3 s1. The Purus River basin has typical charac-teristics of the Amazon region. It is a very large river, the mainland cover of the basin is rain forest and both high rainfall ratesand seasonality play an important role in hydrology. Purus Riverslopes are quite small (


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equal to 625 km2, giving rise to 345 catchments with an average

drainage area of 1000 km2

. The river reaches discretization intosub-reaches and cross sections (Fig. 6d) was performed considering

Dx6 10 km.Fig. 6e shows results of floodplain units raster Phi,ji,

together with model river reaches cross sections. One of the flood-plain units between two model cross sections is highlighted, and

Fig. 6. (a) Digital elevation modelZhi,ji, (b) drainage raster D hi,ji, (c) catchments raster B hi,ji, (d) location of computational cross sections and drainage network and (e)floodplain units rasterPhi,jion the zoom area of the Purus River basin.



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will be used later to show some of the model simulation results. Asan example,Fig. 7shows the curve of water level versus floodedarea (Afl) extracted from DEM (Eq. (9)) of the area highlighted inFig. 6e.

The parameters of the cross sections, i.e. width B(m) and max-imum depthH(m), were estimated as functions of drainage areaA

d(km2), using the following geomorphologic equations:B = 0.8054Ad

0.5289 (R = 0.77) and H = 1.4351Ad0.1901 (R = 0.72).

These equations were developed for the Amazon River basin usingcross sections profiles from 341 gauge stations located in the Bra-zilian part of the Amazon basin, including the rivers Araguaia andTocantins (Paiva, 2009). These geomorphologic equations providesimilar results as those previously developed by Coe et al. (2007)andBeighley et al. (2009).

Fig. 8shows the longitudinal profile of the Purus River on thezoom area extracted from the DEM (black line) and after correc-tion. The systematic errors related to vegetation and surface watereffects were removed (Eq.(7)) considering the following parame-ters: Hveg= 17 m, a= 0.8 and b= 1.0. A single value for Hvegwasadopted for the whole DEM, since rainforest land cover predomi-nates in the Purus basin and its value was estimated through aninspection analysis of the DEM at the border of forested and defor-ested areas. Considering that the number of iterations itmax of theiterative moving average filter is a subjective parameter, differentvalues were tested. The filtered longitudinal profile is smootherwhen larger values of itmax are used, however, when itmax tendsto infinity, the longitudinal profile gets too smoothed, and informa-tion is lost. Therefore we used itmax= 5000, but this parametershould be verified in future applications using a similar inspection.

6.4. Streamflow and water level modeling results

In this section, Purus River simulation results are presented. The

MGB-IPH parameters related to soil water budget were calibrated,using discharge data from the 1985 to 2005 period. Calibration wasperformed using a combination of manual and automatic optimi-zation, as described by Collischonn et al. (2007), using the MO-COM-UA algorithm (Yapo et al., 1998). During the calibrationphase only gauges located at the headwaters of the basin (shownby the grey squares in Fig. 5) where considered for hydrographcomparisons. The model was then validated using the downstreamgauges, identified by numbers 1 to 4 inFig. 5. Gauges 13 provideddischarge data and gauges 24 water level data. The few hydrody-namic model and data preprocessing parameters were not cali-brated and were set to: Mannings n= 0.030, Hveg= 17 m,itmax= 5000, a= 0.8 and b= 1.0. Mannings value was adoptedbased on values for natural rivers presented byChow (1959)and

by other authors who applied models in the Amazon region, suchas LeFavour and Alsdorf (2005) andTrigg et al. (2009). Further

details on model calibration can be found inPaiva et al. (in review)andPaiva (2009).

