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Discharge simulation in the sub-basins of the Amazon
using ORCHIDEE forced by new datasets
Matthieu Guimberteau, Guillaume Drapeau, Josyane Ronchail, Benjamin
Sultan, Jan Polcher, Jean-Michel Martinez, Catherine Prigent, Jean-Loup
Guyot, Gérard Cochonneau, Jhan Carlo Espinoza Villar, et al.
To cite this version:
Matthieu Guimberteau, Guillaume Drapeau, Josyane Ronchail, Benjamin Sultan, Jan Polcher, et al..
Discharge simulation in the sub-basins of the Amazon using ORCHIDEE forced by new datasets.
Hydrology and Earth System Sciences, European Geosciences Union, 2012, 16 (3), p.11171-11232.
<10.5194/hess-16-911-2012>. <hal-00755099>
Hydrol. Earth Syst. Sci., 16, 911–935, 2012
www.hydrol-earth-syst-sci.net/16/911/2012/
doi:10.5194/hess-16-911-2012
© Author(s) 2012. CC Attribution 3.0 License.
Hydrology and
Earth System
Sciences
Discharge simulation in the sub-basins of the Amazon using
ORCHIDEE forced by new datasets
M. Guimberteau1,2, G. Drapeau1,2,3,4, J. Ronchail1,2,3, B. Sultan1,2,5, J. Polcher2,6, J.-M. Martinez7, C. Prigent8,
J.-L. Guyot7, G. Cochonneau7, J. C. Espinoza9,10, N. Filizola11, P. Fraizy12, W. Lavado10,13, E. De Oliveira14,
R. Pombosa15, L. Noriega16, and P. Vauchel12
1Laboratoire d’Oce´anographie et du Climat: expe´rimentations et approches nume´riques (LOCEAN), UMR7159, Paris, France
2Institut Pierre Simon Laplace (IPSL), Paris, France
3Universite´ Paris Diderot, Sorbonne Paris Cite´, Paris, France
4Poˆle de Recherche pour l’Organisation et la Diffusion de l’Information Ge´ographique (PRODIG), Paris, France
5Institut de Recherche pour le De´veloppement (IRD), Paris, France
6Laboratoire de Me´te´orologie Dynamique (LMD), CNRS, Paris, France
7Institut de Recherche pour le De´veloppement (IRD), Brasilia, Brazil
8Laboratoire d’Etudes du Rayonnement et de la Matie`re en Astrophysique (LERMA),
Observatoire de Paris, CNRS, Paris, France
9Instituto Geofisico del Peru´, Lima, Peru´
10Universidad Agraria La Molina, Lima, Peru´
11Universidad Federal de Amazonas, Manaus, Brazil
12Institut de Recherche pour le De´veloppement (IRD), Lima, Peru´
13Servicio Nacional de meteorologı´a e hidrologı´a, Lima, Peru´
14Ageˆncia Nacional de ´Aguas (ANA), Brasilia, Brazil
15Instituto Nacional de meteorologı´a e hidrologı´a, Quito, Ecuador
16Servicio Nacional de meteorologı´a e hidrologı´a, La Paz, Bolivia
Correspondence to: M. Guimberteau (matthieu.guimberteau@upmc.fr)
Received: 24 November 2011 – Published in Hydrol. Earth Syst. Sci. Discuss.: 15 December 2011
Revised: 1 March 2012 – Accepted: 15 March 2012 – Published: 22 March 2012
Abstract. The aim of this study is to evaluate the ability of
the ORCHIDEE land surface model to simulate streamflows
over each sub-basin of the Amazon River basin. For this pur-
pose, simulations are performed with a routing module in-
cluding the influence of floodplains and swamps on river dis-
charge and validated against on-site hydrological measure-
ments collected within the HYBAM observatory over the
1980–2000 period. When forced by the NCC global mete-
orological dataset, the initial version of ORCHIDEE shows
discrepancies with ORE HYBAM measurements with under-
estimation by 15 % of the annual mean streamflow at ´Obidos
hydrological station. Consequently, several improvements
are incrementally added to the initial simulation in order to
reduce those discrepancies. First, values of NCC precipita-
tion are substituted by ORE HYBAM daily in-situ rainfall
observations from the meteorological services of Amazonian
countries, interpolated over the basin. It highly improves
the simulated streamflow over the northern and western parts
of the basin, whereas streamflow over southern regions be-
comes overestimated, probably due to the extension of rainy
spots that may be exaggerated by our interpolation method,
or to an underestimation of simulated evapotranspiration
when compared to flux tower measurements. Second, the
initial map of maximal fractions of floodplains and swamps
which largely underestimates floodplains areas over the main
stem of the Amazon River and over the region of Llanos de
Moxos in Bolivia, is substituted by a new one with a bet-
ter agreement with different estimates over the basin. Simu-
lated monthly water height is consequently better represented
in ORCHIDEE when compared to Topex/Poseidon measure-
ments over the main stem of the Amazon. Finally, a calibra-
tion of the time constant of the floodplain reservoir is per-
formed to adjust the mean simulated seasonal peak flow at
´Obidos in agreement with the observations.
Published by Copernicus Publications on behalf of the European Geosciences Union.
912 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
1 Introduction
The Amazon River basin, the largest basin in the world with
an area of approximately 6.0 million km2, has the highest av-
erage discharge (206 000 m3 s−1) (Callede et al., 2010) and
it contributes to about 15–20 % of the fresh water trans-
ported to the oceans (Richey et al., 1986). The present func-
tioning of this basin is especially complex due to four el-
ements: its extent over large ranges of latitude, longitude
and altitude that organize its mean hydrological character-
istics, the presence of extensive inundation zones that con-
tribute to runoff control at annual and interannual time scales,
the influence of the Atlantic and Pacific oceans that partly
control the hydrological variability at different time scales,
and the land use changes that are increasing since the sev-
enties and lead to changes in the radiation and water bal-
ances. Yet, a good understanding of the present hydrologi-
cal response of the Amazon River basin to various forcings
is required to evaluate future changes. This can be partly
achieved by using numerical models relying on hydroclima-
tological databases. So far, regional discharge simulation
in the Amazon River basin has been conducted by different
groups. Vo¨ro¨smarty et al. (1989) and Costa and Foley (1997)
initiated the simulation efforts in the Amazon River basin.
Coe et al. (2002) simulated discharge in 121 stations of the
Amazon River basin using an integrated biosphere simula-
tor coupled to a hydrological routing algorithm and obtained
good results for Brazilian basins. However, a discharge un-
derestimation was found in the basins lying in other Amazo-
nian countries, due to the lack of reliable rainfall information
except for Brazil. Other deficiencies of the model, related to
the river and floodplain morphology, have been corrected in
Coe et al. (2007) and led to great improvement in the sim-
ulation of discharge, water height and flooded area. In Coe
et al. (2009), the same authors used the model to character-
ize the role of deforestation on runoff evolution. Also us-
ing ISBA (Soil-Biosphere-Atmosphere Interaction) land sur-
face model and the TRIP (Total Runoff integrating Pathways)
river routing model, Decharme et al. (2008) compared their
evaluations in the Parana, Orinoco and Amazon River basins
with satellite-derived inundation estimates as well as in-situ
river discharge observations. Recently an interesting model-
ing effort was introduced by Yamazaki et al. (2011), incor-
porating semi-explicit floodplain process. At the beginning
of 21st century, a distributed Large Basin Simulation Model,
called MGB-IPH (an acronym from the Portuguese for Large
Basins Model and Institute of Hydraulic Research), was de-
veloped by Collischonn and Tucci (2001). Applications of
this model were initially developed for the La Plata basin
(Allasia et al., 2006) and then for some Amazonian rivers, the
Madeira (Ribeiro et al., 2005), the Tapajos, where satellite-
derived rainfall information is being used to run the model
(Collischonn et al., 2008), and the Negro river, where spa-
tial altimetry data is being used to complement the valida-
tion of the simulation (Getirana, 2010; Getirana et al., 2010).
The comparison of different rainfall products used to force
MGB-IPH in the Negro basin shows that observed data give
the most adequate discharge results (Getirana et al., 2011).
Recently, developments towards a better representation of
floodplains in the upper Parana River (Pantanal region) have
been presented in Paz et al. (2010). Beighley et al. (2009)
focused on the representation of water storage in the Ama-
zon River basin and the factors accounting for its variability.
Finally, Paiva et al. (2011) show that it is possible to em-
ploy full hydrodynamic models within large-scale hydrolog-
ical models even using limited data for river geometry and
floodplain characterization.
This present work aims to evaluate the simulation of
discharge in the Amazon main stem and in its prin-
cipal tributaries by the hydrological module SECHIBA
(Sche´matisation des EChanges Hydriques a` l’Interface
Biosphe`re-Atmosphe`re, Ducoudre´ et al., 1993) of the land
surface model (LSM) ORCHIDEE (ORganising Carbon and
Hydrology In Dynamic EcosystEms) considering a 11-level
hydrology (De Rosnay, 1999; De Rosnay et al., 2002;
d’Orgeval, 2006; d’Orgeval et al., 2008), using a routing
module (Polcher, 2003) and the representation of floodplains
and swamps by d’Orgeval (2006). All these characteris-
tics of the model are described in Sect. 2. The model is
forced by NCC atmospheric data (NCEP/NCAR Corrected
by CRU data, Ngo-Duc et al., 2005) detailed in Sect. 3.1.
Ngo-Duc et al. (2005) previously compared observed and
simulated discharge values by ORCHIDEE at the ´Obidos sta-
tion, on the main stem of the Amazon River. In that ex-
periment, the authors found that the quality of simulations
forced by this new 53-yr NCC data is better than the former
ones forced by the GSWP2 (Global Soil Wetness Project 2,
Dirmeyer et al., 2002; Zhao and Dirmeyer, 2003) forcing
dataset. However, the discharge simulations forced by NCC
identified some discrepancies in the annual cycles in some
tributaries of the Amazon River basin and over- or underes-
timations of the mean discharge in the southern and western
tributaries, respectively (J. Ronchail et al., personal commu-
nication, 2005). Improvements to former simulations may be
expected thanks to the recent availability of a comprehensive
observed precipitation dataset for the Amazon River basin
made available within the framework of the ORE (Environ-
mental Research Observatory) HYBAM (Geodynamical, hy-
drological and biogeochemical control of erosion/alteration
and material transport in the Amazon River basin, Cochon-
neau et al., 2006) and of new satellite-derived maps of flood-
plains and swamps distribution (Martinez and Le Toan, 2007;
Prigent et al., 2007). These new data datasets are described
in Sect. 3.3. Our aim is to verify whether ORCHIDEE,
forced by these new datasets, properly reproduces the dif-
ferent specificities of the main stem and of some large sub-
basins of the Amazon when compared with observations (de-
scribed in Sect. 3.2). Therefore, the new datasets are incre-
mentally added to the initial simulation (ORCH1) performed
with NCC and initial maps of flooded areas distribution.
