Hydro-climatological study is difficult in most of the developing countries due to the paucity of monitoring stations. Gridded climatological data provides an opportunity to extrapolate climate to areas without monitoring stations based on their ability to replicate the Spatio-temporal distribution and variability of observed datasets. Simple correlation and error analyses are not enough to predict the variability and distribution of precipitation and temperature. In this study, the coefficient of correlation (R2), Root mean square error (RMSE), mean bias error (MBE) and mean wet and dry spell lengths were used to evaluate the performance of three widely used daily gridded precipitation, maximum and minimum temperature datasets from the Climatic Research Unit (CRU), Princeton University Global Meteorological Forcing (PGF) and Climate Forecast System Reanalysis (CFSR) datasets available over the Niger Delta part of Nigeria. The Standardised Precipitation Index was used to assess the confidence of using gridded precipitation products on water resource management. Results of correlation, error, and spell length analysis revealed that the CRU and PGF datasets performed much better than the CFSR datasets. SPI values also indicate a good association between station and CRU precipitation products. The CFSR datasets in comparison with the other data products in many years overestimated and underestimated the SPI. This indicates weak accuracy in predictability, hence not reliable for water resource management in the study area. However, CRU data products were found to perform much better in most of the statistical assessments conducted. This makes the methods used in this study to be useful for the assessment of various gridded datasets in various hydrological and climatic applications.
References
[1]
Wei, G., Lü, H., Crow, W.T., Zhu, Y., Wang, J. and Su, J. (2018) Evaluation of Satellite-Based Precipitation Products from IMERG V04A and V03D, CMORPH and TMPA with Gauged Rainfall in Three Climatologic Zones in China. Remote Sensing, 10, 30. https://doi.org/10.3390/rs10010030
[2]
Derin, Y. and Yilmaz, K.K. (2014) Evaluation of Multiple Satellite-Based Precipitation Products over Complex Topography. Journal of Hydrometeorology, 15, 1498-1516. https://doi.org/10.1175/JHM-D-13-0191.1
[3]
Worku, L.Y. (2015) Climate Change Impact on Variability of Rainfall Intensity in the Upper Blue Nile Basin. Proceedings of the International Association of Hydrological Sciences, 366, 135-136. https://doi.org/10.5194/piahs-366-135-2015
[4]
IPCC Climate Change (2012) 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 University Press, Cambridge, and New York, 582.
[5]
Ahmed, K., Shahid, S., Wang, X., Nawaz, N. and Najeebullah, K. (2019) Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan. Water (Switzerland), 11, 210. https://doi.org/10.3390/w11020210
[6]
Fuka, D.R., Walter, M.T., Macalister, C., Degaetano, A.T., Steenhuis, T.S. and Easton, Z.M. (2014) Using the Climate Forecast System Reanalysis as Weather Input Data for Watershed Models. Hydrologic Process, 28, 5613-5623. https://doi.org/10.1002/hyp.10073
[7]
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S. and Hsu, K.L. (2018) A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons. Reviews of Geophysics, 56, 79-107. https://doi.org/10.1002/2017RG000574
[8]
Bai, P. and Liu, X. (2018) Evaluation of Five Satellite-Based Precipitation Products in Two Gauge-Scarce Basins on the Tibetan Plateau. Remote Sensing, 10, 1316. https://doi.org/10.3390/rs10081316
[9]
Lin, C., Maurer, E.P., Andreadis, K.M., Rosenberg, E.A., Livneh, B., Lettenmaier, D.P., Nijssen, B. and Mishra, V. (2013) A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions. Journal of Climate, 26, 9384-9392. https://doi.org/10.1175/JCLI-D-12-00508.1
[10]
Yatagai, A., Arakawa, O., Kamiguchi, K. and Kawamoto, H. (2009) A 44-Year Daily Gridded Precipitation Dataset for Asia. Sola, 5, 3-6. https://doi.org/10.2151/sola.2009-035
[11]
Haylock, M.R., Hofstra, N., Klein Tank, A.M.G., Klok, E.J., Jones, P.D. and New, M. (2008) A European Daily High-Resolution Gridded Data Set of Surface Temperature and Precipitation for 1950-2006. Journal of Geophysical Research Atmospheres, 113, D20119. https://doi.org/10.1029/2008JD010201
[12]
Herrera, S., Gutiérrez, J.M., Ancell, R., Pons, M.R., Frías, M.D. and Fernández, J. (2012) Development and Analysis of a 50-Year High-Resolution Daily Gridded Precipitation Dataset over Spain (Spain02). International Journal of Climatology, 32, 74-85. https://doi.org/10.1002/joc.2256
[13]
Schiemann, R., Liniger, M.A. and Frei, C. (2010) Reduced Space Optimal Interpolation of Daily Rain Gauge Precipitation in Switzerland. Journal of Geophysical Research Atmospheres, 115, 1-18. https://doi.org/10.1029/2009JD013047
[14]
Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014) Updated High-Resolution Grids of Monthly Climatic Observations—The CRU TS3.10 Dataset. International Journal of Climatology, 34, 623-642. https://doi.org/10.1002/joc.3711
[15]
Steurer, P.M., Peter, T.C., Heim, R. and Karl, T.R. (1992) The Global Historical Climatology Network: Long-Term Monthly Temperature, Precipitation, Sea Level Pressure, and Station Pressure Data.