Fig. 9shows observed daily stream flow from September 1990

to September 1991 at the stream gauges shown in Fig. 5. Hydro-graphs inFig. 9are arranged from upstream to downstream andthe ratio between daily stream flow and mean stream flow isshown.Fig. 10shows observed and simulated daily stream flowin Purus River at the same stream gauges. In these figures, onecan see that while the flood peaks rise from more or less6000 m3 s1 to almost 15000 m3 s1, the flood wave becomessmoother. Seasonal precipitation in this region causes alternatedhigh and low water periods. Hydrographs of the upper part ofthe basin are noisy, with several peaks related to intense rainfallevents. As the flood wave travels to the lower part of Purus River,it is attenuated and delayed due to the storage of high volumes ofwater on the floodplain. This process produces much smotherhydrographs and flood wave travel times of several months, as

can be seen inFig. 9. These hydrological features were relativelywell represented by the model, as can be seen inFig. 10. Also, errorstatistics comparing observed and simulated stream flow duringthe 19852005 period corroborate the good model performance,although large errors are found in specific time intervals, such asin high flows inFig. 10c. Volume error, or bias, at the four gaugestations was: 9%, 1% and 5%. NashSutcliffe efficiency coeffi-cient values for each of the gauge stations were: 0.84, 0.89 and0.91.

Fig. 11shows observed and simulated water levels from 1987 to1991 at stream gauge stations presented inFig. 5. The main hydro-logical features observed in the water levels of Purus River are wellrepresented by the model. The range of water levels is quite large being 15, 15 and 11 m for the observed data in the three gauge sta-

tions. The simulated range of the water levels is very similar to theobserved one. Large errors were only found at gauge 2 ( 27%),

10 15 20 25 30 35 40 450

50

100

150

200

250

z [m]

Afl

[km

2]

Fig. 7. Flooded area versus water level on the highlighted floodplain unit extractedfrom de DEM.

1300 1400 1500 1600 1700 1800

x [km]

z[km]

0

10

20

30

40

50

60

Fig. 8. Longitudinal profile of Purus River on the zoom area: DEM levels (zDEM) inblack line and estimated bottom levels (z0) with itmax= 50 (light grey line),itmax= 5000 (dark grey line) anditmax= 50,000 (bold black line).

Sep Oct Nov Dec Jan Feb MarApr May Jun Jul Aug Sep0

1

2

3

Fig. 9. Flood wave delay and attenuation in Purus River showed by the rationbetween daily and mean streamflow in the 1990/1991 hydrological year in thegauges 1 (grey line), 2 (black line) and 3 (dashed line) (seeFig. 5).



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while at the other two gauges river stage amplitudes were cor-rectly reproduced (errors of6% and 1%, respectively at gauges3 and 4). As inFig. 10, water levels shown inFig. 11also illustratethe attenuation and delay of the flood wave due to floodplain stor-age as it travels through the Purus River. Analysis of the hydro-graphs and relatively high NashSutcliffe efficiency coefficientvalues at those three gauges (0.84, 0.90 and 0.92) suggest thatthe model is performing good and that the delay and smoothingof the flood wave is being well represented by the propagationmodule of the hydrological model.

6.5. Simulated flood inundation dynamics

Based on model water level results maps of floodplain inunda-tion can be obtained by comparing water levels calculated athydrodynamic cross sections with DEM elevations, according tothe procedure presented in Section5. In this section, we presentsome flood inundation results and discuss the potential of themodel in simulating different types of inundation dynamics.

Fig. 12shows the simulated water depth on the floodplain atthree time intervals throughout the hydrological year 1995/1996(Fig. 12ac). It also shows the computational river reaches andcross sections of the model. The first water depth map was derivedusing water levels calculated at the beginning of the hydrologicalyear, at low water season (15-October-1995), the second is fromthe high water season (22-April-1996) and the third is from the

declining water level period (31-July-1996). Areas appearing whiteinFig. 12are not flooded, while dark areas are flooded to depths of

several meters. It can be seen that the flooded areas expand fromthe dry to the wet season, and that floodplain areas located inthe downstream area (right part ofFig. 12) are still inundated afterthe passing of the flow peak.

Detailed results are given for one of the floodplain units, whichis highlighted inFig. 12. For this floodplain unit we show time ser-ies of the flooded area Afl and exchange flux between river andfloodplainqfl, as calculated by the model (Fig. 13a).Fig. 13b showstime series of the main river dischargeQ, again compared toqfl. Fi-nally,Fig. 13c shows water level zin the floodplain and its tempo-ral derivativedz/dt.

