摘要
Drought acutely affects economic sectors, natural habitats and communities. Understanding the past spatial and temporal patterns of drought is crucial because it facilitates the forecasting of future drought occurrences and informs decision-making processes for possible adaptive measures. This is especially important in view of a changing climate. This study employed the World Meteorological Organization(WMO)-recommended standardized precipitation index(SPI) to investigate the spatial and temporal patterns of drought in Zambia from 1960 to 2016. The relationship between the occurrence of consecutive dry days(CDD; consecutive days with less than 1 mm of precipitation) and SPI was also investigated. Horizontal wind vectors at 850 hPa during the core of the rainy season(December–February)were examined to ascertain the patterns of flow during years of extreme and severe drought; and these were contrasted with the patterns of flow in 2007, which was a generally wet year. Pressure vertical velocity was also investigated. Based on the gamma distribution, SPI successfully categorized extremely dry(with a SPI value less than or equal to –2.0) years over Zambia as 1992 and 2015, a severely dry(–1.9 to –1.5) year as 1995, moderately dry(–1.4 to –1.0) years as 1972, 1980, 1987, 1999 and 2005, and 26 near normal years(–0.9 to 0.9). The occurrence of CDD was found to be strongly negatively correlated with SPI with a coefficient of –0.6. Further results suggest that, during wet years, Zambia is influenced by a clockwise circulating low-pressure zone over the south-eastern Angola, a second such zone over the northern and eastern parts, and a third over the Indian Ocean. In stark contrast, years of drought were characterized by an anti-clockwise circulating high-pressure zone over the south-western parts of Zambia,constraining precipitation activities over the country. Further, wet years were characterized by negative pressure vertical velocity anomalies, signifying ascending motion; while drought years were dominated by positive anomalies, signifying descending motion, which suppresses precipitation. These patterns can be used to forecast drought over Zambia and aid in strategic planning to limit the potential damage of drought.
Drought acutely affects economic sectors, natural habitats and communities. Understanding the past spatial and temporal patterns of drought is crucial because it facilitates the forecasting of future drought occurrences and informs decision-making processes for possible adaptive measures. This is especially important in view of a changing climate. This study employed the World Meteorological Organization(WMO)-recommended standardized precipitation index(SPI) to investigate the spatial and temporal patterns of drought in Zambia from 1960 to 2016. The relationship between the occurrence of consecutive dry days(CDD; consecutive days with less than 1 mm of precipitation) and SPI was also investigated. Horizontal wind vectors at 850 hPa during the core of the rainy season(December–February)were examined to ascertain the patterns of flow during years of extreme and severe drought; and these were contrasted with the patterns of flow in 2007, which was a generally wet year. Pressure vertical velocity was also investigated. Based on the gamma distribution, SPI successfully categorized extremely dry(with a SPI value less than or equal to –2.0) years over Zambia as 1992 and 2015, a severely dry(–1.9 to –1.5) year as 1995, moderately dry(–1.4 to –1.0) years as 1972, 1980, 1987, 1999 and 2005, and 26 near normal years(–0.9 to 0.9). The occurrence of CDD was found to be strongly negatively correlated with SPI with a coefficient of –0.6. Further results suggest that, during wet years, Zambia is influenced by a clockwise circulating low-pressure zone over the south-eastern Angola, a second such zone over the northern and eastern parts, and a third over the Indian Ocean. In stark contrast, years of drought were characterized by an anti-clockwise circulating high-pressure zone over the south-western parts of Zambia,constraining precipitation activities over the country. Further, wet years were characterized by negative pressure vertical velocity anomalies, signifying ascending motion; while drought years were dominated by positive anomalies, signifying descending motion, which suppresses precipitation. These patterns can be used to forecast drought over Zambia and aid in strategic planning to limit the potential damage of drought.
引文
Bordi I,Fraedrich K,Petitta M,et al.2005.Large-scale analysis of drought in Europe using NCEP/NCAR and ERA-40re-analysis data sets.European Water,9(10):35-42.
Burke E J.2011.Understanding the sensitivity of different drought metrics to the drivers of drought under increased atmospheric CO2.Journal of Hydrometeorology,12(6):1378-1394.
