Remote sensing and census based assessment and Remote sensing and census based assessment and scope for improvement of rice and wheat water scope for improvement of rice and wheat water

productivity in the Indo-Gangetic basinproductivity in the Indo-Gangetic basin

Xueliang Cai and Bharat SharmaInternational Water Management Institute (IWMI)

Colombo, Sri Lanka

International Forum on Water Resources and Sustainable Development, 22-24 September, 2009, Wuhan, China

Basin focal projects – a CPWF initiative

An interesting journey:

First lap Global or

local problem?

Journey startsWhere are we all going?

Who’s on the bus?

Second lap

Where’s the water?

Third lap How much do people gain from water?

Fourth lap

Poverty, impacts?Fifth lap

What can change?

Knowledge Exchange (WP6)Who needs to know?

What information tools?Information exchange process?

Data-bases and methods

Basin focal project – the structureBackground

Demography Rural povertyEconomic overview Agriculture

What is the overall situation?

Water availability (WP2)Climate Water account

Water allocation Water hazardsWhat is the water balance?

Water productivity (WP3)Crop water productivity kg/m3

Water value-adding $/m3

Net value / costsHow well is water used?

Water institutions (WP4)Water rights Water policies

Governance Power

Who ‘handles’ the water’?

Farming institutions (WP4)Land rights Infrastructure

Supply chains

Who enables farmer to improve WPr?

Poverty analysis (WP1)Rural poverty trends

Water-food related factors

What links water, food and poverty?

Interventions (WP5)WEAP Trend analysis

Land use change analysisWhat are foreseeable risks and

opportunities for change?

Niger

(KCL)

Where the projects are?

(UC Davis) (FANRPAN)

(IWMI)

(IRD)

(IRD)

(IWMI)

(IFPRI)

(CSIRO)

(IWMI)

Source: Basin Focal Projects, CPWF, 2009

Basin focal project – Indo-Gangetic basin

Basin fact sheet:

Geographic Area: 2.25 million km2

Population: 747 million

Landscape: mountain to plain

Annual precipitation:

100 – 4000 mm

Cropland area: 1.14 million km2

Cropping pattern: rice–wheat

Water use by agri.: 91.4%

Water sources: ground water

and surface water

A basin under extreme pressure…

Source: Xueliang Cai

Photo Credit: Xueliang Cai

Basin water productivity assessment – what to care?

• Magnitude – what’s the current status?

• Spatial Variation – how does it vary within and among regions?

• Causes – why does WP vary (both high and low)?

• Irrigated vs. rainfed – what’s the option for sustainable development under water scarcity and food deficit condition?

• Crop vs. livestock and fisheries – how is livestock and fisheries contributing to water use outputs?

• Scope for improvement – how much potential for where?

Photo Credit: Xueliang Cai

Methodology overview1. Crop productivity (rice as example)

District level yield map of 2005 from censusNDVI composition of 29 Aug – 5 Sept 2005 for rice area

MODIS 250m NDVI at rice heading stage was used to interpolate yield from district average to pixel wise employing rice yield ~ NDVI linear relationship.

Source: IWMI, 2009

Methodology overview2. Evapotranspiration (rice as example)

Actual ET map (2005 Sept 21)

potential ET map (2005 Sept 21)

ETa – the actual Evapotranspiration, mm.

ETf – the evaporative fraction, 0-1, unitless.

ET0 – Potential ET, mm.

Tx – the Land Surface Temperature (LST) of pixel x from thermal data.

TH/TC – the LST of hottest/coldest pixels.

CH

xHf TT

TTET

fpa ETETET

SSEB

ET fraction map (2005 Sept 21)

MODIS LST 2005 Sept 21Daily weather data from 54 stations

Steps: 1. Hargreaves equation for reference

ET.2. FAO56 Kc approach for potential ET.