The inundation dynamics are characterized by a strong season-

ality driven by the main river discharge and water level variations.According to model results, floodplain lateral exchange is driven bythe river water level variations. When the water level is rising(lowering), water flows from the main river (floodplain) to thefloodplain (main river) and the flooded area also increases(decreases).

Since one of the basic assumptions of the model is that there isno water level difference between the main river and the con-nected floodplain, total flooded area exhibits a similar behaviorof water levels. Also, the model assumes that all marginal lakesand other parts of the floodplain are fully connected to the river.This can be usually considered valid, since normally these marginallakes are connected to the main river through small channels (e.g.Bonnet et al., 2008).

Simulation results show that the majority of water in the flood-plain comes from the main river, and that inflow and outflow

1986 1987 1988 1989 1990 1991

Q[m3/s]

Q[m3/s]

Q[m3/s]

1986 1987 1988 1989 1990 1991

1986 1987 1988 1989 1990 1991

0

2000

4000

6000

8000

0

2000

4000

6000

8000

10000

12000

0

5000

10000

15000

(a)

(b)

(c)

Fig. 10. Observed (grey line) and simulated (black line) daily streamflow in thegauges 1 (a), 2 (b) and 3 (c) of Purus River in 19861991 time period (see Fig. 5).

1986 1987 1988 1989 1990 1991

y[m]

1986 1987 1988 1989 1990 1991

y[m]

1986 1987 1988 1989 1990 1991

y[m]

-5

0

5

10

-5

0

5

-5

0

5

(a)

(b)

(c)

Fig. 11. Observed (grey line) and simulated (black line) daily water level in thegauges 2 (a), 3 (b) and 4 (c) of the Purus River in 19861991 time period (seeFig. 5).



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intensity is defined by variation in time of main river water level.

Both features were reported byBonnet et al. (2008)who analyzedinflow and outflow from a marginal lake of the Amazon River near

bidos using water level observations and modeling results. Simi-lar main river driven floodplain inundation dynamics were also ob-served byAlsdorf et al. (2010)in the mainstem Amazon floodplainusing gravimetric and imaging satellite methods.

In addition, inundation results of the model were validated interms of flooded areas extent (Paiva et al., in review; Paiva, 2009)

and a good performance of the model was found.It is worth noting that the model presented here is not able to

simulate a full 2D inundation dynamic, e.g. when two large riverscan exchange water trough floodplain flow. This situation may oc-cur in Amazon rivers, but it is not as common in this area as it is inthe Pantanal region (Paz et al., 2010), and probably it has relativelylow impact on large scale modeling results. To improve the repre-sentation of floodplain flow a 2D model should be more appropri-ate (Bates and De Roo, 2000; Bates et al., 2010). The modelpresented here only considers the floodplain storage in the 1Dhydrodynamic model, taking into account its effects on flood wavedelay and attenuation; and then we use the 1D water levels calcu-lated at cross section locations to generate 2D inundation maps.Although it has some limitations, the approach presented here

can be considered sufficient for large scale hydrological modelingand has much less computational demands.

Fig. 12. Simulated floodplain water depth on the zoom area of Purus River on 15-October-1995 (a), 22-April-1996 (b) and 31-July-1996 (c).

0

100

200

Afl

[km

2]

1994 1995 1996 1997 1998 1999

1994 1995 1996 1997 1998 1999

1994 1995 1996 1997 1998 1999

-500

0

500

qfl

[m3/s]

qfl

[m3/s]

0

1

2x 10

Q[m3/s]

-500

0

500

15

20

25

30

35

z[m]

-0.2

-0.1

0

0.1

0.2

dz/dt[m/day]

4

(a)

(b)

(c)

Fig. 13. (a) Simulated daily flooded area Afl (grey line) and floodplain lateralexchangeqfl(black line), (b) simulated daily dischargeQ(grey line) and floodplainlateral exchangeqfl(black line) and (c) simulated daily water level z(grey line) andits temporal derivative dz/dt(black line) in a floodplain unit of Purus River for theperiod of 19941999.



