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Spatio-temporal evaluation of open access precipitation products with rain gauge observations in Nigeria

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Abstract

Various open access precipitation products are made available for several applications. Evaluating the reliability of these precipitation datasets can be of great importance for both the end-users and data developers. In this study, the performance of 11 open access precipitation products was evaluated against gauge-based data in Nigeria. The evaluation was done on a point-to-pixel basis and at different timescales. Quantitative statistical metrics (correlation coefficient—r, mean error—ME, multiplicative bias—BIAS, root mean square error—RMSE, and Nash–Sutcliffe efficiency coefficient—NSE) were used to evaluate the precipitation products. The results indicate that the open access precipitation products substantially overestimated low rainfall events and underestimated high rainfall events on a daily timescale. Global Precipitation Climatology Centre (GPCC), Climate Hazards Group InfraRed Precipitation with Station data, Version 2.01 (CHIRPSv2.0), Tropical Applications of Meteorology using Satellite and Ground-Based Observations (TAMSAT) African Rainfall Climatology and Time series (TARCAT), Climatic Research Unit Time Series (CRU_TS_4.04), Global Precipitation Measurement (GPM) and Integrated Multi-SatellitE Retrievals for GPM (IMERG), and Global Precipitation Climatology Project (GPCP) performed better in Nigeria. The findings of this study further revealed that cumulative rainfall estimates from the open access precipitation products tended to improve with increasing integration time (i.e., on monthly, seasonal, and annual timescales) and showed better performance in the highlands than in the lowlands. This study provides useful information to potential users about the accuracy of 11 open access precipitation products for various applications in a country characterized by large topographic and rainfall variability.

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References

  • Abera W, Brocca L, Rigon R (2016) Comparative evaluation of different satellite rainfall estimation products and bias correction in the Upper Blue Nile (UBN) basin. Atmos Res 178:471–483

    Article  Google Scholar 

  • Adler RF, Huffman GH, Chang A, Ferraro F, Xie P, Janowiak J, Rudolf B et al (2003) The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167

    Article  Google Scholar 

  • Adler RF, Wang J-J, Sapiano M, Huffman G, Chiu L, Xie PP, …, NOAA CDR Program (2016) Global Precipitation Climatology Project (GPCP) Climate Data Record (CDR), Version 2.3 (Monthly). National Centers for Environmental Information. https://doi.org/10.7289/V56971M6. Accessed 12 December 2018

  • Adler RF, Wang J-J, Sapiano M, Huffman G, Chiu L, Xie PP, …, NOAA CDR Program (2017) Global Precipitation Climatology Project (GPCP) Climate Data Record (CDR), Version 1.3 (Daily). National Centers for Environmental Information. https://doi.org/10.7289/V5RX998Z. Accessed 12 December 2018

  • Akinsanola AA, Ogunjobi KO (2014) Analysis of rainfall and temperature variability over Nigeria. Global Journal of Human-Social Science 14(3):1–19

    Google Scholar 

  • Alemshet B, Gashaw S, Kibrit G, Tirunesh M (2020) Evaluation of satellite precipitation products using HEC-HMS model. Modeling Earth Systems and Environment 6:2015–2032

    Article  Google Scholar 

  • Anjum MN, Ding Y, Shangguan D, Ahmad I, Ijaz MW, Farid HU, Yagoub YE, Zaman M, Adnan M (2018) Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan. Atmos Res 205:134–146

    Article  Google Scholar 

  • Arnold RS, Damasa BM, Pompe CSC, Ronaldo BS, Ireneo BP, Christian JRC (2018) Evaluation and spatial downscaling of CRU TS precipitation data in the Philippines. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-018-0477-2

    Article  Google Scholar 

  • Barrett EC (1989) Satellite remote sensing of rainfall. In Toselli F(ed) Applications of remote sensing to agrometeorology. Kluwer Academic Publishers, Pp 304–326

  • 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

    Article  Google Scholar 

  • Chen S, Hu J, Zhang Z, Behrangi A, Hong Y, Gebregiorgis AS, Cao J, Hu B, Xue X, Zhang X (2015) Hydrologic evaluation of the TRMM multisatellite precipitation analysis over Ganjiang Basin in humid southeastern China. IEEE J Sel Top Appl Earth Obs Remote Sens 8(9):4568–4580