[16]
Chen, M., Xie, P., Janowiak, J.E. and Arkin, P.A. (2002) Global Land Precipitation: A 50-Yr Monthly Analysis Based on Gauge Observations. Journal of Hydrometeorology, 3, 249-266. https://doi.org/10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2
[17]
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., et al. (2010) The NCEP Climate Forecast System Reanalysis. Bulletin of the American Meteorological Society, 91, 1015-1058. https://doi.org/10.1175/2010BAMS3001.1
[18]
Sheffield, J., Goteti, G. and Wood, E.F. (2006) Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling. Journal of Climate, 19, 3088-3111. https://doi.org/10.1175/JCLI3790.1
[19]
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M. and Rudolf, B. (2014) GPCC’s New Land Surface Precipitation Climatology Based on Quality-Controlled in Situ Data and Its Role in Quantifying the Global Water Cycle. Theoretical and Applied Climatology, 115, 15-40. https://doi.org/10.1007/s00704-013-0860-x
[20]
Chaudhuri, A.H., Ponte, R.M. and Nguyen, A.T. (2014) A Comparison of Atmospheric Reanalysis Products for the Arctic Ocean and Implications for Uncertainties in Air-Sea Fluxes. Journal of Climate, 27, 5411-5421. https://doi.org/10.1016/j.jhydrol.2017.01.023
[21]
Rahman, S.H., Sengupta, D. and Ravichandran, M. (2009) Variability of Indian Summer Monsoon Rainfall in Daily Data from Gauge and Satellite. Journal of Geophysical Research Atmospheres, 114. https://doi.org/10.1029/2008JD011694
[22]
Mahe, G., New, M., Paturel, J.E., Cres, A., Dezetter, A., Boyer, J.F., Rouche, N., Servat, E., New, M., Paturel, J.E., et al. (2008) Comparing Available Rainfall Gridded Datasets for West Africa and the Impact on Rainfall-Runoff Modelling Results, the Case of Burkina-Faso. Water SA, 34, 529-536. http://www.wrc.org.za https://doi.org/10.4314/wsa.v34i5.180650
[23]
Revadekar, J.V. and Preethi, B. (2012) Statistical Analysis of the Relationship between Summer Monsoon Precipitation Extremes and Food Grain Yield over India. International Journal of Climatology, 32, 419-429. https://doi.org/10.1002/joc.2282
[24]
Ratan, R. and Venugopal, V. (2013) Wet and Dry Spell Characteristics of Global Tropical Rainfall. Water Resources Research, 49, 3830-3841. https://doi.org/10.1002/wrcr.20275
[25]
Singh, N. and Ranade, A. (2010) The Wet and Dry Spells across India during 1951-2007. Journal of Hydrometeorology, 11, 26-45. https://doi.org/10.1175/2009JHM1161.1
[26]
Singh, D., Tsiang, M., Rajaratnam, B. and Diffenbaugh, N.S. (2014) Observed Changes in Extreme Wet and Dry Spells during the South Asian Summer Monsoon Season. Nature Climate Change, 4, 456-461. https://doi.org/10.1038/nclimate2208
[27]
Vinnarasi, R. and Dhanya, C.T. (2016) Changing Characteristics of Extreme Wet and Dry Spells of Indian Monsoon Rainfall. Journal of Geophysical Research: Atmospheres, 121, 2146-2160. https://doi.org/10.1002/2015JD024310
[28]
Mckee, T.B., Doesken, N.J. and Kleist, J. (1993) The Relationship of Drought Frequency and Duration to Time Scales. AMS 8th Conference on Applied Climatology, January 1993, 179-184.
