There was a typographical error in the abstract of Smiatek et al. (2013). There were extraneous characters that appeared in the seventh sentence of the abstract. The sentence should read, "An expected precipitation decrease of about -11% in winter and -8% in spring, together with increased temperatures of up to -I-1.6°C and a significant decrease in snow mass, can substantially limit the water recharge potential already in the near future until 2050."The staff of the Journal of Hydrometeorology regret any inconvenience this error may have caused. REFERENCE Smiatek, G., S. Kaspar, and H. Kunstmann, 2013: Hydrological climate change impact analysis for the Figeh Spring near Damascus, Syria.

This study investigates the projected precipitation changes of the 21st century in the Mediterranean area with a model ensemble of all available CMIP3 and CMIP5 data based on four different scenarios. The large spread of simulated precipitation change signals underlines the need of an evaluation of the individual general circulation models in order to give higher weights to better and lower weights to worse performing models. The models' spread comprises part of the internal climate variability, but is also due to the differing skills of the circulation models. The uncertainty resulting from the latter is the aim of our weighting approach. Each weight is based on the skill to simulate key predictor variables in context of large and medium scale atmospheric circulation patterns within a statistical downscaling framework for the Mediterranean precipitation. Therefore, geopotential heights, sea level pressure, atmospheric layer thickness, horizontal wind components and humidity data at several atmospheric levels are considered. The novelty of this metric consists in avoiding the use of the precipitation data by itself for the weighting process, as state‐of‐the‐art models still have major deficits in simulating precipitation. The application of the weights on the downscaled precipitation changes leads to more reliable and precise change signals in some Mediterranean sub‐regions and seasons. The model weights differ between sub‐regions and seasons, however, a clear sequence from better to worse models for the representation of precipitation in the Mediterranean area becomes apparent.

A set of downscaled climate change data from transient experiments with regional climate models has been used to access the future climate change signal in the area of the Figeh spring system in Syria and its potential effects on future water availability. The data ensemble at a spatial resolution of 0.258 has been investigated for the period 1961-90 for present-day climate and the periods 2021-50 and 2070-99 for future climate. The focus is on changes to annual, seasonal, and monthly surface air temperature and precipitation. For the first time, the Figeh spring discharge has been assessed with a hydrological runoff model based on an artificial neural network (ANN) approach. The ANN model was formulated and validated for the years 1987-2007, applying daily meteorological driving data. The investigations show that water supply from the spring might face serious problems under changed climate conditions. An expected, a precipitation decrease of about 211% in winter and 28% in spring, together with increased temperatures of up to 11.68C and a significant decrease in snow mass, can substantially limit the water recharge potential already in the near future until 2050. In the period 2070-99, the annual precipitation amount is simulated to decrease by 222% and the annual mean temperature to increase by 148C, relative to the 1961-90 mean. The ensemble mean of the relative change in mean discharge reveals a decrease during the peak flow from March to May, with values up to 220% in 2021-50 and almost 250% in the period 2069-98, both related to the 1961-90 mean.

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Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.