copernicus.org - hess 17-2247-2013 · 2020. 7. 19. · revised: 8 march 2013 – accepted: 1 may...

Hydrol Earth Syst Sci 17 2247ndash2262 2013wwwhydrol-earth-syst-scinet1722472013doi105194hess-17-2247-2013copy Author(s) 2013 CC Attribution 30 License

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Potential effects of climate change oninundation patterns in the Amazon Basin

F Langerwisch1 S Rost1 D Gerten1 B Poulter2 A Rammig1 and W Cramer3

1Earth System Analysis Potsdam Institute for Climate Impact Research (PIK) PO Box 60 12 03 Telegraphenberg A6214412 Potsdam Germany2Laboratoire des Sciences du Climat et de lrsquoEnvironnement UMR8212 CNRS ndash CEA UVSQ Gif-sur Yvette France3Institut Mediterraneen de Biodiversite et drsquoEcologie marine et continentale (IMBE) Aix-Marseille UniversityCNRSBatiment Villemin Europole de lrsquoArbois ndash BP 80 13545 Aix-en-Provence cedex 04 France

Correspondence toF Langerwisch (fannylangerwischpik-potsdamde)

Received 16 December 2011 ndash Published in Hydrol Earth Syst Sci Discuss 6 January 2012Revised 8 March 2013 ndash Accepted 1 May 2013 ndash Published 20 June 2013

Abstract Floodplain forests namely the Varzea and Igapocover an area of more than 97 000 km2 A key factor for theirfunction and diversity is annual flooding Increasing air tem-perature and higher precipitation variability caused by cli-mate change are expected to shift the flooding regime duringthis century and thereby impact floodplain ecosystems theirbiodiversity and riverine ecosystem services To assess theeffects of climate change on the flooding regime we use theDynamic Global Vegetation and Hydrology Model LPJmLenhanced by a scheme that realistically simulates monthlyflooded area Simulation results of discharge and inundationunder contemporary conditions compare well against site-level measurements and observations The changes of calcu-lated inundation duration and area under climate change pro-jections from 24 IPCC AR4 climate models differ regionallytowards the end of the 21st century In all 70 of the 24 cli-mate projections agree on an increase of flooded area in aboutone third of the basin Inundation duration increases dramat-ically by on average three months in western and around onemonth in eastern Amazonia The time of high- and low-waterpeak shifts by up to three months Additionally we find adecrease in the number of extremely dry years and in theprobability of the occurrence of three consecutive extremelydry years The total number of extremely wet years does notchange drastically but the probability of three consecutive ex-tremely wet years decreases by up to 30 in the east andincreases by up to 25 in the west These changes implicatesignificant shifts in regional vegetation and climate and willdramatically alter carbon and water cycles

1 Introduction

Amazonia plays a vital role for the global water and carboncycles through enormous water and carbon stores and fluxesThe Amazon catchment covers six million square kilometersand about 15 of the worldrsquos freshwater runoff is dischargedby the Amazon River (Gaillardet et al 1997) Dissolved inthis water about 33 Tg C yrminus1 are thought to be exported tothe Atlantic Ocean as organic carbon (Moreira-Turcq et al2003) A much larger amount approximately 470 Tg C yrminus1gasses out to the atmosphere as CO2 (Richey et al 2002)

Climate and land use change currently affect Amazonianforests substantially leading to a reduction of biomass biodi-versity and ecosystem services (Fearnside 2004 Foley et al2007 Nepstad et al 2007 Betts et al 2008) Since the forc-ing from changing climate and land use appears to be non-linearly related to the stability of the Amazonian ecosystem(Sitch et al 2008 Nobre and De Simone Borma 2009) thisregion has been identified as one of a set of global ldquotippingelementsrdquo particularly susceptible to global change (Lentonet al 2008)

Much of central Amazonia is influenced by annual flood-ing predominantly caused by precipitation across the basinDuring the flooding season between January and March (Fo-ley et al 2002) the water rises with an amplitude of 5 to 15 m(Junk 1985) and an average speed of 005 m dminus1 (Junk andPiedade 1997) The extent of flooded area in central Ama-zonia increases from about 4 during low water to 16 during high water stage (Richey et al 2002) The recur-rent change between the terrestrial and aquatic phase forms

Published by Copernicus Publications on behalf of the European Geosciences Union

2248 F Langerwisch et al Potential effects of climate change on inundation patterns in the Amazon Basin

characteristic and very diverse habitats for millions of plantand animal species which are intimately related to the recur-rent annual flooding These floodplains are alternately suit-able for aquatic and terrestrial organisms The distributionof these species is especially influenced by the duration ofthe aquatic and terrestrial phase (Junk and Piedade 1997)Floodplain forests cover approximately 97 000 km2 (Parolinet al 2004) and contain about 20 of the Amazonian treespecies (Naiman et al 2005) The vast floodplain areas thusrepresent one of the riches biota on earth providing severalecosystem services such as timber and fish production andcarbon storage (Keddy et al 2009)

Climate change is expected to alter temperature and pre-cipitation patterns which can potentially lead to changes inflood regime such as a reduction in discharge in the Ama-zon River (Arora and Boer 2001) The variability in precip-itation is expected to increase (Seneviratne et al 2012) andmay cause higher spatial and temporal variability in river dis-charge and flooded area (Coe et al 2002) The effect of cli-mate change on the El Nino Southern Oscillation (ENSO) re-mains unclear (Malhi and Wright 2004) but ENSO changesdischarge drastically (Foley et al 2002) Changes in timeduration and height of the flooding has the potential to shiftvegetation distribution which may in turn lead to feedbacksto the atmosphere (eg Cox et al 2004 Malhi et al 2008)

To assess the effects of potential changes in precipita-tion and temperature on discharge and freshwater ecosystemservices usually hydrological models are applied (Vigerstoland Aukema 2011) which are for example WaterGAP (Al-camo et al 2000 Doll and Zhang 2010 Doll et al 2003)WBM (Fekete et al 1999) and SWAT (Arnold and Fohrer2005) and VIC (Liang and Xie 2001 Liang et al 1994)A disadvantage of these models is that they do not incor-porate explicit simulation of vegetation dynamics which arean essential part of the water cycle We use the dynamicglobal vegetation and hydrology model LPJmL (Bondeau etal 2007 Gerten et al 2004 Rost et al 2008 Sitch et al2003) which has been improved for regional application tothe Amazon Basin and includes the dynamic and spatially ex-plicit reproduction of the specific hydrological patterns of themain river stem and its tributaries These patterns consist ofseasonal discharge time and duration of lowhigh water peri-ods and the changing extent of the flooded area during thoseperiods LPJmL combines dynamic terrestrial vegetation de-velopment with carbon and water cycles This enables us toestimate not only the direct effect of changing precipitationand temperature on discharge but also to include the indirecteffects of these changes on vegetation cover and type whichin turn alters runoff and discharge

The main goal of our study is thus to understand and quan-tify the magnitude of impacts of future climate change onthe Amazonian inundation patterns We provide estimates onclimate change induced shifts of inundation patterns whichcomprises of time and duration of lowhigh water periodsand the changing extent of the flooded area during those pe-

riods We describe here a method to calculate monthly in-undated area We evaluate our simulated results against ob-served data for discharge and potentially floodable area andestimate changes in inundation patterns in Amazonia Toquantify the amplitude of shifts in the flooding regime dueto climate change we use forcing data of the 24 General Cir-culation Models (GCMs) from the 4th Assessment Reportof the Intergovernmental Panel on Climate Change (IPCC2007 Randall et al 2007)

2 Methods

We apply the Dynamic Global Vegetation and HydrologyModel LPJmL (Sitch et al 2003 Gerten et al 2004 Bon-deau et al 2007 Rost et al 2008) to understand and to as-sess the effect of climate change on current Amazonian inun-dation patterns LPJmL computes establishment abundancevegetation dynamics growth and productivity of the worldrsquosmajor plant functional types as well as the associated carbonand water fluxes The model is typically applied on a gridof 05

times 05 longitudelatitude and at daily time steps Car-bon fluxes and vegetation dynamics are directly coupled towater fluxes Modelled soil moisture runoff and evapotran-spiration were found to reproduce observed patterns well andtheir quality is comparable to stand-alone global hydrologi-cal models (Wagner et al 2003 Gerten et al 2004 2008Gordon et al 2004 Biemans et al 2009)

The river routing module of LPJmL (described by Rost etal 2008) assumes a surface water storage pool for each gridcell representing the water storage and retention in reservoirsand lakes The change of water storage in the river over timeis represented as the runoff generated in the cell the input ofdischarge accumulated from upstream grid cells the outputto the downstream cell and the outflow of lakes in the respec-tive cell The output to the downstream cell is determinedas a linear transport of discharge depending on the routingvelocity (v) and the distance between the midpoints of theconnected cells Earlier versions of LPJmL used a globallyhomogeneous routing velocity of 1 msminus1 (Rost et al 2008)which had difficulties to reproduce the Amazonian hydro-graph with shifts within the hydrograph of several monthsIn a former study we already improved the reproduction ofthe hydrograph by applying a homogeneously reduced rout-ing velocity of 025 msminus1 to the Amazon Basin leading tosubstantial reductions of the shift for several observation sites(Langerwisch et al 2008) Our new approach is to use het-erogeneous routing velocities which take topographic differ-ences within the Amazon catchment into account for furtherimprovement of the hydrograph

Hydrol Earth Syst Sci 17 2247ndash2262 2013 wwwhydrol-earth-syst-scinet1722472013

F Langerwisch et al Potential effects of climate change on inundation patterns in the Amazon Basin 2249

Table 1Slope classes

Slope range Class

le 15 5gt 15ndashle 3 4gt 3ndashle 6 3gt 6ndashle 10 2gt 10ndashle 35 1gt 35 0

Table 2Relative slope position classes

Distance range Class

0ndash10 cells 2211ndash20 cells 1921ndash40 cells 1341ndash60 cells 1261ndash80 cells 11gt 81 cells 10

