# Combined Use of Graphical and Statistical Approaches for Analyzing Historical Precipitation Changes in the Black Sea Region of Turkey

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area and Data

#### 2.2. Methods

#### 2.2.1. Refined Graphical–statistical CSD Trend Test and Diagnosis

_{1}) with the mean (variance) of zero (one) using

_{1}becomes zero, meaning that e

_{i}= 0 for 1 ≤ i ≤ n. A step-wise summation can be applied to d and e to obtain c and q, respectively, using

_{CSD}can be given by

_{CSD}is approximately normal with the mean of zero and the variance given by

_{CSD}with the mean of zero and variance equal to one can be computed using

_{α/2}denote the standard normal variate at a selected α. The null hypothesis H

_{0}(no trend) is rejected if |Z| > Z

_{α/2}at α; otherwise, the H

_{0}is not rejected.

_{k}versus k or time of observations. Positive and negative trends are shown by the right tail of the scatter plot above and below the reference (q

_{k}= 0 line), respectively. At a selected α, (100–α)% Confidences Interval (CI) limits are constructed and included in the plot of q

_{k}versus k. The (100–α)% CI limits Q based on the V* and Z

_{α/2}given by

_{0}(no trend) is rejected, otherwise, the H

_{0}is not rejected [27].

#### 2.2.2. Mann–Kendall (MK) Test

_{j}and x

_{i}are the sequential data values, n is the length of the data set and $\mathrm{sgn}(\theta )$ is equal to 1, 0, −1 if θ is greater than, equal to, or less than zero, respectively. The variance of S is obtained through:

_{i}is the number of data points in the ith group. Finally, the Mann–Kendall statistic, Z

_{MK}, is given by:

_{MK}values are approximately normally distributed, the null hypothesis, H

_{0}, of no trend is rejected if $\left|{Z}_{MK}\right|>\left|{Z}_{\alpha /2}\right|$, where Z

_{MK}is taken from the standard normal distribution table and α is the significance level. The positive and negative values of Z

_{MK}denote increasing and decreasing trends, respectively.

#### 2.2.3. ITA and Change Boxes

#### 2.2.4. Trend Magnitude

## 3. Results and Discussion

_{0}(no trend) was not rejected (p > 0.05) in the Winter, Spring and Autumn rainfall at all the selected stations. However, the H

_{0}(no trend) was rejected (p < 0.05) in the Summer rainfall from Amasya station. The annual rainfall was mainly characterized by positive trends for which H

_{0}(no trend) was rejected (p < 0.05) in the rainfall from 4 stations including Sinop, Giresun, Trabzon and Artvin. Considering the trends in the first and second halve of the time series, the highest number of significant trends is observed in the Autumn rainfall of the second half sub-series with significant trends at 56% of the selected stations.

_{0}(no trend) was rejected in terms of rainfall at Sinop, Giresun, Trabzon, and Artvin (Figure 4c,f–g,i).

_{MK}denote that the H

_{0}(no trend) was rejected at the 5% significance level. As observed from the table, there are significant increasing trends for the annual rainfall of Sinop, Ordu, Giresun, Trabzon, Artvin and Tokat, while seasons do not have significant trend when full rainfall time series is considered. Only summer season of Samsun has an increasing trend with respect to 5% significance level. Considering the first and second half of the time series, it is observed that most of the annual trends disappeared; there is only one station, Ordu, with increasing trend in the first half while two stations, Trabzon and Amasya have significantly increasing trends in the second half of the annual rainfall series. In this case, however, seasonal trends increased; Winter rainfall of Sinop and Samsun, Spring rainfall of Bartin and Rize and Autumn rainfall of Samsun have decreasing trends in the first halve while in the second sub series, Winter rainfall of Zonguldak and Artvin, Spring rainfall of Trabzon, and Autumn rainfall of Bartin, Zonguldak, Artvin, Kastamonu, Corum and Gumushane have increasing/decreasing trends. Summer rainfall does not show significant trend in subseries (first and second halves).

