Remote sensing based drought monitoring in Zimbabwe

Disaster Prevention and Management Remote sensing based drought monitoring in Zimbabwe Godfrey Mutowo David Chikodzi

Downloaded by GREAT ZIMBABWE UNIVERSITY At 10:05 22 October 2014 (PT)

Article information: To cite this document: Godfrey Mutowo David Chikodzi , (2014),"Remote sensing based drought monitoring in Zimbabwe", Disaster Prevention and Management, Vol. 23 Iss 5 pp. 649 - 659 Permanent link to this document: http://dx.doi.org/10.1108/DPM-10-2013-0181 Downloaded on: 22 October 2014, At: 10:05 (PT) References: this document contains references to 41 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 19 times since 2014*

Users who downloaded this article also downloaded: Rajib Shaw, Huy Nguyen, Umma Habiba, Yukiko Takeuchi, (2011),"Chapter 1 Overview and Characteristics of Asian Monsoon Drought", Community, Environment and Disaster Risk Management, Vol. 8 pp. 1-24 Aki Kogachi, Rajib Shaw, (2012),"Chapter 18 Experience of Community-Based Adaptation in Burkina Faso", Community, Environment and Disaster Risk Management, Vol. 10 pp. 351-371 Vincent Kotwicki, Zaher Al Sulaimani, (2009),"Climates of the Arabian Peninsula – past, present, future", International Journal of Climate Change Strategies and Management, Vol. 1 Iss 3 pp. 297-310

Access to this document was granted through an Emerald subscription provided by 538887 []

For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.

About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download.

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0965-3562.htm

RESEARCH PAPER

Remote sensing based drought monitoring in Zimbabwe Godfrey Mutowo and David Chikodzi

Drought monitoring in Zimbabwe 649


Department of Geography and Environmental Science, Great Zimbabwe University, Masvingo, Zimbabwe Abstract Purpose – Drought monitoring is an important process for national agricultural and environmental planning. Droughts are normal recurring climatic phenomena that affect people and landscapes. They occur at different scales (locally, regionally, and nationally), and for periods of time ranging from weeks to decades. In Zimbabwe drought is increasingly becoming an annual phenomenon, with varying parts of the country being affected. The purpose of this paper is to analyse the spatial variations in the seasonal occurrences of drought in Zimbabwe over a period of five years. Design/methodology/approach – The Vegetation Condition Index (VCI), which shows how close the Normalized Difference Vegetation Index of the current time is to the minimum Normalized Difference Vegetation Index calculated from the long-term record for that given time, was used to monitor drought occurrence in Zimbabwe. A time series of dekadal Normalized Difference Vegetation Index, calculated from SPOT images, was used to compute seasonal VCI maps from 2005 to 2010. The VCI maps were then classified into three drought severity classes (severe, moderate, and mild) based on the relative changes in the vegetation condition from extremely bad to optimal. Findings – The results showed that droughts occur annually in Zimbabwe though, on average, the droughts are mostly mild. The occurrence and the spatial distribution of drought in Zimbabwe was also found to be random affecting different places from season to season thus the authors conclude that most parts of the country are drought prone. Originality/value – Remote sensing technologies utilising such indices as the VCI can be used for drought monitoring in Zimbabwe. Keywords Zimbabwe, Remote sensing, Droughts, Drought monitoring, Vegetation Condition Index, Normalized Difference Vegetation Index Paper type Research paper

