Karamoja is notoriously food insecure and has been in need of food aid for most years during the last two decades. One of the main factors causing food insecurity is drought. Reliable, area-wide, long-term data for detecting and monitoring drought conditions are critical for timely, life-saving interventions and the long-term development of the region, yet such data are sparse or unavailable. Due to advances in satellite remote sensing, characterizing drought in data-sparse regions like Karamoja has become possible. This study characterizes agricultural drought in Karamoja to enable a comprehensive understanding of drought, concomitantly evaluating the suitability of NDVI-based drought monitoring. We found that in comparison with the existing data, NDVI data currently provide the best, consistent, and spatially explicit information for operational drought monitoring in Karamoja. Results indicate that the most extreme agricultural drought in recent years occurred in 2009 followed by 2004 and 2002 and suggest that in Karamoja, moderate to severe droughts (e.g., 2008) often have the same impact on crops and human needs (e.g., food aid) as extreme droughts (e.g., 2009). We present in a proof-of-concept frame, a method to estimate the number of people needing food assistance and the population likely to fall under the integrated food security phase classification (IPC) Phase 3 (crisis) due to drought severity. Our model indicates that 90.7% of the variation in the number of people needing aid can be explained by NDVI data and NDVI data can augment these estimates. We conclude that the biggest drivers of food insecurity are the cultivation of crops on marginal land with insignificant inputs, the lack of irrigation and previous systematic incapacitation of livestock (pastoral) alternatives through government programming. Further research is needed to bridge empirical results with social–economic studies on drought impacts on communities in the region to better understand additional factors that will need to be addressed to ensure livelihood resilience.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Data for September to December 1991, May to December 1997, and January to April 2006 were incomplete.
More information available at http://modis-land.gsfc.nasa.gov/vi.html.
For information about the NextView License, please visit: http://cad4nasa.gsfc.nasa.gov/.
For more information visit http://www.ipcinfo.org/.
Estimated from UBOS 2016 population projection.
As per NUSAF 3 Disaster Risk Financing Handbook.
Alley W (1984) The Palmer Drought Severity Index: limitations and assumptions. J Clim Appl Meteorol 23:1100–1109
Anyamba A, Tucker C, Eastman J (2001) NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event. Int J Remote Sens 22(10):1847–1859
Atzberger C (2013) Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Remote Sens 5(2):949–981
Ayoo S, Opio R, Kakisa OT (2013) Karamoja situational analysis. Technical report, January, Care International in Uganda
Baker S (1974) A background to the study of drought in East Africa. Afr Aff 73(291):170–177
Becker-Reshef I, Justice C, Sullivan M, Vermote E, Tucker C, Anyamba A, Small J, Pak E, Masuoka E, Schmaltz J, Hansen M, Pittman K, Birkett C, Williams D, Reynolds C, Doorn B (2010a) Monitoring global croplands with coarse resolution earth observations: the global agriculture monitoring (GLAM) project. Remote Sens 2(6):1589–1609
Becker-Reshef I, Vermote E, Lindeman M, Justice C (2010b) A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sens Environ 114(6):1312–1323
Brown ME, DeBeurs K (2008) Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sens Environ 112(5):2261–2271
Cervigni R, Morris M (2016) Confronting drought in Africa’s Drylands: opportunities for enhancing resilience. Worldbank. Org, Openknowledge, p 221
DanChurchAid (2010) Climate change and adaptation in the Karamoja sub-region. Technical report, DanChurchAid, Kampala, Uganda
Edwards D (1997) Characteristics of 20th century drought in the United States at multiple time scales. PhD thesis, Colorado State University
