Water and Productivity Impacts for the NBDC
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Transcript of Water and Productivity Impacts for the NBDC
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Water and Productivity Impacts for the NBDC
Charlotte MacAlister Birhanu Zemadim
Teklu ErkossaAmare Haileslassie
Dan FukaTammo SteenhuisSolomon Seyoum
Holger HoffKinde Getnet
Nancy Johnson
Nile Basin Development ChallengeScience and Reflection WorkshopAddis Ababa, 4-6 May 2011
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After measuring and acquiring time series data:-Next step??
• Hydrological Modelling using SWAT/SWAT-WB – Simulating the different physical processes in both time and space with
reasonable accuracy– Determine water use and productivity at different locations in the landscape (e.g.
from trees, cultivated areas and pasture etc.)
• Using the simulated model evaluate the possible implications for hydrological fluxes of different scenarios of RMS interventions
e.g. the installation of ponds and tanks, bunds or terracing etc.
• Finally determine the impact of up-scaling RWM interventions on downstream flows as well as water productivity at different locations in the landscape.
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Field monitoring
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Hydro-Meteorological Monitoring in the Study Landscapes
Objectives1. Developing primary data to understand water budgets, fluxes and
flow pathways- Hydrological processes. 2. Estimating water-use and water productivity within the framework of
Integrated Rainwater Management Strategies, Policies and Institutions.
Data Obtained will be used to calibrate and validate computer models (e.g. SWAT) which can then be used to simulate:
• Hydrological Processes
• Investigate the possible hydrological implications of RMS interventions
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Equipments to be Installed and Variables to be Monitored
• Automatic Weather stations measuring rainfall, temperature, relative humidity, wind speed and wind direction, solar and net radiation and soil temperature. One in each catchment
• Manual Rain Gauges distributed across altitude and space. 5 in each catchment
• Pressure Transducers 1. Stream flow measurement at catchment outlet to measure river stage (converted to flow using a rating equation, determined from current meter measurements). One in each catchment 2. Groundwater level measurement Five in each catchment
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• 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)
• 3 Soil moisture profile inside the catchment to measure volumetric moisture content
• Dip Meters portable, reliable instrument for measuring the water level and total depth in boreholes, wells etc.
• Sediment Sampler Done manually
Equipment to be installed and corresponding variables to be monitored
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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
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Remote monitoring:the solution to upscaling (hydromet) parameters?
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GEONETCASTNRT global network of satellite-based DDS
(space-borne, air-borne, in situ data)
Source: EUMETSAT brochure
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Uses of GEONETCAST:
• Filling precipitation data gaps
• Measurement of 30 minute rainfall used in erosivity estimation
• Aerial rainfall distribution
• Soil moisture estimation
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SEBS input
LST Albedo NDVI LAISZA DSSF DEM
SAF SAF VGT4Africa
VPI
SAF MSG2 SAF SAF SRTM
15mts 1day 10day 1day Calculated 30mts 1day once
Resampling by 2000m georeference
SEBS = estimating atmospheric turbulent fluxes and
surface evaporative fraction using EO data in the
visible, NIR and TIR
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SWAT 2005, User Manual
•I30, Rday and imax can be derived from the 15’ rain intensity time series
APPLICATIONS cont’d…
SWAT 2005, User Manual
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Disturbance Index
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Challenges:
• Continuous validation is required (ground truthing)
• Data storage issues
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Water productivity of mixed crop-livestock systems:
progresses and challenges
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High unproductive water losses
Example: Runoff + EvaporationHigh nutrient depletion
WP scenario ought to change!
-180
-160
-140
-120
-100
-80
-60
-40
-20
0
20
40
N P K
Bala
nces
(kg
ha-1
yr-
1)
RichMediumPoor
Full nutrient balances on Teff
Blue Nile Highlands: >95% rain fed and major sources of livelihoods
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Crop WP gaps are enormous. Implication for LWP
Objective:
Develop strategies and menu of practices for different recommendation domains and understand the impacts of the strategies for water productivity.
WaterDepleted
output alAgricultur ty productiviWater
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How we do: Understanding the system
HH survey(30-40 farms per
system)
Focus farmers:(landscape positions
resources flow monitoring)
Land
scap
e/w
ater
shed
bou
ndar
y
• Feeing and feed sourcing;
• Livestock products;• Livestock services• ME demand
•FU factor•Agricultural water Partitioning
Water flow and biomass H2O
productivity: by land use and crop type
Cropping pattern
Monitor:•Weather and H2O flow •Nutrient flow •Crop management and performance
Crop WP
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How we do: Understanding the system
Crop WPCrop 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
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Out scaling to basin scale
For Crop WP:• Identify similar units
represented by farming systems of the study landscapes
• Acquire baseline data for the identified systems
• Run models for the systems
• Integrate system outputs into basin scale
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Challenges
• Resources demanding! Some tools (e.g. APSIM) are data intensive
• 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
• Capacity of soil laboratories to handle runoff samples
• Data for large scale base line (soil, weather, crop management practices, livestock population)
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Green - Blue Water
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Green-blue water baseline – framing the NBDC
assessing: • green-blue water availability &• variability• storage (buffer capacity)• agricultural productivity
1. CP basins2. Nile sub-basins3. Blue Nile sub-basins
mapping spatial patterns, hotspots, opportunities
Nileblue water: ~ 1300 m3 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 (10th percentile): 3900 kcalIn 2050: 1900 / 1600 kcalBUT: huge spatial variability along the Nile LPJ-based
in a nested approach
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(„difficult hydrology“ – David Grey)
rainfall variability and drought risk(ILRI)
blue per capita 2000 / 2050
green per capita 2000 / 2050
CoV & 10th percentilegreen - blue
water productivity
Nile (framing Blue Nile for taking into account downstream consequences)
IDIS -> NBI subbasins
water availability
1) CPWF, Mulligan2) IWMI, Karimi
CPWF and IIASA data
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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?
