Okan Fistikoglu

Reliable hydrological modelling tools that can operate with the type and quality of data commonly available at the basin scale are essential for effective water resources planning. In recent years, the HIDROTURK model has been developed to support national hydrological assessments in Türkiye, especially in modelling tasks undertaken as part of river basin management planning processes. This study presents one of the first comprehensive evaluations of the newly updated HIDROTURK Phase II model and compares its performance with the AQUATOOL + EVALHID hydrological modelling system. The North Marmara River Basin was selected as the test region due to its complex hydrological structure and diverse sub-basin characteristics.Hydrological simulations were carried out using long-term meteorological inputs derived from precipitation and evapotranspiration records for the period 1989–2014, enabling the examination of the models under a wide range of climatic conditions. Streamflow outputs were compared with observations at 14 calibration points, and model performance was assessed using the Nash–Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) indicators.The results indicate that the meteorological inputs generated through HIDROTURK’s internal processing tools show a high level of agreement with data prepared using more traditional methods, and that both models produced comparable flow patterns under similar conditions. Overall, the findings demonstrate that HIDROTURK Phase II exhibits stable behavior even at this early stage of development and provides a practical and reliable alternative for hydrological simulations.Keywords: Hydrological Modelling; Basin-Scale Simulation; Model Comparison; Model Performance Evaluation; Streamflow CalibrationACKNOWLEDGEMENT: The authors would like to express their gratitude to the projects “Technical Assistance on Preparation of River Basin Management Plans for Six Basins (EuropeAid/140294/IH/SER/TR)” and “Development and Sustainability of the HIDROTURK Model Project” for their support. The authors also thank the Directorate General for Water Management, the State Hydraulic Works, and the General Directorate of Meteorology for providing essential data.

While weighted multi-model approaches are widely used to improve predictive capability, hydrological models (HMs) and their weighted combinations that perform well under past conditions may not guarantee robustness under future climate scenarios. Furthermore, the extent to which weighting schemes influence the propagation of runoff projection uncertainty remains insufficiently explored. Therefore, this study evaluates the capacity of strategies that weight monthly scale HMs to narrow runoff projection uncertainty. Since standard approaches rely only on historical simulation skill and offer static weighting, this study introduces a refined framework, the Uncertainty Optimizing Multi-Model Ensemble (UO-MME), which dynamically considers the trade-offs between calibration performance and projection uncertainty. In performing the uncertainty decomposition, a total of 140 ensemble runoff projections, generated through a modelling chain comprising five GCMs, two emission scenarios, two downscaling methods, and seven HMs, were analyzed for Beydag and Tahtali watersheds in Türkiye. Results indicate that standard techniques, such as Bayesian model averaging, ordered weighted averaging, and Granger–Ramanathan averaging, led to either marginal reductions or noticeable increases in projection uncertainty, depending on the case and projection period. Conversely, the UO-MME achieved average reductions in projection uncertainty of around 30% across the two watersheds by balancing the influences of climate signals produced by GCMs that are reflected in the projections through HMs while maintaining high simulation accuracy, as indicated by Nash–Sutcliffe efficiency values exceeding 0.75. Although not designed to eliminate inherently irreducible uncertainty, the UO-MME framework helps temper the inflation of noisy GCM signals in runoff responses, providing more balanced hydrological projections for water resources planning.

Forested lands in the west coast of Turkey, with their similarity to Mediterranean forests, are often found to be highly susceptible to wildfires, necessitating the development of a forest management program to refine and quantify forest fires and their impacts on the environment. In light of this fact, a multi-temporal approach combining Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI) analysis derived from Sentinel-2 imagery is suggested in the current study. Through PCA of carefully selected bands of Sentinel-2, both recent and historic fire impacts are attempted to be captured. It was found that the first two principal components (PC1 and PC2) predominantly describe landscape characteristics, while the third and fourth components (PC3 and PC4) have high abilities in detecting burn scars. It is worth noting that an increase in the ability to detect burn scars was observed with the inclusion of NDVI and its difference in time (dNDVI) within the PCA process. A high effectiveness level in distinguishing burnt areas from unburnt landscapes was presented by the multi-temporal PCA approach, particularly with dNDVI integration. PC2 and PC3, especially with dNDVI integration, are found to be strong indicative factors of burnt areas. In the classification result, accuracies of different years of fire events differed, and a high accuracy of 98.76% was found in the last fire event year of 2019. However, slight underestimation and overestimation were also observed in older fire scars. Mean accuracy, on average, for the PCA-dNDVI method was found to be higher than that of the MLC method. Furthermore, significant vegetation losses by fire, particularly by the 2019 fire incident, were realized through NDVI assessment. Although it worked well in recent fire scars, overestimating the extent in the case of burned areas from previous years was observed. The potential of multi-temporal PCA integration with NDVI for analysis in mapping burned areas at different scales in fire-prone ecosystems in western Turkey is underlined by the results of this work. Much more successful forest management and assessment strategies after fires have occurred in these ecosystems are helped to be created by this approach. Moreover, the approach is suggested to be one of the strong tools for monitoring fire induced damages across many time scales toward better understanding and management of long-term impacts caused by forest fires in the region.