Egeru A, Osaliya R, MacOpiyo L, Mburu J, Wasonga O, Barasa B, Said M, Aleper D, Majaliwa Mwanjalolo G-J (2014) Assessing the spatio-temporal climate variability in semi-arid Karamoja sub-region in north-eastern Uganda. Int J Environ Stud 71(4):490–509
Ferreri J, Frei B, Ross B, Stoker C (2011) Pastoralists, peace and livelihoods: economic interventions to build peace in Karamoja, Uganda. Technical report, May, The Elliot School for International Affairs
FEWSNET (2005) Conflict baseline study report conducted in the Karamajong cluster of Kenya and Uganda. Technical report, August, USAID Famine Early Warning System Network (USAID/FEWSNET)
FEWSNET (2017) About US—Famine Early Warning Systems Network
Flitcroft ID, Milford JR, Dugdale G (1989) Relating point to area average rainfall in semiarid West Africa and the implications for rainfall estimates derived from satellite data. J Appl Meteorol 28(4):252–266
Franch B, Vermote EF, Becker-Reshef I, Claverie M, Huang J, Zhang J, Justice C, Sobrino JA (2015) Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR growing degree day information. Remote Sens Environ 161:131–148
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J (2015) The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci Data 2:1–21
Funk C, Rowland J, Eilerts G, White L (2012) Famine early warning systems network: informing climate change adaptation series a climate trend analysis of Uganda. Technical report, Famine Early Warning Systems Network
Gao B (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 266:257–266
Gartrell B (1985) 'The roots of famine’: the case of Karamoja. Rev Afr Polit Econ 33:102–110
Gelsdorf K, Maxwell D, Mazurana D (2012) Livelihoods, basic services and social protection in Northern Uganda and Karamoja. Technical report, August, The Overseas Development Institute (ODI), London, UK
Heim RR (2002) A review of twentieth century drought indices used in the United States. Am Meteorol Soc 83:1149–1165
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25(15):1965–1978
Holben BN (1986) Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens 7(11):1417–1434
Jain SK, Keshri R, Goswami A, Sarkar A, Chaudhry A (2009) Identification of drought-vulnerable areas using NOAA AVHRR data. Int J Remote Sens 30(10):2653–2668
Ji L, Peters AJ (2003) Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens Environ 87(1):85–98
Juma ROR (2009) Turkana livelihood strategies and adaptation to drought in Kenya. Thesis, Victoria University of Wellington
Karnieli A, Agam N, Pinker RT, Anderson M, Imhoff ML, Gutman GG, Panov N, Goldberg A (2010) Use of NDVI and land surface temperature for drought assessment: merits and limitations. J Clim 23(3):618–633
Klisch A, Atzberger C (2016) Operational drought monitoring in Kenya using MODIS NDVI time series. Remote Sens 8(4):1–22
Kogan FN (1995) Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull Am Meteorol Soc 76(5):655–668
Kogan FN (1997) Global drought watch from space. Bull Am Meteorol Soc 78(4):621–636
Little PD, McPeak J, Barrett C, Kristjanson P (2008) Challenging orthodoxies: understanding pastoral poverty in East Africa. Dev Change 39:585–609
Liu W, Kogan FN (1996) Monitoring regional drought using the vegetation condition index. Int J Remote Sens 17(14):2761–2782
Lotsch A, Friedl MA, Anderson BT, Tucker C (2003) Coupled vegetation-precipitation variability observed from satellite and climate records. Geophys Res Lett 30(14):8–11
McKee T, Doesken N, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology (vol 17, no 22). American Meteorological Society, p 6
McSweeney C, New M, Lizcano G (2015) UNDP climate change country profile. Technical report, UNDP
Nakalembe C, Dempewolf J, Justice C (2017) Agricultural land use change in Karamoja region, Uganda. Land Use Policy 62:2–12
National Drought Mitigation Center (2015) The Standardized Precipitation Index
NewVision (2015) Govt to distribute relief food to Karamoja
OPM (2009) Karamoja action plan for food security (2009–2014). Technical report, May, Office of the Prime Minister Uganda, Kampala, Uganda
