American Journal of Agriculture and Forestry
Volume 3, Issue 3, May 2015, Pages: 93-98
Received: Mar. 28, 2015;
Accepted: Apr. 18, 2015;
Published: May 7, 2015
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Absalom Mganu Manyatsi, University of Swaziland, Department of Agricultural and Biosystems Engineering, Manzini, Swaziland
Ntobeko Zwane, University of Swaziland, Department of Agricultural and Biosystems Engineering, Manzini, Swaziland
Musa Dlamini, University of Swaziland, Department of Agricultural and Biosystems Engineering, Manzini, Swaziland
Swaziland is generally an arid country, with most rains falling during the period of October to March. The long term average annual rainfall ranges from 400 mm in the lowveld to 1,200 mm in the mountainous highveld. Raingauges have been used as reliable source of rainfall data, but the density of these ground based instruments is too low, offering poor spatial coverage. The use of satellite products to estimate rainfall can fill the gap created by poor spatial coverage of ground based instruments. The African Monitoring of the Environment for Sustainable Development (AMESD) project that was launched in 2007 aims to provide African Nations with resources for climate monitoring application through the use of Meteosat Second Generation (MSG) satellite data. In Swaziland, the satellite receiving station was installed in 2012. The satellite rainfall product has not been evaluated in the country. The objective of this paper was to evaluate the rainfall product by comparing it with rainfall data sourced from raingauge. Daily rainfall data were obtained for five weather stations (Big Bend, Malkerns, Matsapha, Mhlume and Nhlangano) for 1998 to 2006. These daily rainfall data were organized in 10-day (dekadal) totals. Dekadal satellite rainfall data were obtained from the local AMESD receiving station for the respective period. The data were exported to Statistical Package for Social Sciences (SPSS) computer software for analysis. Person correlation and linear regression tests were performed for average dekadals and yearly data for the five weather stations to compared gauged rainfall and satellite rainfall estimates. The correlation for average dekadal rainfall data was significant at 99% level of confidence for all weather stations. Correlation coefficients and R2 were higher for weather stations in the middleveld (Malkerns and Matsapha). The magnitude of underestimation of rainfall by satellite products was higher during the wet season for weather stations receiving relatively higher rainfall. The correlation between yearly gauged rainfall and yearly rainfall estimates from satellite product was significant at 99% level of confidence for Big Bend, Mhlume and Matsapha. It was significant at 95% level of confidence for Malkerns, and not significant for Nhlangano weather station. The regression models that were developed could be used to adjust rainfall estimates from satellite products to ground (gauged) rainfall for an area or community.
Absalom Mganu Manyatsi,
Evaluation of Satellite Rainfall Estimates for Swaziland, American Journal of Agriculture and Forestry.
Vol. 3, No. 3,
2015, pp. 93-98.
Abiola S.F., Mohd-Mokhtar R., Ismail W., Mohamad N., and Mandeep J.S. (2013). Categorical statistics approach to satellite retreieved rainfall data analysis in Nigeria. Scienctific Research and Essays. 8(43), 2123-2137.
Asadullah A., N. McIntyre and Kigobe M. (2010). Evaluation of five satellite products for estiation of rainfall over Uganda. Hydrological Sciences Journal, 53-6, 1137-1150.
Chadwick R.S., Grimes D.I.F., saunders R.W., Francis P.N and Blackmore T.A. (2010). The TAMORA algorithm: satellite rainfall over West Africa using multi-spectral SEVIRI data. Adv. Geosci., 25, 3-9.
Davis, C.L. (2011). Climate Risk and Vulnerability: A Handbook for Southern Africa. Council for Scientific and Industrial Research, Pretoria, South Africa, pp. 92.
Ebert, E. E., Janowiak, J. E. and Kidd, C. (2007) Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Am. Met. Soc. 88, 47–64.
EUMESTSAT, (2015). Monitoring weather and climate from space. http://www.eumetsat.int/website/home/index.html. 20/02/2015.
European Space Agency, (2015). Meteosat Second Generation. http://www.esa.int/Our_Activities/Observing_the_Earth/Meteosat_Second_Generation/MSGoverview2. 20/02/2015
Government of Swaziland (2014). Database of meteorological data. Ministry of Tourism and Environmental Affairs, Mbabane, Swaziland.
Grimes,D.I.F, Pardo-Iguzquiza, E. and Bonifacio, R (1999). Optimal areal estimation using rain gauges and satellite data. Elsevier Journal of Hydrology. 346: 33-50.
Hughes, D. A. (2006). Comparison of satellite rainfall data with observations from gauging stations. http://eprints.ru.ac.za/470/1/Hughes_Comparison_of_satellite_rainfall.pdf. 09/09/2013.
Hughes, D. A., Andersson, L., Wilk, J., Hubert and Savenije, H.H.G (2006). Regional calibration of the Pitman model for the Okavango River. Elsevier Journal of Hydrology 331: 30– 42.
IFRC, (2000). Mozambique, Botswana, Swaziland, Zimbabwe: Floods: http://www.ifrc.org/docs/apeals/oo/o40004.pdf. 26/08/09.
Manyatsi A.M. (2011). Application of indigenous knowledge systems in hydrological disaster management in Swaziland. Current Research Journal of Social Sciences, 3:4, 353-357.
Manyatsi A.M., Mhazo N, and Masarirambi M.T. (2010). Climate variability and changes as perceived by rural communities in Swaziland. Res. J. Environ. Earth Sic., 2: 3, 165-170.
Nicholson, S.E. Some, B., Mccollum, J., Nelkin, E., Klotter, D., Berte, Y., Diallo, B.M., Gaye, I., Kpabeba, G., Ndiaye, O., Noukpozounkou, J.N., Tanu, M.M., Thiam, A And Toure, A.A. and A. K. Traore. (2003). Validation of TRMM and Other Rainfall Estimates with a High-Density Gauge Dataset for West Africa. Part I: Validation of GPCC Rainfall Product and Pre- TRMM Satellite and Blended Products. Journal of Applied Meteorology. 42: 1337-1354.
Ochieng W.O and T.A. Kimaro) undated). Coparative study of performance of satellite derived rainfall estimates: A case study of Mara River basin. http://www.waternetonline.org/Symposium/10/full%20papers/Water%20and%20Environment/Ochieng%20W.O..pdf. 20/03/2015.
Orlandi, A., Ortolania, A., Meneguzzoa, F., Levizzanic V., Torricellac, F. and Turkd, F. J. (2004). Rainfall assimilation in RAMS by means of the Kuo parameterisation inversion. Elsevier Journal of Hydrology. 288: 20–35.
SPSS (2008).Statistical Package for Social Science (SPSS). Polar Engineering and Consultancy. USA.
Teo C.K. and Grimes D.I.F. (2007). Stochastic modelling of rainfall from satellite data. Journal of Hydrology. 346 (1-2), 33-50.
Tote C., Patricio D., Boogaard H, van der Wijngaart R., Tarnavsky E. and Funk C. (2015). Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique. Remote Sens. 7(2), 1758-1776.
WMO (2010). Guide to meteorological instruments and methods of observation. WMO-No 8. Geneva, Switzerland.
Xie. P. and Arkin, P. A. (1995): An intercomparison of gauge observations and satellite estimates of monthly precipitation. Journal of Meteorology. 34: 1134-1160.
Yilmaz, K. K., Houge, T. S., Hsu, K., Sorooshian, S., Gupta, V. H. and Wagener, T. (2005). Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimateswith Emphasis on Hydrologic Forecasting. Journal of Hydrometeorology. 6: 497- 517.