Crop dominance map

Source: IWMI, 2009

Data

1. Census data: crop area, yield and production, livestock and fisheries production;

2. Satellite sensor data: MODIS 250m 16 day NDVI, 1km 16 day Land Surface Temperature (LST);

3. Weather data: daily temperature, humidity, precipitation, wind speed of 58 stations;

4. LULC maps: USGS GLC 1992-93, IWMI IG basin LULC map 2005, IWMI GIAM 500m 2003, Univ. New Hampshire 2002;

5. Other data layers: basin boundary, administrative boundaries, road, railway, and river networks, DEM;

6. Ground truth data.

Photo Credit: Xueliang Cai

Basin cropping patternPredominant crops: irrigated rice/rice-wheat rotation

The predominant crops are mainly cultivated in a belt along the main streams of Ganges and Indus river.

Crop coefficients of the basin as extracted from literature (values) and RS imagery (growth periods)

Source: IWMI, 2009

Rice yield and ETa maps

Huge variation in yield, indicating significant scope for improvement

Yield (ton/ha)

  Pakistan  India  Nepal  Bangladesh Yield  2.6 2.53 3.54 2.75ET 386 417 499 477

ET is high where yield is high. However, ET might also be high where yield is not (so) high. Why?

ETa (mm)

Source: IWMI, 2009

Wheat yield and ETa maps

  Pakistan  India  Nepal  Bangladesh 

Yield  2.77 2.20 1.94 2.33ET 338 291 281 281

Yield (ton/ha)

ETa (mm)

Huge variation in yield, indicating significant scope for improvement

Wheat ET variation is more consistent with yield

Source: IWMI, 2009

Water productivity maps

Rice (kg/m3)

AVG SDV Min Max

0.74 0.33 0.18 1.80

Wheat (kg/m3)

AVG SDV Min Max

0.94 0.43 0.28 2.97

Note: 1% of the points with extremely low and high values are sieved from the statistics

Source: IWMI, 2009

Water productivity maps

Summed economic WP of rice and wheat (USD/m3)

The ratio of rice WP to summed WP

Source: IWMI, 2009

MODIS LST 2005 Sept 21

Causes for variations

Crop water stress (ETa/ETp)

Rice yield

TRMM rainfall (2005 Jun 10 – Oct 15)

Actual ET (Jun 10 – Oct 15) Source: IWMI, 2009

Scope for improvement

Source: IWMI, 2009

Conclusions

1. The productivity of land and water as generated from rice and wheat as well as sugarcane, pulses, and millet etc, is crucial to the livelihoods of the huge rural population in the basin;

2. Basin average yields and water productivity of the predominant crops are generally low despite intensive agricultural activities;

3. Huge variations exist across scales from farm to the basin. An overall declination from North-west to South-east is observed. In contrast to the bright spots of well performing areas, for example, Indian Punjab and Haryana, large areas comes with extremely poor performance (Bihar, Bangladesh…);

4. The variability shows no direct relationship with climate conditions, implying the significance of irrigation and associated crop and water management;

5. Significant scope exists for improvement, which could be achieved mainly by long term yield enhancement. In short term, reducing non-beneficial ET of low yield areas can also largely contribute to improved WP.

*ET- Evapotranspiration, WP- Water Productivity

References

• Roost, N., X.L., Cai, Turral, H., D. Molden, YL. Cui. 2008. An assessment of distributed, small-scale storage in the Zhanghe Irrigation System, China. Part I: Storage capacities and basic hydrological properties. Agricultural Water Management (ISI). 95: 698-706

• Roost, N., X.L., Cai, Turral, H., D. Molden, YL. Cui. 2008. An assessment of distributed, small-scale storage in the Zhanghe Irrigation System, China. Part II: Impacts on the system water balance and productivity. Agricultural Water Management. 95: 685-697

• CAI Xue-liang, CUI Yuan-lai, DAI Jun-feng, 2007. Small Storage Based Return Flows Estimation and Evaluation in Melon-on-the-Vine Irrigation System. Journal of Wuhan University (Engineering edition), 40(2): 46-50. (In Chinese with English abstract)

Thank you

Photo Credit: Xueliang Cai

www.iwmi.org