NERC-DfID-ESRC ESPA

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NERC-DfID-ESRC ESPA
ANDES/AMAZON WP1 DATA BASED ANALYSES OF
ECOSYSTEM SERVICES Mark Mulligan, Leo Saenz,
Edwin Keizer, KCL
KCL OBJECTIVES

• Map state of ES supply (STATE SUPPLY)
• Map human impacts on provision of ES (IMPACTS)
• Review data and assessment tools for
  intervention analysis (ASSESSMENT TOOLS)
• Integrate and put online the baseline datasets
  and analyses (BASELINE)
• ...for services...
• Water quality and quantity
• Reduction of climatic, hydrological and
  geomorphic hazards
• Regulation of the climate system

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SUMMARY AND RECOMMENDATIONS Water and climate
services in the Andes/Amazon

1. Key knowns
2. The NW of the Basin has a surplus of water
   provision but elsewhere there are seasonal
   deficits
3. Climate change will have a much greater impact on
   the provision of hydrological services than land
   use change, especially in the E of the basin.
4. The relationship between land cover and climate
   is complex, with some areas showing an increase
   in rainfall and cloudiness on forest loss and
   others a decrease.
5. Key unknowns
6. Great uncertainties in the measurement of water
   balance (esp. rainfalll distribution).
7. Much variation between models on the magnitude
   and distribution of climate change.
8. Key uncertainties in impact of LUCC on
   non-quantity services
9. Key uncertainities in impacts on global carbon
   budget and climate.

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Water quality and quantity methods

• STATE
• Use FIESTA model to assess water quantity state.
• Switch on erosion component to assess water
  quality (state), combine with population/urban
  centres/industry data as a surrogate for
  pollution loading factors for further detail on
  water quality.
• Assess effect of climate (rainfall and cloud
  cover variability for comparison with later
  scenarios for climate change).
• Compare current state with historical analysis of
  flow data from national records (KCL/UNAL) and
  from satellite radar (largest rivers) (UNAL).
• HUMAN IMPACTS
• Scenario analysis with FIESTA model using
  pre-human (UNEP-WCMC), current (MODIS-VCF) and
  scenario (LUSE model) forest cover to analyse
  impact on total flow, peak flow and baseflow,
  seasonally and annually, indicating the
  environmental service provided by forest cover
  versus other land uses.
• Measure impacts spatially but also at main
  dams.(KCL-MM)
• DATA AND ASSESSMENT TOOLS
• Review of regional and national hydrological data
  gathering efforts.
• Review of hydrological modelling done at the
  Amazon scale

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Model processes
The model works in this way. It is validated at
a range of scales, for a variety of outputs for
Costa Rica
...for diff veg types
Impaction vs. deposition
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Key results water quality and quantity

• Use FIESTA model to assess water quantity state.
• Still a great deal of uncertainty on Amazon water
  balance, depending on the input data used
  (especially rainfall)
• Andes may have highest water balance per unit
  area but their small extent means that on an
  annual basis the inputs are dwarfed by rainfall
  falling on the Amazon
• Wettest catchments are in the N and W and the
  driest in the S and E
• Seasonal deficits in the S and E (and locally bin
  the NW) mean thta inputs from upstream are
  significant seasonally, most of the catchment is
  seasonally dependent on seepage and baseflow

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Mean water balance by km2 (mm/yr) WorldClim
Point values are higher for TRMM data but less
extensive in coverage. Quantification of ES
supply thus limited by climate data.
Mean water balance by km2 (mm/yr) TRMM
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Mean water balance by 100m altitudinal band
(mm/yr) WorldClim
For the Worldclim data the band with the greatest
water balance is the lowest altitude band. For
TRMM the eastern Andean slopes contributes the
most per unit area
Mean water balance by 100m altitudinal band
(mm/yr) TRMM
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Mean water balance by catchment (mm/yr)
WorldClim
Balances are distributed Differently and
generally lower on a catchment basis for TRMM
data
Mean water balance by catchment (mm/yr) TRMM
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Water balances are positive throughout the year
in the Andean flanks and NW of the Basin but
seasonal deficits in the S and SE mean that
inputs from upstream are important.
J
F
M
M
A
J
J
A
S
O
N
D
Monthly water balance for baseline scenario (TRMM
data)
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Minimum monthly rainfall generated runoff (mm).
This shows that only a small area in the NW has
rivers fed continuously by rainfall. All other
rivers rely on baseflow for at least one month.
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Flow validation
Max modelled runoff in the basin is 280 000
cumecs Other estimates 200 000 cumecs (Richey
et al) UNESCO (Studies and Reports on Hydrology
No. 25, 1978) lists the mean annual discharge of
the Amazon river at its mouth at 220,000 cumecs,
based on a discharge of 157,000 measured at the
Obidos narrows. A further 10 of the water
discharged by the Amazon enters downstream of
Óbidos, very little of which is from the northern
slope of the valley. The drainage area of the
Amazon basin above Óbidos is about 5 million km²,
and, below, only about 1 million km², or around
20, exclusive of the 1.4 million km² (600,000
mile²) of the Tocantins basin.
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Key results water quality and quantity