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7. Conclusions

We describe the development of a model for large scale hydro-logic and hydrodynamic modeling. The proposed approach is suit-able for large scale applications in regions with limited data, sinceit uses regionalization methods and GIS algorithms that can be ap-plied using globally available datasets.

The model and methods used to derive the necessary informa-tion were tested in the Purus River basin, which is a representativepart of the Amazon basin in terms of river hydraulics and flood-plain abundance. The case study shows the feasibility of the GISbased algorithms for parameter extraction. A comparison betweenobserved and simulated discharges and water levels at gauging sta-tions shows that the model is capable of reproducing the mainhydrological features of the Purus River basin. Calibration ofparameters related to the hydrodynamic model was not necessary.However, while our modeling approach for flow propagation in riv-ers is relatively complex and complete, our description of flood-plain dynamics is very simple. Our approach does not fullyreproduce what is actually happening in the floodplains, neverthe-less realistic floodplain inundation maps were derived from the re-sults of the model. Our main concern is large scale applications andon the effects that floodplain inundation and emptying has on vari-ables measured at the river gauges.

Model errors may be related to input data, and limitations of themodel itself. In terms of the river flow propagation module, we be-lieve that errors may be related to the input data uncertainty, e.g.DEM precision and errors related to vegetation and cross sectiongeometry provided by geomorphologic relations. Nevertheless, re-sults show that it is possible to employ full hydrodynamic modelswithin large-scale hydrological modeling even using limited datafor river geometry and floodplain characterization. In comparisonto other large scale flow routing algorithms, the model can simu-late additional hydrological features such as backwater effectsand flood inundation dynamics and can provide other output vari-ables as river water levels, total flooded area and 2D water depth as

well.In a forthcoming work (Paiva et al., in review), we present a

model validation including an evaluation of model errors and itssources. We evaluate its advantages if compared with simplifiedflow routing algorithms and also relate the differences in resultsfrom both techniques with the role played by the floodplains andbackwater effects.

Acknowledgements

The authors are grateful for the financial and operational sup-port from the Brazilian agencies FINEP and ANA as for constructivecomments of Marie-Paule Bonnet, Douglas Alsdorf, and anony-mous reviewers.

References

Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration:Guidelines for Computing Crop Water Requirements. Irrigation and DrainagePaper 56. FAO, Rome.

Alsdorf, D., Dunne, T., Melack, J., Smith, L., Hess, L., 2003. Diffusion modeling ofrecessional flow on central Amazonian floodplains. Geophys. Res. Lett. 32,L21405.

Alsdorf, D., Bates, P., Melack, J., Wilson, M., Dunne, T., 2007. The spatial andtemporal complexity of the Amazon flood measured from space. Geophys. Res.Lett. 34, L08402.

Alsdorf, D., Han, S.-C., Bates, P., Melack, J., 2010. Seasonal water storage on theAmazon floodplain measured from satellites. Remote Sens. Environ. 114, 24482456.

Arora, V.K., 2001. Streamflow simulations for continental-scale river basins in aglobal atmospheric general circulation model. Adv. Water Resour. 24, 775791.

Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998. A remote

sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. J.Hydrol. 212213, 198212.

Bates, P.D., De Roo, A.P.J., 2000. A simple raster based model for flood inundationsimulation. J. Hydrol. 236, 5477.

Bates, P.D., Horrit, M.S., Fewtrell, T.J., 2010. A simple inertial formulation of theshallow water equations for efficient two-dimensional flood inundationmodelling. J. Hydrol. 387, 3345.

Beighley, R.E., Eggert, K.G., Dunne, T., He, Y., Gummadi, V., Verdin, K.L., 2009.Simulating hydrologic and hydraulic processes throughout the Amazon RiverBasin. Hydrol. Process. 23 (8), 12211235.

