Analysis of Drought Using Meteorological and Microwave Remote Sensed Data: A Case of Wami Watershed, Tanzania

Authors

  • Mwajuma Juma University of Dar es Salaam
  • Deogratias M.M. Mulungu

Abstract

Agricultural sector is important for the economy of Tanzania, but in recent years there is decline in its growth and performance because of persistent droughts. An in-depth study of droughts was conducted on Wami watershed through rainfall and satellite microwave remote sensing data leading for estimates of meteorological droughts and soil moisture based droughts, respectively. Rainfall data during 1973-2008 was used to obtain Drought Severity Index (DSI) and active imaging microwave radar data during 1997-2009 from ESA ' s SAR missions of ENVISAT and ERS was used to obtain soil moisture anomalies (SMA). Soil map was used to explain discrepancies in droughts from SMA to DSI maps at intervals of time. Seasonality analysis and DSI results showed that the main sub-seasons contributing to rainy season are October through December, January-February and March through May, and drought years were 1984, 1991, 1994, 2004 and 2006. Results showed that the last decade (2000s) had severe droughts that covered 35-39% of the Wami watershed and could have affected 1128000 people. The soil moisture based drought maps showed the same drought conditions as DSI maps in January, March, May and October. This indicated that in most areas the meteorological droughts can be used to infer to droughts conditions in the soil during the rainy season. The obtained drought events and impacts were confirmed in the field through interviews. However, in July SMA map showed normal and wet conditions whereas it was a dry season for DSI map. This showed that when rainy season ends, the soil still holds some moisture, which can be available for simple crops like vegetables. Therefore, it can be concluded that the SMA was able to provide a better alternative to DSI especially for increased spatial coverage and accuracy of drought monitoring for agricultural production. The SMA enables to map droughts conditions at any point spatially rather than point based DSI maps, which may be prone to rainfall data gaps and spatial interpolation errors. The SMA approach for drought monitoring may be useful to rainfall data scarcity areas of Tanzania and for agricultural droughts risk management.

 

Keywords: Active imaging microwave, Agricultural droughts, Drought indices, Meteorological droughts, Soil moisture based droughts, Wami Basin

References

Awange J.L., Aluoch J., Ogallo L.A., Omulo M. and Omondi P. (2007).

Frequency and severity of drought in the Lake Victoria region (Kenya) and its effects on food security. Climate Research, 33: 135-142.

Awange J.L., Ogalo L., Bae K., Were P., Omondi P., Omute P. and Omullo M. (2008). Falling Lake Victoria water levels: Is climate a contributing factor? Climate Change, 89(3-4): 281- 297.

Eastman J.R. (2001). Guide to GIS and Image Processing, IDRISI Production, USA.

Engman E.T. (1990). Progress in microwave remote sensing of soil

moisture. Canadian Journal of Remote

Sensing, 16: 6 €“14.

FAO (2007). Digital Soil Map of the World, 1:5,000,000 scale. United

Nations Food and Agriculture Organization (FAO), Rome, Italy.

Houghton-Carr H. and Fry M. (2006). The decline of hydrological data collection for development of integrated water resources management tools in Southern Africa. In: S. Demuth et al. (eds), International Association of Hydrological Sciences (IAHS), Fifth

FRIEND world conference, Havana, Cuba.

Kogan F. (1993). Development of global drought-watch system using

NOAA/AVHRR data. Advances in Space Research, 13(5): 219-222.

Kong X. and Dorling S.R. (2008). Near- surface soil moisture retrieval from ASAR Wide Swath imagery using a Principal Component Analysis. International Journal of Remote Sensing, 29(10): 2925 €“2942.

Lu D., Mausel P., Brondízio E. and Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12): 2365-2401.

Moran M.S., Hymer D.C., QI J. and Kerr Y. (2002). Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions. Remote Sensing of Environment, 79: 243 €“252.

Mulungu D.M.M., Mtalo F.W., Mkhandi S.H., Fohrer N. and Schamalz B. (2008). Characterisation and monitoring of agricultural droughts in Simiyu River catchment,Tanzania, 9th Waternet/ WARFSA/ GWP-SA Symposium, 29-31 October, Johannesburg, South Africa.