Introduction Drought is one of the main natural hazards that Zimbabwe experiences from time to time (CEDRISA, 2009). Drought is any period of moisture deficiency that deviates from the normal climatic average at a given location or region (Warren and Khogali, 1992; Hulme, 1996). Droughts are normal recurring climatic phenomena, occurring at many scales (locally, regionally, and nationally) for periods varying from weeks to decades (Glantz, 1987; Wilhite, 1993; Bayarjargal et al., 2006). The socio-economic impacts of drought are varied, ranging from famines, food insecurity and a decrease in GDP (Kiros, 1991) to loss of ecosystems, loss of biodiversity, loss of water bodies, and desertification (Adams et al., 2012). Sadly, in the last decades, studies indicate that the frequency and intensity of droughts have increased in some parts of the world (Hulme and Kelly, 1993; McCarthy et al., 2001) including Zimbabwe. It is becoming increasingly unusual for drought not to occur somewhere in Zimbabwe for each agricultural season. This has had consequences on the landscapes, and especially on the livelihoods of the people largely because of the Zimbabwean economy’s dependence on agriculture. The significance of drought mapping and

Disaster Prevention and Management Vol. 23 No. 5, 2014 pp. 649-659 r Emerald Group Publishing Limited 0965-3562 DOI 10.1108/DPM-10-2013-0181

DPM 23,5


650

monitoring to the country cannot therefore be overemphasised (Richardson, 2007). Thus it is important to identify the areas prone to drought conditions as well as their severity. This can aid in an understanding of the possible consequences of drought on annual crop production and livelihoods of local communities, as well as building drought resilient communities, agriculture and infrastructure (Unganai, 1994; Tadesse et al., 2005). In response to the problem of especially agricultural drought, the Zimbabwean government has been relying on food aid and food redistribution, but these are not without their own challenges. Food-aid programmes are open to political manipulation and tend to overshadow the need to develop sustainable solutions to household food insecurity and chronic poverty (Chikodzi and Mutowo, 2013). Although most food shortages that have occurred in Zimbabwe have been attributed to agricultural drought conditions, there are still limited monitoring mechanisms to map the affected areas as well as the severity of the drought in each area. In addition, previous studies on drought were mainly focused on drought mitigation, and the impact of drought on the national economy and food supply. To the best of our knowledge, no initiative has been taken to map the spatial occurrence of drought severity over the country so as to determine the areas that are more vulnerable to droughts. To date, the available information on drought-affected-areas for specific agricultural seasons is derived from individual weather stations that are not so evenly distributed across the country and makes measurements of weather parameters at point level which will be difficult to extrapolate over regions (Unganai and Kogan, 1998; Chikodzi and Mutowo, 2013). The consistency and accuracy of meteorological data in monitoring of agricultural drought largely varies from station to station, with some stations having missing parameters over a number of seasons. Also, this information has largely been archived in tabular, rather than cartographic, form which makes the determination of spatial patterns very difficult. To this end, remotely sensed images become an important data source which is objective and covers large spatial extents. Information from satellite images is especially appropriate over remote areas or in locations where weather stations or other ground observations are sparse or non-existent (Thiruvengadachari and Gopalkrishna, 1993). The use of time series analysis of vegetation indices derived from satellite imagery in vegetation monitoring and classification of land cover is a widespread technique (DeFries and Townshend, 1994; Maselli et al., 1998). In addition, the relationships between such indices to biomass and other biophysical measures of vegetation have been well established by many authors (Goward et al., 1985; Tucker et al., 1985; Peters et al., 1997). For example, the Normalised Difference Vegetation Index (NDVI) and its derivatives such as the Vegetation Condition Index (VCI) (Liu and Kogan, 1996) have been widely used as measures of vegetation health and condition (Tucker et al., 1985). NDVI is the most widely used index for monitoring drought at different spatial scales ranging from the landscape to the global (Goward et al., 1985; Peters et al., 1997). There is a paucity of literature on drought monitoring in Zimbabwe yet there is an increasing prevalence of agricultural drought which has had a devastating impact on people’s livelihoods, particularly in arid and semi arid parts of the country. This has been exacerbated by the fact that most communities are dependent on rain-fed agriculture. In order to reduce vulnerability, Zimbabwe needs to prepare for and adopt drought-mitigating strategies. Profiling the history and occurrences of droughts in Zimbabwe is one of the first steps needed to achieve this. Thus in this study we make use of the VCI to map agricultural drought occurrence and severity in Zimbabwe between 2005-2006 and 2009-2010 agricultural seasons.