Betts, A.K., Ball, J.H., Beljaars, A.C.M., Miller, M.J., Viterbo, P.A., 1996. The landsurfaceatmosphere interaction: a review based on observational and global

modeling perspectives. J. Geophys. Res. 101 (D3), 72097225.Beven, K.J., 2001. Rainfall-runoff Modeling: The Primer. Wiley, 360p.Biancamaria, S., Bates, P.D., Boone, A., Mognard, N.M., 2009. Large-scale coupled

hydrologic and hydraulic modelling of the Ob river in Siberia. J. Hydrol. 379,136150.

Bonnet, M.P., Barroux, G., Martinez, J.M., Seyler, F., Turcq, P.M., Cochonneau, G.,Melack, J.M., Boaventura, G., Bourgoin, L.M., Len, J.G., Roux, E., Calmant, S.,Kosuth, P., Guyot, J.L., Seyler, F., 2008. Floodplain hydrology in an Amazonfloodplain lake (Lago Grande de Curua). J. Hydrol. 349, 1830.

Bourgoin, L.M., Bonnet, M.P., Martinez, J.M., Kosuth, P., Cochonneau, G., Turcq, P.M.,Guyot, J.L., Vauchel, P., Filizola, N., Seyler, P., 2007. Temporal dynamics of waterand sediment exchanges between the Curua floodplain and the Amazon River,Brazil. J. Hydrol. 335, 140156.

Burrough, P.A., McDonnel, R.A., 1998. Principles of Geographical InformationSystems: Spatial Information Systems and Geostatistics. Oxford UniversityPress, Oxford, 333p.

Castellarin, A., Di Baldassarre, G., Bates, P.D., Brath, A., 2009. Optimal cross-sectionalspacing in Preissmann scheme 1D hydrodynamic models. Journal of Hydraulic

Engineering 135 (2), 96105.Chen, Y.H., 1973. Mathematical Modeling of Water and Sediment Routing in NaturalChannels. PhD Dissertation, Colorado State University, USA.

Chow, V.T., 1959. Open Channel Hydraulics. McGraw-Hill, 680p.Coe, M.T., 1997. Simulating continental surface waters: an application to holocene

Northern Africa. J. Clim. 10, 16801689.Coe, M.T., 2000. Modeling terrestrial hydrological systems at the continental scale:

testing the accuracy of an atmospheric GCM. J. Clim. 13, 686704.Coe, M.T., Costa, M.H., Howard, E.A., 2007. Simulating the surface waters of the

Amazon River basin: impacts of new river geomorphic and flowparameterizations. Hydrol. Process. 22 (14), 25422553.

Collini, E.A., Berbery, E.H., Barros, V., Pyle, M.,2008.How does soil moistureinfluencethe early stages of the South American Monsoon? J. Clim. 21, 195213.

Collischonn, W., Haas, R., Andreolli, I., Tucci, C.E.M., 2005. Forecasting river Uruguayflow using rainfall forecasts from a regional weather-prediction model. J.Hydrol. 305, 8798.

Collischonn, W., Allasia, D.G., Silva, B.C., Tucci, C.E.M., 2007. The MGB-IPH model forlarge-scale rainfall-runoff modeling. J. Hydrol. Sci. 52, 878895.

Collischonn, B., Collischonn, W., Tucci, C., 2008. Daily hydrological modeling in the

Amazon basin using TRMM rainfall estimates. J. Hydrol. 360 (14), 207216.Cunge, J.A., Holly, F.M., Verwey, A., 1980. Practical Aspects of Computational RiverHydraulics. Pitman Advanced Publishing Program.

De Roo, A.P.J., Wesseling, C.G., Van Deursen, W.P.A., 2000. Physically based riverbasin modeling within GIS: the LISFLOOD model. Hydrol. Process. 14, 19841992.

Decharme, B., Douville, H., Prigent, C., Papa, F., Aires, F., 2008. A new river floodingscheme for global climate applications: off-line evaluation over South America.