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 913
First, in simulation ORCH2, we test in Sect. 4 the impact
of ORE HYBAM precipitation on simulated water budget
(Sect. 4.1) and simulated streamflow (Sect. 4.2) over the
basin. The different characteristics of the simulated dis-
charges (accuracy of the mean annual value, precision of the
seasonal cycle and representation of the interannual variabil-
ity) are studied in the different locations of the basin. Fi-
nally, the impact of the new spatial distribution of flooded
areas on the time position of flooding and the water height
of the floodplains is investigated through simulation ORCH3
in Sect. 5. Those improvements allowed us to calibrate the
streamflow at ´Obidos (simulation ORCH4). This calibration
can be crucial to use ORCHIDEE as a tool to predict future
changes in Amazon River basin hydrology. Indeed, an in-
crease of extremes (floods and droughts) has already been
observed since the nineties (Espinoza et al., 2009a). It may
be part of the long-term variability described by Callede et al.
(2004) and Marengo (2004) but may be also a consequence
of the climatic change described for South America by the
IPCC (Solomon et al., 2007).
2 The land surface model ORCHIDEE
2.1 Hydrological module and vegetation
SECHIBA is the hydrological module of ORCHIDEE that
simulates the fluxes between the soil and the atmosphere
through the vegetation, and computes runoff and drainage,
which are both discharged to the ocean. The hydrolog-
ical module used in this study is based on developments
by De Rosnay et al. (2000, 2002) and d’Orgeval (2006).
Physical processes of vertical soil flow are represented by
a diffusion-type equation resolved on a fine vertical dis-
cretization (11 levels) and the partitioning between surface
infiltration and runoff is represented in the model. The hy-
drological module is fully described by De Rosnay (1999);
De Rosnay et al. (2002); d’Orgeval (2006); d’Orgeval et al.
(2008).
In order to reduce noise in our simulation of streamflow, no
complex scenario such as deforestation, land use or forest fire
is taken into account in this study. Vegetation distribution and
LAI seasonality are prescribed in the model through global
maps. In each grid-cell, up to thirteen Plant Functional Types
(PFTs) can be represented simultaneously according to the
International Geosphere Biosphere Programme (IGBP, Bel-
ward et al., 1999) and the Olson classification (Olson et al.,
1983). Values of LAI come from the Normalized Differ-
ence Vegetation Index (NDVI) observations (Belward et al.,
1999). The PFTs are grouped into 3 ensembles (bare soil,
trees and grass/crops) and a water balance is computed for
each one given a dominant soil type over the grid box de-
fined by a map derived from Reynolds et al. (1999)’s dataset.
2.2 Routing module
The routing scheme (Polcher, 2003), described in Ngo-Duc
et al. (2007), is activated in the model in order to carry the
water from runoff and drainage simulated by SECHIBA to
the ocean through reservoirs, with some delay. The routing
scheme is based on a parametrization of the water flow on
a global scale (Miller et al., 1994; Hagemann and Dumenil,
1998). Given the global map of the main watersheds (Oki
et al., 1999; Fekete et al., 1999; Vo¨ro¨smarty et al., 2000)
which delineates the boundaries of sub-basins and gives the
eight possible directions of water flow within the pixel, the
surface runoff and the deep drainage are routed to the ocean.
The resolution of the basin map is 0.5◦, higher than usual
resolution used when LSMs are applied. Therefore, we can
have more than one basin in SECHIBA grid cell (sub-basins)
and the water can flow either to the next sub-basin within the
same grid cell or to the neighboring cell. In each sub-basin,
the water is routed through a cascade of three linear reser-
voirs which do not interact with the atmosphere. The water
balance within each reservoir is computed using the follow-
ing continuity equation:
dVi
dt
= Qini − Qouti (1)
where Vi (kg) is the water amount in the reservoir i con-
sidered (i = 1, 2 or 3), Qini and Qouti (both in kg day−1) are,
respectively the total inflow and outflow of the reservoir i.
The slow and deep reservoir (i = 3) collects the deep
drainage D (water moving downward from surface wa-
ter to groundwater) computed by the land surface scheme,
whereas the fast reservoir (i = 2) collects the computed sur-
face runoff R (portion of incoming water such as precipita-
tion and irrigation not infiltrating in the soil but discharged
from the area). Both discharge flows into a third reservoir,
called stream reservoir (i = 1), of the next sub-basin down-
stream. According to Eq. (1), the continuity equations can
be written for each of the three reservoirs as below:
dV1
dt
=
∑
x
Qinx − Qout1 (2)
dV2
dt
= R − Qout2 (3)
dV3
dt
= D − Qout3 (4)
where Qinx (kg day−1) is the total inflow coming from the
neighboring cells or sub-basins x and R and D (both in
kg day−1) are, respectively surface runoff and deep drainage.
The flow chart in Fig. 1 represents the routing channel
modeling in SECHIBA through an example of two sub-
basins (A and B) included in a same grid cell p. Three
reservoirs are allocated to each sub-basin. At each rout-
ing time step 1t = 1 day, the routing scheme computes wa-
ter flows as follows: the sum of the surface runoff and the
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914 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
Fig. 1. Flow chart of the routing channel connected with floodplains/swamps module in ORCHIDEE. Example
of two sub-basins A (with a swamp fraction) and B (with a floodplain fraction) included into one grid cell p.
36
Fig. 1. Flow chart of the routing channel connected with floodplains/swamps module in ORCHIDEE. Example of two sub-basins A (with
a swamp fraction) and B (with a floodplain fraction) included into one grid cell p.
deep drainage is spread in the grid cell p over the two sub-
basins, proportionally to their surface. The surface runoff
of the sub-basins A and B (respectively RAp and RBp ) flows
into their respective fast reservoirs of volume VA2 and VB2.
The deep drainage DAp and DBp flows into the slow reser-
voirs of volume VA3 and VB3. The water amount of the river
is represented by the stream reservoir. The stream reser-
voir of the sub-basin A is assumed to be empty in this ex-
ample (VA1 = 0), the outflows from the neighboring pixels x
(x = sw, w, nw, n, ne) to sub-basin A being null (∑
x
Qoutx = 0).
The stream reservoir of the downstream sub-basin B of vol-
ume VB1 collects the sum of the outflows QoutAp from the three
reservoirs of the sub-basin A (see Eq. 5) and the outflow Qouts
coming from the pixel s (Eq. 6).
QoutAp =
3∑
i=1
QoutAip (5)
where QoutAp (kg day−1) is the total outflow from the sub-
basin A in the pixel p and QoutAip (kg day−1) the outflow from
each reservoir i of the sub-basin A in the pixel p.
QinBp = QoutAp + Qouts (6)
where QinBp (kg day−1) is the total inflow of the sub-basin B
in the pixel p and Qouts (kg day−1) the total outflow from the
pixel s.
The sum of the outflows from the reservoirs of the sub-
basin B in the pixel p goes to the pixel e. Runoff and drainage
are routed through this cascade of reservoirs. In our model,
the volume of water Vi into the reservoir i is assumed to be
linearly related to its outflow Qouti :
Vi = (gi · k) · Qouti (7)
where gi (day m−1) is a property of the reservoir i and k (m)
a water retention index.
The water travel simulated by the routing scheme is depen-
dent on a water retention index k, given by a 0.5◦ resolution
map for each pixel performed from a simplification of Man-
ning’s formula (Dingman, 1994; Ducharne et al., 2003):
k =
√
d3
1z
(8)
where d (m) is the river length from one subgrid basin to the
next subgrid, and 1z (m) the height lost over the path of the
river.
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 915
The value of g in the Eq. (7) has been calibrated for the
three reservoirs over the Senegal river basin only, during the
1◦ NCC resolution simulations (Ngo-Duc et al., 2005; Ngo-
Duc, 2006) and generalized for all the basins of the world.
The “slow reservoir” and the “fast reservoir” have the high-
est value (g2 = g3 = 3.0 days m−1, d’Orgeval, 2006) in order
to simulate the groundwater. The “stream reservoir”, which
represents all the water of the stream, has the lowest value
(g1 = 0.24 day m−1). Those figures are the same for all the
basins of the world. The resulting product gi · k represents
the time constant Ti (day) which is an e-folding time, the
time necessary for the water amount in the stream reservoir
to decrease by a factor e. Hence, it gives an order of mag-
nitude of the travel time through this reservoir between the
sub-basin considered and its downstream neighbor.
2.3 Floodplains and swamps module
Floodplains are land areas adjacent to streams that are sub-
ject to recurring inundation. The stream overflows its banks
onto adjacent lands. Over the Amazon River basin, Richey
et al. (1989) estimated that up to 30 % of the water in the
main stem is derived from water that passed through the
floodplain. Thus, as the floodplain storage is significant in
relation to the streamflow, it must be taken into account ex-
plicitly. Moreover, water can be stored in swamps, it satu-
rates and infiltrates into the soil and does not return to the
river. These inundated areas mainly correspond to flooded
forest areas in the Amazon River basin. Thus, the module of
floodplains/swamps developed in ORCHIDEE by d’Orgeval
(2006) is used for this study in order to better represent
the timing of flow in some regions of the Amazon River
basin strongly affected by flooding. The parametrization
is described in detail by d’Orgeval (2006) and d’Orgeval
et al. (2008). A map of maximal fractions of floodplains
(MFF) and swamps (MFS) derived from the Global Lakes
and Wetlands Database (GLWD, Lehner and Do¨ll, 2004) is
initially prescribed to the model. Floodplains and swamps
in the model derive, respectively from three types of wa-
ter surfaces (Reservoir, Freshwater marsh-Floodplain and
Pan-Brackish/Saline wetland) and one type (Swamp forest-
Flooded forest) according to GLWD database.
Over floodplains areas, the streamflow QinBp from head wa-
ters of the reservoirs of the basin A flows into a reservoir of
floodplains (QinBp =QinFd ) instead of the stream reservoir of
volume VB1 of the next downstream (Fig. 1). The surface
SFd of the floodplain depends on the shape of the bottom of
the floodplain in order to simulate the timing between the
rise of water level and its expansion. Finally, water from the
floodplains reservoir that has not evaporated or reinfiltrated
the soil flows into the stream reservoir of volume VB1 of the
basin B after a delay. This delay is characterized by the time
constant TFd (day) function of the surface of the floodplains
SFd:
Table 1. List of atmospheric forcing variables in NCC.
Name Description Units
Tair Two-meter air temperature K
Qair Two-meter air specific humidity kg kg−1
Wind N Ten-meter wind speed (u component) m s−1
Wind E Ten-meter wind speed (v component) m s−1
Psurf Surface pressure Pa
SWdown Surface downward short wave flux W m−2
LWdown Surface downward long wave flux W m−2
Rainf Rainfall rate kg m−2 s−1
Snowf Snowfall rate kg m−2 s−1
TFd = (gFd · k) · SFd
SB
(9)
where gFd = 4.0 days m−1 is a property of the floodplain
reservoir, SFd (m2) the surface of the floodplain and SB (m2)
the surface of the basin.
The value of gFd has been calibrated through observations
in the Niger Inner Delta and can thus be different for the
Amazon River basin as it will be shown in Sect. 5.1.
Over swamp areas, a fraction of water α = 0.2 is uptaken
from the stream reservoir of volume VA1 (QinSwp =αQinAp ). It
is transferred into soil moisture (Fig. 1) and thus does not re-
turn directly to the river. The swamp storage enhances tran-
spiration of forest where the soil is saturated, reducing bare
soil evaporation.
