Remote Sensing and Census Data Based Water Productivity Analysis for Limpopo Basin –

a preliminary report for discussion

Xueliang Cai and Poolad Karimi

Project Meeting, Pretoria01 July 2009

Structure of the presentation

Introd.

Data

ETa

Introduction

SGVP

Data collection

Simplified Surface Energy Balance model to calculate ETa1

Standardized Gross Value of Production

Results The water productivity mapping results

Discuss. Discussion and plan

ETa1 – Actual Evapotranspiration

What is WP?

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

)2/m3(m inputWater

)2$/m or 2(kg/muse waterfrom derived utputO)3$/m or 3(kg/moductivityPrWater

Introd.DataETa

SGVPResultsDiscuss.

Source: Molden,1997

Why WP?

• 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 land & water resources (e.g.,

increasing productivity of water).

• Considering that:– Land and water resources have 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.

Introd.DataETa

SGVPResultsDiscuss.

Basin WP analysis – what to care?

• Magnitude – what’s the current status?

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

• Irrigated vs. Rainfed – what are the options for sustainable development under water scarcity and food deficit condition?

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

• Scope for improvement – how much potential for where?

Introd.DataETa

SGVPResultsDiscuss.

Water productivity mapping:METHODOLOGY

Introd.DataETa

SGVPResultsDiscuss.

Source: IWMI, 2009

• Production data: - Countries statistic (Mozambique and South Africa) - FAO database in 2005

• Weather data daily temperature, humidity, sea level pressure, precipitation, wind speed collected for 18 stations

• RS images and secondary GIS data

- MODIS 8-day land surface temperature (LST) products

- Land use/land cover (LULC); Basin LULC MAP/ GLC 2008/ IWMI GIAM

- Admin and basin boundaries, road network, ecological zones and DEM

Data sources

Introd.DataETa

SGVPResultsDiscuss.

GLC: Global Land Cover

IWMI GIAM: Global Irrigated Area Mapping

Land use and land cover map

Synthesized by combining the basin LULC map and South Africa Limpopo province map

Introd.DataETa

SGVPResultsDiscuss.

Source: Created by IWMI using data from the basin LULC map and South Africa Limpopo province map

Introd.DataETa

SGVPResultsDiscuss.

Actual ET estimation - SSEB

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.

fpa ETETET

CH

xHf TT

TTET

Simplified surface energy balance (SSEB) is a ET estimate model proposed by Senay (2007). It combines remotely sensed thermal imagery with ground measured climate data, providing quick ET estimate for large scale areas.

Introd.DataETa

SGVPResultsDiscuss.

Actual ET estimation - SSEB

Step 1. Potential ET calculation (2005 Apr 23 - 30 as example)

Steps: 1. Hargreaves equation for reference ET.

Weather stations

Source: IWMI

Introd.DataETa

SGVPResultsDiscuss.

Actual ET estimation - SSEB

Step 2. Actual ET calculation by Simplified Surface Energy Balance (SSEB) approach

Actual ET map (2005 Apr 23 - 30)

ET fraction map DEM corrected MODIS LST

potential ET map

Source: IWMI

Introd.DataETa

SGVPResultsDiscuss.

Standardized gross value of production

SGVP: is an index which helps to compare the economical value of different crops regardless in which country or region they are.

i

icropbaseicrop

cropbase

icropcrops pricenalInternatioproduction

pricelocal

pricelocalSGVP

1

Maize is the major crop in the basin and it is taken as base crop.

Source: Molden, 2001

Introd.DataETa

SGVPResultsDiscuss.

- Average ETo is 1676 mm - standard deviation of 148 mm- Average ETa is 779 mm - deviation of 208 mm.

Evapotranspiration

Limpopo basin annual ETo map 2005 Limpopo basin annual ETa map 2005

Source: IWMI

Introd.DataETa

SGVPResultsDiscuss.

