Identification of an appropriate low flow forecast model\ud for the Meuse River

This study investigates the selection of an appropriate low flow forecast model for the Meuse\ud River based on the comparison of output uncertainties of different models. For this purpose, three data\ud driven models have been developed for the Meuse River: a multivariate ARMAX model, a linear regression\ud model and an Artificial Neural Network (ANN) model. The uncertainty in these three models is assumed to\ud be represented by the difference between observed and simulated discharge. The results show that the ANN\ud low flow forecast model with one or two input variables(s) performed slightly better than the other statistical\ud models when forecasting low flows for a lead time of seven days. The approach for the selection of an\ud appropriate low flow forecast model adopted in this study can be used for other lead times and river basins\ud as well

Identification of appropriate temporal scales of dominant low flow indicators in the Main River, Germany

Models incorporating the appropriate temporal scales of dominant indicators for low flows are assumed to perform better than models with arbitrary selected temporal scales. In this paper, we investigate appropriate temporal scales of dominant low flow indicators: precipitation (P), evapotranspiration (ET) and the standardized groundwater storage index (G). This analysis is done in the context of low flow forecasting with a lead time of 14 days in the Main River, a tributary of the Rhine River, located in Germany. Correlation coefficients (i.e. Pearson, Kendall and Spearman) are used to reveal the appropriate temporal scales of dominant low flow indicators at different time lags between low flows and indicators and different support scales of indicators. The results are presented for lag values and support scales, which result in correlation coefficients between low flows and dominant indicators falling into the maximum 10% percentile range. P has a maximum Spearman correlation coefficient (ρ) of 0.38 (p = 0.95) at a support scale of 336 days and a lag of zero days. ET has a maximum ρ of –0.60 (p = 0.95) at a support scale of 280 days and a lag of 56 days and G has a maximum ρ of 0.69 (p = 0.95) at a support scale of 7 days and a lag of 3 days. The identified appropriate support scales and lags can be used for low flow forecasting with a lead time of 14 days

Discussion of “clustering on dissimilarity Representations for detecting mislabelled Seismic signals at Nevado del Ruiz Volcano” by Mauricio Orozco-Alzate, and César Germán Castellanos-Domínguez

The authors are to be congratulated for a systematic investigationof the accurate and non subjective classifying approach in seismic research. The authors have conducted several clustering algorithms to the seismic event records from Volcanological and SeismologicalObservatory at Manizales. Their objective was to improve the grouping of seismic data (i.e., volcano-tectonic earthquakes, long-period earthquakes and icequakes) digitized at 100.16 Hz sampling frequency.Their study seems adding new approach to their previous work of Langer et al. (2006) who applied different classification techniques to seismic data

Hydrologic homogeneous regions using monthly Streamflow in Turkey

Cluster analysis of gauged streamflow records into homogeneous and robust regions is an important tool for the characterization of hydrologic systems. In this paper we applied the hierarchical cluster analysis to the task of objectively classifying streamflow data into regions encompassing similar streamflow patterns over Turkey. The performance of three standardization techniques was also tested, and standardizing by range was found better than standardizing with zero mean and unit variance. Clustering was carried out using Ward’s minimum variance method which became prominent in managing water resources with squared Euclidean dissimilarity measures on 80 streamflow stations. The stations have natural flow regimes where no intensive river regulation had occurred. A general conclusion drawn is that the zones having similar streamflow pattern were not be overlapped well with the conventional climate zones of Turkey; however, they are coherent with the climate zones of Turkey recently redefined by the cluster analysis to total precipitation data as well as homogenous streamflow zones of Turkey determined by the rotated principal component analysis. The regional streamflow information in this study can significantly improve the accuracy of flow predictions in ungauged watersheds

