Spatiotemporal Characterization of Contemporary Fire Regimes in Russian Boreal Forest Under Impact of Climate Change and Increasing Anthropogenic Activity

Doctoral Dissertation Academic Year 2020

Spatiotemporal Characterization of Contemporary Fire Regimes in Russian Boreal Forest under Impact of Climate Change and Increasing Anthropogenic Activity

Keio University Graduate School of Media and Governance Kiunnei Kirillina Thesis Abstract

Spatiotemporal Characterization of Contemporary Fire Regimes in Russian Boreal Forest under Impact of Climate Change and Increasing Anthropogenic Activity

Forest fire is a complex natural phenomenon affecting and being affected by biogeochemical cycles, global climate and human society. Contemporary fire regimes are becoming susceptible to complex interactions among climate change, land use, and direct human influences such as industrial activities in forests. Therefore, it is critically important to understand the characteristics of these changing fire regimes in order to mitigate today’s wildfire problem, especially in a sensitive ecosystem such as Boreal forest.

This study aims to develop a systems analytic approach for identifying characteristics of contemporary fire regimes for the region recently affected by warming and rapid industrialization and to determine how these trends are related with fire activity on the example of the most fire-hazardous region of Boreal Russia, the Sakha Republic for 1996-2018. The research was conducted in three steps.

The first step was to characterize the fire regimes and their spatiotemporal shifts with original set of fire parameters, and historical trends, evolutionary patterns, fire seasonality and causes. The novel method; based on a regime shift analysis identified the specific inflection points in the evolution of fire regimes such as transitions to a new, more severe fire regimes illustrated by the increased burned area. The relationship between anthropogenic activity and forest fires was examined in GIS-environment. Significant increases of the burned area were found in the forestry districts affected by recent industrialization. The region also evidenced the extension of fire season.

The second step was to find the connections between the identified changes in fire regimes and regional climate change. Based on meteorological observations, the research used the magnitude, pace, inconsistency and significance of climatic changes as signal descriptors of rapid climate changes in fire weather variability, and the related changes in fire regimes by means of descriptive statistics, variability, homogeneity and trend. The results show that significant warming trends in Spring, Autumn and at the beginning of Summer extended the duration of fire season and period of peak fires, which resulted in the increases of the burned area.

The third step projected the future fire weather based on the datasets of the climate models of CMIP 5 Project during the entire fire season. The results show that current warming and some dryness trends will likely to continue in the future which may enforce regime shift of the forest fires.

In conclusions, this study articulated likely a shift of the causes in recent Boreal fire regimes, the effects of anthropogenic activity and short-term climate change. The fire regimes were affected by anthropogenic factors shown by significant increases of the ratio of anthropogenic fires and the increase of the burned area in the forestry districts affected by recent industrialization. The timing of significant upward shifts in the evolution of fire regimes matched with periods of climatic warming, which accompanied the industrial development projects in the same forestry districts.

The findings request updated forest management and fire suppression strategies. Further, this research intends to provide the scientific evidence on new interrelationships between climate change, anthropogenic activities and forest fires as a complex system, which has a potential to affect the global climate.

Keywords: Contemporary Boreal Forest; Fire Regimes; Regime Shifts; Anthropogenic Forest Fires; Climate Change Signals

ii Preface Chapters 5 and 6 of this thesis has been published as articles in the peer-reviewed journals. Chapter 7 has been published as conference proceedings. The author of this thesis, Kiunnei Kirillina, was responsible for data gathering, determining the research methodology, conducting data analysis, presenting the research results at academic conferences, and writing of journal articles. Professor Wanglin Yan is the main supervisor of this thesis. Professor Yan provided the overall guidance and supervision of this thesis. He reviewed this thesis and was the co-author of all published works. Members of the Research Advisory Group, later members of the Academic Degree Evaluation Committee, Professor Tomohiro Ichinose, Professor Norichika Kanie and Professor Lynn Thiesmeyer co-supervised and reviewed this dissertation. Professor Lynn Thiesmeyer also guided and co-authored the both published journal articles. The author thanks Graduate School Committee members for their comments, which helped to improve this dissertation.

List of journal papers and conference papers associated with this thesis:

Kirillina K., Yan. W., Thiesmeyer L., & Shvetsov E.G Identifying possible climate change signals using meteorological parameters in short-term fire weather variability for Russian boreal forest in the Republic of Sakha (Yakutia). Open Journal of Forestry, 10 (2020), 320-359.