Mulungu D.M.M., Sseguya F. and Mashingia F. (2011). Relating Maize

yield with vegetative index and meteorological drought in Ruvu basin, Tanzania, 12th Waternet/ WARFSA/GWP-SA Symposium, 26-28 October 2011, Maputo, Mozambique.

Muthumanickam D., Kannan P., Kumaraperumal R., Natarajan S.,

Sivasamy R. and Poongodi C. (2011). Drought assessment and monitoring through Remote sensing and GIS in western tracts of Tamil Nadu, India. International Journal of Remote Sensing, 32(18): 5157-5176. https://doi.org/10.1080/01431161.201

494642.

NDMC (National Drought Mitigation Center) (2002). Drought basics,

University of Nebraska-Lincoln, USA. Available online at http://drought.unl.edu/Education/DroughtBasics.aspx. Retrieved on 30th December 2018.

Nsubuga F.W.N., Botai O.J., Olwoch J.M., deW Rautenbach C.J., Bevis Y. and Adetunji A.O. (2014). The nature of rainfall in the main drainage sub- basins of Uganda. Hydrological Sciences Journal, 59(1-2): 278-299.

Parkes S.M. and Clifton H.L. (1999). The compression of raw SAR and SAR image data. International Journal of Remote Sensing, 20(18): 3563-3581.

Quiring S.M. and Ganesh S. (2010). Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in Texas. Agricultural and Forest Meteorology, 150(3): 330-339.

Valimba P. (2007). Environmental Flow Assessment Study (EFA), Wami River Sub-basin, Tanzania: The Wami Hydrology. Volume 1 - General Description. Wami-Ruvu Basin Water Office. 42p.

van der Velde R. (2012). Synthetic Aperture Radar data processing with the Next ESA SAR Toolbox (NEST) and Flood and Soil Moisture mapping applications. Unpublished manuscript. University of Twenty, ITC, The Netherlands.

Said S., Kothyari U.C. and Arora M.K. (2012). Vegetation effects on soil moisture estimation from ERS-2 SAR images. Hydrological Sciences, 57(3): 517-534.

Shemsanga C., Omambia A.N. and Gu Y. (2010). The Cost of Climate Change in Tanzania: Impacts and Adaptations. Journal of American Science, 6(3): 182-196.

Stokstad E. (1999). Hydrology-scarcity of rain, stream gages threatens forecasts. Science, 285(5431): 1199-1200.

TMA (Tanzania Meteorological Agency) (2013). Seasonal Forecasts. Tanzania rainfall outlook for October to December. Press release accessed on 10th October 2013 at www.tma.go.tz;

www.meteo.go.tz.

Wagner W., Lemoine G. and Rott H. (1999a). A method for estimating soil moisture from ERS Scatterometer and soil data. Remote Sensing of Environment, 70: 191 €“207.

Wagner W., Noll J., Borgeaud M. and Rott H. (1999b). Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer. IEEE Transactions on Geoscience and Remote Sensing, 37: 206 €“216.

Wagner W. and Scipal K. (2000). Large- scale soil moisture mapping in Western Africa using the ERS scatterometer. IEEE Transactions of

GeoSciences in Remote Sensing.

Wilhite D.A. and Glantz M.H. (1985). understanding the Drought

Phenomenon: The role of definitions. Water International, 10(3): 111-120.

Wilks D.S. (2011). Statistical Methods in the Atmospheric Sciences. Third edition, International geophysics series, Volume 100, USA.

WFP (World Food Program) (2013). Comprehensive Food Security and Vulnerability Analysis (CFSVA) Tanzania 2012. United Nations World Food Programme, Rome, Italy, 74p.

WRBO (2008). Business plan. Wami Ruvu Basin Water Office, Morogoro.

Zribi M., Saux-picart S., Andre C., Descroix L., Ottle C. and Kallel A.

(2007). Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region. International Journal of Remote Sensing, 28(16): 3547- 3565.

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Published

2019-01-30

How to Cite

Juma, M., & Mulungu, D. M. (2019). Analysis of Drought Using Meteorological and Microwave Remote Sensed Data: A Case of Wami Watershed, Tanzania. Tanzania Journal of Engineering and Technology, 37(2). Retrieved from https://journals.udsm.ac.tz/index.php/tjet/article/view/2325