Table of contents
1. PROVISION OF QUALITY, QUANTITY AND WELL REGULATED
WATER .................................................................................................................... 3
1.1 REVIEW OF REGIONAL AND NATIONAL HYDROLOGICAL AND
CLIMATE DATA GATHERING PROGRAMMES ............................................ 3
1.2 FLOW STATIONS DATABASE: A VALIDATION DATASET FOR THE
BASELINE SCENARIO OF RIVER DISCHARGE IN THE OTCA AREA. ..... 7
1.2.1 Real gauge stations. ...................................................................................... 7
1.2.1.1 Limitations and uncertainties. .................................................................... 8
3.1.1.2 Virtual gauge stations .............................................................................. 13
1.3 EVIDENCE FOR MAXIMUM PEAKS, MINIMUM BASEFLOWS AND
FLASHINESS CHANGE FROM FLOW DATA IN THE REGION ................. 14
1.3.1 River flow changes from climate variability .............................................. 14
1.3.2 River flow changes from Land cover change ............................................. 18
1.3.2.1 Amazon deforestation: background ..................................................... 19
1.3.2.2 Effects of forest conversion on flow regimes in the Amazon basin .... 19
1.4
REVIEW OF HYDROLOGICAL MODELLING DONE AT THE
AMAZON SCALE .............................................................................................. 24
1.4.1
Macroescale models ............................................................................. 24
1.4.2
Mesoscale models ................................................................................ 25
1.4.3
Single Column models ......................................................................... 26
1.4.4
Modelling – observational studies for river discharge ......................... 27
1.4.5
Monitoring of vegetation as modelling input to understanding climate
dynamics in the Amazon ...................................................................................... 28
1.4.6
Modelling of flows and soil hydrology ................................................ 29
1.5 New hydroelectric projects proposed in the OTCA area ............................... 30
1.6 Current hydroelectric projects in the OTCA area .......................................... 30
2 REFERENCES .................................................................................................... 31
Table of figures
Figure 1. Flow station places in the OTCA area reported in the literature. ................... 7
Figure 2. Flow stations from IDEAM (yellow placemarks – monthly time series) and
ANA, Hybam Project and GRDC database (red placemarks – average annual
discharge) for which data of river discharge is available. ............................................. 8
Figure 3. 21 virtual stations located in the Negro River basin Amazon. Red place
marks indicate the location of real flow stations used for validation. Blue flags
represent the virtual stations retrieved from radar Altimetry. ...................................... 13
Figure 4. Amazon basin, with the drainage area at Manaus darken. Source of diagram:
William et al (2005). .................................................................................................... 16
Figure 5. A century of river stage records at Manuas – Brazil over the Negro River.
Top trend indicates the annual maximum river stage at Manaus in which the
pronounced drop in water levels can be observed for 1926. The bottom trend indicates
the minimum annual river stage in which paradoxically the lowest observation is
observed in 1963 and not in 1926, the minimum base flow in this year is amongst the
lowest in the whole record. Source of diagram: William et al (2005). ........................ 16
Figure 6. Rainfall deficits in western Amazon upstream Manuas (1926). Data was
collected from rainfall stations available during the 1920 – 1930 period. An east west
dipole is observed indicating greater deficit upstream Manaus in West Amazon, less
pronounced deficit in South west Amazon and surplus to the east of the Amazon
basin. Source of diagram: William et al (2005). .......................................................... 17
Figure 7. NASA’s terra satellite image of the Moderate Resolution Imaging
Spectroradiometer, (MODIS) providing month to month of changes in vegetation
status across the Amazon. Schematic. Source of diagram: Stephen Cole, Goddard
Space Flight Center, NASA (2007) ............................................................................. 19
Figure 8. a. Map of Peru with the localization of Iquitos in the Amazon river and rain
stations considered for analysis by Gentry and Lopez-Parodi (1980). b. Linear
regressions of maximum peak flows (Top) and minimum base flows (bottom) at
Iquitos, indicating an upward trend for Maximum peak flows and no change in
minimum base flows from the 1960s to the 1970s respectively. Source: Gentry and
Lopez-Parodi (1980). ................................................................................................... 20
Figure 9. a. Relation between stage and river discharge at Iquitos. b. Peak stage
records from 1942 to 1980 at Iquitos. Source: Nordin and Meade (1982). ................. 20
Figure 10. a. Study area, Manaus location and main features on the Salamoes river. b.
Maximum peak stages, mean and minimum stages in the Negro river at Manaus with
liner regression indicating the trend of change over the period 1903 – 1985. ............. 21
Figure 11. Location of the study area of the study reported by Costa et al (2003).
Schematic only . To be from diagrams produced in the project. .................................. 22
Figure 12. Seasonal increase in river discharge for the Tocantins river - Brazil.
Bottom curve indicates indicates the period (1949 - 1968) and Top curve indicates the
period (1969 - 1978) cisnistently shwing higher river discharge and early monthly
flood peak for the period of greater forest loss. ........................................................... 22
List of Tables
Table 1. Gathering data programs for hydrological and climate studies in the Amazon
basin ............................................................................................................................... 4
Table 2. Presents a list of the flow stations with information already gathered for the
OTCA area. .................................................................................................................... 9
Table 3. Virtual stations from radar altimetry and routing modelling. Negro basin –
Brazilian part of the OTCA area. Adpated from León et al (2006) ............................. 14
Table 4. Studies reporting the magnitude of ENSO events in the Amazon basin. ..... 18
Table 5: Studies reporting change in river and stream flows from land cover change in
the Amazon basin. ........................................................................................................ 23
Table 6. Main examples of hydrological modelling at the Amazon scale. Macroscale
models. Adapted from D’Almeida et al. (2007) ........................................................ 24
Table 7. List of the main mesoscale models applied in the Amazon. Table adapted
from D’Almeida et al (2007) ....................................................................................... 25
Table 8. List of the main Single Column Models implemented in the Amazon.
Adapted from D’Almeida et al (2007). ........................................................................ 26
Table 9. Presents a list of the main studies reporting radar altimetry coupled with
hydrological modelling. ............................................................................................... 27
Table 10. Presents a list of the main studies reporting vegetation and climate dynamic
interactions ................................................................................................................... 28
1. PROVISION OF QUALITY, QUANTITY AND WELL REGULATED
WATER
1.1 REVIEW OF REGIONAL AND NATIONAL HYDROLOGICAL AND
CLIMATE DATA GATHERING PROGRAMMES
Different multinational and regional research and monitoring programmes have taken
place in the Amazon basin recently (over the last 20 years). Their aim has been to
provide good quality data to support scientific research to enhance the knowledge of
the regional hydrology and climate of the Amazonia as well as the geodynamic,
chemical and land cover controls affecting sedimentation and mass transfer processes
in the main Amazon tributaries.
One of the most comprehensive initiatives is the ORE - HYBAM project
(Environmental Research Observatory - Hydrodynamic of the Amazon Basin project).
ORE – HYBAM is an international scientific effort between France, Brazil, Bolivia,
Colombia, Ecuador, Perú and Venezuela, operating since 2003, which holds close
collaboration with other regional initiatives such as the Large Scale BiosphereAtmosphere Experiment in Amazonía (LBA) and national institutes of research,
hydrology and meteorology such as Instituto National de Pesquisas da Amazonia –
Brazil (INPA), Servicio Nacional de Meteorología e Hidrología del Perú
(SENAMHI), Instituto Nacional de Meteorología e Hidrología del Ecuador
(INAMHI), el Instituto de Hidrología y Meteorología y Estudios Ambientales de
Colombia (IDEAM), SENAMHI – Bolivia, Universidad Agraría de la Molina and
Universidad Nacional de Colombia, among other organizations.
The project was designed to study the hydrology and Geodynamic of the Amazon
Basin in order to predict extreme events in the context of climate variability and
human interventions (ORE-HYBAM 2007). In addition, novel tools such as radar
altimetry techniques have been implemented and validated to monitor river stage and
discharge in river sections lacking of in situ data. MERIS - MODIS imagery
processing has also been used to look at sediments load and transport.
SENAMHI in Peru in collaboration with HYBAM has measured river flows and
sediment load in the Amazon tributaries of Marañon, Ucayali, Huallaga, Santiago,
Nieva and Napo (SENAMHI 2007). Similarly, the INAMHI (2007) in close
collaboration with HYBAM in Ecuador has carried out field commissions to measure
Sediment yields and transport in the Napo river, as well as river discharge other rivers
such as Aguarico, Pastaza and Santiago.
Amongst other regional programmes are the international research initiative Large
Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) led by Brazil. LBA
main focus is to improve the understanding of physical climate, carbon storage and
exchange, biogoechemestry, atmospheric chemistry, impacts of land cover and land
use change on climate and interaction of the Amazonia with other earth systems. This
project has had close collaboration with HYBAM and other various national institutes
of hydrology and meteorology for the gathering of high temporal resolution river
discharge data, rainfall, evaporation, soil water storage, and sediment yields as well as
nutrients exports (LBA 2007). The project is operative since 1995 and holds
collaboration with NASA (National Aeronautics and Space Administration), ESA
(European Space Agency), INPE, INPA, Ministery of Science and Technology –
Brazil and the HYBAM project among other organizations.
