Presented at the BFP Special session in the 13th World Water Congress, Montpelier, France

1. Mapping Basin Level Water Productivity Using Remote Sensing and
Secondary Data in the Karkheh River Basin Iran
Mobin-ud Din Ahmad
Md. Aminul Islam
Ilyas Masih
Lal Muthuwatta
Poolad Karimi
Hugh Turral
Presentation made at XIII IWRA World Water Congress on Global Changes and Water Resources: confronting the
expanding and diversifying pressures, held on September 1-4, 2008, at Montpellier, France.

2. Introduction
• Rapid increase in agricultural production will be required to keep
pace with future food and fiber demands.
– This can be achieved by bringing more area under agriculture or
– by increasing the yields using similar or even reduced water resources
(e.g., increasing productivity of water).
• Considering that:
– Land and water resources are already reached their exploitation limits or are
over exploited in many river basins; and
– There is increasing competition for water among sectors.
• The option of increasing agricultural production using same or
less water resources is the most appropriate one.

3. The case of Iran
• Iran is land abundant and water short country.
– Average Precipitation of 240 mm/year (Dinpashoh et al. 2004)
• less than 200 mm/year over 50 % area
• less than 300 mm/year over 75 % area.
• more than 500 mm/year over 8 %,
– Annual renewable water resources: 135 Km3/year (Vakili et al. 1995)
• Strategic goal of achieving food self-sufficiency need more water
resources development, hence will increase pressures on scarce
water resources
• Addressing these challenges require discovering ways to more
effectively utilize existing resources.
• Unavailability of information on water use performance (e.g.,
water productivity) is yet another bottleneck

4. The case of Karkheh basin
• Very limited information on water productivity
– Field scale estimates exists (e.g. Keshavarz et al., 2003, Moayeri et al.,
2007).
– water productivity estimates beyond field scale are non-existent.
• The major goal of this component of the CPWF’s Karkheh Basin Focal
Project was to fill these information gaps
– The specific objectives are:
• to estimate physical water productivity of major rainfed and irrigated
crops and evaluate the spatial variability in Karkheh basin; and
• to estimate the economic water productivity at sub-catchment to
basin scale both in terms of vegetative areas as well as inclusive of
livestock.

5. METHODOLOGY

6. The Karkheh basin
• Drainage area: 50, 764
Km2
• Population: 4 Million-2/3
rural
• Mediterranean climate
– precipitation 450
mm/year, range: 150 mm
to 750 mm
• Renewable water
resources: 8.5 * 109
m3/year
• distributed among seven
provinces and 32 districts.
• Hydrologically divided
into five main catchments
(sub-basins).

7. Water productivity mapping:
Sub-catchment to basin scale
Benefit
WP =
Consumption
Administrative/district
SRTM 90m DEM Topographic/ GIS Sub-catchment
maps and agricultural (Molden 1997)
Analysis Boundaries statistics
MODIS-TERRA 250m Image Land Use Land Cover
Classification
NDVI time series Map
Land use wise sub-catchment
Gross Value of Production (GVP)
MODIS-TERRA 1000m
Energy Balance Estimation of Actual
Analysis Evapotranspiration ETa
Meteorological Data
Sub-catchment level
Land use wise sub-catchment Land use type
Actual Evapotranspiration ETa Water Productivity
(GVP/ETa)

8. Water productivity mapping:
Field and farm scale
Benefit
WP =
Consumption
(Molden 1997)
Villages: 110
Farmers: 298
Small: 37 Medium: 173 Large: 88
Rainfed: 97 Irrigated: 120 Mixed: 81
Small (11) Small (26)
Medium (45) Medium (62) Medium (66)
Large (41) Large (32) Large (15)

9. RESULTS

10. Field to Farm Analysis

11. Field to Farm Analysis
Variability in land and water productivity-Example of
irrigated wheat
WP (Kg/m of gross inflow)
6000 0.7
5000 0.6
Yield (Kg/ha)
4000 0.5
0.4
3000
0.3
3
2000 0.2
1000 0.1
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12. Field to Farm Analysis:
The case of Irrigated farms
7000 1.20
Yield W ater Productivity
6000
1.00
5000
Water Productivity (kg/m )
3
0.80
4000
Yield (kg/ha)
0.60
3000
0.40
2000
0.20
1000
0 0.00
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131 136 141 146 151 156 161
Gamasiab Qarasou Kashkan Saymareh Lower Karkheh

13. Field to Farm Analysis:
The case of Rainfed farms
3500 1.60
Yield Water Productivity
1.40
3000
1.20
Water Productivity (kg/m )
2500
3
1.00
2000
Yield (kg/ha)
0.80
1500
0.60
1000
0.40
500
0.20
0 0.00
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131 136 141 146
Gamasiab Qarasou Kashkan Saymareh Lower Karkheh