## 4. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Akinsanola, A.A.; Ogunjobi, K.O. Recent homogeneity analysis and long-term spatio-temporal rainfall trends in Nigeria. Theor. Appl. Clim.
**2015**, 128, 275–289. [Google Scholar] [CrossRef] - Pendergrass, A.G.; Knutti, R.; Lehner, F.; Deser, C.; Sanderson, B. Precipitation variability increases in a warmer climate. Sci. Rep.
**2017**, 7, 17966. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Mathbout, S.; Lopez-Bustins, J.A.; Martin-Vide, J.; Bech, J.; Rodrigo, F.S. Spatial and temporal analysis of drought variability at several time scales in Syria during 1961–2012. Atmos. Res.
**2018**, 200, 153–168. [Google Scholar] [CrossRef] - Tabari, H.; Willems, P. More prolonged droughts by the end of the century in the Middle East. Environ. Res. Lett.
**2018**, 13, 104005. [Google Scholar] [CrossRef] [Green Version] - Vicente-Serrano, S.M. Differences in Spatial Patterns of Drought on Different Time Scales: An Analysis of the Iberian Peninsula. Water Resour. Manag.
**2006**, 20, 37–60. [Google Scholar] [CrossRef] - Doblas-Miranda, E.; Martínez-Vilalta, J.; Lloret, F.; Álvarez, A.; Ávila, A.; Bonet, F.J.; Brotons, L.; Castro, J.; Yuste, J.C.; Díaz, M.; et al. Reassessing global change research priorities in Mediterranean terrestrialecosystems: How far have we come and where do we go from here? Glob. Ecol. Biogeogr.
**2015**, 24, 25–43. [Google Scholar] [CrossRef] [Green Version] - Tabari, H.; Willems, P. Lagged influence of Atlantic and Pacific climate patterns on European extreme precipitation. Sci. Rep.
**2018**, 8, 5748. [Google Scholar] [CrossRef] - Giorgi, F. Climate change hot-spots. Geophys. Res. Lett.
**2006**, 33, L08707. [Google Scholar] [CrossRef] - Tabari, H.; Hosseinzadehtalaei, P.; AghaKouchak, A.; Willems, P. Latitudinal heterogeneity and hotspots of uncertainty in projected extreme precipitation. Environ. Res. Lett.
**2019**, 14, 124032. [Google Scholar] [CrossRef] [Green Version] - Ávila, Á.; Guerrero, F.C.; Escobar, Y.C.; Justino, F. Recent Precipitation Trends and Floods in the Colombian Andes. Water
**2019**, 11, 379. [Google Scholar] [CrossRef] [Green Version] - McKitrick, R.; Christy, J. Assessing changes in US regional precipitation on multiple time scales. J. Hydrol.
**2019**, 578, 124074. [Google Scholar] [CrossRef] - Nashwan, M.; Shahid, S.; Xiaojun, W. Uncertainty in Estimated Trends Using Gridded Rainfall Data: A Case Study of Bangladesh. Water
**2019**, 11, 349. [Google Scholar] [CrossRef] [Green Version] - Pandey, B.K.; Khare, D. Identification of trend in long term precipitation and reference evapotranspiration over Narmada river basin (India). Glob. Planet. Chang.
**2018**, 161, 172–182. [Google Scholar] [CrossRef] - Tabari, H. Statistical Analysis and Stochastic Modelling of Hydrological Extremes. Water
**2019**, 11, 1861. [Google Scholar] [CrossRef] [Green Version] - Onyutha, C. Identification of sub-trends from hydro-meteorological series. Stoch. Environ. Res. Risk Assess.
**2015**, 30, 189–205. [Google Scholar] [CrossRef] - Onyutha, C. Statistical analyses of potential evapotranspiration changes over the period 1930–2012 in the Nile River riparian countries. Agric. Forest. Meteorol.
**2016**, 226–227, 80–95. [Google Scholar] [CrossRef] - Tabari, H.; Hosseinzadehtalaei, P. Temporal variability of precipitation over Iran: 1966–2005. J. Hydrol.
**2011**, 396, 313–320. [Google Scholar] [CrossRef] - Westra, S.; Alexander, L.; Zwiers, F.W. Global Increasing Trends in Annual Maximum Daily Precipitation. J. Clim.