    Article  Google Scholar 

  • Chen S, Hong Y, Cao Q, Kirstetter PE, Gourley JJ, Qi Y, Zhang J, Howard K, Hu J, Wang J (2013) Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan: are remote-sensing products ready for gauge denial scenario of extreme events? J Hydrol 506:4–13

    Article  Google Scholar 

  • Dejene TB, Birhanu GA, Zelalem B (2020) Analysis of the changes in historical and future extreme precipitation under climate change in Adama city, Ethiopia. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-01019-x

  • Dembélé M, Zwart SJ (2016) Evaluation and comparison of satellite-based rainfall products in Burkina Faso. West Africa Int J Remote Sens 37(17):3995–4014. https://doi.org/10.1007/s40808-020-00792-z

    Article  Google Scholar 

  • Dinku T, Chidzambwa S, Ceccato P, Connor SJ, Ropelewski CF (2008) Validation of high-resolution satellite rainfall products over complex terrain. Int J Remote Sens 29:4097–4110

    Article  Google Scholar 

  • Dinku T, Hailemariam K, Maidment R, Tarnavsky E, Connor S (2014) Combined use of satellite estimates and rain gauge observations to generate high-quality historical rainfall time series over Ethiopia. Int J Climatol 34:2489–2504

    Article  Google Scholar 

  • Dombai F, Paulitsch H, Cremonini R, Bechini R (2013) MERG-Microwave areal rain gauge: a low cost solid state microwave areal precipitation measurement system. 36th Conference on Radar Meteorology (16–20 September, 2013) of the American Meteorological Society (AMS 2B.4): 1–8

  • Olutoyin F (2014) Physical features and natural setting. Nigeria handbook Abuja, Nigeria. Federal Ministry of Information, Abuja, Nigeria, pp 15–20

    Google Scholar 

  • Fenta AA, Hiroshi Y, Katsuyuki S, Yasuomi I, Nigussie H, Takayuki K, Ashebir SB, Dagnenet S, Kindiye E (2018) Evaluation of satellite rainfall estimates over the Lake Tana basin at the source region of the Blue Nile River. Atmos Res 212:43–53. https://doi.org/10.1016/j.atmosres.2018.05.009

    Article  Google Scholar 

  • Fenta AA, Tsunekawa A, Haregeweyn N, Poesen J, Tsubo M, Borrelli P, Panagos P, Vanmaercke M, Broeckx J, Yasuda H, Kawai T (2020) Land susceptibility to water and wind erosion risks in the East Africa region. Sci Total Environ 703:135016. https://doi.org/10.1016/j.scitotenv.2019.135016

    Article  Google Scholar 

  • Fenta AA, Tsunekawa A, Haregeweyn N, Tsubo M, Yasuda H, Kawai T, Ebabu K, Berihun ML, Belay AS, Sultan D (2021) Agroecology-based soil erosion assessment for better conservation planning in Ethiopian river basins. Environ Res 195:110786. https://doi.org/10.1016/j.envres.2021.110786

    Article  Google Scholar 

  • Funk CC, Peterson PJ, Landsfeld MF, Pedreros DH, Verdin JP, Rowland J D, …, Verdin AP (2014) A quasi-global precipitation time series for drought monitoring.” U.S. Geological Survey Data Series 832, 4. http://pubs.usgs.gov/ds/832/pdf/ds832.pdf

  • Gale T (2004) The Federal Republic of Nigeria. In T.L. Gall (Ed.), Worldmark encyclopedia of the nations, eleventh edition. Farmington Hills: The Gale Group, Inc

  • Gebereetal M, Bitew MM, Hirpa FA, Tesfay GN (2014) Accuracy of satellite rainfall estimates in the Blue Nile basin: lowland plain versus Highland Mountain. Water Resour Res 50:8775–8790