21 Calculation of routing velocity and potentialfloodable area

We extend on earlier work (Langerwisch et al 2008) andtake topography into account by calculating cell specificrouting velocities which are comparable to river flow veloc-ities In the model the routing velocity is used to calculatethe distance that runoff water can move within a time step(see also Rost et al 2008) We also estimate the extent ofpotential floodable area and monthly flooded area

We use a digital elevation model (DEM) provided by theWWF database HydroSHEDS (WWF HydroSHEDS 2007)at a resolution of 15 arc seconds longitudelatitude corre-sponding to approximately 460 m edge length in the study re-gion to calculate the routing velocity and the floodable areaWe apply grid-based elevation data (instead of elevation dataof the actual gauging stations) to obtain a continuous spa-tially consistent basis for our calculations The DEM eleva-tion represents the top of the canopy which issim 30 m lowerthan the actual ground elevation (Anderson et al 2009) Forthe Amazon Basin we assume this to be a systematic er-ror in the DEM elevation and use it directly for calculatingthe routing velocity The calculations of the routing veloc-ity were conducted applying well-established techniques (fordetails see Supplement S1 and S2) The data were processedat the original resolution of HydroSHEDS Final results arere-sampled at 05 times 05 (longitudelatitude) resolution

211 Routing velocity

Based on the DEM we calculate the cellrsquos slope and the cor-responding routing velocity (details see Supplement S1 andS2) The calculation of the high resolution slopeS [degree] is

Table 3Landform types with corresponding slope and mTRMI

Landform type Slope range mTRMI

valley flats lt 3 gt 22nearly level terraces lt 3

le 22gently sloping toe slopes and bottoms ge 3ndashlt 10 gt 18gently sloping ridges ge 3ndashlt 10

le 18very moist steep slopes ge 10ndashle 35

ge 18moderately moist steep slopes ge 10ndashle 35 11ndash17dry steep slopes ge 10ndashle 35 lt 10

based on the work of Burrough (1986) We apply the medianof all subcell values to aggregate the high resolution slope toa 05times 05 cell slope Subsequently we calculate slope de-pendent routing velocityv [msminus1] (Eq 1 Fig S1) based onthe ManningndashStrickler formulation

v =

(tan

(S times

π

180

)) 12times k times R

23 (1)

wherek is the ManningndashStrickler coefficient [m13 sminus1] de-scribing the roughness of the area For natural rivers thisvalue ranges between 28 and 40 m13 sminus1 (Patt 2001) Dueto the lack of detailed cell specific information we setk =

35 m13 sminus1 R is the hydraulic radius [m] It describes theratio between the cross-sectional area [m2] and the wettedperimeter [m] of the channel In wide and shallow watersit corresponds to the depth of the water It is higher in nar-row and deep river sections and lower in wide shallow riversections but cell specific information forR are not avail-able therefore we neglect the influence of this factor and setR = 10 m The median of the calculated routing velocity is025 msminus1 We included an analysis to estimate the sensitiv-ity of the calculated routing velocity tok andR We variedk between 28 and 40 m13 sminus1 (with a constantR = 10 m)which lead to median routing velocities between 020 msminus1

(minus200 ) and 029 msminus1 (+16 ) We variedR between 02and 12 m (with a constantk = 35 m13 sminus1) which lead tomedian routing velocities between 009 msminus1 (minus64 ) and029 msminus1 (+169 ) Additionally we tested all possiblekandR combinations in the range given above (see Fig S2)

212 Floodable area

As a basis for the calculation of inundation we first estimatethe potentially floodable area by applying the same DEMused for the routing velocity calculation (see Sect 211)We determine a modified Topographic Relative Moisture In-dex (mTRMI) based on the work of Parker (1982) on thenative resolution of the DEM (15 arc seconds) This indexis applied to classify structural landscape conditions whichcan be arranged in 7 different landform types such asvalleyflatsanddry steep slopes(also see Tables 1ndash3) It uses sev-eral weighted geomorphologic characteristics such as slopeslope steepness slope configuration relative slope position

wwwhydrol-earth-syst-scinet1722472013 Hydrol Earth Syst Sci 17 2247ndash2262 2013


Table 4 List of the 24 IPCC coupled general circulation models(GCMs) used in this study Details for the climate models see IPCC2007 AR4 chapter 8 (Randall et al 2007)

Model name

BCCR ndash BCM 20 INGV ndash SXGCCCMA ndash CGCM 31 (T47) INM ndash CM 30CCCMA ndash CGCM 31 (T63) IPSL ndash CM 4CNRM ndash CM 3 MIROC 32 (hires)CSIRO ndash Mk 30 MIROC 32 (medres)CSIRO ndash Mk 35 MIUB ndash ECHO-GGFDL ndash CM 20 MPI ndash ECHAM 5GFDL ndash CM 21 MRI ndash CGCM 232aGISS ndash AOM NCAR ndash CCSM 3GISS ndash EH NCAR ndash PCM 1GISS ndash ER UKMO ndash HadCM 3FGOALS ndash g 10 UKMO ndash HadGEM 1

Fig 1 Simulated mean discharge [log m3 sminus1] during June aver-aged over the reference period 1961ndash1990 The white crosses indi-cate the exaple sites Cruzeiro do Sul (CdS ID 3) Porto Velho (PVID 41) andObidos (Obi ID 10 and 42)

and aspect which can be calculated from the DEM We usethe resulting landform type valley flats as potentially flood-able area

In our study mTRMI is the sum of classified slope classi-fied slope configuration and classified relative slope position(Eq 2 see below for definitions)

mTRMI = Sclass+ Sconfigclass+ Sposclass

(2)

We neglect aspect because differences between north andsouth facing slopes are insignificant in the tropics (compareto Donnegan et al 2007) A detailed description of the calcu-lation of mTRMI summands can be found in the Supplement

The first summand is classified slope (Eq 2Sclass) Weuse the previously calculated slope values and slice them insix slope classes (Table 1)

38

823

Figure 2 The 44 sites used for comparison of observed and simulated discharge 824

825

Fig 2The 44 sites used for comparison of observed and simulateddischarge

The second summand is classified slope configuration(Eq 2Sconfclass) Slope configuration describes the convex-ity or concavity of the land surrounding any grid cell basedon the change in elevationZ [m] from cellij to all cells lo-cated at the edge of the 5times 5 cell window We slice the fullrange of resulting values equally into 10 parts and assignthese parts into 3 slope configuration classes slices 0ndash4 toclassminus1 (convex topography) slice 5 to class 0 (flat topog-raphy) slices 6ndash10 to class 1 (concave topography)

The third summand is relative slope position (Eq 2Sposclass

) describing the distance of the cellij to the closestridges and streams We assign the distance [cells] to 6 relativeslope position classes (Table 2)

From the slope classes (Eqs S1ndashS3) the slope configu-ration classes (Eqs S5ndashS9) and the relative slope positionclasses (Eqs S10ndashS11) we calculate mTRMI (Eq 2) Wesum up classified slope (0 to 5) classified slope configura-tion (minus1 to 1) and classified relative slope position (10 to22) The mTRMI ranges from dry to wet (9 to 28) describingsite conditions

We generate a map (15 arc seconds resolution) of landformtypes by combining slopeS and mTRMI For this purpose wegroup sites with defined mTRMI and slope to certain land-form types (details in Table 3) Finally we use the landformtype valley flat which is potentially floodable area to cal-culate the fraction [] of floodable area for each 05times 05

grid cellWe then calculate the fraction of continuously flooded area

from the potentially floodable area (per 05times 05 cell) fromthe work of Richey et al (2002) They estimated that duringlow water stage about 4 and during high water stage about16 of a 177 million km2 quadrant of the central Ama-zon Basin is covered with water This means that 25 of



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e5

Com

paris

onof

obse

rved

and

sim

ulat

eddi

scha

rge

for

the

obse

rvat

ion

perio

d(s

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soF

igs

3an

d4)

Ind

ices

are

give

nfo

rth

est

anda

rdsi

mul

atio

n(s

td)

with

slop

ede

pend

ent

rout

ing

velo

city

and

orig

inal

setti

ng(1

0m

sminus

1)

with

hom

ogen

eous

rout

ing

velo

city

of1

0m

sminus

1in

brac

kets

IDS

tatio

nLa

titud

eLo

ngitu

deD

atab

ase

Num

ber

ofob

sye

ars

Obs

erve

dm

inan

nual

disc

harg

e[m

3sminus

1]

Obs

erve

dm

ean

annu

aldi

scha

rge

[m3

sminus1]

Obs

erve

dm

axan

nual

disc

harg

e[m

3sminus

1]

Sim

ulat

edm

inan

nual

disc

harg

e[m

3sminus

1]

Sim

ulat

edm

ean

annu

aldi

scha

rge

[m3

sminus1]

Sim

ulat

edm

axan

nual

disc

harg

e[m

3sminus

1]

Will

mot

trsquosin

dex

ofag

reem

ent1st

d(1

0m

sminus1)

Err

orof

Qua

lV2

std

(10

msminus

1)

N-R

MS

E3[

]st

d(1

0m

sminus1)

Nas

hndashS

utcl

iffe4

std

(10

msminus

1)

Pea

rson

corr

elat

ion

coef

ficie

nt5st

d(1

0m

sminus1)

1E

stira

odo

Rep

ouso

minus4

37minus

709

3A

NE

EL

1619

325

1847

4915

525

9272

700

870

(07

93)

100

3(0

367

)21

27

(27

75)

037

0(

minus0

073)

079

1(0

678

)2

Sao

Pau

lode

Oliv

enca

minus3

47minus

687

5A

NE

EL

2315

272

4644

278

410

4013

2627

055

717

060

3(0

590

)0

322

(09

56)

347

0(3

596

)minus

136

0(minus

153

5)0

801

(06

94)

3C

ruze

irodo

Sul

minus7

62minus

726

7A

NE

EL

2975

907

2992

2411

0644

890

880

(08

48)

026

3(0

998

)19

68

(22

75)

036

6(0

153

)0

839

(07

96)