28°0’0”E

30°0’0”E

32°0’0”E

16°0’0”S

651

34°0’0”E

N

20°0’0”S

20°0’0”S

18°0’0”S

18°0’0”S

Drought monitoring in Zimbabwe

16°0’0”S

26°0’0”E

Rainfall stations

Total annual rainfall (mm) 1,200 650 335 22°0’0”S


Materials and methods Study area Zimbabwe lies between latitudes 151 and 231S, and longitudes 251 and 341E (Figure 1). The country is divided into ten provinces, and occupies an area of 390,580 km2 and a population of approximately 12.57 million in 2011 (World Bank, 2011). Most of the country is elevated in the central plateau (high veld) stretching from the southwest to the northwest at altitudes between 1,200 and 1,600 m. The country’s east is mountainous with Mount Nyangani as the highest point at 2,592 m. About 20 per cent of the country consists of the low veld under 900 m. The country has a tropical climate with a rainy season usually from late October to March. Rainfall patterns also vary from the wetter eastern highlands to the drier low lying areas such as Zambezi Valley, southeast Lowveld, and the south-western parts of the country (Chenje et al., 1998). Overall, the climate in Zimbabwe shows a lot of influence from the altitude. Zimbabwe is faced with recurring droughts; and severe storms are rare. The country is mostly savanna dominated by miombo woodland on the high veld and mopane woodlands in the low veld. However, the moist and mountainous east supports tropical evergreen and hardwood forests while the dry Kalahari sands to the west support teak and mahogany forests.

280 km

0

26°0’0”E

28°0’0”E

30°0’0”E

32°0’0”E

Note: The rainfall stations used for calculating the standardised precipitation index are also shown in the map

Figure 1. The extent of the study area (Zimbabwe) showing the mean total annual rainfall calculated between 1950 and 2000 (www.worldclim.org), as well as its relative location in Africa (inset)

DPM 23,5


652

Materials We used decadal (ten-day composite) SPOT NDVI data from 2005 to 2010 which was made available by AMESD (2011). The Integrated Land and Water Information Systems (ILWIS) Geographic Information System was used for processing the NDVI data (ITC, 2005). A time series of dekadal NDVI images of Zimbabwe were extracted from the SPOT-VGT ten daily synthesis archive (AMESD, 2011). A dekad is equal to ten days (Delli et al., 2002). The images, which were at 1 km spatial resolution, were retrieved for the period December 2005 to April 2010. The SPOT downloading policy ensures that the satellite data was already pre-processed therefore the data were deemed ready for use. In addition, rainfall data for 46 stations were obtained from the Meteorological Services Department in Zimbabwe. This data were used to calculate the Standardised Precipitation Index (SPI), which was used for cross-comparing the results from the satellite imagery. Method In order to create time series NDVI data for Zimbabwe, the SPOT-VGT images were first imported into ILWIS GIS. The Map List function was then used to group images from the same dekad from the 2005 to 2010 seasons. Next, we calculated the long-term maximum as well as the long-term minimum NDVI for all dekads of the rainy season using the Map List Statistics Operation. For the purposes of this study, the rainy season was taken to be the period between December and April. We then calculated the VCI for the agricultural seasons from the year 2005 to 2010, starting from the first dekad of December 2005 and ending with the third dekad of April 2010. The following formula was used: VCIj ¼

