Presented at the Basin Focal Project workshop 'Clarifying the global picture of water, food and poverty' from 18-20th September in Chiang Mai, Thailand.

1. 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
mark.mulligan@kcl.ac.uk

2. 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

3. Statistics : Bolivia, Colombia, Ecuador and Peru
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

4. Andes : baseline
FAO Percentage of Area sum GDP for 1990
land areas irrigated (millions USD/yr)
Ramankutty Ramankutty CIESIN WCPA WDPA
CIESIN
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

5. 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.

6. ...but spatially very variable

7. 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.

8. How can we help?
questionnaire of 80 water professionals from 7 Andean countries
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
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
Q. What are the most important factors for successful use of PSS?
A. Availability of good data, level of detail
see www.bfpandes.org

9. 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?

10. Colombia Complex
institutional
structures for
water
Ecuador

11. Bolivia
Peru

12. WP4 Institutional analysis (How people manage water and the
agricultural system that consumes it).
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

13. IEI-Col = ∑ (A+B+C+D+E)/5
A = o_Finance_Institutions
Composed
B = Total_enrolled_Students (2005)
C = Health_Investment (2006)
D = Potable_Water_Investment (2006)
representation of key
characteristics of
E = Total_displaced_People_received (2001-2007)
IEI-Ecu ∑ (2(A+B)+C+D+E)/5
A = 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)}/5
A = 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)
IEI-Bol = ∑ (A+B+C+D+E+F+G+H)/5
A = 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)
Environment Indexconditions,
G = on_Social_Invest_USD (2006)
H = o_Finance_Institutions
Tough
High : 9.4
bigger effort
(greater expense)
* required
Low : -2.4
Less difficult
* Standardized for the four countries, main capitals excluded

15. WP2: Assessment of Water resources (how much water? Who uses it?)
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).

16. Rainfall : falling
at the
first hurdle.
Total annual
rainfall
(mm)
TRMM>
<WorldClim
trmm
wclim
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).

17. Wind-driven rainfall is very heterogeneous in a
mountainous environment – even at the scale of individual slopes...
CQ
See at www.ambiotek.com/fiesta (Google Earth viewer required)

18. ...but even in the Andes rainfall stations are sparsely distributed....
WorldClim precipitation stations in Peru and Bolivia

19. 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?

20. Potential Evapotranspiration (mm/yr) Water balance (mm/yr) [worldclim]
Hyper-humid
in the N and E to
hyper-arid in the
SW

21. Per capita water balance
CIESIN
Per capita water availability is high throughout the N and W
Lowest in coastal Peru, Chile, Bolivia and Argentina

22. Water demand vs. supply
Annual water demand Annual water supply (m3) Annual water
(m3) surplus/deficit (m3)
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.

23. Areas of current water deficit (demand>supply)
Water deficits (millions of m3 annually)

24. WP3 Assessment of Water productivity
(How much do people gain from agricultural water use?).
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
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

25. Results : water productivity Dry matter
production
(Kg/Ha./yr)
[without trees]

26. 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

27. DMP (kg/ha/yr) by land use [trees excluded]
Dry matter productivity Dry matter productivity Dry matter productivity
(kg/ha/yr), for pasture (kg/ha/yr), for irrigated (kg/ha/yr), for cropland
cropland
Productivity for pasture highest in Colombia and Ecuador. Highly productive irrigated
cropland in Chile and Argentina. Cropland also productive in E. Bolivia, lowland
Argentina.

28. If we look at the entire countries, not just the Andes, then the lowlands
are clearly more productive [trees excluded]
Dry matter productivity Dry matter productivity Dry matter productivity
(kg/ha/yr) crops (kg/ha/yr) irrigated crops (kg/ha/yr) pasture

29. WP1 Poverty analysis: (What is the linkage between water, agriculture and poverty in basins?)
But, there are noNBI vs. Productivity
relationships between productivity andEcuador Rural Productivity vs. Headcount Index
poverty metrics (by municipality)
50000 45000
45000
40000
Colombia 40000
35000
Ecuador
35000 30000
Productivity
Productivity
30000
MEAN 25000
25000 MEAN
Linear (MEAN) 20000
20000
15000
15000 y = -65.416x + 30132
10000 R2 = 0.035 10000
5000 5000
0 0
0 20 40 60 80 100 120 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000 1.2000
NBI Headcount Index
Peru Rural Productivity vs Malnutrition Bolivia Rural Productivity vs. Headcount Index
50000 50000
45000
40000
Peru 45000
40000
Bolivia
35000 35000
Productivity
Productivity
30000 30000
25000 MEAN
25000
20000
20000
15000
15000
10000
10000
5000
5000
0
0 0.00 0.20 0.40 0.60 0.80 1.00 1.20
0.00 0.20 0.40 0.60 0.80 1.00
% of municipio poor
% malnourished
Note different indices for each country. Analysis by Glenn Hyman, CIAT

30. What about other forms of water productivity : dams turn
water into energy or extra productivity
KCL GLOBAL GEOREFERE CED DAMS DATABASE
Tropics : land areas draining into dams by: Leo Saenz
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.

