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Water and Productivity Impacts for the NBDC Charlotte MacAlister  Birhanu Zemadim Teklu Erkossa Amare Haileslassie Dan Fuka Tammo Steenhuis Solomon Seyoum Holger Hoff Kinde Getnet Nancy Johnson Nile Basin Development Challenge Science and Reflection Workshop Addis Ababa, 4-6 May 2011
After measuring and acquiring time series data:-Next step?? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Field monitoring
Hydro-Meteorological Monitoring in the Study Landscapes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Equipments to be Installed and Variables to be Monitored ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],Equipment to be installed and corresponding variables to be monitored
Challenges: - Limited recording of parameters  e.g. ET creates difficulties for scaling/projecting both at different altitudes and scales - No actual recording of impacts of RMS - Short duration of monitoring period
Remote monitoring: the solution to upscaling (hydromet) parameters?
GEONETCAST NRT global network of satellite-based DDS ( space-borne ,  air-borne ,  in situ data ) Source: EUMETSAT brochure
Uses of  GEONETCAST: ,[object Object],[object Object],[object Object],[object Object]
 
SEBS = estimating atmospheric turbulent fluxes and surface evaporative fraction using EO data in the  visible ,  NIR  and  TIR
SWAT 2005, User Manual ,[object Object],APPLICATIONS cont’d… SWAT 2005, User Manual
Disturbance Index
Challenges: ,[object Object],[object Object]
Water productivity of mixed crop-livestock systems:  progresses and challenges
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Full nutrient balances on Teff  ,[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
[object Object],HH survey (30-40 farms per system) Focus farmers: (landscape  positions resources flow monitoring) Landscape/watershed boundary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Water flow and biomass  H2O productivity: by land use and crop type Cropping  pattern  ,[object Object],[object Object],[object Object],[object Object],Crop WP
[object Object],Crop WP Crop Weather Modeling Crop WP: Current VS RWMS  Potential Models: AquaCrop, APSIM Soil Management: Current Vs Improved Scenarios Map + Management practices (Students) Selected field sample + Sediment quality (students) Review + Field survey RCM +weather stations
Out scaling to basin scale ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
Green - Blue Water
Green-blue water baseline – framing the NBDC  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],mapping spatial patterns, hotspots, opportunities Nile blue water: ~ 1300 m 3  cap -1  yr -1 , green ~ 900 (crop) water productivity: ~ 700 kcal m -3 potential kcal production: 4300 kcal cap -1  day -1 in dry years (10 th  percentile): 3900 kcal In 2050: 1900 / 1600 kcal BUT: huge spatial variability along the Nile LPJ-based in a nested approach
(„difficult hydrology“  –  David Grey ) rainfall variability  and drought risk (ILRI) blue per capita 2000 / 2050 green per capita 2000 / 2050 CoV & 10 th  percentile green - blue water productivity Nile  (framing Blue Nile for taking into account downstream consequences) IDIS -> NBI subbasins water availability 1) CPWF, Mulligan 2) IWMI, Karimi CPWF and IIASA data
additional data: integration with socio-economic data,  e.g. market  access (JRC): blue water storage from „rethinking water storage“ project yield gaps from Licker et al (Wisconsin) Blue Nile Blue Nile SWIM model: focus on water productivity & climate change green-blue water availability at sub-basin scale?
Effects of interventions / SWC e.g. rainwater harvesting and consolidated with other  evidence from the Blue Nile starting from WOCAT database  and suitability mapping (Dile 2010) can local WOCAT experience be generalized / upscaled Lake Tana SWIM model for simulating the effects of outscaling / upscaling for assessing landscape scale effects  of increasing adoption?
Resilience (landscape) approach „ social ecological systems“ integrating  water fluxes and productivities with  institutions and their position /  effectiveness  re uptake of measures node-based WEAP  node-based social networks
[object Object],[object Object]
Hydrological Modelling  with SWAT
Runoff  plots  Maybar) 16  37  43  64 slope of land  Runoff Coefficients Hydrological  processes + impacts Surface runoff decreases with steepness
Hydrological processes + impacts Hill slope Areas Surface runoff infiltration interflow v
Runoff and Scaling issues in SWAT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SWAT-WB initialization method allows continuous scaling to model full basin to sub-catchment scale phenomenon. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
Evaluating Water Resources:  Upscaling Impacts of RMS
Objectives of Water Resources Modeling (WEAP) ,[object Object],[object Object],[object Object]
Methodologies and Approaches ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
WEAP Schematization Dams / Reservoirs Irrigation demands River Networks
Modeling Framework Watersheds Sub-basins Abay Basin SWAT  Model Climate Soil LU WEAP  Model RMS   Scenarios L-S Dev’t   Scenarios Impact Evaluation  Large dams and River Junctions
Challenges ,[object Object],[object Object],[object Object]
Translating hydrology and productivity into environmental, social and economic impacts
WHAT  is evaluated? ,[object Object],[object Object],[object Object],[object Object]
WHY  multicriteria analysis? ,[object Object],[object Object],[object Object]
HOW  to estimate impact? Linkages in N4  ,[object Object],[object Object],[object Object],[object Object],[object Object]
. Biophysical data Hydrologic modeling Externalities Optimization Socio-economic data RWM practices
Some examples on data inputs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Scaling out impacts  - is basin level estimation appropriate? ,[object Object],[object Object],[object Object],[object Object]
Challenges and limitations ,[object Object],[object Object],[object Object],[object Object]
Challenges Summarized: ,[object Object],[object Object],[object Object],[object Object]