• Impacts of (historic) land use change
• Most deforestation historically has taken place
  along the main channel, along the flanks of the
  Andes (esp in the N and S) and throughout the ARC
  of deforestation in the S and E.
• This has a minimum impact on water balances with
  local increses in water balance (runoff) of the
  order of a few mm/year in deforested areas
• This has lead to small increases (lt1) in flow of
  the major rivers draining these areas

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Difference in forest cover (fraction) from
pre-human times. Greatest forest losses have
occurred in the Andes and S of the Basin.
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Impact of historic forest loss on water balance
(mm/yr). Minimal impact slight increases in
water balance in deforested areas.
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Impact of historic forest cover loss on runoff
(mm/yr). Small increases in the major rivers
draining areas of high forest loss in the south.
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Difference in runoff between pre-human and
current times () indicates that the increases in
flow associated with forest loss amount to lt1 of
original flows.
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Key results water quality and quantity

• Impacts of climate change to 2050
• Different GCMs (climate models) produce broadly
  the same pattern but different magnitudes of
  temperature change for the Amazon. Different GCMs
  produce different patterns as well as magnitudes
  of rainfall change
• ECHAM SRES A2 indicates warming throughout the
  basin from 2C in the W to 5C in the E, HADCM3
  SRES A2 gives values of 3C in the W to 7C in the
  E by 2050
• HADCM2 SRES A2 indicates wetting in the S and W
  of the Basin of 400-600 mm/yr and drying in the N
  by 600 to 1000 mm/yr, ECHAM SRES A2 indicates
  wetting throughout the W and central Amazon by
  400-600mm/yr and drying in the E by7 200-400
  mm/yr
• The impacts of climate change on water balance
  and much greater than those of historic land use
  change
• Under HADCM3 evaporation increases throughout the
  basin but especially in the E, water balance
  decreases throughout much of the N and central
  Basin but increases throughout the Andes, N and E
• This leads to increases in runof over the Andes
  (by 100 in the south) and decreases of upto 100
  in the N and central Amazon. Neighbouring rivers
  can show an opposite trend.
• Under ECHAM, evaporation increases throughout but
  especially in the E and water balance increases
  in the W (500mm/yr) and decreases in the E
  (600mm/yr). This leads to increased runoff in
  the Andes and west (30-100) and decreased runoff
  in the NE (-30 to -50)

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Temperature change current to 2050
(ECHAM). Increases throughout but especially in
the E.
Temperature change current to 2050
(HADCM3) Increases throughout but particularly
strong in the NE.
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Precipitation change current to 2050
(HADCM3). Large decreases in the N of the Basin.
Precipitation change current to 2050
(ECHAM). Increases in the W and Andes, Decreases
elsewhere.
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HADCM3_2050
Difference in water balance (mm/yr). Shows
descreases in the north of the Basin and
decreases in parts of the southern Andes.
Difference in evaporation (mm/yr). Shows
increases throughout especially in the southern
Andes and eastern Basin. Much less change in the
N Andes/W Amazon..
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HADCM3_2050
Percentage change in runoff. Shows increases in
runoff over Andes (esp. S) and significant
decreases over the N and SE of the Basin
Change in runoff (mm/yr). Shows differing
responses (positive and negative) between
neighbouring watersheds.
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ECHAM_2050
Difference in water balance (mm/yr). Shows
increases in the west and decreases in the east
of the Basin.
Difference in evaporation (mm/yr). Shows
increases throughout especially in the southern
Andes and eastern Basin.
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ECHAM_2050
Percentage change in runoff Shows increases in
runoff over the southern Andes and Significant
decreases over the eastern Basin
Change in runoff (mm/yr) Shows differing
responses (positive and negative) between
neighbouring watersheds.
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Key results water quality and quantity

• Main research organizations, National Institutes
  and gathering data programmes in the OTCA area
• Different multinational and regional research and
  monitoring programmes have taken place in the
  Amazon recently (over the last 20 years), in
  order to provide good quality data to support
  scientific research on the regional hydrology and
  climate of the basin.
• Some of the main programmes are
• ORE - HYBAM project (Environmental Research
  Observatory - Hydrodynamic of the Amazon Basin
  project), HYBAM SENAMHI (Peru), HYBAM - INAMHI
  (Ecuador), Large Scale Biosphere-Atmosphere
  Experiment in Amazonia (LBA) Project led by
  Brazil, TRMM (Tropical Rainfall Monitoring
  Mission) LBA, Carbon in the Amazon River
  Experiment (CAMREX) and The Amazon Eye (KCL
  2007), amongst other programmes.