Kirillina K., Shvetsov E.G., Protopopova V.V., Thiesmeyer L., & Yan W. Consideration of anthropogenic factors in boreal forest fire regime changes during rapid socio-economic development: case study of forestry districts with increasing burnt area in the Sakha Republic, Russia, Environmental Research Letters, 15 (2020) 035009.

Kirillina K., Goumehei E., & Yan W. (2016). GIS-mapping of forest fires as climate change indicator on North Russia: Case study of the Republic of Sakha (Yakutia), Proceedings of the 3rd International Conference on Engineering and Natural Science – ICENS (pp. 647-662), Kyoto, Japan, 12-14 July 2016.

Fujisawa 2020

Kiunnei Kirillina

iii Spatiotemporal Characterization of Contemporary Fire Regimes in Russian Boreal Forest under Impact of Climate Change and Increasing Anthropogenic Activity

A dissertation submitted to the Environmental Design and Governance Program, Graduate School of Media and Governance, Keio University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Academic Year 2020

Kiunnei Kirillina

Dissertation Advisory Committee:

Professor Wanglin Yan Keio University, Japan Professor Tomohiro Ichinose Keio University, Japan Professor Norichika Kanie Keio University, Japan Professor Lynn Thiesmeyer Keio University, Japan

iv Acknowledgements

I would like to thank my supervisor, Professor Wanglin Yan for his kind guidance, support, and understanding over all duration of the Doctoral course. Professor Yan always provided me useful advices, comments and research supervision to improve the quality of my research work. It is due to Professor Yan that I had the opportunity to study and to do my research at the Graduate School of Media and Governance of Keio University.

I would like to express my gratitude to my co-supervisors, members of my Research Advisory Group Professor Tomohiro Ichinose, Professor Norichika Kanie and Professor Lynn Thiesmeyer for sharing their knowledge and research experience toward my research project. Professor Norichika Kanie for suggestions for environmental policy. Professor Tomohiro Ichinose for suggestions for forest environment. I am very thankful to my co-supervisor, Professor Lynn Thiesmeyer, who has been very kind, helpful, and supportive during my research, especially on scientific method for environmental issues. I am very grateful for all time you have spent in helping me and always being open for discussions.

I would like to extend my gratitude to a number of people who have kindly assisted me during my research work. I am thankful for Dr. Pavel Groisman and all members of the Northern Eurasia Future Initiative (NEFI) and the Alaska Fire Science Consortium Staff, Alison York and Randi Jandt, for immersing me into the real science world and helping me to build research networks. Your help and guidance are invaluable.

I would like to express my sincere appreciation to the co-author of my journal papers, Dr. Evgeny Shvetsov from the Sukachev Institute of Forest of the Russian Academy of Sciences for the fruitful research collaboration.

Many thanks to all of my colleagues from EcoGIS Lab for their kind help and friendship throughout the years. Especially, Haitham Alkhalaf, Hossein Vahidi, Elham Goumehei, Muriadi Arip, Lindelwa Manyatsi, Bismark Adu Gyamfi, Fahd Al-Guthmy and Hua Jinling.

I would like to express my gratitude to all of my family members, especially for my parents, my aunt Anna and uncle Prokopyi, for supporting me unconditionally.