J. Geophys. Res. 113.DHI, 2003. MIKE 11 A Modelling System for Rivers and Channels Reference

Manual. DHI Software.Dijkshoorn, J.A., Huting, J.R.M., Tempel, P., 2005. Update of the 1:5 million Soil and

Terrain Database for Latin America and the Caribbean (SOTERLAC, Version 2.0).Report 2005/01, ISRIC World Soil Information, Wageningen.

Eva, H.D., De Miranda, E.E., Di Bella, C.M., Gond, V., 2002. A Vegetation Map of SouthAmerica. EUR 20159 EN, European Commission, Luxembourg.

Farr, T.G., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M.,Rodriguez, E., Rosen, P., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J.,Werner, M., Burbank, D., Oskin, M., Alsdorf, D., 2007. The shuttle radar

topography mission. Rev. Geophys. 45 (2).Foley, J.A., Prentice, I.C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., Haxeltine, A.,

1996. An integrated biosphere model of land surface processes, terrestrialcarbon balance, and vegetation dynamics. Global Biogeochem. Cycles 10 (4),603628.

Frappart, F., Calmant, S., Cauhop, M., Seyler, F., Cazenave, A., 2006. Preliminaryresults of ENVISAT RA-2-derived water levels validation over the Amazon basin.Remote Sens. Environ. 100, 252264.

Galland, J.C., Goutal, N., Hervouet, J.M., 1991. TELEMAC a new numerical model forsolving shallow-water equations. Adv. Water Resour. 14 (3), 138148.

Getirana, A.C.V., Bonnet, M.-P., Rotunno Filho, O.C., Collischonn, W., Guyot, J.-L.,Seyler, F., Mansur, W.J., 2010. Hydrological modelling and water balance of theNegro River basin: evaluation based on in situ and spatial altimetry data.Hydrol. Process.doi:10.1002/hyp.7747.

Hamilton, S.K., Sippel, S.J., Melack, J.M., 2002. Comparison of inundation patternsamong major South American floodplains. J. Geophys. Res., 107(D20), LBA-1LBA-4.

Hess, L.L., Melack, J.M., Novo, E.M.L.M., Barbosa, C.C.F., Gastil, M., 2003. Dual-season

mapping of wetland inundation and vegetation for the central Amazon basin.Remote Sens. Environ. 87, 404428.

http://dx.doi.org/10.1002/hyp.7747http://dx.doi.org/10.1002/hyp.7747


12/12

Jenson, S.K., Domingue, J.O., 1988. Extracting topographic structure from digitalelevation data for geographic information system analysis. PhotogrammetricEng. Remote Sens. 54 (11), 15931600.

Kalnay, E. et al., 1996. The NCEP/NCAR 40-years reanalysis project. Bull. Am.Meteorol. Soc. 77, 437471.

Kellndorfer, J., Walker, W., Pierce, L., Dobson, C., Fites, J.A., Hunsaker, C., Vona, J.,Clutter, M., 2004. Vegetation height estimation from shuttle radar topographymission and national elevation datasets. Remote Sens. Environ. 93, 339358.

Koussis, A.D., 2009. Assessment and review of the hydraulics of storage floodrouting 70 years after the presentation of the Muskingum method. Hydrol. Sci. J.

54 (1).Kouwen, N. et al., 1993. Grouping response units for distributed hydrologic

modelling. J. Water Resour. Manage. Planning 119 (3), 289305.LeFavour, G., Alsdorf, D., 2005. Water slope and discharge in the Amazon River

estimated using the Shuttle Radar Topography Mission digital elevation model.Geophys. Res. Lett. 32, L17404.

Lehner, B., Verdin, K., Jarvis, A., 2006. HydroSHEDS Technical Documentation. WorldWiuldlife Fund US, Washington, DC. .

Lian, Y., Chan, I.-C., Singh, J., Demissie, M., Knapp, V., Xie, H., 2007. Coupling ofhydrologic and hydraulic models for the Illinois River Basin. J. Hydrol. 344, 210222.

