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Deciphering the performance of satellite based daily rainfall products over Zambia

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The amount and distribution of precipitation plays a vital role in the management of water resources, agriculture and foodrisk preparedness. Unfortunately, Zambia like many other developing countries is a highly data-scarce country with few and unevenly distributed meteorological stations. The objective of this study was to run a comparative analysis of satellite-based rainfall products (SRPs) and gauge data to ascertain the reliability of using SRPs for daily rainfall measurements in Zambia. The four daily SRPs examined in this study include the following: The Tropical Applications of Meteorology using Satellite and ground-based observations version 3 (TAMSATv3), Precipitation Estimation from Remotely Sensed Information using Artifcial Neural Networks (PERSIANN), the Climate Hazards group InfraRed Precipitation with Station data version 2 (CHIRPSv2.0), and the African Rainfall Climatology Version 2 (ARCv2). SRPs were compared to rain gauge data from 35 meteorological, agrometeorological, and climatological stations in Zambia for the period 1998–2015. Statistical analyses were extensively carried out at temporal scales inter alia daily, monthly, seasonal and annual. Comparisons were also done for three stations lying at the highest, middle and lowest elevations to examine the ability of SRPs to capture precipitation occurrences on complex topography. Strong coefcient of determination (>0.9) of all the SRPs and gauge data were found at the monthly scale even over multifaceted topography. However, the ability of these products to capture rain gauge data at daily, seasonal and annual scales difers markedly. Specifcally, PERSIANN outperforms all the other SRPs at all scales, CHIRPSv2.0 is rated second, followed by TAMSATv3 and ARCv2, respectively. These results suggest that PERSIANN can reliably be used in studies that seek to estimate rainfall in data-sparse regions of Zambia at any temporal scale and arrive at similar results to rain gauge data.
Czasopismo
Rocznik
Strony
903--919
Opis fizyczny
Bibliogr. 65 poz.
Twórcy
  • School of Geosciences, The University of Edinburgh, Edinburgh EH9 3FF, UK
autor
  • School of Civil Engineering and Geosciences, Newcastle University, Newcastle NE1 4LY, UK
autor
  • Ministry of Lands, Natural Resources and Environmental Protection, P.O. Box 50694, Lusaka, Zambia
  • Ministry of Energy and Water Development, P.O. Box 53930, Lusaka, Zambia
Bibliografia
  • 1. AghaKouchak A, Mehran A, Norouzi H, Behrangi A (2012) Systematic and random error components in satellite precipitation data sets. Geophys Res Lett 39:9. https://doi.org/10.1029/2012GL051592
  • 2. Agnihotri G, Dimri AP (2015) Simulation study of heavy rainfall episodes over the southern Indian peninsula. Meteorol Appl 22:223–235. https://doi.org/10.1002/met.1446
  • 3. Alexandersson H, Moberg A (1997) Homogenization of Swedish temperature data. Part I: homogeneity test for linear trends. Int J Climatol 17:25–34
  • 4. Amitai E, Unkrich L, Goodrich D, Habib E, Thill B, Thill B (2012) Assessing satellite-based rainfall estimates in semiarid watersheds using the USDA-ARS walnut gulch gauge network and trmm PR. J Hydrometeorol 13:1579–1588. https://doi.org/10.1175/JHM-D-12-016.1
  • 5. Ananthakrishnan R, Soman MK (1989) Statistical distribution of daily rainfall and its association with the coefficient of variation of rainfall series. Int J Climatol 9:485–500. https://doi.org/10.1002/joc.3370090504
  • 6. Ashouri H, Hsu K, Sorooshian S, Braithwaite D, Knapp K, Cecil D, Nelson B, Prat O (2015) PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull Am Meteorol Soc 96:69–83. https://doi.org/10.1175/BAMS-D-13-00068.1