    Article  Google Scholar 

  • Harris IC, Jones PD, Osbon T (2020) CRU TS4.04: Climate research unit (CRU) time-series (TS) version 4.04 of high-resolution gridded data of monthly-by-monthly variation in climate (Jan. 1901- Dec. 2019). Centre for Environmental Data Analysis. https://catalogue.ceda.ac.uk/uuid/89e1e34ec3554dc98594a5732622bce9

  • Herman A, Kumar VB, Arkin PA, Kousky JV (1997) Objectively determined 10-day African rainfall estimates created for famine early warning. Int J Rem Sensing 18:2147–2159

    Article  Google Scholar 

  • Hirpa FA, Gebremichael M, Hopson T (2010) Evaluation of high-resolution satellite precipitation products over very complex terrain in Ethiopia. J Appl Meteorol Climatol 49:1044–1051

    Article  Google Scholar 

  • Huffman G J, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, …, Stocker EF (2017) The TRMM multi-satellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8 (1):38–55. https://doi.org/10.1175/JHM560.1. Accessed 12 November 2018

  • Huffman GJ, Bolvin DT, Braithwaite D, Hsu K, Joyce R, Xie P, Yoo SH (2015) NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm Theoretical Basis Document (ATBD) Version, 4, 26

  • Huffman GJ, Stocker EF, Bolvin DT, Nelkin EJ, Adler RF (2014) TRMM Version 7 3B42 and 3B43 Data Sets. NASA/GSFC, Greenbelt, MD. http://mirador.gsfc.nasa.gov/cgibin/mirador/presentNavigation.pl?tree=project&project=TRMM&dataGroup=Gridded&CGISESSID=5d12e2ffa38ca2aac6262202a79d882a. Accessed 7 November 2018

  • Huffman GJ, Bolvin DT, Nelkin EJ (2007) Integrated multi-satellite retrievals for GPM (IMERG) TECHNICAL documentation. IMERG Tech Document, 1–54

  • Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503

    Article  Google Scholar 

  • Kidd C (2001) Satellite rainfall climatology: a review. Int J Climatol 21:1041–1066

    Article  Google Scholar 

  • Levizzani V, Amorati R, Meneguzzo F (2002) A review of satellite-based rainfall estimation methods. In: European Commission Project MUSIC report, pp 66

  • Liu Z (2015) Comparison of versions 6 and 7 3-hourly TRMM multisatellite precipitation analysis (TMPA) research products. Atmos Res 163:91–101

    Article  Google Scholar 

  • Maidment RI, Grimes D, Allan RP, Tarnavsky E, Stringer M, Hewison T, Roebeling R, Black E (2014) The 30-year TAMSAT African Rainfall Climatology And Time-series (TARCAT) data set. Journal of Geophysical Research 119 (1): 619–10,644. https://doi.org/10.1002/2014JD021927.

  • Maidment R, Grimes D, Black E, Tarnavsky E, Young M, Greatrex H,…, Allan RP (2017) A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa. Nature Scientific Data 4:170063. https://doi.org/10.1038/sdata.2017.63

  • Melo DDC, Xavier AC, Bianchi T, Oliveira PT, Scanlon BR, Lucas MC, Wendland E (2015) Performance evaluation of rainfall estimates by TRMM multi-satellite precipitation analysis 3B42V6 and V7 over Brazil. J Geophys Res Atmos 120(18):9426–9436

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900. https://doi.org/10.13031/2013.23153

  • Mosier TM, Hill DF, Sharp KV (2014) 30-Arcsecond monthly climate surfaces with global land coverage. Int J Climatol 34:2175–2188. https://doi.org/10.1002/joc.3829

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290. https://doi.org/10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  • National Centre for Atmospheric Research (NCAR) (2020) Precipitation data sets - overview and comparison table. Accessed 09/02/2020 from https://climatedataguide.ucar.edu/climate-data/precipitation-data-sets-overview-comparison-table

  • Nigerian Meteorological Agency-NiMets (2019) Seasonal rainfall prediction. NiMETs, The Weather and Climate Research centre, Bill Clinton Road, Abuja Airport, Nigeria

  • Odekunle TO (2004) Rainfall and the length of the growing season in Nigeria. Int J Climatol 24:467–479. https://doi.org/10.1002/joc.1012