3 Datasets
In this section, the datasets used in this work are presented:
the atmospheric forcing, the validation data and the new
rainfall and flooded areas distribution datasets used to force
ORCHIDEE.
3.1 Atmospheric forcing
The atmospheric data set used as input to ORCHIDEE is
NCC (NCEP/NCAR Corrected by CRU data, Ngo-Duc et al.,
2005) based on the NCEP/NCAR reanalysis project (Kistler
et al., 2001) and in-situ observations. The spatial resolution
is 1◦× 1◦ for the whole globe. The temporal resolution is
six hours and the time series cover the 1948–2000 period.
All variables present in the forcing are summarized in Ta-
ble 1. The NCC precipitation is a hybridization of NCEP and
CRU precipitation (New et al., 2000); the radiation is a hy-
bridization of NCEP and SRB radiation (Surface Radiation
Budget data produced at NASA Langley Research Center)
used for a bias correction of the reanalysis product. The data
have allowed 50-yr river flows to be simulated over the planet
(Ngo-Duc et al., 2005).
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916 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
Table 2. List of ORE HYBAM gauge stations over the Amazon River basin. Except for the streamflow at the river mouth (1972–2003), the
mean annual discharge (Qmean) corresponds to the mean annual value of ORE HYBAM data for the mean time period 1980–2000.
Station Abbreviation River Latitude Longitude Qmean (m3 s−1) Area (km2)
Mouth (Callede et al., 2010) AMAZ Amazonas ∼ 0.00 ∼−50.5 206 000 5 961 000
´Obidos OBI Amazonas −1.95 −55.30 169 515 4 680 000
Manacapuru MANA Solimoes −3.31 −60.61 100 819 2 242 400
Sao Paulo de Olivenc¸a SPO Solimoes −3.45 −68.75 46 206 990 781
Tamshiyacu TAM Solimoes −4.00 −73.16 30 530 726 400
Fazenda Vista Alegre FVA Madeira −4.68 −60.03 28 374 1 293 600
Porto Velho PVE Madeira −8.74 −63.92 19 418 954 400
Guajara-Mirim GMIR Mamore −10.99 −65.55 8041 532 800
Rurrenabaque RUR Beni −14.55 −67.55 1986 67 500
Labrea LAB Purus −7.25 −64.80 5472 230 000
Gaviao GAV Jurua −4.84 −66.85 4632 170 400
Acanaui ACA Japura −1.82 −66.60 14 075 251 800
Serrinha SER Negro −0.48 −64.83 16 193 291 100
Caracarai CARA Branco +1.83 −61.08 2780 130 600
Sao Francisco SFR Jari −0.41 −52.33 990 51 343
Altamira ALT Xingu −3.38 −52.14 8000 469 100
Itaituba ITA Tapajos −4.24 −56.00 11 789 461 100
3.2 Data of validation
3.2.1 ORE HYBAM gauge stations
Discharge data has been gathered and complemented within
the frame of the ORE (Environmental Research Observa-
tory) HYBAM (Geodynamical, hydrological and biogeo-
chemical control of erosion/alteration and material transport
in the Amazon River basin – http://www.ore-hybam.org/),
a partnership which associates the meteorological and hy-
drological services of the Amazonian countries (Age´ncia Na-
cional de ´Aguas Water National Office/ANA in Brazil (http:
//www2.ana.gov.br/), Servicio Nacional de Meteorologı´a
e Hidrologı´a/National Meteorology and Hydrology Ser-
vice/SENAMHI in Peru (http://www.senamhi.gob.pe/) and
Bolivia (http://www.senamhi.gob.bo/), Instituto Nacional de
Meteorologı´a e Hidrologı´a / National Meteorology and Hy-
drology Institute/INAMHI in Ecuador (http://www.inamhi.
gov.ec/)) and the French Institute of Research for Develop-
ment (IRD – http://www.ird.fr/). The rating curves have been
determined using the stream gauging measurements, recently
with Acoustic Doppler Current Profiler (ADCP), and have
been used to convert the water level series into discharge
data. The daily water level data were corrected when neces-
sary, with missing values estimated by correlation with data
from upstream or downstream stations. Sixteen stations out
of eighty were chosen to realize the comparison with simu-
lated data (Fig. 2 and Table 2). The choice of these stations
depended on:
– The length of the records. Those beginning in the late
seventies were preferred to the others in order to get
Fig. 2. Map of the Amazon River sub-basins and the main rivers. Localization of the main ORE HYBAM gauge
stations (see Table 2 for their coordinates). Color is used to distinguish the different sub-basins. Topographic
scale is indicated.
37
Fig. 2. Map of the Amazon River sub-basins and the main rivers.
Localization of the main ORE HYBAM gauge stations (see Table 2
for their coordinates). Color is used to distinguish the different sub-
basins. Topographic scale is indicated.
longer common series with the simulated data. How-
ever, in order to have information about most sub-
basins, some short series were retained in places where
no other record exists.
– The proximity of the stations. A choice was made be-
tween stations close to each other based on the reliabil-
ity of their records (absence of missing values).
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 917
– The size of the river. Some very small rivers were
not recognizable with ORCHIDEE due to the coarse
resolution.
3.2.2 Topex/Poseidon measurements
Surface monitoring by satellite altimetry has been performed
on the whole Earth since 1993 (Cre´taux et al., 2011). Altime-
try datasets are available over the main stem of the Amazon
and the Rio Negro-Branco rivers for a time period common
to ours (1993–2000) thanks to Topex/Poseidon satellite mis-
sion. These measurements are used to assess the accuracy of
ORCHIDEE to simulate floodplain height variability.
3.3 New basis of observations over the Amazon River
basin
In order to simulate the streamflow with higher accuracy over
the Amazon River basin, a new set of observations of precipi-
tation is used to force ORCHIDEE and validate NCC. A new
map of MFF and MFS based on observations is also tested
and compared with the initial map prescribed to the model.
The details of the construction and implementation of these
new datasets are described in this section.
3.3.1 ORE HYBAM precipitation
Global rainfall datasets usually rely on very sparse in-situ
observations over the Amazon River basin. Consequently,
the Amazonian precipitation is often poorly represented in
such datasets, especially regarding the plain of the Andean
countries (Espinoza et al., 2009b). Within the frame of the
ORE HYBAM, daily rainfall data from 1488 rain gauges
have been gathered, from 1975 to 2009. A quality con-
trol based on the application of the Regional Vector Method
(RVM) on the rainfall values (Espinoza et al., 2009b) was
then performed over the Amazon River basin. RVM en-
ables to discriminate stations with lowest probability of er-
rors in their series. Finally, 752 rain gauges approved by
RVM were retained, with data covering more than five-year
continuous periods. The density of ORE HYBAM stations
is about 125 [/106 km2] over the Amazon basin. This den-
sity is compared to the minimum rain gauge density require-
ments to prevent the effect of poor forcing precipitation on
runoff simulation. ORE HYBAM density is higher than Oki
et al. (1999)’s (30 [/106 km2]) and Rudolf et al. (1994)’s
(80 [/106 km2]) recommendations. Moreover, it is close to
WMO (World Meteorological Organization) recommenda-
tion (100 to 400 [/106 km2], WMO, 1994) for operational
purposes. Because a few extremely rainy spots located on the
foothills of the Eastern Andes bring large amounts of water
to the western and south-western parts of the basin (Killeen
et al., 2007), we then chose to replace missing values of
these particular stations by estimated values using linear re-
gressions or by long-term climatological means. A strong
underestimation of the rainfall input is thus avoided for the
periods when records of one or several of these wet spots
are missing. The concerned locations are the Chapare region
in Bolivia (Cristal Mayu and Misicuni stations), the Manu-
Tambopata area (Quincemil and San Gaban stations) and the
Selva Central region (Tingo Maria station) in Peru. In-situ
observations were afterward spatially interpolated to the res-
olution of NCC (1◦× 1◦). Geostatistics have been widely
used to interpolate environmental variables such as rainfall
(Goovaerts, 2000; Hevesi et al., 1992). Ordinary kriging has
notably been shown to provide better estimates than con-
ventional methods, as it takes into account the spatial de-
pendence between neighbouring observations, which is ex-
pressed by a semi-variogram. In this study, ordinary kriging
was thus performed to generate an observation-based grid-
ded daily rainfall dataset. Finally, because other NCC vari-
ables were available at a 6-h temporal resolution, daily rain-
fall grids were segmented at this resolution following a di-
urnal cycle as described in NCC precipitation data. If the
NCC precipitation is null for the four 6 h time-steps in the
day, the ORE HYBAM daily value is spread equally over the
time-steps.
Mean annual value of NCC precipitation over the basin
is about 2044 mm yr−1 whereas ORE HYBAM precipitation
shows a higher value than NCC (2190 mm yr−1 i.e. +7.1 %).
The mean annual spatial distribution in precipitation is
shown over the Amazon River basin for both datasets in
Fig. 3a,b and their difference is calculated (Fig. 3c). In
both datasets, the spatial distribution of precipitation of the
Amazon River basin is quite similar. The rainiest regions
(3000 mm yr−1 and more) are located in the northwest of the
basin (Colombia, North of the Ecuadorian Amazon, North-
east of Peru and Northwest of Brazil). Rainfall is also abun-
dant close to the average position of the South Atlantic Con-
vergence Zone (SACZ), established during austral summer
from the Northwest of the Amazon to the Subtropical South
Atlantic (Vera et al., 2006). Rainfall decreases toward the
tropics, reaching less than 1500 mm yr−1 in the Peruvian-
Bolivian plain and toward the north in the Roraima Brazil-
ian state. Rainfall also diminishes with altitude: in the An-
des, over 2000 m, annual rainfalls lower than 1000 mm are
the most frequent. However, some differences in precipita-
tion rate exist between the two datasets. ORE HYBAM de-
picts a sharp increase by 250 to 750 mm yr−1 of the amount
of rainfall in the north-west of the basin toward the south-
ern and eastern part of this area. Extremely high values can
be measured in the wet spots of the Eastern Andes foothills,
in positions that favor strong air uplift; in these very rainy
spots scattered along the Cordillera, annual rainfall reaches
5000 to 6000 mm with ORE HYBAM (regions of Churuy-
acu in Colombia, of the Reventador Volcano in Ecuador, of
San Gaba´n and Tingo Maria in Peru, of the Chapare´ in Bo-
livia) whereas these spots of precipitation are much less sig-
nificant in NCC dataset. Finally, drier regions are present in
ORE HYBAM compared to NCC over the south-east of the
basin, the region of the Amazon mouth, the extreme north of
the basin and over Ecuador and Central Peru.
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918 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
(a) (b)
(c)
Fig. 3. Precipitation (mm yr−1) over the Amazon River basin from (a) NCC and (b) ORE HYBAM. (c) Differ-
ences between (b) and (a). For topographic scale, see Fig. 2.
38
Fig. 3. Precipitation (mm yr−1) over the Amazon River basin from (a) NCC and (b) ORE HYBAM. (c) Differences between (b) and (a).
