Water and Poverty in the Andes: Results from the CPWF Andes Basin Focal Project

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

[email protected]

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


(kg/ha/yr), for irrigated

cropland


(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.


(kg/ha/yr) pastureDry matter productivity

(kg/ha/yr) irrigated crops


(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]


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


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]


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