v Table of Contents

pp. THESIS ABSTRACT…………………………………………………………… ii PREFACE……………………………………………...... iii ACKNOWLEDGEMENTS…………………………………………………...... v TABLE OF CONTENTS………………………………………………………... vi-viii LIST OF TABLES………………………………………………………………. ix-x LIST OF FIGURES…………………………………………………………...... xi-xii LIST OF ABBREVIATIONS………………………………………………...... xii LIST OF APPENDICES………………………………………………………... xiv CHAPTER 1 – INTRODUCTION 1.1 General introduction…………………………………………………………... 1-3 1.2 Research background on observed and projected climate change impacts on forests and forest fire regimes……………………………………………………... 3-4 1.3 Fire disturbance and climate change implications on global fire activity: approaches to define the changing fire regimes…………………………………… 4-5 1.4 Boreal forest fires: characteristics and challenges under climate change……. 5-6 1.5 Boreal forest fires as a growing natural disaster in the Sakha Republic of Russia...... 6-8 CHAPTER 2 – IDENTIFIED RESEARCH GAPS IN UNDERSTANDING CONTEMPORARY BOREAL FIRE REGIMES AS A KEY CONCEPT FOR BOREAL FOREST FIRE ECOLOGY AND FORESTRY MANAGEMENT 2.1 State of knowledge on fire regimes: fire regime as a key concept in Fire Ecology and Forestry Management………………………………………………. 9-13 2.2 Fire regime descriptors: weather and climate…………………………………. 13-14 2.3 Fire regime descriptors: anthropogenic activities…………………………….. 14-15 2.4 Contemporary boreal fire regimes model…………………………………… 15 CHAPTER 3 – RESEARCH APPROACH AND METHODOLOGY 3.1 General background and the significance of the research……………………... 16 3.2 Definition of terms………………………………………………………...... 17 3.3 Research questions and hypothesis………………………………………...... 18 3.4 Research objectives…………..……………………………………...... 18-19 3.5 Research framework and methodology……………………………………….. 19-22 3.6 Organization of thesis……………………………………………………...... 22-23

vi CHAPTER 4 – STUDY AREA AND INITIAL DATA DESCRIPTION 4.1 Study area: The Republic of Sakha (Yakutia) of Russia…. …………………… 24-25 4.2 Information on Yakutian Forestry Service and its administrative division……. 25-26 4.3 Recent changes in forest laws and regulation in Russia and Sakha Republic: the increasing anthropogenic pressure on forests and their suspected relation to the changes in the regional fire activity…………………………………………… 26-28 CHAPTER 5 – CONSIDERATION OF ANTHROPOGENIC FACTORS FOR CHARACTERIZATION OF CONTEMPORARY BOREAL FIRE REGIMES DURING RAPID SOCIO-ECONOMIC DEVELOPMENT: THE CASE STUDY OF THE FORESTRY DISTRICTS WITH INCREASING BURNED AREA IN THE SAKHA REPUBLIC 5.1 Introduction “Characterization of contemporary boreal fire regimes under impact of climate change and increasing human alteration to forests”…………... 29-31 5.2 Conceptual framework for characterizing contemporary boreal fire regimes… 32-33 5.3 Compilation of fire features for fire regime characterization…………………. 33-34 5.4 Data analysis and discussion 5.4.1 Assessment of recent fire occurrences and burned area extent, 1996–2018… 34-35 5.4.2 Analysis of interannual variability and trends in fire regime………….…… 35-37 5.4.3 Analysis of changes in fire seasonality and related changes in burned area.... 37-39 5.4.4 Analysis of fire causes……………………………………………………… 39-41 5.5 Conclusion……………………………………………………………………. 41-42 CHAPTER 6 – IDENTIFYING POSSIBLE CLIMATE CHANGE SIGNALS IN SHORT-TERM FIRE WEATHER VARIABILITY AND THE RELATED CHANGES IN FIRE SEASONALITY AND FIRE ACTIVITY FOR YAKUTIAN BOREAL FOREST 6.1 Introduction “Climate change signals in fire weather and related changes in fire seasonality and fire activity” …………………………………………………. 43-44 6.2 Conceptual framework for detection of climate change signals in fire weather data………………………………………………………………………………... 44-45 6.3 Data organization…………………………………………………………...... 45-46 6.4 Methodology………………………………………………………………….. 46-47 6.5 Data analysis and discussion “Detecting climate change signals in fire weather variability and related changes in fire seasonality and fire activity” 6.5.1 Assessment of recent fire weather variability………………………………… 47-55 6.5.2 Analysis of inconsistency and abrupt changes in the evolution of fire weather 55-70 6.5.3 Trend analysis of fire weather…………………………………………………… 70-71

