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

1. “Agricultural Research Towards
Sustainable Development Goals ”
Agricultural water interventions for
sustainable intensification –
upstream downstream trade-offs
and opportunities
Yihun Dile and Louise Karlberg
Stockholm Environment Institute
Stockholm Resilience Centre

2. Two sister projects on AWI
Ethiopia
Scattered ponds
Subsistence agriculture
Implications of potential AWI
DSS location + size of dams
India
Watershed dev. prog.
Commercial farming
Actual changes
Livelihoods
SWAT tool used for analysis
Implications on downstream water availability

3. Research Area
WH suitability study
Upper Blue Nile Basin
Total area: 10 sq.km
 Subbasin size: 1-6ha
Hydrological Modelling
Lake Tana Basin
Understanding implications
Meso-scale

4. Water harvesting implementation
Suitability class
HRUs of slope: <8%; Soil: Luvisols, and vertisols; and agricultural land.
Area = 3.79km2 (38% of watershed)
 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

5. Water Harvesting Implementation Scenarios
Nutrient scenarios
ONION
 Onion – 1st stage: UREA – 85kg/ha, and DAP – 30kg/ha
2nd stage: UREA – 85kg/ha
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

6. Crop growth constraints

7. Crop production
Scenarios
2.5th percentile
WH+Baseline Nutrient 15
WH+BNR1
94.7
WH+BNR2
148.56
Percent change in teff yield
Median
97.5th percentile
57.2
667.3
134.3
674.5
217.4
363.6
Onion yield (ton/ha)
2.5th percentile
1.33
median
7.66
97.5th percentile
8.22

8. Change in Teff yield (%)

9. Change in crop yield (%)

10. Onion production (ton/ha)

11. Water Productivity
Baseline
WH + Baseline N
WH + BNR1
WH + BNR2
2.5
0.14
0.17
0.29
0.38
Water productivity
Median
0.17
0.27
0.40
0.45
97.5th
0.20
1.12
1.13
0.75
Year
1995
2001
IRR Vol (m3)
532,486
309,326
WYLD (m3)
1,839,334
7,063,383
Percentage
29
3.95
th

15. The Kothapally Case, India

21. Implications on livelihoods
60000
50000
40000
Vegetable crop
Main crop
)
R
N
I
(
e
o
c
n
i
m
r
a
F
30000
20000
10000
0
S. No C. No C. Max
int. int. int.
Dry year
S. No C. No C. Max
int. int. int.
Normal year
S. No C. No C. Max
int. int. int.
Wet year

22. Spatial variability
No intervention
Dry
Wet
WDP

23. Water balance Kothapally
100%
80%
60%
Outflow
40%
GW recharge
ET
)
m
(
i
l
f
o
g
a
t
n
c
r
e
P
20%
0%
No int
Dry
WDP
No int
WDP
Medium
No int
WDP
Wet

24. Water outflow Kothapally
350
300
250
200
)
m
(
w
o
l
f
t
u
O
150
100
50
0
No int
WDP
Dry
No int
WDP
Medium
No int
WDP
Wet

25. Soil loss analysis
100
70
90
WSD
60
80
50
Soil loss (ton/ha)
No int
40
30
)
(
s
L
o
S
e
v
i
t
a
l
m
u
C
20
Scenario-1
70
Scenario-4
60
50
40
30
20
10
10
0
0
0
1
4
7
10
13
16 19
Year
22
25 28
31
50
100
150
Daily rainfall (mm)
200
250
300

27. Downstream consequences
3)
140
120
100
80
Storm flow
60
Base flow
m
M
(
i
v
s
e
r
S
O
t
a
w
o
l
f
n
I
40
20
0
Current Max int Current Max int Current Max int
Dry
Medium
Wet

28. Conclusions
• Total annual runoff reduced by 5 - 30% (Eth) and around 60% (In). At the mesoscale 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

29. Thanks for the attention

31. Model setup and simulation
 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

32. Management
 Two reserviors
Elevation
1784
2135
*
Lake Tana
Angereb
Reservior
Principal spillway
Area(km2)
Volume(Mm3)
2,766
20,300
0.5
3.53
Elevation
1787
2138
Emergency spillway
Area(km2)
Volume(Mm3)
2983
29,100
0.6
5.16
 Fertilizer application
 Tillage operations
 depth of till of 15cm, and
 mixing efficiency of 0.3
 tillage frequency of 4
 Pescticide application
 2.4.D amine weed killer
 1 liter/ha ~ 0.379kg/ha
32

33. Model setup and simulations
 Subbasins No.: 482
 HRUs No.: 786
 Total area: 10 sq.km
 Subbasin size: 1-6ha
Pond
 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

34. Model Calibration and Validation at Megech
NSE=0.76
PBIAS=4.0%
NSE=0.74
PBIAS=40.2%