For topographic scale, see Fig. 2.
3.3.2 Map of maximal fractions of floodplains and
swamps
Initially, the maps of MFF and MFS prescribed to OR-
CHIDEE were derived from the GLWD dataset of Lehner
and Do¨ll (2004) (hereafter called “GLWD”). In this study,
a new map (hereafter called “PRIMA”) is produced where
MFF and MFS are, respectively derived from Prigent
et al. (2007) and Martinez and Le Toan (2007) dataset
at 0.25◦× 0.25◦ and interpolated at 0.5◦× 0.5◦ for OR-
CHIDEE. Prigent et al. (2007) estimated monthly inundated
fractions over the world by multisatellite method for eight
years (1993–2000). Then, for each 0.5◦ pixel of the Amazon
River basin, as we need it for ORCHIDEE, we determine
the maximum value that has been recorded during the 8 yr
observation. As there is no distinction between floodplains
and swamps in Prigent et al. (2007)’s estimates, we apply to
the maximum value a ratio of their maximal distribution us-
ing the 1995–1996 flood estimate by Martinez and Le Toan
(2007) who distinguish floodplains and flooded forests areas.
The GLWD and PRIMA maps are compared for flood-
plains (Fig. 4) and swamps (Fig. 5). For both water sur-
faces, the difference between the two maps is performed. For
both datasets, floodplains are located along the main stem of
the Solimoes-Amazon River, in the southern region of the
basin in Llanos de Moxos and to a lesser extent along the
Ireng river (in GLWD) or the Branco river (in PRIMA) in the
northernmost region of the basin (Fig. 4). However, MFF of
PRIMA map are globally higher than GLWD (respectively
about 4.2 and 2.6 % of the total area of the Amazon River
basin according to Table 3). Moreover, in PRIMA map,
many MFF lower than 5 % within the mesh cover almost all
the basin whereas GLWD does not give data over these re-
gions. The difference in MFF between the two maps is par-
ticularly high along the main stem of the Solimoes-Amazon
River and especially near the mouth (between +5 to +15 %
before Manacapuru to more than +70 % around ´Obidos, see
Fig. 2 and Table 2 for localization of the two stations). We
note that a small percentage of this difference (up to about
2 % around ´Obidos) is explained by the fact that Prigent et al.
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M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 919
(a) (b)
(c)
Fig. 4. Fraction of floodplains within the mesh (% of the mesh area) over the Amazon River basin from
(a) GLWD and (b) PRIMA. (c) Differences between (b) and (a). Dark green and brown colors indicate the
topography of the region. For topographic scale, see Fig. 2.
39
Fig. 4. Maximal fraction of floodplains within the mesh (% of the mesh area) over the Amazon River basin from (a) GLWD and (b) PRIMA.
(c) Differences between (b) and (a). Dark green and brown colors indicate the topography of the region. For topographic scale, see Fig. 2.
(2007)’s data take into account the surface of the river while
Lehner and Do¨ll (2004) data does not. In the south, the re-
gion of Llanos de Moxos shows the same order of increase
in MFF with PRIMA dataset particularly over the Mamore´
river in Bolivia. In Llanos de Moxos and along the main
stem of the Solimoes-Amazon River, PRIMA is in better
agreement with Hamilton et al. (2002)’s estimates with an
underestimation of MFF by 30 % which is much lower than
GLWD (near 100 %) according to Table 4. In the North, at
Roraima region, PRIMA is again in better agreement with
Hamilton et al. (2002)’s estimates (about +15 %) than GLWD
(about −70 %). For MFS, PRIMA presents half the extent
of GLWD across the river basin (Table 3). According to
Fig. 5c, this decrease is observed over the western part of the
basin and mainly in the Northern Peruvian region (up to 45 %
more over some pixels), the south-easternmost part of the
basin and along the main stem near the mouth of the Amazon
River. On the other hand, an increase of MFS compared to
GLWD is observed over southern regions of the basin mainly
over Llanos de Moxos (up to 45 % more over some pixels)
and along the Negro and Branco rivers.
Table 3. Proportions of maximal fractions of floodplains (MFF),
swamps (MFS) and lakes (% of the areas of the basin) over the
Amazon River basin according to GLWD and PRIMA datasets.
Flooded areas GLWD PRIMA
MFF 2.59 % 4.22 %
MFS 15.4 % 7.97 %
Lakes 0.79 %
Total 18.8 % (1.17× 106 km2) 12.9 % (0.80× 106 km2)
To summarize, combining floodplains and swamps, the to-
tal maximal fractions in PRIMA are lower on average over
the Amazon River basin when compared to GLWD (Ta-
ble 3). Estimates over Central Amazon were performed by
Hess et al. (2003) for the flood period May–August 1996
where water surfaces have been differentiated and classified.
We sum the two classes “Non vegetated-flooded” and “Non
woody-flooded” for comparison with our “Floodplains” class
and “Woody-flooded” class is considered to be equivalent to
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920 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
Table 4. Maximal areas in floodplains (km2) from satellite estimates (Hamilton et al., 2002; Hess et al., 2003) and the two datasets GLWD
and PRIMA. Number in brackets is the relative error (in %) between the datasets and the estimates.
Region Lon Lat Hamilton et al. (2002) Hess et al. (2003) GLWD PRIMA
Central Amazon 72.0◦ W:54.0◦ W 8.00◦ S:0.00◦ S – 277 440∗ 173 395∗ (−37.5) 231 418∗ (−16.6)
Mainstem of the Amazon 70.0◦ W:52.0◦ W 2.00◦ S:5.00◦ S 97 360 – 6245 (−93.6) 69 862 (−28.2)
Llanos de Moxos 68.0◦ W:61.0◦ W 16.0◦ S:12.0◦ S 92 094 – 5217 (−94.3) 64 879 (−29.6)
Roraima 61.5◦ W:59.0◦ W 2.25◦ N:4.50◦ N 16 530 – 4466 (−73.0) 19 168 (+16.0)
∗ Swamps are included.
(a) (b)
(c)
Fig. 5. Fraction of swamps within the mesh (% of the mesh area) over the Amazon River basin from (a) GLWD
and (b) PRIMA. (c) Differences between (b) and (a). Dark green and brown colors indicate the topography of
the region. For topographic scale, see Fig. 2.
40
Fig. 5. Maximal fraction of swamps within the mesh (% of the mesh area) over the Amazon River basin from (a) GLWD and (b) PRIMA.
(c) Differences between (b) and (a). Dark green and brown colors indicate the topography of the region. For topographic scale, see Fig. 2.
“Swamps”. On average over the Central Amazon, PRIMA
shows an underestimation of less than 20 % of total MFF
and MFS compared to Hess et al. (2003)’s estimates whereas
GLWD underestimation is near by 40 % (Table 4). Moreover,
according to Hess et al. (2003), flooded forest (i.e. swamps)
constituted nearly 70 % of the entire wetland area during
high water period in this region. The distribution of swamps
according to PRIMA is close to this estimate with a value
of 61 % whereas GLWD largely overestimates it (95 %).
Figure 6 shows a comparison in MFF and MFS between
Hess et al. (2003)’s estimates, GLWD and PRIMA datasets
in three points over the main stem of the Amazon River. MFF
with GLWD is systematically underestimated throughout the
main stem whereas PRIMA is in better agreement with esti-
mates even if MFF remains lower than the estimates by Hess
et al. (2003). MFS in PRIMA is well distributed at Curuai
(about 20 %) and Cabaliana (about 40 %) but for the west-
ern region of the main stem, GLWD gives much higher MFS
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 921
Fig. 6. Comparison of MFF and MFS to Hess et al. (2003)’s observations in different parts of the main stem:
Mamiraua near Tefe´, Cabaliana near Manacapuru and Curuai near O´bidos. Floodplains from Hess et al. (2003)
are considered as the sum of “Non vegetated-flooded” and “Non woody-flooded” classes. Swamps are consid-
ered as the equivalent of “Woody-flooded” class. Color is used to distinguish the different sub-basins.
41
Fig. 6. Comparison of MFF and MFS to Hess et al. (2003)’s observations in different parts of the main stem: Mamiraua near Tefe´, Cabaliana
near Manacapuru and Curuai near ´Obidos. Floodplains from Hess et al. (2003) a e considered as the sum of “Non vegetat d-flooded” and
“Non woody-flooded” classes. Swamps are considered as the equivalent of “Woody-flooded” class. Color is used to distinguish the different
sub-basins.
(about 65 %) than PRIMA (about 35 %) in agreement with
estimates (about 70 %).
4 Simulated water budget and streamflows over the
basin: impact of NCC precipitation corrected by
ORE HYBAM
4.1 Simulated water budget
Water balance analysis led to many estimates from mod-
els, reanalysis and, subsequently, measurements of fluxes.
A non-exhaustive list of the annual values from some esti-
mates in the literature (mainly from Large Scale Biosphere-
Atmosphere Experiment in Amazonia, LBA, measurements)
is given in Table 5 in average over the Amazon River
basin and in different regions of the basin. In compari-
son, results from simulations ORCH1 (ORCHIDEE forced
by NCC) and ORCH2 (ORCHIDEE forced by NCC cor-
rected by ORE HYBAM precipitation) are shown. Accord-
ing to the different estimates, the water budget components
over the whole basin are about 6.2± 1.1 mm d−1 in precip-
itation P , 3.9± 0.7 mm d−1 in evapotranspiration (ET) and
2.99 mm d−1 in runoff (R). We note that the uncertainty is
high in the estimations in P and E (their standard deviations,
calculated from the annual values, are around 1.0 mm d−1).
Runoff is estimated from one value from Callede et al.
(2010)’s estimate with an error of 6 %. Moreover, accord-
ing to Marengo (2006), the different estimates of areas of
the Amazon River basin generate uncertainty in the estima-
tions of runoff at the mouth of the Amazon. Thus, an uncer-
tainty also exists in comparison with simulated runoff. In the
model, it is computed from a total surface of basin equal to
5 853 804 km2 (Fekete et al., 1999) which is lower (−1.8 %)
than Callede et al. (2010)’s estimate (5 961 000 km2). Precip-
itation over the whole basin is about 5.6 mm d−1 according
to NCC. It is underestimated when compared to the average
value of the estimations. Furthermore, it is also lower than
the median of the observations (5.9 mm d−1). NCC value is
higher than GPCP estimate (5.2 mm d−1), equal to CMAP
but underestimated when compared to the five other esti-
mates. ORE HYBAM data (6.0 mm d−1) is closer to the av-
erage value and the median of the estimations. It is equal to
CRU average value and close to the estimates by Marengo
(2005) (5.8 mm d−1) and LW (5.9 mm d−1). Simulated ET
over the whole basin (about 2.8 mm d−1) seems to be un-
derestimated when compared to the average value given by
Da Rocha et al. (2009). ET variation between the two simu-
lations is not significant. ET is more limited by the amount
of incident energy, which is the same in both simulations,
rather than by precipitation change. The resulting simulated
runoff at the mouth of the Amazon is underestimated by 6 %
compared to Callede et al. (2010)’s estimate when NCC pre-
cipitation is used. The correction by ORE HYBAM data
leads to an overestimation of R by 8.4 %. If we consider
that ORE HYBAM precipitation is closer to the observations
than NCC, the overestimation of R is mainly due to the low
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922 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
T
able
5.M
ean
an
n
u
al
v
alues
ofprecipitation(P),
ev
apotranspiration(ET)
and
total
ru
n
off(R)(allin
m
m
d −
1)from
estim
ates
and
sim
ulationsO
RCH
1
and
O
RCH
2
o
v
er
the
A
m
azon
Riv
erbasin
and
different
regions
of
the
basin.V
aluesbetw
een
brack
ets
are
the
m
ean
an
n
u
al
relativ
e
erro
rs(in
%)betw
een
sim
ulationsO
RCH
s
and
estim
ates.T
otal
sim
ulated
ru
n
offis
the
su
m
of
su
rface
ru
n
off
and
deep
drainage
m
inus
the
reinfiltration
of
w
ater
o
v
erfloodplains
and
the
returnflo
w
to
the
soil
o
v
er
the
sw
am
ps.