Liang, X., Lettenmaier, D.P., Wood, E.F., Burges, S.J., 1994. A simple hydrologicallybased model of land surface water and energy fluxes for general circulationmodels. J. Geophys. Res. 99, 1441514428.

Liston, G.E., Sud, Y.C., Wood, E.F., 1994. Evaluating GCM land surface hydrologyparameterizations by computing river discharges using a runoff routing model:application to the Mississippi Basin. J. Appl. Meteorol. 33, 394405.

Liu, Z., Todini, E., 2002. Towards a comprehensive physically-based rainfall-runoff

model. Hydrol. Earth Syst. Sci. 6 (5), 859881.Lohmann, D., Nolte-Holube, R., Raschke, E., 1996. A large-scale horizontal routingmodel to be coupled to land surface parametrization schemes. Tellus Ser. A:Dyn. Meteorol. Oceanogr. 48 (5), 708721.

Ludwig, K., Bremicker, M., 2006. The Water Balance Model LARSIM Design,Content and Applications. Freiburger Schiriften zr Hydrologie, Band 22,Institut fur Hydrologie der Universitt Freiburg.

Maidment, D., 2002. Arc Hydro GIS for Water Resources. ESRI Press, Redlands, CA.Matheussen, B., Kirschbaum, R.L., Goodman, I.A., ODonnell, G.M., Lettenmaier, D.P.,

2000. Effects of land cover change on streamflow in the interior Columbia Riverbasin (USA and Canada). Hydrol. Process. 14, 867885.

Meade, R.H., Rayol, J.M., Da Conceio, S.C., Natividade, J.R.G., 1991. Backwatereffects in the Amazon River basin of Brazil. Environ. Geol. Water Sci. 18 (2),105114.

Mohamed, Y.A., van den Hurk, B.J.J.M., Savenije, H.H.G., Bastiaanssen, W.G.M., 2005.Impact of the Sudd wetland on the Nile hydroclimatology. Water Resour. Res.41, W08420.

Monteith, J.L., 1965. Evaporation and environment. In: Proc. 19th Symp. Soc. Exp.Biol. Swansea (1964).

Moore, R.J., Clarke, R.T., 1981. A distribution function approach to rainfall-runoffmodeling. Water Resour. Res. 17 (5), 13671382.Nijssem, B., Lettenmaier, D.P., Liang, X., Wetzel, S.W., Wood, E.F., 1997. Streamflow

simulation for continental-scale river basins. Water Resour. Res. 33 (4), 711724.

Nijssen, B., ODonnell, G.M., Hamlet, A.F.E., Lettenmaier, D.P., 2001. Hydrologicsensitivity of global rivers to climate change. Clim. Change 50 (12), 143175.

Paiva, R.C.D., 2009. Large Scale Hydrological and Hydrodynamic Modeling: the RioSolimes Case Study (in Portuguese). M.Sc. Dissertation, Instituto de PesquisasHidrulicas, Universidade Federal do Rio Grande do Sul. .

Paiva, R.C.D., Collischonn, W., Buarque, D.C., in review. Validation of a fullhydrodynamic model for large scale hydrologic modelling in the Amazon.Hydrol. Process.

Paz, A.R., Bravo, J.M., Allasia, D., Collischonn, W., Tucci, C.E.M., 2010. Large-scalehydrodynamic modeling of a complex river network and floodplains. J. Hydrol.Eng. 15 (2), 152165.

Pitman, A.J., 2003. The evolution of, and revolution in, land surface schemesdesigned for climate models. Int. J. Climatol. 23, 479510.

Rabus, B., Eineder, M., Roth, A., Bamler, R., 2003. The shuttle radar topographymissiona new class of digital elevation models acquired by spaceborne radar.

J. Photogrammetry Remote Sens. 57, 241262.RADAMBRASIL. 1982. Programa de Integrao Nacional, Levantamento de Recursos

Naturais. Ministrio das Minas e Energia, Secretaria-Geral.

