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Climate change threatens mental health via increasing exposure to the social and economic disruptions created by extreme weather and large-scale climatic events, as well as through the anxiety associated with recognising the existential threat posed by the climate crisis. Considering the growing levels of climate change awareness across the world, negative emotions like anxiety and worry about climate-related risks are a potentially pervasive conduit for the adverse impacts of climate change on mental health. In this study, we examined how negative climate-related emotions relate to sleep and mental health among a diverse non-representative sample of individuals recruited from 25 countries, as well as a Norwegian nationally-representative sample. Overall, we found that negative climate-related emotions are positively associated with insomnia symptoms and negatively related to self-rated mental health in most countries. Our findings suggest that climate-related psychological stressors are significantly linked with mental health in many countries and draw attention to the need for cross-disciplinary research aimed at achieving rigorous empirical assessments of the unique challenge posed to mental health by negative emotional responses to climate change.

In hydro-meteorological trend analysis, an alteration in the given variable is detected by considering the long-term series as a whole. Whereas the long-term trend may be absent, the significance of hidden (short-durational) sub-trends in the series may be important for environmental management practices. In this paper, a graphical approach of identifying trend or sub-trends using nonparametric cumulative rank difference (CRD) was proposed. To confirm the significance of the visualized trend, the CRD was translated from the graphical to a statistical metric. To assess its capability, the performance of the CRD method was compared with that of the well-known Mann-Kendall (MK) test. The graphical and statistical CRD techniques were applied to detect trends and subtrends in the annual rainfall of 10 River Nile riparian countries (RNRCs). The co-occurrence of the trend evolutions in the rainfall with those of the large-scale oceanatmosphere interactions was analyzed. The power of the CRD method was shown to closely agree with that of the MK test under the various circumstances of sample sizes, variations, linear trend slopes, and serial correlations. At the level of significance a = 5 %, the long-term trends were found present in 30 % of the RNRCs. However at a = 5 %, the main downward (upward) sub-trends were found significant in 30 (60 %) of the RNRCs. Generally at a = 1 %, linkages of the trend evolutions in the rainfall of the RNRCs were found to those of the influences from the Atlantic and Indian Oceans. At a = 5 %, influences from the Pacific Ocean on the rainfall trends of some countries were also evident.

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This study demonstrates the existence of uncertainty in hydrometeorological trend analyses using historical rainfall from Kenya in East Africa. With respect to the influence of short- and long-term persistence on trend analyses, a total of 13 approaches of rank-based techniques including Mann-Kendall, Spearman’s Rho, and Cumulative Rank Difference (CRD) tests were employed. Graphically, CRD-based diagnoses of trends and subtrends were performed. To assess data-related uncertainty, a resampling procedure was applied. It was shown that at a selected significance level, the null hypothesisH0(no trend) can be rejected for trend direction while the evidence to rejectH0(zero trend magnitude) is statistically insufficient. Graphical and statistical approaches when combined yield more influential and comprehensive information for inference than relying purely on statistical results. Alongside an apparent linear trend, variations in the nonlinear (e.g., cyclical) component of the series may also not be due to natural randomness. Method-related uncertainty is not negligible especially for series with persistent fluctuations. These findings shed light on the need to assess uncertainty on trend results. Furthermore, it is recommended that the conclusiveness of trends be cautiously premised not only on statistical grounds but also on more considered physical and/or theoretical understanding of the hydrometeorological processes.

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Abstract. Spatiotemporal variability in annual and seasonal rainfall totals were assessed at 37 locations of the Nile Basin in Africa using quantile perturbation method (QPM). To get insight into the spatial difference in rainfall statistics, the stations were grouped based on the pattern of the long-term mean (LTM) of monthly rainfall and that of temporal variability. To find the origin of the driving forces for the temporal variability in rainfall, correlation analyses were carried out using global monthly sea level pressure (SLP) and sea surface temperature

Trends and variability in series comprising the mean of fifteen highest daily rainfall intensities in each year were analyzed considering entire Uganda. The data were extracted from high-resolution (0.5° × 0.5°) gridded daily series of the Princeton Global Forcings covering the period 1948–2008. Variability was analyzed using nonparametric anomaly indicator method and empirical orthogonal functions. Possible drivers of the rainfall variability were investigated. Trends were analyzed using the cumulative rank difference approach. Generally, rainfall was above the long-term mean from the mid-1950s to the late 1960s and again in the 1990s. From around 1970 to the late 1980s, rainfall was characterized by a decrease. The first and second dominant modes of variability correspond with the variation in Indian Ocean Dipole and North Atlantic Ocean index, respectively. The influence of Niño 3 on the rainfall variability of some parts of the country was also evident. The southern and northern parts had positive and negative trends, respectively. The null hypothesisH0(no trend) was collectively rejected at the significance level of 5% in the series from 7 out of 168 grid points. The insights from the findings of this study are vital for planning and management of risk-based water resources applications.