Drought Monitoring in Zambia using Meteosat and Noaa Avhrr Data
Cumulative Rainfall Departure: Derived from the rainfall images and climatological rainfall images.
Figure 5 Cumulative Rainfall Departure from normal 1 to 10th April, 2000
Vegetation index, NDVI: Derived from the NOAA data by the usual combination of NIR and Red reflectances. These are produced consistently with the rainfall outputs (same window, resolution)
Figure 6 Zambia Vegetation Status 21- 30th November 1999
Users: The users of our information range from Government Institutions (like Disaster Management Unit, Ministry of Agriculture, Department of Water Affairs e.t.c), International Organisations like FEWS, farming community, researchers from local and International Universities, Non Governmental Organisations, Zambia Electricity Supply Company, Regional Organisations involved with management of regional water bodies like Zambezi River Authority.
This information has been well received by users who have used it in various ways like agriculture, water management, research, disaster management e.t.c. Most of the users have been very active and have helped in refining our products by giving us feed back and suggestions on how best to improve our information to suit their needs. One particular example is one in which a non-governmental organisation (Program Against Malnutrition) asked for rainfall data normally represented at a national scale broken down to administrative districts. This suggestion was incorporated into our dekadal tasks and the product has proved more helpful to the NGO whose work is mainly at district level. Another example was one in which FEWS asked for Excel plots of point extracted values from a cumulative rainfall image against the normal. This helps them in verifying independent field reports.
Validation: National Early Warning Cards are usually sent to Meteorological and part time Rainfall reporting Stations. At the end of every dekad, fully completed cards are then sent back to us containing all the rainfall amounts for that dekad and comments on agriculture performance. We then use the rainfall reports to improve the accuracy of Rainfall Estimates and the Agriculture performance reports to independently validate data that we produce.
Dissemination: Given widespread access to e-mail the maps (JPEG) derived from the system are sent as e-bulletins (Word files) via e-mail. Web versions are prepared and we hope to have them available soon. All data is produced in a regular lat-long grid (20 pixels/degree) covering most of the Zambezi Catchment.
Training: The installation of the AMIS was accompanied by an extensive training program. Given the need for sustainability it was crucial for the operators to be fully familiar with the background, the methodologies and the software the system had been implemented in. Depending on the initial status of the staff, training also included basic programming principles and algorithms. The choices made for software (IDRISI+AWK) provided a very shallow learning curve allowing the operators to quickly build up significant amounts of work and a good confidence level.
Conclusion: The temporal and spatial resolution of satellite derived information has practically proven in this particular case to be more handy and timely to decision makers to make well informed decisions on time. A steady stream of quality data has been accumulating and we hope to benefit in future from improved calibrations and refinement in the interpretation of NOAA-AVHRR data. We hope in particular to start the usage of the produced rainfall and vegetation related data in applications such as rainfall-runoff models for flood warning and river management. We have been archiving data and would like to invite other institutions to participate in cooperative applied research ventures.
References
- Grimes, D.G., E. Pardo and R. Bonifácio, 1999, Optimal Areal Rainfall Estimation using raingauges and satellite data, Journal of Hydrology, 222, 93-108,
- Nawa.,K 2000, Drought Monitoring in Zambia using Meteosat and AVHRR Data.