Constraints and perceived freedom levels ın the leisure of university students

The purpose of this research is to examine the leisure constraints and level of perceived freedom of university students according to different variables. 250 university students in total, 174 (69%) of whom are males and 78 (31%) of whom are females selected by random sampling have voluntarily attended the research study. A survey model has been used in the study. The sample of the study consists of students studying at Istanbul University's Faculty of Sports Sciences. In order to identify the leisure obstacles of the participants, the Obstacles of Leisure Scale, which was developed by Alexandris and Carroll (1997) and adopted into Turkish by Karaküçük and Gürbüz (2007); and in order to identify the level of perceived freedom of the participants, the Perceived Freedom in Leisure Scale, which was developed by Witt and Ellis (1985) and adopted into Turkish by Yerlisu Lapa and Ağyar (2011), were used along with a personal information form prepared by the researcher as a data collection tool in the research. Percent (%) and frequency methods have been utilized to identify the distribution of the personal information of the participants and the Shapiro-Wilks normality test has been applied to identify whether data had normal distribution. Mann-Whitney U and Kruskall Wallis tests have been applied to determine the significant differences after it was determined that the data were suitable to non-parametric test conditions. According to the gender variable, no significant difference (except Individual Psychology) has been identified in the sub-dimensions of the obstacles of the leisure scale and perceived freedom in leisure scale (p>0.05). While there is a significant difference in all sub-dimensions of the perceived freedom in leisure scale (p<0.05), no significant difference has been found in the sub-dimensions of the obstacles of the leisure time scale (p>0.05) according to age. In conclusion, it has been determined that while there are no differences between the leisure obstacles according to the gender of the participants, the level of perceived freedom increases as the age increases

University students’ opinions of the meaning of leisure and their perceived freedom in leisure

The aim of this study is to examine the perceptions of university students towards leisure and perceived freedom in leisure on the basis of different variables. To this end, a total of 250 university students in total, 174 (69%) of whom are males and 78 (31%) of whom are females selected by random sampling have voluntarily attended the research study. The sample of the study consists of students studying at Istanbul University's Faculty of Sports Sciences. The data collection instruments of the study include the personal information form developed by the researcher, as well as the Leisure Meanings Inventory which was developed by Esteve et al. (1999) and adapted into Turkish by Gürbüz et al. (2007) and aims to determine the leisure perceptions of the participants. The Perceived Freedom in Leisure Scale, which was developed by Witt and Ellis (1985) and adapted into Turkish by Yerlisu, Lapa and Ağyar (2011) was used to determine the participants’ perceived freedom levels in leisure. Additionally, frequency methods have been utilized to identify the distribution of the personal information of the participants and the Shapiro-Wilks normality test has been applied to identify whether data had normal distribution. Mann-Whitney U and Kruskall Wallis tests have been applied to determine the significant differences after it was determined that the data were suitable to non-parametric test conditions. According to the gender variable, no significant difference has been identified in the sub-dimension of the perceived freedom in leisure scale (p>0.05). In the active-passive participation and goal orientation sub-dimensions in the leisure meanings inventory, the female participants were found to score more than male participants. Based on the age variable, there were no significant differences found in any sub dimension of the perceived freedom scale (p<0.05) or in the leisure meanings inventory (p>0.05). In conclusion, the perceived leisure levels of the female participants were higher than the male while it was also seen that as age increases, the perceived freedom levels in leisure increases as well

Sensitivity of Columbia Basin Runoff to Long-Term Changes in Multi-Model CMIP5 Precipitation Simulations

In this study, we used precipitation elasticity index of streamflow, to reflect on the sensitivity of streamflow to changes in future precipitation. We estimated precipitation elasticity of streamflow from: (1) simulated streamflow by the VIC model using observed precipitation for the current climate (1963–2003); (2) simulated streamflow by the VIC model using simulated precipitation from 10 GCM - CMIP5 dataset for the future climate (2010–2099) including two different pathways (RCP4.5 and RCP8.5) and two different downscaled products (BCSD and MACA). The hydrological model was calibrated at 1/16 latitude-longitude resolution and the simulated streamflow was routed to the subbasin outlets of interest i.e. Hungry Horse subbasin. We used hydrological model simulations from 19063-2003 and calculated streamflow sensitivities and precipitation elasticity for the same period using observed climate (case 1) and simulated climate (case 2). The runoff sensitivity to long-term (e.g., 30-year) average annual changes in precipitation is calculated based on the elasticity of streamflow for three different 30 year blocks (2010-40, 2040-70 and 2070-99), which are of importance to reservoir management in the Columbia River basin. These two cases and different periods are compared to assess the effects of forcing by different climate models and different pathways on the precipitation elasticity of streamflow