Dai A.2011.Drought under global warming:a review.Wiley Interdisciplinary Reviews:Climate Change,2(1):45-65.
Dee D P,Uppala S M,Simmons A J,et al.2011.The ERA-interim reanalysis:configuration and performance of the data assimilation system.Quarterly Journal of the Royal Meteorological Society,137(656):553-597.
Dogan S,Berktay A,Singh V.2012.Comparison of multi-monthly rainfall-based drought severity indices,with application to semi-arid Konya closed basin,Turkey.Journal of Hydrology,470-471:255-268.
Duan Y,Ma Z,Yang Q.2016.Characteristics of consecutive dry days variations in China.Theoretical and Applied Climatology,130(1-2):701-709.
Field C B,Barros V,Stocker T F,et al.2012.IPCC-Intergovernmental Panel on Climate Change.Managing the risks of extreme events and disasters to advance climate change adaptation.A special report of working groups I and II of the intergovernmental panel on climate change.Cambridge:Cambridge University Press,65-108.
Fotso-Nguemo T,Chamani R,Yepdo Z D,et al.2018.Projected trends of extreme rainfall events from CMIP5 models over Central Africa.Atmospheric Science Letters,19(2):e803.
Frich P,Alexander L V,Della-Marta P,et al.2002.Observed coherent changes in climatic extremes during the second half of the twentieth century.Climate Research,19(3):193-212.
Grinn-GofrońA,Bosiacka B.2015.Effects of meteorological factors on the composition of selected fungal spores in the air.Aerobiologia,31(1):63-72.
Hachigonta S,Reason C J C.2006.Interannual variability in dry and wet spell characteristics over Zambia.Climate Research,32:49-62.
Hayes M J,Svoboda M D,Wall N,et al.2011.The Lincoln declaration on drought indices:universal meteorological drought index recommended.Bulletin of the American Meteorological Society,92(4):485-488.
Hill M.2016.World's Biggest Dam Has'Extremely Dangerous'Low Water Levels.[2017-11-24].https://www.bloomberg.com/news/articles/2016-01-08/world-s-biggest-dam-has-extremely-dangerous-low-water-levels.
Holton J R.1992.An Introduction to Dynamic Meteorology.San Diego:Academic Press,166-175.
Hottenstein J,Ponce-Campos G,Moguel-Yanes J,et al.2015.Impact of varying storm intensity and consecutive dry days on grassland soil moisture.Journal of Hydrometeorology,16(1):106-117.
Karl T R,Nicholls N,Ghazi A.1999.CLIVAR/GCOS/WMO workshop on indices and indicators for climate extremes workshop summary.Climate Change,42(1):3-7.
Libanda B,Barbara N,Bathsheba M.2015.Rainfall variability over northern Zambia.Journal of Scientific Research and Reports,6(6):416-425.
Libanda B,Ogwang B,Ongoma V,et al.2016.Diagnosis of the 2010 DJF flood over Zambia.Natural Hazards,81(1):189-201.
Libanda B,Zheng M,Banda N.2017a.Variability of extreme rainfall over Malawi.Geographical Pannonica,4(21):212-223.
Libanda B,Allan D,Noel B,et al.2017b.Predictor selection associated with statistical downscaling of precipitation over Zambia.Asian Journal of Physical and Chemical Sciences,1(2):1-9.
Libanda B,Ngonga C.2018.Projection of frequency and intensity of extreme precipitation in Zambia:a CMIP5 study.Climate Research,76(1):59-72.
Loomis R S.1999.Feeding a world population of more than eight billion people:A challenge to science.Crop Science,39(4):1250.
McKee T B,Doesken N J,Kleist J.1993.The relationship of drought frequency and duration to time scales.In:Proceedings of the 8th Conference on Applied Climatology.Anaheim:American Meteorological Society,179-184.
Monacelli G.2005.Drought assessment and forecasting.Geneva:World Meteorological Organization(WMO).http://www.wmo.int/pages/prog/hwrp/documents/regions/DOC8.pdf
Muchuru S,Landman W,DeWitt D,et al.2014.Seasonal rainfall predictability over the Lake Kariba catchment area.Water SA,40(3):461,doi:http://dx.doi.org/10.4314/wsa.v40i3.9.