NDVIj NDVImin 100 NDVImax NDVImin

ð1Þ

where NDVI j is the NDVI for the dekad, NDVI min is the minimum long-term NDVI for the dekad and NDVI max is the long-term maximum NDVI for the dekad (Kogan, 1995). Averaging the VCI for all the dekads of a season produced the average state of vegetation for the season. The condition of the vegetation is measured as a percentage and different degrees of a drought severity are indicated by VCI values below 50 per cent. In order to determine the areas which experienced drought during the agricultural seasons from 2005 to 2010, we classified only those pixels whose VCI value was less than 50 per cent. Although drought is one of the major natural hazards facing the world, there is no universally accepted definition of it (Agnew and Anderson, 1992; Wilhite, 1993), but only operational definitions exist. The following classification was used to come up with the nominal drought severity classes: VCI less than 36 per cent was classified as severe drought, VCI of between 36 and 45 per cent was classified as moderate while VCI of between 45 and 49 per cent was classified as a mild drought. Areas that had a VCI of 50 per cent or more were treated as non-drought areas and therefore are not shown on the resulting maps (Liu and Kogan, 1996). In order to show drought severity in agricultural fields only, we crossed the map of rainfed agricultural fields (Fischer et al., 2008) with the drought maps for each agricultural season. We also used the statistics function to come up with the total area under each severity class for all the seasons. The area data were then aggregated and plotted in Microsoft Excel to show the seasonal patterns of severity.

In order to cross validate results from the satellite imagery, we used rainfall data to calculate the SPI. The SPI is a probability index that considers only precipitation. It is based on the probability of recording a given amount of precipitation, and the probabilities are standardized so that an index of 0 indicates the median precipitation amount (half of the historical precipitation amounts are below the median, and half are above the median) (McKee et al., 1993, 1995; Guttman, 1998; Hayes et al., 1999). SPI is calculated using the formula:


SPI ¼

X m s

where X is the total precipitation within a specified period, m is the long-term mean of total rainfall within the period and s is the long-term standard deviation of rainfall totals with the given period. The index is negative for drought, and positive for wet conditions. As the dry or wet conditions become more severe, the index becomes more negative or positive, respectively. In this study, SPI was calculated over five months (December to April) to match the time period over which the VCI was calculated. The SPI was calculated over a period of 41 years (1965/1966 to 2005/2006 wet seasons). We calculated SPI only for the 2005/2006 agricultural season because it is the only season for which we have both SPI and VCI. Results Figures 2 and 3 show the spatial and temporal variations of the drought severity in Zimbabwe between 2005/2006 and 2009/2010 seasons. Figure 2 shows the spatial distribution of drought severity in both rangelands and agricultural fields. It can be noted that for the period under consideration, the 2007/2008 agricultural season was the “worst” season (had the largest proportion of the country under drought) whilst the 2008/2009 agricultural season was the “best” season (lowest proportion of the country under drought) in terms of drought severity. For every season under consideration, at least some parts of the country experienced a drought. Figure 3 shows that for the period under study, close to half (41 per cent) of the country’s rangelands and agricultural areas were under moderate to severe drought during the worst agricultural season (2007/2008 agricultural season). However, it can be observed that, with the exception of 2007/2008, the mild to moderate droughts are the most recurrent severity classes. On average, the moderate and mild drought categories would constitute less than 25 per cent of the country’s rangelands and agricultural lands. This same generalisation can also be observed in Figure 2. The SPI shows a close covariation with the VCI, with the exception of a few areas. In Binga, for example, the VCI indicates a severe drought while the SPI points to a near normal season of above average rainfall totals (Table I). In addition results from the study also show that different agricultural drought conditions as explained by VCI fall in one nominal category of the SPI. This pattern is especially pronounced in rainfall stations that are located in different climate regimes (Table II and Figure 1). Discussion The seasonal occurrence of drought shows that Matabeleland North and South Provinces, as well as Midlands provinces are more likely to be affected by severe droughts than the other provinces. In the other provinces, droughts range from


DPM 23,5

N

N Zimbabwe (2006/07 Agricultural Season)

Zimbabwe (2005/06 Agricultural Season)

654


Drought Intensity Mild Moderate Severe 0

Drought Intensity Mild Moderate Severe 200 km 0

200 km

N

N Zimbabwe (2008/09 Agricultural Season)