**2013**, 26, 3904–3918. [Google Scholar] [CrossRef] [Green Version] - Karandish, F.; Mousavi, S.S.; Tabari, H. Climate change impact on precipitation and cardinal temperatures in different climatic zones in Iran: Analyzing the probable effects on cereal water-use efficiency. Stoch. Environ. Res. Risk Assess.
**2016**, 31, 2121–2146. [Google Scholar] [CrossRef] - Shiru, M.S.; Shahid, S.; Chung, E.S.; Alias, N. Changing characteristics of meteorological droughts in Nigeria during 1901–2010. Atmos. Res.
**2019**, 223, 60–67. [Google Scholar] [CrossRef] - Dudley, R.; Hirsch, R.; Archfield, S.; Blum, A.; Renard, B. Low streamflow trends at human-impacted and reference basins in the United States. J. Hydrol.
**2020**, 580, 124254. [Google Scholar] [CrossRef] - Kundzewicz, Z.W.; Robson, A. Detecting Trend and Other Changes in Hydrological Data; World Climate Program—Water, WMO/UNESCO, WCDMP-45, WMO/TD-No.1013; WMO: Geneva, The Netherlands, 2000; p. 157. [Google Scholar]
- Onyutha, C. Statistical Uncertainty in Hydrometeorological Trend Analyses. Adv. Meteorol.
**2016**, 2016, 1–26. [Google Scholar] [CrossRef] [Green Version] - Wu, H.; Qian, H. Innovative trend analysis of annual and seasonal rainfall and extreme values in Shaanxi, China, since the 1950s. Int. J. Clim.
**2016**, 37, 2582–2592. [Google Scholar] [CrossRef] - Wang, X.; Xiao, J.; Li, X.; Cheng, G.; Ma, M.; Zhu, G.; Arain, M.A.; Black, T.A.; Jassal, R.S. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun.
**2019**, 10, 2389. [Google Scholar] [CrossRef] [PubMed] - Sen, Z. An innovative trend analysis methodology. J. Hydrol. Eng.
**2012**, 17, 1042–1046. [Google Scholar] [CrossRef] - Onyutha, C. An improved method to quantify trend slope and its significance. Front. Earth Sci. 2020; under review. [Google Scholar]
- Şen, Z. Trend identification simulation and application. J. Hydrol. Eng.
**2014**, 19, 635–642. [Google Scholar] [CrossRef] - Şen, Z. Innovative trend significance test and applications. Theor. Appl. Clim.
**2015**, 127, 939–947. [Google Scholar] [CrossRef] - Güçlü, Y.S. Multiple Şen-innovative trend analyses and partial Mann-Kendall test. J. Hydrol.
**2018**, 566, 685–704. [Google Scholar] [CrossRef] - Alashan, S. An improved version of innovative trend analyses. Arab. J. Geosci.
**2018**, 11, 50. [Google Scholar] [CrossRef] - Güçlü, Y.S.; Dabanli, I.; Sisman, E.; Sen, Z. Air quality (AQ) identification by innovative trend diagram and AQ index combinations in Istanbul megacity. Atmospheric Pollut. Res.
**2019**, 10, 88–96. [Google Scholar] [CrossRef] - Tabari, H.; Taye, M.T.; Onyutha, C.; Willems, P. Decadal Analysis of River Flow Extremes Using Quantile-Based Approaches. Water Resour. Manag.
**2017**, 2, 527–3387. [Google Scholar] [CrossRef] - Öztopal, A.; Şen, Z. Innovative Trend Methodology Applications to Precipitation Records in Turkey. Water Resour. Manag.
**2016**, 31, 727–737. [Google Scholar] [CrossRef] - Şen, Z. Innovative Trend Methodologies in Science and Engineering. In Innovative Trend Methodologies in Science and Engineering; Springer: Cham, Switzerland, 2017. [Google Scholar]
- Mohorji, A.M.; Şen, Z.; Almazroui, M. Trend Analyses Revision and Global Monthly Temperature Innovative Multi-Duration Analysis. Earth Syst. Environ.
**2017**, 1, 9. [Google Scholar] [CrossRef] [Green Version] - Amrhein, V.; Greenland, S.; McShane, B. Scientists rise up against statistical significance. Nature
**2019**, 567, 305–307. [Google Scholar] [CrossRef] [Green Version] - Deniz, A.; Toros, H.; Incecik, S. Spatial variations of climate indices in Turkey. Int. J. Clim.
**2011**, 31, 394–403. [Google Scholar] [CrossRef] - Duzenli, E.; Tabari, H.; Willems, P.; Yilmaz, M.T. Decadal variability analysis of extreme precipitation in Turkey and its relationship with teleconnection patterns. Hydrol. Process.
**2018**, 32, 3513–3528. [Google Scholar] [CrossRef] - Sensoy, S.; Demircan, M.; Ulupınar, U.; Balta, I. Turkey Climate. DMI. 2008. Available online: http://www.dmi.gov.tr/iklim/iklim.aspx (accessed on 20 February 2020). (In Turkish)
- Akçar, N.; Yavuz, V.; Ivy-Ochs, S.; Kubik, P.W.; Vardar, M.; Schlüchter, C. Palaeoglacial records from Kavron Valley, NE Turkey: Field and cosmogenic exposure dating evidence. Quat. Int.