Tim
e
period
can
be
different
acco
rding
the
study
.B
etw
een
1980–2000,
estim
ates
are
co
m
pared
w
ith
O
RCH
ID
EE
resultsforthe
co
rresponding
tim
e
period
ofthe
sim
ulation.O
therw
ise,the
av
erage
ofthe
21yr
of
sim
ulation
is
u
sed.(a)Fro
m
29
estim
ated
v
alues
calculated
by
m
ass
and
en
ergy
b
alance
m
odels
and
global
m
odel
rean
alysisdata.(b)Estim
ated
v
aluesfrom
rain
g
auge
stations(M
arengo
,2005)
and
sev
eraldata
so
u
rces:G
lobalH
istoricalClim
atology
N
etw
o
rk(GHCN,V
o
se
et
al.
,1992),N
O
A
A
Clim
ate
Prediction
Center(CPC)M
erged
A
nalysis
ofPrecipitation(CM
AP
,X
ie
and
A
rkin
,1997),G
lobalPrecipitation
Clim
atology
Project(GPCP
,H
uffm
an
et
al.
,1995),N
ationalCentersforEnvironm
entalPrediction(NC
EP),Leg
ates-W
ilm
ott(LW
,Leg
ates
and
W
illm
ott
,1992)
and
Clim
ate
R
esearch
U
nit(CRU
,N
ew
et
al.
,2000)data.
Site
Study
D
ata
O
RCH
1
O
RCH
2
Site(City
,State)
Site
ID
Lon( ◦W
)
Lat( ◦S)
R
eference
Tim
e
period
Site
type
P
ET
R
P
ET
R
P
ET
R
–
–
D
a
R
ocha
et
al.(2009)(a)
–
–
–
2.70
to5.20
–
2.82
2.76
A
m
azon
Riv
erbasin
–
–
M
arengo(2006)(b)
1970–1999
–
5.20
to8.60
–
–
5.63
6.00
–
–
Callede
et
al.(2010)
1972–2003
–
–
–
2.99
2.81(−
6.02)
3.24(+8.36)
ZF2–K
m
14
K
14
60.12
2.59
M
alhi
et
al.(2002)
Sep
1995–A
ug
1996
Flux
to
w
er
5.72
3.08
–
6.65(+16.3)
2.88(−
6.49)
–
6.80(+18.9)
2.79(−
9.42)
–
(M
anaus,A
m
azonas)
ZF2–K
m
34
K
34
60.20
2.60
A
raujo
et
al.(2002)
Jul1999–Sep
2000
Flux
to
w
er
7.48
3.10
–
6.26(−
16.3)
2
.93(−
5.48)
–
5.96(−
20.3)
2.84(−
8.39)
–
(M
anaus,A
m
azonas)
D
uck
e
reserv
e
D
CK
59.95
2.95
Shuttlew
o
rth(1988)
Sep
1983–Sep
1985
Field
site
7.19
3.66
–
5.83(−
18.9)
2.63(−
28.1)
–
6.90(−
4.03)
2.52(−
31.1)
–
(M
anaus,A
m
azonas)
K
m
83–Logged
forest
K
83
55.00
3.02
D
a
R
ocha(2004)
Jul2000–July
2001
Flux
to
w
er
6.03
3.45
–
5.48(−
9.12)
3.03(−
12.2)
–
5.55(−
7.96)
2.90(−
15.9)
–
(Santare´m
,P
ara
w
estern)
Jav
aes
riv
er
atB
ananalIsland
JAV
50.15
9.82
B
orm
a
et
al.(2009)
O
ct2003–Sep
2006
Flux
to
w
er
4.64
3.65
–
5.19(+11.9)
3.35(−
8.22)
–
5.43(+17.0)
3.36(−
7.95)
–
(Pium,T
o
cantins)
BiologicalR
eserv
e
ofJaru
RJA
61.93
10.08
V
o
n
R
ando
w
et
al.(2004)
Feb
1999–Sep
2002
Flux
to
w
er
5.95
3.70
–
5.23(−
12.1)
3
.02(−
18.4)
–
5.64(−
5.21)
2.93(−
20.8)
–
(Ji–P
aran
a,R
ondonia)
Sinop
SIN
55.33
11.41
V
o
u
rlitis
et
al.(2002)
A
ug
1999–Jul2000
Flux
to
w
er
5.83
3.07
–
6.28(+
7.72)
3.34(+8.79)
–
6.14(+5.32)
3.17(+3.26)
–
(Sinop,M
ato
G
rosso)
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 923
Table 6. Comparison of the mean annual values (all in mm d−1) of precipitation (P ), transpiration (Tv) and evaporation of water over the
leaves (Ev) between measurements by Shuttleworth (1988) near Manaus (Ducke Reserve, see Table 5 for the coordinates) and results from
the two simulations ORCH1 and ORCH2, over the mean time period September 1983–September 1985. Values between brackets and r2 are,
respectively the mean annual relative error (%) and the coefficient of correlation between simulation and observation.
P Ev Tv
mm d−1 (%) r2 mm d−1 (%) r2 mm d−1 (%) r2
Shuttleworth (1988) 7.19 – 0.91 – 2.75 –
ORCH1 5.83 (−18.9) 0.66 1.61 (+76.9) 0.61 0.93 (−66.2) 0.71
ORCH2 6.90 (−4.03) 0.71 1.16 (+27.5) 0.73 1.27 (−53.8) 0.77
Fig. 7. Map of locations of flux towers stations (in red). See Table 5 for the coordinates of the stations. Color
is used to distinguish the different sub-basins. For topographic scale, see Fig. 2.
42
Fig. 7. Map of locations of flux towers stations (in red). See Table 5
for the coordinates of the stations. Color is used to distinguish the
different sub-basins. For topographic scale, see Fig. 2.
ET simulated in the model. Simulated P and ET are also
compared with measurements (Table 5) from six LBA flux
towers and one field site. They are located along the main
stem of the Amazon River and over the south-eastern part of
the basin (see Fig. 7 for the localization). The results con-
firm the underestimation in simulated ET for both simula-
tions in the central region of the basin (K14, K34, DCK and
K83) and in the south (RJA and JAV). The underestimation
at JAV is only 8 % but may be more since P in the model
is high compared to observation (+12 to +17 %). Moreover,
the low underestimation at JAV and the slight overestimation
at SIN (+3 to 9 %) in ET compared to measurements occur
in a region facing deforestation. However, in this region, the
tropical forest covers 98 % of the grid cell area in the model,
whereas the measurements have been performed over a tran-
sitional tropical forest. Thus, since ORCHIDEE does not
take into account deforestation, the comparison with obser-
vation in this region may be biased where simulated ET can
be overestimated.
Regarding the underestimation in ET near Manaus, mea-
surements of precipitation and ET are available for two years
(September 1983–September 1985) (Shuttleworth, 1988).
Moreover, evaporation over leaves (Ev) and transpiration
(Tv) data are distinguished. Table 6 shows a comparison of
the annual rate of these variables between the two simula-
tions and observations. First, the results in precipitation show
an underestimation in annual rate between observations and
both simulations. However, underestimation is lower (−4 %)
when NCC is corrected by ORE HYBAM data. Moreover,
a better accuracy in variability is found compared to mea-
surements (r2 of ORCH1 and ORCH2 are, respectively 0.66
and 0.71). Both forcings give less precipitation than observed
at the end of 1984 and particularly in December (Fig. 8a).
Then, ORCH2 is in better agreement with measurements dur-
ing the dry period. During the next wet period in 1985, the
variability of precipitation is not well represented in both
simulations. ORE HYBAM overestimates precipitation at
the end of the wet 1985 period. The use of daily precip-
itation from ORE HYBAM database does not change the
underestimated simulated ET (Table 5). In fact, the radia-
tive budget is not affected and the energy available to evap-
orate remains the same. However, the use of ORE HYBAM
daily rainfall dataset change the ratio between evaporation
of water over the leaves (Ev) and transpiration (Tv). Vari-
ation of Ev during the time period is improved in ORCH2
compared to ORCH1 (the coefficients of correlation with ob-
servations are, respectively 0.6 and 0.7 with ORCH1 and
ORCH2, Table 6) where a low seasonality was simulated
throughout the period (Fig. 8b). Moreover, Ev overestima-
tion observed with ORCH1 (+77 %) is reduced by more than
half (+27.5 %) on average over the period with ORCH2 (Ta-
ble 6). As Ev is reduced, Tv increases with ORCH2 but re-
mains underestimated (respectively about 54 and 66 % for
ORCH1 and ORCH2 according to Table 6) throughout the
time period (Fig. 8c). However, the seasonal variation is in
agreement with observations (r2 is, respectively 0.7 and 0.8
with ORCH1 and ORCH2 according to Table 6) where dry
season and wet season are well differentiated (Fig. 8c).
www.hydrol-earth-syst-sci.net/16/911/2012/ Hydrol. Earth Syst. Sci., 16, 911–935, 2012
924 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
(a)
(b) (c)
Fig. 8. Comparison of times series of (a) precipitation (P ), (b) evaporation of water over the leaves (Ev) and
(c) transpiration (Tv) (all in mm d−1) between the two simulations (ORCH1 and ORCH2) and estimates from
Shuttleworth (1988) near Manaus (Ducke Reserve), over the period September 1983–September 1985.
43
Fig. 8. Comparison of times series of (a) precipitation (P ), (b) evaporation of water over the leaves (Ev) and (c) transpiration (Tv) (all in
mm d−1) between the two simulations (ORCH1 and ORCH2) and estimates from Shuttleworth (1988) near Manaus (Ducke Reserve), over
the period September 1983–September 1985.
The precipitation over the Amazon River basin is im-
proved by the use of ORE HYBAM daily dataset. However,
ET does not change between the two simulations and remains
underestimated compared to observations. This discrepancy
may be attributed to the low transpiration simulated by the
model that has been pointed out at Manaus. In ORCH2,
the resulting simulated runoff at the mouth of the Amazon is
consequently overestimated. In the next section, the annual
simulated streamflow variation will be studied in the main
sub-basins of the Amazon. Moreover, results in streamflow
will be discussed when the observations of precipitation and
floodplains/swamps distribution are implemented in the forc-
ing of the model.
