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




  Mark Mulligan and Jorge...
The Andes ‘basin’ (all basins above 500 masl) and the 13 key CPWF
                             sub-basins

Context:
1.Not ...
Statistics : Bolivia, Colombia, Ecuador and Peru
Area: 3.8 million km2
Population: of 95 million (Col, Ecu, Peru, Bol, 200...
Andes : baseline




                                                          FAO Percentage of      Area sum GDP for 199...
Most countries on the way up....




Latin America is comparatively water rich and some sub-regions have developed
nicely....
...but spatially very variable
WP5 Intervention analysis; (Analysis of change and potential
change in basins)

What do water policy makers in the region ...
How can we help?
questionnaire of 80 water professionals from 7 Andean countries
                 Q. In your experience wh...
BFPA DES : Aim
The aim of the BFPANDES is “to have the best available
(social) science used by local institutions in the f...
Colombia             Complex
                     institutional
                     structures for
                     w...
Bolivia



Peru
WP4 Institutional analysis (How people manage water and the
             agricultural system that consumes it).
      U DE...
IEI-Col = ∑ (A+B+C+D+E)/5
  A = o_Finance_Institutions
                                                               Comp...
WP2: Assessment of Water resources (how much water? Who uses it?)

                         Water availability : Methods

...
Rainfall : falling
                                           at the
                                       first hurdle.
...
Wind-driven rainfall is very heterogeneous in a
mountainous environment – even at the scale of individual slopes...




  ...
...but even in the Andes rainfall stations are sparsely distributed....




                   WorldClim precipitation sta...
WorldClim precipitation stations in central Peru




The points are transparent and an image lies beneath, but what image?...
Potential Evapotranspiration (mm/yr)     Water balance (mm/yr) [worldclim]




                                       Hype...
Per capita water balance




   CIESIN


Per capita water availability is high throughout the N and W
Lowest in coastal Pe...
Water demand vs. supply




                Annual water demand   Annual water supply (m3)      Annual water
             ...
Areas of current water deficit (demand>supply)




               Water deficits (millions of m3 annually)
WP3 Assessment of Water productivity
      (How much do people gain from agricultural water use?).
Water productivity : Me...
Results : water productivity     Dry matter
                                 production
                                (K...
Dry matter
                                       production
                                     DMP (in kg/ha/yr)

     ...
DMP (kg/ha/yr) by land use [trees excluded]




              Dry matter productivity    Dry matter productivity     Dry m...
If we look at the entire countries, not just the Andes, then the lowlands
              are clearly more productive [trees...
WP1 Poverty analysis: (What is the linkage between water, agriculture and poverty in basins?)
But, there are noNBI vs. Pro...
What about other forms of water productivity : dams turn
water into energy or extra productivity

 KCL GLOBAL GEOREFERE CE...
Water productivity : dams in the Andes
Dams : points in the landscape at
which water=productivity

Andes : 174 large dams
...
Ecosystem services : cloud forest example
Rules of thumb for the water service benefits of protected areas
            Water quantity services
           •Protected...
Tracing the impact of protected areas on water



    As you travel downstream
    from the protected areas their
    cont...
umber of urban people consuming water originating in a protected
             area – WDPA 2009 (Colombia)    [gl_sumurbpc]...
But who should pay to manage nature to
                     maintain these services?
1. Everyone
       -through national ...
Percentage of water arriving at tropical dams that fell as rain on protected areas




                                   ...
Institutional questionnaire did not find interest in
              climate change. Why?
Don’t we have enough to deal with ...
But we do not know what the future holds. What
                 can we do?




  ...use our best guess. A
  general circul...
But these are highly uncertain?
      How can we reduce uncertainty?

