Hakan Tongal

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).

Phase-space reconstruction and self-exciting threshold models in lake level forecasting: a case study of the three largest lakes of Sweden

Lake water level forecasting is very important for an accurate and reliable management of local and regional water resources. In the present study two nonlinear approaches, namely phase-space reconstruction and self-exciting threshold autoregressive model (SETAR) were compared for lake water level forecasting. The modeling approaches were applied to high-quality lake water level time series of the three largest lakes in Sweden; Vänern, Vättern, and Mälaren. Phase-space reconstruction was applied by the k-nearest neighbor (k-NN) model. The k-NN model parameters were determined using autocorrelation, mutual information functions, and correlation integral. Jointly, these methods indicated chaotic behavior for all lake water levels. The correlation dimension found for the three lakes was 3.37, 3.97, and 4.44 for Vänern, Vättern, and Mälaren, respectively. As a comparison, the best SETAR models were selected using the Akaike Information Criterion. The best SETAR models in this respect were (10,4), (5,8), and (7,9) for Vänern, Vättern, and Mälaren, respectively. Both model approaches were evaluated with various performance criteria. Results showed that both modeling approaches are efficient in predicting lake water levels but the phase-space reconstruction (k-NN) is superior to the SETAR model.

Quantification of parametric uncertainty of ANN models with GLUE method for different streamflow dynamics

This study describes the parametric uncertainty of artificial neural networks (ANNs) by employing the generalized likelihood uncertainty estimation (GLUE) method. The ANNs are used to forecast daily streamflow for three sub-basins of the Rhine Basin (East Alpine, Main, and Mosel) having different hydrological and climatological characteristics. We have obtained prior parameter distributions from 5000 ANNs in the training period to capture the parametric uncertainty and subsequently 125,000 correlated parameter sets were generated. These parameter sets were used to quantify the uncertainty in the forecasted streamflow in the testing period using three uncertainty measures: percentage of coverage, average relative length, and average asymmetry degree. The results indicated that the highest uncertainty was obtained for the Mosel sub-basin and the lowest for the East Alpine sub-basin mainly due to hydro-climatic differences between these basins. The prediction results and uncertainty estimates of the proposed methodology were compared to the direct ensemble and bootstrap methods. The GLUE method successfully captured the observed discharges with the generated prediction intervals, especially the peak flows. It was also illustrated that uncertainty bands are sensitive to the selection of the threshold value for the Nash–Sutcliffe efficiency measure used in the GLUE method by employing the Wilcoxon–Mann–Whitney test.

Analysis of dam-induced cyclic patterns on river flow dynamics

ABSTRACT This paper investigates the impact of the Hungry Horse Dam on streamflow dynamics in the South Fork of the Flathead River, Montana, USA. To this end, pre- and post-dam periods of raw and naturalized streamflow data were analysed. Pettitt’s change point analysis indicated a significant change point in streamflow dynamics due to dam construction. Complexities in the pre- and post-dam periods were evaluated by sample and multi-scale entropy analyses, and the entropies of the post-dam period were found to be higher than those of the pre-dam period. Possible reasons for this, unrelated to the natural hydrological cycle caused by the dam, were analysed using wavelet analyses. The wavelet analyses showed a clear change in the phase relationship between precipitation and streamflow. Finally, weak positive trends found in the hydrological variables indicated the effects of human activities (e.g. dam construction). The results also revealed distorted lead times, which can improve the streamflow forecasts for different lead times.

Entropy analysis for spatiotemporal variability of seasonal, low, and high streamflows

Climate variability and change lead to changes in spatiotemporal variability in streamflow, which complicate management of water resources. This problem is particularly critical for small island regions, such as the small island state of Tasmania in Australia. In Tasmania, water resources play an important role for hydro-electricity generation and agriculture, whose water demands are highly seasonal in nature. However, identification of possible changes in seasonal streamflow variability can be difficult due to the inherent uncertainties resulting from the seasonal variability of climate. Entropy theory can provide a suitable framework to analyze the spatiotemporal variability in streamflows. In this study, we propose to use Shannon entropy with Chao–Shen estimator to assess the space–time variability of seasonal as well as low and high streamflows (i.e., 25th and 75th percentiles of streamflows) in Tasmania. In conjunction with isoentropy maps that depict spatial variability of seasonal, low, and high flows, trend detection analyses are performed to evaluate the significance of temporal variability. The results indicate that there is a distinct pattern between summer–autumn and winter–spring streamflow entropies, with the entropies of streamflows observed in winter–spring found to be higher than those observed in summer–autumn. The results also suggest that the spatial variability of uncertainty in streamflow is closely associated with the spatial pattern of rainfall in Tasmania. Finally, statistically insignificant trends in entropies of seasonal, low, and high streamflows possibly imply consistency in cyclic patterns and underlying probability distributions of these streamflows.

Akarsu Akımlarında Volatilitenin Non-Lineer Varyans Modelleri ile İncelenmesi: Köprüçay Nehri Örneği

Akarsu akim serilerinin, varyansin sabit kabul edildigi klasik zaman serisi modelleriyle (Otoregresif Hareketli Ortalama - ARMA) modellenmesinde surecin ortalama davranisina odaklanilmakta ve varyans degiskenligine dayali non-lineer etkiler goz ardi edilmektedir. Duragan olmayan varyans degisikligine dayali bu etkilerin (volatilite) Otoregresif Kosullu Degisen Varyans (ARCH) tipi modeller ve bu modellerin genisletilmis hali olan Genellestirilmis Otoregresif Kosullu Degisen Varyans (GARCH) modelleri ile incelenmesinde ve tahmini su kaynaklari yonetimindeki risk ve belirsizlik iceren hidrolojik sureclerde onem kazanmaktadir. Bu calismada, ilk once Koprucay Nehri’ ne ait gunluk ve yillik akim serilerinin ortalama davranislari lineer zaman serisi modelleri (AR, MA, ARMA) ile temsil edilerek en uygun modeller secilmis, daha sonra bu modellerden elde edilen kalintilar uzerinde volatilitenin varligi Engle Lagrange Multiplier (LM) testi ile arastirilarak kosullu degisen varyans modelleri (ARCH-GARCH) kurulmustur. Gunluk akim verilerinde en iyi modelin ARMA(1,1)-GARCH(2,3) oldugu, yillik akim serisinde ise volatilitenin olmadigi gorulmustur. Gunluk akim serilerindeki volatilite kumelenmesi, kosullu degisen standart sapma ve varyans grafikleriyle ortaya konulmustur. Bu calisma, zamanla degisen varyans kaynakli non-lineer etkilerin akim degiskenligine etkisini ortaya koymakta olup akim sureclerinin istatistiksel modellenmesine bir katki saglayacaktir.