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Comparison of sub series with diferent lengths using şen innovative trend analysis

Alashan Sadık

Acta Geophysica

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2023

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Vol. 71, no. 1

373--383

EN

Climate change causes trends in hydro-meteorological series. Traditional trend analysis methods such as Mann-Kendall and Spearman Rho are sensitive to dependent series and cannot detect non-monotonic trends. Şen-innovative trend analysis method is launched into literature in order to overcome these restrictions. It does not require any restrictive assumptions as serial independence and normal distribution and examines a given time series as equally divided into two sub-series. The Şen multiple innovative trend analysis methodology is improved to detect partial trends on different sub-series, again with equal lengths. Climate change strongly affects hydro-meteorological parameters today compared to the last twenty or thirty years and gives asymmetrical trend change points in hydro-meteorological time series. Due to asymmetric trend change points, it may be necessary to analyze sub-series with different lengths to use all measured data. In this study, the Şen innovative trend analysis method is revised to satisfy these requirements (ITA_DL). The new approach compared with the traditional Mann-Kendall (MK) and Şen innovative trend analysis (Şen_ITA) gives successful and consistent results. The ITA_DL gives four monotonic trends on May, July, September, and October rainfall series of Oxford although the MK gives three mono tonic trends in the May, July, and December and cannot detect trends on the September and October. In the ITA_DL visual inspection, the December rainfall series does not show an overall or partial trend. The ITA_DL trend results are consistent with the Şen_ITA except for the September rainfall series, although it has different trend slope amounts.

2

Assessing the changing pattern of hydro-climatic variables in the Aghanashini River watershed, India

Kumar Hanumapura Kumaraswamy Yashas, Varija Kumble

Acta Geophysica

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2023

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Vol. 71, no. 6

2971--2988

EN

Growing population and climate change have altered the hydro-climatic trend from past decades. This manuscript analyses the abrupt shift in these time series and their changing pattern using historical data sets. The Pettitt test and the Standard Normal Homogeneity Test were used to evaluate the time series' homogeneity. The Concentration Index, Precipitation Concentration Index and Seasonality Index were employed to analyse the spatial variability of daily, monthly and seasonal rainfall patterns over the Aghanashini River watershed. Furthermore, the temporal trend in the rainfall, streamflow, and temperature time series was investigated using Mann–Kendall (MK) and the graphical Innovative-Şen (IŞ) test. Clear evidence of climate change impact on the rainfall and streamflow pattern was recognized, as there is an upward shift in the maximum temperature time series and a downward shift in the rainfall and streamflow time series after 2001. The rainfall indices showed that the watershed has fewer percentage of rainy days and stronger rainfall seasonality, indicating a possible risk of flash floods in the downstream of the watershed. Additionally, the results of the MK and IŞ trend tests paralleled each other and provided support for the findings emphasized by rainfall indices.

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.

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Changes in the Large-Scale Atmospheric Circulation over Romania Between 1961 and 2010 on Seasonal Basis

Barbu N., Burada C., Stefan S., Georgescu F

Acta Geophysica

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2016

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Vol. 64, no. 2

510--520

EN

The aim of this paper is to investigate the trends and shifts of the circulation types over Romania for 50-year period (1961-2010) on seasonal basis. In order to achieve this, two objective catalogues, namely GWT and WLK, from COST733 Action were employed. Daily circulation types were grouped according to the cyclonicity and anticyclonicity and were used to calculate the seasonal occurrence frequency of cyclonic and anticyclonic types. The trend of seasonal time series was investigated by using Mann–Kendall test and the shifts points were determined by using Pettitt test. The results reveal that the occurrence frequency of anticyclonic types increases in summer and winter seasons and the occurrence frequency of cyclonic ones decreases for the summer season (for alpha = 0.05).