# Impacts of Global Climate Change on Duration of Logging Season in Siberian Boreal Forests

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## Abstract

**:**

## 1. Introduction

_{2}during the photosynthetic process, forests contribute to climate change mitigation, and thus serving as a carbon sink. They also supply multiple ecosystem services and non-timber products such as mushrooms, berries, and medicinal plants [1]. Meanwhile, the ability of forests to sustain these services is changing due to climate change. According to the Intergovernmental Panel on Climate Change (IPCC), the second half of the 20th century was the warmest in the last millennia. In particular, the global average surface temperature has already gone up by 0.85 ${}^{\circ}$C since the industrial revolution and has continued to rise [2].

## 2. Materials and Methods

#### 2.1. Data

- three-hourly meteorological observations (SROK8C);
- daily soil temperature at depths down to 320 cm (TPG);
- daily air temperature and precipitation (TTTR).

#### 2.2. Calculation of Logging Season Duration

#### 2.3. Trend Presence Testing

#### 2.4. Modeling and Forecasting of Logging Season Duration

- Box–Cox transformation (if needed). If the variance of a time series is not constant, the Box–Cox transformation makes the data approximate to a normal distribution according to the following rule:$$y\left(\lambda \right)=\left(\right)open="\{"\; close>\begin{array}{cc}({y}^{\lambda}-1)/\lambda ,\hfill & \mathrm{if}\phantom{\rule{4.pt}{0ex}}\lambda \ne 0;\hfill \\ ln\left(y\right),\hfill & \mathrm{otherwise}.\hfill \end{array}$$To select the value of $\lambda $ parameter, we used the technique proposed by V. M. Guerrero [61].
- Selection of the differencing degree. The most common tests for stationarity/non-stationarity are Augmented Dickey–Fuller (ADF) [62] and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) [63]. For our research, we used the KPSS-test, which assumes a more easy-to-operate hypothesis of stationarity. When at some step the KPSS null-hypothesis of stationarity is not rejected, the d parameter of ARIMA model should be set to the current order of differencing.
- Fitting of p and q parameters. As there is no finite algorithm to calculate the numbers of AR and MA model components, the straightforward evaluation of all possible combinations was used. The maximum values of p and q were usually set to five. We used the Akaike information criterion (AIC) to select the best-fitted model. In our study, we employed Corrected Akaike information criterion (AICc), a modified AIC criterion for small samples. Its formula is given as:$$AICc=2(p+q+k+1)-2ln\left(\widehat{L}\right)+{\displaystyle \frac{2(p+q+k+1)(p+q+k+2)}{T-p-q-k-2}},$$
- Checking that residuals look like white noise. The Ljung–Box test was used to test whether there is no serial correlation in the residuals of the fitted model. If it is not the case, another model should be selected.

## 3. Results and Discussion

#### 3.1. Logging Season Duration Shortening

#### 3.2. Tendencies of Expectable Season Start/End Boundaries

#### 3.3. Logging Season Duration Modeling and Forecasting

## 4. Conclusions

- There is strong evidence of logging season duration shortening during the retrospective period of 1966–2018 for almost all considered stations. Although the considered stations are located in similar natural conditions, the local climate varies significantly and affects the economic potential of logging activity.
- The gradual reduction of logging season durations has an uneven effect on the start and end boundaries of the season. Climate warming has almost no effect on the start date of the season in winter, but it significantly shifts the boundaries of the season end in spring.
- Despite some limitations of ARIMA modeling framework forecasting performance caused by the lack of prolonged-time series of temperature and wind speed available for calculating the logging season durations, a set of ARIMA models of acceptable quality was elaborated. These forecasting models show that in the nearest future, the trends of gradual shortening of logging season duration will hold for the most part of stations. The most pronounced effect is observed for Achinsk station, where, according to our calculations the logging season will decrease from $148.4\pm 17.3$ days during the historical sample (1966–2018) to $136.2\pm 30$ days in 2028.
- In our opinion, the identified downward trends in the duration of the potential logging season in the largest Siberian logging regions are a direct consequence of the global climate change observed during the period on which the database used was based.
- From an economic perspective, shorter duration of logging season means fewer wood stocks available for cutting that would make companies unable to comply with their logging plans and lead them to suffer losses in the future. In this regard, logging companies will have to adapt to these changes by redefining their economic strategies in terms of intensifying timber harvesting operations.
- The approach we used in this study might be applied to the prediction of establishment and then the loss of ice roads to access remote mines and communities in circumpolar areas.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Diagram of the temperature and calculated logging season (shapes ${L}^{\prime}$ and ${L}^{\u2033}$) and non-logging period (shape S) for the sample Boguchany station (World Meteorological Organization (WMO) index = 29,282) in 2000.

