Bharat Sharma

Basin focal Project on

Indo-Gangetic Basin

Analysis of Agricultural Water Productivity ( WP-3)

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Water Productivity – The Concept

Water productivity (WP) is “the physical mass of production or the economic value of production measured against gross inflow, net inflow, depleted water, process depleted water, or available water” (Molden, 1997, SWIM 1). It measures how the systems convert water into goods and services. The generic equation is:

)/m(m inputWater

)$/m or (kg/muse waterfrom derived utputO)$/m or (kg/moductivityPrWater

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2233

=

3

Why map water productivity ?

The overarching goal of Water Productivity assessment is to identify opportunities to improve the net gain from water by either:

• increasing the productivity (physical/ economic) for the same quantum of water; or

• reduce the water input without or with little productivity decrease.

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Basin WP Mapping – What to Care ?

• Magnitude of agricultural and water productivity;

• Spatial variation of WP;

• Scope for improvement: How much and where;

• Irrigated vs. rainfed;

• Crop vs. livestock and fisheries.

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Basin WP: Multi-indicators

• Land productivity– Individual crop yield (kg/m2)– Standardized gross value production (SGVP) ($/ha)

• Livestock and fisheries– Production ($)

• Water use (IWMI water accounting framework)– Available water (m3)– Irrigation diversion (m3)– Potential ET (mm)– Actual ET (mm)

• Water productivity– Combination of above productivity (numerator)

and water use (denominator)

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Basin WP: The Methodology

• Basin WP initial assessment

• Sub-catchment modeling and verification

• Scaling up-down

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District level WP Estimates based on Crop Productivity Census data and Consumptive Use Estimates

Source: Upali & Sharma, 2008

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KHARIF RABI ALL

Trends in Water Productivity in Rice, Bangladesh Districts (1968-2004)

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Irrigation canal commands in Punjab (Pakistan) and spatial variation in annual actual evapotranspiration (ETa) in Punjab for year 2004-05

(using Surface Energy Balance Algorithm for Land, SEBAL)

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Sampling variation in productivity

Average Farm Size in Rechna Doab

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10.4

9.3

10.7

0

2

4

6

8

10

12

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Upper Middle Low er Overall Rechna

Fa

rm S

ize

(H

a)

Average Farm Area (ha)

Land Distribution Pattern in Rechna Doab

0

5

10

15

2025

30

35

40

45

50

Landless Less than

1 ha

1.01 to

2.0 ha

2.01 to3

ha

3.01 to5

ha

5.01 to10

ha

10.01

to20 ha

Greater

than 20

haFarm Categories

Pe

rce

nta

ge

Sh

are

Percent Households Percent Share of Land

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Cropping intensity across Rchna-Doab

0

50

100

150

200

250

Cro

pp

ing

In

ten

sit

y

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Annual Water Use Patterns from Major Sources across Sub-divisions of Rechna Doab

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Basin WP Initial AssessmentAgricultural productivity calculation flow chart

Censusproduction data

Crop productivity

map (district wise)

Time series MODIS data

Biomass estimate(pixel wise)

Crop productivity map(kg/m2, pixel wise)

GT data

Census data

Literature info.

MODIS NPP

Yield

Biomass

Harvest index

Crop group/LULC map

Disaggregation*

Local and international prices

Crop standardized gross value productivity map

($/m2, pixel wise)

LivestockProduction*price

FisheryProduction*price

productivity map($/m2, district average)

Agriculturalproductivity map($/m2, pixel wise)

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Disaggregation*

The disaggregation procedure takes district wise average yield from census data. Assuming harvest index (HI) does not vary for same crop, the yield of pixel i is calculated as:

Average yield of district *Biomass of pixel i

Average biomassYieldpixel i =

Basin WP Initial Assessment

Agricultural productivity calculation flow chart

from district wise average yield value to pixel wise average yield value

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Basin WP Initial Assessment

Water depletion estimate flow chart

Points weather data

Points reference ET

Points potential ET

MODIS Land Surface Temperature data

Evaporative fraction map (SSEB*)

Actual ET map(mm) ET

act– the actual Evapotranspiration, mm.

ETfrac

– 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

xHfrac

TT

TTET

−−−−

−−−−====

fracact ETETET ∗=0

*SSEB: Simplified Surface Energy Balance Model

SSEB assumes linear relationship between latent heat flux (ET) and land surface temperature (Gabriel et al., 2007). Hot pixels and cold pixels are identified to represent no ET andmaximum ET.

Water depletionmap (mm)

Seasonal time series

Kc

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Sub-catchment Modeling and Links to Basin WP Assessment

Agro-hydrologicalModel (OASIS)

Time series Landsat data

Biomass estimate(pixel wise)

Data input Weather data

Biomassmodeling

SSEBSEBALValidation

Validation

Validation

Model unitAverage WP

LandsatWP map

Water accountingcomponents

yieldYield estimate

(kg/m2)Actual ET maps

Basin MODISWP map

Verifications

Water productivity values, variations, factors and potential assessment

scenarios

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Dataset

• 58 weather stations

• Data period: 1995-2007 (more to come)

• Item: daily mean, max, min temperature; mean sea level pressure; mean humidity; precipitation; mean & max wind speed.

Weather data Agricultural data

• District wise crop area and production

• State wise livestock and fishery production

• Local and international prices

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DatasetC..LULC Map

10km, GIAM, 1999 500m, Thenkabail et al, 20051km, USGS, 1992-1993

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DatasetHHMODIS 250m 16 day NDVI mega-dataset (2006)

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DatasetHH.MODIS 1km 8 day Land surface temperature mega-dataset (2006)

Note: Curve breakdown is due to existence of clouds

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DatasetH..

Groundtruthing (8th -17th Oct, 2008)

• Across Indus-Gangetic river basin

• >2700km covered

• 175 samples

– LULC

– Cropping pattern

– Agricultural productivity

– Water use (surface/GW)

– Social-economic survey

Rice (cultivated)Dual irri. Canal system

CottonRice mixed with tree plantation

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