4G

avia

ominus

483

minus66

75

AN

EE

L23

603

4732

9932

151

5683

1786

80

855

(07

54)

096

4(0

993

)29

05

(36

94)

013

1(

minus0

405)

082

6(0

652

)5

Aca

naui

minus1

8minus

665

5A

NE

EL

2329

0013

778

2553

329

5511

611

2588

20

693

(06

12)

099

1(0

953

)22

26

(25

51)

minus0

120

(minus0

471)

051

4(0

358

)6

Flo

riano

Pei

xoto

minus9

05minus

673

8A

NE

EL

3027

583

2062

881

335

790

835

(07

93)

024

5(0

974

)26

45

(30

32)

000

7(

minus0

305)

081

0(0

751

)7

Labr

eaminus

725

minus64

8A

NE

EL

3072

354

4411

739

101

6241

2279

90

867

(07

71)

099

3(0

991

)30

60

(40

14)

023

2(

minus0

321)

082

5(0

666

)8

Ser

rinha

minus0

45minus

648

3A

NE

EL

2035

9915

980

3043

434

8615

975

3567

60

910

(08

12)

029

1(1

002

)13

71

(20

37)

064

9(0

225

)0

832

(06

68)

9C

arac

arai

18

minus61

13

AN

EE

L30

244

2898

1117

816

3681

1739

60

868

(08

13)

100

1(0

995

)21

22

(25

95)

015

0(

minus0

271)

086

7(0

790

)10

Obi

dos

minus1

9minus

555

05A

NE

EL

2875

602

172

696

306

318

4275

814

783

429

433

40

873

(06

13)

099

6(0

995

)17

08

(30

76)

035

6(

minus1

090)

086

8(0

426

)11

Por

todo

sG

auch

osminus

116

5minus

572

3A

NE

EL

2233

376

217

021

1077

4248

054

7(0

501

)0

986

(09

45)

714

9(7

786

)minus8

188

(minus9

899)

073

8(0

663

)12

Cac

hoei

rao

minus11

75

minus55

77

AN

EE

L20

224

815

2287

214

4364

340

541

(05

00)

024

6(0

962

)68

39

(73

59)minus

894

9(minus

105

22)

078

3(0

716

)13

Inde

cominus

101

3minus

554

2A

NE

EL

2037

011

4834

650

685

3899

074

2(0

735

)0

304

(10

02)

240

5(2

450

)minus0

200

(minus0

245)

067

4(0

659

)14

Tre

sM

aria

sminus

763

minus57

88

AN

EE

L15

429

3759

1036

59

5801

2244

40

715

(06

60)

098

4(0

966

)44

81

(49

23)minus

202

1(minus

264

7)0

791

(06

93)

15Ja

toba

minus5

15minus

568

3A

NE

EL

2236

4910

790

2633

430

1448

948

219

077

6(0

729

)0

996

(09

89)

414

8(4

512

)minus

125

5(minus

166

9)0

872

(07

75)

16S

aoF

elix

doX

ingu

minus6

58minus

520

5A

NE

EL

2369

750

8821

660

979

6331

724

072

4(0

646

)0

324

(09

50)

295

7(3

340

)minus1

331

(minus1

974)

075

5(0

618

)17

Bel

oH

oriz

onte

minus5

38minus

528

8A

NE

EL

2275

952

3218

818

2085

4234

269

075

1(0

673

)0

995

(10

02)

362

4(4

126

)minus1

518

(minus2

263)

086

2(0

717

)18

Mou

thminus

572

minus54

43

AN

EE

L23

3989

734

6812

2468

8961

054

0(0

483

)0

211

(10

00)

673

2(7

289

)minus7

068

(minus8

460)

077

6(0

659

)19

Ped

rado

Ominus

457

minus54

05

AN

EE

L19

7125

5710

241

2744

4015

755

077

3(0

700

)0

988

(10

02)

319

3(3

690

)minus0

590

(minus1

123)

078

7(0

669

)20

Alta

mira

minus3

2minus

522

2A

NE

EL

2780

886

0932

298

9114

232

5187

70

798

(07

19)

098

1(0

998

)30

75

(35

75)minus0

578

(minus1

132)

086

1(0

714

)21

Sao

Fel

ipe

057

minus67

32

AN

EE

L19

1220

7406

1700

074

965

6018

057

087

7(0

814

)0

979

(09

72)

150

6(1

910

)0

572

(03

12)

079

9(0

684

)22

Uar

acu

055

minus69

17

AN

EE

L20

159

2459

5835

156

2675

6955

087

5(0

834

)0

991

(03

85)

166

4(1

957

)0

497

(03

04)

078

3(0

717

)23

Moc

idad

e3

45minus

610

5A

NE

EL

1316

213

0446

522

1291

7795

075

6(0

785

)0

494

(04

73)

255

2(2

346

)minus0

387

(minus0

172)

065

1(0

682

)24

Mal

oca

doC

onta

ominus

395

minus60

43

AN

EE

L23

3027

795

24

113

519

026

8(0

268

)0

997

(09

89)

325

2(3

252

)minus1

936

(minus1

936)

minus0

437

(minus0

437)

25M

ato

Gro

sso

minus15

02

minus59

97

AN

EE

L27

117

171

20

166

1029

073

5(0

724

)0

983

(10

02)

261

9(2

687

)minus0

152

(minus0

213)

056

0(0

544

)26

Pal

mei

ral

minus10

03

minus64

45

AN

EE

L12

121

486

10

200

767

083

1(0

831)

027

7(0

993

)18

73

(18

74)

039

7(0

397

)0

701

(07

01)

27A

rique

mes

minus10

03

minus62

97

AN

EE

L11

2832

211

350

123

474

061

0(0

610

)0

264

(02

63)

262

4(2

622

)minus0

201

(minus0

198)

062

2(0

623

)28

Sao

Car

los

minus9

7minus

631

3A

NE

EL

1244

341

1338

064

523

640

596

(05

92)

026

1(0

986

)46

42

(46

77)minus

326

0(minus

332

5)0

599

(05

90)

29C

acho

eira

doS

amue

lminus

905

minus63

47

AN

EE

L4

8626

011

332

636

2247

043

1(0

409

)0

581

(09

98)

616

2(6

384

)minus8

788

(minus9

506)

057

5(0

544

)30

San

taIs

abel

minus9

1minus

637

2A

NE

EL

1745

206

624

035

113

810

684

(06

76)

024

7(0

988

)48

26

(48

93)minus

298

1(minus

309

3)0

835

(08

23)

31C

acho

eira

Prim

aver

aminus

119

minus61

23

AN

EE

L19

9414

2942

801

508

2217

061

1(0

610

)0

967

(08

64)

320

2(3

209

)minus0

189

(minus0

193)

064

8(0

641

)32

Pim

enta

Bue

nominus

116

5minus

612

AN

EE

L24

2242

416

071

499

2217

078

6(0

784

)1

002

(09

91)

260

4(2

623

)minus0

028

(minus0

043)

065

4(0

649

)33

Boc

ado

Gua

riba

minus7

68minus

603

AN

EE

L29

4854

6531

070

618

6463

450

572

(05

60)

097

9(0

983

)17

78

(18

11)minus

013

5(minus

017

7)0

692

(06

17)

34S

anta

rem

Suc

undu

riminus

675

minus58

95

AN

EE

L22

013

183

96

980

3398

011

8(0

114

)0

992

(09

72)

151

23(1

520

7)minus

566

89(minus

573

31)

minus0

027

(minus0

041)

35E

stira

oda

Ang

elic

aminus

097

minus57

07

AN

EE

L22

1125

496

91

367

2211

049

8(0

498

)0

566

(09

97)

436

7(4

384

)minus2

819

(minus2

848)

034

0(0

342

)36

Boc

ado

Infe

rno

minus1

57minus

548

3A

NE

EL

1688

362

1257

361

330

110

385

(03

82)

046

6(0

464

)64

95

(64

61)minus

654

1(minus

646

4)0

306

(02

99)

37P

orto

Ron

cado

rminus

135

8minus

553

2A

NE

EL

2257

127

712

021

412

970

363

(03

63)

100

3(1

002

)40

69

(40

64)minus

860

1(minus

857

6)0

392

(03

93)

38Lu

cas

minus13

15

minus56

05

AN

EE

L26

111

560

50

181

1212

025

2(0

247

)0

971

(09

46)

455

2(4

581

)minus3

541

(minus3

598)

minus0

143

(minus0

155)

39B

arra

gem

Jusa

nte

minus12

78

minus54

27

AN

EE

L16

148

016

540

328

1921

022

4(0

219

)0

987

(09

91)

403

1(4

086

)minus1

711

(minus1

787)

minus0

342

(minus0

350)

40F

azen

daP

aqui

raminus

042

minus53

72

AN

EE

L23

2415

693

84

930

4435

007

9(0

079

)0

997

(10

01)

139

77(1

406

4)minus

924

42(minus

936

09)

004

2(0

042

)41

Por

toVe

lho

minus8

76minus

639

1R

ivD

IS11

3753

1753

938

975

327

1706

454

381

093

2(0

824

)0

094

(01

27)

184

4(2

896

)0

603

(00

20)

093

2(0

737

)42

Obi

dos

minus1

91minus

555

5N

A56

7151

715

603

624

600

030

567

148

176

294

334

091

1(0

626

)0

994

(09

95)

179

2(3

843

)0

527

(minus

117

7)0

880

(04

32)

43Lo

cota

lminus

170

4minus

660

2R

ivD

IS4

412

440

513

00

352

(03

52)

100

9(1

009

)53

35

(53

43)minus

499

4(minus

501

2)0

192

(01

92)

44A

ngos

tode

lBal

aminus

145

5minus

675

5R

ivD

IS4

404

2313

8614

1151

918

590

562

(05

62)

019

8(1

008

)28

74

(28

76)minus

041

3(minus

041

5)0

884

(08

84)

45V

illa

Bar

rient

osminus

163

2minus

672

5R

ivD

IS4

1578

252

047

227

085

4(0

854

)0

188

(01

87)