14. Field to Farm Analysis:
Main observations
• Large variability and presence of closable gaps.
– The difference between the top 10% of cases and average water
productivity is about 0.40 kg/m3.
• Increase yield by 1500kg/ha with no increase in water use.
– Reduce over irrigation: Farmers apply 2-8 irrigations to wheat crops. The highest yield
can generally be attained by 3-4 irrigations in most cases.
– Interventions regarding improving field layouts, leveling, irrigation scheduling and
fertilizer inputs are essentially required.
– For rainfed areas, exploring means of supplemental irrigation.
– In spatial terms more scope for land and water productivity
improvements exists in the upper than lower Karkheh.

15. Sub-Basin to Basin Scale Analysis

16. Sub-Basin to Basin Scale Analysis:
Land use and ETa

17. Sub-Basin to Basin Scale Analysis:
Water Consumption and GVP
• Precipitation:
18.51×109 m3/year
(Muttuwatte et al.,
2008)
• Overall ETa:
16.68×109 m3/year
• Overall GVP:
0.98×109 $/year

18. Sub-Basin to Basin Scale Analysis:
WP of rainfed and irrigated crops
• Rainfed WP: 0.051$/m3;0.027 to 0.071$/m3
• Rainfed water productivity has a declining
trend from upper to lower Karkheh.
• Irrigated WP: 0.22 $/m3 ;0.12 to 0.524 $/m3.
• Higher irrigated WP values are concentrated
in middle and lower parts
• High performing areas are:
– Irrigated case; Jelogir, Pole Dokhtar,
Ghore Baghestan, Doab, Abdul Khan
and Hamedieh,
– Rainfed case; Dartoot, Holilan, Ghore
Baghestan

19. Sub-Basin to Basin Scale Analysis:
WP of vegetative and livestock
• Vegetative WP: 0.097 $/m3; 0.004 to
0.36 $/m3.
– The higher values are mainly due to
higher proportion of irrigated lands
• WP vegetative and livestock: 0.129 $/m3
; 0.022 to 0.408 $/m3.
• Magnitude and distribution of agricultural
economic water productivity changes
substantially when livestock is included.

20. SUMMARY AND CONCLUSIONS

21. Summary and Conclusions
• The study shows that land and water productivity exhibit large inter-
and intra-sub-basin variations.
– Indicating that considerable scope exists for farm scale productivity
improvement both in irrigated and rainfed areas.
– Key interventions could be:
• Irrigated areas: improving field layouts, leveling and irrigation scheduling are
essentially required, balanced use of fertilizer
• Rainfed areas: Tapping opportunities for providing additional water wherever
possible

22. Summary and Conclusions
(Cont.)
• The identified bright spots in upper (Jelogir, Pole Dokhtar, Ghore
Baghestan and Doab) and lower Karkheh (Abdul Khan and Hamedieh).
Similarly for rainfed areas Dartoot, Holilan, Ghore Baghestan could be
help in interventions in the neighboring low performing areas
– The intervention focusing on reasons attributed to high performance such as
irrigation, agronomic and markets in case of bright spots could be instructive
to reduce productivity gap of low performing neighbors (Hot spots).
– Shifting to higher values crops could also contribute to increasing water
productivity but might contradict national food sufficiency targets.
• Inclusion of livestock in economic water productivity estimates
substantially changes the map of basin water productivity and the
magnitude of results.
– This highlights the importance of fully accounting for all agricultural
production systems in calculations, especially if they are to be used for the
purpose of possible reallocation of water away from the rural sector.

23. Summary and Conclusions
(Cont.)
• The approach presented in the paper exemplifies how the combined
use of freely available remote sensing data and routine secondary
data/statistics coupled with advanced GIS techniques can be used to
compute water productivity at different scales such as sub-catchment
to river basin.
• This methodology provides essential information to water managers
and policy makers on water use performance/water productivity
helping them to identify high and low performing regions for better
targeting resources reallocation and productivity enhancement
campaigns within a river basin.

24. Acknowledgments:
Ministry of Jihad-e-Agriculture, Iran
(AERO, Iran)
Statistical Department, Iran
Ministry of Water and Power, Iran
Meteorological Department, Iran
IWMI and CPWF colleagues

25. International Water Management Institute (IWMI)
PO Box 2075, Colombo, Sri Lanka
E-mail: iwmi@cgiar.org
Corresponding author: Dr. Mobin-ud Din Ahmad
E-mail: a.mobin@cgiar.org; mobin.ahmad@csiro.au