  • 7. Awange JL, Ferreira VG, Forootan E, Khandu Andam-Akorful SA, Agutu NO, He XF (2016) Uncertainties in remotely sensed precipitation data over Africa. Int J Climatol 36:303–323. https://doi.org/10.1002/joc.4346
  • 8. Bajracharya SR, Palash W, Shrestha MS, Khadgi VR, Duo C, Das PJ, Dorji C (2015) Systematic evaluation of satellite-based rainfall products over the Brahmaputra Basin for hydrological applications. Adv Meteorol 2015:1–17. https://doi.org/10.1155/2015/398687
  • 9. Basheer M, Elagib NA (2018) Performance of satellite-based and GPCC 70 rainfall products in an extremely data-scarce country in the Nile Basin. Atmos Res. https://doi.org/10.1016/j.atmosres.2018.08.028
  • 10. Becker J (2009) Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Mar Geod 32:37–41. https://doi.org/10.1080/01490410903297766
  • 11. Blanc J, Hall JW, Roche N, Dawson RJ, Cesses Y, Burton A, Kilsby CG (2012) Enhanced efficiency of pluvial flood risk estimation in urban areas using spatial-temporal rainfall simulations. J Flood Risk Manag 5:143–152. https://doi.org/10.1111/j.1753-318X.2012.01135.x
  • 12. Bowman KP (2005) Comparison of TRMM precipitation retrievals with rain gauge data from ocean buoys. J Clim 18(1):178–190. https://doi.org/10.1175/JCLI3259.1
  • 13. Chabala LM, Kuntashula E, Kaluba P (2013) Characterization of temporal changes in rainfall, temperature, flooding hazard and dry spells over Zambia. Univ J Agric Res 1:134–144. https://doi.org/10.13189/ujar.2013.010403
  • 14. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?—arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
  • 15. Challinor AJ, Wheeler TR, Craufurd PQ, Slingo JM, Grimes DIF (2004) Design and optimisation of a large-area process-based model for annual crops. Agric For Meteorol 124:99–120
  • 16. Chanda K, Maity R (2015) Meteorological drought quantification with standardized precipitation anomaly index for the regions with strongly seasonal and periodic precipitation. J Hydrol Eng 12:1–8. https://doi.org/10.1061/(asce)he.1943-5584.0001236
  • 17. Chen AS, Djordjević S, Leandro J, Savić DA (2010) An analysis of the combined consequences of pluvial and fluvial flooding. Water Sci Technol 62:1491–1498. https://doi.org/10.2166/wst.2010.486
  • 18. Clark DB (2011) Model Development the Joint UK Land Environment Simulator (JULES), model description—part 2: carbon fluxes and vegetation dynamics. Geosci Model Dev 4:701–722
  • 19. Creutin JD, Obled C (1982) Objective analyses and mapping techniques for rainfall fields: an objective comparison. Water Resour Res 18:413–431
  • 20. CSO (2010) 2010 Census of population and housing. Lusaka, Zambia. Retrieved from https://www.zamstats.gov.zm/phocadownload/ZambiaCensusProjection2011-2035.pdf. Accessed 8 Aug 2018
  • 21. Derin Y, Yilmaz KK, Derin Y, Yilmaz KK (2014) Evaluation of multiple satellite-based precipitation products over complex topography. J Hydrometeorol 15:1498–1516. https://doi.org/10.1175/jhm-d-13-0191.1
  • 22. Duan ZW, Bastiaanssen WGM, Junzhi L (2012). Monthly and annual validation of TRMM mulitisatellite precipitation analysis (TMPA) products in the caspian sea region for the period 1999–2003. In: IEEE International Geoscience and Remote Sensing Symposium, Munich, 2012, pp. 3696–3699. https://doi.org/10.1109/IGARSS.2012.6350613
  • 23. 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. https://doi.org/10.1038/sdata.2015.66
  • 24. Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228:113–129
  • 25. Haberyan KA (2018) A %3e22,000 years diatom record from the plateau of Zambia. Quaty Res (US) 89:33–42. https://doi.org/10.1017/qua.2017.31
  • 26. Hachigonta S, Reason CJC (2006) Interannual variability in dry and wet spell characteristics over Zambia. Clim Res 32:49–62. https://doi.org/10.3354/cr032049
  • 27. Hachigonta S, Reason CJC, Tadross M (2008) An analysis of onset date and rainy season duration over Zambia. Theoret Appl Climatol 91:229–243. https://doi.org/10.1007/s00704-007-0306-4