207

2(2

068

)0

506

(05

08)

083

7(0

838

)46

Alta

mira

minus3

2minus

522

1R

ivD

IS4

1007

8610

2698

414

614

394

4621

20

813

(07

26)

007

0(0

132

)35

52

(42

60)

minus0

538

(minus1

212)

090

8(0

748

)

1W

illm

ott(

1982

)ra

nge

1ndash0

perf

ectm

atch

(pm

)is

12

Jach

ner

etal

(20

07)

0ndashinfinp

mi

s0

3M

ayer

and

But

ler

(199

3)0

ndashinfinp

mi

s0

N-R

SM

E=10

0(R

SM

E(

obs m

ax-o

bsm

in)

4N

ash

and

Sut

cliff

e(1

970)

minusinfin

ndash1p

mi

s1

5se

ee

gJa

chne

ret

al(

2007

)minus

1ndash0ndash

1p

mi

s1



39

40

41

42

Figure 3 Observed and simulated discharge [m3 s

minus1] for all 44 sites Observed 826

discharge as solid grey line simulated discharge with routing velocity of 10 ms-1

as 827

dashed grey line and simulated discharge with slope depending routing velocity as 828

solid black line 829

830

Fig 3 Observed and simulated discharge [m3 sminus1] for all 44 sites Observed discharge as solid grey line simulated discharge with routingvelocity of 10 msminus1 as dashed grey line and simulated discharge with slope depending routing velocity as solid black line



Table 6Comparison of observed floodplain area and calculated floodable area in the subregions of the basin R denotes the rectangle numberin Fig 5

North-west corner South-east corner

Floodplain

Source R area [103 km2] fraction []

published calculated published calculated

Richey et al (2002) 1 072 W 8 S54 W 2900 2395 163 135Melack et al (2004) 1 072 W 8 S54 W 1903 2395 107 135Hess et al (2003) 1 072 W 8 S54 W 3030 2395 170 135Hamilton et al (2002) 2 2 S70 W 5 S52W 974 914 146 137Hamilton et al (2002) 3 12 S68 W 16 S61 W 921 494 274 147

the high water flooded area is also covered during low waterWe therefore assume that 25 of the potential floodable areais continuously covered with water

Estimations of the inundation with models andor remotesensing has besides Richey et al (2002) already conductedfor example by Alsdorf et al (2007 2010) and Bates andDe Roo (2000) A comparison of remotely sensed inunda-tion and modelled inundation has been conducted by Wilsonet al (2007) and Bates (2012) These studies also discuss theapplicability of modeling and remote sensing to the inunda-tion estimation Due to the high spatial and temporal vari-ability in large catchments these methods are excellent toolsto investigate inundation patterns

The actual monthly flooded area is calculated by assum-ing that under current conditions (reference period 1961ndash1990) the floodable area is totally covered with water if thereference mean of the maximal monthly discharge per year(ie high water stage) plus the standard deviation for this pe-riod is reached Therefore it is possible that more than themaximal floodable area is flooded during anomalously highwater discharge years

22 Data and simulations

LPJmL is run in its natural vegetation mode at 05times 05 spa-tial resolution for the period 1901ndash2099 Transient runs arepreceded by 1000 yr spin up during which the pre-industrialCO2 level of 280 ppm and the climate of the years 1901ndash1930are repeated to obtain equilibrium for vegetation carbon andwater pools

For the model evaluation we perform model runs usingclimate forcing data from a homogenized and extended CRUTS21 global climate dataset covering the years 1901 to 2003(Osterle et al 2003 Mitchell and Jones 2005) For the pro-jections we take climate forcing data from 24 coupled gen-eral circulation models (GCMs Table 4) chosen for the 4thAssessment Report of the IPCC (Nakicenovic et al 2000Meehl et al 2007) calculated under the SRES A1B sce-nario Since all current climate models show considerable bi-ases for the Amazon Basin we apply an anomaly approach(Rammig et al 2010) The anomaly approach determines the

climate model bias for the reference period (1961ndash1990) asthe difference (for temperature) or the ratio (for precipitationand cloud cover) of the 30 yr means of climate model out-put (24 climate projections from IPCC-AR4) and observedclimate (CRU) for each month and each grid cell With thisapproach climate model bias is removed and the climate in-put for LPJmL is standardized (Rammig et al 2010)

To get quasi-daily values the monthly values of tempera-ture and cloud cover are linearly interpolated Daily precipi-tation amount and distribution of wet days to calculate coreprocesses such as photosynthesis water fluxes and vegeta-tion growth are inferred using a stochastic method (Gertenet al 2004) This method of using monthly inputs and recal-culate them to quasi-daily values is used in most large-scalemultiple-scenario studies (Alcamo et al 2003 Biemans etal 2011 Rost et al 2008) Whether the treatment of climatedata with the present implementation of the weather gener-ator in our model significantly affects simulating results rel-ative to the climate change signal is being investigated in anon-going study (Gerten et al 2012) Soil information is de-rived from the FAO global database (FAO 1991 Sitch et al2003)

23 Model evaluation and projections

231 Current conditions

We compare observed monthly discharge from the ldquoRiverDischarge Databaserdquo of the ldquoCenter for Sustainability andthe Global Environmentrdquo (2007) with simulated monthly dis-charge at 44 sites for corresponding time periods Addi-tionally to the simulation with the improved slope depen-dent routing velocity we also compare the simulated dis-charge calculated with the original LPJmL routing velocityof 10 msminus2 This shows the improvement of the introductionof the slope dependent routing velocity The observed andsimulated discharge for the 44 observation sites (Fig 2) areshown in Fig 3 (details in Table 5) We evaluate the qualityof our model simulations with the Willmottrsquos index of agree-ment which ranges from 0 to 1 with 1 indicating completeagreement (Willmott 1982) and the error of the qualitative



4

3

8

31

Fig 4 Comparison of observed and simulated discharge for all 44 observation sites with 5 indices (details in Table 5) Sites are sortedaccording the observed mean annual discharge [m3 sminus1] with the lowest discharge site at the left hand site

validation (QualV) which ranges from 0 to infinite with lowvalues indicating high agreement (Jachner et al 2007) Wealso calculate the normalised RMSE NashndashSutcliffe coeffi-cient and Pearson correlation coefficient (Mayer and Butler1993 Nash and Sutcliffe 1970) A summary of these resultsis given in Table 5 and Fig 4

For further evaluation we compare the calculated flood-able area with published values of floodplain area for 3 sub-regions of the basin (Hamilton et al 2002 Richey et al2002 Melack et al 2004 Lehner and Doll 2004 details inTable 6 and Fig 5)

232 Projections

Future changes in inundated area duration of inundation andhigh and low water peak month are evaluated by comparingthe years 1961 to 1990 (reference period) with data from thelast 30 model years 2070 to 2099 (future period) We ex-tend our analysis to identify changes in frequency of extremeevents (ie droughts and very high floods) In this context wedefine ldquoextreme floodrdquo as the flooded area being larger thanthe 30 yr median flooded area added by the standard devi-ation (for the considered time period) We define ldquoextremedroughtrdquo as the flooded area being smaller than the meanflooded area reduced by the standard deviation We calcu-late proportion of models in agreement in certain events bycombining results of the 24 different model runs If all modelruns (2424) show this event the proportion is 100 and 4 if only one model run shows this event

45

836

Figure 5 Fraction classes of floodable area per cell Class 1 representing lt5 class 2 837

representing ge5-10 class 3 representing ge10-15 class 4 representing ge15-45 of 838

floodable area For a comparison of simulated floodable area with floodplain area 839

(rectangles R1ndashR3) see Table 6 840

841

Fig 5 Fraction classes of floodable area per cell Class 1 repre-sentinglt 5 class 2 representingge 5ndash10 class 3 representingge 10ndash15 class 4 representingge 15ndash45 of floodable area For acomparison of simulated floodable area with floodplain area (rect-angles R1ndashR3) see Table 6



46

842

Figure 6 Proportion of models in agreement [] in (a) an increase and (b) a decrease 843

of mean annual inundated area per cell The proportion represents the agreement 844

between the 24 model runs showing an increase or a decrease in inundated area 845

respectively 846

847

Fig 6Proportion of models in agreement [] in(a) an increase and(b) a decrease of mean annual inundated area per cell The propor-tion represents the agreement between the 24 model runs showingan increase or a decrease in inundated area respectively

3 Results and discussion



The calculated routing velocity is highest in the Andean re-gion where the slopes are steepest and lowest in the depres-sion of the basin (Fig S1) Both the Guiana Highlands andthe Brazilian Highlands (north-west and south of the mouthrespectively) can be identified with a slightly higher veloc-ity than the lowland For the three example sites Cruzeirodo Sul Porto Velho andObidos we calculate routing veloci-ties of 025 msminus1 which agree with those reported by Birkettet al (2002) and Richey et al (1989) who measured a flowvelocity of 035plusmn 005 and 03 msminus1 respectively A sensi-tivity analysis carried out to estimate the effect of alteredR

andk values (Eq 1) on the routing velocity showed that the

47

848

Figure 7 Lengthening (blue) and shortening (red) of duration of inundation in months 849

(mean over 24 model realizations) between future and reference period 850

851

Fig 7Lengthening (blue) and shortening (red) of duration of inun-dation in months (mean over 24 model realisations) between futureand reference period

calculated velocities are less sensitive to changes ink than inR (for details see Fig S2) Depending onk andR the cal-culated basin wide mean velocity ranges between 007 and033 msminus1 while the applied velocity is 025 msminus1

Our model input velocities are calculated using slope me-dians over 05times 05 cells and thereby steep and plane areasare combined which leads to differences between simulatedrouting velocities and the observed flow velocities We areaware that our approach of applying the standard ManningndashStrickler formulation to such large spatial scales is limitedand that information on the parameterisation is missing Weattribute the uncertainty of the parameters by conducting ananalysis to estimate the sensitivity of the routing velocity tok

andR (see Supplement S1) This in combination with the ef-fective reproduction of observed hydrographs (details in Ta-ble 5) confirms that our method is suitable for our simulationpurposes