vii 6.5.4 Analysis of fire weather and related changes in fire activity illustrated by burned area: The assessment of 2014 and 2018 fire seasons………………………… 72-82 6.6 Conclusion……………………………………………………………………. 82-84 CHAPTER 7 – FUTURE PROJECTIONS OF FIRE WEATHER IN YAKUTIAN BOREAL FOREST UNDER IMPACT OF CLIMATE CHANGE 7.1 Presentation of selected data…………………………………………………. 85 7.2 Methodological approach for modeling future climate conditions during flammable season under impact of climate change using different climate scenarios from CMIP5 Project……………………………………………………. 85-86 7.3 Projections of future fire weather in Yakutian boreal forest…………………. 86-92 7.4 Conclusion……………………………………………………………………. 92 CHAPTER 8 – CONCLUSIONS 8.1 General conclusions and recommendations…………………..………………. 93-95 8.2 Discussion “Managing forest and fires in a changing climate and increasing 96-100 human alteration: Regional implications for Sakha Republic”…………………… 8.3 Concluding remarks and future work……………………………………...... 100-101 REFERENCES…………………………………………………………………... 102-111 APPENDICES…………………………………………………………………… 112-130

viii List of Tables pp. CHAPTER 1 Table 1.1 Fire disturbance and climate change implications to global boreal fire activity……………………………………………………… 4-5 Table 1.2 Boreal fires characteristics and measurement approaches………. 6

Table 2.1 Approaches to characterization of contemporary boreal forest fire regimes………………………………………………………….. 12-13 CHAPTER 3 Table 3.1 Definition of terms related with fire activity in the Sakha Republic of Russia………………………………………………. 17 Table 3.2 Definition of terms related with detection of climate changes signals in fire weather variability………………………………. 17 CHAPTER 4 Table 4.1 Information on the Forestry Service of the Sakha Republic…….. 26 CHAPTER 5 Table 5.1 Burned area trend detection analysis for Sakha Republic and five selected forestry districts, 1996–2016………………………….. 35 Table 5.2 Significant changes in fire seasonality in the Sakha Republic up to 2016………………………………………………………….. 38 Table 5.3 Peak fire months in the Sakha Republic from 2000 to 2018 correlated with burned area…………..………………………….. 38 CHAPTER 6 Table 6.1 Characteristics of meteorological stations………………………. 46 Table 6.2 Historical normals for monthly, annual, seasonal and peak fire period means of temperature……………………………………. 47-48 Table 6.3 Basic descriptive statistics for the monthly, annual, seasonal and peak fire period means of temperature for the period 1996-2018 and their differences from historical normals (DFHN)………….. 48-49 Table 6.4 Historical means for monthly, annual, seasonal and peak fire period total precipitation……………………………………….. 49 Table 6.5 Basic descriptive statistics for the monthly, annual, seasonal and peak fire period means of total precipitation for the period 1996- 2018 and their differences from historical means (DFHM)…………………………………………………………. 50 Table 6.6 Variability analysis for temperature, 1996-2018……………….. 51-53 Table 6.7 Variability analysis for precipitation, 1996-2018……………….. 53-55 Table 6.8 The results of homogeneity tests for temperature……………….. 62 Table 6.9 The results of the homogeneity tests for precipitation…………… 69 Table 6.10 Mann-Kendall test results for temperature, 1996-2018…………. 71

ix Table 6.11 Mann-Kendall test results for precipitation, 1996-2018………… 71 Tables Summary tables for analysis of impact of fire weather on fire 6.12-6.21 activity…………………………………………………………... 73-79 CHAPTER 7 Table 7.1 The deviations of the norms of modeled precipitation from observed precipitation, in mm and %...... 86 Table 7.2 Air temperature for future period till 2100……………………… 91 Table 7.3 Atmospheric precipitation for future period till 2100…………… 91

x List of Figures pp. CHAPTER 2 Figure 2.1 The definition of fire regime suggested in this research……… 10 Figure 2.2 Historical trends of the burned area in the Sakha Republic, Russia…………………………………………………………. 12 Figure 2.3 The suggested Model of contemporary boreal fire regimes….. 15 CHAPTER 3 Figure 3.1 The research framework of the study………………………….. 20 Figure 3.2 Methodological approach for fire regimes characterization…… 21 Figure 3.3 Methodological approaches for assessment of the impact of climate change on fire weather variability and preparation of predictions of future fire weather under projected impact of climate change………………………………………………… 21 CHAPTER 4 Figure 4.1 Study area…………………………………………………….. 25 Figure 4.2 Impact of recent forest legislation changes on forest fire risk….. 27 Figure 4.3 Interrelations between climate change, forest fires and industrialization……………………………………………….. 28 CHAPTER 5 Figure 5.1 Long-term trends of the number of fires and burned area in the Sakha Republic, Russia…………………………...…………… 35 Figure 5.2 Temporal evolution of burned area in the Sakha Republic, 1996–2018: Regime shift analysis for Sakha Republic……….. 36 Figure 5.3 Temporal evolution of burned area in Verkhnevilyuisky forestry district, 1996–2016…………………………………… 37 Figure 5.4 The fire causes in the Sakha Republic, 2000–2016. …………… 39 Figure 5.5 Map of current industrial and agro-industrial areas with burned area for five forestry districts in the Sakha Republic, 2000– 41 2016…………………………………………………………… CHAPTER 6 Figures Monthly, annual and seasonal upward shifts in the temperature, 6.1.1-6.5.2 for 1996-2018…………………………………………………. 56-62 Figures Monthly, annual and seasonal shifts in precipitation, for 1996- 6.6.1-6.10.2 2018……………………………………………………...... 63-68

