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Water and Poverty in the Andes: Results from the
CPWF Andes Basin Focal Project
Mark Mulligan and Jorge Rubiano, King’s College London and the BFPANDES team : Condesan, CIAT, National University, Colombia
The Andes ‘basin’ (all basins above 500 masl) and the 13 key CPWF
sub-basins
Context:
1.Not a single basin!
2.All mountains
3.Transnational, globally important
4.Heterogeneous (hyper humid to hyper
arid)
5.Steep slopes, competing demands on
land use
6.Environmentally sensitive
7.Hydropower is important
8.Complex water legislation
9.Climate change
Area: 3.8 million km2
Population: of 95 million (Col, Ecu, Peru, Bol, 2005)
Pop growth: 2.5% p.a. (1980-2005)
Highly urbanised: (<15% of population is rural)
46.9 million considered poor (income<essential needs)
People below poverty line (US$1/day) 15-20%: Bolivia, 14%; Colombia, 14%;
Ecuador, 20%; Peru 15.5% (reporting year varies by country; mid- to late 1990s).
Contribution of agriculture to GDP: 10-20% : Bolivia, 20%; Colombia, 13%;
Ecuador, 11%; Peru, 10% (2002 est.)
Climate: varies from humid and tropical to cold and semi-arid
Annual precipitation: 1,835 mm (average) but range from approx. 0 to >10,000mm
Total renewable water resources: 5,100 km3/yr (total)
Annual water use by sector, Andean sub-region (includes Argentina, Chile and
Venezuela): agriculture, 36.5 km3 (73% of total); domestic consumption, 10.5 km3
(21%); industry, 3.1 km3 (6%)
Agricultural area and fertiliser use increasing since the 1960s
Cultivated land: 3.7 % of total
Irrigated land: 30,870 km2
Rainfed land: 108,750 km2 (2000)
Protected areas: 434,058 km2
Statistics : Bolivia, Colombia, Ecuador and Peru
FAO Percentage of
land areas irrigated
Area sum GDP for 1990
(millions USD/yr)
Andes : baseline
1. Much pasture and cropland, especially in the N and W
2. Large urban areas throughout but especially in the N
3. Complex network of large and globally important protected areas
4. Significant irrigated agriculture especially in coastal Peru and the drier parts of
Ecuador and Colombia
5. Highest GDPs concentrated around urban centres, large rural areas with low
GDP
Ramankutty Ramankutty CIESIN WCPA WDPA
CIESIN
Most countries on the way up....
Latin America is comparatively water rich and some sub-regions have developed
nicely. But areas such as northeast Brazil, the maize-beans farming system in Meso-
america and the Andes mountain region face natural resources limitations, including
drought and poor access to, and use of, water. These sub-regions are the ones that have
been by-passed by overall improvement in well-being in the region and poverty in the
Andean region persists.
...but spatially very variable
WP5 Intervention analysis; (Analysis of change and potential
change in basins)
What do water policy makers in the region need?
Questionnaire of 80 water professionals from 7 Andean countries. Of the
respondents: 46% were development workers, 26% scientists, 21% as
students, and 9% public sector employees.
1.Highest priority in Andean watersheds is soil erosion (71%),
2.44% said that the effects of soil erosion on agricultural livelihoods should
be considered more in the policy making process ,
3.48% said reform in the institutional approach regarding the management
of water resources is important,
4.66% of respondents observed that equality of access to water is important,
5.58% said the implementation of Payment for Environmental Services is a
priority.
How can we help?
questionnaire of 80 water professionals from 7 Andean countries
Q. What are the most important factors for successful use of PSS?
A. Availability of good data, level of detail
Q. What are the reasons for the low uptake of policy support tools
such as for example SWAT in the Andes?
A. Lack of knowledge of them, lack of or expensive data, lack
of training/capacity
see www.bfpandes.org
Q. In your experience which phrase best describes the use of
scientific data/informatiopn in policy formulation in the Andes?