**Figure 3.**Dynamics of within-year non-working days number for Boguchany (BOG), Yeniseysk (ENI), and Yerbogachen(YER): 1966–2018. Note: for other stations, the average number of non-working days does not significantly differ from 0.

**Figure 4.**Pearson correlations between expectable durations of logging seasons by meteorological stations of Krasnoyarsk Krai and Irkutsk Oblast: 1966–2018.

**Figure 7.**Point forecasts and forecast intervals at 80% and 95% levels of the expected duration of logging season by stations predicted with autoregressive-integrated-moving average modeling (ARIMA) models.

Station Name | WMO Index | Latitude (N) | Longitude (E) | Altitude, m a.s.l. | Onset of Observations | Relocation Note |
---|---|---|---|---|---|---|

Yeniseysk (ENI) | 29,263 | ${58}^{\circ}{27}^{\prime}$ | ${92}^{\circ}{09}^{\prime}$ | 77 | 1853 | 15 times until 1915 without coordinates change |

Boguchany (BOG) | 29,282 | ${58}^{\circ}{23}^{\prime}$ | ${97}^{\circ}{27}^{\prime}$ | 131 | 1930 | 2 km NW in 1960 |

Achinsk (ACH) | 29,467 | ${56}^{\circ}{17}^{\prime}$ | ${90}^{\circ}{31}^{\prime}$ | 265 | 1940 | 1 km S in 1956 |

Minusinsk (MIN) | 29,866 | ${53}^{\circ}{43}^{\prime}$ | ${91}^{\circ}{42}^{\prime}$ | 254 | 1885 | 800 m NW in 1990 |

Bratsk (BRT) | 30,309 | ${56}^{\circ}{17}^{\prime}$ | ${101}^{\circ}{45}^{\prime}$ | 410 | 1901 | 4 km NW in 1956 |

Kirensk (KIR) | 30,230 | ${57}^{\circ}{46}^{\prime}$ | ${108}^{\circ}{04}^{\prime}$ | 256 | 1892 | 1.5 E in 1909, to the waterfront of the Lena river in 1931, to the right bank of the Lena river in 1933 and 1.5 km S—SW in 1942 |

Tulun (TUL) | 30,504 | ${54}^{\circ}{36}^{\prime}$ | ${100}^{\circ}{38}^{\prime}$ | 523 | 1940 | 1.3 km W in 1951 |

Yerbogachen (YER) | 24,817 | ${61}^{\circ}{16}^{\prime}$ | ${108}^{\circ}{01}^{\prime}$ | 284 | 1936 | No relocations |

**Table 2.**Temperature (t, ${}^{\circ}$C) and wind speed (w, km/h) thresholds to declare actirovannye dni (non-working days) in Krasnoyarsk Krai and Irkutsk Oblast.

Krasnoyarsk | Irkutsk | ||
---|---|---|---|

$\mathit{t}$ | $\mathit{w}$ | $\mathit{t}$ | $\mathit{w}$ |

$-40$ | NA | 40 | NA |

NA | 22 | ||

$-35$ | 5 | $-35$ | 3 |

$-25$ | 10 | ||

$-15$ | 15 | ||

$-5$ | 20 |

**Table 3.**Basic descriptive statistics for calculated logging season durations and the Mann–Kendall test results for eight considered meteorological stations in Krasnoyarsk Krai and Irkutsk Oblast: 1966–2018.