xi Figures Relation between meteorological parameters (air temperature 6.11.1- / atmospheric precipitation) and burned area………………… 73-80 6.11.12 CHAPTER 7 Figure 7.1 The deviations of the norms of modeled temperature from observed temperature for historical experiment, in °C……….. 86 Figure 7.2 The deviations of the norms of modeled temperature from observed temperature for historical experiment, in °C ………. 87 Figure 7.3 Maps of projected air temperature changes over Sakha Republic for the fire season (according to the RCP 4.5 and 8.5 Scenarios)…………………………………………………….. 89 Figure 7.4 Maps of projected atmospheric precipitation changes over Sakha Republic for the fire season (according 4.5 and 8.5 RCP Scenarios)………………………………………………...... 90

xii List of Abbreviations pp.

GIS Geographic Information Systems 2 CMIP 5 Coupled Model Intercomparison Project Phase 5 2 v. Versus 4 NOAA AVHRR The Advanced Very High Resolution Radiometer 32 MODIS Moderate Resolution Imaging Spectroradiometer 32 HANDS Hotspot and Normalized Difference Vegetation Index 32 Algorithm Differencing Synergy Algorithm MAKESENS Program for Trends Detection Developed by the Finnish Meteorological Research Institute 33 ISDM- Forest Fires Remote Monitoring Information System of the Rosleskhoz Federal Agency for Forestry of Russia 34 NOAA GSOD The Global Summary of the Day Product from the United States National Oceanic and Atmospheric Administration 45 SD Standard Deviation 46 CV Coefficient of Variation 46 PCI The Precipitation Concentration Index 47 SNHT Standard Normal Homogeneity Test 47 DFHN Differences from Historical Normals 48 DFHM Differences from Historical Means 50 SE Standard Error 70 BA Burned Area 73 AMT Average Monthly Temperature 73 AMP Mean of Total Monthly Precipitation 74 BCC-CM1 Climate model of the Beijing Climate Centre, China Model 85 CM3 Model Climate model of the Meteo-France, Centre National de Recherches Meteorologiques 85 HadCM3 Model Climate model of the Hadley Centre for Climate Prediction, Met Office, the United Kingdom 85 INM-CM4 Climate model of the Institute for Numerical Mathematics Model (INM), Russia 85 ECHAM5 / MPI Climate model of the Max Planck Institute of Meteorology, OM Model Germany 85 IPSL Model Climate model of the Institute Pierre-Simon Laplace, 85 France DRR Disaster Risk Reduction Framework 100 GHG Greenhouse Gas Emissions 101

xiii List of Appendices pp. 1 Air temperature changes for study area (five selected forestry districts)……… 112-116 2 Research design………………………………………………………………… 117 3 Trends of temperature and precipitation across Sakha Republic, 1996-2018… 118-127 4 Relation between fire weather and burned area changes……………………… 128-130

xiv CHAPTER 1 – INTRODUCTION 1.1 General introduction

Forest fire is a complex natural phenomenon, driven by multiple factors at several different spatial and temporal scales (Torres, 2013). Conventional drivers of fire activity include fire weather, fuel and vegetation structure, and natural and anthropogenic fire ignition sources (Rogers, Balch, Goetz, Lehmann, & Turetsky, 2020). However, contemporary forest fires and their regimes are becoming susceptible to complex interactions among climate change, land use, and direct human influences such as industrial activities. Recently, many forest regions of the world have attracted large industrial development projects, especially in the countries with extractive industry-based economies such as Russia, thereby by, further complicating the existing complexities of forest fire drivers. Hence, it is critically important to understand the characteristics, patterns, and causes of these changing fire regimes in order to mitigate today’s wildfire problem, especially in the sensitive ecosystems such as Boreal forests.