  • 28. He Z, Yang L, Tian F, Ni G, Hou A, Lu H (2017) Intercomparisons of rainfall estimates from TRMM and GPM multisatellite products over the Upper Mekong River Basin. J Hydrometeorol 18:413–430. https://doi.org/10.1175/JHM-D-16-0198.1
  • 29. Huygen J (1989) Estimation of rainfall in Zambia using meteosat-tir data. Report 12, Wageningen. Retrieved from https://core.ac.uk/download/pdf/29358705.pdf. Accessed 8 Aug 2018
  • 30. Janjai S, Nimnuan P, Nunez M, Buntoung S, Cao J (2015) An assessment of three satellite-based precipitation data sets as applied to the Thailand region. Phys Geogr 36:282–304. https://doi.org/10.1080/02723646.2015.1045286
  • 31. Kang HM, Yusof F (2012) Homogeneity tests on daily rainfall series in Peninsular Malaysia. Int J Contemp Math Sci 7:9–22
  • 32. Kar AK, Lohani AK, Goel NK, Roy GP (2015) Rain gauge network design for flood forecasting using multi-criteria decision analysis and clustering techniques in lower Mahanadi river basin, India. J Hydrol Reg Stud 4:313–332. https://doi.org/10.1016/j.ejrh.2015.07.003
  • 33. Lee G, Juncher D (2015) Modelling the local and global cloud formation on HD. Astronomy & Astrophysics, 1–23. Retrieved from https://arxiv.org/pdf/1505.06576.pdf. Accessed 12 Dec 2018
  • 34. Libanda B, Ngonga C (2018) Projection of frequency and intensity of extreme precipitation in Zambia: a CMIP5 Study. Clim Res 76:59–72. https://doi.org/10.3354/cr01528
  • 35. Libanda B, Ngonga C, Zheng M (2019) Spatial and temporal patterns of drought in Zambia. J Arid Land 11:180–191. https://doi.org/10.1007/s40333-019-0053-2
  • 36. Limao N, Venables AJ (2001) Infrastructure, geographical disadvantage, transport costs, and trade. World Bank Econ Rev 15:451–479
  • 37. Maggioni V, Vergara HJ, Anagnostou EN, Gourley JJ, Hong Y, Stampoulis D (2013) Investigating the applicability of error correction ensembles of satellite rainfall products in river flow simulations. J Hydrometeorol 14:1194–1211. https://doi.org/10.1175/JHM-D-12-074.1
  • 38. Mahmood MI, Elagib NA, Horn F, Saad SAG (2017) Lessons learned from Khartoum flash flood impacts: an integrated assessment. Sci Total Environ 601:1031–1045. https://doi.org/10.1016/j.scitotenv.2017.05.260
  • 39. Maidment RI, Grimes D, Black E, Tarnavsky E, Young M, Greatrex H, Alcántara EMU (2017) A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa. Sci Data 4:170063. https://doi.org/10.1038/sdata.2017.63
  • 40. Majidi M, Alizadeh A, Farid A, Vazifedoust M (2015) Estimating evaporation from lakes and reservoirs under limited data condition in a semi-arid region. Water Resour Manag 29:3711–3733. https://doi.org/10.1007/s11269-015-1025-8
  • 41. Miceli R, Sotgiu I, Settanni M (2008) Disaster preparedness and perception of flood risk: a study in an alpine valley in Italy. J Environ Psychol 28:164–173. https://doi.org/10.1016/j.jenvp.2007.10.006
  • 42. Mudenda O, Nkonde E (2018) Lessons from the Modernization of National Meteorological and Hydrological Services. Retrieved from https://www.wmo.int/pages/prog/www/IMOP/documents/O3_8_Mudenda_ExtendedAbstract.pdf. Accessed 2 Mar 2020
  • 43. Nasrollahi N (2015) False alarm in satellite precipitation data. In: Improving infrared-based precipitation retrieval algorithms using multi-spectral satellite imagery. Springer Theses (Recognizing Outstanding Ph.D. Research). Springer, Cham
  • 44. Novella NS, Thiaw WM (2013) African rainfall climatology version 2 for famine early warning systems. J Appl Meteorol Climatol 52:588–606. https://doi.org/10.1175/JAMC-D-11-0238.1
  • 45. Met Office (2018) Rain and snow—what is precipitation? Retrieved from https://www.metoffice.gov.uk/weather/learn-about/met-office-for-schools/other-content/other-resources/what-is-precipitation
