Mehmet C. Demirel

Seasonality of low flows and dominant processes in the Rhine River

Low flow forecasting is crucial for sustainable cooling water supply and planning of river navigation in the Rhine River. The first step in reliable low flow forecasting is to understand the characteristics of low flow. In this study, several methods are applied to understand the low flow characteristics of Rhine River basin. In 108 catchments of the Rhine River, winter and summer low flow regions are determined with the seasonality ratio (SR) index. To understand whether different numbers of processes are acting in generating different low flow regimes in seven major sub-basins (namely, East Alpine, West Alpine, Middle Rhine, Neckar, Main, Mosel and Lower Rhine) aggregated from the 108 catchments, the dominant variable concept is adopted from chaos theory. The number of dominant processes within the seven major sub-basins is determined with the correlation dimension analysis. Results of the correlation dimension analysis show that the minimum and maximum required number of variables to represent the low flow dynamics of the seven major sub-basins, except the Middle Rhine and Mosel, is 4 and 9, respectively. For the Mosel and Middle Rhine, the required minimum number of variables is 2 and 6, and the maximum number of variables is 5 and 13, respectively. These results show that the low flow processes of the major sub-basins of the Rhine could be considered as non-stochastic or chaotic processes. To confirm this conclusion, the rescaled range analysis is applied to verify persistency (i.e. non-randomness) in the processes. The estimated rescaled range statistics (i.e. Hurst exponents) are all above 0.5, indicating that persistent long-term memory characteristics exist in the runoff processes. Finally, the mean values of SR indices are compared with the nonlinear analyses results to find significant relationships. The results show that the minimum and maximum numbers of required variables (i.e. processes) to model the dynamic characteristics for five out of the seven major sub-basins are the same, but the observed low flow regimes are different (winter low flow regime and summer low flow regime). These results support the conclusion that a few interrelated nonlinear variables could yield completely different behaviour (i.e. dominant low flow regime).

Validation of an ANN Flow Prediction Model Using a Multt-Station Cluster Analysis

The objective of this study is to validate a flow prediction model for a hydrometric station using a multistation criterion in addition to standard single-station performance criteria. In this contribution we used cluster analysis to identify the regional flow height, i.e., water- level patterns and validate the output of an artificial neural network (ANN) model of the Alportel River in Portugal. Measurements of pre- cipitation, temperature, and flow height were used as input variables to the ANN model with a lead time of 12 h. The lead time of 12 h is assumed to be appropriate for a short-term hydrological prediction since it is meaningful for physical processes. The ANN model with three inputs, four hidden neurons, and ten epochs was tested using the new model-validation criterion. The high performance of the model (i.e., Nash-Sutcliffe coefficient is equal to 0.922) was confirmed by the cluster-analysis criterion. It can be concluded that a multistation-based approach can be used as an additional validation criterion and might result in a rejection of a model which initially passed a single-station validation criterion. DOI: 10.1061/(ASCE)HE.1943-5584.0000426.