Browsing by Author "Şan, Murat"

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Daily precipitation performances of regression-based statistical downscaling models in a basin with mountain and semi-arid climates
(Springer, 2023-04-01) Şan, Murat; Nacar, Sinan; Bayram, Adem; Kankal, Murat; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü; 0000-0003-0897-4742; AAC-6221-2021
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 degrees x 1.50 degrees resolution showed similar performance to 0.25 degrees x 0.25 degrees 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.
Innovative and polygonal trend analyses applications for rainfall data in Vietnam
(Springer, 2021-02-16) Şan, Murat; Linh, Nguyen Thi Thuy; Pham, Quoc Bao; Kankal, Murat; Akçay, Fatma; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020; CBG-3616-2022; 24471611900; 57222226657
It is a known fact that the size, frequency, and spatial variability of hydrometeorological variables will irregularly increase under the impact of climate change. Among the hydrometeorological variables, rainfall is one of the most important. Trend analysis is one of the most effective methods of observing the effects of climate change on rainfall. Recently, new graphical methods have been proposed as an alternative to classical trend analysis methods. Innovative Polygon Trend Analysis (IPTA), which evolved from Innovative Trend Analysis (ITA), is currently one of the proposed methods and it does not contain any assumptions. The aim of this study is to compare IPTA, ITA with the Significance Test and Mann-Kendall (MK) methods. To achieve this, the monthly total rainfall trends of 15 stations in the Vu Gia-Thu Bon River Basin (VGTBRB) of Vietnam have been examined for the period 1979-2016. The analyses show that rainfall tends to increase (decrease) in March (June) at nearly all stations. IPTA and ITA with the Significance Test are more sensitive than MK in determining the trends. While trends were detected in approximately 90% of all months in IPTA and ITA with the Significance Test, this rate was only 23% in the MK test. Although the arithmetic mean graphs in the 1-year hydrometeorological cycle are considerably regular at almost all stations, their standard deviations are relatively irregular. The most critical month for trend transitions between consecutive months for all the stations is October, which has an average trend slope of -1.35 and a trend slope ranging from -3.98 to -0.21, which shows a decreasing trend.
Innovative polygon trend analyses with star graph for rainfall and temperature data in agricultural regions of Turkey
(Springer, 2022-12-01) Şan, Murat; Acar, Emine; Kankal, Murat; KANKAL, MURAT; Akçay, Fatma; ; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; JTU-9268-2023; AAZ-6851-2020
Agriculture is affected by climate change, such as extreme increases or decreases rainfall and temperature patterns. It is possible to research that effect by using trend analysis methodologies. This paper investigates trends of monthly total rainfall and mean temperature data of nine selected stations from agricultural regions of Turkey between 1969 and 2020. To the end, the classical Mann-Kendall, Innovative Trend Significance Test (ITST), and Innovative Polygon Trend Analysis (IPTA) with Star Graph methods providing the opportunity to examine seasonal behavior were used for trend analysis. The analysis reveals that about 96% of all monthly rainfall in the Mann-Kendall test has no trend. However, nearly all stations tend to decrease (increase) in November (September) in both innovative approaches. For temperature, it is seen that increasing trend or no trend dominated in general. There were increasing trends in the innovative approaches throughout the year except for April. Temperatures have increased significantly throughout the year in all regions over the last decades. With the help of IPTA, it was also concluded that the seasonal internal variability of rainfall over the entire time and in the last 30 years is quite complex and persists in all agricultural regions. The results show that irregular changes in rainfall and rising temperatures in all stations negatively affected crop yield and/or required more irrigation. In addition, according to the results obtained by comparing trend methods, innovative approaches are very insistent on determining of trend and provide additional information through a visual review of trend behaviors.
Reply to discussion of "innovative approaches to the trend assessment of streamflows in the eastern Black Sea basin, Turkey"
(Taylor & Francis Ltd, 2023-03-31) Akçay, Fatma; Kankal, Murat; Şan, Murat; Akçay, Fatma; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 000-0001-8129-3009; 0000-0003-0897-4742; CBG-3616-2022; AAZ-6851-2020
In this reply we thank the authors for their comments on our article "Innovative approaches to trend assessment of streamflows in the Eastern Black Sea basin, Turkey." They stated that the trend slope calculations in our study are incorrect. A response to the discussion on this topic offered.