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This talk presented two sister projects in Ethiopia and India. In both case studies the SWAT model was used to analyze how scenarios of upstream water harvesting and nutrient application interventions impact downstream water availability. The case study in Ethiopia shows that crop yields significantly increase with water harvesting and nutrient applications. By only implementing water harvesting yield scenarios show an increase by 65 % and by adding nutrient applications yields improved by up to 200 %. Water productivity also increases with water harvesting and application of nutrients. However, there is upstream-downstream water availability trade-offs that need to be take into account. More at www.siani.se
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Agricultural water interventions for sustainable intensification –
upstream downstream trade-offs and opportunities
“Agricultural Research Towards Sustainable Development Goals ”
Yihun Dile and Louise Karlberg
Stockholm Environment Institute
Stockholm Resilience Centre
Two sister projects on AWI
EthiopiaScattered pondsSubsistence agricultureImplications of potential AWIDSS location + size of dams
IndiaWatershed dev. prog.Commercial farmingActual changesLivelihoods
SWAT tool used for analysisImplications on downstream water availability
Research Area
WH suitability studyUpper Blue Nile Basin
Hydrological ModellingLake Tana Basin
Understanding implicationsMeso-scale
Total area: 10 sq.km Subbasin size: 1-6ha
Water harvesting implementation
Ponds dimension size that can store water for ONSEASON and OFFSEASON irrigation size determined for combination of different climatic years & nutrient application
Crop rotation is applied ONSEASON (July-Dec) – TEFF OFFSEASON (Jan-April) – Onion
Suitability class HRUs of slope: <8%; Soil: Luvisols, and vertisols; and agricultural land. Area = 3.79km2 (38% of watershed)
Water Harvesting Implementation Scenarios
Nutrient scenarios
TEFF Current nutrient application rate (WH + BaselineN)
TEFF – 1st stage: UREA - 15kg/ha and DAP – 30kg/ha 2nd stage: UREA – 15kg/ha
Blanket Nutrient Recommendation (WH + BNR1) TEFF – 1stage: UREA – 50kg/ha, and DAP – 30kg/ha 2nd stage: UREA – 50kg/ha
Blanket Nutrient Recommendation (WH + BNR2) TEFF – 1st stage: UREA – 85kg/ha, and DAP – 30kg/ha 2nd stage: UREA – 85kg/ha
ONION Onion – 1st stage: UREA – 85kg/ha, and DAP – 30kg/ha 2nd stage: UREA – 85kg/ha
Crop growth constraints
Scenarios Percent change in teff yield2.5th percentile Median 97.5th percentile
WH+Baseline Nutrient
15 57.2 667.3
WH+BNR1 94.7 134.3 674.5WH+BNR2 148.56 217.4 363.6
2.5th percentile median 97.5th percentileOnion yield (ton/ha) 1.33 7.66 8.22
Crop production
Change in Teff yield (%)
Change in crop yield (%)
Onion production (ton/ha)
Water Productivity
Water productivity2.5th Median 97.5th
Baseline 0.14 0.17 0.20WH + Baseline N 0.17 0.27 1.12WH + BNR1 0.29 0.40 1.13WH + BNR2 0.38 0.45 0.75
Year IRR Vol (m3) WYLD (m3) Percentage1995 532,486 1,839,334 292001 309,326 7,063,383 3.95
The Kothapally Case, India
Implications on livelihoods
0
10000
20000
30000
40000
50000
60000
S. No int.
C. No int.
C. Max int.
S. No int.
C. No int.
C. Max int.
S. No int.
C. No int.
C. Max int.
Farm
inco
me
(IN
R)
Vegetable crop
Main crop
Normal year Wet yearDry year
Spatial variability
No intervention WDP
Dry
Wet
Water balance Kothapally
0%
20%
40%
60%
80%
100%
No int WDP No int WDP No int WDP
Dry Medium Wet
Perc
enta
ge o
f to
tal r
ainf
all
(mm
)
Outflow
GW recharge
ET
Water outflow Kothapally
0
50
100
150
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300
350
No int WDP No int WDP No int WDP
Dry Medium Wet
Outf
low
(mm
)
Soil loss analysis
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10
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30
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50
60
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80
90
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0 50 100 150 200 250 300
Daily rainfall (mm)S
oil l
oss
(ton
/ha)
Scenario-1
Scenario-4
0
10
20
30
40
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60
70
1 4 7 10 13 16 19 22 25 28 31
Year
Cum
ulat
ive
Soil
Los
s (m
m) WSD
No int
Downstream consequences
0
20
40
60
80
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120
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Current Max int Current Max int Current Max int
Dry Medium Wet
Inflo
w a
t O
S re
serv
oir
(Mm
3 )
Storm flow
Base flow
Conclusions• Total annual runoff reduced by 5 - 30% (Eth) and around 60% (In). At the meso-scale level the total runoff reduction was 30% (In).
• Peak flows reduce and low flows increase – flooding problems, bank ersion and channel sedimentation reduce + more water available during dry seasons.
• Sediment loss reduction
• Crop yield and biomass increase upstream, in particular when combined with nutrient management – food availability and material flow will improve (upstream + downstream)
• Drought proofing? Only for some farms during dry seasons, but significantly higher incomes with WDP during normal and wet years
• DSS tool for location and size of dams
Thanks for the attention
Basin Area: 15129 km2
Total No subbasins: 959 Subasin sizes: 500-3000ha Total No HRUs: 9963 Flow calibrated at 3 gauging stations
Climate data rainfall, Max & Min - 1990-2011 Global weather data – weather genrator
Evapotranspiration Hargreaves’s method
Surface runoff estimation Curve number method
Stream routing Variable storage method
Hydrological data 1990-2007
Model setup and simulation
32
Management
Two reserviorsPrincipal spillway Emergency spillway
Elevation* Area(km2) Volume(Mm3)
Elevation Area(km2) Volume(Mm3)
Lake Tana 1784 2,766 20,300 1787 2983 29,100AngerebReservior
2135 0.5 3.53 2138 0.6 5.16
Tillage operations
depth of till of 15cm, and mixing efficiency of 0.3 tillage frequency of 4
Fertilizer application
Pescticide application 2.4.D amine weed killer 1 liter/ha ~ 0.379kg/ha
Subbasins No.: 482 HRUs No.: 786 Total area: 10 sq.km Subbasin size: 1-6ha
Climate data rainfall, Max & Min - 1990-2011
Evapotranspiration Hargreaves’s method Global weather data – weather genrator
Surface runoff estimation Curve number method
Stream routing Variable storage method
Model setup and simulations
Pond
Model Calibration and Validation at Megech
NSE=0.76PBIAS=4.0%
NSE=0.74PBIAS=40.2%
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