Rawls, W.J., Ahuja, L.R., Brakensiek, D.L., Shirmohammadi, A., 1993. Infiltration andsoil water movement. In: Maidment, D.R. (Ed.), Handbook of Hydrology.McGraw-Hill, New York.

Remo, J.W.F., Pinter, N., 2007. Retro-modeling the Middle Mississippi River. J.Hydrol. 337, 421435.

Richey, J.E., Melack, J.M., Aufdenkampe, A.K., Ballester, V.M., Hess, L.L., 2002.Outgassing from Amazonian rivers and wetlands as a large tropical source ofatmospheric CO2. Nature 416, 617620.

Schmugge, T.J., Kustas, W.P., Ritchie, J.C., Jackson, T.J., Rango, A., 2002. Remotesensing in hydrology. Adv. Water Resour. 25, 13671385.

Shuttleworth, W.J., 1993. Evaporation. In: Maidment, D.R. (Ed.), Handbook ofHydrology. McGraw-Hill, New York.

Silva Dias, M.A.F., Silva Dias, P.L., Longo, M., Fitzjarrald, D.R., Denning, A.S., 2004.River breeze circulation in eastern Amazonia: observations and modelingresults. Theor. Appl. Climatol. 78, 111122.

Sun, G., Ranson, K.J., Kharuk, V.I., Kovacs, K., 2003. Validation of surface height fromshuttle radar topography mission using shuttle laser altimeter. Remote Sens.Environ. 88, 401411.

Thielen, J., Bartholmes, J., Ramos, M.-H., de Roo, A., 2009. The European flood alertsystem Part 1: concept and development. Hydrol. Earth Syst. Sci. 13, 125140.Todini, E., 1996. The ARNO rainfall-runoff model. J. Hydrol. 175, 339382.Todini, E., 2007. A mass conservative and water storage consistent variable

parameter Muskingum-Cunge approach. Hydrol. Earth Syst. Sci. 11 (5), 16451659.

Tomasella, J., Borma, L.S., Marengo, J.A., Rodriguez, D.A., Cuartas, L.A., Nobre, C.A.,Prado, M.C.R., 2010. The Droughts of 19961997 and 20042005 in Amazonia:Hydrological Response in the River Main-stem. Hydrol. Process.

Trigg, M.A., Wilson, M.D., Bates, P.D., Horritt, M.S., Alsdorf, D.E., Forsberg, B.R., Vega,M.C., 2009. Amazon flood wave hydraulics. J. Hydrol. 374, 92105.

Tucci, C.E.M., 1978. Hydraulic and Water Quality Model for a River Network. Ph.D.Dissertation, Colorado State University, Fort Collins, USA.

USACE, 2002. HEC-RAS River Analysis System: Hydraulic Reference Manual, Version3.1. US Army Corps of Engineers, Hydrologic Engineering Center. .

Viney, N.R., Sivapalan, M., Deeley, D., 2000. A conceptual model of nutrientmobilisation and transport applicable at large catchment scales. J. Hydrol. 240(12), 2344.

Wood, E.F., Lettenmaier, D.P., Zartarian, V.G., 1992. A land-surface hydrologyparameterization with subgrid variability for general circulation models. J.Geophys. Res. 97, 27172728.

Wood, A.W., Maurer, E., Kumar, A., Lettenmaier, D.P., 2002. Long-rangeexperimental hydrologic forecasting for the eastern United States. J. Geophys.Res. 107, D20.

Xu, C.-Y., Widn, E., Halldin, S., 2005. Modelling hydrological consequences ofclimate change progress and challenges. Adv. Atmos. Sci. 22 (6), 789797.

Yapo, P.O., Gupta, H.V., Sorooshian, S., 1998. Multi-objective global optimization forhydrologic models. J. Hydrol. 204, 8397.

Zhang, Y., Li, C., Trettin, C.C., Li, H., Sun, G., 2002. An integrated model of soil,hydrology, and vegetation for carbon dynamics in wetland ecosystems. GlobalBiogeochem. Cycles 16 (4), 1061.

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