31. 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
PWS
Catchments of Andean dams

32. Ecosystem services : cloud forest example

33. Rules of thumb for the water service benefits of protected areas
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

34. Tracing the impact of protected areas on water
As you travel downstream
from the protected areas their
contribution to flow diminishes as
rivers are swamped with water
from non-protected areas
% of water originating in a protected area – WDPA 2009 (Colombia) [gl_pc_wc_fin]
see www.kcl.ac.uk/geodata

35. umber of urban people consuming water originating in a protected
area – WDPA 2009 (Colombia) [gl_sumurbpc]
The beneficiaries can easily
number millions of people. A
strong case for PWS.
see www.kcl.ac.uk/geodata

36. 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)

37. Percentage of water arriving at tropical dams that fell as rain on protected areas
More
conservation Development of PES
to improve schemes to sustain
ES at dam existing conservation
see www.kcl.ac.uk/geodata
% water supply from protected areas
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.

38. 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

39. 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.

40. But these are highly uncertain?
How can we reduce uncertainty?
Use many models and see what they agree and
disagree on:

41. Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs
cnrm_cm3
bccr_bcm2_0 cccma_cgcm2 cccma_cgcm3_1 cccma_cgcm3_t_t63
csiro_mk3_0 gfdl_cm2_0 gfdl_cm2_1 giss_aom
°C hccpr_hadcm3
All GCMS agree warming.
There is some consistency in the pattern of warming for the Andes but all
GCMs disagree elsewhere....
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

42. Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs
miroc3_2_medres miub_echo_g mpi_echam5
ipsl_cm4 miroc3_2_hires
mri_cgcm2_3_2a ncar_pcm1
°C
....the magnitude as well as the spatial pattern vary considerably (for the same
scenario) between different models

43. 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
mm/yr csiro_mk3_0 gfdl_cm2_0 gfdl_cm2_1 giss_aom hccpr_hadcm3
For precipitation there is disagreement on the direction of change as well as
the magnitude. All models indicate wetting in the Andes...
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

44. Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs
ipsl_cm4 miroc3_2_hires miroc3_2_medres miub_echo_g mpi_echam5
mri_cgcm2_3_2a ncar_pcm1
mm/yr
...many models indicate considerable trying in parts of N Colombia,
Venezuela and the Amazon

45. Mean change and uncertainty (sd) of 17 models
Warming and wetting.
Greatest uncertainty at high latitudes, coastal and Amazon margins

46. Temperature seasonality of change : mean of 17 models
J F M A M J
J A S O N D
Monthly temperature change to 2050s (°C)
Greatest increase in S Andes in J,J,A,S

47. Rainfall seasonality of change : mean of 17 models
J F M A M J
J A S O N D
Monthly precipitation change to 2050s (mm)
More or less even seasonal distribution of change.

48. 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

49. Regional scale hydrological impact
Mean annual temperature Mean annual precipitation Mean annual evapo- Mean annual water balance
change to 2050s (°C) change to 2050s (mm) transpiration change to change to 2050s (mm)
2050s (mm)
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)

50. 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

51. 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)

52. DMP (in Dg/ha/day)
Rainfall (mm/yr)
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.
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.

53. 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

54. Sensitivity to change
Runoff sensitivity to tree Runoff sensitivity to Runoff sensitivity to
cover change (% change in precipitation change (% temperature change (%
runoff per % change in tree change in runoff per % change in runoff per %
cover) change in precipitation) change in precipitation)

55. 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

56. 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
Thank you

57. 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).

60. Persons per km2 of urban population drinking water originating in a
protected area – WDPA 2009 (Colombia) [gl_mnurbpc]
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
see www.kcl.ac.uk/geodata

61. Like carbon, water is not just a national issue
Flows of virtual water (transpiration) embedded in traded agricultural products
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.

62. 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.
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 /

63. <Crop per drop of
rainfall (RUE)
(g/Ha./yr/mm)
[without trees].
Averaged by
catchment
Crop per drop >
(g/Ha./yr/mm)
[without trees].
for areas with <500mm
rainfall
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).

64. Crop per drop
(g/ha/yr/mm water), for
cropland
Crop per drop highest in high Andes (Colombia, Ecuador) and SE
Bolivia