Water and Productivity Impacts for the NBDC


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Presented by Charlotte MacAlister, Birhanu Zemadim , Teklu Erkossa , Amare Haileslassie, Dan Fuka, Tammo Steenhuis, Solomon Seyoum, Holger Hoff, Kinde Getnet, and Nancy Johnson to the Nile Basin Development Challenge Science and Reflection Workshop, Addis Ababa, 4-6 May 2011

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  • Are all three optimized or just profit?
  • Is this why optimization or why multicriteria?
  • Water and Productivity Impacts for the NBDC

    1. 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. 2. After measuring and acquiring time series data:-Next step?? <ul><li>Hydrological Modelling using SWAT/SWAT-WB </li></ul><ul><ul><li>Simulating the different physical processes in both time and space with reasonable accuracy </li></ul></ul><ul><ul><li>Determine water use and productivity at different locations in the landscape (e.g. from trees, cultivated areas and pasture etc.) </li></ul></ul><ul><li>Using the simulated model evaluate the possible implications for hydrological fluxes of different scenarios of RMS interventions </li></ul><ul><li>e.g. the installation of ponds and tanks, bunds or terracing etc. </li></ul><ul><li>Finally determine the impact of up-scaling RWM interventions on downstream flows as well as water productivity at different locations in the landscape. </li></ul>
    3. 3. Field monitoring
    4. 4. Hydro-Meteorological Monitoring in the Study Landscapes <ul><li>Objectives </li></ul><ul><li>Developing primary data to understand water budgets, fluxes and flow pathways- Hydrological processes. </li></ul><ul><li>Estimating water-use and water productivity within the framework of Integrated Rainwater Management Strategies, Policies and Institutions. </li></ul><ul><li>Data Obtained will be used to calibrate and validate computer models (e.g. SWAT) which can then be used to simulate: </li></ul><ul><ul><li>Hydrological Processes </li></ul></ul><ul><ul><li>Investigate the possible hydrological implications of RMS interventions </li></ul></ul>
    5. 5. Equipments to be Installed and Variables to be Monitored <ul><li>Automatic Weather stations measuring rainfall, temperature, relative humidity, wind speed and wind direction, solar and net radiation and soil temperature. One in each catchment </li></ul><ul><li>Manual Rain Gauges distributed across altitude and space. 5 in each catchment </li></ul><ul><li>Pressure Transducers </li></ul><ul><li>1. Stream flow measurement </li></ul><ul><li>at catchment outlet to measure river stage (converted to flow using a rating equation, determined from current meter measurements). One in each catchment </li></ul><ul><li>2. Groundwater level measurement </li></ul><ul><li> Five in each catchment </li></ul>
    6. 6. <ul><li>20 Stage Boards at the catchment outlet to enable manual measurement of stage (converted to flow using a rating equation, determined from current meter measurements) </li></ul><ul><li>3 Soil moisture profile inside the catchment to measure volumetric moisture content </li></ul><ul><li>Dip Meters portable, reliable instrument for measuring the water level and total depth in boreholes, wells etc. </li></ul><ul><li>Sediment Sampler Done manually </li></ul>Equipment to be installed and corresponding variables to be monitored
    7. 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. 8. Remote monitoring: the solution to upscaling (hydromet) parameters?
