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 Research Towards Sustainable Development Goals
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
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
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
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
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