OPM (2010) The Karamoja integrated disarmament and development programme. Technical report, January 2007, Office of the Prime Minister, Kampala, Uganda
OPM (2015) Karamoja food security situation. Technical report, September, Office of the Prime Minister
Palmer WC (1965) Meteorological drought. Weather Bureau Research Paper, US
Peters AJ, Walter-Shea EA, Ji L, Viña A, Hayes M, Svoboda MD (2002) Drought monitoring with NDVI-based standardized vegetation index. Photogramm Eng Remote Sens 68(1):71–75
Quiring SM, Ganesh S (2010) Evaluating the utility of the Vegetation Condition Index (VCI) for monitoring meteorological drought in Texas. Agric For Meteorol 150(3):330–339
Rowley RJ, Price KP, Kastens JH (2007) Remote sensing and the rancher: linking rancher perception and remote sensing remote sensing and the rancher: linking rancher perception and remote sensing. Management 60(4):359–368
Stark J (2011) Climate change and conflict in Uganda: the cattle corridor and Karamoja. Technical report, United States Agency for International Development
Stites E, Akabwai D, Mazurana D, Ateyo P (2007) Angering Akuju: survival and suffering in Karamoja. Technical report, Feinstein International Center
Suwanprasert K, Seto S, Kaewrueng S (2013) Integrated drought risk indices from climate based and satellite-based observation for agricultural drought. J Jpn Soc Civil Eng 69(4):1_25–1_30
Trewartha G (1961) Tropical East Africa. In: The Earth’s problem climates, chapter 9. University of Wisconsin Press, Madison, p 334
Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150
Turvey CG, Mclaurin MK (2012) Applicability of the Normalized Difference Vegetation Index (NDVI) in index-based crop insurance design. Weather Clim Soc 4:271
Uganda-Government (2007) Peace, recovery and development plan for northern Uganda (PRDP). Technical report, September 2007, Uganda of Government
Vermote E, Justice CO, Bréon FM (2009) Towards a generalized approach for correction of the BRDF effect in MODIS directional reflectances. IEEE Trans Geosci Remote Sens 47(3):898–908
Vicente-Serrano SM (2006) Evaluating the impact of drought using remote sensing in a mediterranean, semi-arid region. Nat Hazards 40(1):173–208
Vicente-Serrano SM, Beguería S, Gimeno L, Eklundh L, Giuliani G, Weston D, El Kenawy A, López-Moreno JI, Nieto R, Ayenew T, Konte D, Ardö J, Pegram GGS (2012) Challenges for drought mitigation in Africa: the potential use of geospatial data and drought information systems. Appl Geogr 34(3):471–486
Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A Multiscalar Drought Index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J Clim 23(7):1696–1718
Wilhelmi OV, Wilhite DA (2002) Assessing vulnerability to agricultural drought: a Nebraska case study. Nat Hazards 25:37–58
Wilhite D (1985) Understanding the drought phenomenon: the role of definitions. Water Int 10:111–120
World Food Programme (2017) World Food Programme
Xu Y-P, Lin S-J, Huang Y, Zhang Q-Q, Ran Q-H (2011) Drought analysis using multi-scale standardized precipitation index in the Han River Basin, China. J Zhejiang Univ Sci 12(6):483–494
Yang SE, Wu BF (2010) Calculation of monthly precipitation anomaly percentage using web-serviced remote sensing data. In: Proceedings—2nd IEEE international conference on advanced computer control, ICACC 2010, vol 5, pp 621–625
Zhang A, Jia G (2013) Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens Environ 134:12–23
I would like to thank Dr. Christopher Justice, Professor, Department of the Geographical Sciences University of Maryland for his guidance during this research, and his invaluable comments on earlier versions of this manuscript. I would also like to thank anonymous reviewers for the comments on previous versions of this paper.
About this article
Cite this article
Nakalembe, C. Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices. Nat Hazards 91, 837–862 (2018). https://doi.org/10.1007/s11069-017-3106-x
- Agricultural drought
- Remote sensing
- East Africa