Zimbabwe (2007/08 Agricultural Season)

Drought Intensity

Drought Intensity

Mild Moderate Severe 0

Mild Moderate Severe 200 km

0

200 km

N N

Zimbabwe (2009/10) Agricultural Season)

Overall drought severity 2005/06 to 2008/09

Figure 2. Drought occurrence in Zimbabwe (in both rangelands and rain fed agricultural fields) from 2005-2006 agricultural season to 2009-2010 agricultural season, as well as the overall drought occurrence for the period under study

Mild Moderate Severe

Drought Intensity Mild Moderate Severe 0 0

200 km

200 km

Note: The black lines show the boundaries of the provinces

severe to mild and occur randomly hence some form of crop production that is adapted to drought conditions like small grains can do well in these areas. In addition early warning system on drought in Zimbabwe need to be strengthened because they form a major element of disaster risk reduction, can help prevent loss of life and reduce the potential economic impacts of a disaster if people are prepared and know what to do in response to the warning. This is also supported by Unganai (1994) and Ndlovu (2009)

Drought monitoring in Zimbabwe

30


Per cent of total country

25 20

655

15 10

Figure 3. Drought occurrence in Zimbabwe, as a percentage of affected rangelands and agricultural fields, from 2005-2006 agricultural season to 2009-2010 agricultural season

5 0 Mild

Moderate

Severe

Drought intensity 2005/2006

2007/2008

2006/2007

2008/2009

Station name

VCI

VCInarr SPInarr

Karoi Beatrice Bulawayo Chakari Chisumbanje Kezi Lupane Murehwa Plumtree Arcturus Banket Beitbridge Bikita Bindura Binga Buffalo Range Buhera Centenary Chegutu Chimanimani Chinhoyi Chipinge Chivhu

52.81 51.20 58.14 59.54 65.08 56.32 64.01 46.69 70.06 42.46 63.01 40.47 54.93 40.51 24.53 40.82 68.89 35.60 39.95 49.26 54.36 49.64 56.88

ND ND ND ND ND ND ND MI ND MO ND MO ND MO SD MO ND SD MO MI ND ND ND

MD MW MW MW MW MW MW MW MW NN NN NN NN NN NN NN NN NN NN NN NN NN NN

SPI 0.84 1.22 1.51 1.61 1.05 1.48 1.57 0.72 1.20 0.09 0.68 0.01 0.93 0.59 0.14 0.53 0.88 0.12 0.76 0.55 0.77 0.64 0.98

2009/2010

Name

VCI

VCInarr SPInarr

Concession Gokwe Guruve Gweru Harare Hwange Kadoma Kwekwe Marondera Mberengwa Mhondoro Mt Darwin Mukandi Mutare Mutoko Mvurwi Nyanga Victoria Falls West Nicholson Zaka Kanyemba Masvingo Rusape

43.26 23.77 33.50 47.32 53.63 39.32 53.29 46.13 49.54 62.02 52.58 57.69 56.60 41.97 34.27 49.38 49.90 49.89 73.06 57.96 49.92 68.01 63.74

MO SD SD MI ND MO ND MI ND ND ND ND ND MO SD MI MI ND ND ND ND ND ND

NN NN NN NN NN NN NN NN NN NN NN NN NN NN NN NN NN NN NN NN SW SW SW

SPI 0.72 0.60 0.60 0.97 0.16 0.26 0.80 0.04 0.53 0.42 0.37 0.10 0.64 1.05 0.32 0.82 0.43 0.79 0.14 0.45 1.54 1.71 1.74

Notes: VCInarr, nominal class for the Vegetation Condition Index; SPInarr, nominal class for the Standardised Precipitation Index. The following class names apply to the Vegetation Condition Index: SD, severe drought; MO, moderate drought; MI, mild drought; and ND, no drought. The following nominal names apply to the SPI: MW, moderately wet; NN, near normal; and SW, severely wet