A. Data are not used (46%), spatial analysis and modelling are
encouraging wider use, decisions are taken using local or expert
knowledge
BFPA=DES : Aim
The aim of the BFPANDES is “to have the best available
(social) science used by local institutions in the formulation
and testing of land and water policy for improved water
productivity and better livelihoods in the Andes”.
BFPA=DES : Key issues
Institutions. Are the institutions using and sharing the best available
information and if not why not?
Optimal allocation. What are the biophysical, knowledge and
power/equity barriers to optimal least-conflict allocation of water?
Sustainability. Which (soft/hard) management interventions
maximize economic returns (production) whilst minimizing
degradation of water, soil and environment?
Colombia
Ecuador
Complex
institutional
structures for
water
Bolivia
Peru
U=DERSTA=DI=G I=STITUTIO=AL CAPACITY :
THE I=STITUTIO=AL E=VIRO=ME=T I=DEX
1. IEI : A selection of key social, economic and political variables that indicate where an intervention will require higher effort and more investment because of a lack on institutional capacity.
2. Can also be used as indicators of progress in development and poverty reduction strategies.
3. Developed with the most reliable country data at municipal level. Methods for data processing include PCA, cluster and spatial analyses.
4. Variables considered:
•Social : Poverty measures (UBN and Poverty lines), Current status of education, health (Chronic and Total Malnutrition), demography, public services infrastructure, social and non social investment (including potable water and irrigation)
•Economic : Per capita consumption, purchasing power (di), number of financial institutions.
•Political : People displaced by violence
5. Feeds into the cost side of intervention cost:benefit
WP4 Institutional analysis (How people manage water and the
agricultural system that consumes it).
Composed
representation of key
characteristics of
IEI-Bol = ∑ (A+B+C+D+E+F+G+H)/5A = Education_Units
B = "o_of_teaching_rooms
C = Human_Development_Index (2001)
D = Yearly_Average_expenditure
E = PerCapita_compsumption_USD-Year (2001)
F = Social_Investments_USD (2006)
G = "on_Social_Invest_USD (2006)
H = "o_Finance_Institutions
IEI-Col = ∑ (A+B+C+D+E)/5A = "o_Finance_Institutions
B = Total_enrolled_Students (2005)
C = Health_Investment (2006)
D = Potable_Water_Investment (2006)
E = Total_displaced_People_received (2001-2007)
IEI-Ecu ∑ (2(A+B)+C+D+E)/5A = Iliteracy_rate
B = Unsatisfied_Basic_"eeds
C = Global_malnutrition_in_kids<5
D = %_Poor_below_PovLine
E = %_poor_below_extreme_PovLine
IEI-Per = ∑ {(A+B+C+D+E+F) – (G+H+I)}/5A = "o_kids_primary_school_completed
B = "o_kids_primary_school_finished_on_time
C = "o_educated_kids_between_4&5
D = "o_educated_kids_between_12&16
E = "o_young_Secondary_School_completed
F = "o_young_Secondary_School_finished_on_time
G = Malnutrition_rate (1999)
H = pople_no_electricity
I = Adult_Iliteracy_rate (2005)
Environment Index
High : 9.4
Low : -2.4
Tough conditions,
bigger effort
(greater expense)
required
Less difficult
* Standardized for the four countries, main capitals excluded
*
Water availability : Methods
1. Whole-Andes analysis of water availability at 1km spatial resolution using
the FIESTA delivery model (http://www.ambiotek.com/fiesta) and long
term climatologies from WORLDCLIM (1950-) and TRMM (1996-). Per
capita supply and demand.
2. Analysis of potential impacts of historic and projected land use change
(results not presented – see www.bfpandes.org).
3. Analysis of potential impacts of multiple-model, multiple scenario climate
change and assessment of hydrologically sensitive areas.
4. Understanding uncertainty and sensitivity to change.
5. Detailed hydrological modelling for smaller areas using AguA Andes PSS
(results not presented – see www.bfpandes.org).
WP2: Assessment of Water resources (how much water? Who uses it?)
Total annual
rainfall
(mm)
<WorldClim
TRMM>
trmm
wclim
Rainfall : falling
at the
first hurdle.
Hyper humid in the N and E.