**2007**, 164, 170–183. [Google Scholar] [CrossRef] - Tatli, H.; Dalfes, N.; Menteş, S. A statistical downscaling method for monthly total precipitation over Turkey. Int. J. Clim.
**2004**, 24, 161–180. [Google Scholar] [CrossRef] - Sariş, F.; Hannah, D.M.; Eastwood, W.J. Spatial variability of precipitation regimes over Turkey. Hydrol. Sci. J.
**2010**, 55, 234–249. [Google Scholar] [CrossRef] [Green Version] - Biyik, G.; Unal, Y.; Onol, B. Assessment of Precipitation Forecast Accuracy over Eastern Black Sea Region using WRF-ARW. In Proceedings of the 11th Plinius Conference on Mediterranean Storms, Barcelona, Spain, 7–10 September 2009. [Google Scholar]
- Mann, H.B. Nonparametric Tests against Trend. Econometrica
**1945**, 13, 245. [Google Scholar] [CrossRef] - Kendall, M.G. Rank Correlation Methods, 4th ed.; Griffin, C., Ed.; Griffin: London, UK, 1975. [Google Scholar]
- Theil, H. A rank-invariant method of linear and polynomial regression analysis. Nederl. Akad. Wetench. Ser. A
**1950**, 53, 386–392. [Google Scholar] - Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc.
**1968**, 63, 1379–1389. [Google Scholar] [CrossRef] - Tabari, H.; Willems, P. Seasonally varying footprint of climate change on precipitation in the Middle East. Sci. Rep.
**2018**, 8, 4435. [Google Scholar] [CrossRef] [PubMed] - Baglee, A.; Connell, R.; Haworth, A.; Rabb, B.; Bugler, W.; Ulug, G.; Capalov, L.; Hansen, D.S.; Glenting, C.; Jensen, C.H.; et al. Climate Risk Case Study, Plot Climate Change Adaptation Market Study: Turkey. 2013. Available online: https://www.ebrd.com/downloads/sector/sei/turkey-adaptation-study.pdf (accessed on 20 February 2020).
- Onyutha, C.; Tabari, H.; Rutkowska, A.; Nyeko-Ogiramoi, P.; Willems, P. Comparison of different statistical downscaling methods for climate change rainfall projections over the Lake Victoria basin considering CMIP3 and CMIP5. HydroResearch
**2016**, 12, 31–45. [Google Scholar] [CrossRef] - Yue, S.; Pilon, P.; Cavadias, G. Power of the Mann–Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J. Hydrol.
**2002**, 259, 254–271. [Google Scholar] [CrossRef] - Pirnia, A.; Golshan, M.; Darabi, H.; Adamowski, J.F.; Rozbeh, S. Using the Mann–Kendall test and double mass curve method to explore stream flow changes in response to climate and human activities. J. Water Clim. Chang.
**2018**, 10, 725–742. [Google Scholar] [CrossRef] - Tang, L.; Zhang, Y. Considering Abrupt Change in Rainfall for Flood Season Division: A Case Study of the Zhangjia Zhuang Reservoir, Based on a New Model. Water
**2018**, 10, 1152. [Google Scholar] [CrossRef] [Green Version] - Onyutha, C. Trends and variability in African long-term precipitation. Stoch. Environ. Res. Risk Assess.
**2018**, 32, 2721–2739. [Google Scholar] [CrossRef] - Vido, J.; Nalevanková, P.; Valach, J.; Šustek, Z.; Tadesse, T. Drought Analyses of the Horné Požitavie Region (Slovakia) in the Period 1966–2013. Adv. Meteorol.
**2019**, 2019, 1–10. [Google Scholar] [CrossRef] - Onyutha, C.; Tabari, H.; Taye, M.T.; Nyandwaro, G.N.; Willems, P. Analyses of rainfall trends in the Nile River Basin. HydroResearch
**2016**, 13, 36–51. [Google Scholar] [CrossRef]

