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Presentation at CoCoon meeting in Cali, 22 of Sept, 09

Presentation at CoCoon meeting in Cali, 22 of Sept, 09

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    Andes BFP Andes BFP Presentation Transcript

    • BFP Andes: concepts and advances
      • J. Rubiano and Andes Basin Focal Project Team
      • King’s College, University of London, jerubiano@gmail.com
      CoCoon Matchmaking Meeting
      Cali, Colombia - 22-23 September, 2009
    • Outline
      What is the Andes BFP
      Work and products
      Network of partners
    • BFPANDES : Aim
      The aim of the BFPANDES is “to have the best available science used in the formulation and testing of land and water policy for better livelihoods in the Andes”.
      BFPANDES : 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 management interventions maximize economic returns (production), alleviate poverty whilst minimizing degradation of water, soil and environment?
    • The Andes ‘basin’ (all basins above 500 masl) and the 13 key sub-basins
      Context:
      Transnational, globally important
      Heterogeneous (hyper humid to hyper arid)
      Steep slopes, competing demands on land use
      Environmentally sensitive
      www.ambiotek.com/aguaandes
    • Silvia Benitez Water Conservation
      Programme Co-ordinator
      Carmen Candelo Reina
      Governance and Livelihoods Program
      Director
      Noel Trejos Chief Scientist in
      Integral Management
      John Pender
      Economist
      Meagan
      Keefe
      Agricultural
      Economist
      Jairo Valderrama
      Biologist
      Edwin Pajares
      Director of Natural
      Resource Sharing
      Program
      Alonso Moreno
      Natural Resource
      Sustainable
      Management
      Programme
      Mario Aquirre Senior Officer
      Water Program
      Ernesto Guhl Cam
      SEI, Cauca University, Valle University, CAN, Proyecto GEF Paramo, CIAT, UNAL, CONDESAN, Kings College London, Universidad Autonoma,deOccidente,
    • Where are the poor?
      Why they are poor?
      Which are the related factors?
      Which are the opportunities?
    • % of population
      with unmet
      basic needs
    • Andean socio-economic selected indicators (1=FAO, 2=WHO, 3=CEPAL, 4=DHS, U=Urban, R=Rural)
    • Agriculture in theEconomy
    • 0.55
      0.50
      IMPORTS AND EXPORTS
      IN THE ANDEAN REGION
      1980 – 2007
    • Migration in theRegion
    • Urbanization of poverty
    • Public and private debt in the Andes
    • What is the current institutional context?
      What are their main constraints and advantages?
      What needs to be changed?
    • Colombia
    • Ecuador
      Peru
      Bolivia
    • INSTITUTIONAL ENVIRONMENTAL INDEXVariables 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, purchase power, financial support.
      • Political
      People displaced by violence
    • Composed representation of key characteristics of
      IEI-Col = ∑ (A+B+C+D+E)/5
      A = No_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)/5
      A = Iliteracy_rate
      B = Unsatisfied_Basic_Needs
      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 = No_kids_primary_school_completed
      B = No_kids_primary_school_finished_on_time
      C = No_educated_kids_between_4&5
      D = No_educated_kids_between_12&16
      E = No_young_Secondary_School_completed
      F = No_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 = No_of_teaching_rooms
      C = Human_Development_Index (2001)
      D = Yearly_Average_expenditure
      E = PerCapita_compsumption_USD-Year (2001)
      F = Social_Investments_USD (2006)
      G = Non_Social_Invest_USD (2006)
      H = No_Finance_Institutions
      Tough conditions, bigger effort
      Less difficult
      *
      * Standardize for the four countries, main capitals excluded
    • How much water?
      Where?
      When?
    • Methods : water availability
      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-)
    • Results : water availability
      Total annual rainfall
      (mm)
      TRMM>
      <WorldClim
      trmm
      wclim
    • Actual evapotranspiration (mm/yr)
      Water balance (mm/yr) [worldclim]
    • J
      F
      M
      A
      M
      J
      J
      A
      S
      O
      N
      D
      Rainfall (mm/month) - highly variable spatially and seasonally, hyper-humid to hyper-arid
    • How water is used, by whom and where?
      What are the current and potential benefits out of water?
    • Methods : water productivity
      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
      Whole-Andes analysis of plant production based on dry matter production calculated from SPOT VGT (1998-2008), masked to exclude trees.
      Whole Andes analysis of production per unit rainfall (crop per drop)
      Precise digitisation of all dams in the Andes using Google Earth Dams Geowiki (http://www.kcl.ac.uk/schools/sspp/geography/research/emm/geodata/geowikis.html)
      Calculation of dam watersheds using HydroSHEDS
    • Dry matter production
      (Kg/Ha./yr)
      [without trees]
      Results : water productivity
    • Dry matter production
      DMP (in g/ha/yr)
      <Averaged in
      500m elev. bands
      Averaged by
      Catchment>
