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N4 Activity update, May 2012

N4 Activity update, May 2012. Original research questions from project brief: How can the consequences of improved rainwater management (RMS) systems be anticipated ( and measured) ? What methods are appropriate under different circumstances?

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N4 Activity update, May 2012

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  1. N4 Activity update, May 2012 Original research questions from project brief: • How can the consequences of improved rainwater management (RMS) systems be anticipated (and measured)? What methods are appropriate under different circumstances? • How can the contribution of improved RMS be assessed relative to the contributions of other factors? • How can research on performance be used to further improve RMS design?

  2. N4 Activity update, May 2012 N4 team (9 staff; 4 consultants; 10 students): TL/hydrology/modelling: Charlotte MacAlister Hydrology/modelling: Solomon Seyoum, Dan Fuka, Zach Easton, Tammo Steenhuis, Francisco Flores Soils/crop productivity: Teklu Erkossa Livestockproductivity: Amare Haileslassie, Don Peden Economics/Livelihoods:Kinde Getnet, Nancy Johnston Economic data review: Gerba Leta Spatial Analysis/data: Yenenesh Abebe Students: MSc - BedasaEba (also N2), Ayele Abebe (also N2), AlemayehuWudneh, Bamlaku Desalegn, Getnet Taye, Negasa Bane, AddisuAsfaw, Nurelegn Mekuriaw. PhD - Abeyou Wale, HaimanoteBayabil 2012 budget: $359K reducing to $319K

  3. N4 Activity update, May 2012 *Biophysical *Socio-economic N4 ‘themes’: • Developing hydrological (process based) and water resource models of the BNB to anticipate the impact of (large scale) RMS implementation • Assessing sediment and nutrient transport, loss and contamination • Investigating crop-water and livestock-water productivity > relating to RMS potential • Livelihoods and poverty impact analysis • Economic assessment of the water, sediment and agronomic components of the primary farming approaches, and modelling anticipated impacts of potential RMS on livelihoods in the BNB • Linking to N3 targeting for recommendation of appropriate ‘development domains’ for RMS • Analysis of policy implications of basin scale implementation of RMS

  4. Biophysical Impacts of RMS N4 Activity update, May 2012 • Improvements in RMS optimize distribution of rainfall amongst different hydrologic components to: • increase water availability > less loss and more water storage (soil, surface, GW) • reduce evaporation and increase transpiration i.e. crop water productivity = fodder availability and improved livestock water productivity • improve soil conditions (reduce sediment loss) and reverse land degradation • Evaluate current status of hydrologic components in relation to rainfall from global data • Initialize hydrologic and water resource models to evaluate impacts of RMS on water availability, sediment load, soil moisture (and groundwater recharge)

  5. N4 Activity update, May 2012 • Challenges: • Properly describing hydro-physical processes e.g. runoff • Parameterizing hydrological components (P, ET, soil moisture etc) • Representing RMS practices in the process based model (SWAT) • Accurate representation of plant water use at large scale (LU-LC) • Reliable sediment data • Sediment routing in reservoirs within WEAP model • Definition of RMS scenarios for impact modelling • Linking hydrological, water resource and economic models

  6. Optimizing rainfall partitioning and quantifying rainfall-runoff processes Rainfall Evaporation Transpiration Canopy Evaporation Soil Evaporation Vegetation Land Surface Water Body Throughfall Infiltration Overland flow Capillary Rise Minimize: Unproductive water use Soil Stream Interflow Maximize: TARGET/Productive water use Capillary Rise Baseflow Percolation Optimize: Aquifer Watershed discharge Transfers

  7. Proportion of Rainfall Contributing to Major Hydrologic Components (Climate Forecast System Reanalysis, 31 year mean)

  8. Proportion of Rainfall Contributing to Major Hydrologic Components (Climate Forecast System Reanalysis, 31 year mean - Wet Season )

  9. Traditional SWAT Soils Hydrological Units are defined by a coincidence of soil type and landuse Landuse Hydrological Response Units So runoff here is calculated the same.. …as here but we know this is not the case

  10. Incorporating Topographic Index in the SWAT model

  11. ‘Automating’ the Topographic Index in ArcGIS

  12. TI

  13. Global Soil – to be replaced by Masterplan soil coverage

  14. HRU’s

  15. SWAT-WEAP Interface (WEAP schema) SWAT sub-catchments Dams / Reservoirs Irrigation demands River Networks

  16. Next: how to parameterize the RMS in the SWAT process model…..

  17. Anticipating RMS impacts on Crop Water Productivity – on and off site

  18. N4 Activity update, May 2012 Anticipating economic impacts of RMS on households and catchments: Establish a baseline of current situation using HH, hydrological/sediment and secondary data (at hydrological unit scale or HRU) Done: Primary HH data gathered at Jeldu, Diga, Fogera ECOSAUT populated for Jeldu and Fogera Preliminary analysis completed for Jeldu Challenges: ‘Validating’ the model and analysis Incorporating crop, sediment and runoff data from 3 sites Scenario development with N2, N3 and stakeholders Extrapolation of economic impacts of RMS scenarios to larger scale

  19. Analysis of policy implications of basin scale implementation of RMS No output so far……..

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