![]() Water resources engineers are in need of hydrological information that accounts for nonstationarity in a changing world. Existing climate models are not reliable and detailed enough to project changes in runoff. Such changes cannot be estimated with a sufficient accuracy from short hydrological records. It is very difficult, however, to predict how the climate change will affect the water cycle in a certain region. Means and extremes of precipitation, evapotranspiration and streamflow are now changing. In reality, however, anthropological intervention in river basins, and more recently climate change in the world-wide scale have resulted in change and variability of hydrological variables to increase so that they are not sufficiently small to assume stationarity. These variables in annual scale have time-invariant probability density functions (pdfs), whose properties can be estimated from an available record, which are then used to design and operate water resources projects. Stationarity means that hydrological variables fluctuate randomly within an unchanging envelope of variability. 2008) it is announced that “Stationarity is dead.” In a widely cited paper by 7 authors from different institutions published in 2008 in the journal Science (Milly et al. The assumption of stationarity which has been made so far in water resources planning and management studies is now being challenged. Human effects in river basins such as land-cover and land-use changes, urbanization, changes in impervious surfaces and drainage network, deforestation and mining also play an important role. But this is not the only reason for hydrometeorological change. The main reason for this, obviously, is the change of our climate (global warming) due to increase of greenhouse gas concentrations in the atmosphere. ![]() In recent years, there is an increasing trend in the number of papers concerning nonstationarity and trends in hydrological time series, published in hydrological periodicals. 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. 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. Design life level is another concept that can be used in a nonstationary context. Design concepts such as return period and hydrological risk should be redefined in a changing world. Annual maxima or peaks-over-threshold series can be analyzed incorporating a trend component to the parameters. Frequency analysis of nonstationary processes can be made by fitting a trend to the parameters of the probability distribution. ![]() 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. The power of a test depends on the chosen level of significance, sample size and the accuracy of prediction of trends. Some authors criticized the use of significance levels in statistical tests and recommended using confidence intervals around the estimated effect size. Long-term persistence in hydrological processes also affects the results of the test. 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. The estimation of extremes (floods and low flows) is more important but also much more difficult. 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. A stationary model is sometimes preferable to a nonstationary one when the evolution in time of hydrological processes cannot be predicted reliably. It must be remembered, however, that all hydrological systems include a stationary element, at least in the form of a random component. Detailed climate models and long hydrological records are needed to predict the future conditions in a changing world. ![]() 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. Recent climate change due to global warming has given an impetus to trend analysis of hydrological time series.
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