1. Water availability and Productivity in the
Andes Region
Mark Mulligan, King’s College London
mark.mulligan@kcl.ac.uk
and the BFPANDES team : Condesan, CIAT, National University, Colombia
mark.mulligan@kcl.ac.uk
[30 mins]
DATA AND MODELS AVAILABLE AT:
www.policysupport.org/links/aguaandes and www.kcl.ac.uk/geodata

2. Water in the Andes ‘basin’ (all basins above 500 masl) and the 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
10. Industrial and extractive impacts on
water quality

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

4. WP 2 : 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 estimated.
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 of uncertainty and sensitivity to change.
5. Detailed hydrological modelling for smaller areas using AguAAndes Policy
support system (PSS) (results not presented – see www.bfpandes.org).
6. Issues of water access discussed in other presentations

5. Rainfall : falling at the
first hurdle.
Total annual rainfall
(mm)
TRMM>
<WorldClim
trmm
wclim
1. Hyper humid in the N and E.
2. At these scales there is uncertainty even in the fundamentals such as rainfall
inputs (especially because of complex topography/wind driven rain).

6. 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)

7. ...but even in the Andes rainfall stations are sparsely distributed....
Precipitation stations used by WorldClim in Peru
and Bolivia

8. WorldClim precipitation stations in central Peru
interpolation
points
The points are transparent and an image lies beneath, but what image?
If we cannot understand the distribution of rainfall how are we to understand water resources?
Development agencies please note : there is still a lot of hydrological science we do not know
(including where the rain falls). Sound decisions need sound data.

9. Potential Evapo-transpiration (mm/yr) Water balance (mm/yr) [worldclim]
Water balance is
dominated by the
rainfall, which can be an
order of
Magnitude > PET
Makes it Important to
know the rainfall!
Hyper-humid in the N
and E to hyper-arid in
the SW

10. Per capita water balance
CIESIN
Per capita water availability is high throughout the N and W.
Availability ≠ access
Some low spots at densely populated urban centres.
Lowest in coastal Peru, Chile, Bolivia and Argentina.

11. Water demand vs. supply
- =
Annual water supply (m3)
- Annual water demand
(m3)
= Annual water surplus/deficit
(m3)
Agricultural demand (green water) is accounted for in the ET/water balance calculation.
Industrial demand highly localised. Domestic demand estimated here from mean p.c. water use and
population density. Deficits in the S.

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

13. Water storage and use: dams in the Andes
Dams : points in the landscape at which
water=productivity
Andes : 174 large dams
10.5% of land area drains into a dam Catchments of
Andean dams
Accessing around 20% of streamflow
At least 100 km3 of water storage capacity
At least 20,000 MW HEP capacity
Also used for drinking water, irrigation and
industrial purposes (100 million people)
20% of the Andean population lives
upstream of dams – importance of careful
land management
See presentation of Leo Saenz for detail

14. Impacts on water availability I
Water quality
Parts of the Andes have a lot of water but not all water is usable because of:
1. Lack of access
2. Lack of storage
3. Water quality is not fit for purpose

15. Point sources can have a direct influence on downstream users
% of water in streams that fell as rain
on a mine:
1. There are a lot of mines in the Andes
and there will be more
2. Mines can have significant
downstream impacts so need careful
management and planning.

16. % of water that is human impacted
Human activities (agriculture,
roads, mining, oil and gas and
urban areas influence
downstream water quality.
Likely reflected in higher
sediment loads, organic and
inorganic contaminants, incl.
pesticides and fertiliser etc.
Influence decays downstream by
dilution of human influenced
water with runoff from less
influenced areas.
Maps potential quality of water,
usually poor around people!
See: Wednesday 11th 4:40 - 5:10 pm en el Bloque 4 session:
Manejo del Agua en Zonas Urbanas

17. Impacts on water availability II
Climate variability and change
Climate has always changed and will continue to do so.
But we do not know what the future holds, how can we understand
the water resource implications?
...use our best guess. A general circulation model (GCM) projection of
future climate.

18. But these are highly uncertain because there is a lot about the
climate we just do not know?
How can we reduce uncertainty?
Use many models and see what they agree and
disagree on and indeed if there is any consensus:

19. Mean change and uncertainty (s.d.) of 17 GCMs
Warming and wetting for the Andes.
Greatest T uncertainty at high latitudes, coastal and Amazon margins
Rainfall change highly certain

20. 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 and in in J,J,A,S

21. 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)
Mostly even seasonal distribution of change.
Therefore, no major negative changes in seasonal deficits likely

22. 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? Methods
1. Use monthly anomalies (deltas) (mean of 17 models) to force
FIESTA hydrological model at Andes scale
2. Look into implications for evapo-transpiration and water
balance

23. Regional scale hydrological impact
4 mm/yr loss 100-300 mm/yr gain
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)

24. ??Uncertainty??
Remember the Mona Lisa?
We cannot even measure rainfall properly at the Andean scale
and the systems that determine access and productivity of water
are much more complex than just rainfall.
How do we deal with this complexity and uncertainty?
1. 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 –
look at system sensitivity?
2. We run with multiple datasets and multiple parameters to
understand the levels of uncertainty.
3. Instead of providing answers, we tie data and knowledge into a
system for providing answers (a PSS) that can be applied to
geographically and sectorally specific questions.

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

26. The AGUAANDES POLICY SUPPORT SYSTEM
-Online (web service)
-All data supplied (1km or 1 Ha.)
-Detailed and easy to use PSS
SimTerra : the most -Bilingual
detailed global -Testable climate and land use scenarios
databases, tiled and policy options e.g. dam building
+
Detailed grid –based
process models
+
Tools to test
scenarios and policy
options
http://www.policysupport.org/links/aguaandes
More details and Demo BFPANDES workshop Tuesday 10-11

27. 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. Water quality is currently and will likely continue to be more of a
problem for the Andes than climate change, especially for potable water.
Requires careful legal regulation and benefit sharing mechanisms
3. Climate change will likely have a positive or neutral effect on water
quantity in the Andes but may create regulation or quality issues.
4. There is still an enormous lack of knowledge about the biophysical
components of water resources – do not consider it well known because it
is not.
Much more detail in mid-term and final reports : www.bfpandes.org
Thank you

29. 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/

30. Dry matter
Results : water productivity
production
(Kg/Ha./yr)
[without trees]
A coarse scale (1km) estimate
of broad differences in
productivity, not an estimate
of yield.

31. 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.

32. 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 is highest in Colombia and Ecuador.
Highly productive irrigated cropland in Chile and Argentina.
Cropland also productive in E. Bolivia, lowland Argentina.

33. 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

34. 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 (rainfall and temperature)
Draw implications for impacts of climate change scenaria
Ignore water quality issues (for now)
But then there are also effects of seasonality, CO2 fertilisation, nutrient
limitation, respiration, pests and diseases.... All of which change with
climate.........so we cannot give a definitive answer but rather start the process
of building a system to provide answers

35. 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.

36. WP 3 : 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/geodata)
4. Calculation of dam watersheds using HydroSHEDS and estimation of
their productivity (dams discussed in presentation by Leo Saenz)
5. Freshwater fisheries productivity (discussed in presentation by
UNAL).

37. Dams turn water into energy, urban, industrial and irrigation water
KCL GLOBAL GEOREFERENCED 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 40N and 40S
23% are in South America
32% of land area between 40S and 40N 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.