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SADC activities on the use of GIS and RS for Agricultural Meteorology T. Tamuka Magadzire SADC Regional Remote Sensing Unit, USGS/FEWSNET WMO/FAO Training.

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Presentation on theme: "SADC activities on the use of GIS and RS for Agricultural Meteorology T. Tamuka Magadzire SADC Regional Remote Sensing Unit, USGS/FEWSNET WMO/FAO Training."— Presentation transcript:

1 SADC activities on the use of GIS and RS for Agricultural Meteorology T. Tamuka Magadzire SADC Regional Remote Sensing Unit, USGS/FEWSNET WMO/FAO Training Workshop on GIS and Remote Sensing Application in Agricultural Meteorology for SADC Countries. November 14-18, 2005 Gaborone, Botswana

2 Outline Background to SADC Region SDC RRSU Background Available EO-based data Modelling Applications of EO data End-user information products RRSU Database Partnerships - GMFS

3 14 Member States million people. Varied climate regions. Mostly uni-modal rainfall systems (bi-modal in the north). Varied cropping systems. Maize (corn) dominant crop Cassava and tubers important in the north. Rain fed agriculture – irrigation only significant in South Africa and Zimbabwe. The SADC Region - Background Prone to floods and droughts. Southern African Development Community

4 Climatic Hazards in SADC Floods and droughts are the major climatic hazards in the SADC Region. Serious drought in : Flooding in Mozambique, Zimbabwe, Botswana and South Africa in 2000: –Cyclones Eline and Gloria responsible. –4 million people affected. Lessons learnt. – SADC Disaster Management Strategy formulated. Further flooding in ensuing years (e.g. in 2003 from Cyclone Delfina (January) and Cyclone Japhet (March)) Serious droughts between 2001 and 2005 in several SADC countries: The SADC Region - Background


6 SADC RRSU: Organizational Context The SADC Secretariat is comprised of four directorates, including the Food, Agriculture and Natural Resources (FANR) Directorate The SADC Regional Remote Sensing Unit (RRSU) is a project within the FANR Directorate

7 Institutional Setting SADC RRSU Cooperating Partners: Technical support and training. Emergency food assessments. Supply of satellite data. Technical support and training. Vulnerability assessment activities. Support to the Regional Disaster Management Strategy. Supply of satellite data.

8 Main Objective of RRSU Strengthen national and regional capabilities in the area of Remote Sensing, Agrometeorology and GIS. Support early warning for food security and natural resources and disaster management. Principal contact institutions: National Meteorological Services (NMSs). National Early Warning Units (NEWUs). National Disaster Management Units.

9 SADC RRSU: Operational Activities Training of agro-meteorologists in the use of satellite imagery products and GIS for early warning for food security. Monitoring crops, vegetation and weather developments during the crop growing period using satellite images and GIS techniques. Developing and maintaining database of satellite images, maps and associated data.

10 RRSU: Agromet & GIS Training Subject- or application- specific workshops conducted at national and regional levels. Creating trained experts in RS and GIS applications. National staff seconded to RRSU Backstopping missions organized for on-the-job training in Member States.

11 NEWU SADC Region Early Warning Information flow NEWU Outgoing: satellite-based information and analysis

12 NEWU Incoming: ground-based information and analysis SADC Region Early Warning Information flow

13 Available EO-based Data Available satellite-based data used for Agromet activities are vegetation products and rainfall estimates. These products are analyzed and further processed into application specific products for flood and drought monitoring by USGS/FEWSNET and RRSU

14 Monitoring Rainfall Activity Rainfall Estimate (RFE) images. Combine satellite images with rain gauge observations. RRSU receives RFE images from USGS – EROS Data Center.