At these scales there is uncertainty even in the fundamentals such as rainfall inputs
(especially because of complex topography/wind driven rain).
See at www.ambiotek.com/fiesta (Google Earth viewer required)
Wind-driven rainfall is very heterogeneous in a
mountainous environment – even at the scale of individual slopes...
CQ
WorldClim precipitation stations in Peru and Bolivia
...but even in the Andes rainfall stations are sparsely distributed....
WorldClim precipitation stations in central Peru
The points are transparent and an image lies beneath, but what image?
Do the points give a good impression of the complexity which lies
beneath?
If we cannot understand the distribution of rainfall how are we to
understand water resources?
Hyper-humid
in the N and E to
hyper-arid in the
SW
Potential Evapotranspiration (mm/yr) Water balance (mm/yr) [worldclim]
Per capita water balance
Per capita water availability is high throughout the N and W
Lowest in coastal Peru, Chile, Bolivia and Argentina
CIESIN
Annual water demand
(m3)
Annual water supply (m3) Annual water
surplus/deficit (m3)
Water demand vs. supply
Agricultural demand (green water) is accounted for in the ET/water balance calculation.
Industrial demand highly localised. Domestic demand estimated from mean p.c. water use
and population density. Deficits in the S.
Water deficits (millions of m3 annually)
Areas of current water deficit (demand>supply)
1. Whole-Andes analysis of plant production based on dry matter
production calculated from SPOT VGT (1998-2008), masked to
exclude trees.
2. Whole Andes analysis of production per unit rainfall (crop per drop,
not shown).
3. Accurate digitisation of all dams in the Andes using Google Earth
Dams Geowiki
(http://www.kcl.ac.uk/schools/sspp/geography/research/emm/geodat
a/geowikis.html)
4. Calculation of dam watersheds using HydroSHEDS
Water productivity : Methods
Water productivity : often defined as the crop per drop or yield per
unit of water use but in BFPANDES defined more broadly as the
contribution of water to human wellbeing through production of food,
energy and other goods and services
WP3 Assessment of Water productivity
(How much do people gain from agricultural water use?).
Dry matter
production
(Kg/Ha./yr)
[without trees]
Results : water productivity
Dry matter
production
DMP (in kg/ha/yr)
<Averaged in
500m elev. bands
Averaged by
Catchment>
By elevation : lowest elevations have highest productivity.
By catchment : Colombian and Ecuadorian Andean catchments have highest
productivity along with Eastern foothill catchments in the South
Dry matter productivity
(kg/ha/yr), for cropland
Dry matter productivity
(kg/ha/yr), for irrigated
cropland
Dry matter productivity
(kg/ha/yr), for pasture
DMP (kg/ha/yr) by land use [trees excluded]
Productivity for pasture highest in Colombia and Ecuador. Highly productive irrigated
cropland in Chile and Argentina. Cropland also productive in E. Bolivia, lowland
Argentina.
Dry matter productivity
(kg/ha/yr) pastureDry matter productivity
(kg/ha/yr) irrigated crops
Dry matter productivity
(kg/ha/yr) crops
If we look at the entire countries, not just the Andes, then the lowlands
are clearly more productive [trees excluded]
NBI vs. Productivity
y = -65.416x + 30132
R2 = 0.035
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
0 20 40 60 80 100 120
NBI
Productivity
MEAN
Linear (MEAN)
Ecuador Rural Productivity vs. Headcount Index
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000
Headcount Index
Productivity
MEAN
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
0.00 0.20 0.40 0.60 0.80 1.00
Productivity
% malnourished
Peru Rural Productivity vs Malnutrition Bolivia Rural Productivity vs. Headcount Index
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
0.00 0.20 0.40 0.60 0.80 1.00 1.20
% of municipio poor
Productivity
MEAN
Colombia Ecuador
Peru Bolivia
Note different indices for each country. Analysis by Glenn Hyman, CIAT
But, there are no relationships between productivity and poverty metrics (by municipality)
WP1 Poverty analysis: (What is the linkage between water, agriculture and poverty in basins?)