Station Name | Mean | S.D. | $\mathit{\tau}$ | z | p-Value | Trend Characteristics | |
---|---|---|---|---|---|---|---|

1 | Acninsk | 148.42 | 17.27 | $-0.30$ | $-3.15$ | 0.00 | Decreasing trend |

2 | Boguchany | 156.40 | 17.19 | $-0.24$ | $-2.53$ | 0.01 | Decreasing trend |

3 | Bratsk | 157.72 | 15.72 | $-0.26$ | $-2.74$ | 0.01 | Decreasing trend |

4 | Kirensk | 164.64 | 15.32 | $-0.17$ | $-1.77$ | 0.08 | Decreasing trend |

5 | Minusinsk | 137.58 | 14.37 | $-0.20$ | $-2.08$ | 0.04 | Decreasing trend |

6 | Tulun | 156.79 | 15.96 | $-0.29$ | $-3.07$ | 0.00 | Decreasing trend |

7 | Yeniseysk | 153.75 | 14.48 | $-0.22$ | $-2.34$ | 0.02 | Decreasing trend |

8 | Yerbogachen | 172.15 | 16.89 | $-0.00$ | $-0.01$ | 0.99 | No trend |

Station Name | $(\mathit{p},\mathit{d},\mathit{q})$ | Mean/Drift | ${\mathit{\phi}}_{1}$ | ${\mathit{\theta}}_{1}$ | ${\mathit{\theta}}_{2}$ | ${\mathit{\theta}}_{3}$ | ${\mathit{\theta}}_{4}$ | AICc | |
---|---|---|---|---|---|---|---|---|---|

1 | Achinsk | $(1,1,4)$ | $-64.75$ | $-0.99$ | $-0.29$ | $-1.22$ | $0.26$ | $0.36$ | $959.27$ |

$\left(18.12\right)$ | $\left(0.03\right)$ | $\left(0.14\right)$ | $\left(0.15\right)$ | $\left(0.15\right)$ | $\left(0.17\right)$ | ||||

2 | Boguchany | $(0,0,1)$ | $513.87$ | $0.25$ | $606.45$ | ||||

$\left(11.87\right)$ | $\left(0.15\right)$ | ||||||||

3 | Bratsk | $(0,1,1)$ | $-0.90$ | $963.56$ | |||||

$\left(0.06\right)$ | |||||||||

4 | Kirensk | $(0,0,1)$ | $164.64$ | $-0.05$ | $445.05$ | ||||

$\left(1.98\right)$ | $\left(0.15\right)$ | ||||||||

5 | Minusinsk | $(0,1,4)$ | $-1.32$ | $0.27$ | $-0.17$ | $0.34$ | $293.81$ | ||

$\left(0.16\right)$ | $\left(0.28\right)$ | $\left(0.27\right)$ | $\left(0.14\right)$ | ||||||

6 | Tulun | $(0,1,1)$ | $-0.89$ | $-217.51$ | |||||

$\left(0.07\right)$ | |||||||||

7 | Yeniseysk | $(1,0,0)$ | $153.81$ | $0.13$ | $438.38$ | ||||

$\left(2.24\right)$ | $\left(0.14\right)$ | ||||||||

8 | Yerbogachen | $(0,0,2)$ | $172.44$ | $-0.05$ | $-0.23$ | $455.74$ | |||

$\left(1.66\right)$ | $\left(0.14\right)$ | $\left(0.17\right)$ |

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**MDPI and ACS Style**

Chugunkova, A.V.; Pyzhev, A.I.
Impacts of Global Climate Change on Duration of Logging Season in Siberian Boreal Forests. *Forests* **2020**, *11*, 756.
https://doi.org/10.3390/f11070756

**AMA Style**

Chugunkova AV, Pyzhev AI.
Impacts of Global Climate Change on Duration of Logging Season in Siberian Boreal Forests. *Forests*. 2020; 11(7):756.
https://doi.org/10.3390/f11070756

**Chicago/Turabian Style**

Chugunkova, Anna V., and Anton I. Pyzhev.
2020. "Impacts of Global Climate Change on Duration of Logging Season in Siberian Boreal Forests" *Forests* 11, no. 7: 756.
https://doi.org/10.3390/f11070756