**Figure 4.**CSD-based graphical diagnoses of the trend significance for annual rainfall at stations (

**a**–

**p**) selected across the study area.

**Table 1.**Statistical information of monthly rainfall data and the record length at the observation stations (X

_{min}, X

_{max}and X

_{mean}: minimum, maximum and mean monthly precipitation; SD: standard deviation; C

_{v}: coefficient of variation; C

_{sx}: coefficient of skewness).

Station | Full Period | First Half | Second Half | X_{min} | X_{max} | X_{mean} | SD | C_{v} | C_{sx} |
---|---|---|---|---|---|---|---|---|---|

Bartın | 1965–2015 | 1964–1989 | 1990–2015 | 0 | 349.1 | 86.7 | 61.65 | 0.71 | 1.15 |

Zonguldak | 1960–2015 | 1960–1987 | 1988–2015 | 0 | 359.8 | 101.3 | 68.6 | 0.68 | 0.86 |

Sinop | 1960–2015 | 1960–1987 | 1988–2015 | 0 | 324.0 | 57.2 | 42.9 | 0.75 | 1.44 |

Samsun | 1960–2015 | 1960–1987 | 1988–2015 | 0 | 350.3 | 58.8 | 40.4 | 0.69 | 1.68 |

Ordu | 1963–2015 | 1963–1989 | 1990–2015 | 0.3 | 267.6 | 85.8 | 50.7 | 0.59 | 1.00 |

Giresun | 1960–2015 | 1960–1987 | 1988–2015 | 0.2 | 521.6 | 104.97 | 61.89 | 0.59 | 1.41 |

Trabzon | 1960–2015 | 1960–1983 | 1984–2005 | 2.8 | 293.0 | 68.1 | 43.8 | 0.64 | 1.28 |

Rize | 1960–2015 | 1960–1987 | 1988–2015 | 8.2 | 516.6 | 186.4 | 102.1 | 0.55 | 0.84 |

Artvin | 1960–2015 | 1960–1987 | 1988–2015 | 0.9 | 342.2 | 59.2 | 43.2 | 0.73 | 1.99 |

Bolu | 1960–2015 | 1960–1987 | 1988–2015 | 0 | 174.4 | 46.4 | 29.1 | 0.63 | 0.94 |

Duzce | 1960–2015 | 1961–1987 | 1988–2015 | 0 | 227.2 | 68.5 | 43.1 | 0.63 | 0.75 |

Kastamonu | 1960–2015 | 1960–1987 | 1988–2015 | 0 | 278.7 | 41.6 | 31.7 | 0.76 | 1.80 |

Corum | 1960–2015 | 1960–1987 | 1988–2015 | 0 | 220.1 | 36.8 | 28.4 | 0.77 | 1.37 |

Amasya | 1960–2015 | 1960–1987 | 1988–2015 | 0 | 144.6 | 38.3 | 28.4 | 0.74 | 0.87 |

Tokat | 1961–2015 | 1960–1987 | 1988–2015 | 0 | 141.1 | 36.1 | 27.3 | 0.76 | 0.87 |

Gumushane | 1965–2015 | 1965–1989 | 1990–2015 | 0 | 141.9 | 38.5 | 26.8 | 0.70 | 0.78 |

**Table 2.**Statistical results of trend analysis for seasonal and annual rainfall using the CSD method.

Rainfall Station | Full Time Series | First Half Sub−series | Second Half Sub−series | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Win | Spr | Sum | Aut | Ann | Win | Spr | Sum | Aut | Ann | Win | Spr | Sum | Aut | Ann | |

Bartin | −0.63 | −0.48 | 0.20 | 0.26 | 0.57 | −1.69 | −2.44 | 0.68 | 0.21 | −0.83 | 1.58 | 1.64 | −0.84 | 2.88 | 0.03 |

Zonguldak | −0.61 | −1.14 | −1.31 | −0.43 | −1.22 | −1.65 | −1.66 | −0.01 | −0.11 | −1.89 | 2.58 | 1.41 | −1.64 | 2.77 | −1.99 |

Sinop | 0.62 | 0.51 | 0.98 | 0.14 | 2.11 | −2.32 | −1.38 | 0.65 | −2.16 | −1.52 | 1.48 | 0.67 | 0.40 | −0.38 | 0.36 |

Samsun | −0.06 | −0.19 | 1.22 | 0.04 | 0.97 | −1.92 | −0.42 | 1.01 | −2.28 | −1.00 | 1.59 | 0.84 | 0.79 | 1.71 | 0.86 |

Ordu | 0.63 | 1.92 | −0.74 | 1.25 | 1.88 | 0.45 | 1.76 | 0.08 | 1.40 | 1.93 | −0.75 | −0.31 | 1.37 | 0.20 | 0.92 |

Giresun | −0.26 | 0.73 | 0.53 | 0.19 | 3.08 | 0.00 | 0.23 | 1.21 | −0.31 | 1.68 | 1.35 | 0.01 | −0.36 | 0.66 | 1.36 |

Trabzon | 1.93 | 1.53 | 1.64 | 1.58 | 3.35 | 0.15 | −0.99 | 0.64 | −0.36 | −0.09 | 1.53 | 2.01 | 0.33 | 0.32 | 1.99 |

Rize | −0.29 | −1.16 | 1.35 | −0.70 | 1.75 | −0.04 | −2.24 | −0.43 | −0.37 | −0.64 | −0.81 | 0.41 | 1.37 | −0.92 | 0.47 |

Artvin | 0.30 | 1.51 | 1.47 | 0.70 | 2.64 | 1.15 | 0.07 | −1.31 | 1.17 | 0.21 | −2.16 | 1.21 | 0.86 | −2.39 | −0.76 |