15 NOAA Rainfall Estimates Rainfall Estimates (RFE) are produced by NOAA for the FEWSNET activity, and distributed in southern Africa through RRSU Uses a number of datasets –Meteosat data used to composite a CCD image at -38 o C, a rainfall estimate is generated from the CCD using the GOES Precipitation Index (GPI). GPI = CCD x 3 –WMO GTS rainfall data from approx stations (not all stations used at any given time), and are taken as the true rainfall within 15-km radius of each station –Two satellite microwave instruments, SSM/I (Special Sensor Microwave/Imager) and the AMSU (Advanced Microwave Sounding Unit), which acquire data every 6 hours and every 12 hours respectively. The four datasets are merged to produce an improved product

16 RFE – Related Activities SADC RRSU operates the WinTRES system and generates daily and dekadal Cold Cloud Duration (CCD) images from Meteosat-7 TIR images RRSU currently working on computer algorithms in collaboration with Botswana Met Services to enable the use of MSG Meteosat-8 images in CCD generation Interest has been expressed by SADC nationals in improving RFE using local rain gauge data Some workshops have been held by NOAA on implementation of their RFE production technique locally in Africa – RRSU installed this technique locally for short time –Limited by operational availability of rain-gauge information

17 Monitoring Vegetation Condition Normalized Difference Vegetation Index (NDVI) images. Sources of NDVI are NOAA AVHRR (8km), SPOT VGT (1.1km) and MODIS (250m) MODIS: 250m SPOT: 1 km AVHRR: 8 km

18 Seasonal Trends Time series curves for visualizing seasonal trends. – Comparing against long-term (average) trends. Main crop-growing regions in SADC monitored.

19 Monitoring Crop Condition: WRSI The Water Requirements Satisfaction Index (WRSI) is a crop specific water balance approach that models the effect of seasonal rainfall availability on potential crop yields. Two approaches are used in the SADC region – using satellite-based, distributed approach, and a ground- based point-specific approach The model is being used in several SADC countries to monitor crop water use with a view to yield forecasting and estimation. SADC RRSU is providing training Operational model run at USGS but modern modelling software now publicly available from FAO and USGS.

20 Crop Water Balance Modeling Water Requirements Satisfaction Index (WRSI) WRSI=100*AET/WR Regression models Yield Estimation Water Requirements Satisfaction Index

21 WRSI Water Balance - Products WRSIWRSI Anomaly Start of Season Soil Water Index

22 Can model for multiple regions or countries Can enter field information on planting, soils, maturity Can model using information for multiple planting dates multiple varieties (maturity periods) multiple crop types Range of outputs: SOS, WRSI, WRSI Anom, SWI etc SWI, West Africa WRSI Anom, East Africa SOS, Southern Africa WRSI, Zambia

23 End-user Information Products A number of bulletins are produced to meet information requirements, including: –Regular agrometeorological updates at 10-daily and monthly intervals –Ad-hoc “Significant Weather Developments” (SWD) bulletin which aims to “ provide timely highlights of developing weather patterns and their potential impacts to human lives and property” –Other special bulletins to address current or issues e.g. forecast interpretation; drought alert

24 Agro-Meteorological Update Rainfall Areas Crops Models Agromet Up-dates

25 Agro-Meteorological Update Rainfall Areas Crops Models

26 Significant Weather Developments Forecast Cyclone Tracks Major River Basins

27 Examples from SWD bulletins

28 RRSU Database  Developed and maintained on central computer at the RRSU  Abridged onto CD for external use.  Simple and open data formats make data portable. RRSU Standard Vector Data. Satellite data. Raster images with climatic parameters. Tabular data with agricultural statistics and population data. Free WinDisp 3.5 & 4 software for data viewing. Current CD-ROM version is 2.0. Details from

29 RRSU Data holdings comprises both baseline datasets and earth observation datasets, compiled from a variety of sources uniform regional standard vector data set for SADC at a scale of 1:1 million was compiled as part of this dataset –originated from the DCW –updated using inputs from the SADC countries

30 RRSU Data holdings Administrative (borders, subnational boundaries, cities) Elevation Land use and land cover Hydrology (water bodies, rivers, lakes) Infrastructure (roads, railroads, bridges, airports, utility lines) Soil Agriculture (crop zone maps) Climate (rainfall, temperature etc) Demography Satellite images