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Water Impacts NBDC Ethiopia Workshop 2011

  • 1. Water and Productivity Impacts for the NBDC Charlotte MacAlister Birhanu Zemadim Teklu Erkossa Amare Haileslassie Dan Fuka Tammo Steenhuis Solomon Seyoum Holger Hoff Kinde Getnet Nancy Johnson Nile Basin Development Challenge Science and Reflection Workshop Addis Ababa, 4-6 May 2011
  • 2.
  • 4.
  • 5.
  • 6.
  • 7. Challenges: - Limited recording of parameters e.g. ET creates difficulties for scaling/projecting both at different altitudes and scales - No actual recording of impacts of RMS - Short duration of monitoring period
  • 8. Remote monitoring: the solution to upscaling (hydromet) parameters?
  • 9. GEONETCAST NRT global network of satellite-based DDS ( space-borne , air-borne , in situ data ) Source: EUMETSAT brochure
  • 10.
  • 11.  
  • 12. SEBS = estimating atmospheric turbulent fluxes and surface evaporative fraction using EO data in the visible , NIR and TIR
  • 13.
  • 15.
  • 16. Water productivity of mixed crop-livestock systems: progresses and challenges
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23. Green - Blue Water
  • 24.
  • 25. („difficult hydrology“ – David Grey ) rainfall variability and drought risk (ILRI) blue per capita 2000 / 2050 green per capita 2000 / 2050 CoV & 10 th percentile green - blue water productivity Nile (framing Blue Nile for taking into account downstream consequences) IDIS -> NBI subbasins water availability 1) CPWF, Mulligan 2) IWMI, Karimi CPWF and IIASA data
  • 26. additional data: integration with socio-economic data, e.g. market access (JRC): blue water storage from „rethinking water storage“ project yield gaps from Licker et al (Wisconsin) Blue Nile Blue Nile SWIM model: focus on water productivity & climate change green-blue water availability at sub-basin scale?
  • 27. Effects of interventions / SWC e.g. rainwater harvesting and consolidated with other evidence from the Blue Nile starting from WOCAT database and suitability mapping (Dile 2010) can local WOCAT experience be generalized / upscaled Lake Tana SWIM model for simulating the effects of outscaling / upscaling for assessing landscape scale effects of increasing adoption?
  • 28. Resilience (landscape) approach „ social ecological systems“ integrating water fluxes and productivities with institutions and their position / effectiveness re uptake of measures node-based WEAP node-based social networks
  • 29.
  • 31. Runoff plots Maybar) 16 37 43 64 slope of land Runoff Coefficients Hydrological processes + impacts Surface runoff decreases with steepness
  • 32. Hydrological processes + impacts Hill slope Areas Surface runoff infiltration interflow v
  • 33.
  • 34.
  • 35.
  • 36. Evaluating Water Resources: Upscaling Impacts of RMS
  • 37.
  • 38.
  • 39. WEAP Schematization Dams / Reservoirs Irrigation demands River Networks
  • 40. Modeling Framework Watersheds Sub-basins Abay Basin SWAT Model Climate Soil LU WEAP Model RMS Scenarios L-S Dev’t Scenarios Impact Evaluation Large dams and River Junctions
  • 41.
  • 42. Translating hydrology and productivity into environmental, social and economic impacts
  • 43.
  • 44.
  • 45.
  • 46. . Biophysical data Hydrologic modeling Externalities Optimization Socio-economic data RWM practices
  • 47.
  • 48.
  • 49.
  • 50.

Notas do Editor

  1. Are all three optimized or just profit?
  2. Is this why optimization or why multicriteria?