    9. 9. GEONETCAST NRT global network of satellite-based DDS ( space-borne , air-borne , in situ data ) Source: EUMETSAT brochure
    10. 10. Uses of GEONETCAST: <ul><li>Filling precipitation data gaps </li></ul><ul><li>Measurement of 30 minute rainfall used in erosivity estimation </li></ul><ul><li>Aerial rainfall distribution </li></ul><ul><li>Soil moisture estimation </li></ul>
    11. 12. SEBS = estimating atmospheric turbulent fluxes and surface evaporative fraction using EO data in the visible , NIR and TIR
    12. 13. SWAT 2005, User Manual <ul><li>I 30 , R day and i max can be derived from the 15’ rain intensity time series </li></ul>APPLICATIONS cont’d… SWAT 2005, User Manual
    13. 14. Disturbance Index
    14. 15. Challenges: <ul><li>Continuous validation is required (ground truthing) </li></ul><ul><li>Data storage issues </li></ul>
    15. 16. Water productivity of mixed crop-livestock systems: progresses and challenges
    16. 17. <ul><ul><li>High unproductive </li></ul></ul><ul><ul><li>water losses </li></ul></ul><ul><ul><li>Example: </li></ul></ul><ul><ul><li>Runoff + Evaporation </li></ul></ul><ul><ul><li>High nutrient depletion </li></ul></ul><ul><ul><li>WP scenario ought to change! </li></ul></ul>Full nutrient balances on Teff <ul><ul><li>Blue Nile Highlands: </li></ul></ul><ul><ul><li>>95% rain fed and major </li></ul></ul><ul><ul><li>sources of livelihoods </li></ul></ul>
    17. 18. <ul><ul><li>Crop WP gaps are enormous. Implication for LWP </li></ul></ul><ul><ul><li>Objective: </li></ul></ul><ul><ul><li>Develop strategies and menu of practices for different recommendation domains and understand the impacts of the strategies for water productivity. </li></ul></ul>
    18. 19. <ul><ul><li>How we do: Understanding the system </li></ul></ul>HH survey (30-40 farms per system) Focus farmers: (landscape positions resources flow monitoring) Landscape/watershed boundary <ul><li>Feeing and feed sourcing; </li></ul><ul><li>Livestock products; </li></ul><ul><li>Livestock services </li></ul><ul><li>ME demand </li></ul><ul><li>FU factor </li></ul><ul><li>Agricultural water Partitioning </li></ul>Water flow and biomass H2O productivity: by land use and crop type Cropping pattern <ul><ul><li>Monitor: </li></ul></ul><ul><ul><li>Weather and H2O flow </li></ul></ul><ul><ul><li>Nutrient flow </li></ul></ul><ul><ul><li>Crop management and performance </li></ul></ul>Crop WP
    19. 20. <ul><ul><li>How we do: Understanding the system </li></ul></ul>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
    20. 21. Out scaling to basin scale <ul><li>For Crop WP: </li></ul><ul><li>Identify similar units represented by farming systems of the study landscapes </li></ul><ul><li>Acquire baseline data for the identified systems </li></ul><ul><li>Run models for the systems </li></ul><ul><li>Integrate system outputs into basin scale </li></ul>
    21. 22. <ul><ul><li>Challenges </li></ul></ul><ul><ul><li>Resources demanding! Some tools (e.g. APSIM) are data intensive </li></ul></ul><ul><ul><li>Whether biophysical factors such as soil, climate, landscape and crop types … details are considered in the similarity analysis for basin wide scaling up needs clarification </li></ul></ul><ul><ul><li>Capacity of soil laboratories to handle runoff samples </li></ul></ul><ul><ul><li>Data for large scale base line (soil, weather, crop management practices, livestock population) </li></ul></ul>
    22. 23. Green - Blue Water
    23. 24. Green-blue water baseline – framing the NBDC <ul><li>assessing: </li></ul><ul><li>green-blue water availability & </li></ul><ul><li>variability </li></ul><ul><li>storage (buffer capacity) </li></ul><ul><li>agricultural productivity </li></ul><ul><li>CP basins </li></ul><ul><li>Nile sub-basins </li></ul><ul><li>Blue Nile sub-basins </li></ul>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
    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
    25. 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?
    26. 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?
    27. 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
    28. 29. <ul><li>Challenges: </li></ul><ul><li>Lack of data at smaller scale </li></ul>
    29. 30. Hydrological Modelling with SWAT
    30. 31. Runoff plots Maybar) 16 37 43 64 slope of land Runoff Coefficients Hydrological processes + impacts Surface runoff decreases with steepness
    31. 32. Hydrological processes + impacts Hill slope Areas Surface runoff infiltration interflow v
    32. 33. Runoff and Scaling issues in SWAT <ul><li>Current limitations: </li></ul><ul><ul><li>Limited scaling abilities due coarseness of soils and land use data base </li></ul></ul><ul><ul><li>Can upscale by grouping HRUs, but no ability to downscale above the resolution of the finest initialization dataset. </li></ul></ul><ul><ul><li>Predicts infiltration excess runoff instead of saturation excess </li></ul></ul><ul><ul><li>Topographic data not used that is driving force for hydrology </li></ul></ul><ul><li>  </li></ul>
    33. 34. SWAT-WB initialization method allows continuous scaling to model full basin to sub-catchment scale phenomenon. <ul><li>Allows </li></ul><ul><li>TI/STI based </li></ul><ul><li>Predicts overland flow locations correctly </li></ul><ul><li>Down-scaling to DEM Resolution </li></ul><ul><li>Up-scaling with physical attribute instead of judgment </li></ul><ul><li>  </li></ul>