    Article  Google Scholar 

  • Ojeda JJ, Volenec JJ, Brouder SM, Caviglia OP, Agnusdei MG (2017) Evaluation of agricultural production systems simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments. GCB Bioenergy 9(4):796–816

    Article  Google Scholar 

  • Olaniran OJ (1988) The distribution in space of rain-days of rainfall of different amounts in the tropics: Nigeria as a case study. Geoforum 19:507–520. https://doi.org/10.1016/S0016-7185(88)80021-6

    Article  Google Scholar 

  • Ralph F (2014) Rainfall. In: Njoku EG (ed) Encyclopaedia of remote sensing. Springer, London, pp 640–653

    Google Scholar 

  • Romilly TG, Gebremichael M (2011) Evaluation of satellite rainfall estimates over Ethiopian river basins. Hydrol Earth Syst Sci 15:1505–1514

    Article  Google Scholar 

  • Salami AA (2019) Temporal variations of selected climatic parameters in Osogbo, Nigeria for the period of four decades (1975–2014). American Journal of Earth and Environmental Sciences 2(1):9–14

    Google Scholar 

  • Salami AA, Olorunfemi JF, Olanrewaju RM (2021) Geo-spatial analysis of rainfall amounts, and rainy days using satellites and ground-based data in Nigeria. J Met Clim Sci 19(1):29–42

    Google Scholar 

  • Sanogo S, Fink HF, Omotosho JA, Ba A, Redl R, Ermert V (2015) Spatio-temporal characteristics of the recent rainfall recovery in West Africa. Int J Climatol 35(15):4589–4605. https://doi.org/10.1002/joc.4309

    Article  Google Scholar 

  • Savary S, Nelson A, Willocquet L, Pangga I, Aunario J (2012) Modeling and mapping potential epidemics of rice diseases globally. Crop Protection 34(Supplement C):6–17. https://doi.org/10.1016/j.cropro.2011.11.009

    Article  Google Scholar 

  • Schamm K, Ziese M, Raykova K, Becker A, Finger P, Meyer-Christoffer A, Rudolf B, Schneider U (2016) GPCC full data daily version 1.0: daily land-surface precipitation from rain gauges built on GTS based and historic data. Research Data Archive at the National Centre for Atmospheric Research, Computational and Information Systems Laboratory. Accessed 15 June 2019

  • Schneider U, Finger P, Meyer-Christoffer A, Rustemeier E, Ziese M, Becker A (2017) Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the Global Precipitation Climatology Centre (GPCC). Atmosphere 8 (3) 52:1–17. https://doi.org/10.3390/atmos8030052

  • Sharifi E, Steinacker R, Saghafian B (2016) Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: preliminary results. Remote Sensing 8(2):135. https://doi.org/10.3390/rs8020135

    Article  Google Scholar 

  • Singh J, Knapp HV, Demissie M (2004) Hydrologic modeling of the iroquois river watershed using HSPF and SWAT. Illinois State Water Survey, Champaign

  • Sorooshian S, Hsu K, Braithwaite D, Ashouri H, Program NOAACDR (2014) NOAA Climate Data Record (CDR) of precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN-CDR), version 1 revision 1. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V51V5BWQ.Accessed22January2019

    Article  Google Scholar 

  • Sparks AH (2018) NASAPOWER: A NASA POWER global meteorology, surface solar energy and climatology data client for R. Journal of Open Source Software 3(30):1035. https://doi.org/10.21105/joss.01035

  • Stackhouse PW, Jr, Zhang T, Westberg D, Barnett AJ, Bristow T, Macpherson B, Hoell JM (2018) POWER release 8 (with GIS applications) methodology (data parameters, sources, & validation) documentation date may 1, 2018 (all previous versions are obsolete) (data version 8.0.1). NASA. https://power.larc.nasa.gov/documents/POWER_Data_v8_methodology.pdf. Accessed 22 January 2020

  • Stampoulis D, Anagnostou EN (2012) Evaluation of global satellite rainfall products over continental Europe. J Hydrometeorol 13(2):588–603

