Abstract
Like most other countries of Africa, one of the main problems threatening effective impact modelling in Nigeria including Upper Benue river basin, dwells in lack of high-quality in-situ observation datasets at appropriate spatiotemporal scales. Gridded meteorological variables can serve as promising alternatives to in-situ measurements in data sparse regions, but then, require validations to assess quantitatively their level of accuracies and reliabilities. As a consequence, this study makes comparative analysis of two gauge-based, spatially interpolated surface atmospheric temperature datasets with in-situ measurements in seven distinct meteorological stations covering the period of 1982-2006. Correspondingly, spatial analysis and statistical measures were used to assess the performances of the gridded datasets from the Climate Research Unit (CRU) and the Climate Prediction Centre (CPC). Results from spatial distributions depict 8, 11 and 10 °C as observed minimum temperatures and 33, 36, 42 °C as observed maximum temperatures over the Cameroon highland (Gembu), the Jos plateau and at the northern fringes of the basin respectively. Consequently, both the CRU and CPC datasets captured remarkably well the observed temperature gradients along the varying topography, though with differing margins. The interannual variabilities indicate CRU dataset to better capture the signs and magnitudes of the observed anomalies as compared to the CPC data. Moreover, the CRU data was noted to be more outstanding in representing the observed features in seasonal temperature variations over most stations. Also, the shapes of the probability density function (PDF) for both datasets in minimum and maximum temperatures measured closely the shapes of the observed PDF. Trend analysis suggests CRU datasets to better represent the warming and the cooling trends than the CPC. Overall, the CRU datasets are the most outstanding in this study and is therefore preferred for water resource application over the study area.
Highlights
The variations of near-surface air temperature are influential on agriculture, hydrology, energy and ecosystems; as it is one of the key elements, which represent the state of the atmosphere (Adeniyi and Dilau, 2015; Chen et al, 2014)
Gauge-based, minimum and maximum temperature datasets namely; Climate Research Unit (CRU) and Climate Prediction Centre (CPC) are validated in this study relative to reference observation data for Upper Benue river basin between 1982 and 2006
The spatial representations of the minimum annual minimum temperature and maximum annual maximum temperature for the 25 years period over Upper Benue river basin are shown in Figure 2 for the reference observational data, CRU and CPC
Summary
The variations of near-surface air temperature are influential on agriculture, hydrology, energy and ecosystems; as it is one of the key elements, which represent the state of the atmosphere (Adeniyi and Dilau, 2015; Chen et al, 2014). The significant input from Africa continent to the global climate system is well documented in literatures, yet, ground observation networks are non-existence in most remote areas (Hassan et al, 2020), where they are needed for analysis. Even where they exist, the datasets are usually characterized with gaps, limited and restricted accessibility and inadequate spatiotemporal continuity and distributions (Piyoosh and Ghosh, 2016). The datasets are usually characterized with gaps, limited and restricted accessibility and inadequate spatiotemporal continuity and distributions (Piyoosh and Ghosh, 2016) This is true for most developing countries. Advancement in computational technologies in recent decades has aided the availability of climate datasets in digital forms over the entire globe (Daly, 2006), which serve as key input data for computer models, in water resource and environmental managements as well hydroclimatological impact assessments
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