Table I. VCI and SPI values and narration from the 46 rainfall stations used in this study

DPM 23,5


656

Table II. Classification of SPI

who go on to say early warning system based on indigenous knowledge systems need to be adopted and used more often as they have been proven the be effective. The drought pattern of Zimbabwe indicates that the famine, food shortages and poor harvests cannot be attributed to drought alone. This is because in any agricultural season, not all the areas are affected by continuous droughts, more so for successive years except for some parts of Matabeleland North and South Provinces. This observation tends to lend support to the assertion that the decline in agricultural output is mainly due to inappropriate government policies rather than drought (Richardson, 2007). In addition, Nash and Endfield (2002) also noted that perceptions about the increasing severity of droughts are often influenced by the tendency for humans to compare recent drought years to previous wet periods during their lifetimes. Thus perceptions about droughts increasing in their frequencies and severity in Zimbabwe may be due to a comparison of the dry 1980s and 1990s to the wet 1970s. This was corroborated by Mazvimavi (2010) who, based on rainfall records at 40 stations covering the period from 1892 to 2000 concluded that there was no evidence of changes in the median, high or low rainfall during the beginning (October to December), mid-to-end (January to March) of the rainy season and for the whole year. The discrepancies in the covariation between SPI and VCI can be understood in terms of how the two indices are calculated. SPI is calculated using rainfall data only whilst the vegetation condition of an area is a function of both rainfall and temperature. Thus, while the SPI is “conservative” on drought intensity because it utilises only one climate variable, VCI is more robust because it takes into account both rainfall and temperature. In addition, the differences in the aridity levels of the rainfall stations result in different sensitivity levels of vegetation to rainfall deviations. All other factors being constant, vegetation in the drier areas is more sensitive to rainfall fluctuations than vegetation in relatively wetter areas. Thus a slight deviation of rainfall amounts (near normal class in SPI) results in a pronounced increase or decrease in the vegetation condition (VCI) of that particular area. However, it should be noted that recently, there has been high inter-annual variability of rainfall inmost parts of southern Africa (O’Hare et al., 2005). This was confirmed by Simba et al. (2012) who observed that dryspell events at Buffalo Range weather station in Chiredzi and Zaka station were increasing in length and frequency. This may mean that the intra seasonal variations are averaged out by the total for the annual rainfall resulting in a “normal” rainfall season. Further studies can investigate the occurrence of intra-seasonal drought and its impact on food crops, especially maize. An understanding of the intra-seasonal droughts (dry spells) is important because they are most relevant to agricultural activities and are the major determinants of rainfed agricultural output. SPI value

Narration

42 1.5 to 1.49 0.99 to 0.99 1.0 to 1.49 1.5 to 1.99 o2.0

Extremely wet Moderately wet Near normal Moderately dry Severely dry Extremely dry

Source: McKee et al. (1993)