    Article  Google Scholar 

  • Tesfa GA, Demelash AM, Mamuye TE (2020) Performance evaluation of integrated multi-satellite retrieval for global precipitation measurement products over Gilgel Abay watershed, Upper Blue Nile Basin, Ethiopia. Model Earth Syst Environ 6:1853–1861. https://doi.org/10.1007/s40808-020-00795-w

    Article  Google Scholar 

  • Usman M, Nichol JE, Ibrahim AT, Baba LF (2018) Spatio-temporal analysis of trends in rainfall from long term satellite rainfall products in the Sudano Sahelian zone of Nigeria. Agric for Meteorol 260–261:273–286. https://doi.org/10.1016/j.agrformet.2018.06.016

    Article  Google Scholar 

  • van Wart J, Grassini P, Cassman KG (2013) Impact of derived global weather data on simulated crop yields. Glob Change Biol 19(12):3822–3834. https://doi.org/10.1111/gcb.12302

    Article  Google Scholar 

  • van Wart J, Grassini P, Yang H, Claessens L, Jarvis A, Cassman KG (2015) Creating long-term weather data from thin air for crop simulation modeling. Agricultural and Forest Meteorology 209–210(Supplement C):49–58. https://doi.org/10.1016/j.agrformet.2015.02.020

    Article  Google Scholar 

  • Verdin J, Funk C, Senay G, Choularton R (2005) Climate science and famine early warning. Philos Trans Roy Soc London B360:2155–2168. https://doi.org/10.1098/rstb.2005.1754

    Article  Google Scholar 

  • Wang W, Lu H, Zhao T, Jiang L, Shi J (2017a) Evaluation and comparison of daily rainfall from latest GPM and TRMM products over the Mekong River Basin. IEEE J Sel Top Appl Earth Observ Remote Sens 10(6):2540–2549

    Article  Google Scholar 

  • Wang Z, Zhong R, Lai C, Chen J (2017b) Evaluation of the GPMIMERG satellite-based precipitation products and the hydrologicalutility. Atmos Res 196:151–163

    Article  Google Scholar 

  • Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30:79–82

    Article  Google Scholar 

  • Worqlul AW, Maathuis B, Adem AA, Demissie SS, Langan S, Steenhuis TS (2014) Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground observed data for the Lake Tana basin in Ethiopia. Hydrol Earth Syst Sci 18:4871–4881

    Article  Google Scholar 

  • Xie P, Arkin P (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Amer Meteor Soc 78:2537–2558

    Article  Google Scholar 

  • Xie P, Joyce R, Wu S, Yoo SH, Yarosh Y, Sun F, Lin R, NOAA CDR Program (2019) NOAA Climate Data Record (CDR) of CPC Morphing Technique (CMORPH) high-resolution global precipitation estimates, version 1 [daily]. NOAA National Centres for Environmental Information. https://doi.org/10.25921/w9va-q159. Accessed 29 August 2020

  • Young MP, Williams CJ, Chiu JC, Maidment RI, Chen SH (2014) Investigation of discrepancies in satellite rainfall estimates over Ethiopia. J Hydrometeorol 15:2347–2369

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the Nigerian Meteorological Agency (NiMet) for the provision of the ground-based rainfall data. We thank all the research centers, institutions, and agencies such as the University of Reading, University of Maryland, NASA GSFC, US Geological Survey Climate Hazards Group at University of California, National Oceanic and Atmospheric Administration Climate Prediction Center, University of East Anglia, NOAA/ESRL/PSL, and NASA Langley Research Centre (LaRC) POWER Project for producing and sharing the precipitation estimates used in this study.

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  1. Department of Geography and Environmental Management, University of Ilorin, P.M.B. 1515, Ilorin, Nigeria

    Afeez Salami

  2. Arid Land Research Centre, Tottori University, Tottori, Hamasaka, 1390680-0001, Japan

    Ayele Almaw Fenta

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  1. Afeez Salami
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Salami, A., Fenta, A.A. Spatio-temporal evaluation of open access precipitation products with rain gauge observations in Nigeria. Arab J Geosci 15, 1785 (2022). https://doi.org/10.1007/s12517-022-11071-9

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