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1

Performance of bias corrected monthly CMIP6 climate projections with diferent reference period data in Turkey

Oruc Sertac

Acta Geophysica

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2022

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Vol. 70, no 2

777--789

EN

Decisions that are based on the future climate data, and its consequences are significantly important for many sectors such as water, agriculture, built environment, however, the performance of model outputs have direct influence on the accuracy of these decisions. This study has focused on the performance of three bias correction methods, Delta, Quantile Mapping (QM) and Empirical Quantile Mapping (EQM) with two reference data sets (ERA and station-based observations) of precipitation for 5 single CMIP6 GCM models (ACCESS-CM2, CNRM-CM6-1-HR, GFDL-ESM4, MIROC6, MRI-ESM2-0) and ensemble mean approach over Turkey. Performance of model-bias correction method-reference data set combinations was assessed on monthly basis for every single station and regionally. It was shown that performance of GCM models mostly affected by the region and the reference data set. Bias correction methods were not detected as effective as the reference data set over the performance. Moreover, Delta method outperformed among the other bias correction techniques for the computation that used observation as reference data while the difference between bias correction methods was not significant for the ERA based computations. Besides ensemble approach, MIROC6 and MRI-ESM2-0 models were selected as the best performing models over the region. In addition, selection of the reference data sets also found to be a dominant factor for the prediction accuracy, 65% of the consistent performance at the stations achieved by the ERA reference used bias correction approaches.

2

Visual and statistical inference of hourly and sub hourly extreme rainfall trends Central Anatolia, Turkey case

Oruc Sertac

Acta Geophysica

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2021

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Vol. 69, no. 1

199--216

EN

The analysis of long-term rainfall data in a changing climate is important because it has many sectoral applications such as agriculture, infrastructure, and water resources management. Statistical analyses of the annual maximum rainfall data were performed using the Mann–Kendall (MK) trend test to evaluate the annual maximum trend characteristics of the rainfall time series, the innovative trend analysis (ITA) method to detect categorial trends, and the ITA indicator to digitize the ITA results. Storm durations of 5, 10, 15, 30 min and 1, 3, 6, 24 h annual maximum rainfall series at 13 central stations in Central Anatolia, Turkey were used. According to the MK test results, there were no signifcant upward or downward monotonic trend at four stations, whereas the remaining nine stations showed a signifcant upward or downward monotonic trend. Signifcant negative and positive trends were identifed for the sub-hourly and hourly rainfall, whereas signifcant positive trends were detected for hourly storm durations. Signifcant trend results were mostly consistent with the general ITA results. The sub hourly storm duration data were more consistent in terms of signifcant trends. Conversely, when evaluated according to low, medium, and high data values in the rainfall series (categories), the high data values showed diferent trends. Although no trend was detected with the MK test, the ITA results showed an upward or downward trend for 25 rainfall series. 29 of 30 signifcant MK test results were consistent with the ITA indicator results, compared with 24 of 30 results of the visually inspected ITA results.

3

Trend and nonstationary relation of extreme rainfall: Central Anatolia, Turkey

Oruc Sertac

Acta Geophysica

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2021

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Vol. 69, no. 1

243--255

EN

The frequency of extreme rainfall occurrence is expected to increase in the future and neglecting these changes will result in the underestimation of extreme events. Nonstationary extreme value modelling is one of the ways to incorporate changing conditions into analyses. Although the defnition of nonstationary is still debated, the existence of nonstationarity is determined by the presence of signifcant monotonic upward or downward trends and/or shifts in the mean or variance. On the other hand, trend tests may not be a sign of nonstationarity and a lack of signifcant trend cannot be accepted as time series being stationary. Thus, this study investigated the relation between trend and nonstationarity for 5, 10, 15, and 30 min and 1, 3, 6, and 24 h annual maximum rainfall series at 13 stations in Central Anatolia, Turkey. Trend tests such as Mann– Kendall (MK), Cox–Stuart (CS), and Pettitt’s (P) tests were applied and nonstationary generalized extreme value models were generated. MK test and CS test results showed that 33% and 27% of 104 time series indicate a signifcant trend (with p<0.01–p<0.05–p<0.1 signifcance level), respectively. Moreover, 43% of time series have outperformed nonstationary (NST) models that used time as covariate. Among fve diferent time-variant nonstationary models, the model with a location parameter as a linear function of time and the model with a location and scale parameter as a linear function of time performed better. Considering the rainfall series with a signifcant trend, increasing trend power may increase how well fitted nonstationary models are. However, it is not necessary to have a signifcant trend to obtain outperforming nonstationary models. This study supported that it is not necessarily time series to have a trend to perform better nonstationary models and acceptance of nonstationarity solely depending on the presence of trend may be misleading.

4

Quantile trends of subhourly extreme rainfall: Marmara Region, Turkey

Oruc Sertac

Acta Geophysica

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2021

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Vol. 69, no. 6

2453--2473

EN

Global climate change will probably cause intensification of the hydrologic cycle, which can lead to alterations in extreme precipitation properties. In this study, we investigated the trend of 5-, 10-, 15-, and 30-min annual maximum rainfall series at 12 stations in the Marmara Region, Turkey, using quantile regression. The data ranges were from 46 to 71 years long. Five quantiles were used to examine the extreme rainfall series, and their quantile regression parameters were calculated. The results show that quantile regression is a powerful tool to compute trends with a more inferential context, which was validated with the notable differences between the trends at chosen quantiles and the classical ordinary least squares method. Concerning the problem of the analysis of climate trends, the quantile regression method seems to provide a perspective from a more detailed understanding of processes in the climate system in terms of characteristics of climate variability and extremity.

5

Extreme precipitation indices trend assessment over Thrace region, Turkey

Oruc Sertac, Yalcin Emrah

Acta Geophysica

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2021

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Vol. 69, no. 1

307--321

EN

The frequency and the severity of extreme weather events are increasing globally and will continue to do so in the coming decades as a consequence of our changing climate. Understanding the characteristics of these events is crucial due to their signifcant negative impacts on social, physical and economic environments. In this study, 14 extreme rainfall indices are determined and examined in terms of trends and statistical characteristics for the four meteorological stations located in the Thrace region of Turkey, namely Edirne, Tekirdag, Kirklareli and Sariyer (Istanbul). The results indicate that annual total precipitation has an increasing trend for the Kirklareli and Sariyer stations (z=1.730 and z=2.127) and a decreasing trend for the Edirne and Tekirdag stations (z=− 0.368 and z=− 0.401). However, the precipitation intensity indices (SDII) of all stations show increasing trends that are statistically signifcant for the Edirne and Kirklareli stations. The Kirklareli station tends to have more days with heavy, very heavy and extremely heavy rainfall events (z=2.241, z=2.076 and z=1.684, respectively). It is also anticipated that maximum amount of rainfalls in daily and consecutive fve- and ten-day time scales will probably increase at all stations. Moreover, rainfall from very wet days and extremely wet days and fraction of total wet day rainfall that comes from very wet days and extremely wet days indices also show increasing trend tendencies for all stations. The remarkable point is the decreasing total precipitation trend at the Edirne and Tekirdag stations, contrary to the Kirklareli and Sariyer stations, which indicates that the annual total precipitation does not necessarily depend on extreme precipitation for the analyzed period.