4.2 Simulated river discharge at ´Obidos and over the
sub-basins of the Amazon River
The mean annual streamflow estimated at the mouth
of the Amazon River basin is about 206× 103 m3 s−1
(2.99 mm d−1) with an error of 6 % due to the method,
for a total area of 5 961 000 km2 and the time period
1972–2003 (Callede et al., 2010). Simulated streamflow
is given at the pixel of the routing network correspond-
ing to the mouth of the basin of a total surface equal to
5 853 804 km2 in the model (Fekete et al., 1999). Accord-
ing to ORCH1, simulated streamflow at the mouth is un-
derestimated (191× 103 m3 s−1) compared to Callede et al.
(2010)’s estimates (−7.42 %) whereas with ORCH2, an over-
estimation is found (220× 103 m3 s−1 i.e. +6.56 %). We note
that the differences in streamflow between simulation and
observation are of the same order than the estimated error in
observation. The increase of about 15 % in streamflow of the
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 925
(a) (b)
Fig. 9. Mean relative error of Qmean (%) between simulations ((a) ORCH1, (b) ORCH2) and observation over
the time period 1980–2000. The color indicates the sign of the error: red is negative error, blue is positive error
and grey is very low error. The error is represented for the different stations (colored circles) and their associ-
ated sub-basins (same color than the corresponding station). Five residual basins are also represented: SPO*,
MANA*, OBI*, PVE* and FVA*. Qmean of each residual basin is the difference between the downstream
and the upstream station(s) Qmean. Thus, the color of the residual basin can be different from the color of the
downstream station. For topographic scale, see Fig. 2.
44
Fig. 9. Mean relative error of Qmean (%) between simulations – (a) ORCH1, (b) ORCH2 – and observation over the time period 1980–2000.
The color indicates the sign of the error: red is negative error, blue is positive error and grey is very low error. The error is represented for
the different stations (colored circles) and their associated sub-basins (same color than the corresponding station). Five residual basins are
also represented: SPO*, MANA*, OBI*, PVE* and FVA*. Qmean of each residual basin is the difference between the downstream and the
upstream station(s) Qmean. Thus, the color of the residual basin can be different from the color of the downstream station. For topographic
scale, see Fig. 2.
Amazon between ORCH1 and ORCH2 is mainly due to the
increase in precipitation (+6.75 %) (ET did n t change) when
NCC data is corrected by ORE HYBAM. At the ´Obidos sta-
tion, which is the nearest gauged station from the outlet of
the basin, mean annual simulated str amflow with ORCH1 is
highly underestimated (−15 %) compared to ORE HYBAM
discharge measurements, whereas with ORCH2 it is in good
agreement with ORE HYBAM (−0.25 %) (Table 7). Thus,
between ´Obidos and the mouth of the Amazon, a large quan-
tity of water, overestimated in both simulations, comes from
the south-eastern river basins (Xingu and Tapajos Rivers) as
found at Itaituba (+20 %) and Altamira (more than +90 %)
according to Table 7 (see Fig. 2 and Table 2 for localization
of the stations).
At ´Obidos station, about 20 % of discharge comes
from southern basins (Fazenda Vista Alegre), 20 % from
northern basins (Acanaui, Serrinha, Caracarai), 30 % from
western/south-western basins (Sao Paulo de Olivenc¸a,
Gaviao, Labrea) and 30 % from central residual basins (be-
tween Sao Paulo de Olivenc¸a and Manacapuru – hereafter
called “MANA*” – and between Manacapuru and ´Obidos –
hereafter called “OBI*”) (Espinoza et al., 2009a) (see Fig. 2
and Table 2 for localization of the stations). With simula-
tion ORCH1, the underestimation of streamflow at ´Obidos
is mainly due to the low streamflow over western/south-
western regions of the basin (near −35 % at Tamshiyacu)
and over the two central residual basins (between −25 to
more than −35 %) (Fig. 9a). The streamflow coming from
the south is close to the observations but it is a compensation
between th overestimation at Rurrenabaque and Guajara-
Mirim and the underestimation over the two southern resid-
ual basins (hereafter called “PVE*” and “FVA*”). Simu-
lat d streamflow in orthern stations like Acanaui and Ser-
rinha is close to the observations and an overestimation is ob-
served over the northernmost region of the basin at Caracarai.
The correction of NCC precipitation by ORE HYBAM data
leads to a decrease of the error with observation over An-
dean sub-basins (−24 % at Tamshiyacu) and over the resid-
ual basin of MANA* where the precipitation has been sig-
nificantly increased (Fig. 9b). Simulation of streamflow over
the residual basin of OBI* is not improved. It can be due
to the lack of available rainfall gauges for kriging over this
region (see Fig. 1 of Espinoza et al., 2009b). The overes-
timation of streamflow over all the southern sub-basins (ex-
cept Rurrenabaque) by 25 % to more than 35 %, mainly due
to the increase of the rainy spots, leads to an excess of water
at Fazenda Vista Alegre. Consequently, simulated stream-
flow from ORCH2 at ´Obidos station, close to the observa-
tions, is a result of a compensation between southern and
western/south-western regions.
The observed streamflow at ´Obidos has a pronounced
seasonality during the year (Fig. 10a). Flow is high-
est, on average, during May and June with a maximum
value of about 230× 103 m3 s−1. Then, a decrease occurs
during five months until the low-flow in November (near
103× 103 m3 s−1). Simulated streamflow is time shifted in
www.hydrol-earth-syst-sci.net/16/911/2012/ Hydrol. Earth Syst. Sci., 16, 911–935, 2012
926 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
T
able
7.
Statistical
results
of
observ
ed
and
sim
ulated
discharges(Q
m
ean
,
Q
m
in
,
Q
m
ax (allin
m
3
s −
1),N
ash
co
efficient
and
N
-R
M
SE
(%))for
the
studied
stations
o
v
er
the
period
1980–2000.V
aluesbetw
een
brack
ets
are
relativ
e
differences(%)betw
een
sim
ulation
and
estim
ate.
O
bserv
ed
O
RCH
1
O
RCH
2
O
RCH
3
O
RCH
4
Sta.
Q
m
ean
Q
m
in
Q
m
ax
Q
m
ean
Q
m
in
Q
m
ax
N
ash
N
-RM
SE
Q
m
ean
Q
m
in
Q
m
ax
N
ash
N
-R
M
SE
Q
m
ean
Q
m
in
Q
m
ax
N
ash
N
-RM
SE
Q
m
ean
Q
m
in
Q
m
ax
N
ash
N
-RM
SE
O
BI
169515
101934
231719
144084
60440
242756
0.08
27.80
169091
84409
260867
0.40
22.50
166913
106951
215933
0.54
19.60
167188
91254
237118
0.80
12.90
(−
15.0)
(−
40.7)
(4.80)
(−
0.25)
(−
17.2)
(12.6)
(−
1.54)
(4.92)
(−
6.81)
(−
1.37)
(−
10.5)
(2.33)
M
A
N
A
100819
62135
135592
80455
34660
130141
−
0.65
34.31
96232
50131
142233
0.15
24.67
96566
48572
144304
0.24
23.36
96584
48029
145463
0.11
25.24
(−
20.2)
(−
44.2)
(−
4.00)
(−
4.55)
(−
19.3)
(4.90)
(−
4.22)
(−
21.8)
(6.43)
(−
4.20)
(−
22.7)
(7.28)
SPO
46206
23773
67958
33154
12586
57803
−
0.21
37.33
38228
17399
59197
0.53
23.11
37910
15965
60558
0.49
24.15
*
*
*
(−
28.2)
(−
47.1)
(−
14.9)
(−
17.3)
(−
26.8)
(−
12.9)
(−
17.9)
(−
32.8)
(−
10.9)
TA
M
30530
15062
46086
19884
7207
36859
−
0.69
46.04
23120
10666
36181
0.26
30.45
22593
9721
36085
0.19
31.93
*
*
*
(−
34.9)
(−
52.1)
(−
20.0)
(−
24.3)
(−
29.2)
(−
21.5)
(−
26.0)
(−
35.5)
(−
21.7)
G
AV
4653
948
8788
6389
500
15050
−
0.67
84.01
7244
899
15983
−
1.30
98
.79
7197
839
16105
−
1.30
98.67
*
*
*
(37.3)
(−
47.3)
(71.2)
(55.7)
(−
5.16)
(81.9)
(54.7)
(−
11.6)
(83
.3)
LA
B
5472
1010
11043
6217
383
16448
0.30
59.47
6543
510
15892
0
.46
52.00
6755
514
16450
0.38
55,98
*
*
*
(13.6)
(−
62.0)
(48.9)
(19.6)
(−
49.5)
(43.9)
(23.4)
(−
49.1)
(49
.0)
FVA
28374
6311
56730
32098
3582
79042
0.35
52.60
43080
7232
96767
−
0.59
82
.27
42004
6934
93545
−
0.23
72.36
42062
6712
93903
−
0.28
73.65
(13.1)
(−
43.2)
(39.3)
(51.8)
(14.6)
(70.6)
(48.0)
(9.88)
(64.9)
(48.2)
(6.36)
(65.5)
PV
E
19418
5136
36677
19151
2714
47145
0.52
41.32
28705
5521
64734
−
0.65
76
.48
27870
5287
62290
−
0.34
69.03
27923
5031
62696
−
0.38
69.87
(−
1.40)
(−
47.1)
(28.5)
(47.8)
(7.50)
(76.5)
(43.5)
(2.94)
(69.8)
(43.8)
(−
2.04)
(70.9)
G
M
IR
8041
1782
15405
6377
885
15956
0.29
54.46
10111
1115
24883
−
0.45
77
.81
9567
970
24632
−
0.05
66.34
9620
871
24524
−
0.07
66.92
(−
20.7)
(−
50.3)
(3.60)
(25.7)
(−
37.4)
(61.5)
(18.9)
(−
45.6)
(59
.9)
(19.6)
(−
51.1)
(59.2)
RU
R
1986
546
4865
1137
114
3389
0.34
60.06
1802
314
4800
0.68
42.13
1796
308
4793
0.67
42.46
*
*
*
(−
42.7)
(−
79.2)
(−
30.3)
(−
9.27)
(−
42.6)
(−
1.34)
(−
9.58)
(−
43.6)
(−
1.48)
ITA
11902
4006
23470
14168
624
40387
−
0.32
71.52
14153
830
35467
0.19
55.98
13962
651
35768
0.24
54.18
13964
651
35795
0.24
54.46
(19.0)
(−
84.4)
(72.1)
(18.9)
(−
79.3)
(51.1)
(17.3)
(−
83.7)
(52
.4)
(17.3)
(−
83.8)
(52.5)
A
LT
7956
1164
20568
15164
536
47426
−
2.29
164.6
15436
796
42111
−
1.60
146.4
15325
650
43349
−
1.51
144.0
15327
657
43450
−
1.54
145.0
(90.6)
(−
54.0)
(131)
(94.0)
(−
31.6)
(105)
(92.6)
(−
44.2)
(111)
(92.7)
(−
43.5)
(111)
ACA
14089
7165
21816
13677
7302
21918
−
0.05
40.34
17515
10149
27074
−
0.07
40
.72
17633
10278
27258
−
0.06
40.45
*
*
*
(−
2.90)
(1.90)
(0.47)
(24.3)
(41.6)
(24.1)
(25.2)
(43.4)
(24.9)
SER
16193
7621
26921
14433
7472
23208
0.56
26.88
16692
8998
26685
0.79
18.76
16354
8828
26034
0.80
18.18
16354
8825
26040
0.80
18.22
(−
10.9)
(−
2.00)
(−
13.8)
(3.08)
(18.1)
(−
0.88)
(0.99)
(15.8)
(−
3.30)
(0.99)
(15
.8)
(−
3.27)
CA
RA
2777
691
7232
4742
559
13567
−
0.89
120.2
3809
601
9918
0.49
62.64
3755
600
9780
0.49
62.74
3768
588
9772
0.49
62.46
(70.7)
(−
19.2)
(87.6)
(37.1)
(−
13.0)
(37.1)
(35.2)
(−
13.2)
(34
.9)
(35.6)
(−
14.9)
(35.1)
SFR
990
199
2371
2220
86
6331
−
5.45
209.0
2024
192
5297
−
3.02
165.0
*
*
*
(124)
(−
57.1)
(167)
(105)
(−
3.81)
(123)
*
*
*
M
eans
thatthere
w
as
n
o
change
co
m
pared
to
previous
sim
ulation
.