Moreover, from 1998 to 1999 the LBA Brazil in Collaboration with NASA carried
out the TRMM – LBA experiment as a main component of the TRMM validation
program in which data of dynamical, microphysical, electrical and adiabatic heating
characteristics of tropical convection in the Amazon basin were gathered (Rutledge
1999).
The Global Energy and Water Cycle Experiment (GEWEX), established by the World
Climate Research Programme (WCRP), was designed to model the water cycle
towards understanding of potential impacts of o climate change in the basin. The
programme includes field intensive measurements and modelling with the aim of
providing the best available eater balance of the Amazon basin (Marengo 2006).
The Carbon in the Amazon River Experiment (CAMREX) has focused on
distributions and transformation of water and bioactive elements (C, N, P and O) in
the basin. This information has been used to build models to understand hydrological
and biogeochemical cycles and their interactions at different scales (from regional to
continental). The project is a joint initiative of the University of Washington (UW,
Seattle), the Centro de Energia Nuclear na Agricultura (CENA), NASA, INPA,
among other organizations (CAMREX 2007).
EOS – Amazon project linked in the central Andes with the Cornell EOS project
(EOS 2007), from 1988 to 1998 produced modelling datasets of the distribution of
rainfall and flow accumulations in the river basin as well as used radar technology to
understand the geomorphology and hydrology of central Andes glaciers.
The Amazon – Eye (KCL 2007) is one of the latest environmental information
systems for the Amazon basin. The system provides easy access to a large set of
datasets of terrain, hydrology, climate, land cover change, which in association with a
data base of existing and proposed dams in the region, are been used to support
studies that enhance the knowledge of the human and climate change impacts upon
bio-stability and maintenance of environmental services from regional to continental
scales in the basin.
Another KCL’s environmental datasets supporting hydrological research in the
Amazon is the Conservation – Eye (KCL 2007b). This initiative is a land use change
alert system based on MODIS VCF % of tree cover products, which is been used to
track patterns of rapid forest conversion as well as human interventions in protected
areas and explore interactions with observed hydrological anomalies.
The Table 5 presents a list of the main programs operating in the Amazon region and
the main data gathering and research activities.
Table 1. Gathering data programs for hydrological and climate studies in the Amazon basin
Project name
ORE - HYBAM
Organizations
involved
LBA
Focus
research
Hydrology
of
and
Period
work
2003
of
Data gathering activities
Study
the
hydrology
and
SENAMHI
INAMHI
IDEAM
SENAMHI
National University
of Colombia
Universidad
Agraria
de
la
Molina,
among
other organizations
geodynamic
the Amazon
Hydrology and
geodynamic of
the Amazon
Operative
since 1997
–
Hydrology and
geodynamic of
the Amazon
Operative
since 1997
NASA
ESA
INPE
INPA
Ministery
of
Science
and
Technology
–
Brazil
HYBAM
University
of
Washington (UW,
Seattle)
CENA
INPA
RSRG
AARAM
NFS Ecosystems
NASA LBA –
ECO
FAPESP
Land use change
Hydrology and
water chemistry
Physical climate
Carbon
dynamics:
storage
and
exchange
1996 – 2007
Building
of
Baseline datasets
and Model of
hydrology and
biogeochemical
cycles
from
regional
to
continental
scales
Operative
from
the
1980s
Distributions and transformation of
water and bioactive elements (C,
N, P and O)
GEWEX
LBA
Hydrology
water cycle
–
Since 1988
Amazon Eye
KCL
Ambiotek
2005-2007
Conservation
Eye
KCL
Ambiotek
UNEP -WCMC
NASA - EOS
Collection
of
high resolution
large datasets of
climate,
ecosystems, land
cover
and
infrastructure of
the
Amazon
basin
Land
Cover
change
Measurements of hydrology data,
for better understanding of the
Amazon water cycle and provide a
more accurate estimate of the
water balance.
Support hydrological analysis
about the impacts of land cover
and climate change upon the biostability
and
environmental
services provision of the basin.
Geomorphology
1988 - 1998
HYBAM
SENAMHI
-
SENAMHI – Peru
HYBAM
INAMHI
-
INAMHI
Ecuador
LBA
CAMREX
Cornell EOS
of
Geodynamic of the mazon Basin in
order to predict extreme events in
the context of climate variability
and human interventions. Radar
altimetry and in situ validation for
21 virtual flow stations in the
Amazon basin.
2005 - 2007
Measurement of river flows and
sediment load in the Amazon
tributaries of Marañon, Ucayali,
Huallaga, Santiago, Nieva and
Napo.
River flows measurements in the
rivers of Aguarico, Napo, Pastaza,
Santiago.
Sediment yields and transport in
the Napo river.
River flow measurements and
sediment yields in Collaboration
with the HYBAM project.
Also the project has implemented
the LBA-Hydronet is a protorype
for the Worldwide web based
regional hydrological data bank
(LBA-Hydronet V1)
Tracking
of
hydrological
anomalies from land cover change
in the basin
Use of Synthetic Aperture Radar
and hydrology of
central Andes
Glaciers
SAR to study glaciers in the central
Andes
1.2 FLOW STATIONS DATABASE: A VALIDATION DATASET FOR THE
BASELINE SCENARIO OF RIVER DISCHARGE IN THE OTCA AREA.
In order to validate modelled against observed river discharge for a baseline scenario
of water resources in the OTCA area, a preliminary flow stations database has been
built providing river discharge data for different sub-basins of the different countries
comprising the Amazonia. In addition, several virtual gauge stations instrumented
from radar altimetry have been also incorporated on the database.
1.2.1 Real gauge stations.
The database is composed by about 1000 flow stations places digitized according to
infrastructure of flow stations informed by different national institutes of meteorology
and hydrology within the OTCA area and from the Global Runoff Data Centre
(GRDC 2007) (Figure 1).
The national institutes of hydrology in the OTCA area from which data has been
collected are: ANA – Brazil (Brazilian Water National Agency), IDEAM - Colombia
(Colombian Institute of Hydrology and Meteorology), Hybam (Hydrology and
Geochemistry of the Amazon Basin project), SENAMHI – Peru (Peruvian National
Service of Hydrology and Meteorology), INMET - Brazil (Brazilian National
Institute of Meteorology), among other sources.
Figure 1. Flow station places in the OTCA area reported in the literature.
The database accounts for 20 year time series of monthly river discharge, for about 70
stations along the main Colombian OTCA - Amazon Rivers (Meta, Putumayo,
Guainia, Caqueta, Vaupes and Amazonas, among others). Annual river discharge
(only) is reported for about 100 stations along the main Amazon tributaries (Negro,
Solimoes, Putumayo, Vaupes, Caqueta, Guainia, Napo, Marañon, Ucayali, Beni,
Purus, Madeira, Tapajos, Branco, Orinoco, Maroni and Oyapcok, among others)
(Figure 2). Data has been gathered from ANA, Hybam project (ORE-HYBAM 2007)
and the GRDC database (GRDC 2007).
Figure 2. Flow stations from IDEAM (yellow placemarks – monthly time series) and ANA, Hybam
Project and GRDC database (red placemarks – average annual discharge) for which data of river
discharge is available.
1.2.1.1 Limitations and uncertainties.
Most of the databases used to build this dataset for the OTCA area present significant
limitations, since an important number of the flow stations are not currently in
operation or are not well maintained, and thus most of the time series are significantly
incomplete. Furthermore, most of the stations on the main Amazon tributaries do not
report river discharge; instead they inform the river stage, preventing the direct
comparison of modelled river discharge estimates. Also, providing that for most cases
the flow stations provide estimates rather than actual measures of river discharge, due
to large dimension of some of the Amazon tributaries, further uncertainty is observed.
Finally, the coordinates for a considerable number of flow stations are not well placed
on the river stream, introducing additional error.
Table 1 presents a list of flow stations with data online and from national institutes of
hydrology in the OTCA area.
Table 2. Presents a list of the flow stations with information already gathered for the OTCA area.
