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This research entailed the acquisition of long-term (1980–2020) meteorological data (rainfall, temperature, and runoff) which was used to compute the values for the mean annual, monthly, and seasonal hydro-climate variables to establish their trend and behavior. To substantiate if the patterns of runoff are in any way due to the changing climate, the meteorological data were subjected to non-parametric tests—Mann–Kendall (MK), Sen's slope estimator, Kendall tau, and partial correlation. MK trend test for runoff, rainfall, and temperature (on an annual and seasonal basis) revealed significant trends on an annual scale. Nevertheless, rainfall data did not exhibit any, observable trend on a seasonal scale. On both annual and seasonal scales, temperature values showed increasing trends. In the entire period considered (1980–2020), runoff also exhibited a remarkable increase in trend. Sen's estimator values ranged from − 0.04 to 3.23. The results of the Kendall tau correlation showed that runoff is positively correlated to rainfall (tau = 0.222), with a high confidence level (95%). Again, the partial correlation analysis of runoff and climatic variables for annual and seasonal timescale, results show that the runoff is significantly (rxy.z = 0.315) affected by both rainfall and temperature. It is therefore recommended that future research should utilize robust input–output models to determine the amount of water stress in the basin. As a sustainable management approach, artificial surface water reservoirs should be constructed to complement the available water from the Sokoto-Rima river and alleviate the water stress experienced by the population. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the projected regional responses of univariate and bivariate behaviors of extreme precipitation to climate change over the upper-middle Huaihe River Basin. Based on twelve GCM outputs under historical, RCP4.5 and the observations at 32 rainfall stations, the equidistant cumulative distribution function matching method (EDCDFm) was utilized to bias correct daily precipitation during the historical (1961–2005) and future (2021–2080) periods. Four precipitation indices combinations were introduced based on eight precipitation indices to characterize the regional-scale changes of precipitation events, which designate the duration, intensity and amount of heavy and weak precipitation in a year. Their dependence structures were captured by Copulas. Kendall return period (KRP) were applied to discuss hazard scenarios and we quantified the spatial variability of KRPs under different marginal values. The results indicated that projected precipitation characteristics including the average intensity, the amount of annual precipitation, the intensity and amount of extreme precipitation together with annual extremes displayed increasing trends, while the changes of consecutive wet and dry days did not present pronounced trends. Decreased KRPs in the vast majority of the territory manifested that the frequency of simultaneous floods and droughts in a year as well as that of extreme heavy precipitation events would augment. Obvious spatial heterogeneity of the changes of KRP was partly attributed to the topography difference, especially the coastal areas along the main stream of the Huaihe River. Consequently, there will be a higher risk of water resources-related issues in this region for upcoming decades. • Significant decreases will occur in the intensity and amount of heavy precipitation for upcoming decades. • Change rates of precipitation indices combinations illustrate obvious spatial heterogeneity. • The frequency of simultaneous floods and droughts in a year and that of extreme heavy precipitation events will augment. [ABSTRACT FROM AUTHOR]