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Mark Mulligan: Water availability and Productivity in the Andes Region


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Mark Mulligan: Water availability and Productivity in the Andes Region

  1. 1. Water availability and Productivity in the Andes Region Mark Mulligan, King’s College London and the BFPANDES team : Condesan, CIAT, National University, Colombia [30 mins] DATA AND MODELS AVAILABLE AT: and
  2. 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. 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. 4. WP 2 : Water availability : Methods 1. Whole-Andes analysis of water availability at 1km spatial resolution using the FIESTA delivery model ( 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 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 6. Issues of water access discussed in other presentations
  5. 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. 6. Wind-driven rainfall is very heterogeneous in a mountainous environment – even at the scale of individual slopes... CQ See at (Google Earth viewer required)
  7. 7. ...but even in the Andes rainfall stations are sparsely distributed.... Precipitation stations used by WorldClim in Peru and Bolivia
  8. 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. 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. 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. 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. 12. Areas of current water deficit (demand>supply) Line of water deficit Water deficits (millions of m3 annually)
  13. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 More details and Demo BFPANDES workshop Tuesday 10-11
  27. 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 : Thank you
  28. 28. 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’
  29. 29. 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.
  30. 30. 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.
  31. 31. 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.
  32. 32. 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
  33. 33. 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 we cannot give a definitive answer but rather start the process of building a system to provide answers
  34. 34. 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.
  35. 35. 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 ( 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).
  36. 36. 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 ( 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.