312 Simulated discharge

For most of the sites the characteristics of the simulated hy-drograph such as time and height of high and low waterphase agree with observed hydrographs (Fig 3 Table 5) Dueto scaling effects the model underestimates however the dis-charge at several sites (Fig 3) These scaling effects may inpart be caused by the averaging out of the high discharge incells that do not fully cover the measured river reach as wellas the false estimation of sites which belong to the same sim-ulated cell This problem could be overcome by applying themodel on a smaller scale or by including a larger amount ofmeasurement data if available to better represent the aver-age discharge of the certain area

In our further analysis of shifts of high and low water peakmonths due to climate change we compare the simulatedpeak month during a reference period with simulated peak



48

852

Figure 8 Proportion of models in agreement [] in a forward shift (a c) and 853

backward shift (b d) between future and reference period of at least 3-months of high 854

water peak month (a b) and low water peak month (c d) 855

856

Fig 8 Proportion of models in agreement [] in a forward shift(a c) and backward shift(b d) between future and reference period of atleast 3-months of high water peak month(a b) and low water peak month(c d)

months during a future period We are therefore confidentthat the small deviations to earlier peak month in the modelwill not affect the calculated differences between referenceand future period

In our work we simulate the discharge for sites withless than 10 m3 sminus1 as well as sites with more than100 000 m3 sminus1 mean June discharge (see Fig 1) Concern-ing this wide range of discharge within the basin the repro-duction of the overall discharge pattern is unprecedented fora model with predictive capacity We reproduce the order ofmagnitude of discharge and regarding the standard deviationof the observed discharge we see that our model results canreproduce the discharge in low medium and high dischargesites

For the comparison of observed and simulated dischargefor all 44 gauging stations we calculated Willmottrsquos index ofagreement error of qualitative validation normalised RMSENashndashSutcliffe coefficient and the Pearson correlation coef-ficient The wide range of indices offers the possibility tocompare the discharge data under different aspects Four outof the five indices show for most of the sites a high agreementbetween simulated and observed discharge The Willmottrsquos

index of agreement is in 23 of the 44 sites (52 ) larger than07 compare to 18 sites (41 ) simulated with the originalhomogenous routing velocity of 10 msminus1 (Table 5) The er-ror of the QualV is in 18 sites (41 ) smaller than 05 com-pared to 8 sites (18 ) For the three example sites Cruzeirodo Sul Porto Velho andObidos the Willmottrsquos index ofagreement is 088 093 and 091 respectively The error ofQualV for these sites is 026 009 and 099 respectively Ithas to be taken into account that for this analysis the modelwas run in its natural vegetation mode It is known that defor-estation changes the hydrological flows eg due to changesin surface runoff (Foley et al 2007) This might lead to smalldifferences between observed and simulated discharge

313 Floodplain area

A comparison of floodplain area in three part of the basinshows that calculated floodable area agrees with publishedvalues for floodplain area Cells with a high fraction of flood-able area are concentrated along the main stems of the rivernetwork (Fig 5) In addition to the Amazon main stem alarge potentially floodable area is calculated in the south ofthe region (around 15 S 65 W) realistically reproducing



49

857

Figure 9 Difference of the number of (a) extremely dry years and (b) extremely wet 858

years between future and reference period 859

860

Fig 9 Difference of the number of(a) extremely dry years and(b) extremely wet years between future and reference period

the Llanos de Moxos wetland (upper Rio Madeira basin)This vast area of about 150 000 km2 is inundated annually for3 to 4 months (Hamilton et al 2002) According to Melacket al (2004) about 14 of the whole Amazon Basin is flood-able this agrees with our result of 126 Detailed compari-son with published data for 3 subregions of the basin (rectan-gles R1ndashR3 in Fig 5) with Hamilton et al (2002) Richey etal (2002) Hess et al (2003) and Melack et al (2004) showsthat calculated and observed values are in comparable rangefor 3 of the 4 regions (Fig 5 Table 6) In the central basin(R1 and R2 in Fig 5) our values of floodable area are closeto observed values For R1 the value is in between reportedvalues We underestimate by 174 compared to Richey etal (2002) and by 21 compared to Hess et al (2003) andwe overestimate by 26 compared to Melack et al (2004)For R2 also situated in central Amazonia we underestimatedby 6 in comparison to Hamilton et al (2002) Bigger dif-ferences were found for the Llanos de Moxos (R3) Here weunderestimated the floodable area by about 46 compared to

50

861

Figure 10 Difference in the probability of at least three consecutive extremely dry 862

years (a) and extremely wet years (b) between the future and reference period 863

Fig 10 Difference in the probability of at least three consecutiveextremely dry years(a) and extremely wet years(b) between thefuture and reference period

Hamilton et al (2002) However he and his colleagues exam-ine only the Llanos de Moxos while our rectangular regionalso includes parts outside this area Thus our underestima-tion of the floodable area in this region is probably a result ofthe comparison of two slightly different spatial subsets

The connectivity between floodplain and river depends onsmall-scale characteristics such as small channels We ap-proximate this connectivity on a large-scale of 05 by build-ing our analysis on a high resolution DEM and thus capturesmall-scale characteristics We are aware that this simplifi-cation cannot fully represent the actual characteristics of thefloodplain Studying small scale changes such as the shift offloodplain forest into Terra firme forest of few hundred me-tres would require a much finer resolution but to roughly as-sess the possible changes in floodplain extend this approachis appropriate (see also Guimberteau et al 2012) Howeverthe estimated floodplain area and therewith the inundatedpatterns must be regarded as a first assessment at large spatialscales Our large-scale approach may be applied to various



river catchments where a digital elevation model is availableIt is especially useful for large catchments where measuredvalues are insufficiently available Besides this we can alsoreasonably reproduce floodable area with the approach of themodified TRMI This is a basis to calculate actual inundatedarea which enables us to estimate the extent of the area ofstrong interactions between land and river as this landndashriverinterface is of importance for plant and animal diversity

32 Future inundation patterns

For the Amazon region temperature is projected to increaseby up to 35 K until the end of the century in the A2 scenario(Meehl et al 2007) A decrease of precipitation is expectedby the end of the century (especially during the southern-hemisphere winter) in southern Amazonia whereas an in-crease in precipitation is expected in the northern part (seeeg Rammig et al 2010) Precipitation is of course a di-rect driver for inundation patterns in the Amazon Basin andthus the quality of precipitation projections is crucial for pro-jecting future inundation patterns It is well known that un-til now precipitation projections from General CirculationModels (GCMs) for the Amazon Basin are highly uncertain(eg Jupp et al 2010) mainly due to model uncertaintiesin projecting land surface feedbacks cloud formation andtropical Pacific and Atlantic sea surface temperature changes(eg Li et al 2006) Rowell (2011) found highest uncertain-ties in the deep tropics especially over South America Byapplying the model results of 24 GCMs from the IPCC-AR4we apply here the best available range of future precipitationand temperature projections and we therefore also includethe uncertainties in our results and discussions

Our approach of slope dependent routing velocity success-fully reproduces the hydrograph for the period 1961ndash1990and we therefore compare this period to the projections for2070ndash2099 to estimate changes in inundation patterns

We identify several spatial and temporal shifts in the inun-dation regime under future climate conditions For the west-ern part of Amazonia 60ndash100 of the models agree in dis-playing an increase in inundated area (Fig 6a) For the east-ern part there is no clear trend about half of the modelsproject an increase and half show a decrease in inundationarea with proportions of 50ndash60 and 40ndash60 respectively(Fig 6b) Inundation tends to be lengthened by 2 to 3 monthsin the western basin while in the east a shortening of the in-undation duration is likely to occur on average by 05 to 1months (Fig 7) Furthermore our results indicate that in thenorth-west temporal shifts in the time of high and low wa-ter peak month will occur (Fig 8) But the exact spatial andtemporal dimensions of these changes remain unclear Wecalculate a proportion of models in agreement of 40 to 50 (Fig 8a) for a 3-months-forwards shift of the high water peakmonth in some parts of north-western Amazonia There is asimilar proportion for a 3-months-backwards shift in other

parts of this region (Fig 8b) A comparable pattern can beseen for the low water peak month (Fig 8c and d)

The analysis of extreme years reveals that in a 30 yr periodthe number of extremely dry years decreases by up to 3 yrin north-west and south-east Amazonia (Fig 9a) The pro-portion of models in agreement for 3 consecutive dry yearsdecreases in most parts of Amazonia by 30 to 90 with anespecially pronounced decrease in north-east and south-westAmazonia (Fig 10a) Thus dry years are expected to occurless often and more discrete leading to less predictable con-ditions The analysis of extreme wet years shows changes inthe number ofminus15 to +15 yr (Fig 9b) in a 30 yr periodwith no clear spatial pattern The proportion for 3 consecu-tive years with extreme floods shows spatial differences Itdecreases by up to 70 in the east and it increases by upto 40 in the north-west (Fig 10b) The extreme wet yearspersist longer in the west and are expected to occur morediscrete in eastern Amazonia

4 Conclusions

Under future conditions modifications in flooded area andinundation duration as well as high and low water peakmonths and extreme years are likely to occur Already in thisdecade three years with extraordinary water amounts tookplace (Marengo et al 2008 2011 Nobre and De SimoneBorma 2009) The ldquoCore Amazonrdquo (Killeen and Solorzano2008) in the north-western part of the Amazon Basin ex-periences in our simulations most of these changes An in-crease in inundated area a lengthening of inundation dura-tion changes in low and high water peak month combinedwith shifts in occurrence and duration of extreme events havethe potential to change this region substantially The large-scale changes in floodplain patterns found in this study mayamplify projected climate and land-use change effects Weassume that the shifted flooding situation will influence sev-eral plant and animal species This will lead to changes inspecies composition due to shifts in competition and foodweb networks Since the biodiversity increases from east towest in the Amazon Basin (Worbes 1997 ter Steege et al2003) the predicted changes in the north-western part willinfluence an area with a large and valuable species pool