      Lowest elevations have highest productivity.
      Colombian and Ecuadorian Andean catchments have
      Highest productivity along with Eastern foothill catchments in the South
    • <Crop per drop of rainfall (RUE)
      (g/Ha./mm)
      [without trees].
      Averaged by
      catchment
      Crop per drop > (g/Ha./mm)
      [without trees].
      for areas with <500mm rainfall
      Lowest elevations have greatest crop per drop. Small lowland-dominated Pacific and Eastern foothill catchments have greatest crop per drop
    • DMP (in Dg/ha/day)
      DMP (in Dg/ha/day)
      Elevation(m)
      Rainfall (mm/yr)
      Crop per drop of rainfall (RUE) (g/Ha./mm)
      Rainfall (mm/yr)
      Rainfall (mm/yr)
    • Dams : points in the landscape at which water=productivity
      Tropics : land areas draining into dams
      by: Leo Saenz
      Developed the first georeferenced global database of dams (www.kcl.ac.uk/geodata)
      There are at least 29,000 large dams between 40N 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. 80% are in the largest countries (China, India, Brazil, South Africa, Zimbabwe, Mexico)
      33% of land area between 40S and 40N drains into a dam (capturing some 24% of rainfall and thissurface provides important environmental and ecosystem services to specific companies.
    • Water productivity : dams in the Andes
      Andes : 174 large dams
      Area draining into dams : 389,190 km2 (10.5% of land area)
      At least 80,300Mm3 of water storage capacity
      At least 20,000 MW HEP capacity
      Also used for drinking water, irrigation and industrial purposes
    • Environmental services : the role of cloudforests
      Peru/Bolivia % of water derived from cloud stripping
    • Tracing the impact of protected areas on water
      Assuming that water originating
      from protected areas is better than
      that originating elsewhere:
      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
    • Number of urban people drinking 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
    • What have been made/attempted before?
      What is feasible to do from now on?
      With whom, where, how?
    • Strategic Interventions so far
      • Agricultural and income diversification
      • Compensation/ payment for environmental services
      • Risk Management
      • Institutional capacity building and policy dialogues
      • Access to irrigation infrastructure
    • Environmental vs. ecosystem services : cloud forest example
      An environmental service:
      Cloud forests occur underneath persistent ground level cloud in the tropics. This cloud generates:
      • high rainfall inputs
      • low evapo-transpiration
      The pan-tropical average cloud-forest water balance is 452 mm/yr cf 124 mm/yr for the tropics as a whole. This is a function of the climate in which the cloud forest sits not the cloud forest itself and would occur even in the absence of the forest.
      An ecosystem service:
      Cloud forests strip passing cloud/fog water very efficiently and this water ends up in the rivers. If the cloud forests are replaced by pasture, this stripping does not occur and the extra water is lost. This service is dependent on the ecosystem as well as the environment.
      Example of water from montane forests
      Peru/Bolivia % of water derived from cloud stripping
    • Potential for Aquaculture in the Andes
      Food Security - Is Aquacultureanalternative in Andean system?
      How this activity compete with others?
    • Products
      capacity built in local students, institutions/stakeholders through training, workshops and tools,
      (b) 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)
      The AguAAndes Policy Support System – a web based tool for understanding the likely impact of particular scenarios of change and policy options on water and water poverty in any Andean catchment (http://www.policysupport.org/links/aguaandes).
    • The AGUAANDES POLICY SUPPORT SYSTEM
      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
    • Networking and Knowledge Sharing
      • Andes BFP Lima, Peru Workshop
      • Andes BFP Fuquene, Cundinamarca - Workshop
      • CONDESAN - Environmental Services Conference – Manizales, Colombia
      • Water Workshop at Externado University – Bogota
      • International Forum on Water and Food, Ethiopia
      • PROSUL Workshop, Brasil
      • Policy Support System On line questionnaire.
      • FINAL ANDES BFP MEETING - Agua2009 Conference, Cali – Colombia 9 – 11 NOV 2009 www.ambiotek.com/bfpandesworkshop
    • Gracias
    • Methods : Institutions
      Composed representation of a selection of key social, economical and political variables that helps answering where an intervention will face hash conditions, need higher effort and more investment.
      It also expresses which characteristics can be used as indicators of progress for development and poverty reduction strategies.
      It is made with the most reliable country data at municipal level.
      Methods for data processing include PCA, Cluster and Spatial Analyses.
    • COLOMBIA
      PERU
      ECUADOR
      BOLIVIA
      http://www.latin-focus.com/
      http://www.bcb.gov.bo/webdocs/Diciembre2008/estadodeuda2008.pdf