31 RRSU Data holdings Administrative National borders of SADC countries Sub-national boundaries of SADC countries National borders of SADC countries (FAO/GIEWS version) Level 1 sub-national boundaries of SADC countries (FAO/GIEWS version) Major cities and towns of SADC countries (FAO/GIEWS version) Cities and towns of SADC countries Urbanised areas of SADC countries Cultural landmarks of SADC countries Elevation Digital elevation model Elevation contours in SADC countries Spot elevations in SADC countries Land use and land cover Land cover areas of SADC countries Forest types in SADC countries Managed areas (including national parks) in SADC countries Centre points of managed areas in SADC countries Hydrology Small water bodies of SADC countries Rivers of SADC countries Surface water bodies of SADC countries Perennial and non-perennial water layers in SADC countries Wetland types in SADC countries Lakes of SADC countries Small islands and lakes of SADC countries Small coastal islands of SADC countries Infrastructure Roads in SADC countries Railroads in SADC countries Road and Railroad Bridges in SADC countries Airports in SADC countries Utility lines in SADC countries Soil Soil types in SADC countries Agriculture Crop zone maps of SADC countries (FAO/GIEWS version) Crop harvest dates of SADC countries (FAO/GIEWS version) Crop planting dates of SADC countries (FAO/GIEWS version) Historical crop statistics for the SADC countries Crop Water Satisfaction index (1996 – 2005) Start of rainfall season estimates ( ) Climate Dekadal, long term average: rainfall, temperature, evapotranspiration Monthly, long term average: radiation, humidity, wind Satellite Rainfall estimates from 1995 to 2005 Demography Population for SADC countries, by province Satellite images MODIS imagery ( ) AVHRR NDVI vegetation images ( ) Landsat imagery: regional coverage for 1970s, 1990s, 2000s ASTER (partial SADC coverage) imagery for late 2001, early 2002, and 2003 Meteosat thermal infrared and cold-cloud duration imagery SPOT-4 VGT NDVI vegetation images ( )

32 Partnerships - GMFS SADC RRSU has been collaborating with the GMFS consortium over the last couple of years GMFS is developing products for estimation of yield and area planted to crops, as well as other monitoring products –Concentrating on using a combination of SAR and optical EO data to id crop extent and phenological stages GMFS has done some preliminary work for product development in Malawi, with potential for spreading to SADC region Products are currently being validated by GMFS

33 Contacts RRSU Coordinator –Dr. Kennedy Masamvu: Regional Agrometeorologist –Dr. Elijah Mukhala: Database Specialist –Mrs. Dorothy Nyamhanza: Research Assistant –Mr. Blessing Siwela: GeoInformatics Scientist (USGS/FEWSNET Regional Rep. Southern Africa) –Mr. T. Tamuka Magadzire: Website:

34 Gracias Tinotenda Re a leboha Thank You Asante sana Merci Beaucoup Obrigado Zikomo Siyabonga Grazie

35 Websites for cyclone monitoring Forecasted cyclone track: – – Latest cyclone track: – Latest satellite imagery: – Other useful websites for extreme-weather monitoring: –http://www.sadc-hazards.net – – – – – – – – Note: Although the SADC national meteorology websites have not been included here, they are very good for monitoring. Links to most of them can be found at: – ???

36 NOAA RFE - Limitations Weaknesses in datasets –Microwave inputs have 6hr and 12hr repeat rate: estimates can either miss out some storms altogether, or overestimate rainfall when the satellite image is taken at the peak of a storm –Rainfall is estimated most accurately in the vicinity of GTS gauges –Meteosat-derived GPI estimates capture convectional rainfall very well. However, other rainfall types (e.g. orographic) are not estimated as accurately. Also cirrus clouds can cause over- estimation * Note that the merging process makes these datasets complementary

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