Validating Gauge-based Spatial Surface Atmospheric Temperature Datasets for Upper Benue River Basin, Nigeria

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.

Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups

“…The potential evapotranspiration values were extracted from the CRU TS dataset. The precipitation values for runoff reconstruction were extracted from the Global Precipitation Climatology Centre (GPCC) Precipitation Total Full V2018 data provided by the NOAA/OAR/ESRL (PSL, Boulder, Colorado, USA) because these were found to agree better with observations in previous research compared to the CRU TS precipitation dataset (Ahmed et al, 2019;Degefu et al, 2022;Fiedler and Döll, 2007;Hu et al, 2018;Salaudeen et al, 2021).…”