The first georeferenced global database of dams (www.kcl.ac.uk/geodata)
There are at least 29,000 large dams between 40= and 40S
57% in Asia, 23% in South America, 12% in Africa, 6.5 % in Asia and the
Caribbean, 1.3 % Australia, 0.2 % Middle East.
33% of land area between 40S and 40= drains into a dam (capturing some
24% of rainfall) and this surface provides important environmental and ecosystem
services to specific companies if carefully managed.
Tropical montane cloudforests cover 4% of these watersheds but receive 15% of
rainfall.
Tropics : land areas draining into dams by: Leo Saenz
KCL GLOBAL GEOREFERE=CED DAMS DATABASE
What about other forms of water productivity : dams turn
water into energy or extra productivity
Water productivity : dams in the Andes
Dams : points in the landscape at
which water=productivity
Andes : 174 large dams
Area draining into dams : 389,190 km2
(10.5% of land area)
Accessing around 20% of streamflow
At least 80,300Mm3 (80.3 km3) of water
storage capacity
At least 20,000 MW HEP capacity
Also used for drinking water, irrigation
and industrial purposes
20% of the Andean population lives
upstream of dams – importance of
careful land management – valuation for
PWSCatchments of Andean dams
Ecosystem services : cloud forest example
Water quantity services•Protected ecosystems do not necessarily generate more
rainfall than agricultural land uses.
•Protected ecosystems may have higher evapotranspiration
and thus lower water yields
Thus quantity benefits difficult to prove
Water regulation services•Protected ecosystems do not protect against the most destructive
floods
•For ‘normal’ events they do encourage more subsurface flow and
thus more seasonally regular flow regimes
Likely benefits especially in highly seasonal environments
Water quality services (quantity for a purpose)•Protected ecosystems encourage infiltration leading to lower soil
erosion and sedimentation
•Unprotected land will tend to have higher inputs of pesticides,
herbicides, fertilisers ...
Clear benefits of PA’s: generation of higher quality water than non-
protected areas
Rules of thumb for the water service benefits of protected areas
% of water originating in a protected area – WDPA 2009 (Colombia) [gl_pc_wc_fin]
see www.kcl.ac.uk/geodata
As you travel downstream
from the protected areas their
contribution to flow diminishes as
rivers are swamped with water
from non-protected areas
Tracing the impact of protected areas on water
=umber of urban people consuming water originating in a protected
area – WDPA 2009 (Colombia) [gl_sumurbpc]
see www.kcl.ac.uk/geodata
The beneficiaries can easily
number millions of people. A
strong case for PWS.
But who should pay to manage nature to
maintain these services?
1. Everyone-through national or international taxation (e.g. The CR fuel tax model)
2. International users of the virtual water embedded in commodities
-transfers of virtual water are denying downstream users of this water
(assuming transpiration is not locally recycled as rainfall)
- the cost of commodities need to incorporate the costs of sustained and
equitable water provision
3. Downstream urban, agricultural and industrial users of water
supplied by water treatment plants and dams- sustaining protected areas to avoid paying higher treatment costs
- insurance against critical supply problems
4. Voluntary personal contributions- bundling water offsets with carbon offsets (avoiding multiple
disbenefits)
Percentage of water arriving at tropical dams that fell as rain on protected areas
see www.kcl.ac.uk/geodata
Method: For all 29,000 dams calculated the percentage of rainfall draining into them
that fell on protected areas upstream.
Result: Indicates the contribution of PA’s to the economic output of those hydro’
companies. Important for the development of PWS schemes to fund conservation.
More
conservation
to improve
ES at dam
Development of PES
schemes to sustain
existing conservation
% water supply from protected areas
Institutional questionnaire did not find interest in
climate change. Why?
Don’t we have enough to deal with : why also worry
about climate change?
...because climate change changes everything and
policy support based on current climate can be
rendered irrelevant if it does not take climate
change into account
But we do not know what the future holds. What
can we do?
...use our best guess. A
general circulation
model (GCM)
projection of future
climate.
But these are highly uncertain?
How can we reduce uncertainty?