[29]
Ashouri, H., Nguyen, P., Thorstensen, A., Hsu, K., Sorooshian, S. and Braithwaite, D. (2016) Assessing the Efficacy of High-Resolution Satellite-Based Persiann-CDR Precipitation Product in Simulating Streamflow. Journal of Hydrometeorology, 17, 2061-2076. https://doi.org/10.1175/JHM-D-15-0192.1
[30]
Chaudhary, S., Dhanya, C.T. and Vinnarasi, R. (2017) Dry and Wet Spell Variability during Monsoon in Gauge-Based Gridded Daily Precipitation Datasets over India. Journal of Hydrology, 546, 204-218. https://doi.org/10.1175/JCLI-D-13-00424.1
[31]
Willmott, C.J. and Matsuura, K. (2006) On the Use of Dimensioned Measures of Error to Evaluate the Performance of Spatial Interpolators. International Journal of Geographical Information Science, 20, 89-102. https://doi.org/10.1080/13658810500286976
[32]
Willmott, C. and Matsuura, K. (2005) Climate Research. Climate Research, 30, 79-82. http://www.int-res.com https://doi.org/10.3354/cr030079
[33]
Amadi, A.N. (2014) Impact of Gas-Flaring on the Quality of Rain Water, Groundwater and Surface Water in Parts of Eastern Niger Delta, Nigeria. Journal of Geosciences and Geomatics, 2, 114-119.
[34]
Yin, H., Donat, M.G., Alexander, L.V. and Sun, Y. (2015) Multi-Dataset Comparison of Gridded Observed Temperature and Precipitation Extremes over China. International Journal of Climatology, 35, 2809-2827. https://doi.org/10.1002/joc.4174
[35]
Hu, Z., Hu, Q., Zhang, C., Chen, X. and Li, Q. (2016) Evaluation of Reanalysis, Spatially Interpolated and Satellite Remotely Sensed Precipitation Data Sets in Central Asia. Journal of Geophysical Research Atmospheres, 121, 5648-5663. https://doi.org/10.1002/2016JD024781
[36]
Prakash, S., Gairola, R.M. and Mitra, A.K. (2015) Comparison of Large-Scale Global Land Precipitation from Multisatellite and Reanalysis Products with Gauge-Based GPCC Data Sets. Theoretical and Applied Climatology, 121, 303-317. https://doi.org/10.1007/s00704-014-1245-5
[37]
Nkiaka, E., Nawaz, N.R. and Lovett, J.C. (2017) Evaluating Global Reanalysis Precipitation Datasets with Rain Gauge Measurements in the Sudano-Sahel Region: Case Study of the Logone Catchment, Lake Chad Basin. Meteorological Applications, 24, 9-18. https://doi.org/10.1002/met.1600
[38]
Kursinski, A.L. and Zeng, X. (2006) Areal Estimation of Intensity and Frequency of Summertime Precipitation over a Midlatitude Region. Geophysical Research Letters, 33, 1-5. https://doi.org/10.1029/2006GL027393
[39]
Salman, S.A., Shahid, S., Ismail, T., Ahmed, K. and Wang, X.J. (2018) Selection of Climate Models for Projection of Spatiotemporal Changes in Temperature of Iraq with Uncertainties. Atmospheric Research, 213, 509-522. https://doi.org/10.1016/j.atmosres.2018.07.008
[40]
Robeson, S.M. and Ensor, L.A. (2006) Daily Precipitation Grids for South America. Bulletin of the American Meteorological Society, 86, 1567-1570. https://doi.org/10.1175/1520-0477(2006)87[1095:DPGFSA]2.0.CO;2
[41]
