The use of remote sensing and gis for drought risk assessment: the case of southern province, Zambia.
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Water plays a major role in human livelihoods and has significant implications to food security. Hydrological extremes are a major threat to society and can have extensive effects. Droughts compound the already increasing pressures on water resources from population growth, economic activities and escalating competition between users. Remote Sensing and Geographic Information Systems (GIS) have emerged as essential tools in the assessment and analysis of natural resources and hazards for drought prone areas such as the Southern Province of Zambia. The Southern Province is drought prone and is typified by unreliable rainfall and arid conditions despite being one of the food baskets of Zambia. Therefore, it is important to assess the risk of drought in the region. However, there are only 11 meteorological stations present in the vast region, of which only six have historical records of more than 10 years. This limits the use of conventional drought risk assessments techniques as they require well populated networks of weather stations. Remote sensing and GIS are not reliant on station based data. Satellite data is easily obtainable in almost real-time and at high resolutions from several platforms. Therefore, it is important to explore the use of Remote Sensing and GIS to assess drought in the Southern Province. In this study, Normalised Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) for the years 2000 to 2016 were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS). Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) gridded rainfall estimates and in situ rainfall data were also obtained. Standardised Precipitation Index (SPI) and rainfall anomaly percentage were calculated using monthly CHIRPS in SPIRITS software and used to assess meteorological drought. NDVI was transformed to Vegetation Condition Index (VCI) in order to assess agricultural drought. Soil Moisture Index (SMI) was determined using NDVI and LST. Correlations analyses were conducted between NDVI and in situ rainfall, NDVI and CHIRPS, VCI and maize production and yield, and SMI and Soil Moisture. Drought risk comprised of three components, which were hazard, exposure and vulnerability. Meteorological drought hazard was determined using rainfall anomaly percentage and SPI, whilst agricultural drought hazard was determined using VCI and SMI. Drought exposure comprised of two elements, which were human populations and land iv cover. Drought vulnerability was a combination of socioeconomic indicators, rainfall variability and slope. The results show that NDVI responded quickly to increments in rainfall but slower to reductions due to residual soil moisture. Droughts of the Southern Province were categorised as aggressive or regressive in nature. Aggressive droughts were those that increased in magnitude and/or intensity as the season advanced, whereas regressive droughts where those that saw reductions in intensity/magnitude as the season progressed. Aggressive and regressive behaviour were true for both meteorological and agricultural droughts. The 2001/2002 season saw an aggressive drought occur as the average SPI for the Southern Province dropped from -0.26 (normal) in November 2001 to - 2.29 (extremely dry) in January 2002. In the 2002/2003, SPI rose from - 1.55 (severely dry) in November 2002 to 0.57 (normal) in March 2003, which indicated a regressive drought. Similarly, VCI dropped from 53 (no drought) in November 2001 to 36 (mild drought) in January 2002, whereas it rose from 36 in November 2002 to 50 in Mach 2003. Regressive droughts occurred as a result of El Niño and were characterised by higher surface temperatures. SMI was lower in agro-ecological Region I and higher in Region IIa. NDVI was positively correlated to in situ rainfall measurements (0.59) and was reliable as an index to assess drought. VCI was correlated to maize production and yield (0.66 and 0.84, respectively). Similarly, SMI was correlated to soil moisture at 5 centimetre depth with a correlation of 0.71. The most at risk to drought districts in the province were Kalomo, Sinazongwe and Zimba. The least at risk were Mazabuka and Chikankata, which were both in Region IIa. Kalomo and Zimba had a combined population of 258,570 people of which about 75% were estimated to be living under the poverty line of 96.37 Zambian Kwacha rebased in 2015. The two districts were also estimated to have planted 96,427.53 hectares of maize in 2015, which at the time was the most in the Southern Province. However, 37.62% was estimated to have been harvested. Remote sensing and GIS were successfully used to assess meteorological and agricultural drought in the Southern Province of Zambia. It was possible to assess areas in which meteorological data was absent as the remote sensing data used covered the whole province. Furthermore, remote sensing data had a few gaps and correlated well with precipitation values. It is recommended that the Disaster Management and Mitigation Unit (DMMU) integrate remote sensing drought indices and GIS work flows to drought early warning systems. It is also recommended that further studies be carried out in the drought hot spot districts of Kalomo, Sinazongwe and Zimba by the Zambia Meteorological Department and the DMMU in collaboration with the University of Zambia Integrated Water Resources Management Centre (UNZA IWRM) .
The University of Zambia
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