Use many models and see what they agree and
disagree on:
bccr_bcm2_0 cccma_cgcm2 cccma_cgcm3_1 cccma_cgcm3_t_t63cnrm_cm3
csiro_mk3_0 gfdl_cm2_0 giss_aom hccpr_hadcm3
Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs
gfdl_cm2_1
Climate data source : Ramirez, J.; Jarvis, A. 2008. High Resolution Statistically Downscaled Future Climate Surfaces.
International Centre for Tropical Agriculture, CIAT. Available at: http://gisweb.ciat.cgiar.org/GCMPage/home.html
All GCMS agree warming.
There is some consistency in the pattern of warming for the Andes but all
GCMs disagree elsewhere....
°C
miroc3_2_hiresmiroc3_2_medres miub_echo_g mpi_echam5
mri_cgcm2_3_2a ncar_pcm1
ipsl_cm4
....the magnitude as well as the spatial pattern vary considerably (for the same
scenario) between different models
Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs
°C
Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs
bccr_bcm2_0 cccma_cgcm2 cccma_cgcm3_1 cccma_cgcm3_t_t63 cnrm_cm3
csiro_mk3_0gfdl_cm2_0 giss_aom hccpr_hadcm3
gfdl_cm2_1
Climate data source : Ramirez, J.; Jarvis, A. 2008. High Resolution Statistically Downscaled Future Climate Surfaces.
International Centre for Tropical Agriculture, CIAT. Available at: http://gisweb.ciat.cgiar.org/GCMPage/home.html
For precipitation there is disagreement on the direction of change as well as
the magnitude. All models indicate wetting in the Andes...
mm/yr
miroc3_2_hiresmiroc3_2_medres miub_echo_g mpi_echam5
mri_cgcm2_3_2ancar_pcm1
ipsl_cm4
...many models indicate considerable trying in parts of N Colombia,
Venezuela and the Amazon
Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs
mm/yr
Mean change and uncertainty (sd) of 17 models
Warming and wetting.
Greatest uncertainty at high latitudes, coastal and Amazon margins
Monthly temperature change to 2050s (°C)
FJ M MA J
J A S O N D
Temperature seasonality of change : mean of 17 models
Greatest increase in S Andes in J,J,A,S
Monthly precipitation change to 2050s (mm)
FJ M MA J
J A S O N D
Rainfall seasonality of change : mean of 17 models
More or less even seasonal distribution of change.
So what will happen?
1. Who knows?
2. It will be warmer and wetter
3. Mean of 17 models warming is highest in the S Andes
4. Mean of 17 models wetting is highest in the W and S coastal
Andes
5. Uncertainty in temperature change is low in the Andes (the
models agree) [but is much greater in the Amazon]
6. Uncertainty in rainfall is greatest in the areas of highest rainfall
7. Seasonality of change is high for temperature and low for
rainfall
What will be the hydrological impacts?
1. Use monthly anomalies (mean of 17 models) to force FIESTA
hydrological model at Andes scale
2. Look into implications for evapo-transpiration and water
balance
Mean annual evapo-
transpiration change to
2050s (mm)
Mean annual temperature
change to 2050s (°C)
Mean annual precipitation
change to 2050s (mm)
Mean annual water balance
change to 2050s (mm)
Regional scale hydrological impact
Temperature and rainfall will increase and this drives up evapo-transpiration . But,
the balance between increased evapo-transpiration and increased rainfall tends
towards more available water (water balance increases)
But then there is the issue of water quality.....% of water in streams originating
from mine.
1.This pattern is repeated throughout
the Andes.
2.Is and will be more of a problem
than climate change, especially for
potable water
3.Requires careful legal regulation
and benefit sharing mechanisms
So what are the implications for agriculture?
Method:
Examine the current distribution of productivity from 10 years of 10-daily
remote sensing data
Look at relationships between current productivity and current climate
conditions
Draw implications for impacts of climate change scenario
Ignore water quality (for now)
DMP (in Dg/ha/day)
Relationships between productivity and rainfall indicate a linear trend between 0 and 1000 mm/yr
but little effect in wetter areas. So productivity may increase in drier areas that wet.