Item No
River
Name
Country
State
Municipality
Responsible
River
stage
Station
type
Mean
discharge
(m3 s-1)
Lat
Long
1
Francisco de
Orellana
Napo
Ecuador
Orellana
Orellana
HYBAM
1230
-0.4414
-76.9892
2
Borja
Marañon
Peru
Amazonas
Borja
SENAMHI
4600
-4.4703
-77.5483
3
Atalaya
Ucayali
Peru
Ucayali
Ucayali
HYBAN
0
-10.6783
-73.8178
4
Rurrenabaque
Beni
Bolivia
El Beni
Beni
SENAMHI
2010
-13.5547
-66.4658
5
Nazareth
Amazonas
Colombia
Amazonas
Leticia
IDEAM
32814.56
-3.8792
-69.9642
6
Tabatinga
Amazonas
Brazil
Amazonas
Tabatinga
ANA
42800
-3.7500
-68.0833
7
Porto Velho
Madeira
Brazil
Porto Velho
Porto Velho
ANA
18600
-7.2633
-62.0797
8
Labrea
Purus
Brazil
Amazonas
Labrea
ANA
LM
5530
-6.7478
64.8000
9
Serrinha
Negro
Brazil
Amazonas
Santa Izabel do rio Negro
ANA
YES
LM
16070
0.4819
64.8300
10
Caracarai
Brazil
Roraima
Caracarai
INMET
YES
LM
2880
1.8214
-60.8764
11
Brazil
Amazonas
Manacapuru
ANA
YES
LM
102470
-2.6917
-59.3906
12
Manacapuru
Fazenda Vista
Alegre
Branco
RIO
SOLIMÕES/AMAZONAS
Madeira
Brazil
Amazonas
NOVO ARIPUANÃ
ANA
YES
LM
27940
-3.1017
-59.9833
13
Itaituba
Tapajos
Brazil
Para
ITAITUBA
ANA
YES
LM
16500
-3.7167
-54.0167
14
Obidos
Amazonas
Brazil
Para
ÓBIDOS
ANA
YES
LM
170070
-0.0528
55.5111
15
Langa Tabiki
Maroni
French Guyana
OBHI
NO
1680
4.9861
-54.4367
16
Saut Maripa
Oyapock
French Guyana
OBHI
NO
840
3.8017
-51.8847
ANA
YES
LM
11481
-0.2003
-66.8014
ANA
YES
LM
2512
0.4769
-69.1281
LM
3012
YES
No
17
Curicuriari
Negro
Brazil
Amazonas
18
Uaracu
Negro
Brazil
Amazonas
19
Taraqua
Negro
Brazil
Amazonas
SÃO GABRIEL DA
CACHOEIRA
SÃO GABRIEL DA
CACHOEIRA
SÃO GABRIEL DA
CACHOEIRA
ANA
YES
20
Manaus
Negro
Brazil
Amazonas
Manaus
ANA
YES
21
Careiro
Brazil
Amazonas
Manaus
ANA
YES
LM
22
Jatuarana
PARANA DO CAREIRO
RIO
SOLIMÕES/AMAZONAS
Brazil
Amazonas
ANA
YES
23
Cucui
Negro
Brazil
Amazonas
Manaus
SÃO GABRIEL DA
CACHOEIRA
ANA
YES
0.1303
-67.4614
-2.8633
-59.9731
12400
-2.8044
-58.1669
LM
119270
-2.9478
-58.3219
LM
5043
1.2153
66.8525
24
Sao Felipe
Negro
Brazil
Amazonas
SÃO GABRIEL DA
CACHOEIRA
ANA
YES
LM
25
Los Cerros
Vaupes
Colombia
Vaupes
Mitu
IDEAM
NO
27
Mitu Automatica
Vaupes
Colombia
Vaupes
Mitu
IDEAM
NO
29
Pituna
Cudayari
Colombia
Vaupes
Mitu
IDEAM
30
San Antonio
Vaupes
Colombia
Vaupes
Mitu
35
Nazareth
Amazonas
Colombia
Amazonas
37
Las Mercedes
Caqueta
Colombia
41
Pto Las Brisas
Caqueta
57
Pto Gaitan
Manacacias
58
Camp Yucao
59
8133
0.3717
67.3128
LM - LG
867.87
1.1000
-70.7333
LM - LG
1253.92
1.0833
-69.5000
NO
LM - LG
81.09
1.2167
-70.7167
IDEAM
NO
LM - LG
1128.08
0.8667
-71.0333
Leticia
IDEAM
NO
LM - LG
32814.56
4.1500
-69.9500
Amazonas
Leticia
IDEAM
NO
LM - LG
4781.76
4.1333
-70.0167
Colombia
Amazonas
Leticia
IDEAM
NO
LM - LG
4697.18
1.0333
-74.1667
Colombia
Meta
Puerto Gaitan
IDEAM
No
LM - LG
456
4.3167
-72.0833
Yucao
Colombia
Meta
Puerto Lopez
IDEAM
No
LM - LG
88.4
4.3500
-71.8333
Pte Texas
Meta
Colombia
Casanare
Mani
IDEAM
No
LM - LG
2161
4.4333
-71.9167
60
La Estacion
Cravo
Colombia
Casanare
Nunchia
IDEAM
No
LM - LG
237.24
4.7667
71.6167
61
Pte Carretera5
Cno Duya
Colombia
Casanare
Orocue
IDEAM
No
LM - LG
26.73
4.9500
-70.5500
62
Bonanza
Meta
Colombia
Vichada
La Primavera
IDEAM
No
LM - LG
2858.24
5.1667
-69.1667
63
Santa Maria
Meta
Colombia
Vichada
La Primavera
IDEAM
No
LM - LG
3467.05
5.2667
-69.2833
64
Agua Verde
Meta
Colombia
Vichada
La Primavera
IDEAM
No
LM - LG
3342.74
5.8167
-69.9667
65
Guayare
Guaviare
Colombia
Guainia
Inirida
IDEAM
No
LM - LG
6887.28
0.6494
-67.8167
66
Pto Arturo
Guaviare
Colombia
Guaviare
San Jose del Guaviare
IDEAM
No
LM - LG
1929.91
0.0431
-71.3000
67
Mapiripana
Guaviare
Colombia
Guainia
Guaviare
IDEAM
No
LM - LG
2640.42
0.0467
70.5333
68
Arabia Arrecifal
Guaviare
Colombia
Guainia
Inirida
IDEAM
No
LM - LG
3224.36
3.8833
-67.6000
69
Sta Rita
Vichada
Colombia
Vichada
Santa Rita
IDEAM
No
LM - LG
1038.32
4.8667
-67.9833
70
Pte Lleras
Meta
Colombia
Meta
Puerto Lopez
IDEAM
No
LM - LG
422.91
4.1000
-71.0833
71
Orocue
Meta
Colombia
Casanare
Orocue
IDEAM
No
LM - LG
2902.78
4.8000
-70.6667
72
Patevacal
Meta
Colombia
Vichada
La Primavera
IDEAM
No
LM - LG
4642.41
6.2000
-69.0667
73
Aceitico
Meta
Colombia
Vichada
Pto Carreno
IDEAM
No
LM - LG
4764.24
6.1833
-68.4500
74
Bonanza2
Meta
Colombia
Vichada
La Primavera
IDEAM
No
LM - LG
2858.24
5.1667
-70.8333
75
Agua verde 2
Meta
Colombia
Vichada
La Primavera
IDEAM
No
LM - LG
3342.74
5.8167
-69.9667
76
Roncador
Orinoco
Colombia
Vichada
Pto Carreno
IDEAM
No
LM - LG
15210.8
5.8833
-67.5667
77
Pto Narino
Orinoco
Colombia
Vichada
Santa Rita
IDEAM
No
LM - LG
14574.47
4.9333
-67.8500
78
La Balsora
Guayavero
Colombia
Meta
San juan de Arama
IDEAM
No
LM - LG
565.5
2.5000
-73.9333
79
Cejal
Guaviare
Colombia
Vichada
Santa Rita
IDEAM
No
LM - LG
3854.05
3.9833
-68.3500
80
El Barro
Metica
Colombia
Meta
San Carlos Guaroa
IDEAM
No
LM - LG
153.05
2.7500
-73.1833
81
Rancho Alegre
Orotoy
Colombia
Meta
Castilla la Nueva
IDEAM
No
LM - LG
9.28
3.8667
-73.5333
82
Pte Camacho
Cno Camoa
Colombia
Meta
San Martin
IDEAM
No
LM - LG
1.58
7.7000
-73.7167
83
Bajo Nare
Metica
Colombia
Meta
Puerto Lopez
IDEAM
No
LM - LG
373.24
4.0000
-72.9667
84
Pte Carretera
Guayuriba
Colombia
Meta
Villviciencio
IDEAM
No
LM - LG
157.37
4.0500
-73.7667
85
pte Carretera2
Q. Pipiral
Colombia
Meta
Villviciencio
IDEAM
No
LM - LG
4.28
4.2000
-73.7167
86
Peralonso
Pachaquiar
Colombia
Meta
Villviciencio
IDEAM
No
LM - LG
0.97
4.1000
-73.4500
87
Guacapate
Negro
Colombia
Cundinamarca
Quetame
IDEAM
No
LM - LG
26.47
4.3000
-73.8500
88
El Palmar
Blanco
Colombia
Cundinamarca
Guayabetal
IDEAM
No
LM - LG
47.23
4.2167
-73.8333
89
Caseteja delicia
Negro
Colombia
Cundinamarca
Guayabetal
IDEAM
No
LM - LG
99.68
4.2000
-73.7667
90
Pte Abadia
Guatiquia
Colombia
Meta
Villviciencio
IDEAM
No
LM - LG
93.19
4.2333
-73.6333
91
Palmarito
Cno Chivo
Colombia
Meta
Restrepo
IDEAM
No
LM - LG
0.35
4.2333
-73.5333
92
Pte El Amor
Ocoa
Colombia
Meta
Villviciencio
IDEAM
No
LM - LG
8.14
4.1167
-73.6167
93
Pte Carretera 3
Guacavia
Colombia
Meta
Cumaral
IDEAM
No
LM - LG
52.17
4.3167
-73.4167
94
El Cable
Humea
Colombia
Cundinamarca
Medina
IDEAM
No
LM - LG
126.73
4.3667
-73.2667
95
Pte Carretera 4
Q. Salinas
Colombia
Meta
Restrepo
IDEAM
No
LM - LG
2.28
4.2333
-73.5833
96
Cabuyaro
Meta
Colombia
Meta
Cabuyaro
IDEAM
No
LM - LG
876.25
4.3000
-72.7667
97
Humapo
Meta
Colombia
Meta
Puerto Lopez
IDEAM
No
LM - LG
1531.27
4.3333
-72.3500
98
Pto Inirida
Guainia
Colombia
Inirida
Inirida
IDEAM
No
LM - LG
2926.66
3.8667
-67.9500
99
Pueblo Nuevo