Use many models and see what they agree and
        ...
Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs




                                                     ...
Temperature change AR4-A2a (1961-90) to 2050 – 17 different GCMs




                                          miroc3_2_me...
Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs




    bccr_bcm2_0       cccma_cgcm2                  ...
Precipitation change AR4-A2a (1961-90) to 2050 – 17 different GCMs




        ipsl_cm4    miroc3_2_hires    miroc3_2_medr...
Mean change and uncertainty (sd) of 17 models




Warming and wetting.
Greatest uncertainty at high latitudes, coastal and...
Temperature seasonality of change : mean of 17 models
                     J         F                  M              A  ...
Rainfall seasonality of change : mean of 17 models
                   J        F                   M              A       ...
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
...
Regional scale hydrological impact




Mean annual temperature   Mean annual precipitation     Mean annual evapo-      Mea...
But then there is the issue of water quality.....
% of water in streams originating
from mine.
1.This pattern is repeated ...
So what are the implications for agriculture?
Method:

Examine the current distribution of productivity from 10 years of 1...
DMP (in Dg/ha/day)




                                      Rainfall (mm/yr)
Relationships between productivity and rainf...
But then there are effects of seasonality, CO2 fertilisation,
nutrient limitation, respiration, pests and diseases.... All...
Sensitivity to change




 Runoff sensitivity to tree           Runoff sensitivity to      Runoff sensitivity to
cover cha...
SimTerra : the
 most detailed
    global
databases, tiled
       +
Detailed grid –
based process
   models
       +
 Tools...
Concluding:
1.Water productivity is much more than crop per drop and includes
productivity for energy (HEP), domestic and ...
BFPA DES : Outputs
(a)capacity built in local students, institutions/stakeholders through
training, workshops, tools, diss...
Persons per km2 of urban population drinking water originating in a
          protected area – WDPA 2009 (Colombia)     [g...
Like carbon, water is not just a national issue
Flows of virtual water (transpiration) embedded in traded agricultural pro...
The “world water crisis”

                                                                          1.Humans have availabl...
<Crop per drop of
                            rainfall (RUE)
                             (g/Ha./yr/mm)
                  ...
Crop per drop
                                                    (g/ha/yr/mm water), for
                                ...
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
Water and Poverty in the Andes: Results from the CPWF Andes Basin Focal Project
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Water and Poverty in the Andes: Results from the CPWF Andes Basin Focal Project

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

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

  1. 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. 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. 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. 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. 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. 6. ...but spatially very variable
  7. 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. 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. 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. 10. Colombia Complex institutional structures for water Ecuador
  11. 11. Bolivia Peru
  12. 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. 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
  14. 14. 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).
  15. 15. 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).
  16. 16. 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)
  17. 17. ...but even in the Andes rainfall stations are sparsely distributed.... WorldClim precipitation stations in Peru and Bolivia
  18. 18. 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?
  19. 19. Potential Evapotranspiration (mm/yr) Water balance (mm/yr) [worldclim] Hyper-humid in the N and E to hyper-arid in the SW
  20. 20. Per capita water balance CIESIN Per capita water availability is high throughout the N and W Lowest in coastal Peru, Chile, Bolivia and Argentina
  21. 21. 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.
  22. 22. Areas of current water deficit (demand>supply) Water deficits (millions of m3 annually)
  23. 23. 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
  24. 24. Results : water productivity Dry matter production (Kg/Ha./yr) [without trees]
  25. 25. 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
  26. 26. 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.
  27. 27. 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
  28. 28. 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
  29. 29. 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.
  30. 30. 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
  31. 31. Ecosystem services : cloud forest example
  32. 32. 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
  33. 33. 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
  34. 34. 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
  35. 35. 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)
  36. 36. 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.
  37. 37. 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
  38. 38. 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.
  39. 39. But these are highly uncertain? How can we reduce uncertainty? Use many models and see what they agree and disagree on:
  40. 40. 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
  41. 41. 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
  42. 42. 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
  43. 43. 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
  44. 44. Mean change and uncertainty (sd) of 17 models Warming and wetting. Greatest uncertainty at high latitudes, coastal and Amazon margins
  45. 45. 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
  46. 46. 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.
  47. 47. 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
  48. 48. 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)
  49. 49. 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
  50. 50. 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)
  51. 51. 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.
  52. 52. 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
  53. 53. 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)
  54. 54. 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
  55. 55. 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
  56. 56. 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).
  57. 57. 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
  58. 58. 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.
  59. 59. 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 /
  60. 60. <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).
  61. 61. Crop per drop (g/ha/yr/mm water), for cropland Crop per drop highest in high Andes (Colombia, Ecuador) and SE Bolivia

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