TURKISH ADAPTATION OF STUDY-LEISURE CONFLICT SCALE, ITS VALIDITY AND RELIABILITY

This study aimed to adapt to Turkish the measurement of work-leisure conflict developed by Tsaur et al. (2012) to measure work-leisure conflict and to present the causes and dimensions of the conflict and to develop a new study-leisure conflict scale for university students based on the items of this scale and to undertake reliability and validity studies for the new measure. A total of 306 students took part in the study. First of all, “Measurement of Work-Leisure”, the foundation for this study, was translated into Turkish in order to ensure linguistic equivalence. Validity was investigated by using explanatory and confirmatory factor analyses. Reliability was calculated by utilizing Cronbach Alpha internal consistency coefficient, split halves method and Guttman reliability coefficient. Statistical results obtained from the study show that the adapted Turkish version of the scale was a valid and reliable measurement instrument.  Article visualizations

Low flow forecasting with a lead time of 14 days for navigation and energy supply in the Rhine River

Low flow forecasting, days or even months in advance, is particularly important to the efficient operation of power plants and freight shipment. This study presents a low flow forecasting model with a lead time of 14 days for the Rhine River. The forecasts inherit uncertainty sources mainly because of model parameterization. Therefore, a systematic uncertainty analysis is applied to indicate the major uncertainty sources in the results. Firstly, the Rhine basin is divided into 7 major sub-basins. Each sub-basin is modeled separately with a data-driven model and the output discharges are routed to Lobith after German-Dutch border with another data-driven model. Five pre-selected low flow indicators (basin averaged precipitation, basin averaged potential evapotranspiration; basin averaged fresh snow depths, basin averaged groundwater levels and major lake levels in the sub-basins) are used as inputs to the models. The basin discretization and the selection of indicators are based on a literature study and seasonality analysis of the discharge time series from 108 sub-basins. The correlations between indicator and low flows with varying temporal resolution and varying lags between indicator and low flows were used to identify appropriate temporal scales of the model inputs.We assume that a suitable model structure for the Rhine basin has been determined; that is, the sub-system boundaries have been specified, the important state variables and input and output fluxes to be included have been identified and selected for each sub-basin. The results in this study show that the data-driven models used for each sub-basin are capable of representing the essential characteristics of the system based on lagged and temporally averaged low flow indicators and are forecasting low flows adequately. In addition to the forecast results, the uncertainties due to specific model parameters in the calibrated data-driven model corresponding to key processes are also given

Uncertainty analysis of a low flow model for the Rhine River

It is widely recognized that hydrological models are subject to parameter uncertainty. However, little attention has been paid so far to the uncertainty in parameters of the data-driven models like weights in neural networks. This study aims at applying a structured uncertainty analysis to a data-driven model for low flow forecasting with a lead time of 14 days in the Rhine River. In the modeling phase, the Rhine basin is divided into seven major sub-basins. Each sub-basin is modeled separately with a data-driven model and the output discharges were routed to Lobith with another data-driven model. Basin averaged precipitation, basin averaged potential evapotranspiration, basin averaged fresh snow depths, basin averaged groundwater levels and major lake levels in the sub-basins are selected as low flow indicators and used as inputs to the models. The basin discretization and the selection of low flow indicators were not arbitrary since the dominant processes were considered by applying seasonality analysis to discharge time series from 108 sub-basins. Moreover, the correlations between indicators and low flows with varying temporal resolution and varying lags were used to identify appropriate temporal scales of the model inputs.\ud The structure of the model can inherit uncertainty too due to many factors, including the lack of a robust hydrological theory at the spatial scale of the seven sub-basins. However, the parameter uncertainty is assumed to be the largest uncertainty source compared to other uncertainty sources. The effects of the input uncertainty were not assessed since averaging over sub-basins significantly reduces the measurement uncertainties. The model parameter sets were estimated using inverse modeling. The uncertainty of each weight is expressed as a probability distribution. Sensitivity analysis was applied for reducing the dimension and size of parameter space before uncertainty analysis. Finally, Monte Carlo Simulation was used to estimate the posterior distributions of the model outputs. The results in this study provide the effects of uncertainties in low flow model parameters on the model outputs. It has also been shown that the explicit assessment of uncertainties in the data-driven model parameters can lead to significant improvements in the information supply for low flow forecasting