Conclusions It can be concluded that droughts occur annually in Zimbabwe, although the severity and spatial distribution varies. We also conclude that Matabeleland North and South, Midlands and Mashonaland Central Provinces are prone to moderate and severe droughts. However, on a long-term basis the country shows mild drought conditions, with a few isolated cases of severe and moderate droughts. Above all, we have shown the utility of remotely sensed images in mapping drought severity in Zimbabwe. This has implications for successful building of drought resilient communities and infrastructure as well as strengthening early warning systems thereby reducing the vulnerability of communities by assisting them to plan and prepare for droughts. References Adams, H.D., Luce, C.H., Breshears, D.D., Allen, C.D., Weiler, M., Cody, V.C., Smith, A.M.S. and Huxman, T.E. (2012), “Ecohydrological consequences of drought and infestation-triggered tree die-off: insights and hypotheses”, Ecohydrology, Vol. 5 No. 2, pp. 145-159. Agnew, C.T.E. and Anderson, E. (1992), Water Resources in the Arid Realm, Routledge, London. AMESD (2011), available at: www.amesd.co.bw Bayarjargal, Y., Karnieli, A., Bayasgalan, M., Khudulmur, S., Gandush, C. and Tucker, C.J. (2006), “A comparative study of NOAA-AVHRR derived drought indices using change vector analysis”, Remote Sensing of Environment, Vol. 105 No. 1, pp. 9-22. CEDRISA (2009), Droughts and Floods in Southern Africa: Environmental Change and Human Vulnerability, UNEP and SARDC, Nairobi. Chenje, M., Sola, L. and Paleczny, D. (1998), The State of Zimbabwe’s Environment, MMET, Harare. Chikodzi, D. and Mutowo, G. (2013), “Drought monitoring for Masvingo Province in Zimbabwe: a remote sensing perspective”, Herald Journal of Geography and Regional Planning, Vol. 2 No. 1, pp. 056-060. DeFries, R.S. and Townshend, J.R.G. (1994), “NDVI-derived land cover classifications at a global scale”, International Journal of Remote Sensing, Vol. 15 No. 17, pp. 3567-3586. Delli, G., Sarfatti, P. and Cadi, A. (2002), “Classification of historical series of NDVI: an application for northern Algeria”, Journal of Agriculture and Environment for International Development, Vol. 96 Nos 3/4, p. 2002. Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H.T., Verelst, L. and Wiberg, D. (2008), Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008), IIASA, Laxenburg and FAO, Rome. Glantz, M.H. (1987), “Drought and economic development in sub-Saharan Africa”, in Glantz, M. (Ed.), Drought and Hunger in Africa: Denying Famine a Future, Cambridge University Press, Cambridge, pp. 37-58. Goward, S.A., Tucker, C.J. and Dye, D. (1985), “North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer”, Vegetation, Vol. 64 No. 1, pp. 3-14. Guttman, N.B. (1998), “Comparing the palmer drought index and the standardised precipitation index”, Journal of the American Water Resource Association, Vol. 34 No. 1, pp. 113-121. Hayes, M.J., Svoboda, M.D., Wilhite, D.A. and Vanyarkho, O.V. (1999), “Monitoring the 1996 drought using the standardised precipitation index”, Bulletin of the American Meteorological Society, Vol. 80 No. 3, pp. 429-438. Hulme, M. (1996), Climate Change and Southern Africa: An Exploration of Some Potential Impacts and Implications in the SADC Region, Climatic Research Unit, University of East Anglia, and World Wide Fund International, Gland.