Guaviare
Colombia
Guainia
Barranco Minas
IDEAM
No
LM - LG
3037.41
3.4167
-69.8833
100
Barranco Murciel
Guaviare
Colombia
Guainia
Barranco Minas
IDEAM
No
LM - LG
3097.14
3.5667
-69.6000
101
Venecia
Orteguaza
Colombia
Caqueta
Florencia
IDEAM
No
LM - LG
96.07
1.5833
-75.5167
102
Florencia
Hacha
Colombia
Caqueta
Florencia
IDEAM
No
LM - LG
36.51
1.6000
-75.6000
103
Bocatoma
Paujil
Colombia
Caqueta
Paujil
IDEAM
No
LM - LG
0.28
1.5667
-75.2833
104
Itarca
San Pedro
Colombia
Caqueta
La Montanita
IDEAM
No
LM - LG
79.09
1.5000
-75.4667
105
Bocatoma1
Dedo
Colombia
Caqueta
Florencia
IDEAM
No
LM - LG
0.83
1.6000
-75.6333
106
San Ignacio
Caguan
Colombia
Caqueta
San Vicente del Caguan
IDEAM
No
LM - LG
242.44
2.0667
-74.7667
107
Doncello
Doncello
Colombia
Caqueta
El Doncello
IDEAM
No
LM - LG
1.93
1.0667
-75.2500
108
Puerto Rico
Guayas
Colombia
Caqueta
Puerto Rico
IDEAM
No
LM - LG
194.03
1.9667
-75.2000
109
Puerto Rico 2
Guayas
Colombia
Caqueta
Puerto Rico
IDEAM
No
LM - LG
194.03
1.9667
-75.2000
110
La Quilla
Caguan
Colombia
Caqueta
Cartagena del Chaira
IDEAM
No
LM - LG
1090.33
0.5500
-74.3500
111
Sibundoy
San Pedro
Colombia
Putumayo
San Francisco
IDEAM
No
LM - LG
5.03
1.2000
-76.8833
112
El Eden
Putumayo
Colombia
Putumayo
San Francisco
IDEAM
No
LM - LG
28.83
1.1167
-76.9333
113
Canal B
Quinchoa
Colombia
Putumayo
Santiago
IDEAM
No
LM - LG
13.68
1.1333
-76.9500
114
Canal B2
Quinchoa
Colombia
Putumayo
Santiago
IDEAM
No
LM - LG
13.68
1.1333
-76.9500
115
Pte Canal
San Pedro
Colombia
Putumayo
Colon
IDEAM
No
LM - LG
4.04
1.1833
-76.9333
116
La Joya
Guineo
Colombia
Putumayo
Villagarzon
IDEAM
No
LM - LG
58.61
1.8333
-76.5667
117
Piedra Lisa
Mocoa
Colombia
Putumayo
Mocoa
IDEAM
No
LM - LG
47.16
1.2000
-76.6333
118
Angosturas
Caqueta
Colombia
Putumayo
Caqueta
IDEAM
No
LM - LG
660.09
119
Piedra Lisa II
Mocoa
Colombia
Putumayo
Mocoa
IDEAM
No
LM - LG
660.09
120
Yunguillo
Caqueta
Colombia
Putumayo
Mocoa
IDEAM
No
LM - LG
232.56
1.4000
-76.6000
121
Maria Manteca
Caqueta
Colombia
Amazonas
Miriti - Parana
IDEAM
No
LM - LG
8521.9
-1.4000
-70.6000
122
Sta Isabel
Caqueta
Colombia
Amazonas
Puerto Santander
IDEAM
No
LM - LG
7092.5
-1.1333
-71.1000
123
Pte Cordoba
Caqueta
Colombia
Amazonas
La Pedrera
IDEAM
No
LM - LG
9307.08
-1.2667
-69.7333
124
Bacuri
Caqueta
Colombia
Amazonas
La Pedrera
IDEAM
No
LM - LG
9866.95
-1.2167
-69.4667
125
Villareal
Caqueta
Colombia
Amazonas
La Pedrera
IDEAM
No
LM - LG
6843.22
-1.3000
-69.6167
126
La Mar Balsayaco
Putumayo
Colombia
Putumayo
Santiago
IDEAM
No
LM - LG
5.72
-1.1167
-76.9667
127
Canal
Putumayo
Colombia
Putumayo
Santiago
IDEAM
No
LM - LG
8.19
1.1167
-76.9500
128
Pte Texas
Putumayo
Colombia
Putumayo
Pto Caicedo
IDEAM
No
LM - LG
508.83
0.6000
-76.5667
129
La Chorrera
Igara Parana
Colombia
Amazonas
La Chorrera
IDEAM
No
LM - LG
318.56
-1.4333
-72.7500
130
Tarapaca
Putumayo
Colombia
Amazonas
Tarapaca
IDEAM
No
LM - LG
7068
-2.8667
-69.7333
0.3833
1.2
-76.3500
-76.65
3.1.1.2 Virtual gauge stations
The database also comprises 21 virtual stations at the upper Negro river basin, 14
located along the river Negro main stream and between Cucui and Serrinha flow
stations and 7 along the Vaupes river from the Uaracu station to the confluence with
Negro Main steam (Figure 3).
The rating curves generated trough radar altimetry and flow routing modelling
reported by León et al. (2006) provide an estimate of river discharge with an error of
less than 10% (mean absolute difference of water depth of less than 1.1m between
modelled and observed water depth), which indicates a god reliability of the method
described. The use of these techniques and their further improvement holds a great
potential for the estimate of river discharge in large rivers lacking of in situ data in the
Amazon basin.
Figure 3 presents the location of virtual gauge stations and real gauge stations (used
for calibration) in the Negro basin and Table 2 presents the main characteristics of the
virtual stations.
Figure 3. Virtual gauge stations in the Negro basin (blue flags) and real gauge
stations used for calibration and hydrodynamic modelling.
Figure 3. 21 virtual stations located in the Negro River basin Amazon. Red place marks indicate
the location of real flow stations used for validation. Blue flags represent the virtual stations
retrieved from radar Altimetry.
Table 3. Virtual stations from radar altimetry and routing modelling. Negro basin – Brazilian
part of the OTCA area. Adpated from León et al (2006)
Station
River
Latitude –
longitude
Type of
data
Dry/wet
season
crosssection
width (km)
T493_1
T89_22
T536_1
T536_2
T536_3
T536_4
T89_26
T994_1
T493_2
T450_1
T951_1
T254_22
T908_1
T407_1
T121_1
T178_7
T622_1
T579_1
T78_1
T35_1
T536_5
Negro
Negro
Negro
Negro
Negro
Negro
Negro
Negro
Negro
Negro
Negro
Negro
Negro
Negro
Uaupes
Uaupes
Uaupes
Uaupes
Uaupes
Uaupes
Uaupes
0.87, -66.89
0.91, -67.00
0.92, -67.19
0.72, -67.23
0.60, -67.26
0.37, -67.31
0.09, -67.29
-0.23, -66.73
-0.33, -66.62
-0.32, -66.03
-0.31, -65.91
-0.24, -65.81
-0.37, -65.32
-0.41, -65.15
-.43, -68.94
0.43, -68.89
0.35, -68.75
0.12, -68.16
0.11, -68.09
0.11, -67.45
0.09, -67.36
ENVISAT
T/P
ENVISAT
ENVISAT
ENVISAT
ENVISAT
T/P
ENVISAT
ENVISAT
ENVISAT
ENVISAT
T/P
ENVISAT
ENVISAT
ENVISAT
T/P
ENVISAT
ENVISAT
ENVISAT
ENVISAT
ENVISAT
1.72/2.23
1.4/2.08
0.76/1.29
1.02/1.98
0.98/2.19
1.06/2.19
0.8/0.84
1.12/1.52
2.16/2.48
3.65/3.65
1.81/2.06
2.72/7.70
2.91/2.91
2.44/2.44
0.8/1.29
0.98/0.98
1.06/1.69
1.21/2.64
1.42/1.42
0.89/1.41
1.02/1.34
Discharge(m3 s1
) measured by
ADCP in
05/2005
7071
7071
7623
8582
8647
11,625
12,524
18,590
18,569
20,361
20,445
21,841
22,388
23,460
No_data
No_data
No_data
4850
4791
5190
5204
Average
water
depth(m)
by ADCP
in
05/2005
8.23
8.4
10.18
11.43
9.32
12.24
11.95
12.92
11.49
7.58
11.1
11.48
12.44
11.78
No_data
No_data
No_data
5.48
6.13
8.98
10.6
Upstream In
situ station
distance(km)
Cucui(47)
Cucui(60)
Cucui(85)
Cucui(113)
Cucui(128)
Sao_Felipe(0)
Sao_Felipe(33)
Curicuriari(10.6)
Curicuriari(26.5)
Curicuriari(100)
Curicuriari(114)
Curicuriari(126)
Curicuriari(188)
Curicuriari(207)
Uaracu(50.3)
Uaracu(57)
Uaracu(80.4)
Taracua(60)
Taraqua(69)
Taraqua(160)
Taraqua(168)
1.3 EVIDENCE FOR MAXIMUM PEAKS, MINIMUM BASEFLOWS AND
FLASHINESS CHANGE FROM FLOW DATA IN THE REGION
This section looks into the evidence from literature reviews, on the potential changes
on Amazon River discharges as a result of natural climate variability or large scale
deforestation in the basin.
1.3.1 River flow changes from climate variability
It has been widely reported that the discharge of many tropical rivers is affected by
decadal and inter-decadal climate variability such as ENSO phenomena (El Niño
Southern Oscilation – drier years) and La Niña (wetter years) (Sternberg 1987;
Amarasekera et al 1997; Costa et al 2003; Marengo et al 2005; Williams et al 2005;
Schöngart and Junk 2006;).
In the Amazonia such impacts have likely been observed since the last tow millennia
producing a dynamic of intensive drought and floods, which have determined the life
stile of riverside communities (Meggers 2005).
However, the longest river stage record in the Amazonia at Manaus – Negro River
(Figure 4), only goes back just over a 100 years, starting in 1903, to be used to
confirm impacts of such Mega-Niño events on flows. Nonetheless, these time series
have allowed the study of some of the most severe droughts from ENSO in the region
over the last century (for the years 1912, 1926, 1983, 1997).
Particularly the most severe drought recorded at Manuas in 1926 indicates that ENSO
phenomena can produce deficits of annual river discharge of up to 30 to 40% in base
flows and as much as 50% of the peak discharges (The annual maximum peak water
stage dropped 6m, from 27.8m to 21.8m) (Figure 5) (Carneiro 1957; Sternberg 1987;
Richey et al. 1989; Williams et al 2005). In addition, drought from ENSO coincides
with annual rainfall deficits in western Amazon covering areas in the countries of
Brazil, Venezuela, Peru, Ecuador and Colombia, which in 1926 were of up to 40% of
annual rainfall, while rainfall surplus are observed in northeast Brazil (Williams et al
2005) (Figure 6).
Exceptionally low Sea Surface Temperatures in the northern boundary of South
America alongside rainfall deficits and drought causing soil moisture depletion and
widespread fires have been blamed to have increased the magnitude of drought
(Williams et al. 2005).
Figure 4 shows a map of the Amazon basin highlighting the river basin upstream
Manaus, Figure 5 shows the annual time series of maximum peak flows and base
flows of the Amazon at the confluence with the Negro river and Figure 6 shows the
rainfall deficits and surplus in western and northeast Amazonia respectively.
Figure 4. Amazon basin, with the drainage area at Manaus darken. Source of diagram: William
et al (2005).
Figure 5. A century of river stage records at Manuas – Brazil over the Negro River. Top trend
indicates the annual maximum river stage at Manaus in which the pronounced drop in water
levels can be observed for 1926. The bottom trend indicates the minimum annual river stage in
which paradoxically the lowest observation is observed in 1963 and not in 1926, the minimum
base flow in this year is amongst the lowest in the whole record. Source of diagram: William et al
(2005).
Figure 6. Rainfall deficits in western Amazon upstream Manuas (1926). Data was collected from
rainfall stations available during the 1920 – 1930 period. An east west dipole is observed
indicating greater deficit upstream Manaus in West Amazon, less pronounced deficit in South
west Amazon and surplus to the east of the Amazon basin. Source of diagram: William et al
(2005).
Strong drought events in the Amazon have also been reported in association to other
climate variability phenomena different from El Niño. The Amazon drought in 2005,
the worst over the last 100 years over south-western Amazonia, according to Marengo
et al (2005) was attributed to:
“Exceptionally warm sea surface temperatures of the Tropical North Atlantic,
anomalous lower intensity in northeast wind moisture transport into southern
Amazonia during the summer season and the weakened upward motion over that part
of the Amazonia, resulting in reduced convective development and rainfall”.
In addition, low humidity, warmer air temperature (from 3 to 5 C°) and increased
number of forest fires are blamed to have exacerbated the drought (Marengo et al
2005).
The consequences of the Amazon drought 2005 were catastrophic for riverside
communities in South-West Amazon, such as Iquitos, whose economies, transport
systems and access to health care systems, among other aspects, depend upon the
normal flow regimes of the Amazon tributaries (particularly Napo, Ucayali,
Purtumayo, Salomoes, among other rivers).
The characteristics of the previous climatic events in the Amazon allow speculating
that ongoing climate change might change the magnitude of decadal or inter-annual
climate variability phenomena throughout the basin exacerbating their impacts.
The study of ENSO impacts for the whole basin is limited by the scarcity of
information and the research gaps in countries of the OTCA area other than Brazil.
Thus the studies available reviewed have been overwhelmingly large scale (basinscale) and point studies.
Finally, to confirm speculations on the impacts of climate change potentially
worsening the impacts of climate variability phenomena on river flows, the use of
improved and more powerful models, especially macroscale and mesoscale AGCM
models as well as more detailed and accurate datasets, especially rainfall in order to
reduce the uncertainty in the estimation of the basin water balance, are fundamental.
Table 3. presents some of the main studies referring to ENSO phenomena impacting
river flows in the region.
Table 4. Studies reporting the magnitude of ENSO events in the Amazon basin.
Reference
Type of analysis
River
side
community
Manaus
–
Brazil
William et
al (2005).
Comparison
of
river stage levels
and rainfall time
series
Sternberg
(1987)
Statistical analysis
of river stages
Manaus
Brazil
–
1903
1985
Marengo et
al (2005)
Analysis of climate
data (rainfall, air
temperatures)
Drought 2005
Solimoes and
Madeira rivers
2005
Richey et Analysis of river Manaus
discharge
time
al. 1989
series
Period of
analysis
1920
1930
1903
1985
-
-
Key findings
1926 – Niño year of most severe
drought of the last century in
west-central Amazon.
Rainfall deficits of up to 50%.
Flow deficits down to 40%.
Rainfall surplus to the northeast
of the basin.
Almost statistically significant
upward trend of minimum base
flows at Manaus over the period
1903 - 1985
Causes of drought were not
related to El Niño but to warmer
tropical Atlantic.
/Humidity was lower than
normal and air temperatures
higher (3 to 5 C°).
There is statiscally significant
change of the river discharge
over the period with interannual
variability occurring on periods
between two and three years.
1.3.2 River flow changes from Land cover change
Land use and land cover changes upstream the river basin, river damming and flow
diverting for irrigation purposes are human interventions affecting river discharge,
maximum peak and minimum base flows (Costa et al 2003). Moreover, Land cover
changes might affect climate and consequently the hydrological cycle as already
informed for some largest catchments such as Yangtze, Mekong and Mississipi
(Charney et al., 1975; Williams and Balling 1996; Yin and Li, 2001; Goteti and
Lettenmaier, 2001; Yang et al., 2002; D’Almeida et al 2007). Moreover, conversion
from forest to pasture changes significantly soil infiltration dynamics and the way
water reaches the river streams as well as weakens the ecosystem ability to tap up
significant amounts of water from deep soils (down to about 20m) with implications
on evaporation, cloud formation and rainfall (Sternberg 1987; Moraes et al 2005).
1.3.2.1
Amazon deforestation: background
In the Amazon deforestation initially greatest in lower Amazonia has widespread
further up the river basin towards the Andes including the countries of Colombia,
Ecuador, Peru and Bolivia. Large population increase, roads, oil pipelines
construction in Ecuador, Illicit crops phenomenon and oil palm cultivation in
Colombia, and soybean cultivation in Matogrosso - Brazil have been the main drivers
to widespread deforestation recently (Gentry and Lopez-Parodi 1980; Bubb et al
2005; NASA 2006). Since the early 1970s to the early 1990s the loss of tropical forest
in the region might have gone up to 10% of the basin area (Gentry and Lopez-Parodi
1980; Fearnside 2001; Laurance et al., 2001; NASA 2005; INPE 2005),
However, new remote sensing technologies such as MODIS (Moderate Resolution
Imaging Spectroradiometer) derived maps of Vegetation Continuous Fields (VCF) are
helping to successfully mitigate deforestation in many parts of the basin. Because,
these technologies have allowed the implementation of rapid detection deforestation
observatories at regional scales to subsequently help focus local forest loss mitigation
efforts in the basin (Figure 7) (Townshend et al 1999; Zeng 1999; Zhan et al 2000;
Ichii et al 2003; Hansen et al 2003; NASA 2005; Mulligan and Burke 2005a;
Mulligan and Burke 2005b).
