Performance Evaluation of Climate Products with Validation Using Bootstrapping and Climate Extremes in the Hadejia-Jama’are River Basin, Nigeria
- Authors
-
-
Idris M. WADA
Department of Agricultural Engineering, Faculty of Engineering, Federal University, Wukari, Nigeria
Author
-
Abba IBRAHIM
Department of Agricultural and Environmental Engineering, Faculty of Engineering, Bayero University, Kano, Nigeria
Author
-
Genesis ISHAYA
Department of Agricultural Engineering, Faculty of Engineering, Federal University, Wukari, Nigeria
Author
-
- Keywords:
- Climate Products, Compromise Programming, Multi-Criteria Group Decision Making, Hadejia-Jama’are River basin, Statistical evaluation, Climate extremes.
- Abstract
-
The increasing availability of satellite, reanalysis, and gauge-based Climate Products (CPs) has significantly advanced hydroclimatic research, particularly in data-scarce regions. Despite their usefulness, these CPs often carry substantial uncertainties, necessitating thorough evaluation before use in regional or basin-level studies. This study assesses the performance of nine precipitation and four temperature gridded datasets over the Hadejia-Jama’are River Basin (HJRB), Nigeria, using observational data from Kano, Jigawa, and Bauchi for the period 1990–2015. Daily and monthly precipitation (Pr), maximum temperature (Tmax), and minimum temperature (Tmin) temperature records were evaluated using Kling-Gupta Efficiency (KGE), Normalized Root Mean Square Error (NRMSE), Pearson correlation (r), Percentage Bias (PBIAS), and Mean Difference (MD). The fifth generation of the European Centre for Medium-Range Weather Forecasts (ERA5) dataset demonstrated superior performance in mean annual assessment for both Tmin and Tmax with minor underestimation. Tmax biases ranged from 2.2% to 3.5% and Tmin from 0.3% to 2.1%. Climate Forecast System Reanalysis (CFSR) had the weakest performance among the temperature products. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) outperformed other precipitation products, with modest underestimations of 3.5% (Kano), 9% (Bauchi), and 12% (Jigawa), while Climate Prediction Center (CPC) showed the poorest alignment with observed data. Monthly statistical evaluations yielded stronger statistical performance than daily analyses, especially for temperature datasets. For precipitation, correlation coefficients exceeded 0.5 across all models at daily resolution, with CHIRPS showing the highest values. Overall model ranking using Compromise Programming Index (CPI) and Multi-Criteria Group Decision-Making Method (MCGDM) confirmed ERA5 (for Tmax and Tmin) and CHIRPS (for precipitation) as the most reliable products. Further validation using climate extremes and bootstrapped uncertainty analysis showed that the ratio of modeled to observed extremes consistently exceeded 0.5, indicating suitability for hydrological applications. The bootstrapped 95% uncertainty bands were narrow, reflecting low uncertainty in the selected CPs. ERA5 and CHIRPS emerged as the most robust datasets for hydroclimatic studies in the Hadejia-Jama’are River Basin.
- References
- Downloads
- Published
- 08-08-2025
- Section
- Articles
- License
-
Copyright (c) 2025 FUDMA Journal of Engineering and Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Similar Articles
- Sirajo ALHASSAN, Abdullahi A. ADAMU, Mohammed T. JIMOH, Experimental Characterization and Seasonal Energy Potential Assessment of Municipal Solid Waste in Kano City, Nigeria , FUDMA Journal of Engineering and Technology: Vol. 2 No. 1 (2026): June 2026
- Abdulkarim AMINU, Mohammed ALIYU, Ibrahim S. MOHAMMED, Iyanda M. ANIMASHAUN, Peter DANIEL, Mohammad USMAN, Production and Optimisation of Parameters for Density and Ash Content of Briquettes Produced from Sorghum Stalks and Groundnut Shells Using African Locust Bean (Parkia biglobosa) Pulp as a Binding Agent , FUDMA Journal of Engineering and Technology: Vol. 1 No. 1 (2025): July 2025
- Bala YAHAYA, Mukhtar N. YAHYA, Characterization of Soil Properties Under Continuous Irrigation Practice , FUDMA Journal of Engineering and Technology: Vol. 1 No. 2 (2025): December 2025
- Muhammad M. HAMIDU, Development of MATLAB-Based Model for Solar Radiation Prediction and Photovoltaic Panel Tilt Optimization Angle for Maiduguri Region, Nigeria , FUDMA Journal of Engineering and Technology: Vol. 1 No. 2 (2025): December 2025
- Nafisa A. ABDULHAMID, Danladi D. USMAN, Kabiru I. JAHUN, Rotkangmwa J. SATI, Yahaya U. JAURO, Abdulmajid M. HASHIR, Yusuf G. SHIMBURA, Development of a Solar Dryer with Multi-Products and a Residual Moisture Control System , FUDMA Journal of Engineering and Technology: Vol. 2 No. 1 (2026): June 2026
- Mokọ́ládé JOHNSON, Bashir SALIM, Enobong EQUERE, Miriam I. CHUKWUMA-UCHEGBU, Emma EKPO, Ali B. ABDULSALAM, Conserving Indigenous Geometries: A Vital Approach to Integrating Cultural Heritage into Architectural Education , FUDMA Journal of Engineering and Technology: Vol. 2 No. 1 (2026): June 2026
- Adebola S. AKOLADE, Precious A. OLAOMOTITO, Abibat Y. ADEBIYI, Oluwatunmise P. ABOLARIN, Opeyemi I. ADETUNJI, Kola OGEDENGBE, Production and Evaluation of Cement-Bonded Composites Board Made from Banana Pseudo Stem , FUDMA Journal of Engineering and Technology: Vol. 1 No. 2 (2025): December 2025
- Martins Y. OTACHE, Iyanda M. ANIMASHAUN, Sadeeq A. MOHAMMED, Emmanuel O. AGBESE, Spatiotemporal Variability and Trend Analysis of Rainfall and Temperature in Northern Nigeria , FUDMA Journal of Engineering and Technology: Vol. 1 No. 1 (2025): July 2025
- Victoria A. UMEJURU, Isaac E. IHUA-MADUENYI, Adaobi S. NWOSI-ANELE, Performance Evaluation and Economic Assessment of Electrical Submersible Pumps for Heavy Oil Production in Mature Niger Delta Oilfields , FUDMA Journal of Engineering and Technology: Vol. 2 No. 1 (2026): June 2026
- Caleb A. ABORISADE, Jide E.T. AKINSOLA, Ifeoluwa M. OLANIYI, Fathia O. ONIPEDE, Emmanuel A. OLAJUBU, Ganiyu A. ADEROUNMU, Machine Learning-Based Polycystic Ovary Syndrome Generative Modelling via Ensemble Learning and Neural Networks for Infertility Prediction , FUDMA Journal of Engineering and Technology: Vol. 1 No. 1 (2025): July 2025
You may also start an advanced similarity search for this article.
