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.
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