    34. 35. <ul><li>Challenges: </li></ul><ul><li>Current data availability – for calibration and validation </li></ul><ul><li>How to simulate actual RMS within the model </li></ul>
    35. 36. Evaluating Water Resources: Upscaling Impacts of RMS
    36. 37. Objectives of Water Resources Modeling (WEAP) <ul><li>Assess integrated impacts of rainwater management interventions and large-scale development projects on water availability, sediment load (and groundwater recharge) </li></ul><ul><li>Device mechanisms of incorporating rainwater management interventions in IWRM planning </li></ul><ul><li>Investigate up- and out-scaling options of rainwater management interventions using sustainability criteria of satisfying downstream flow requirements </li></ul>
    37. 38. Methodologies and Approaches <ul><li>General Approaches: </li></ul><ul><li>Represent RMS interventions scenarios in SWAT (parameters and management options) </li></ul><ul><li>Perturb inflows to large dams and river junctions according SWAT simulation of RMS interventions </li></ul><ul><li>Evaluate the bio-physical sustainability criteria for large-scale and RMS scenarios </li></ul><ul><li>Potential Scenarios: </li></ul><ul><li>Baseline (current) </li></ul><ul><li>Medium-term </li></ul><ul><li>Medium-term + RMS </li></ul><ul><li>Long-term </li></ul><ul><li>Long-term + RMS </li></ul>
    38. 39. WEAP Schematization Dams / Reservoirs Irrigation demands River Networks
    39. 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
    40. 41. Challenges <ul><li>The WEAP reservoir and river junction nodes should match with SWAT sub-basins </li></ul><ul><li>WEAP doesn't have sediment routing capability </li></ul><ul><li>Aggregated RMS scenarios are not yet defined (targeting) </li></ul>
    41. 42. Translating hydrology and productivity into environmental, social and economic impacts
    42. 43. WHAT is evaluated? <ul><li>Current practice (crops, livestock, trees, management practices) is compared to selected alternatives in terms of their impacts on: </li></ul><ul><ul><li>Farm profit ( economic benefit ) </li></ul></ul><ul><ul><li>Employment opportunities ( social benefit ) </li></ul></ul><ul><ul><li>Environmental externalities ( ecosystem benefit ) </li></ul></ul>
    43. 44. WHY multicriteria analysis? <ul><ul><li>To know the possible outcome in terms of profit, employment, and externality </li></ul></ul><ul><ul><li>To understand trade-offs between criteria and between upstream and downstream land and water users </li></ul></ul><ul><ul><li>To estimate values for environmental services that could be the basis for compensation schemes </li></ul></ul>
    44. 45. HOW to estimate impact? Linkages in N4 <ul><li>Bio-physical and hydrological characterization of watersheds and prioritization of areas for interventions (SWAT & WEAP) </li></ul><ul><li>Identify RWM practices and how they would be incorporated into land use systems </li></ul><ul><li>Gather data on productivity and environmental impacts </li></ul><ul><li>Gather data on socio-economic costs and benefits </li></ul><ul><li>Optimization model to get economic, employment and environmental impacts of RWMS scenarios </li></ul>
    45. 46. . Biophysical data Hydrologic modeling Externalities Optimization Socio-economic data RWM practices
    46. 47. Some examples on data inputs <ul><li>Externalities </li></ul><ul><li>Socio-economic issues </li></ul><ul><li>Erosion and sediments through land use </li></ul><ul><li>NPK release to water resources </li></ul><ul><li>Additional water available </li></ul><ul><li>Land ( allocation and constraints ) </li></ul><ul><li>Crops , forages , livestock , forest ( production ) </li></ul><ul><li>Costs and prices for crops, livestock, forests </li></ul><ul><li>Management practices and costs </li></ul><ul><li>Labor (costs and incomes) </li></ul>
    47. 48. Scaling out impacts - is basin level estimation appropriate? <ul><li>Run model for representative sites </li></ul><ul><li>Adopt results of similarity analysis </li></ul><ul><li>Scale out model results of representative sites (to similar sites) at basin level </li></ul><ul><li>Aggregate the scaled-out results </li></ul>
    48. 49. Challenges and limitations <ul><li>Characterizing and prioritizing scenarios with one or more RWMS </li></ul><ul><li>Scaling out impacts to a basin level </li></ul><ul><li>Incorporating gender and equity issues </li></ul><ul><li>How to validate the model outputs? </li></ul>
    49. 50. Challenges Summarized: <ul><li>How do we deal with data gaps at field and basin scale; validating the model outputs? (Dan + Birhanu) </li></ul><ul><li>How do we characterize/ aggregate/prioritize RMS scenarios – especially when we don’t have actual data measuring the impact of any specific RMS? (Solomon + Amare) </li></ul><ul><li>What alternatives exist to address scaling out RMS practices and process/impacts to a basin level? (Tammo +Teklu) </li></ul><ul><li>What alternative options do we have to link biophysical and livelihoods issues? (Kinde + Nancy) </li></ul>