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Droughts as destructive climatic extreme events affect natural environment of an area. Identifying droughts has a vital importance in the planning and management of water resource systems of a river basin. This study investigates the trends of maximum hydrologic drought variables, annual maximum duration (AMD), and annual maximum severity (AMS), by using Mann-Kendall, modified Mann-Kendall, and recently developed Şen's innovative trend analysis (ITA) methods. These methods are applied to the AMD and AMS of nine different stations located in Coruh River Basin, Turkey, in which numerous highly critical power plants have recently been constructed, under construction, or planned. Basic Mann-Kendall test indicated no trend in the investigated stations while the modified Mann-Kendall gave significantly decreasing trend for AMS series of station 2304 and for AMD series of station 2321 at 10% significance level. Modified Mann-Kendall and ITA approaches indicate that the AMS of station 2304 show significantly decreasing trend. According to the ITA method, the peak AMS values of the stations 2315 and 2322 have also significantly decreasing trend after 1986. Both modified MK and ITA methods provide increasing trends in drought severities of the stations 2316 and 2323. It should be noted that some droughts or water stress may be occurred in these stations in the future. Modified Mann-Kendall test provides no significant trend for the seven stations while ITA results indicated some positive or negative trends at these stations. The primary points of interest of Şen's ITA method are that it is not subject to any assumption such as serial relationship, non-normality, sample size, and trends of low, medium, and high data can be seen by this method easily. The study indicates that the ITA method could be simply and successfully used by hydrologist or water resources decision makers or in identifying droughts to prepare efficient management plans. [ABSTRACT FROM AUTHOR]

Prediction of a stage-discharge relationship is of immense importance for reliable planning, design and management of most water resources projects. The aim of this study is to compare the performance of artificial intelligence methods, namely, Artificial Neural Network, Adaptive Nero- Fuzzy Inference System ( ANFIS), and Gene- Expression Programming, with those of two conventional methods, i.e., stage-rating curve and Regression techniques in deriving a stage-discharge curve in the Kizilirmak River, Turkey. The daily minimum, mean and maximum river discharge, and stage data for period of 2005-2007 were used for training and testing the models. The comparison includes visual and parametric approaches, namely coefficient of correlation, mean absolute error and root mean square error. The results proved the high ability of the artificial intelligence methods in developing stage-discharge relationship. Furthermore, the performance of the ANFIS model was found to be superior to all the models. [ABSTRACT FROM AUTHOR]

Summary: Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, an adaptive neuro-fuzzy approach is proposed to estimate suspended sediment concentration on rivers. The daily rainfall, streamflow and suspended sediment concentration data from Mad River Catchment near Arcata, USA are used as a case study. In the first part of the study, various combinations of current daily rainfall, streamflow and past daily streamflow, suspended sediment data are used as inputs to the neuro-fuzzy computing technique so as to estimate current suspended sediment. In the second part of the study, the potential of neuro-fuzzy technique is compared with those of the three different artificial neural networks (ANN) techniques, namely, the generalized regression neural networks (GRNN), radial basis neural networks (RBNN) and multi-layer perceptron (MLP) and two different sediment rating curves (SRC). The comparison results reveal that the neuro-fuzzy models perform better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. [Copyright &y& Elsevier]

The growing shortage of freshwater resources and increasing environmental awareness give rise to the use of treated wastewater as an alternative resource for water supply. Accurate estimation of wastewater evaporation (WWE), as the main cause of water losses, is necessary for proper water resources management. Unfortunately, few studies have focused on modelling WWE despite its vital importance. This study investigates the ability of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN) techniques to estimate WWE as a function of variables including wastewater properties, clear water evaporation and climatic factors. The study uses measured data from an experiment conducted in Neishaboor municipal wastewater treatment plant, Iran. Results indicate that the ANN model is superior among the three methods, and also demonstrates higher accuracy when compared with those of a dimensional analysis model using the F-test statistic. [ABSTRACT FROM AUTHOR]