Analysing the variations in hydrological cycle components is essential for water resources planning and management. In this study, the relationship between the streamflow data belonging to five ...discharge gauging stations in the Eastern Black Sea Basin in Türkiye and the Arctic Oscillation (AO), East Atlantic-Western Russia (EAWR), North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP) was investigated. For this purpose, Spearman’s correlation test, ensemble empirical mode decomposition (EEMD) and relative importance analysis were used. Accordingly, Spearman’s correlation coefficients were calculated between raw streamflow data, decomposed streamflow data via EEMD and atmospheric teleconnections. Then, the relative importance analysis was applied to determine the atmospheric teleconnections’ influences on streamflow data. The findings showed that the relationship between raw streamflow data and atmospheric teleconnections is generally more significant and negative in the winter and spring. Furthermore, it was observed that the linkage between the decomposed streamflow data and atmospheric teleconnections could differentiate. Although no significant correlation between atmospheric teleconnections and raw streamflow data was detected in some months, significant correlations were detected between atmospheric teleconnections and decomposed streamflow data. This reveals the importance of examining the relationship between atmospheric teleconnections and streamflow data for different periods. The relative importance analysis revealed that the influence of atmospheric teleconnections on streamflow data could change from station to station and from component to component. This study showed that investigating the effects of atmospheric teleconnections on streamflow data for different components and periods is important.
The investigation of variability in hydrometeorological variables is crucial for water resources management and planning. In this study, trends in monthly streamflow were investigated using a novel ...wavelet-based Innovative Polygon Trend Analysis (W-IPTA) approach in the Konya Closed Basin, Turkey, which has semi-arid and changing climate conditions. Initially, homogeneity tests were applied to the streamflow data of thirteen discharge gauging stations, and eight of them were found homogeneous. Then, IPTA was performed, and mostly decreasing trends in mean streamflow were detected, particularly in the southern part of the basin. Increasing trends were generally observed in the standard deviation of streamflow data and the transitional period between winter and spring months in mean values in the western part of the basin. The W-IPTA indicated that decreasing trends were in short-term periods in the southern part of the basin while increasing trends were in the western part of the basin. In addition, decreasing trends were determined in the long-term period in most stations in the basin. The relationship between atmospheric teleconnections (i.e., North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and North Sea Caspian Pattern (NCP)) and streamflow data was investigated using Spearman’s rank correlation and wavelet coherence analysis. A significant negative relationship between the NAO, AO, NCP, and streamflow was determined, especially in the winter months. Accordingly, it was found that the atmospheric teleconnections could be influential on the trends in the streamflow regime as well as anthropogenic and geographical factors in the basin.
Rain-on-snow (ROS) floods can cause economic damage and endanger human lives due to the compound effect of rainfall and snowmelt, especially under climate change. In this study, possible future ...changes of seasonality, magnitude and frequency characteristics of ROS floods were investigated for the selected catchments in Slovenia, Europe. For this purpose, five global/regional climate models (GCM/RCM) combinations were applied using the RCP4.5 climate scenario for the period 1981–2100. To determine ROS floods’ characteristics in the future, a lumped conceptual hydrological model Génie Rural à 6 paramètres Journalier (GR6J) with snow module CemaNeige was applied. The results indicate that the number of ROS floods could increase in the future. Moreover, also the magnitudes of extreme ROS floods could increase, while a slight decrease in the median values of ROS flood magnitudes was observed. The strength of seasonality for a high-altitude catchment could decrease in the future. A slight shift in the average ROS floods’ timing could be expected. Furthermore, a catchment located in a temperate continental climate could have a different response to the climate change impact in comparison to a catchment located in a mountain climate with alpine characteristics. Additionally, differences among investigated climate models show a large variability.
Forecasting evaporation, an important variable in the hydrological cycle, is crucial for managing water resources and taking precautions against severe phenomena, such as droughts and floods. In this ...study, the prediction of daily pan evaporation was carried out in the Euphrates sub-basin, Turkey, which has different climate characteristics and is a critical region for Turkey or neighbouring countries. In this regard, two empirical models, namely the Griffith model and calibrated Hargreaves-Samani, and four ensemble empirical mode decomposition (EEMD) based data-driven models, namely EEMD-Random Forests (EEMD-RF), EEMD-Artificial Neural Network (EEMD-ANN), EEMD-Gradient Boosting Machines (EEMD-GBM), and EEMD-Regression Tree (EEMD-RT) were used for evaporation forecasting. The EEMD and Recursive Feature Elimination (RFE) were implemented as a signal decomposition technique and determination of the importance of the EEMD components, respectively. Although the empirical models yielded satisfactory performance, they predicted low and high evaporation values poorly, in general. The EEMD-RF, EEMD-ANN, and EEMD-GBM models performed better than the EEMD-RT model. The data-driven models, except EEMD-RT, outperformed the empirical models, especially regarding predicting extreme evaporation values. The sensitivity analysis indicated that wind speed, humidity, and maximum temperature could influence evaporation forecasting. This study shows that using data-driven models benefitting from EEMD and RFE can be a good alternative to empirical models for predicting evaporation.
