Assessment of regional best-fit probability density function of annual maximum rainfall using CFSR precipitation data
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Ó Indian Academy of Sciences (0123456789().,-volV)(0123456789( ).,-volV)
Assessment of regional best-Bt probability density function of annual maximum rainfall using CFSR precipitation data NKPA M OGAREKPE1,* , IMOKHAI T TENEBE2,5, PRAISEGOD C EMENIKE3, OBIANUJU A UDODI4 and RICHARD E ANTIGHA1 1 Department of Civil Engineering, Cross River University of Technology, Calabar, Nigeria. 2 Ingram School of Engineering, Texas State University, San Marcos, TX, USA. 3 Department of Civil Engineering, Covenant University, Ota, Nigeria. 4 Shell Centre for Environmental Management and Control, University of Nigeria, Enugu Campus, 5
Enugu, Nigeria. Texas Commission on Environmental Quality, Critical Infrastructure Division, Austin, TX, USA. *Corresponding author. e-mail: [email protected] MS received 15 October 2019; revised 20 April 2020; accepted 4 May 2020
The upper Cross River basin (UCRB) Bts a true description of a data scarce watershed in respect of climatic data. This paper seeks to determine the best-Bt probability density function (PDF) of annual maximum rainfall for the UCRB using the Climate Forecast System Reanalysis (CFSR) precipitation data. Also, to evaluate the performance of the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Inter-comparison Project (CMIP3) Fourth Assessment Report (AR4) Global Circulation Models (GCMs) in simulating the monthly precipitation in the UCRB considering 1979–2014 data. For the determination of the best-Bt PDF, the models under review included the generalized extreme value (GEV), normal, gamma, Weibull and log-normal (LN) distributions. Twenty-four weather station datasets were obtained and subjected to frequency distribution analysis on per station basis, and subsequently Btted to the respective PDFs. Also, simulated monthly precipitation data obtained from 16 AR4 GCMs, for weather station p6191, were subjected to frequency distribution analysis. The results showed the percentages of best-Bt to worst-Bt PDFs, considering the total number of stations, as follows: 54.17%, 45.83%, 37.50%, 45.83%, and 50%/50%. These percentages corresponded to GEV, Weibull, gamma, gamma, and LN/normal, respectively. The comparison of the predicted and observed values using the Chi-square goodness-of-Bt test revealed that the GEV PDF is the best-Bt model for the UCRB. The correlation coefBcient values further corroborated the correctness of the test. The PDF of the observed data (weather station p6191) and the simulations of the 16 GCMs computed using monthly rainfall datasets were compared using a mean square error (MSE) dependent skill score. The result from this study suggested that the CGCM3.1 (T47) and MRI-CGCM2.3.2 provide the best representations of precipitation, considering about 36 years trend for station p6191. The results have no inCuence on how well the models perform in other geographical locations. Keywords. Rainfall; models; Btting; probability density function; CMIP3; climate models.
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1. Introduction The analyses of rainfall events on s
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