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 927
(a)
(b)
Fig. 10. Mean monthly discharge (m3 s−1) at O´bidos for simulations ORCH1 and ORCH2 compared to obser-
vations. (a) Mean annual seasonality and (b) interannual variation over the time period 1980–2000.
45
Fig. 10. Mean monthly discharge (m3 s−1) at ´Obidos for simulations ORCH1 and ORCH2 compared to observations. (a) Mean annual
seasonality and (b) interannual variation over the time period 1980–2000.
both simulations compared to the observations (Fig. 10a). In
fact, according to cross-correlation statistic (see Appendix
for more details), correlation between simulated and ob-
served discharges would be optimal if a time lag of 1 month
was applied (dt = 1 and rcross = 0.91, rcross = 0.93 for ORCH1
and ORCH2, respectively). However, Nash coefficient (see
Appendix for more details) is increased when ORCHIDEE
is forced by NCC precipitation corrected by ORE HYBAM
(0.08 and 0.40, respectively for ORCH1 and ORCH2 ac-
cording to Table 7) indicating a significant improvement of
the simulation in streamflow at ´Obidos when ORE HYBAM
precipitation is used. The simulated interannual variation in
streamflow is better captured with ORCH2 according to the
coefficient of variation of the root mean squared error (see
Appendix for more details) which is lower (22.5 %) than
with ORCH1 (28 %). The simulated low-flow is improved
(−17 % with ORCH2 compared to−41 % with ORCH1) but
remains underestimated for most years whereas simulated
high-flow is slightly overestimated with ORCH2 (+5 %) even
more during some dry years (1981, 1985, 1992) (Fig. 10b).
Figure 11 depicts all the simulated seasonal cycles for both
simulations at the sixteen stations across the Amazon River
basin and are compared to the observed seasonality. Northern
stations show an underestimation in high-flow in the west-
ern part (Acanaui and Serrinha) and an overestimation in
the eastern part (Caracarai and Sao Francisco). Low-flow is
underestimated everywhere except in the north at Acanaui
and Serrinha. The improvement of the seasonal cycles at
Tamshiyacu and Sao Paulo de Olivenc¸a is pointed out when
precipitation is corrected by ORE HYBAM data. The un-
derestimation in low-flow is reduced by about 50 %. The
Nash coefficient becomes positive in these stations, reach-
ing 0.5. This mainly contributes to the increase in low flow
at ´Obidos with ORCH2 compared to ORCH1 and becomes
in better agreement with observations. Moreover, the in-
crease in precipitation in the north-western part of the basin
induces a better seasonality. At station Acanaui, no change
in Nash coefficient is shown but at Serrinha, the seasonality
is well captured when compared to observations (Nash coef-
ficient reaches about 0.80). Interannual variation of stream-
flow at this station is also well simulated as shown in Fig. 12
(N-RMSE for ORCH1 and ORCH2 are, respectively about
27 % and 19 %). A better capture of high-flow with ORCH2
is shown (−1 % of mean annual relative error with obser-
vations) and high-flow of each year from 1986 to 1993 is
in better agreement with observations than with ORCH1
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928 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
Fig. 11. Mean seasonal discharges (m3 s−1) over the sub-basins for simulations ORCH1 and ORCH2 compared
to observations, over the mean period 1980–2000. The stations are organized according their locations (North
(N), North-East (NE), East (E), South-East (SE), South-West (SW), West (W) and North-West (NW)).
46
Fig. 11. Mean seasonal discharges (m3 s−1) over the sub-basins for simulations ORCH1 and ORCH2 compared to observations, over the
mean period 1980–2000. The stations are organized according their locations – North (N), North-East (NE), East (E), South-East (SE),
South-West (SW), West (W) and North-West (NW).
Fig. 12. Interannual variation of monthly discharge (m3 s−1) at Serrinha for simulations ORCH1 and ORCH2
compared to observations, over the time period 1980–2000.
47
Fig. 12. Interannual variation of monthly discharge (m3 s−1) at Serrinha for simulations ORCH1 and ORCH2 compared to observations,
over the time period 1980–2000.
where they are systematically underestimated. In north-
ern and north-eastern regions, simulated streamflow is im-
proved (high-flow indeed) by the decrease in precipitation at
Caracarai and Sao Francisco. Interannual variation of high-
flow is better captured at Caracarai with ORCH2 (Fig. 13):
the coefficient of correlation with observations is about 0.86
compared to 0.74 with ORCH1. Improvements are less
pronounced for stations in south-eastern regions (Altamira
and Itaituba) where a decrease in precipitation was intro-
duced. Concerning southern stations, simulated streamflow
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 929
Fig. 13. Interannual variation of maximal monthly discharge (m3 s−1) at Caracarai for simulations ORCH1 and
ORCH2 compared to observations, over the time period 1980–2000.
48
Fig. 13. Interannual variation of maximal monthly discharge (m3 s−1) at Caracarai for simulations ORCH1 and ORCH2 compared to
observations, over the time period 1980–2000.
(a) (b)
Fig. 14. Mean monthly seasonal discharge (m3 s−1) at O´bidos for simulation ORCH3 compared to (a) ORCH2
and (b) ORCH4. Comparison with observations is shown over the mean time period 1980–2000.
49
Fig. 14. Mean monthly seasonal discharge (m3 s−1) at ´Obidos for simulation ORCH3 compared to (a) ORCH2 and (b) ORCH4. Comparison
with observations is shown over the mean time period 1980–2000.
is degraded following the increase in precipitation. The over-
estimation in high flow simulated with ORCH1 is accentu-
ated except for Rurrenabaque where streamflow seasonality
is well simulated (−1.3 % of mean annual relative error with
ORCH2). One can expect an underestimation in ET by OR-
CHIDEE over southern regions of the Amazon River basin
as far as ORE HYBAM precipitation is satisfactory.
5 Impact of the new distribution in maximal fractions
of floodplains and swamps on river discharge
5.1 Discharge at ´Obidos and model calibration
The introduction of a new map of MFF and MFS (simula-
tion ORCH3), improves the seasonality of the streamflow
at ´Obidos – ORCH3 Nash coefficient is higher (0.54) than
ORCH2 one (0.40) and N-RMSE is lower (19.6 compared
to 22.50) according to Table 7. The increase in MFF over the
main stem of the Amazon smoothes the increase in stream-
flow similarly to the observations (Fig. 14a) but delays the
high-flow by one month (dt =−1 and rcross = 0.90). In order
to improve the timing of the high-flow, a calibration of the
time constant of the floodplains reservoir, which was evalu-
ated over the Niger Inner Delta (gFd = 4.0 days), is performed
in simulation ORCH4. The delay is corrected with a value of
2.5 days for the parameter gFd (dt = 0 and rcross = 0.91) as
shown in Fig. 14b, leading to a value of high-flow similar to
observation (+2 % of error in Qmax). The Nash coefficient is
consequently increased (0.80).
The increase in MFF over the region of Llanos de Moxos
delays the peak of high-flow to March–April in agreement
with observations leading to a better capture of low-flow
period from August to November (Fig. 15a). Moreover,
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930 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
(a) (b)
Fig. 15. Mean monthly seasonal discharge (m3 s−1) at (a) Guajara-Mirim and (b) Caracarai for simulation
ORCH3 compared to ORCH2 and observations, over the mean time period 1980–2000.
50
Fig. 15. Mean monthly seasonal discharge (m3 s−1) at (a) Guajara-Mirim and (b) Caracarai for simulation ORCH3 compared to ORCH2
and observations, over the ean time period 1980–2000.
Fig. 16. Map of locations of virtual stations on Topex/Poseidon ground tracks available for the period 1993–
2000: 4 locations on the mainstem (ID An with n=1 to 4 in light blue) and 4 locations on the Rio Negro-Branco
(ID Bn with n= 1 to 4 in red). See Table 7 for coordinates of the stations. Color is used to distinguish the
different sub-basins.
51
Fig. 16. Map of locations of virtual stations on Topex/Poseidon ground tracks available for the period 1993–2000: 4 locations on the
mainstem (ID An with n= 1 to 4 in light blue) and 4 l cations n the Rio Negro-Branco (ID Bn with n= 1 to 4 in red). See Table 8 for
coordinates of the stations. Color is used to distinguish the different sub-basins.
the mean annual streamflow at Guajara-Mirim decreases in
ORCH3 (about 500 m3 s−1) due to increased swamps area in
PRIMA map. The same effect is simulated inside the basin of
the upper Rio Branco with ORCH3. A delay of the peak flow
by one month occurs and a better capture of high-flow evolu-
tion during June to August is performed (Fig. 15b). The cal-
ibration at ´Obidos does not change significantly the stream-
flows at the other stations compared to the previous results
obtained with ORCH3 (see Table 7).
5.2 Simulated water height of the floodplains
Streamflow seasonality of the Amazon can be highly af-
fected by floodplains distribution mainly between Manaca-
puru and the mouth, where large amounts of water are
transferred through the floodplains (Bonnet et al., 2008).
Thus, variations of water height of the floodplains simu-
lated in ORCHIDEE are compared with observations from
Topex/Poseidon. Flooded fraction extension cannot be com-
pared with observations as long as the swamps do not have
a spatio-temporal variability simulated in ORCHIDEE. That
would be an interesting perspective for further develop-
ment of the model, but high uncertainties exist for this rep-
resentation according to the poorly known topography in
forested areas. Over the period 1993–2000, 8 locations
of Topex/Poseidon measurements are distributed along the
main stem of the Amazon, the Rio Branco and the Rio Negro
(Fig. 16).