Bolu | −0.98 | 0.72 | 0.39 | 0.07 | 0.32 | 0.24 | −1.58 | −0.16 | 1.22 | −0.69 | 1.52 | 1.94 | 0.12 | 1.99 | 1.31 |

Duzce | −1.15 | −1.33 | −0.71 | −0.87 | −1.61 | −0.87 | −0.93 | 0.60 | 0.20 | −0.31 | −0.48 | 0.18 | −0.71 | 0.75 | −0.83 |

Kastamonu | 0.01 | 1.36 | 0.94 | −0.47 | 1.40 | 0.33 | 1.19 | 0.16 | 0.37 | 0.96 | 1.31 | 0.91 | 1.39 | 2.19 | 1.82 |

Corum | −1.32 | 0.29 | 1.13 | −0.38 | 0.79 | −0.20 | 1.12 | −0.28 | 0.56 | 1.00 | 0.86 | −1.05 | −0.21 | 2.49 | −0.19 |

Amasya | −0.99 | 1.10 | 2.11 | −0.73 | 1.22 | −2.02 | −0.43 | 1.59 | −0.46 | 0.35 | 1.86 | 0.37 | 1.60 | 3.11 | 1.94 |

Tokat | −1.21 | 1.33 | 0.89 | −0.06 | 1.80 | −0.81 | 1.15 | 0.51 | 1.23 | 1.60 | 0.42 | −1.13 | 0.44 | 2.48 | −0.62 |

Gumushane | −0.91 | 0.41 | −0.25 | 1.36 | 0.99 | 0.09 | −0.41 | 0.11 | 1.15 | 1.03 | 1.01 | 0.36 | −0.62 | 2.90 | 0.90 |

_{0}(no trend) was rejected (p < 0.05) are presented in bold.

Rainfall Station | Full Time Series | First Half Sub−series | Second Half Sub−series | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Win | Spr | Sum | Aut | Ann | Win | Spr | Sum | Aut | Ann | Win | Spr | Sum | Aut | Ann | |

Bartin | −0.71 | −0.35 | 0.19 | 0.29 | 0.95 | −4.6 | −3.54 | 1.28 | 0.82 | −4.95 | 3.19 | 2.14 | −1.84 | 6.47 | −0.72 |

Zonguldak | −0.51 | −0.63 | −0.97 | −0.43 | −1.45 | −2.3 | −3.46 | −0.05 | −0.01 | −6.77 | 6.06 | 1.31 | −3.26 | 6.62 | −6.78 |

Sinop | 0.40 | 0.24 | 0.51 | 0.09 | 2.46 | −3 | −1.52 | 0.97 | −2.20 | −6.22 | 1.55 | 0.79 | 0.67 | −0.42 | 0.78 |

Samsun | 0.04 | −0.05 | 1.21 | 0.01 | 1.20 | −2.8 | −0.55 | 1.24 | −3.39 | −3.65 | 2.46 | 0.79 | 1.43 | 1.92 | 2.61 |

Ordu | 0.33 | 0.88 | −0.54 | 1.16 | 2.22 | 0.6 | 1.52 | −0.10 | 2.44 | 9.24 | −0.9 | −0.51 | 2.68 | 0.49 | 1.33 |

Giresun | −0.18 | 0.41 | 0.36 | 0.15 | 3.44 | −0 | 0.59 | 1.34 | −0.42 | 3.94 | 1.76 | 0.26 | −1.59 | 1.31 | 5.86 |

Trabzon | 1.33 | 0.60 | 0.68 | 1.34 | 4.32 | 0.63 | −0.93 | 0.89 | −0.58 | −0.09 | 2.7 | 2.77 | 0.24 | 1.24 | 6.61 |

Rize | −0.53 | −0.81 | 1.07 | −0.92 | 3.94 | −0.5 | −3.07 | −1.24 | −0.64 | −4.15 | −2.1 | 0.37 | 5.12 | −1.82 | 3.21 |

Artvin | 0.13 | 0.35 | 0.64 | 0.49 | 2.23 | 2.35 | 0.01 | −0.71 | 1.80 | 0.58 | −5.8 | 1.00 | 0.95 | −8.19 | −2.55 |

Bolu | −0.14 | 0.21 | 0.38 | 0.06 | 0.39 | 0.03 | −1.54 | −0.13 | 1.68 | −0.92 | 1.85 | 2.19 | 0.16 | 2.83 | 2.36 |

Duzce | −1.28 | −0.37 | −0.39 | −0.71 | −1.85 | −1.4 | −1.58 | 0.83 | 0.50 | −1.32 | −1.3 | 0.47 | −2.26 | 1.17 | −1.84 |