The projected changes in inundation have to potential forlarge-scale changes in water and carbon fluxes The reduc-tion of terrestrial evapotranspiration caused by an increase ofinundated area and projected deforestation could lead to a re-duced recycling of precipitation and further amplify droughtconditions The South American Low Level Jet is transport-ing atmospheric moisture from the Amazon southwards tothe Parana-La Plata Basin (Marengo et al 2009) A reduc-tion of moisture in Amazonia might therefore reduce the pre-cipitation of the entire region of South America The out-gassing CO2 from the Amazon Basin which is currentlyestimated to be about 470 Tg C yrminus1 (Richey et al 2002)



is likely to increase due to longer inundation in the westThe less pronounced shortening of inundation in the eastmight not be sufficiently able to balance the additional fluxChanges in regional climate will significantly change vege-tation patterns in Amazonia and may cause additional carbonemissions We conclude that changes in inundation patternscaused by climate change will significantly alter the highlycomplex system in the Amazon Basin

Supplementary material related to this article isavailable online athttpwwwhydrol-earth-syst-scinet1722472013hess-17-2247-2013-supplementpdf

AcknowledgementsWe thank ldquoPakt fur Forschung der Leibniz-Gemeinschaftrdquo for funding the TRACES project We also thankJohn Lowry from Utah State University for providing AML scripttemplates to calculate mTRMI and landform types We thankHeike Zimmermann-Timm Pia Parolin and Florian Wittmann forfruitful discussion We also thank the anonymous reviewers fortheir helpful remarks Finally we thank our LPJmL and AmazonGroup colleagues at PIK

Edited by A Bronstert

References

Alcamo J Henrichs T and Rosch T World Water in 2025 ndashGlobal modeling and scenario analysis for the World Commis-sion on Water for the 21st Century Report A0002 Center forEnvironmental Systems Research University of Kassel KurtWolters Strasse 3 34109 Kassel Germany 2000

Alsdorf D E Rodrıguez E and Lettenmaier D P Measur-ing surface water from space Rev Geophys 45 RG2002doi1010292006RG000197 2007

Alsdorf D Han S-C Bates P and Melack J Sea-sonal water storage on the Amazon floodplain measuredfrom satellites Remote Sens Environ 114 2448ndash2456doi101016jrse201005020 2010

Anderson L O Malhi Y Ladle R J Aragao L E O CShimabukuro Y Phillips O L Baker T Costa A C L Es-pejo J S Higuchi N Laurance W F Lopez-Gonzalez GMonteagudo A Nunez-Vargas P Peacock J Quesada C Aand Almeida S Influence of landscape heterogeneity on spatialpatterns of wood productivity wood specific density and aboveground biomass in Amazonia Biogeosciences 6 1883ndash1902doi105194bg-6-1883-2009 2009

Arnold J G and Fohrer N SWAT2000 current capabilities andresearch opportunities in applied watershed modelling HydrolProcess 19 563ndash572 doi101002hyp5611 2005

Arora V K and Boer G J Effects of simulated climate changeon the hydrology of major river basins J Geophys Res-Atmos106 3335ndash3348 2001

Bates P D Integrating remote sensing data with flood inundationmodels how far have we got Hydrol Process 26 2515ndash2521doi101002hyp9374 2012

Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236 54ndash77doi101016S0022-1694(00)00278-X 2000

Betts R A Malhi Y and Roberts J T The future ofthe Amazon new perspectives from climate ecosystem andsocial sciences Philos T R Soc B 363 1729ndash1735doi101098rstb20080011 2008

Biemans H Hutjes R W A Kabat P Strengers B J GertenD and Rost S Effects of precipitation uncertainty on dischargecalculations for main river basins J Hydrometeorol 10 1011ndash1025 doi1011752008jhm10671 2009

Biemans H Haddeland I Kabat P Ludwig F Hutjes RW A Heinke J Von Bloh W and Gerten D Impactof reservoirs on river discharge and irrigation water supplyduring the 20th century Water Resour Res 47 W03509doi1010292009WR008929 2011

Birkett C M Mertes L A K Dunne T Costa M H and Jasin-ski M J Surface water dynamics in the Amazon Basin Appli-cation of satellite radar altimetry J Geophys Res-Atmos 107261ndash2621 doi1010292001JD000609 2002

Bondeau A Smith P C Zaehle S Schaphoff S LuchtW Cramer W Gerten D Lotze-Campen H Muller CReichstein M and Smith B Modelling the role of agri-culture for the 20th century global terrestrial carbon bal-ance Global Change Biol 13 679ndash706 doi101111j1365-2486200601305x 2007

Burrough P A Digital elevation models in Principles of ge-ographical information systems for land resources assessmentedited by Burrough P A 39ndash55 Oxford University Press NewYork 1986

Center for sustainability and the global environment (SAGE)River Discharge Database available athttpwwwsagewisceduriverdata(last access 12 October 2007) 2007

Coe M T Costa M H Botta A and Birkett C Long-termsimulations of discharge and floods in the Amazon Basin JGeophys Res-Atmos 107 8044 doi1010292001JD0007402002

Cox P M Betts R A Collins M Harris P P HuntingfordC and Jones C D Amazonian forest dieback under climate-carbon cycle projections for the 21st century Theor Appl Cli-matol 78 137ndash156 doi101007s00704-004-0049-4 2004

Doll P and Zhang J Impact of climate change on freshwa-ter ecosystems a global-scale analysis of ecologically relevantriver flow alterations Hydrol Earth Syst Sci 14 783ndash799doi105194hess-14-783-2010 2010

Doll P Kaspar F and Lehner B A global hydrological modelfor deriving water availability indicators model tuning and vali-dation J Hydrol 270 105ndash134 2003

Donnegan J A Butler S L Kuegler O Stroud B J HiseroteB A and Rengulbai K Palaursquos forest resources 2003 in Re-sour Bull PNW-RB-252 1ndash52 US Department of AgricultureForest Service Pacific Northwest Research Station PortlandOR 2007

FAO The digitized soil map of the world (Release 10) Food andAgriculture Organization of the United Nations Rome Italy1991

Fearnside P M Are climate change impacts already affectingtropical forest biomass Global Environ Chang 14 299ndash302doi101016jgloenvcha200402001 2004



Fekete B M Vorosmarty C J and Grabs W Global com-posite runoff fields of observed river discharge and simulatedwater balances Global Runoff Data Center Koblenz availableat httpwwwbafgdenn298486GRDCEN02Services04 Report Seriesreport22templateId=rawproperty=publicationFilepdfreport22pdf 1999

Foley J A Botta A Coe M T and Costa M H ElNino-Southern Oscillation and the climate ecosystems andrivers of Amazonia Global Biogeochem Cy 16 791ndash7917doi1010292002GB001872 2002

Foley J A Asner G P Costa M H Coe M T DeFriesR Gibbs H K Howard E A Olson S Patz J Ra-mankutty N and Snyder P Amazonia revealed forest degra-dation and loss of ecosystem goods and services in the Ama-zon Basin Front Ecol Environ 5 25ndash32 doi1018901540-9295(2007)5[25ARFDAL]20CO2 2007

Gaillardet J Dupre B Allegre C J and Negrel P Chemical andphysical denudation in the Amazon River basin Chem Geol142 141ndash173 1997

Gerten D Schaphoff S Haberlandt U Lucht W and Sitch STerrestrial vegetation and water balance ndash hydrological evalua-tion of a dynamic global vegetation model J Hydrol 286 249ndash270 doi101016jjhydrol200309029 2004

Gerten D Rost S Von Bloh W and Lucht W Causes of changein 20th century global river discharge Geophys Res Lett 35L20405 doi1010292008gl035258 2008

Gerten D Schaphoff S Rastgooy J Deryng D Wallace CWarren R and Edwards N Quantification of Crop and Wa-ter Impacts under Scenarios from D51 ERMITAGE project re-port Open University Milton Keynes UK available athttpermitagecsmanacuksitesdefaultfilesD52pdf 2012

Gordon W S Famiglietti J S Fowler N L Kittel T G F andHibbard K A Validation of simulated runoff from six terrestrialecosystem models results from VEMAP Ecol Appl 14 527ndash545 doi10189002-5287 2004

Guimberteau M Drapeau G Ronchail J Sultan B Polcher JMartinez J-M Prigent C Guyot J-L Cochonneau G Es-pinoza J C Filizola N Fraizy P Lavado W De OliveiraE Pombosa R Noriega L and Vauchel P Discharge sim-ulation in the sub-basins of the Amazon using ORCHIDEEforced by new datasets Hydrol Earth Syst Sci 16 911ndash935doi105194hess-16-911-2012 2012

Hamilton S K Sippel S J and Melack J M Com-parison of inundation patterns among major South Amer-ican floodplains J Geophys Res-Atmos 107 51ndash514doi1010292000JD000306 2002

Hess L L Melack J M Novo E Barbosa C C F and GastilM Dual-season mapping of wetland inundation and vegetationfor the central Amazon basin Remote Sens Environ 87 404ndash428 2003

IPCC Summary for policy makers in Climate change 2007 Thephysical science basis Contribution of working group I to thefourth assessment report of the Intergovernmental Panel on Cli-mate Change edited by Solomon S Qin D Manning MChen Z Marquis M Averyt K B Tignor M and MillerH L Cambrigde University Press Cambrigde UK and NewYork NY USA available athttpwwwipccchpublicationsanddataar4wg1encontentshtml 2007

Jachner S Van den Boogaart K G and Petzoldt T Statisticalmethods for the qualitative assessment of dynamic models withtime delay (R package qualV) J Stat Softw 22 1ndash30 2007

Junk W J The Amazon floodplain ndash A sink or source for organiccarbon Mitteilungen des Geologisch-Palaontologischen Insti-tuts der Universitat Hamburg 58 267ndash283 1985

Junk W J and Piedade M T F Plant life in the floodplain withspecial reference to herbaceous plants in The Central AmazonFloodplain edited by Junk W J 147ndash185 Springer BerlinGermany 1997