Figure 7. NASA’s terra satellite image of the Moderate Resolution Imaging Spectroradiometer,
(MODIS) providing month to month of changes in vegetation status across the Amazon.
Schematic. Source of diagram: Stephen Cole, Goddard Space Flight Center, NASA (2007)
1.3.2.2
Effects of forest conversion on flow regimes in the Amazon
basin
- Studies of river stage records
Not many studies report the analysis of change in river flows as an effect of
deforestation in the OTCA area apart from some insightful examples at the Amazon
scale reported at Manaus – Brazil, Iquitos – Peru and the Tocantins basin in Brazil.
The scarcity of long term high resolution rainfall and river discharge records
coinciding with reliable land cover change analysis over the same periods, at the
country scale, has limited the scope of this type of research.
In this sense, conclusions from the literature on the impacts of deforestation on river
flows do not totally agree. While, Sternberg (1987) report no discernable trends
towards higher flood peaks at Manaus during the period 1903 – 1955, Gentry and
Lopez-Parodi (1980) and Costa et al. (2003) inform the increase in the duration of
floods with deforestation.
For instance, Gentry and Lopez-Parodi (1980), attributed higher flood crests with
almost constant base flows of the Amazon river at Iquitos – Peru (Figure 8a) during
the 1970s decade, compared to the 1960s decade, to greatly enhanced patterns of
deforestation in the upper parts of the river basin in Peru and Ecuador (Figure 8b).
Whereas, Nordin and Meade (1982) attributed the changes to normal climate
variability. Yet river stage changes due to alterations of streambed at Iquitos (Figure
9a), higher rainfall patterns and carry over storage effects from one year to the next
should be considered in these analyses (Nordin and Meade 1982).
b
a
Figure 8. a. Map of Peru with the localization of Iquitos in the Amazon river and rain stations
considered for analysis by Gentry and Lopez-Parodi (1980). b. Linear regressions of maximum
peak flows (Top) and minimum base flows (bottom) at Iquitos, indicating an upward trend for
Maximum peak flows and no change in minimum base flows from the 1960s to the 1970s
respectively. Source: Gentry and Lopez-Parodi (1980).
Figure
9.
a.
Relation between
stage and river
discharge
at
Iquitos. b. Peak
stage records from
1942 to 1980 at
Iquitos.
Source:
Nordin and Meade
(1982).
In a similar way, Sternberg (1987) points out the limitations of these analyses based
upon river stages records only because of the uncertainties arising from inter-annual
climate variability (Figure 10a and Figure 10b). Nonetheless, Sternberg (1987) points
out a potential upward trend of river stage of the Amazon river at Manaus due to
deforestation over the period 1903 – 1985 considering that the change in the slope of
the low water stage record was almost statistically significant (Figure 10b).
In addition, Chu et al. (1994) also points out the significant uncertainty in the absolute
amounts of rainfall over the contributory watershed at Taperinha – Brazil to achieve
consistent rainfall – runoff relationships. Furthermore, Harden (2006) stating the
impacts upon fluvial systems at local scales due to reduced storage capacity of
Andean landscapes of Ecuador, mainly due to paramo removal, still points out the
limitations to extrapolating these impacts at regional contexts in which geomorphic
adjustments play a role.
b
a
Figure 10. a. Study area, Manaus location and main features on the Salamoes river. b. Maximum
peak stages, mean and minimum stages in the Negro river at Manaus with liner regression
indicating the trend of change over the period 1903 – 1985.
- Comparison of rainfall – runoff ratios in the Amazon basin
Perhaps the most comprehensive study looking at the impacts of deforestation on river
flows in the Amazon basin at large scale is that of Costa et al (2003), which
considered rainfall - runoff ratios to ovoid potential effects of climate variability upon
rainfall, which could affect flow regimes in the basin.
By considering a 50 years time series (1949 - 1998) of river flows at Porto National
station of the Tocantins river (drainage area of 175360 km2 in southeast Amazon),
rainfall data from the New et al (2000) dataset and land cover change from two
periods of great contrasts in deforestation extent (1949 to 1968 with 30% of
deforestation and 1979 to 1998 of 50% of deforestation (Figure 11)), Costa et al
(2003), informs 24% and 28% increase in mean annual river discharge and maximum
peak flows for the period of greater deforestation (1979 – 1998) when compared to
the initial period (1949 - 1968). This conclusion was backed by the statistically
significant increase in the rainfall – runoff ratio (from 0.237 to 0.285 respectively),
with negligible rainfall changes between the two periods (Figure 12).
Figure 11. Location of the study area of the study reported by Costa et al (2003). Schematic only .
To be from diagrams produced in the project.
Figure 12. Seasonal increase in river discharge for the Tocantins river - Brazil. Bottom curve
indicates indicates the period (1949 - 1968) and Top curve indicates the period (1969 - 1978)
cisnistently shwing higher river discharge and early monthly flood peak for the period of greater
forest loss.
This means that land cover changes could have already affected the long-term and
seasonal maximum and mean flows of the Tocantins river. However, the rainfall
dataset (New et al 2000) still remains as a source of uncertainty due to its coarse
nature and thus important rainfall contributing areas might not be well distinguished
(D’Almeida et al. 2007).
- Comparison of rainfall – runoff ratios: small scale studies
At small catchment scales (1h) some studies have reported changes in rainfall – runoff
ratios due to land use changes in the Amazon basin. Chaves et al (2007) in a paired
catchments study in Rancho Grande, Rondonia, studying the impacts on flow regimes
of the conversion from forest to pasture, reports the increase in surface stream flow
from 0.8% to 17% of rainfall from a forest to a pasture site, which was attributed to
changes in soil hydraulic conductivity leading to more frequent overland saturation in
pasture soils (Biggs et al. 2006). These results coincide with similar studies reported
for the Amazon states of Para (Moraes et al. 2006) and Manaus (Troncoso et al 2007)
These studies highlight the potential impacts of forest conversion to pasture on the
regularization of floods, hydrological budgets disruption and sedimentation at larger
scales in the Amazon.
No studies referring to the change in peak flows, mean and minimum base flows are
reported for flow stations in the Amazon tributaries in the countries of Colombia,
Ecuador, Venezuela and Bolivia and again most studies have been single point at very
few locations in the Amazon basin.
Table 4 presents the main studies referring to changes in flow regimes from land
cover disturbances in the OTCA area.
Table 5: Studies reporting change in river and stream flows from land cover change in the Amazon
basin.
Reference
Type of analysis
Costa et al
(2003).
Statistical analysis
of flow records,
rainfall
surfaces
(New et al (2000)
dataset and land
cover change.
Paired catchments
Study. Small scale
(1h).
Chaves et
al (2007)
River side
communit
y
Manaus –
Brazil
Period of
analysis
Key findings
1949
1998
-
Annual river flows and rainfall –
runoff ratios increase significantly
with the conversion of forest to other
land uses. Not statistically significant
change in rainfall patterns is
observed over the period.
Increase in surface stream flow from
conversion of forest to pasture,
which can
potentially affect
hydrological budgets at larger scales
in the Amazon
Potential
impacts
of
forest
conversion to pasture on the
regularization of floods in wet
seasons and drought in dry season at
large scales in the Amazon.
Not significant increase of rainfall.
Increase in high river stages while
low stages remained constant.
Effects attributed to deforestation
Changes in river stage at Iquitos
could have been the result of bank
erosion.
Changes might respond to decadal
climate
cycles
rather
than
Rancho
Grande,
Rondonia
Troncoso et
al (2007)
Paired catchments
Study. Small scale
(1km2).
Manaus
Brazil
-
Gentry and
Lopez
–
Parodi
(1980)
Nordin and
Meade
(1982)
Analysis of river
stage
data
at
Iquitos as well as
rainfall
Constesting
to
Gentry and Lopez
– Parodi (1980)
Iquitos
Peru
-
1962
1978
-
Iquitos
Peru
-
1942
1980
-
deforestation.
1.4
REVIEW OF HYDROLOGICAL MODELLING DONE AT THE
AMAZON SCALE
A set of modelling studies have been developed to improve our understanding of the
impacts of deforestation on the hydrology of the Amazon. These studies have varied
from macroescale (> 105 km2) to mesoescale (102 - 105 km2) as well as one
dimensional models (Single Column Models - SCM).
1.4.1 Macroescale models
These models of coarse scale nature applied at the Amazon scale implement AGCM
(general Circulation Models of the Atmosphere) and the comparison between
scenarios of baseline forest and extreme deforestation.
