Chain Link Time Chainage Software Applications' title='Chain Link Time Chainage Software Applications' />PETROLEUM AND NATURAL GAS REGULATORY BOARD. NOTIFICATION. New Delhi, the 12th February 2016. G. S. R. Infra T4S PPPPL01 2014. In exercise of the powers conferred. This paper presents a method for appropriate coupling of deterministic and statistical models. In the decisionsupport system for the Elbe river, a conceptual. A Core Network comprises of Through Routes and Link Routes. Through routes are the ones which collect traffic from several link roads or a long chain. Chainage 0. The ultimate resource for project management terminology. Get easytounderstand explanations of all key concepts to make sure you use the right PM terms. PETROLEUM AND NATURAL GAS REGULATORY BOARD. NOTIFICATION. New Delhi, the 11th November, 2009. G. S. R. 808E. In exercise of the powers conferred by section 61 of the. Deterministic statistical Model Coupling in a DSS for River Basin Management. Palette Cad Keygen. Consistency of the models is essential to ensure transparency of the DSS, enhance computational efficiency, and support the model selection process. Here, we define consistency as the condition that the output accuracy of the deterministic, rainfall runoff model matches the required input accuracy for the statistical vegetation model. This accuracy cannot be determined by means of sensitivity and uncertainty analyses because the deterministic outcomes of the rainfall runoff model are translated into statistical parameters before use in the vegetation model. This means that discharge time series of different accuracy can result in similar vegetation patterns if the mean and variance of the discharge time series remain the same, although too large deviations will lead to undesirable differences in the predicted pattern. To solve this problem, a number of artificial discharge time series are generated to reflect the hypothetical outcomes of the HBV calibrations of different quality levels. The quality of a rainfall runoff model is usually expressed in terms of the NashSutcliffe coefficient NS and the relative volume error RVE see section 3. Both criteria measure the extent to which the model describes the data correctly. In this case, the NS value is chosen to assess the rainfall runoff model accuracy, because it is a well known criterion and it measures structural errors as in RVE as well as errors in the distribution of the discharge during the year. To estimate the required accuracy of the HBV model prior to the calibration of the model, the hypothetical output Qg of HBV is generated from the observed discharge series Qo and the statistical properties of the error time series QoQm. This is done by adding an auto correlated noise term to the observed discharge Q0. Qtextg left t right Qtexto left t right varepsilon left t right,6where. Qtexto left t right alpha varepsilon left t 1 right,7with t a time varying scaling factor randomly drawn from a uniform distribution on the interval, and the auto correlation coefficient of the error time series QoQm. The error between the observed and generated discharge series and thus the NS value can be varied by adapting the parameter in the range 0,1. Using observed and modeled discharge values for the period 1. Elbe river and was found to be 0. This value corresponds well to the value of 0. Meuse river 2. 7. The Meuse river is, like the Elbe river, a rain fed river with a similar discharge pattern e. This results in similar differences between the observed and modeled discharge time series and thus in similar error time series. To avoid confusion, it is necessary to distinguish between the required input accuracy P1 of the vegetation model and the output accuracy P2 of the rainfall runoff model. The value of P1 is expressed in terms of the minimal difference in the number of flood days that leads to a change in the vegetation type, and depends on the sensitivity of the vegetation model for changes in this input variable. Due to the rules underlying the model, MOVER2. P1 can be 1. 0 days per year or more. The value of P1 can be increased if a different, less sensitive vegetation model is used. The value of P2 reflects the accuracy of a set of discharge years and is expressed in terms of the percentage of correct years. An artificially generated discharge year is considered to represent the data correctly if the difference between the observed and modeled number of flood days for that year does not exceed P1. Equations 6 and 7 are used to generate auto correlated discharge time series Qg of different quality. The percentage of correctly predicted years P2, which depends on the value of P1, is stored for each time series. The appropriate NS value that corresponds to a particular combination of the values of P1 and P2 can then be obtained from the discharge time series that approaches the chosen value of P2 best. The sensitivity of the NS values for simultaneous changes in the values of P1 and P2 was determined for the location at Wittenberg Fig. Fig.  3. Sensitivity of the NS value at Wittenberg Elbe km 2. P1 of the vegetation model and the percentage of correctly predicted years P2 by the rainfall runoff model. Figure 3 indicates that, for instance, a vegetation model requiring an accuracy of 2. NS value of 0. 8. HBV based on 9. 0 correctly predicted years. In principle, this value should be determined for each subbasin or river location for which observed discharge time series are available. Depending on the local hydraulic and geographical conditions, this may lead to different requirements for the calibration of the sub basins.