Among various monthly water balance models, one of the models that has the simplest structure and offers a well-behaved conceptual platform is the GR2M. Despite the widespread use of the model with two-free parameters, the fact that it tends to produce relatively large errors in peak flow months necessitates some modifications to the model. The reason for the mentioned simulation deficiencies could be that the relationship between the routing reservoir and the external environment of the basin is controlled by a single parameter, making the storage–discharge relationship linear. Therefore, in this study, least squares support vector regression, one of the nonlinear data-driven models, has replaced the routing part of the GR2M to enhance the monthly runoff simulation. The performance of the three-parameter hybrid model (GR3M), which was developed by considering the parameter parsimony point of view and including a machine learning (ML)-based nonlinear routing scheme, was examined in some locations in the Gediz River Basin in western Turkey. Statistical performance measures have shown that GR3M, which both leverages the capabilities of an ML model and blends conceptual outputs within a nested scheme, clearly outperforms the original GR2M. The proposed modification has brought significant improvements, especially to high-flow simulations.

İklim değişikliğinin akarsu akımları üzerindeki etkilerinin hidrolojik modeller yardımıyla belirlenmesinde küresel atmosferik veri setlerinden sıklıkla faydalanılmaktadır. Sunulan çalışmada, Ege bölgesinde yer alan Gördes Havzası akımlarının hem küresel veri setleri hem de lokal istasyon verileri ile akışlarının modellenmesi ve bu model sonuçları dikkate alınarak performansın değerlendirmesi amaçlanmıştır. Küresel veri seti olarak ECMWF (European Centre for Medium-Range Weather Forecasts) tarafından sunulan 1959-2022 yılları arasında veri sağlayan ERA5 Land veri seti ve hidrolojik model olarak da abcd aylık yağış akış modeli kullanılmıştır. Model performansını değerlendirmek için Nash-Shutcliffe performans fonksiyonu seçilmiş olup optimum model parametrelerin tahmini için Parçacık Sürü Optimizasyonu Algoritması (PSO) kullanılmıştır. Çalışmada elde edilen bulgular ERA5 Land veri setinin Gördes Havzası’nın aylık akımlarının modellenmesinde havzanın hidrolojik özelliklerini başarılı bir şekilde yansıttığı ve bu veri setini kullanmanın modelleme çalışmalarında kolaylaştırıcı olacağı gösterilmiştir.

The emergence of the Gravity Recovery And Climate Experiment (GRACE) paved the way for remote tracking of hydrological water cycle components at large scales. With the main motivation of evaluating the feasibility of the coarse resolution GRACE data for small-scale analysis, the GRACE data and large-scale hydrological models were utilized in an integrated manner to monitor the variations of the flood potential index (FPI) over the Western Anatolian Basin (WAB). The results show an ascending trend for monthly and annual FPI over the WAB. The results also suggest that the monthly FPI in 2015, 2003, 2009, and 2016 was the highest, from which the highest potentiality of flood appertains to 2015/07 with an FPI of 0.92. The lowest and highest annual FPI is 0.26 (in 2007) and 0.76 (in 2015), respectively. The validation of the results indicates that variations of FPI coincide with that of the flood incidents, stream discharge, Standardized Precipitation Index (SPI), and the simulated flood risk. The findings accentuate the high feasibility of the GRACE JPL Mascons for better surveillance of floods over local scale areas.

The study proposes a new approach that combined statistical downscaling, bias correction, and disaggregation of rainfall techniques in order to derive the IDF curve from large scale atmospheric reanalysis data. The applied methodology details the NCEP/NCAR reanalysis dataset being downscaled by an ANN-based approach to estimate the daily rainfall of Izmir. The annual maximum rainfall series of the study area were sampled from the daily downscaled rainfall series. The sampled daily maximum rainfalls were then bias-corrected by the quantile mapping method and disaggregated into the annual maximum standard-duration rainfall heights regarding the rainfalls' scale-invariant properties. Finally, the IDF curves of the study area were determined by using the disaggregated rainfall heights. The results confirmed that the IDF curves dependent on short-duration extreme rainfall heights could be reasonably estimated from the large-scale atmospheric variables using the statistical downscaling approach.