Rainfall (mm/yr)
DMP (in Dg/ha/day)
Mean annual temperature (°C)Temperature strongly increases productivity in the range 0-20 with a decline from 20-30°. So
productivity may decline in the warmest areas.
But then there are effects of seasonality, CO2 fertilisation,
nutrient limitation, respiration, pests and diseases.... All of
which change with climate
How do we deal with this complexity and uncertainty?
1. Since climate change will always be uncertain we change
the question from what will the future be like and how will
that affect system A? to how much change can system A
stand?
2. Instead of providing answers we tie data and knlwedge
into an answering systems (PSS) that can be applied to
geographically and sectorally specific questions
Runoff sensitivity to
precipitation change (%
change in runoff per %
change in precipitation)
Runoff sensitivity to
temperature change (%
change in runoff per %
change in precipitation)
Runoff sensitivity to tree
cover change (% change in
runoff per % change in tree
cover)
Sensitivity to change
SimTerra : the
most detailed
global
databases, tiled
Detailed grid –
based process
models
Tools to test
scenarios and
policy options
+
+
http://www.policysupport.org/links/aguaandes
Thank you
Concluding:
1.Water productivity is much more than crop per drop and includes
productivity for energy (HEP), domestic and industrial supply and sustaining
environmental flows. Dams are clearly important.
2.The environmental, institutional and socio-economic domains in the Andes
are highly spatially variable and complex, precluding the development of a
single answer to the water-productivity-poverty question
3.Our focus on developing a system for providing answers to geographically
and sectorially focused questions (a PSS) may help bridge the gap between
available knowledge and knowledge lacking in policy formulation.
Much more detail in mid-term and final reports : www.bfpandes.org
BFPA=DES : Outputs
(a)capacity built in local students, institutions/stakeholders through
training, workshops, tools, dissemination
(b) freely available report, maps and baseline data diagnosing current
status of water poverty, water productivity, environmental security and
their social and institutional context along with likely future impacts
(http://www.bfpandes.org) . Released at upcoming conf.
(c)The AguAAndes Policy Support System – a simple, accessible web
based tool for understanding the likely impact of particular scenarios of
change and policy options on water and water poverty in detail in any
Andean catchment . Batteries included! -all data supplied.
(http://www.policysupport.org/links/aguaandes).
Persons per km2 of urban population drinking water originating in a
protected area – WDPA 2009 (Colombia) [gl_mnurbpc]
see www.kcl.ac.uk/geodata
Where there are large cities
downstream of protected areas, a
significant proportion of the people
in these cities benefit from water
that fell as rain on a protected area
Regional virtual water balances and net inter-regional virtual water flows related to
the trade in agricultural products. Period: 1997-2001.
Only the largest net flows (>10 Gm3/yr) are shown.
Like carbon, water is not just a national issue
Flows of virtual water (transpiration) embedded in traded agricultural products
Visualisation by David Tryse based on data from The 2nd UN World Water Development Report: 'Water, a shared
responsibility’ http://www.unesco.org/water/wwap/wwdr/wwdr2/
The “world water crisis”
1.Humans have available less
than 0.08% of all the Earth's
water.
2.Over the next two decades our
use is estimated to increase by
about 40%, more than half of
which to is needed to grow
enough food.
3.One person in five lacks safe
drinking water now and the
situation is not likely to get
better.
<Crop per drop of
rainfall (RUE)
(g/Ha./yr/mm)
[without trees].
Averaged by
catchment
CPD or RUE (rainfall use efficiency) meaningless where rainfall is high
(significant runoff), better to use WUE (production/transpiration) where
possible.
Small lowland-dominated Pacific and Eastern foothill catchments have
greatest crop per drop. For low rainfall areas high water productivity is
highly localised (irrigation).
Crop per drop >
(g/Ha./yr/mm)
[without trees].
for areas with <500mm
rainfall
Crop per drop
(g/ha/yr/mm water), for
cropland
Crop per drop highest in high Andes (Colombia, Ecuador) and SE
Bolivia