New Yorker.2016.One of Africa's biggest dams is falling apart.[2017-10-11].https://www.newyorker.com/tech/elements/one-of-africas-biggest-dams-is-falling-apart.
Ngarukiyimana J P,Fu Y,Yang Y,et al.2017.Dominant atmospheric circulation patterns associated with abnormal rainfall events over Rwanda,East Africa.International Journal of Climatology,38(1):187-202.
Nicholson S E,Grist J P.2003.The seasonal evolution of the atmospheric circulation over West Africa and equatorial Africa.Journal of Climate,16(7):1013-1030.
Novella N S,Thiaw W M.2013.African Rainfall Climatology Version 2 for Famine Early Warning Systems.Journal of Applied Meteorology and Climatology,52(3):588-606.
Ogwang B A,Tan G,Haishan C.2012.Diagnosis of September-November drought and the associated circulation anomalies over Uganda.Pakistan Journal of Meteorology,9(17):11-24.
Okoola R E.1999.A diagnostic study of the Eastern Africa monsoon circulation during the northern hemisphere spring season.International Journal of Climatology,19(2):143-169.
Ongoma V,Guirong T,Ogwang B A,et al.2015.Diagnosis of seasonal rainfall variability over East Africa:A case study of2010-2011 drought over Kenya.Pakistan Journal of Meteorology,11(22):13-21.
Ongoma V,Chen H,Gao C,et al.2017.Future changes in climate extremes over Equatorial East Africa based on CMIP5multimodel ensemble.Natural Hazards,90(2):901-920.
Ongoma V,Chen H,Gao C.2018.Evaluation of CMIP5 twentieth century rainfall simulation over the equatorial East Africa.Theoretical and Applied Climatology,doi:https://doi.org/10.1007/s00704-018-2392-x.
Salehnia N,Alizadeh A,Sanaeinejad H,et al.2017.Estimation of meteorological drought indices based on AgMERRAprecipitation data and station-observed precipitation data.Journal of Arid Land,9(6):797-809.
Santos J F,Pulido-Calvo I,Portela M M.2010.Spatial and temporal variability of droughts in Portugal.Water Resources Research,46,W03503,doi:10.1029/2009WR008071.
Scheff J,Frierson D M W.2012.Robust future precipitation declines in CMIP5 largely reflect the poleward expansion of model subtropical dry zones.Geophysical Research Letters,39(18):L18704,https://doi.org/10.1029/2012GL052910.
Schneider U,Ziese M,Meyer-Christoffer A,et al.2016.The new portfolio of global precipitation data products of the Global Precipitation Climatology Centre suitable to assess and quantify the global water cycle and resources.Proceedings of the International Association of Hydrological Sciences,374:29-34.
Singh U S,Rathor H S.1974.Reduction of the complete omega equation to the simplest form.Pure and Applied Geophysics,112(1):219-223.
Taylor K E.2001.Summarizing multiple aspects of model performance in a single diagram.Journal of Geophysical Research,106(D7):7183-7192.
Taylor K E,Stouffer R J,Meehl G A.2012.An overview of CMIP5 and the experiment design.Bulletin of the American Meteorological Society,93(4):485-498.
Tiffen M,Mulele M R.1994.The environmental impact of the 1991-92 drought on Zambia.A report for IUCN-The world conservation union.[2018-03-06].https://portals.iucn.org/library/sites/library/files/documents/1994-017.pdf.
Trenberth K E,Fasullo J T.2009.Global warming due to increasing absorbed solar radiation.Geophysical Research Letters,36(7):L07706,https://doi.org/10.1029/2009GL037527.
Udmale P,Ichikawa Y S,Kiem A,et al.2014.Drought Impacts and Adaptation Strategies for Agriculture and Rural Livelihood in the Maharashtra State of India.The Open Agriculture Journal,8(1):41-47.
Uhe P,Philip S,Kew S,et al.2017.Attributing drivers of the 2016 Kenyan drought.International Journal of Climatology,38(S1):e554-e568.
Zargar A,Sadiq R,Naser B,et al.2011.A review of drought indices.Environmental Reviews,19(1):333-349.
Zhao T,Dai A.2015.The magnitude and causes of global drought changes in the twenty-first century under a low-moderate emissions scenario.Journal of Climate,28(11):4490-4512.