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Key results water quality and quantity

• Main research organizations, National Institutes
  and gathering data programmes in the OTCA area

Project name Organizations involved Focus of research Period of work Data gathering activities
CAMREX University of Washington (UW, Seattle) CENA INPA RSRG AARAM NFS Ecosystems NASA LBA ECO FAPESP 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)
Amazon Eye KCL Ambiotek Collection of high resolution large datasets of climate, ecosystems, land cover and infrastructure of the Amazon basin 2005-2007 Support hydrological analysis about the impacts of land cover and climate change upon the bio-stability and environmental services provision of the basin.
Conservation Eye KCL Ambiotek UNEP -WCMC Land Cover change 2005 - 2007 Tracking of hydrological anomalies from land cover change in the basin
Cornell EOS NASA - EOS Geomorphology and hydrology of central Andes Glaciers 1988 - 1998 Use of Synthetic Aperture Radar SAR to study glaciers in the central Andes
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Key results water quality and quantity

• Flow stations database.
• About 1000 known flow stations digitized
  according to different National Institutes of
  Hydrology and Meteorology and the Global Runoff
  Data Centre (GRDC 2007).
• The database accounts for about 70 flow stations
  reporting 20 year monthly time series and for
  almost 100 indicating annual average river
  discharge.
• 21 virtual stations with rating curves derived
  from radar altimetry and validated against in
  situ observations with errors of less than 10
  (Leon et al 2006).
• National Institutes and regional projects
  providing data
• ANA (Brazilian Water National Agency), IDEAM
  (Colombian Institute of Hydrology and
  Meteorology), Hybam (Hydrology and Geochemistry
  of the Amazon Basin project), SENAMHI (Peruvian
  National Service of Hydrology and Meteorology)
  and GRDC, amongst other organizations.
• Main limitations and data uncertainties
• Most time series are significantly incomplete
  since an important number of flow stations are
  not currently in operation.
• Most flow station coordinates are not well placed
  on the river stream and provide estimates of
  river discharge rather than measurements,
  especially at large rivers.

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Key results water quality and quantity

• Flow stations database. About 1000 flow stations
  known in the OTCA area (Source ESPA 2007, Google
  Earth 2007)
• Flow station places in the OTCA area reported in
  the literature. National Institutes of Hydrology
  and Meteorology and Global Runoff Data Centre -
  GRDC (2007).

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Key results water quality and quantity

• Flow stations database. About 70 stations with
  monthly records going back 20 years and about 100
  stations with information online (Source ESPA
  2007, Google Earth 2007)
• 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.

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Key results water quality and quantity

• Flow stations database. 21 virtual stations
  derived from radar altimetry (Source Leon et al
  (2006), Google Earth 2007)
• 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.

Negro River Basin
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Key results water quality and quantity

• Evidence for maximum peaks, minimum baseflows and
  flashiness change from flow data in the region
• Decadal and inter-annual climate variability has
  affected the discharge of the Amazon river in
  different parts of the basin.
• ENSO phenomena have produced severe drought and
  strong discharge and rainfall deficits in
  Western Amazon (of up to 50 - reported at Manaus
  1926) with consequent widespread fires (Carvalho
  1952 Sternberg 1987Williams et al 2005).
• Other climate variability phenomena such as the
  Amazon drought in 2005, the worse in Southwest
  Amazon over a century, attributed to anomalous
  sea surface temperatures of North Atlantic, low
  humidity, warmer air temperatures (3 to 5C) and
  reduced convective development and rainfall,
  produced catastrophic impacts upon riverside
  communities (e.g. Iquitos) (Marengo et al 2005).
• Potential Climate change Scenarios
• Climate change might exacerbate the impacts of
  climate variability throughtout the river basin
  (Marengo et al 2005).
• Limitations and uncertainties
• The use of improved and more powerful models
  (AGCM models) as well as more representative
  datasets, especially for rainfall, are
  fundamental to provide more representative
  predictions of future impacts of climate change.
• Available studies are overwhelmingly large-scale
  and single point analysis and are not reported
  for countries in the OTCA area other than Brazil.
  