  • 46. Ogwang BA, Guirong T, Haishan C (2012) Diagnosis of September–November drought and the associated circulation anomalies over Uganda. Pak J Meteorol 9:11–24
  • 47. Ogwang BA, Chen H, Li X, Gao C (2014) The influence of topography on east African October–December climate: sensitivity experiments with RegCM4. Adv Meteorol. https://doi.org/10.1155/2014/143917
  • 48. Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inform Syst 4:313–332. https://doi.org/10.1080/02693799008941549
  • 49. Oreggioni WF, Báez BJ (2018) Assessment of satellite-based precipitation estimates over Paraguay. Acta Geophys 66:369–379. https://doi.org/10.1007/s11600-018-0146-x
  • 50. Scofield RA, Kuligowski K (2003) Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Weather Forecast 18:1037–1051
  • 51. Shen Y, Xiong A, Wang Y, Xie P (2010) Performance of high-resolution satellite precipitation products over China. J Geophys Res 115(D2):D02114. https://doi.org/10.1029/2009JD012097
  • 52. Sonkoué D, Monkam D, Fotso-Nguemo TC, Yepdo ZD, Vondou DA (2019) Evaluation and projected changes in daily rainfall characteristics over Central Africa based on a multi-model ensemble mean of CMIP5 simulations. Theoret Appl Climatol 137:2167–2186. https://doi.org/10.1007/s00704-018-2729-5
  • 53. Stampoulis D, Anagnostou EN, Nikolopoulos EI (2013) Assessment of high-resolution satellite-based rainfall estimates over the Mediterranean during heavy precipitation events. J Hydrometeorol 14:1500–1514. https://doi.org/10.1175/JHM-D-12-0167.1
  • 54. Stern RD, Dennett MD, Dale IC (1982) Analysing daily rainfall measurements to give agronomically useful results. Exp Agric 18:223–236
  • 55. Tabios G, Salas J (1985) A comparative analysis of techniques for spatial interpolation of precipitation. JAWRA J Am Water Resour Assoc 21:365–380
  • 56. TAHMO (2018) The Trans-African HydroMeteorological Observatory. Retrieved from https://tahmo.org/about-tahmo-2/. Accessed 8 Aug 2018
  • 57. Tarek MH, Hassan A, Bhattacharjee J, Choudhury SH, Badruzzaman AB (2017) Assessment of TRMM data for precipitation measurement in Bangladesh. Meteorol Appl 24:349–359. https://doi.org/10.1002/met.1633
  • 58. Thomson MC, Connor SJ, Zebiak SE, Jancloes M, Mihretie A (2011) Africa needs climate data to fight disease. Nature 471:440–442. https://doi.org/10.1038/471440a
  • 59. Tshimanga RM, Tshitenge JM, Kabuya P, Alsdorf D, Mahe G, Kibukusa G, Lukanda V (2016) A regional perceptive of flood forecasting and disaster management systems for the Congo River Basin. Flood Forecast Glob Perspect. https://doi.org/10.1016/B978-0-12-801884-2.00002-5
  • 60. Washington R, Harrison M, Conway D, Black E, Challinor A, Grimes D, Todd M (2006) African climate change: taking the shorter route. Bull Am Meteor Soc 87:1355–1366. https://doi.org/10.1175/BAMS-87-10-1355
  • 61. Watson J, Challinor A (2013) The relative importance of rainfall, temperature and yield data for a regional-scale crop model. Agric For Meteorol 170:47–57. https://doi.org/10.1016/j.agrformet.2012.08.001
  • 62. WMO (2010) CIMO survey on national summaries of methods and instruments for solid precipitation measurement at automatic weather stations. Retrieved from https://library.wmo.int/doc_num.php?explnum_id=9443. Accessed 2 Feb 2020
  • 63. Zambrano-Bigiarini M, Nauditt A, Birkel C, Verbist K, Ribbe L (2017) Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile. Hydrol Earth Syst Sci 21:1295–1320. https://doi.org/10.5194/hess-21-1295-2017
  • 64. Zeng Q, Wang Y, Chen L, Wang Z, Zhu H, Li B (2018) Intercomparison and evaluation of remote sensing precipitation products over China from 2005 to 2013. Remote Sens 10:168. https://doi.org/10.3390/rs10020168
  • 65. ZMD (2020) Weather information is key. Retrieved from https://zamweather.com/about/. Accessed 2 Mar 2020
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4f6a3b38-b299-4808-952b-a17bf8cce210
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