Recent climate change due to global warming has given an impetus to trend analysis of hydrological time series. Climate change as well as low-frequency climate variability and human intervention in river basins violate the assumption of stationarity, which is claimed to be dead by some researchers. Detailed climate models and long hydrological records are needed to predict the future conditions in a changing world. It must be remembered, however, that all hydrological systems include a stationary element, at least in the form of a random component. A stationary model is sometimes preferable to a nonstationary one when the evolution in time of hydrological processes cannot be predicted reliably. It is attempted to generate synthetic nonstationary time series of future climates by means of a global climate model, which are then used in water resources optimization under uncertainty. The estimation of extremes (floods and low flows) is more important but also much more difficult. The statistical significance of a trend can be detected by means of statistical tests such as the nonparametric Mann-Kendall test, which must be modified when there is serial correlation, possibly by prewhitening. Long-term persistence in hydrological processes also affects the results of the test. Some authors criticized the use of significance levels in statistical tests and recommended using confidence intervals around the estimated effect size. The power of a test depends on the chosen level of significance, sample size and the accuracy of prediction of trends. In some cases, it is more important to increase the power so that errors of estimation that may lead to damages due to inadequate protection are prevented. Frequency analysis of nonstationary processes can be made by fitting a trend to the parameters of the probability distribution. Annual maxima or peaks-over-threshold series can be analyzed incorporating a trend component to the parameters. Design concepts such as return period and hydrological risk should be redefined in a changing world. Design life level is another concept that can be used in a nonstationary context. In management decisions of water structures, a risk-based approach should be used where errors that result in under-preparedness are considered as well as those resulting in over-preparedness. In a changing world, decision making in water resources management requires long-term projections of hydrological time series that include trend due to anthropogenic intervention and climate change.