Kastamonu | −0.04 | 0.49 | 0.56 | −0.17 | 1.39 | 0.41 | 1.36 | 0.01 | 0.43 | 2.16 | 1.21 | 1.39 | 2.92 | 1.88 | 5.40 |

Corum | −0.59 | 0.06 | 0.30 | −0.13 | 0.30 | −0.2 | 0.94 | −0.52 | 0.99 | 1.88 | 1.33 | −0.91 | −0.59 | 2.77 | −0.71 |

Amasya | −0.80 | 0.33 | 0.37 | −0.53 | 0.83 | −2.6 | −0.51 | 1.27 | −0.59 | 0.71 | 2.96 | 0.73 | 1.12 | 3.90 | 4.26 |

Tokat | −0.53 | 0.43 | 0.28 | 0.02 | 1.27 | −0.9 | 0.82 | 0.24 | 1.88 | 3.34 | 0.24 | −0.96 | 0.28 | 1.89 | −1.25 |

Gumushane | −0.29 | 0.20 | −0.03 | 0.50 | 0.87 | 0.07 | −0.52 | 0.15 | 1.13 | 2.62 | 1.12 | 0.71 | −0.32 | 2.26 | 1.94 |

Rainfall Station | Full Time Series | First Half Sub−series | Second Half Sub−series | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Win | Spr | Sum | Aut | Ann | Win | Spr | Sum | Aut | Ann | Win | Spr | Sum | Aut | Ann | |

Bartin | −0.26 | −0.49 | 0.26 | 0.32 | 0.50 | −0.63 | −2.31 | 0.63 | 0.30 | −1.17 | 1.76 | 1.45 | −0.66 | 2.71 | −0.49 |

Zonguldak | −0.73 | −0.94 | −1.38 | −0.76 | −1.16 | −1.63 | −1.72 | −0.02 | 0.02 | −1.92 | 2.70 | 1.52 | −1.52 | 2.53 | −1.70 |

Sinop | 0.78 | 0.62 | 1.11 | −0.11 | 2.18 | −2.75 | −1.32 | 0.77 | −1.80 | −1.88 | 1.18 | 0.85 | 0.49 | −0.86 | 0.38 |

Samsun | 0.09 | −0.11 | 2.36 | −0.17 | 1.32 | −2.23 | −0.49 | 1.01 | −2.43 | −1.01 | 1.32 | 0.85 | 1.02 | 1.22 | 1.05 |

Ordu | 0.96 | 1.84 | −0.77 | 1.10 | 2.11 | 1.04 | 0.88 | −0.04 | 1.28 | 1.97 | −0.66 | −0.93 | 1.42 | 0.29 | 0.48 |

Giresun | −0.31 | 0.70 | 0.48 | 0.19 | 3.28 | −0.54 | 0.24 | 0.97 | −0.10 | 1.68 | 0.29 | 0.04 | −0.45 | 0.53 | 1.48 |

Trabzon | 1.35 | 1.40 | 1.58 | 1.18 | 3.39 | 0.21 | −1.00 | 0.58 | −0.32 | −0.05 | 0.74 | 2.17 | 0.24 | −0.03 | 2.01 |

Rize | −0.46 | −1.34 | 1.19 | −1.02 | 1.77 | −0.36 | −2.23 | −0.53 | −0.26 | −0.57 | −1.44 | 0.22 | 1.26 | −1.18 | 0.55 |

Artvin | 0.12 | 1.35 | 1.93 | 0.97 | 2.25 | 0.79 | 0.02 | −1.13 | 1.28 | 0.30 | −2.31 | 1.09 | 0.85 | −2.23 | −0.69 |

Bolu | −0.48 | 0.63 | 0.72 | −0.12 | 0.56 | 0.19 | −1.42 | −0.10 | 1.40 | −0.61 | 1.52 | 1.76 | 0.13 | 1.50 | 1.05 |

Duzce | −1.66 | −0.78 | −0.66 | −1.08 | −1.54 | −0.13 | −0.79 | 0.64 | 0.52 | −0.33 | −0.17 | 0.25 | −0.79 | 0.51 | −0.83 |

Kastamonu | −0.12 | 0.98 | 0.85 | −0.75 | 1.58 | 0.42 | 1.17 | 0.02 | 0.40 | 1.01 | 1.05 | 0.77 | 1.40 | 1.96 | 1.92 |

Corum | −1.32 | 0.19 | 0.58 | −0.69 | 0.41 | −0.34 | 1.21 | −0.28 | 0.69 | 1.03 | 0.38 | −1.15 | −0.26 | 2.13 | −0.38 |