Worqlul, A.W., Maathuis, B., Adem, A.A., Demissie, S.S., Langan, S. and Steenhuis, T.S. (2014) Comparison of Rainfall Estimations by TRMM 3B42, MPEG and CFSR with Ground-Observed Data for the Lake Tana Basin in Ethiopia. Hydrology and Earth System Sciences, 18, 4871-4881. https://doi.org/10.5194/hess-18-4871-2014
[42]
Willmott, C.J. (1981) On the Validation of Models. Physical Geography, 2, 184-194. https://doi.org/10.1080/02723646.1981.10642213
[43]
Sushama, L., Ben Said, S., Khaliq, M.N., Nagesh Kumar, D. and Laprise, R. (2014) Dry Spell Characteristics over India Based on IMD and Aphrodite Datasets. Climate Dynamics, 43, 3419-3437. https://doi.org/10.1007/s00382-014-2113-9
[44]
Dash, S.K., Kulkarni, M.A., Mohanty, U.C. and Prasad, K. (2009) Changes in the Characteristics of Rain Events in India. Journal of Geophysical Research Atmospheres, 114, 1984-2012. https://doi.org/10.1029/2008JD010572
[45]
Cordano, E. (2016) Package “RMRAINGEN” (R Multi-Site RAINfall GENeretor): A Package to Generate Daily Time Series of Rainfall from Monthly Mean Values. CRAN, 23. https://github.com/cran/RMRAINGEN/blob/master/R/dw.spell.RD
[46]
Shahid, S. (2008) Spatial and Temporal Characteristics of Droughts in the Western Part of Bangladesh. Hydrological Processes: Wiley Interscience, 2274, 2267-2274.
[47]
Lloyd-Hughes, B. and Saunders, M.A. (2002) A Drought Climatology for Europe. International Journal of Climatology, 22, 1571-1592. https://doi.org/10.1002/joc.846
[48]
Komuscu, A.U. (1999) Using the SPI to Analyze Spatial and Temporal Patterns of Drought in Turkey Using the SPI to Analyze Spatial and Temporal Patterns of Drought in Turkey. Drought Network News, 11, 6-13.
[49]
Mahe, G., L’Hote, Y., Olivry, J. and Wotling, G. (2001) Tendances et discontinuités dans des séries de pluies régionales en Afrique de l’Ouest et Centrale: 1951-1989. Hydrological Sciences Journal, 46, 211-226. https://doi.org/10.1080/02626660109492817
[50]
Fu, Y., Xia, J., Yuan, W., Xu, B., Wu, X., Chen, Y. and Zhang, H. (2016) Assessment of Multiple Precipitation Products over Major River Basins of China. Theoretical and Applied Climatology, 123, 11-22. https://doi.org/10.1007/s00704-014-1339-0
[51]
Sun, Q., Miao, C., Duan, Q., Kong, D., Ye, A., Di, Z. and Gong, W. (2014) Would the “Real” Observed Dataset Stand Up? A Critical Examination of Eight Observed Gridded Climate Datasets for China. Environmental Research Letters, 9, Article ID: 055007. https://doi.org/10.1088/1748-9326/9/1/015001
[52]
Salman, S.A., Shahid, S., Ismail, T., Al-Abadi, A.M., Wang, X. and Chung, E.S. (2019) Selection of Gridded Precipitation Data for Iraq Using Compromise Programming, Measurement. Journal of the International Measurement Confederation, 132, 87-98. https://doi.org/10.1016/j.measurement.2018.09.047
[53]
New, M., Lister, D., Hulme, M. and Makin, I. (2002) A High-Resolution Data Set of Surface Climate over Global Land Areas. Climate Research, 21, 1-25. https://doi.org/10.3354/cr021001
[54]
Jones, P.D. and Harris, I.C. (2008) Climatic Research Unit (CRU) Time-Series Datasets of Variations in Climate with Variations in Other Phenomena. NCAS British Atmospheric Data Centre. http://catalogue.ceda.ac.uk/uuid/3f8944800cc48e1cbc29a5ee12d8542d
