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Trend analysis and forecasting of the Gökırmak River streamflow (Turkey) Cover

Trend analysis and forecasting of the Gökırmak River streamflow (Turkey)

Open Access
|Sep 2020

Figures & Tables

Figure 1

The Gökırmak River and the location of the streamflow gauging station
The Gökırmak River and the location of the streamflow gauging station

Figure 2

The autocorrelation functions (ACF) and partial autocorrelation functions (PACF) of the natural logarithm of annual and mean seasonal streamflow data. The lines represent the 95% confidence interval.
The autocorrelation functions (ACF) and partial autocorrelation functions (PACF) of the natural logarithm of annual and mean seasonal streamflow data. The lines represent the 95% confidence interval.

Figure 3

The autocorrelation functions (ACF) of the natural logarithm of mean monthly streamflow data. The lines represent the 95% confidence interval.
The autocorrelation functions (ACF) of the natural logarithm of mean monthly streamflow data. The lines represent the 95% confidence interval.

Figure 4

The partial autocorrelation functions (PACF) of the natural logarithm of the mean monthly streamflow data. The lines represent the 95% confidence interval.
The partial autocorrelation functions (PACF) of the natural logarithm of the mean monthly streamflow data. The lines represent the 95% confidence interval.

Figure 5

Trend analysis results for mean annual streamflow. In variable box; actual is the observed value; forecasts are the predicted values; fits are calculated values that best fitting to forecast. The accuracy of models was assessed by using commonly used performance measures which are mean absolute deviation (MAD), mean squared deviation (MSD), mean absolute percentage error (MAPE).
Trend analysis results for mean annual streamflow. In variable box; actual is the observed value; forecasts are the predicted values; fits are calculated values that best fitting to forecast. The accuracy of models was assessed by using commonly used performance measures which are mean absolute deviation (MAD), mean squared deviation (MSD), mean absolute percentage error (MAPE).

Figure 6

Trend analysis results for mean seasonal streamflow. In variable box; actual is the observed value; forecasts are the predicted values; fits are calculated values that best fitting to forecast. The accuracy of models was assessed by using commonly used performance measures which are mean absolute deviation (MAD), mean squared deviation (MSD), mean absolute percentage error (MAPE).
Trend analysis results for mean seasonal streamflow. In variable box; actual is the observed value; forecasts are the predicted values; fits are calculated values that best fitting to forecast. The accuracy of models was assessed by using commonly used performance measures which are mean absolute deviation (MAD), mean squared deviation (MSD), mean absolute percentage error (MAPE).

Figure 7

Trend analysis results for mean monthly streamflow. In variable box; actual is the observed value; forecasts are the predicted values; fits are calculated values that best fitting to forecast. The accuracy of models was assessed by using commonly used performance measures which are mean absolute deviation (MAD), mean squared deviation (MSD), mean absolute percentage error (MAPE).
Trend analysis results for mean monthly streamflow. In variable box; actual is the observed value; forecasts are the predicted values; fits are calculated values that best fitting to forecast. The accuracy of models was assessed by using commonly used performance measures which are mean absolute deviation (MAD), mean squared deviation (MSD), mean absolute percentage error (MAPE).

Descriptive statistics of the streamflow data

StreamflowMeanSDCVCSMaximum valueMinimum valueRange
Annual28.483.430.45202.4649.9010.4639.44
Spring64.898.160.47415.31111.7725.3786.40
Summer21.274.170.7378.3950.713.6447.06
Autumn8.700.890.3869.8413.901.3812.52
Winter19.072.530.50105.5834.735.0029.73

Results of skewness, kurtosis and normality tests

StreamflowSkewnessSEskewnessZskewnessKurtosisSEkurtosisZkurtosisKolmogorov–Smirnov*Shapiro–Wilk
Statisticsp-valueStatisticsp-value
Annual0.040.5970.067−1.351.154−0.0010.1750.200*0.9350.200*
Spring0.180.5970.302−1.531.154−0.0010.1390.200*0.9210.200*
Summer0.420.5970.704−1.011.154−0.0010.1820.200*0.9240.200*
Autumn−0.580.597−0.9720.551.1540.0000.1290.200*0.8530.042
Winter0.210.5970.352−1.121.154−0.0010.1370.200*0.9560.200*

Parameters of ARIMA models for annual streamflow data

ParametersModels
ARIMA (1, 1, 0)ARIMA (0, 1, 1)ARIMA (1, 1, 1)
ARMAARMA
  Coefficient−0.6370.890−0.1260.894
  SE0.2550.3140.377344
  p-value0.0290.0160.7460.027
  Normalized BIC15.61615.40915.863
  R2−0.565−0.272−0.479
  Ljung–Box Statistics25.3119.1118.12
  Ljung–Box p-value0.0050.0390.034

Values of non-parametric tests and trend status

PeriodStreamflowKendall’s taupTrendSpearman’s rhopTrend
AnnualAnnual−0.0550.784−0.0550.852
SeasonalSpring−0.2090.298−0.2970.303
Summer0.1430.4770.2400.409
Autumn−0.2970.169−0.4370.118
Winter−0.0990.622−0.1080.714
MonthlyJanuary−0.2090.298−0.2620.366
February−0.1650.412−0.2040.483
March−0.0550.784−0.0990.737
April−0.2090.298−0.2880.318
May−0.1430.477−0.1430.626
June0.2090.2980.2440.401
July0.0110.9560.1560.594
August−0.2310.250−0.2880.318
September−0.2090.298−0.3410.233
October−0.2310.250−0.3490.221
November−0.2090.298−0.2260.436
December0.0330.870−0.0110.970
DOI: https://doi.org/10.1515/ohs-2020-0021 | Journal eISSN: 1897-3191 | Journal ISSN: 1730-413X
Language: English
Page range: 230 - 246
Submitted on: Feb 25, 2020
Accepted on: Apr 20, 2020
Published on: Sep 25, 2020
Published by: University of Gdańsk
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2020 Gökhan Arslan, Semih Kale, Adem Yavuz Sönmez, published by University of Gdańsk
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.