This study aims to develop a systems analytic approach for identifying characteristics of contemporary fire regimes for the region recently affected by warming and rapid industrialization to determine how these two phenomena are related to fire activity in one of the most fire-hazardous regions of Boreal Russia; the Sakha Republic, between 1996-2018.

The research starts with the assessment of the temporal variability through regime shift approach. As suggested by Jiménez-Ruano et al. (Adrián Jiménez-Ruano, Marcos Rodrigues Mimbrero, & Juan de la Riva Fernández, 2017), we should establish not only the type of changes of fire activity that occur, but also know when the changes take place to capture the particular change points in the temporal evolution of fire regimes in order to find the reasons underlying them.

The other research focus of this study is the assessment of the impact of short-term climate change on fire activity. Despite the abundance of fire-climate studies (Rolstad, Blanck, & Storaunet, 2017; Stocks et al., 1998; Wastl & Menzel, 2012), there still limited research assessing the impact of short-term climate variability on fire weather. Furthermore, “the long-term averages may conceal climate changes that have appeared in more recent decades and may not detect the initial signal of rapid climate changes” (Bateman et al., 2016; Farinotti, 2013).

1 Finally, the study additionally focuses on the assessment of anthropogenic sources of fire ignition including impacts of industrial activities on fire activity and climate change effects accompanying the industrial development.

This research is conducted in three steps. The first step is to characterize the fire regimes and their spatiotemporal shifts with an original set of fire parameters, and historical trends, evolutionary patterns, seasonality and causes of fires. The novel method; based on the regime shift analysis identifies the specific inflection points in the evolution of fire regimes, which possibly might be considered as transitions to new fire regimes. The relationship between anthropogenic activities and fires are examined in GIS-environment using spatial correlation analysis.

The second step is devoted to finding the connections between the identified changes in fire regimes and regional climate change. Based on meteorological observations, the study uses the magnitude, pace, inconsistency and significance of climatic changes as signal descriptors of rapid climate changes in fire weather variability. Related changes in fire regimes are then examined by means of descriptive statistics, variability, homogeneity and trend analysis. These assessments are carried out for the entire fire season, including the onset, the peak fire occurrence period and the end of the season. The identified changes in fire weather are related with changes in fire seasonality and activity.

The third step is a projection of the future fire weather conditions for the entire fire season. In order to define whether a warming trend will continue in the future, fire weather simulation based on a set of climate models from CMIP 5 Project. That is; first, the best climate model is chosen for each fire season month depending on the ability of the climate model to simulate the current climate. Then, using the best fitting climate models’ simulations of weather conditions during the entire fire season were done for the period until 2100.

In conclusions, this study discusses shifts in recent Boreal fire regimes that have taken place due to the effects of anthropogenic activities as well as rapid and short-term climate changes.

The results discuss the necessity to update the existing Boreal forest management and fire suppression strategies based on the findings this study. Furthermore, this research provides scientific evidence on new interrelationships between climate change, anthropogenic activities and forest fires as a complex system, which has a potential to affect the global climate. However, continuous efforts should be taken towards developing common approaches for fire regimes

2 characterization, which would be possible to apply to any region suffering from persistent fire events. Again, further studies have to be done to translate the suggested fire regime characterization approaches into operational fire management practices as well as forestry legislation and policy, which are vitally needed to mitigate today’s wildfire problem.

1.2 Research background on observed and projected climate change impacts on forests and forest fire regimes

“The Earth’s average global temperature has risen by 0.8°C since 1880; however, the average rate of increase since 1981 (0.17°C) became two times more faster” (NOAA, 2019). Climate change has reflected in many atmospheric, oceanic and terrestrial processes. The observed changes in mean values have transpired into changes in weather extremes. The corresponding changes in meteorological parameters such as temperature and precipitation as well as the extension of the growing season and the fast retreat of snow cover have caused changes in the natural frequency and magnitude of forest fires (Groisman et al., 2007), which have been observed across the world’s forest ecosystems (Bradstock, 2010; De Groot et al., 2013; M. Flannigan, Stocks, & Wotton, 2005; M. D. Flannigan, Stocks, & Wotton, 2000; A. Jiménez-Ruano, M. Rodrigues Mimbrero, & J. De la Riva Fernández, 2017; Stocks et al., 1998). Projections from climate models predict that the trends on warming and dryness will likely continue in the future (Moritz et al., 2012).