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Key results water quality and quantity

• Evidence for maximum peaks, minimum baseflows and
  flashiness change from flow data in the region.
  Impacts of land cover change.
• Some studies suggest that land cover changes have
  already affected the river discharge of some
  Amazon tributaries at local and large scales,
  though still there is a great deal of uncertainty
  as to whether some of these changes are the
  impact of decadal or inter-annual climate
  variability at the basin scale.
• Analysis of rainfall - runoff ratios and land
  cover changes at large scales (e.g. Tocantins
  basin - Costa et al 2003 ) have indicated
  statistically significant runoff increases
  attributed to deforestation. At local scales
  (1h), studies of the impacts of forest conversion
  to pasture have reported similar conclusions
  (e.g. Rancho Grande, Rondonia - Chaves et al
  (2007), Manaus - Troncoso et al (2007)).
• Controversy is greatest when studies are based
  upon river stage analysis only. While some
  attribute river discharge changes to land use
  change (Gentry and Lopez-Parodi (1980, 1982),
  Harden (2006)) others attribute such changes to
  climate variability and geo-tectonic adjustments
  affecting the streambed (Nordin and Meade (1982))
• Main uncertainties and limitations
• Long river discharge time series as well as more
  detailed rainfall datasets are essential to
  properly distinguish potential impacts of land
  use change regarding those attributable to
  climate variability.
• Climate variability impacts are potentially
  larger than those of land use change.

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Key results water quality and quantity

• Main studies referring to evidence of river
  discharge changes from Land Use change in the
  OTCA area.

Reference Type of analysis River side community Period of analysis Key findings
Costa et al (2003). Statistical analysis of flow records, rainfall surfaces (New et al (2000) dataset and land cover change. Manaus Brazil 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.
Chaves et al (2007) Paired catchments Study. Small scale (1h). Rancho Grande, Rondonia Increase in surface stream flow from conversion of forest to pasture, which can potentially affect hydrological budgets at larger scales in the Amazon
Troncoso et al (2007) Paired catchments Study. Small scale (1km2). Manaus - Brazil 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.
Gentry and Lopez Parodi (1980) Analysis of river stage data at Iquitos as well as rainfall Iquitos - Peru 1962 - 1978 Not significant increase of rainfall. Increase in high river stages while low stages remained constant. Effects attributed to deforestation
Nordin and Meade (1982) Constesting to Gentry and Lopez Parodi (1980) Iquitos - Peru 1942 - 1980 Changes in river stage at Iquitos could have been the result of bank erosion. Changes might respond to decadal climate cycles rather than deforestation.
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Key results water quality and quantity

• Evidence of river discharge changes from land
  cover change in the OTCA area.

Seasonal increase in river discharge for the
Tocantins river - Brazil. Bottom curve (1949
1968 less deforestation) Top curve (1969 1978
greatest deforestation) For greater
deforestation higher river discharge and early
monthly flood peaks are observed. Source Costa
et al (2003).
Linear regressions of maximum peak flows (Top)
and minimum base flows (bottom) at Iquitos -
Peru. The increase in maximum peak flows was
attributed to deforestation Source Gentry and
Lopez-Parodi (1980).
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Key results water quality and quantity

• Review of hydrological modelling done at the
  Amazon scale
• From Macroescale (gt 105 km2) to mesoescale (102 -
  105 km2) models as well as Single Column Models
  SCM have been used to understand impacts of
  deforestation on the Amazon hydrology.
• Macroscale models suggest an overall decrease in
  water resources at the Amazonia scale attributed
  to reduced evapo-transpiration affecting its
  significant rainfall recirculation (DAlmeida et
  al 2007 Franken and Leopoldo 1984 Salati and
  Nobre 1991).
• Mesoscale models, of greater detail than
  macroescale 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 DAlmeida 2007 NASA
  2007c). However, results might vary depending
  upon climatic conditions and topography of
  different areas.
• Single Column models indicate greater
  precipitation over forested areas due to greater
  evapo-transpiration flux.
• Main uncertainties and limitations
• The coarse nature of macroscale models limits
  their use at local or regional scales.
• More reliable rainfall datasets are fundamental
  to better associate deforestation to changing
  patterns of rainfall and cloud cover.
• SCM results sometimes differ from observations
  due to lack of the ability to consider horizontal
  discontinuities (as those produced by forest
  fragmentation).

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Key results water quality and quantity

• Review of hydrological modelling done at the
  Amazon scale Observational studies for river
  discharge
• Radar altimetry products, such as ENVISAT and
  TOPEX coupled with routing modelling and in situ
  data 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 (Leon et al
  2006 Zakharova et al 2006 and Frappart, et al
  2006).
• Limitations still remain to establish a direct
  relation between floods extent and floods volume
  in the Amazon due to highly variable topography
  and distribution of flood plains.
• Monitoring of vegetation as modelling input to
  understand 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 (Franken and
  Leopoldo, 1984 Vorosmarty et al., 1989 Salati
  and Nobre)
• Studies focussing on the distribution of NDVI in
  the basin for wet and dry periods hold a great
  potential to improve the knowledge of
  Hydro-ecological processes in the basin, hotspot
  areas where forest are more prone to severe
  drought impacts and scale up ecosystems response
  to drought through out the basin.