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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 experiencebe generalized / upscaled
Lake Tana SWIM model for simulating the effects of outscaling / upscaling
for assessing landscape scale effects of increasing adoption?
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Resilience (landscape) approach
„social ecological systems“
integrating water fluxes and productivitieswith institutions and their position / effectiveness re uptake of measures
node-based WEAP node-based social networks
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Challenges:
•Lack of data at smaller scale
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Hydrological Modelling with SWAT
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Runoff plots
Maybar)
16 37 43 64 slope of land
Runoff Coefficients
Hydrological processes + impacts
Surface runoff decreases with steepness
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Hydrological processes + impacts
Hill slope Areas
Surface runoff
infiltration
interflowv
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Runoff and Scaling issues in SWAT
Current limitations:– Limited scaling abilities due
coarseness of soils and land use data base
– Can upscale by grouping HRUs, but no ability to downscale above the resolution of the finest initialization dataset.
– Predicts infiltration excess runoff instead of saturation excess
– Topographic data not used that is driving force for hydrology
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SWAT-WB initialization method allows continuous scaling to model full basin to sub-catchment
scale phenomenon.
Allows
TI/STI based
Predicts overland flow locations correctly
Down-scaling to DEM Resolution
Up-scaling with physical attribute instead of judgment
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Challenges:
•Current data availability – for calibration and validation•How to simulate actual RMS within the model
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Evaluating Water Resources: Upscaling Impacts of RMS
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Objectives of Water Resources Modeling (WEAP)
1. Assess integrated impacts of rainwater management interventions and large-scale development projects on water availability, sediment load (and groundwater recharge)
2. Device mechanisms of incorporating rainwater management interventions in IWRM planning
3. Investigate up- and out-scaling options of rainwater management interventions using sustainability criteria of satisfying downstream flow requirements
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Methodologies and Approaches
General Approaches:
• Represent RMS interventions scenarios in SWAT (parameters and management options)
• Perturb inflows to large dams and river junctions according SWAT simulation of RMS interventions
• Evaluate the bio-physical sustainability criteria for large-scale and RMS scenarios
Potential Scenarios:1. Baseline (current) 2. Medium-term3. Medium-term + RMS 4. Long-term5. Long-term + RMS
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WEAP Schematization
Dams / Reservoirs
Irrigation demands
River Networks
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Modeling FrameworkW
ate
rsh
ed
sS
ub
-ba
sin
sA
ba
y B
as
in
SWAT Model
ClimateSoilLU
WEAP Model
RMS Scenarios
L-SDev’t
Scenarios
ImpactEvaluation
Large dams and
River Junctions
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Challenges• The WEAP reservoir and
river junction nodes should match with SWAT sub-basins
• WEAP doesn't have sediment routing capability
• Aggregated RMS scenarios are not yet defined (targeting)
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Translating hydrology and productivity into environmental, social and economic
impacts
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WHAT is evaluated?
Current practice (crops, livestock, trees, management practices) is compared to selected alternatives in terms of their impacts on:
• Farm profit (economic benefit)
• Employment opportunities (social benefit)
• Environmental externalities (ecosystem benefit)
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WHY multicriteria analysis?
• To know the possible outcome in terms of profit, employment, and externality
• To understand trade-offs between criteria and between upstream and downstream land and water users
• To estimate values for environmental services that could be the basis for compensation schemes
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HOW to estimate impact? Linkages in N4
• Bio-physical and hydrological characterization of watersheds and prioritization of areas for interventions (SWAT & WEAP)
• Identify RWM practices and how they would be incorporated into land use systems
• Gather data on productivity and environmental impacts
• Gather data on socio-economic costs and benefits
• Optimization model to get economic, employment and environmental impacts of RWMS scenarios
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.Biophysical data
Hydrologic modeling
Externalities
OptimizationSocio-
economic data
RWM practices
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Some examples on data inputs
• Externalities
• Socio-economic issues
• Erosion and sediments through land use
• NPK release to water resources• Additional water available
• Land (allocation and constraints)• Crops, forages, livestock, forest
(production) • Costs and prices for crops,
livestock, forests• Management practices and costs• Labor (costs and incomes)
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Scaling out impacts - is basin level estimation appropriate?
• Run model for representative sites
• Adopt results of similarity analysis
• Scale out model results of representative sites (to similar sites) at basin level
• Aggregate the scaled-out results
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Challenges and limitations
• Characterizing and prioritizing scenarios with one or more RWMS
• Scaling out impacts to a basin level
• Incorporating gender and equity issues
• How to validate the model outputs?
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Challenges Summarized:• How do we deal with data gaps at field and basin
scale; validating the model outputs? (Dan + Birhanu)
• 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)
• What alternatives exist to address scaling out RMS practices and process/impacts to a basin level? (Tammo +Teklu)
• What alternative options do we have to link biophysical and livelihoods issues? (Kinde + Nancy)