Main findings indicate an overall decrease of water resources in the Amazonia, due to
reduced evapotranspiration from deforestation. If deforestation overtakes a given
deforestation threshold it could lead to a significant drop in rainfall and runoff
because of the significant rainfall recirculation in the basin (D’Almeida et al 2007;
Franken and Leopoldo 1995; Salati and Nobre 1991; Laurence and Williamson 2001).
Table 6 presents a descriptive summary of the main macroescale models applied at
the Amazon scale.
In addition, large scale atmosphere circulations and thus water and energy cycles as
well as sensible to latent heat flux could potentially be affected by deforestation and
forest fragmentation due to decline in surface roughness length and increase in
Albedo leading to reductions in surface net radiation (Eltahir 1996; Berbet and Costa
2003; Nobre et al. 1991; Roy and Avissar 2002; Durieux et al. 2003; Voldoire and
Royer, 2004; D’Almeida 2007). Table 6 presents a list of the main macroscale
modelling efforts carried out in the Amazon.
Table 6. Main examples of hydrological modelling at the Amazon scale. Macroscale models.
Adapted from D’Almeida et al. (2007)
Reference
Lean and
Warrilow, 1989
Nobre et al.,
1991
HendersonSellers et al.,
1993
Lean and
Rowntree, 1993
Dirmeyer and
Shukla, 1994
Polcher and
Laval, 1994a
Polcher and
AGCM
Resolution
(lat × lon)
Simulation
(months)
Change in
Precipitation
(mm/d)
Change in
Evapotranspiration
(mm/d)
Change
in
Runoff
(mm/d)
Change
in T
(°C)
UKMO
2.5° × 3.75°
36
−1.43
−0.85
−0.40
2.4
NMC
2.5° × 3.75°
12.5
−1.76
−1.36
−0.40
2.5
CCM1
4.5° × 7.5°
72
−1.61
−0.64
−0.90
0.6
UKMO
2.5° × 3.75°
36
−0.81
−0.55
−0.20
2.1
COLA
4.5° × 7.5°
48
LMD
LMD
2.0° × 5.6°
2.0° × 5.6°
13.5
132
−0.51
0.24
−0.31
1.08
−2.07
−0.35
−0.16
0.02
2
3.7
3.8
0.14
Laval, 1994b
Sud et al., 1996
Manzi and
Planton, 1996
Lean et al., 1996
Lean and
Rowntree, 1997
Hahmann and
Dickinson, 1997
Costa and Foley,
2000
Kleidon and
Heimann, 2000
Voldoire and
Royer, 2004
36
−1.48
−1.22
2.8° × 2.8°
2.5° × 3.75°
50.5
120
−0.40
−0.43
−0.31
−0.81
0.33
0.39
−0.50
2.3
HC
2.5° × 3.75°
120
−0.27
−0.76
0.51
2.3
CCM2
2.8° × 2.8°
120
−0.99
−0.41
−0.50
1
GENESIS
4.5° × 7.5°
180
−0.70
−0.60
−0.10
1.4
ECHAM4
5.6° × 5.6°
240
−0.38
−1.30
ARPEGE
2.8° × 2.8°
360
−0.40
−0.40
GLA
4.0° × 5.0°
EMERAUDE
HC
−0.26
2
0.92
−0.01
2.5
−0.01
1.4.2 Mesoscale models
They are used to model deforestation impacts upon atmospheric circulations at finer
scales (D’Almeida 2007; Roy and Avissar 2002).
In the Amazon these models predict the alteration of intensity and distribution of
precipitation as well as the increase in the seasonality of clouds in areas of high
deforestation extent (Shu et al. 1994; Avissar and Liu, 1996; D’Almeida 2007; NASA
2004). These effects are attributed to disruption of atmosphere circulations due to
induced heterogeneities and gradients on the convective Boundary layer depth, soil
moisture, surface temperatures and sensible to latent heat flux (Chen and Avissar,
1994; Roy and Avissar 2002; Durieux et al. 2003).
In addition, it has been suggested that changes in cloud cover are significant for
seasonal and diurnal distributions in areas of large forest conversion to pasture
(Durieux et al 2003; Roy and Avissar 2002; NASA 2007). Lower level clouds are
observed in early afternoons and less convection at night and early morning in the dry
season, while convective cloudiness increases over deforested areas at night in the wet
season (Roy and Avissar 2002; Durieux et al. 2003). Though, the wind field in some
areas might disperse partially the impacts of these factors (Pielke et al., 1991;
D’Almeida et al. 2007).
Overall, results from mesoscale models might vary depending upon climatic
conditions of different areas. Therefore, while Eltahir and Bras (1994) report weaker
impact (reduction) of deforestation on the water cycle in west-central Amazonia,
Chou et al. (2002) informs stronger effects.
Table 7 Presents a list of the main mesoscale modelling efforts carried out in the
Amazon.
Table 7. List of the main mesoscale models applied in the Amazon. Table adapted from
D’Almeida et al (2007)
Reference
Mesoscale
Resolution (km
Simulation
gris
Key findings
model
× km)
(days)
Eltahir and Bras, 1994
MM4a
50 × 50
Silva Dias and Regnier,
1996
RAMSb
4
Dolman et al., 1999
RAMSb
20 × 20
16 (4, 1) × 16 (4,
1)e,
60 (20) × 60 (20)d
4
10 °S, 60
°W
10.5 °S, 62
°W
Wang et al., 2000
MM5V2c
12 (4) × 12 (4)e
6
11 °S, 63
°W
Baidya Roy and Avissar,
2002
RAMSb
16 (4, 1) × 16 (4,
1)e
1
10°S, 62.5
°W
Tanajura et al., 2002
ETA/SSiBd
80 × 80
30
22 °S, 60
°W
Weaver et al., 2002
ClimRAMSb
2
10 °S, 62
°W
93
16 (4, 1) × 16 (4,
1)e,
16 (4, 2) × 16 (4, 2)e,
16 (4, 4) × 16 (4, 4)e
Center
6.5 °S, 67.5
°W
Less rainfall, less
evaporation
Greater vertical
motion
Deeper convective
layer
More convection
during
dry-season
More convection
triggered by surface
heterogeneity
Less rainfall, less
evaporation
Effects predicted
depend
on correct model
configuration
1.4.3 Single Column models
Results from Single Column Models (SCM) sometimes differ from observations due
to the lack of the ability to consider horizontal discontinuities such as thermal
instabilities. In the Amazon, the use of SCM to model the Continuous Boundary
Layer (CBL) (in Rondonia) underestimates in situ observations (D’Almeida et al.
2007).
Nonetheless, results from these models indicate greater precipitation over forested
areas in Amazonia due to greater evapotranspiration flux (Rocha, et al. 1996;
D’Almeida et al. 2007).
Table 8 presents a list of the main Single Column Models implemented in the
Amazon.
Table 8. List of the main Single Column Models implemented in the Amazon. Adapted from
D’Almeida et al (2007).
Reference
SCM
da Rocha et al.,
1996
SiB-1D a
Fisch et al., 1996
CBL
typee
Dolman et al.,
1999
MESONHg
Study sites
2°19’S,
60°19’Wb;
2°57’S, 59°57’Wc;
10°45’S, 62°22’Wd
10°05’S,
61°55’Wf;
10°45’S, 62°22’Wc
10°05’S,
61°55’Wf;
Simulation
(h)
Period of
simulations
Key findings
52
Jul-93
More convection over
forest
9
Jul-93
Deeper CBL over pasture
12
Aug-94
Deeper CBL over pasture
10°45’S, 62°22’Wc
1.4.4 Modelling – observational studies for river discharge
Radar altimetry coupled with routing modelling and field commissions in order to
estimate river discharge in un-gauged parts of Large Amazon tributaries has been an
area of significant advances and great potential for operational hydrology.
León et al. (2006) using ENVISAT and TOPEX Poseidon radar data reports the good
agreement between river stage and discharge with regard to field observations for 21
virtual stations in the Negro basin (more than 600 km upstream Manaus) along the
Negro and Vaupes Amazon tributaries. In addition, Zakharova et al. (2006) also
reports the use of TOPEX Poseidon to estimate river stage and discharge in three
places along the river : Manacapuru (Solimoes river), Jatuarana (Solimoes and Negro
river) and Obidos (Amazon) informing good agreement.
In that direction, Frappart, et al. (2006) recommends the combination of different
radar products such as ENVISAT and TOPEX as the best alternative to assemble
robust datasets for operational hydrology. Though, Frappart et al. (2005) points out
the limitations, which still remain from using these technologies in aspects such as the
establishment of a direct relation between flooded extent and floods volume in the
Amazon due to the highly variable topography and distribution of flood plains.
Table 9 summarizes the main studies reporting radar altimetry techniques as well as
hydrological modelling to improve the monitoring of river discharges in the basin.
Table 9. Presents a list of the main studies reporting radar altimetry coupled with hydrological
modelling.
Reference
Type of analysis
Scale of analysis
Leon et al
(2006)
Radar
altimetry
processing (ENVISAT,
TOPEX Poseidon)
flow routing modelling
Radar
altimetry
processing
(TOPEX
Poseidon)
Empirical estimates of
River discharge
Negro river basin
Zakharova
et al 2006
Frappart, F.