DPM 23,5


658

Hulme, M. and Kelly, M. (1993), “Exploring the links between desertification and climate change”, Environment, Vol. 35 No. 6, pp. 4-11. ITC (2005), “ILWIS software (version 3.3)”, available at: www.itc.nl Kiros, F.G. (1991), “Economic consequences of drought, crop failure and famine in Ethiopia, 1973-1986”, Ambio, Vol. 20 No. 5, pp. 183-185. Kogan, E. (1995), “Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data”, Bulletin of the American Meteorological Society, Vol. 76 No. 5, pp. 655-668. Liu, W.T. and Kogan, F.N. (1996), “Monitoring regional drought using vegetation condition index”, International Journal of Remote Sensing, Vol. 17 No. 14, pp. 2761-2782. McCarthy, J.J., Canziani, O.F., Leary, N.A., Dokken, D.J. and White, K.S. (2001), Climate Change 2001: Impacts. Adaptation and Vulnerability, Cambridge University Press, Cambridge. McKee, T.B., Doesken, N.J. and Kleist, J. (1993), “The relationship of drought frequency and duration to time scales”, Eighth Conference on Applied Climatology, American Meteorological Society, Anaheim, CA, pp. 179-184. McKee, T.B., Doesken, N.J. and Kleist, J. (1995), “Drought monitoring with multiple time scales”, Ninth Conference on Applied Climatology, American Meteorological Society, Dallas, TX, pp. 233-236. Maselli, F., Gilabert, M.A. and Conese, C. (1998), “Integration of high and low resolution NDVI data for monitoring vegetation in Mediterranean environments”, Remote Sensing of Environment, Vol. 63 No. 3, pp. 208-218. Mazvimavi, D. (2010), “Investigating changes over time of annual rainfall in Zimbabwe”, Hydrology and Earth System Sciences, Vol. 14 No. 12, pp. 2671-2679. Nash, D.J. and Endfield, G.H. (2002), “A 19th century climate chronology for the Kalahari Region of central southern Africa derived from missionary correspondence”, International Journal of Climatology, Vol. 22 No. 7, pp. 821-841. Ndlovu, S. (2009), “Coping with drought research findings from Bulilima and Mangwe districts, Matabeleland South, Zimbabwe”, available at: practicalaction.org/docs/southern_africa/ Zimbabwe-coping-with-drought.pdf (accessed 22 May 2013). O’Hare, G., Sweeney, J. and Wilby, R.L. (2005), Weather, Climate and Climate Change: Human Perspectives, Pearson Education Ltd., London. Peters, A.J., Eve, M.D., Holt, E.H. and Whitford, W.G. (1997), “Analysis of desert plant community growth patterns with high temporal resolution satellite spectra”, Journal of Applied Ecology, Vol. 34 No. 2, pp. 418-432. Richardson, C.J. (2007), “How much did drought matter? Linking rainfall and GDP growth in Zimbabwe”, African Affairs, Vol. 106 No. 424, pp. 463-478. Simba, F.M., Murwendo, T., Chikodzi, D., Mapurisa, B., Munthali, A. and Seyitini, L. (2012), “Environmental changes and farm productivity: a case study of Masvingo Province in Zimbabwe”, Sacha Journal of Environmental Studies, Vol. 2 No. 1, pp. 114-129. Tadesse, T., Brown, J.F. and Hayes, M.J. (2005), “A new approach for predicting drought-related vegetation stress: integrating satellite, climate, and biophysical data over the US central plains”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 59 No. 4, pp. 244-253. Thiruvengadachari, S. and Gopalkrishna, H.R. (1993), “An integrated PC environment for assessment of drought”, International Journal of Remote Sensing, Vol. 14 No. 17, pp. 3201-3208. Tucker, C.J., Townshend, J.R.G. and Goff, T.E. (1985), “African land-cover classification using satellite data”, Science, Vol. 227 No. 4685, pp. 369-375.


Unganai, L. (1994), Drought and Southern Africa: A Note from the Harare Regional Drought Monitoring Centre, Drought Monitoring Centre, Harare. Unganai, L.S. and Kogan, F.N. (1998), “Drought monitoring and corn yield estimation in Southern Africa from AVHRR data”, Remote Sensing of Environment, Vol. 63 No. 3, pp. 219-232. Warren, A. and Khogali, M. (1992), Assessment of Desertification and Drought in the Sudan–Sahelian Region 1985–1991, UNSO, New York, NY. Wilhite, D.A. (1993), “The enigma of drought”, in Wilhite, D.A. (Ed.), Drought Assessment, Management, and Planning: Theory and Case Studies, Kluwer Academic Publishers, Boston, MA; Dordrecht, and London, pp. 3-15. World Bank (2011), available at: http://data.worldbank.org/country/zimbabwe (accessed 4 February 2013) Further reading Davenport, M.L. and Nicholson, S.E. (1993), “On the relation between rainfall and the normalized difference vegetation index for diverse vegetation types in East Africa”, International Journal of Remote Sensing, Vol. 14 No. 12, pp. 2369-2389. Schulze, R.E. (2000), “Modelling hydrological responses to land use and climate change: a Southern African perspective”, Ambio, Vol. 29 No. 1, pp. 12-22. Corresponding author Godfrey Mutowo can be contacted at: [email protected]

To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints