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Key results water quality and quantity

• Main examples of hydrological modelling at the
  Amazon scale. Macroscale models - Adapted from
  DAlmeida et al (2007)

Reference AGCM Resolution Simulation Change in Precipitation Change in Evapotranspiration Change in Runoff
(lat lon) (months) (mm/d) (mm/d) (mm/d)

Lean and Warrilow, 1989 UKMO 2.5 3.75 36 -1.43 -0.85 -0.40
Nobre et al., 1991 NMC 2.5 3.75 12.5 -1.76 -1.36 -0.40
Henderson-Sellers et al., 1993 CCM1 4.5 7.5 72 -1.61 -0.64 -0.90
Lean and Rowntree, 1993 UKMO 2.5 3.75 36 -0.81 -0.55 -0.20
Dirmeyer and Shukla, 1994 COLA 4.5 7.5 48 0.24 -0.31 0.02
Polcher and Laval, 1994a LMD 2.0 5.6 13.5 1.08 -2.07 3.7
Polcher and Laval, 1994b LMD 2.0 5.6 132 -0.51 -0.35 -0.16
Sud et al., 1996 GLA 4.0 5.0 36 -1.48 -1.22 -0.26
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Key results water quality and quantity

• Main examples of hydrological modelling at the
  Amazon scale. Macroscale models - Adapted from
  DAlmeida et al (2007)

Reference AGCM Resolution Simulation Change in Precipitation Change in Evapotranspiration Change in Runoff Change in T
(lat lon) (months) (mm/d) (mm/d) (mm/d) (C)
Manzi and Planton, 1996 EMERAUDE 2.8 2.8 50.5 -0.40 -0.31 0.33
Lean et al., 1996 HC 2.5 3.75 120 -0.43 -0.81 0.39

Lean and Rowntree, 1997 HC 2.5 3.75 120 -0.27 -0.76 0.51 2.3
Hahmann and Dickinson, 1997 CCM2 2.8 2.8 120 -0.99 -0.41 -0.50 1
Costa and Foley, 2000 GENESIS 4.5 7.5 180 -0.70 -0.60 -0.10 1.4
Kleidon and Heimann, 2000 ECHAM4 5.6 5.6 240 -0.38 -1.30 0.92 2.5
Voldoire and Royer, 2004 ARPEGE 2.8 2.8 360 -0.40 -0.40 -0.01 -0.01
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Key results water quality and quantity

• Main examples of hydrological modelling at the
  Amazon scale. Mesoscale models - Adapted from
  DAlmeida et al (2007)

Reference Mesoscale Resolution (km Simulation gris Key findings Key findings
model km) (days) Center
Eltahir and Bras, 1994 MM4a 50 50 93 6.5 S, 67.5 W Less rainfall, less Less rainfall, less
evaporation evaporation
Silva Dias and Regnier, 1996 RAMSb 20 20 4 10 S, 60 W Greater vertical motion Greater vertical motion
Dolman et al., 1999 RAMSb 16 (4, 1) 16 (4, 1)e, 4 10.5 S, 62 W Deeper convective layer Deeper convective layer
60 (20) 60 (20)d 60 (20) 60 (20)d
Wang et al., 2000 MM5V2c 12 (4) 12 (4)e 6 11 S, 63 W More convection during More convection during
dry-season dry-season
Baidya Roy and Avissar, 2002 RAMSb 16 (4, 1) 16 (4, 1)e 1 10S, 62.5 W More convection More convection
triggered by surface triggered by surface
heterogeneity heterogeneity
Tanajura et al., 2002 ETA/SSiBd 80 80 30 22 S, 60 W Less rainfall, less Less rainfall, less
evaporation evaporation
Weaver et al., 2002 ClimRAMSb 16 (4, 1) 16 (4, 1)e, 2 10 S, 62 W Effects predicted depend Effects predicted depend
16 (4, 2) 16 (4, 2)e, 16 (4, 2) 16 (4, 2)e, on correct model on correct model
16 (4, 4) 16 (4, 4)e 16 (4, 4) 16 (4, 4)e configuration configuration
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Key results water quality and quantity

• Main examples of hydrological modelling at the
  Amazon scale. Observational studies of river
  discharge

Reference Type of analysis Scale of analysis Period of analysis Key findings
Leon et al (2006) Radar altimetry processing (ENVISAT, TOPEX Poseidon) flow routing modelling Negro river basin 2005 Rating curves validated for 21 virtual stations in the Napo river. Good agreement between modelled and measured water depths and discharges.
Zakharova et al 2006 Radar altimetry processing (TOPEX Poseidon) Empirical estimates of River discharge Manacapuru (Solimoes river), Jatuarana (Solimoes and Negro river) and Obidos (Amazon) 1992 2002 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 un-gauged large rivers
Frappart, F. (2006) Radar altimetry processing (ENVISAT, Tabatinga, River negro basin at Manaus, Salimoes Negro confluence and Tapajos river 2003 - 2004 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 hydrology.
Frappart, et al (2005) Radar altimetry processing (SAR, JERS-1 and TOPEX) Negro river at Manaus 1995 - 1996 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.
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Key results water quality and quantity

• Main examples of hydrological modelling at the
  Amazon scale. Vegetation as modelling input to
  hydro-climatic models

Reference Type of analysis Scale of analysis Period of analysis Key findings
Poveda and Salazar (2004) Space-time variability of NDVI Amazon basin scale 1981 - 2002 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
Wittmann, et al (2004) Forest and climatologic disturbances Tefe and Manaus Brazilian Amazon 2000 Tree species richness are well defined with gradients of flooding and sedimentation
NASA (2007) Field measurements Effects of drought on forest Amazon - Tapajos national forest 2005 Collaboration with the LBA. Stress related signals due to drought are reported to monitor of forest health state from space.
NASA (2007b) Deforestation and climate implications Climatic models using MODIS data suggest forest conversion to crops derives warmer and drier conditions.
NASA (2007c) Deforestation and climate implications Use of TRMM Area of Porto Velho 2000 - 2001 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.
Huete, et al (2006) Field observations soil moisture modelling impacts of dry seasons on rainforest productivity Amazon scale 2006 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
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Key results water quality and quantity

• New dam projects in the OTCA area and
  Characteristics

Dam name Country River State Municipality Key characteristics
San Antonio Brazil Madeira Rondonia Porto Velho Projects not licensed yet due to threats to endemic catfish in the basin (Manyari and Carvhalo 2007).
Jirau Brazil Madeira Rondonia Porto Velho Projects not licensed yet due to threats to endemic catfish in the basin (Manyari and Carvhalo 2007).
Sao Luis Brazil Tapajos Para Sao Luis 9000 MW of installed capacity (Switkes 2007)
Belo monte Brazil Xingu Para Belomonte 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).
Babaquara Brazil Xingu Para Altamira Dam not funded by the World Bank since indigenous communities put pressure on the project preventing its construction (McCully 2001).
Bela Vista Brazil Xingu Para Bela Vista Belo monte dam system in the Xingu river ( Belomonte 2007).
Pimental Brazil Xingu Para Belo monte dam system in the Xingu river ( Belomonte 2007).
Sumapaz Colombia Cundinamarca / Meta Future expansion of the Bogotá aqueduct with a potential water flow of about 16 m3 s-1 from the Sumapaz Paramo.
Amazon Brazil Amazon 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)
44
Key results water quality and quantity

• Main current and proposed dam projects in the
  OTCA area

45
Key results water quality and quantity

• Main current dam projects in the OTCA area -
  Characteristics

Dam name Country River State Municipality Year Key characteristics
Tucurui Brazil Tocantins Para Tucuri 1984 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).
Isamu Ikeda Brazil Tocantins Tocantins Ponte Alta do Tocantins 1982 The third hydroelectric in the Tocantins state with a generation capacity of 30 MW.
Serra Da Mesa Brazil Tocantins Goias Campinacu 1996 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).
46
Key results water quality and quantity

• Main current dam projects in the OTCA area -
  Characteristics

Balbina Brazil Jatapu Amazon Jatapu 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).
Guri Venezuela Caroni Bolivar Bolivar 1978/1986 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)
47
Reduction of climatic, hydrological and
geomorphic hazards methods

• STATE
• Assess sensitivity of peak flow and baseflow to
  vegetation cover change around current value
  using FIESTA model. Where are the sensitive
  areas?
• HUMAN IMPACTS
• Compare maximum peaks, minimum baseflows and
  flashiness for pre-human, current and scenario
  forest cover from model.
• Review of evidence for maximum peaks, minimum
  baseflows and flashiness change from flow data in
  the region
• DATA AND ASSESSMENT TOOLS
• Review of regional and national hydrological data
  gathering efforts.
• Review of hydrological modelling done at the
  Amazon scale.

48
Key results Reduction of climatic, hydrological
and geomorphic hazards

• Flow sensitivity to land use change and impact of
  land use and climate change on peak flows and
  low flows
• Runoff sensitivity to forest cover is low overall
  but spatially variable through the basin.
• Historic forest loss has led to small increases
  in low flows especially in the N and W of the
  basin and small decreases in high flows
  especially in the E of the Basin.
• Climate change scenaria lead to much greater
  changes in minimum and maximum flows. Under
  ECHAM minimum flows increase especially in the W
  of the Basin whiule they decrease under HADCM3
  everywhere except the extreme west
• Maximum flows decrease in most of the basin under
  HADCM3 whilst under ECHAM maximum flows decrease
  in the E but increase elsewhere.

49
Sensitivity to land use change ( change in
runoff per change in forest cover)
Some parts of the basin have a greater runoff
response to LUCC for reasons of climate and
landscape. These areas are thus hydrologically
sensitive.
50
The difference in minimum flow between pre-human
and current forest cover conditions averaged by
catchment (mm/hr)
The difference in maximum flow between pre-human
and current forest cover conditions averaged by
catchment (mm/hr)
51
The difference in maximum flow between current
and climate change scenaria conditions averaged
by catchment (mm/hr)
Minimum flow
Maximum flow
52
Reference Type of analysis River side community Period of analysis Key findings
Costa et al (2003). Statistical analysis of flow records at , rainfall surfaces from the New et al (2000) dataset and land cover change over the 1949 1998 period. 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.
Chaves et al (2007) Paired catchments Study. Small scale (1h). Rancho Grande, Rondonia Increase in surface stream flow from conversion of forest to pasture, which can potentially affect hydrological budgets at larger scales in the Amazon
Troncoso et al (2007) Paired catchments Study. Small scale (1km2). - 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.
Gentry and Lopez Parodi (1980) Analysis of river stage data at as well as rainfall - 1962 - 1978 Not significant increase of rainfall. Increase in high river stages while low stages remained constant. Effects attributed to deforestation
Nordin and Meade (1982) Constesting to Gentry and Lopez Parodi (1980) - 1942 - 1980 Changes in river stage at could have been the result of bank erosion. Changes might respond to decadal climate cycles rather than deforestation.
Studies reporting change in river and stream
flows from land cover change in the Amazon basin.
53
Regulation of the climate system methods

• STATE
• Assess relationship between forest cover change
  and cloud generation and rainfall generation
  using MODIS VCFchange (www.ambiotek.com/trees ,
  TRMM rainfall (www.ambiotek.com/1kmrainfall ),
  MODIS cloud climatology (www.ambiotek.com/clouds)
  for the Andes-Amazon for the largest historic
  forest cover change events. Review and assess
  potential carbon stocks again using MODIS-VCF
  (coupled with existing plot data and published
  studies) and sequestration and their fossil fuel
  offsetting equivalent.(KCL-MM)
• HUMAN IMPACTS
• Examine impacts of LUCC on carbon stocks and loss
  of sequestration potential. (KCL-MM and KCL-EK)
• DATA AND ASSESSMENT TOOLS
• Review national and international climate data
  gathering efforts.
• Review of previous research on climate-vegetation
  feedbacks.
• Review of available models (KCL-EK)

54
Key results regulation of the climate system

• The relationship between forest cover change and
  cloud generation and rainfall
• There seems to be no consistent relationship
  bertween the difference in forest cover and
  rainfall of neighbouring cells, thus forets loss
  can be associated with increases or decreases in
  rainfall. The greater differences between
  neighbours with similar vegetation covers
  probably reflects the greater frequency of those
  areas.
• Spatial distribution indicates much spatial
  variation with forest loss leading to rainfall
  increases of 10 in N and S Andes, S and E
  Brazil but declines in rainfall in the central
  Andes and Pacific
• Similarly change in cloud frequency shows no
  relastionship with change in forest cover (though
  ba land sea effect is apparent, especially for
  convective (afternoon/evening rains)).
• Spatial cloud frequency increases significantly
  on forest loss in some parts of SE Amazon and E
  Brazil whereas it decreases significantly on
  forest loss throughout the central and S Andes
  and E Brazil.

55
The impact of differences in forest cover on mean
annual rainfall (1997-2006)
Indicates that there is no consistent pattern
whereby rainfall increases or decreases on forest
loss rather, rainfall may increase or decrease
56
The impact of differences in forest cover on
evening and night-time rainfall (1997-2006)
Pattern is repeated for rainfall at different
time of day, forest loss or gain can coincide
with increases or decreases in rainfall depending
on the setting. These changes are more
pronounced for evening (convective) rainfall.
57
Percent change in rainfall for areas with forest
loss (rainfall enhancement only). Forest loss
leads to rainfall increases in N and S Andes, S
and E Brazil.
Percent change in rainfall for areas with forest
loss (rainfall decline only). Forest loss leads
to large decreases in rainfall in parts of the
central Andes and Pacific.
58
The impact of differences in forest cover on mean
annual cloud frequency (2000-2006)
Difference in mean annualcloud frequency between
cell and its westernmost neighbour (fraction)
Difference in forest cover between cell and its
westernmost neighbour ()
Indicates that change between forest and
non-forest can lead to increases or decreases in
cloud frequency, depending on the context.
59
The impact of differences in forest cover on
diurnal and seasonal cloud frequency (2000-2006)
Pattern is repeated, forest loss or gain can lead
to increases or decreases in cloud depending on
the setting. These changes are more pronounced
for evening (convective) rainfall. Evening and
nightime cloud show land-sea effects.
60
Percent change in cloud for areas with forest
loss (cloud enhancement only). Shows large
increases in cloud freq on forest loss in some
parts of SE Amazon and E Brazil
Percent change in cloud for areas with forest
loss (cloud decline only). Large decreases in
cloud on forest loss throughout central and S
Andes and E Brazil.
61
Biomass Stock Actual
Biomass Stock Change
Decision selection biomass Stock Saatchi map
LUCC scenarios
Correlation (227 plots)
Biomass Saachi et al., 2007
Carbon Loss/ Emission
Carbon Sequestration
R²

• Carbon loss / ecosystem

• Carbon sequestration / ecosystem (literature)
• Forest terra firme
• Forest black water
• Forest white water
• Savanna ?
• Cerrado ?
• Agro-forest systems
• Etc..

Biomass Malhli et al., 2007
Comment 3 different maps differ in ecosystem ,
geographical coverage and modelling approach ?
result, not correlated!
R²
R²
Biomass Euler et al., 2007
Mapping and Quantification of Carbon change
according to 2 LUCC scenarios for 2007-2050
Ecosystems TNC (288 classes)
Biomass /Ecosystem (Statistics min, max, mean
etc)
Biomass Saachi et al., 2007
Comment Few references, numbers missing for
many ecosystem and land use/ cover types, besides
carbon sequestration is spatial variable which
complicates extrapolation
Aggregation / reclassification to functional
classes
Map with final classes biomass stock per class
Forest cover change ? Climate
Comment References to be collected
GCM experiments ? Deforestation ? regional
global climate
Comment Comparison of results to be done
Comment Which final classes ?
E.W.H. Keizer Edwin.keizer_at_kcl.ac.ul 02/01/2008
62
Further material The Hydro-climatology of the
Amazon Basin
63
Modelled potential solar radiation (W/m2)
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
DEC
SEP
OCT
NOV
Strong seasonality in solar, even in the absence
of cloud effects
64
Modelled total annual potential solar radiation
(MJ)
Latitudinal, altitudinal and aspect related
effects
65
Also topographic shading can be important,
especially in the mountains or at low latitudes
where the sun is frequently directly overhead
66
New pan-tropical 1km MODIS cloud climatology
(Mulligan,2006)
Annual average cloud frequency (fraction of total
observations)
Developed from all the MODIS MOD35 product ever
produced
Available at www.ambiotek.com/clouds
67
For Amazon Basin
gt80 of observations cloudy in the cloudforest
areas of the Andes and in the NW of the Basin
leads to lower sunlightPET. 40-70 elsewhere.
68
0000-0600
0600-1200
1800-2400
1200-1800
Strongly diurnal with frequencies of 100 on
isolated mountains during the day Subject to
errors because of time differences within single
satellite swath (hence squares)
69
Also highly seasonal with greatest frequencies of
cloud in DJF and MAM and Even sustained through
dry season in NW of the Basin
70
New pan-tropical 1km TRMM based rainfall
climatology (Mulligan,2006)
Total annual rainfall (mm/yr)
Developed from all TRMM data ever retrieved
(1997-2006)
Available at www.ambiotek.com/1kmrainfall
71
Previous most spatially detailed rainfall
climatology was WORLDCLIM (Hijmans et al,2004),
based on spline interp. of station data
72
TROPICLIM (Mulligan,2006), is similar in overall
magnitudes but shows a much finer scale of
variation much more patchy rainfall
73
WCgtTRMM
TRMMgtWC
Absolute differences between the two
climatologies are generally low but in isolated
rainfall hotspots the TRMM product produces much
higher values
74
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
Seasonal variability of WORLDCLIM
75
FEB
APR
JAN
MAR
MAY
AUG
JUN
JUL
SEP
OCT
NOV
DEC
Seasonal variability of TROPICLIM
Very similar in pattern, Much more spotty
76
3B42
0000
0600
0300
0900
1200
1500
2100
0000
1800
Mean 1997-2006) using the 3 hourly product, shows
well the diurnal patterns