Water availability and Productivity in the Andes Region

Mark Mulligan, King’s College Londonmark.mulligan@kcl.ac.uk

and the BFPANDES team : Condesan, CIAT, National University, Colombiamark.mulligan@kcl.ac.uk

[30 mins]

DATA AND MODELS AVAILABLE AT:www.policysupport.org/links/aguaandes and www.kcl.ac.uk/geodata

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

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 W2. Large urban areas throughout but especially in the N3. Complex network of large and globally important protected areas4. Significant irrigated agriculture especially in coastal Peru and the drier parts of

Ecuador and Colombia5. Highest GDPs concentrated around urban centres, large rural areas with low

GDP

Ramankutty Ramankutty CIESIN WCPA WDPA

CIESIN

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

Total annual rainfall(mm)

<WorldClim

TRMM>

trmm

wclim

Rainfall : falling at the

first hurdle.

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).

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

Precipitation stations used by WorldClim 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?

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.

points

interpolation

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

Potential Evapo-transpiration (mm/yr) Water balance (mm/yr) [worldclim]

Per capita water balance

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.

CIESIN

Annual water demand (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 here from mean p.c. water use and population density. Deficits in the S.

Annual water supply (m3)

- =

- =

Water deficits (millions of m3 annually)

Areas of current water deficit (demand>supply)

Line of water deficit

Water storage and use: dams in the Andes

Dams : points in the landscape at which water=productivity

Andes : 174 large dams10.5% of land area drains into a dam

Accessing around 20% of streamflowAt least 100 km3 of water storage capacityAt 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

Catchments of Andean dams

Impacts on water availability IWater quality

Parts of the Andes have a lot of water but not all water is usable because of:1. Lack of access2. Lack of storage3. Water quality is not fit for purpose

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.

% 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

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.

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 anddisagree on and indeed if there is any consensus:

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

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 and in 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

Mostly even seasonal distribution of change.

Therefore, no major negative changes in seasonal deficits likely

So what will happen?1. Who knows?2. It will be warmer and wetter3. Mean of 17 models warming is highest in the S Andes4. Mean of 17 models wetting is highest in the W and S coastal

Andes5. 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 rainfall7. Seasonality of change is high for temperature and low for

rainfall

What will be the hydrological impacts? Methods1. Use monthly anomalies (deltas) (mean of 17 models) to force

FIESTA hydrological model at Andes scale2. 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)

4 mm/yr loss 100-300 mm/yr gain

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.

??Uncertainty??

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

The AGUAANDES POLICY SUPPORT SYSTEM

-Online (web service)

-All data supplied (1km or 1 Ha.)

-Detailed and easy to use PSS

-Bilingual

-Testable climate and land use scenarios

and policy options e.g. dam building

SimTerra : the most detailed global databases, tiled

Detailed grid –based process models

Tools to test scenarios and policy

options

+

+

http://www.policysupport.org/links/aguaandesMore details and Demo BFPANDES workshop Tuesday 10-11

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. 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

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.

Dry matter production(Kg/Ha./yr)

[without trees]

Results : water productivity

A coarse scale (1km) estimate of broad differences in productivity, not an estimate of yield.

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 is 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) pasture

Dry 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]

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

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.

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).

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

The first georeferenced global database of dams (www.kcl.ac.uk/geodata)There are at least 29,000 large dams between 40N and 40S23% are in South America32% 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.

Tropics : land areas draining into damsby: Leo Saenz

KCL GLOBAL GEOREFERENCED DAMS DATABASE

Dams turn water into energy, urban, industrial and irrigation water