Jupp T E Cox P M Rammig A Thonicke K Lucht Wand Cramer W Development of probability density functionsfor future South American rainfall New Phytol 187 682ndash693doi101111j1469-8137201003368x 2010

Keddy P A Fraser L H Solomeshch A I Junk W J Camp-bell D R Arroyo M T K and Alho C J R Wet and won-derful The worldrsquos largest wetlands are conservation prioritiesBioscience 59 39ndash51 doi101525bio20095918 2009

Killeen T J and Solorzano L A Conservation strategies to mit-igate impacts from climate change in Amazonia Philos T RSoc B 363 1881ndash1888 doi101098rstb20070018 2008

Langerwisch F Rost S Poulter B Zimmermann-Timm H andCramer W Assessing carbon dynamics in Amazonia with thedynamic global vegetation model LPJmL ndash discharge evaluationVerh Internat Verein Limnol 30 455ndash458 2008

Lehner B and Doll P Development and validation of a globaldatabase of lakes reservoirs and wetlands J Hydrol 296 1ndash22doi101016jjhydrol200403028 2004

Lenton T M Held H Kriegler E Hall J W Lucht WRahmstorf S and Schellnhuber H J Tipping elements in theEarthrsquos climate system Proc Natl Acad Sci 105 1786ndash1793doi101073pnas0705414105 2008

Li W H Fu R and Dickinson R E Rainfall and its seasonalityover the Amazon in the 21st century as assessed by the coupledmodels for the IPCC AR4 J Geophys Res-Atmos 111 1ndash14doi1010292005JD006355 2006

Liang X and Xie Z A new surface runoff parameterizationwith subgrid-scale soil heterogeneity for land surface mod-els Adv Water Resour 24 1173ndash1193 doi101016S0309-1708(01)00032-X 2001

Liang X Lettenmaier D P Wood E F and Burges S J Asimple hydrologically based model of land surface water and en-ergy fluxes for general circulation models J Geophys Res 9914415ndash14428 doi10102994JD00483 1994

Malhi Y and Wright J Spatial patterns and recent trends in theclimate of tropical rainforest regions Philos T R Soc B 359311ndash329 doi101098rstb20031433 2004

Malhi Y Roberts J T Betts R A Killeen T J Li W andNobre C A Climate change deforestation and the fate of theAmazon Science 319 169ndash172 2008

Marengo J A Nobre C A Tomasella J Cardoso M F andOyama M D Hydro-climatic and ecological behaviour of thedrought of Amazonia in 2005 Philos T R Soc B 363 1773ndash1778 doi101098rstb20070015 2008

Marengo J Nobre C A Betts R A Cox P M Sampaio Gand Salazar L Global warming and climate change in Ama-zonia Climate-vegetation feedback and impacts on water re-sources in Amazonia and Global Change edited by Keller MBustamante M Gash J and Silva Dias P 273ndash292 American



Geophysical Union Washington 2009Marengo J A Tomasella J Alves L M and Soares W

R The drought of 2010 in the context of historical droughtsin the Amazon region Geophys Res Lett 38 L12703doi1010292011GL047436 2011

Mayer D G and Butler D G Statistical validation Ecol Modell68 21ndash32 doi1010160304-3800(93)90105-2 1993

Meehl G A Stocker T F Collins W D Friedlingstein P GayeA T Gregory J M Kitoh A Knutti R Murphy J M NodaA Raper S C B Watterson I G Weaver A J and ZhaoZ-C Global climate projections in Climate Change 2007 ThePhysical Science Basis Contribution of Working Group I to theFourth Assessment Report of the Intergovernmental Panel onClimate Change edited by Solomon S Qin D Manning MChen Z Marquis M Averyt K B Tignor M and Miller HL Cambridge University Press Cambrigde UK and New YorkNY USA 2007

Melack J M Hess L L Gastil M Forsberg B R HamiltonS K Lima I B T and Novo E M L Regionalization ofmethane emissions in the Amazon Basin with microwave remotesensing Global Change Biol 10 530ndash544 doi101111j1529-8817200300763x 2004

Mitchell T D and Jones P D An improved method of con-structing a database of monthly climate observations and as-sociated high-resolution grids Int J Climatol 25 693ndash712doi101002joc1181 2005

Moreira-Turcq P Seyler P Guyot J L and Etcheber H Expor-tation of organic carbon from the Amazon River and its main trib-utaries Hydrol Process 17 1329ndash1344 doi101002hyp12872003

Naiman R J Decamps H and McClain M E Riparia Ecol-ogy conservation and management of streamside communitiesElsevier Academic Press ISBN-13978-0126633153 2005

Nakicenovic N Davidson O Davis G Grubler A KramT Lebre La Rovere E Metz B Morita T Pepper WPitcher H Sankovski A Shukla P Swart R Watson R andDadi Z IPCC Special report on emission scenarios availableat httpwwwipccchipccreportssresemissionindexphpidp=0 2000

Nash J E and Sutcliffe J V River flow forecasting through con-ceptual models part I ndash A discussion of principles J Hydrol 10282ndash290 doi1010160022-1694(70)90255-6 1970

Nepstad D C Tohver I M Ray D Moutinho P and CardinotG Mortality of large trees and lianas following experimen-tal drought in an Amazon forest Ecology 88 2259ndash2269doi10189006-10461 2007

Nobre C A and De Simone Borma L ldquoTipping pointsrdquo for theAmazon forest Current Opinion in Environmental Sustainability1 28ndash36 doi101016jcosust200907003 2009

Osterle H Gerstengarbe F W and Werner P C Ho-mogenisierung und Aktualisierung des Klimadatensatzes desClimate Research Unit der Universitaet of East Anglia Norwich6 Deutsche Klimatagung 2003 Potsdam Germany Terra Nostra2003 326ndash329 2003

Parker A J The Topographic Relative Moisture Index An ap-proach to soil-moisture assessment in mountain terrain PhysGeogr 3 160ndash168 1982

Parolin P De Simone O Haase K Waldhoff D RottenbergerS Kuhn U Kesselmeier J Kleiss B Schmidt W Piedade

M T F and Junk W J Central Amazonian floodplain forestsTree adaptations in a pulsing system Bot Rev 70 357ndash3802004

Patt H Hochwasser-Handbuch Auswirkungen und SchutzSpringer Verlag Berlin 2001

Rammig A Jupp T Thonicke K Tietjen B Heinke J Os-tberg S Lucht W Cramer W and Cox P Estimating therisk of Amazonian forest dieback New Phytolog 187 694ndash706doi101111j1469-8137201003318x 2010

Randall D A Wood R A Bony S Colman R Fichefet TFyfe J Kattsov V Pitman A Shukla J Srinivasan J Stouf-fer R J Sumi A and Taylor K E Climate models and theirevaluation in Climate Change 2007 The Physical Science Ba-sis Contribution of Working Group I to the Fourth AssessmentReport of the Intergovernmental Panel on Climate Change editedby Solomon S Qin D Manning M Chen Z Marquis MAveryt K B Tignor M and Miller H L Cambridge Univer-sity Press 2007

Richey J E Mertes L A K Dunne T Victoria R L ForsbergB R Tancredi A C N S and Oliveira E Sources and routingof the Amazon River flood wave Global Biogeochem Cy 3191ndash204 1989

Richey J E Melack J M Aufdenkampe A K Ballester V Mand Hess L L Outgassing from Amazonian rivers and wetlandsas a large tropical source of atmospheric CO2 Nature 416 617ndash620 doi101038416617a 2002

Rost S Gerten D Bondeau A Lucht W Rohwer J andSchaphoff S Agricultural green and blue water consumptionand its influence on the global water system Water Resour Res44 W09405 doi1010292007wr006331 2008

Rowell D P Sources of uncertainty in future changes in local pre-cipitation Clim Dynam 39 1929ndash1950 doi101007s00382-011-1210-2 2011

Seneviratne S I Nicholls N Easterling D Goodess C MKanae S Kossin J Luo Y Marengo J McInnes K RahimiM Reichstein M Sorteberg A Vera C and Zhang XChanges in climate extremes and their impacts on the naturalphysical environment in Managing the Risks of Extreme Eventsand Disasters to Advance Climate Change Adaptation A Spe-cial Report of Working Groups I and II of the IntergovernmentalPanel on Climate Change edited by Field C B Barros VStocker T F Qin D Dokken D J Ebi K L MastrandreaM D Mach K J Plattner G-K Allen S K Tignor Mand Midgley P M 109ndash230 Cambrigde University Press Cam-brigde UK and New York NY USA 2012

Sitch S Smith B Prentice I C Arneth A Bondeau ACramer W Kaplan J O Levis S Lucht W Sykes M TThonicke K and Venevsky S Evaluation of ecosystem dy-namics plant geography and terrestrial carbon cycling in the LPJdynamic global vegetation model Global Change Biol 9 161ndash185 doi101046j1365-2486200300569x 2003

Sitch S Huntingford C Gedney N Levy P E Lomas MPiao S L Betts R Ciais P Cox P Friedlingstein PJones C D Prentice I C and Woodward F I Evalua-tion of the terrestrial carbon cycle future plant geography andclimate-carbon cycle feedbacks using five Dynamic Global Veg-etation Models (DGVMs) Global Change Biol 14 2015ndash2039doi101111j1365-2486200801626x 2008



ter Steege H Pitman N Sabatier D Castellanos H Van derHout P Daly D C Silveira M Phillips O Vasquez RVan Andel T Duivenvoorden J De Oliveira A A Ek RLilwah R Thomas R Van Essen J Baider C Maas PMori S Terborgh J Nunez Vargas P Mogollon H andMorawetz W A spatial model of tree alpha-diversity and treedensity for the Amazon Biodivers Conserv 12 2255ndash2277doi101023A1024593414624 2003

Vigerstol K L and Aukema J E A comparison of tools for mod-eling freshwater ecosystem services J Environ Manage 922403ndash2409 doi101016jjenvman201106040 2011