Rainfall–runoff modeling is significant for efficient water resources management and planning. The hydrological conceptual models can have challenges, such as dealing with nonlinearity and needing ...more data, whereas data-driven models are generally lacking in reflecting the physical process in the basin. Accordingly, two-hybrid model structures, namely Génie Rural à 6 paramètres Journalier (GR6J)-wavelet-based-genetic algorithm-artificial neural network
1
(GR6J-WGANN
1
) and GR6J-wavelet-based genetic algorithm-artificial neural network
2
(GR6J-WGANN
2
) models, were proposed in this study to develop rainfall–runoff modeling performance. The novel GR6J-WGANN
1
model used the routing store outflow (
QR
), exponential store outflow (
QRexp
), and direct flow (
QD
) obtained from the GR6J, and the GR6J-WGANN
2
model used the soil moisture index (
SMI
) obtained from the GR6J as input data. The wavelet transformation and Boruta algorithm were implemented to decompose the input data into components and select important wavelet components, respectively. The performance of the GR6J, standalone WGANN models, and hybrid models were tested in three sub-basins of Konya Closed Basin, Turkey, which generally has arid and changing climate conditions. The hybrid models performed better than the conceptual and data-driven models, particularly regarding the extreme flow predictions. Using soil moisture index, routing store outflow, exponential store outflow, and direct flow as the output of the GR6J in GR6J-WGANN
1
and GR6J-WGANN
2
improved the rainfall–runoff modeling performance remarkably. The findings of this study indicated that hybrid models, which integrate strong sides of conceptual and data-driven models, can be more useful for producing more accurate forecasting results.
Hydrological modelling can be complex in nonhomogeneous catchments with diverse geological, climatic, and topographic conditions. In this study, an integrated conceptual model including the snow ...module with machine learning modelling approaches was implemented for daily rainfall-runoff modelling in mostly karst Ljubljanica catchment, Slovenia, which has heterogeneous characteristics and is potentially exposed to extreme events that make the modelling process more challenging and crucial. In this regard, the conceptual model CemaNeige Génie Rural à 6 paramètres Journalier (CemaNeige GR6J) was combined with machine learning models, namely wavelet-based support vector regression (WSVR) and wavelet-based multivariate adaptive regression spline (WMARS) to enhance modelling performance. In this study, the performance of the models was comprehensively investigated, considering their ability to forecast daily extreme runoff. Although CemaNeige GR6J yielded a very good performance, it overestimated low flows. The WSVR and WMARS models yielded poorer performance than the conceptual and hybrid models. The hybrid model approach improved the performance of the machine learning models and the conceptual model by revealing the linkage between variables and runoff in the conceptual model, which provided more accurate results for extreme flows. Accordingly, the hybrid models improved the forecasting performance of the maximum flows up to 40 % and 61 %, and minimum flows up to 73 % and 72 % compared to the CemaNeige GR6J and stand-alone machine learning models. In this regard, the hybrid model approach can enhance the daily rainfall-runoff modelling performance in nonhomogeneous and karst catchments where the hydrological process can be more complicated.
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•Hybrid models integrating conceptual and machine learning models were implemented in the karst Ljubljanica River catchment.•The conceptual model CemaNeige GR6J yielded a very good performance, but it overestimated low flows.•The stand-alone machine learning models yielded poor performance compared to the conceptual and hybrid models.•The hybrid models outperformed the stand-alone models, particularly in simulating low and high flows.
Arctic oscillation (AO) and the North Sea Caspian pattern (NCP) are atmospheric teleconnections that affect climate parameters such as precipitation, temperature and stream flow. The purpose of this ...study is to identify the relationship of the AO and NCP with Turkey’s mean temperature and precipitation totals. First, Pearson correlation coefficients between the AO and NCP and climate data were calculated and the results were assessed using Student’s
t
test. Although the results vary from region to region, highly negative correlation coefficients were observed between either the NCP or AO and the temperature, especially in the winter. Furthermore, the NCP and AO have a remarkably strong relationship with winter precipitation. Second, the NCP index (NCPI) and AO index were divided into negative and positive phases; then, the impacts of both phases on the climatic data were determined as annual and seasonal. Accordingly, the annual and seasonal mean temperature values under the effect of NCP (−) and AO (−) are higher than the annual and seasonal temperature values under the effect of NCP (+) and AO (+). In this context, the temperature differences are significant, especially in winter. The precipitation amount, under the effect of positive and negative phases of global indices, was also investigated in this study. Thus, substantial results were obtained, particularly for winter precipitation in Turkey’s western regions. Finally, scatter diagrams were also prepared to examine the relationship between negative and positive phases of the AO or NCP and the temperature or precipitation.
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