(2006)
Radar
altimetry
processing (ENVISAT,
Manacapuru
(Solimoes river),
Jatuarana
(Solimoes
and
Negro river) and
Obidos
(Amazon)
Tabatinga, River
negro basin at
Manaus,
Salimoes Negro
confluence and
Tapajos river
Period
of
analysis
2005
1992 –
2002
2003
2004
-
Key findings
Rating curves validated for 21 virtual
stations in the Napo river.
Good agreement between modelled and
measured water depths and discharges.
Successful production of rating curves
between river levels derived from radar
altimetry and in situ measurements,
which can be used to estimate
successfully river discharge at ungauged large rivers
The Ice 1 re-tracking algorithm
performs best to estimate river stages
with ENVISAT RA-2 data with errors
not superior to 0.5 m. Accuracies are
from two to three times better than
those for TOPEX.
Combination of different radar
altimetry products is essential to
assemble robust datasets for operational
Frappart, et
al (2005)
Radar
altimetry
processing
(SAR,
JERS-1 and TOPEX)
Negro river at
Manaus
1995
1996
-
hydrology.
Delineation of flood plain extent for
high and low stages.
No direct relation between flooded
extent and floods volume observed,
attributed to highly variable topography
and distribution of flood plains.
1.4.5 Monitoring of vegetation as modelling input to
understanding climate dynamics in the Amazon
Deforestation could potentially affect the lands ability to absorb carbon dioxide,
threatening also the natural flow regimes of the Amazon river and its tributaries,
intimately linked to the daily life of riverside communities (Vorosmarty et al., 1989;
Salati and Nobre, 1991; Victoria et al. 1991; D’Almeida et al 2007; Eltahir and Bras,
1994; Trenberth, 1999; D’Almeida et al 2007; Bruijnzeel 2004; CSIRO Australia
(2007, May 11)).
Poveda and Salazar (2004) studying the Space-time variability of NDVI throughout
the Amazon basin report a reasonable well defined pattern of distribution of NDVI for
wet and dry periods. They also point out the increase in NDVI Variability in wet
periods (Niña events), compared to dry periods (El Niño events). These analyses
improve the knowledge on Hydro-ecological processes as well as provide rules for the
spatial scaling of ecosystems response to drought throughout the basin (Poveda and
Salazar 2004; Wittmann, et al 2004; NASA 2007). Table 10 present a list of some of
the most relevant studies looking at the forest responses to drought from NDVI
estimates.
Table 10. Presents a list of the main studies reporting vegetation and climate dynamic
interactions
Reference
Type of analysis
Poveda and
Salazar
(2004)
Space-time
of NDVI
Wittmann,
et al (2004)
Forest and climatologic
disturbances
NASA
(2004)
Deforestation
and
climate implications
Use of TRMM
NASA
(2005)
Field measurements
Effects of drought on
forest
NASA
Deforestation
variability
and
Scale
of
analysis
Amazon
basin scale
Period of
analysis
1981
2002
Tefe
and
Manaus
–
Brazilian
Amazon
Area
of
Porto Velho
2000
Amazon
Tapajos
national
forest
2005
-
2000
2001
-
Key findings
Well defined pattern of wet and dry
period for the distribution of NDVI.
NDVI increases in wet Niña events.
Hydro-ecological processes can be better
understood through spatial scaling of
ecosystems response to drought and
surplus of water
Tree species richness are well defined
with gradients of flooding and
sedimentation
Deforested areas warm up faster
increasing cloudiness and rainfall in dry
seasons. Annual effects are likely to be
small compared to seasonal and daily
cycles.
Collaboration with the LBA. Stress
related signals due to drought are reported
to monitor of forest health state from
space.
Climatic models using MODIS data
(2006)
climate implications
Huete, et al
(2006)
Field observations
soil moisture modelling
impacts of dry seasons
on
rainforest
productivity
Amazon
scale
2006
suggest forest conversion to crops derives
warmer and drier conditions.
Normal dry seasons is a period of higher
greening up of forest.
forest roots (down to 20m) allow forest to
tap up storage water not accessible to less
stature vegetation
1.4.6 Modelling of flows and soil hydrology
Data gathering efforts and hydrological modelling in the Amazon river have been
used to provide a forecast of river discharge and stage to help plan human activities
such as navigation, agricultural production and support flood warning systems, among
other purposes.
Elias (1983) reports the hydrological model PLUMUS, which was coupled with the
Muskingum routing method in order to forecast river stages and flows for navigation
and assist flood warning systems in Manaus and Obidos. In the same way the Woods
hole research centre (2005) in collaboration with the University of Viçosa in Minas
Gerais has gathered a large number of datasets and produced a comprehensive model
of the Amazon river and floodplain to help understand the flow dynamics of the river
system and its variation to climate change.
Even though, many initiatives have been carried out to improve estimates from river
discharge modelling, there is still a great deal of uncertainty on producing accurate
water balances in some parts of the basin, which could be mainly attributed to the
uncertainties associated with the accuracy of distribution and detail of rainfall datasets
available as well as the lack of good soils data at the basin scale to improve the
modelling of evapotranspiration dynamics.
1.5 New hydroelectric projects proposed in the OTCA area
Dam name
San Antonio
Country
Brazil
River
Madeira
State
Rondonia
Municipality
Porto Velho
Jirau
Brazil
Madeira
Rondonia
Porto Velho
Sao Luis
Brazil
Tapajos
Para
Sao Luis
Belo monte
Brazil
Xingu
Para
Belomonte
Babaquara
Brazil
Xingu
Para
Altamira
Bela Vista
Brazil
Xingu
Para
Bela Vista
Pimental
Brazil
Xingu
Para
Belo monte dam system in the
Xingu river ( Belomonte 2007).
Sumapaz
(dam
about
25km outside the
OTCA
area,
though being a
water contributor
area)
Amazon
Colombia
Cundinamar
ca / Meta
Future expansion of the Bogotá
aqueduct with a potential water
flow of about 16 m3 s-1 from the
Sumapaz Paramo.
Para
Plan of the 1960s to dam the
Amazon river, with a potential
of 80000 MW of generation
capacity, 190000 km2 of
reservoir area and 64 Km dam
wall (McCully 2001)
Brazil
Amazon
Key characteristics
Projects not licensed yet due to
threats to endemic catfish in the
basin (Manyari and Carvhalo
2007).
Projects not licensed yet due to
threats to endemic catfish in the
basin (Manyari and Carvhalo
2007).
9000 MW of installed capacity (
Switkes 2007).
11182 MW. First of a series of
dams in the Xingu river. The
dam would displace a bout
16000 people including 450
indigenous people (Belomonte
2007; Switkes 2007).
Dam not funded by the World
Bank
since
indigenous
communities put pressure on the
project
preventing
its
construction (McCully 2001).
Belo monte dam system in the
Xingu river ( Belomonte 2007).
1.6 Current hydroelectric projects in the OTCA area
Dam
name
Count
ry
River
State
Municipality
Yea
r
Key characteristics
Tucurui
Brazil
Tocantins
Para
Tucuri
198
4
Isamu
Ikeda
Brazil
Tocantins
Tocanti
ns
Ponte Alta
do Tocantins
198
2
Serra
Da
Mesa
Brazil
Tocantins
Goias
Campinacu
199
6
Balbina
Brazil
Jatapu
Amazon
Jatapu
Guri
Vene
zuela
Caroni
Bolivar
Bolivar
197
8/1
986
Dam generates 7920 MW and has an
area of 2435km2 covering tropical rain
forest
and
affecting
riverside
communities. Population of Tucuri
increased from 10000 in 1970 to 88000
nowadays
stimulated
but
dam
infrastructure and opening up of roads
in this part of the Amazon. Amongst
the main impacts observed since the
creation of the dam are: poor water
quality at the discharge point,
disappearance of species, reduced
fishing
catches
and
fishermen
migration upstream the dam (Manyari
and Carvhalo 2007).
The third hydroelectric in the Tocantins
state with a generation capacity of 30
MW.
Hydroeletric generation of 1275 MW
and inundated area of 1.784 km2. A
15000h park was built to compensate
indigenous for the construction of the
dam. The park was a joint effort
between Furnas Centrais Electricas and
Fundacao Nacional do Indio (FUNAI).
On the other hand, amphibian species
showed a substantial decline before and
after the flooding of the reservoir
(Brandao, A. and Araujo F, B. 2007).
3150 km2 of rain forest inundated by
the dam. The reservoir produces
deoxygenated water, which is corrosive
to the turbines. Balbina reservoir also
flooded two villages, in which lived
107 of the 374 remaining members of
the tribe Waimiri-Atroari (McCully
2001).
Second biggest dam of the world in
hydropower generation (10200MW –
87 billion KW h) and eight in the
volume of water dammed. The project
was heavily criticised by the
destruction of thousands of squared
kilometres of rain forest of reach
biological diversity. 1500 Km2 of
rainforest submerged. Great problems
of green house gas emissions(Methane,
CO2)and oxygen depletion due to
organic matter discomposing ( McCully
2001)
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