Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ1/r/gumushane/12112
  Stationarity or nonlinear nonstationarity? Assessing standard duration extreme rainfall under Türkiye’s complex topography and climate variability

  Şahin, Nagehan; Şan, Murat; Kankal, Murat

  The impact of climate change on extreme rainfall regimes requires a reassessment of the design processes of traditional hydrological structures. For this purpose, conducting an in-depth analysis of the nonstationary nature of extreme rainfall in regions with complex topography and climate is of great importance. This study examines the stationary and nonstationary states of standard duration (5 min–24 h) annual maximum rainfall data across Türkiye. For this purpose, the intensity-duration-frequency relationship was analyzed using data at 100 homogeneous stations during 1980–2020. The analyses were performed using the generalized extreme value (GEV) distribution with eight linear and non linear location and scale parameters, based on stationary and nonstationary models. Time was included as a covariate in nonstationary models. The corrected akaike information criterion and likelihood ratio test were used to select the appropriate GEV distribution. Findings indicate that stationarity is generally dominant, but nonstationarity is relatively higher in a nonlinear manner in cold climates and complex topography. The effect of location and scale parameters was found to be individual in the short duration and combined in the long duration. It was observed that nonstationarity was relatively higher in complex topography than in flatter regions up to 8 h. Still, the diversity of nonlinear models decreased over the long duration. In conclusion, this study emphasizes the need to consider nonstationary approaches in designing hydrological structures, especially in climate change.

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  AtıfOpenAlex1

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• MakaleAçık Erişim2023Scopus SJRQ1/r/gumushane/11558
  Daily precipitation performances of regression-based statistical downscaling models in a basin with mountain and semi-arid climates

  Şan, Murat; Nacar, Sinan; Kankal, Murat; Bayram, Adem

  The impacts of climate change on current and future water resources are important to study local scale. This study aims to investigate the prediction performances of daily precipitation using five regression-based statistical downscaling models (RBSDMs), for the first time, and the ERA-5 reanalysis dataset in the Susurluk Basin with mountain and semi-arid climates for 1979–2018. In addition, comparisons were also performed with an artificial neural network (ANN). Before achieving the aim, the effects of atmospheric variables, grid resolution, and long-distance grid on precipitation prediction were holistically investigated for the first time. Kling-Gupta efficiency was modified and used for holistic evaluation of statistical moments parameters at precipitation prediction comparison. The standard triangular diagram, quite new in the literature, was also modified and used for graphical evaluation. The results of the study revealed that near grids were more effective on precipitation than single or far grids, and 1.50° × 1.50° resolution showed similar performance to 0.25° × 0.25° resolution. When the polynomial multivariate adaptive regression splines model, which performed slightly higher than ANN, tended to capture skewness and standard deviation values of precipitations and to hit wet/dry occurrence than the other models, all models were quite well able to predict the mean value of precipitations. Therefore, RBSDMs can be used in different basins instead of black-box models. RBSDMs can also be established for mean precipitation values without dry/wet classification in the basin. A certain success was observed in the models; however, it was justified that bias correction was required to capture extreme values in the basin.

  AtıfScopus26·WoS27

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