Wagner W Scipal K Pathe C Gerten D Lucht W andRudolf B Evaluation of the agreement between the first globalremotely sensed soil moisture data with model and precipitationdata J Geophys Res 108 4611 doi1010292003JD0036632003

Willmott C J Some comments on the evaluation of model perfor-mance B Am Meteorol Soc 63 1309ndash1313 1982

Wilson M Bates P Alsdorf D Forsberg B Horritt MMelack J Frappart F and Famiglietti J Modeling large-scaleinundation of Amazonian seasonally flooded wetlands GeophysRes Lett 34 L15404 doi1010292007GL030156 2007

Worbes M The forest ecosystem of the floodplains in TheCentral Amazon Floodplain edited by Junk W J 223ndash265Springer Berlin Germany 1997

WWF HydroSHEDS HydroSHEDS available athttphydroshedscrusgsgov(last access 15 October 2007)2007



characteristic and very diverse habitats for millions of plantand animal species which are intimately related to the recur-rent annual flooding These floodplains are alternately suit-able for aquatic and terrestrial organisms The distributionof these species is especially influenced by the duration ofthe aquatic and terrestrial phase (Junk and Piedade 1997)Floodplain forests cover approximately 97 000 km2 (Parolinet al 2004) and contain about 20 of the Amazonian treespecies (Naiman et al 2005) The vast floodplain areas thusrepresent one of the riches biota on earth providing severalecosystem services such as timber and fish production andcarbon storage (Keddy et al 2009)

Climate change is expected to alter temperature and pre-cipitation patterns which can potentially lead to changes inflood regime such as a reduction in discharge in the Ama-zon River (Arora and Boer 2001) The variability in precip-itation is expected to increase (Seneviratne et al 2012) andmay cause higher spatial and temporal variability in river dis-charge and flooded area (Coe et al 2002) The effect of cli-mate change on the El Nino Southern Oscillation (ENSO) re-mains unclear (Malhi and Wright 2004) but ENSO changesdischarge drastically (Foley et al 2002) Changes in timeduration and height of the flooding has the potential to shiftvegetation distribution which may in turn lead to feedbacksto the atmosphere (eg Cox et al 2004 Malhi et al 2008)

To assess the effects of potential changes in precipita-tion and temperature on discharge and freshwater ecosystemservices usually hydrological models are applied (Vigerstoland Aukema 2011) which are for example WaterGAP (Al-camo et al 2000 Doll and Zhang 2010 Doll et al 2003)WBM (Fekete et al 1999) and SWAT (Arnold and Fohrer2005) and VIC (Liang and Xie 2001 Liang et al 1994)A disadvantage of these models is that they do not incor-porate explicit simulation of vegetation dynamics which arean essential part of the water cycle We use the dynamicglobal vegetation and hydrology model LPJmL (Bondeau etal 2007 Gerten et al 2004 Rost et al 2008 Sitch et al2003) which has been improved for regional application tothe Amazon Basin and includes the dynamic and spatially ex-plicit reproduction of the specific hydrological patterns of themain river stem and its tributaries These patterns consist ofseasonal discharge time and duration of lowhigh water peri-ods and the changing extent of the flooded area during thoseperiods LPJmL combines dynamic terrestrial vegetation de-velopment with carbon and water cycles This enables us toestimate not only the direct effect of changing precipitationand temperature on discharge but also to include the indirecteffects of these changes on vegetation cover and type whichin turn alters runoff and discharge

The main goal of our study is thus to understand and quan-tify the magnitude of impacts of future climate change onthe Amazonian inundation patterns We provide estimates onclimate change induced shifts of inundation patterns whichcomprises of time and duration of lowhigh water periodsand the changing extent of the flooded area during those pe-

riods We describe here a method to calculate monthly in-undated area We evaluate our simulated results against ob-served data for discharge and potentially floodable area andestimate changes in inundation patterns in Amazonia Toquantify the amplitude of shifts in the flooding regime dueto climate change we use forcing data of the 24 General Cir-culation Models (GCMs) from the 4th Assessment Reportof the Intergovernmental Panel on Climate Change (IPCC2007 Randall et al 2007)

2 Methods

We apply the Dynamic Global Vegetation and HydrologyModel LPJmL (Sitch et al 2003 Gerten et al 2004 Bon-deau et al 2007 Rost et al 2008) to understand and to as-sess the effect of climate change on current Amazonian inun-dation patterns LPJmL computes establishment abundancevegetation dynamics growth and productivity of the worldrsquosmajor plant functional types as well as the associated carbonand water fluxes The model is typically applied on a gridof 05

times 05 longitudelatitude and at daily time steps Car-bon fluxes and vegetation dynamics are directly coupled towater fluxes Modelled soil moisture runoff and evapotran-spiration were found to reproduce observed patterns well andtheir quality is comparable to stand-alone global hydrologi-cal models (Wagner et al 2003 Gerten et al 2004 2008Gordon et al 2004 Biemans et al 2009)

The river routing module of LPJmL (described by Rost etal 2008) assumes a surface water storage pool for each gridcell representing the water storage and retention in reservoirsand lakes The change of water storage in the river over timeis represented as the runoff generated in the cell the input ofdischarge accumulated from upstream grid cells the outputto the downstream cell and the outflow of lakes in the respec-tive cell The output to the downstream cell is determinedas a linear transport of discharge depending on the routingvelocity (v) and the distance between the midpoints of theconnected cells Earlier versions of LPJmL used a globallyhomogeneous routing velocity of 1 msminus1 (Rost et al 2008)which had difficulties to reproduce the Amazonian hydro-graph with shifts within the hydrograph of several monthsIn a former study we already improved the reproduction ofthe hydrograph by applying a homogeneously reduced rout-ing velocity of 025 msminus1 to the Amazon Basin leading tosubstantial reductions of the shift for several observation sites(Langerwisch et al 2008) Our new approach is to use het-erogeneous routing velocities which take topographic differ-ences within the Amazon catchment into account for furtherimprovement of the hydrograph




Slope range Class

















v =

(tan

(S times

π

180


23 (1)




212 Floodable area





Model name






(2)



38

823


825











Tabl

e5

Com

paris

onof

obse

rved

and

sim

ulat

eddi

scha

rge

for

the

obse

rvat

ion

perio

d(s

eeal

soF

igs

3an

d4)

Ind

ices

are

give

nfo

rth

est

anda

rdsi

mul

atio

n(s

td)

with

slop

ede

pend

ent

rout

ing

velo

city

and

orig

inal

setti

ng(1

0m

sminus

1)

with

hom

ogen

eous

rout

ing

velo

city

of1

0m

sminus

1in

brac

kets

IDS

tatio

nLa

titud

eLo

ngitu

deD

atab

ase

Num

ber

ofob

sye

ars

Obs

erve

dm

inan

nual

disc

harg

e[m

3sminus

1]

Obs

erve

dm

ean

annu

aldi

scha

rge

[m3

sminus1]

Obs

erve

dm

axan

nual

disc

harg

e[m

3sminus

1]

Sim

ulat

edm

inan

nual

disc

harg

e[m

3sminus

1]

Sim

ulat

edm

ean

annu

aldi

scha

rge

[m3

sminus1]

Sim

ulat

edm

axan

nual

disc

harg

e[m

3sminus

1]

Will

mot

trsquosin

dex

ofag

reem

ent1st

d(1

0m

sminus1)

Err

orof

Qua

lV2

std

(10

msminus

1)

N-R

MS

E3[

]st

d(1

0m

sminus1)

Nas

hndashS

utcl

iffe4

std

(10

msminus

1)

Pea

rson

corr

elat

ion

coef

ficie

nt5st

d(1

0m

sminus1)

1E

stira

odo

Rep

ouso

minus4

37minus

709

3A

NE

EL

1619

325

1847

4915

525

9272

700

870

(07

93)

100

3(0

367

)21

27

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mi

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39

40

41

42




as 827



830






Floodplain

















4

3

8

31




232 Projections


45

836





841




46

842




respectively 846

847







47

848



851










48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References





























































































Slope range Class

















v =

(tan

(S times

π

180


23 (1)




212 Floodable area





Model name






(2)



38

823


825











Tabl

e5

Com

paris

onof

obse

rved

and

sim

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scha

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for

the

obse

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perio

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igs

3an

d4)

Ind

ices

are

give

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est

anda

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atio

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td)

with

slop

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rout

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velo

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and

orig

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1)

with

hom

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eous

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tatio

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titud

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ase

Num

ber

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Obs

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dm

inan

nual

disc

harg

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3sminus

1]

Obs

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Obs

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Sim

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39

40

41

42




as 827



830






Floodplain

















4

3

8

31




232 Projections


45

836





841




46

842




respectively 846

847







47

848



851










48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References





























































































Model name






(2)



38

823


825











Tabl

e5

Com

paris

onof

obse

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and

sim

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with

slop

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and

orig

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Obs

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Obs

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Obs

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Sim

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39

40

41

42




as 827



830






Floodplain

















4

3

8

31




232 Projections


45

836





841




46

842




respectively 846

847







47

848



851










48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References




























































































Tabl

e5

Com

paris

onof

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for

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39

40

41

42




as 827



830






Floodplain

















4

3

8

31




232 Projections


45

836





841




46

842




respectively 846

847







47

848



851










48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References




























































































39

40

41

42




as 827



830






Floodplain

















4

3

8

31




232 Projections


45

836





841




46

842




respectively 846

847







47

848



851










48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References






























































































Floodplain

















4

3

8

31




232 Projections


45

836





841




46

842




respectively 846

847







47

848



851










48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References




























































































4

3

8

31




232 Projections


45

836





841




46

842




respectively 846

847







47

848



851










48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References




























































































46

842




respectively 846

847







47

848



851










48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References




























































































48

852




856






313 Floodplain area




49

857



860



50

861















4 Conclusions









References




























































































49

857



860



50

861















4 Conclusions









References



































































































4 Conclusions









References
































































































References



























































































copernicus.org - hess 17-2247-2013 · 2020. 7. 19. · revised: 8 march 2013 – accepted: 1 may...

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