Evapotranspiration

Average ETa is less than half of ETo, indicating significant water stress

ETa: 779 mm ETo: 1676 mm

His

togr

am

ET (mm)

Source: IWMI

CLASS_NAME AreaLULC Ratio

ETa_MEAN

ETa_STD

ETa_SUM

Rainfall_MEAN

[km2] [%] [mm] [mm] [106 m3] [mm]Waterbodies 124 0.0 861.8 220.3 106.9 550.6Rock, mines, scars, river bed 299 0.1 737.7 163.9 220.8 506.7

Shrubland 255578 61.7 776.2 244.9 198387.9 580.2Urban, builtup 2412 0.6 646.1 219.1 1558.3 627.2Wetland 20 0.0 607.5 89.7 12.1 501.2Grassland 5964 1.4 634.6 187.8 3784.8 527.5Deciduous Broadleaf Forest 48999 11.8 791.5 197.3 38781.2 519.4Evergeen Broadleaf Forest 1392 0.3 812.1 393.9 1130.8 677.4Cropland/Grassland Mosaic 69302 16.7 733.7 154.5 50844.2 619.8Cropland/Woodland Mosaic 3583 0.9 927.5 347.9 3323.5 701.8Dryland Cropland and pasture 9526 2.3 753.7 279.8 7179.5 597.2Commercial irrigated, permanent 581 0.1 900.5 204.7 522.8 565.5Commercial irrigated, temporary 1618 0.4 761.2 201.9 1231.5 541.9Commercial dryland, permanent 417 0.1 986.4 231.3 411.8 613.6Commercial dryland, temporary 6760 1.6 617.3 172.3 4173.0 554.1Semicommercial dryland, temporary 7941 1.9 657.0 197.8 5217.6 550.7

Average 779.0 208.0 592.2

SUM 414517 316886.5

Evapotranspiration

Source: IWMI

SGVP

CountryTotal Cropped are a (ha)

Total SGVP (Million US$)

Average SGVP (US$/ha)

Major cultivated crops

CropYield (kg/ha)

Percentage of cropped area

Contribution to total SGVP

South Africa 6,043,944 8,216.4 1,360Maize 3,635 53% 17%Wheat 2,366 13% 5%Sunflower 1,348 8% 2%

Mozambique 4,525,760 1,068.3 286Maize 1,141 27% 16%Cassava 5,882 24% 55%Sorghum 629 11% 3%

Zimbabwe 2,975,330 1,724.7 580Maize 529 58% 7%cotton 668 10% 8%Groundnuts 288 7% 1%

Botswana 142,525Introd.DataETa

SGVPResultsDiscuss.

SGVP calculate based on FAO data

While SGVP calculated using countries major crops production value at

Source: IWMI

SGVP

Introd.DataETa

SGVPResultsDiscuss.

The SGVP figures estimated through country statistic for the part fall in the basin boundary:

- South Africa; 450 US$/ha ; from 442 to 453 US$/ha - Mozambique; 80 US$/ha ; from 47 to 126 US$/ha Which much are lower than the one calculated by FAO data.

Within the Mozambique districts Beline, Chibuto and Xai xai Districts in south east part of the basin have higher SGVP Whereas, Chicualacuala has the lowest

Source: IWMI

WP

Introd.DataETa

SGVPResultsDiscuss.

Source: IWMI

Causes for variations and scope for improvement

Introd.DataETa

SGVPResultsDiscuss. Source: IWMI

Causes for variations and scope for improvement

Source: IWMI

Basin water productivity analysis - the road ahead

Introd.DataETa

SGVPResultsDiscuss.

Issues need to be resolved:

- Better basin land use and land cover map and assessment of the influences on final water productivity maps;

-Validation of ET (with local expert knowledge);

-Crop production data for a better land productivity map;

-Livestock and fishery to be included in WP (data?);

-Linkages with other work packages of Limpopo BFP ( especially water availability and interventions);

-Field level water productivity assessment for validation and causes studies (scaling down/up).

Project Report, Forthcoming. For more information visit: www.iwmi.org

N.B. This is not a form of technical output. Data and figures shown are subject to change.