Results from simulation ORCH4 are compared to these
estimates for the same time period. The effect of the change
from the old distribution of the MFF (ORCH2) to the new
one (ORCH3) and the effect of the calibration at ´Obidos
(ORCH4) on the simulated water level height is also shown
(Table 8). Water height of the floodplains is not directly con-
sidered because ORCHIDEE does not take into account the
height of the river bed. Then, an index of water height vari-
ation is performed for simulation and observations data. The
minimal value of the water height during the 8 yr is consid-
ered as the height of the river bed and it is consequently sub-
tracted each month to the water level height from the simu-
lated and observed data.
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 931
Table 8. Comparison of the water height index Hindex (m) between simulations ORCH2 to ORCH4 and observations, in 4 locations on the
mainstem (ID A) and 4 locations on the Rio Negro-Branco (ID B) (see Fig. 16 for locations), for the time period 1993–2000. Monthly mean
correlation coefficient r2 and monthly standard deviation σ are given.
Stations Hindex (m) r2 σ (m)
ID Lat. Lon. Observed ORCH2 ORCH3 ORCH4 ORCH2 ORCH3 ORCH4 Observed ORCH2 ORCH3 ORCH4
A1 −1.98 −53.85 2.85 75.98 8.82 6.39 0.82 0.71 0.90 1.20 44.80 4.17 3.43
A2 −2.51 −56.50 4.76 66.35 51.88 36.93 0.79 0.73 0.92 1.74 37.56 26.74 20.22
A3 −3.23 −59.08 7.98 43.66 22.27 14.07 0.66 0.84 0.77 3.13 24.50 12.30 7.99
A4 −3.86 −61.69 7.70 40.40 14.70 9.16 0.80 0.85 0.81 3.02 22.14 7.82 4.96
B1 −3.18 −60.00 7.72 19.28 10.19 6.37 0.87 0.92 0.89 3.57 10.49 5.35 3.40
B2 −1.28 −62.00 5.38 3.14 2.14 1.35 0.89 0.91 0.90 3.03 1.77 1.21 0.76
B3 −1.06 −63.00 3.56 – 1.21 – 0.54 0.54 2.32 – 0.78
B4 −0.46 −62.00 4.32 5.06 2.19 1.37 0.72 0.74 0.73 2.59 2.91 1.23 0.77
Fig. 17. Interannual variation of monthly water height index Hindex (m) at station B1 (see Fig. 16 for location)
on the Rio Negro, for the simulations ORCH2 to ORCH4 compared to the Topex/Poseidon observations, over
the time period 1993–2000.
52
Fig. 17. Interannual variation of monthly water height index Hindex (m) at station B1 (see Fig. 16 for location) on the Rio Negro, for the
simulations ORCH2 to ORCH4 compared to the Topex/Poseidon observations, over the time period 1993–2000.
Over the main stem, water height level is highly overes-
timated at the four stations (A1 to A4) with the old dis-
tribution of MFF (ORCH2) according to Table 8. More-
over, the overestimated standard deviation indicates a too-
high variation in simulated water height through the year,
despite a good correlation with observations. The new dis-
tribution of MFF (ORCH3) increases the rate of MFF over
the main stem and logically reduces the water height of the
floodplains. The calibration (ORCH4) improves the mean
water height for the four stations (Table 8) and gives bet-
ter correlations with observations (up to 0.92). However,
the water height remains overestimated for all the stations,
mainly around ´Obidos. This is consistent when compared
to Fig. 6 where Hess et al. (2003) estimate 25 % more MFF
than ORCHIDEE at Curuai near ´Obidos. Around Manaca-
puru (stations A3 and A4), the simulated water height is in
better agreement with Topex/Poseidon measurements with
a small overestimation. This corroborates the findings by
Hess et al. (2003) who estimate only 5 % more of MFF than
ORCHIDEE at Cabaliana near Manacapuru (see Fig. 6).
Over the Rio Negro, the change of the distribution in MFF
does not improve the correlation with measurements which
is good near the main stem at B1 and B2 (about 0.9) and low
at B3 and B4 (between 0.5 and 0.7) (Table 8). We note that
no fraction of floodplains were present in the old map at sta-
tion B3, whereas the new distribution now enables a compar-
ison with observations. The water height is underestimated
at all the stations when compared to measurements, for all
the simulations except B1 close to the main stem. In fact, the
interannual seasonality of water height of floodplains is well
captured at this station with ORCH4 compared to measure-
ments during the eight years (Fig. 17). The old distribution in
MFF largely overestimates the water height in flooded areas.
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932 M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets
6 Summary and final remarks
In this work, the simulation by ORCHIDEE of discharge val-
ues in the main tributaries of the Amazon River basin and
in the last gauged station ´Obidos has benefited from two
major inputs. A first improvement results from the intro-
duction of a comprehensive daily rainfall observations de-
rived from ORE HYBAM dataset, that includes data from
upstream regions of the basin, especially in Bolivia, Peru,
Ecuador and Colombia that were not taken into considera-
tion in former simulations. Additionally to a good density
of rainfall stations, a distribution that takes into account very
rainy spots in the Andes or in the North-West contribute to
a better reliability of rainfall input. Indeed, the addition of
high rainfall in the northwestern equatorial region and in
some rainy spots spread along the Andes tends to improve
the mean rainfall over the basin (ORCH2) that was previ-
ously underestimated (ORCH1). Simulated evapotranspira-
tion remains underestimated when using ORE HYBAM rain-
fall forcing, but the use of daily values in precipitation seems
to highly improve the seasonal variation of evaporation of
water over the leaves (ORCH2) when compared to Shuttle-
worth (1988)’s measurements near Manaus. The forcing by
ORE HYBAM rainfall results in an increase in streamflow
by 15 % at ´Obidos (ORCH2) that is more realistic than the
previous one (ORCH1) when compared to the observations.
However, the amplitude of the annual cycle is still inade-
quate. The flood is higher than observed at ´Obidos, which
may be attributed to overestimated high-flow in the south-
ern basins and the low water stage is too low in accordance
with low values in the northwestern basins. At a regional
scale, ORE HYBAM rainfall forcing considerably improves
the discharge in the western basins where the seasonality and
the interannual variability are reasonably well captured. This
is also observed in the northern basins where the previously
overestimated streamflow is reduced. However, streamflow
is degraded in some southern basins where simulated evap-
otranspiration may be too low and the extension of rainy
spots exaggerated by our interpolation method. An ongo-
ing work thus aims at defining a better interpolation method,
that would allow for a better description of the rainfall in the
Andean part of the basin. A more accurate spatial pattern
of the rainy spots should reduce the amount of precipitation
over the respective sub-basins. In simulation ORCH2, the ex-
tremes in streamflow occur earlier than observed at ´Obidos.
That is why a second modification has been realized by in-
troducing a new map of maximal fractions of floodplains and
swamps in the model (ORCH3). The improvements are es-
pecially significant over large areas such as the main stem of
the Amazon River and the Llanos de Moxos region.
A better capture of the streamflow seasonality is also found
over small basins such as the Branco River at Caracarai.
However, for small basins, product quality is probably not
sufficient since the spatial resolution of the satellite obser-
vation is approximately 25× 25 km2 and the error bar on
the product is about 10 % (Prigent et al., 2007). Together
with a calibration at ´Obidos of the time constant of the
floodplains reservoir in the model (ORCH4), the change in
floodplains/swamps maps has improved the simulated water
height of floodplains in the main stem of the Amazon River
and corrected the discharge seasonality at ´Obidos and in the
involved sub-basins. Some extensions of this study can be
considered, such as a vegetation map that would introduce
the recent extension of deforested areas and finally, a better
representation of evapotranspiration in the model. In addi-
tion, the improvements performed in the study in the ability
of the ORCHIDEE model to represent the hydrological dy-
namics in the Amazon River basin make this model a power-
ful tool for studying the impact of climate change scenarios
on the river discharge.
Appendix A
Statistical tools
Various indicators are used to compare observed and simu-
lated discharge.
A1 Coefficient of variation of the root mean squared
error
The Coefficient of Variation of the Root Mean Squared Error
(CV(RMSE), Eq. A1) is similar to an R2 error as it measures
the degree of data scatter.
CV(RMSE) =
√√√√√ N∑
i=1
(
QORCHi − Qobsi
)2
N
× 100
Qobs
(A1)
where N is the time-step number, QORCHi and Qobsi
(m3 s−1), respectively the simulated and observed stream-
flows for time-step i, Qobs (m3 s−1) the mean of observed
streamflows for the serie.
A2 Nash-Sutcliffe coefficient
The Nash-Sutcliffe efficiency coefficient (Nash, Eq. A2) as-
sesses the predictive power of hydrological models (Nash
and Sutcliffe, 1970). Nash-Sutcliffe efficiencies can range
from −∞ to 1. An efficiency of 1 corresponds to a perfect
match of simulated discharge to the observed data. An effi-
ciency of 0 indicates that the model predictions are as accu-
rate as the mean of the observed data, whereas an efficiency
less than zero occurs when the observed mean is a better pre-
dictor than the model. Essentially, the closer the model effi-
ciency is to 1, the more accurate the model is.
Nash = 1 −

N∑
i=1
(
QORCHi − Qobsi
)2
N∑
i=1
(
Qobs − Qobsi
)2
 (A2)
Hydrol. Earth Syst. Sci., 16, 911–935, 2012 www.hydrol-earth-syst-sci.net/16/911/2012/
M. Guimberteau et al.: Amazon discharge simulation using ORCHIDEE forced by new datasets 933
A3 Coefficient of cross-correlation
The cross correlation computation is useful to illustrate the
time lag existing between two seasonalities. In other words,
it deduces the necessary delay dt to get the best correlation
between the two seasonalities. The cross-correlation r at de-
lay dt is defined as:
r (dt ) =
n∑ [(
QORCHn − QORCH
)
×
(
QOBS(n−dt ) − QOBS
)]
√
n∑ (
QORCHn − QORCH
)2 √ n∑ (
QOBS(n−dt ) − QOBS
)2 (A3)
where n is the month, dt (months) the delay, QORCH and
QORCH (m3 s−1), respectively monthly and monthly mean
streamflow simulated by the model and QOBS and QOBS
(m3 s−1), respectively monthly and monthly mean observed
streamflows.
Acknowledgements. We want to acknowledge all our colleagues
in the national hydrological services (ANA in Brazil, SENAMHI
in Peru and Bolivia and INAMHI in Ecuador) and the French
Institute of Research for Development (IRD) who participated to
the field campaigns of the HYBAM Program and thus contributed
to collecting the data used in this work, which is available on the
web page of the ORE-HYBAM (http://www.ore-hybam.org). This
work was financially supported by the GIS (Groupement d’Inte´reˆt
Scientifique) REGYNA (REGioNAlisation des pre´cipitations
et impacts hYdrologiques et agronomiques) and the EU-FP7
AMAZALERT (Raising the alert about critical feedbacks between
climate and long-term land use change in the Amazon) projects.
Correction of NCC forcing in precipitation by ORE HYBAM and
simulations with ORCHIDEE were performed using computational
facilities of the Institut du De´veloppement et des Ressources en
Informatique Scientifique (IDRIS, CNRS, France).
Edited by: W. Buytaert
The publication of this article is financed by CNRS-INSU.
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