Amasya | −1.26 | 0.86 | 1.61 | −1.39 | 1.36 | −1.42 | −0.38 | 1.06 | −0.46 | 0.30 | 1.80 | 0.71 | 1.03 | 1.93 | 2.04 |

Tokat | −0.88 | 1.13 | 0.99 | −0.33 | 2.03 | −0.21 | 0.96 | 0.13 | 1.04 | 1.63 | 0.22 | −0.87 | 0.18 | 1.37 | −0.54 |

Gumushane | −0.43 | 0.39 | −0.13 | 1.60 | 1.19 | 0.53 | −0.44 | 0.12 | 1.23 | 1.48 | 1.05 | 0.48 | −0.49 | 2.27 | 1.19 |

_{MK}denote that the H

_{0}(no trend) was rejected at the 5% significance level.

**Table 5.**Trends and change rates of annual rainfall at all stations using the ITA-CB (Minimum, mean and maximum percentage changes for each group are presented).

Station | Low Group | High Group | All Group | ||||||
---|---|---|---|---|---|---|---|---|---|

Min | Mean | Max | Min | Mean | Max | Min | Mean | Max | |

Bartin | 4.25 | 6.09 | 7.50 | −0.88 | 6.75 | 11.90 | −0.88 | 6.65 | 11.90 |

Zonguldak | −8.04 | 4.29 | 13.39 | 5.12 | 15.27 | 3.27 | 13.39 | 3.27 | 20.00 |

Sinop | 35.17 | 46.70 | 59.29 | −3.49 | 4.15 | 10.24 | 59.29 | 19.25 | 59.29 |

Samsun | 14.42 | 17.70 | 21.54 | −6.33 | −4.05 | −1.29 | −6.33 | 6.52 | 21.54 |

Ordu | 16.69 | 18.00 | 20.69 | −3.90 | −1.04 | 3.22 | −3.90 | 6.06 | 20.69 |

Giresun | −8.18 | 1.61 | 5.08 | 3.48 | 13.01 | 22.42 | −8.18 | 7.58 | 22.42 |

Trabzon | 9.72 | 16.69 | 19.46 | 10.52 | 12.27 | 13.60 | 6.43 | 13.70 | 19.46 |

Rize | −35.57 | 13.40 | 4.82 | 3.65 | 3.93 | 21.12 | −35.57 | 7.23 | 21.12 |

Artvin | 18.81 | 21.29 | 27.23 | 3.86 | 8.96 | 15.09 | 3.86 | 15.09 | 27.23 |

Bolu | −6.57 | −3.43 | 1.35 | −4.80 | 1.54 | 5.26 | −6.57 | 0.98 | 6.15 |

Duzce | −11.81 | −1.54 | 3.68 | −9.72 | −5.38 | 3.06 | −11.81 | −5.20 | 3.68 |

Kastamonu | −6.83 | −2.22 | 8.91 | 2.80 | 20.10 | 5.95 | −6.83 | 5.95 | 40.51 |

Corum | −21.80 | 4.81 | 17.53 | 4.47 | 6.69 | 8.29 | −21.80 | 1.75 | 17.53 |

Amasya | −1.60 | 1.68 | 7.73 | −4.00 | 4.56 | 2.29 | −4.00 | 2.29 | 11.15 |

Tokat | 6.64 | 9.61 | 13.08 | 5.33 | 6.64 | 8.18 | 13.09 | 8.18 | 13.09 |

Gumushane | −11.02 | −0.82 | 3.04 | −5.58 | 1.86 | 8.70 | −11.02 | 1.02 | 8.70 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Cengiz, T.M.; Tabari, H.; Onyutha, C.; Kisi, O.
Combined Use of Graphical and Statistical Approaches for Analyzing Historical Precipitation Changes in the Black Sea Region of Turkey. *Water* **2020**, *12*, 705.
https://doi.org/10.3390/w12030705

**AMA Style**

Cengiz TM, Tabari H, Onyutha C, Kisi O.
Combined Use of Graphical and Statistical Approaches for Analyzing Historical Precipitation Changes in the Black Sea Region of Turkey. *Water*. 2020; 12(3):705.
https://doi.org/10.3390/w12030705

**Chicago/Turabian Style**

Cengiz, Taner Mustafa, Hossein Tabari, Charles Onyutha, and Ozgur Kisi.
2020. "Combined Use of Graphical and Statistical Approaches for Analyzing Historical Precipitation Changes in the Black Sea Region of Turkey" *Water* 12, no. 3: 705.
https://doi.org/10.3390/w12030705