Despite the fact that the wildfires are becoming an emerging environmental, economic and social issue in a global scale, some regions are being affected by fires because of their high susceptibility to climate change. This is particularly evidenced in Boreal forests. Existing studies have demonstrated the increasing frequency of forest fires and continuous growth of burned areas in Boreal forest zones in countries such as Canada, United States, and Russia (De Groot et al., 2013; Kirilenko & Sedjo, 2007; Price et al., 2013; Wotton, Martell, & Logan, 2003). There are changes in climate and fire drivers such as fuel conditions, ignition sources, and anthropogenic-induced changes in land use distorting the natural characteristics of fire regimes (Pechony & Shindell, 2010).

Therefore, climate change manifestations; such as warming, prolonged growing season, extended and more frequent droughts will elevate the future fire risk. However, little is known about the interactions between climate change, forest fires and anthropogenic activities in forests, such as industrial activities. Even-though, these interactions have the potential to affect the global climate. The mentioned interactions are used for building the research framework for this study and for

3 developing a systems analytic approach for identifying characteristics of contemporary fire regimes for the regions affected by warming and rapid industrialization.

1.3 Fire disturbance and climate change implications on global fire activity: approaches to define the changing fire regimes

“Changes in disturbance regimes are naturally part of all ecosystems” (Dale et al., 2001), including forest ecosystems. For Boreal forests, forest fires are important component within their regeneration and development process (Whitman, Parisien, Thompson, & Flannigan, 2019). By virtue of climate change, forests across the globe have experienced the modifications of the timing, frequency, extent and severity of fires. In order to maintain effective forest fire management and fire suppression strategies, it is necessary to learn how to express these forest fire disturbances by quantitative means.

Understanding the current picture of global boreal fire activity requires a comprehensive literature review. The results shown in Table 1.1 “Fire disturbance and climate change implications to global boreal fire activity”. Fire-climate studies in Boreal countries including Canada, the USA and Russia usually use four major fire activity metrics such as burned area, fire occurrence, fire weather and fire seasonality. These metrics are used for construction of original set of fire parameters for characterization of contemporary boreal fire regimes, as further used as fire activity descriptors later in this study. However, this study additionally analyzes fire causality with a focus on anthropogenic sources of fire ignition and impact of short-term climate change on fire weather development.

Table 1.1. Fire disturbance and climate change implications to global boreal fire activity. Fire activity Study Change/ Projected Time period or General References metric area change scenario trend

Fire Canada Fire season length: average 3 x CO2 + Kochtubajda et weather, (Boreal) increase of 30-50 days. al. 2006 seasonality Fire weather: increase of SSR on 19-44% Burned USA Area burned: 2 or 3.5-5.5 Reference 1970-2000 + Balshi et al area, (Alaska), times. v. 2091-2100 (BA); 2008 occurrence Canada Fire occurrence: FRI will 2070-2100 (FO) (Boreal) decrease in Alaska on 50% and in Canada – on 40% Burned area USA Area burned to increase 14- 1922-96 v. 2025-99 + Bachelet et al. (Alaska) 34% 2005

4 Continued

Burned Canada Area burned: increase by 1/3. 2 x CO2 + Amiro et al. area, (Boreal) Fire severity: increase 0-18% 3 x CO2 2009 severity

Fire Canada Fire season: earlier start. Fire 2 x CO2 + Stocks et al. weather, and weather: increase in severity 1998 seasonality Russia (Boreal) Fire Russia Increase in the number of days 1981-2000 v. 2100 + Malevsky- weather, (Boreal) with high fire danger of up to Malevich et occurrence 12 days; areas of maximum al. 2008 fire danger risk will double by 2050 Data Source: Compiled by author from Flannigan et al. 2009. Implications of changing climate for global Wildland fire.

1.4 Boreal forest fires: characteristics and challenges under climate change

Boreal forest zone is a key region of the world “where both its sensitivity to change and its size make it likely to affect the global climate system” (Soja et al., 2007). However, forest fire is a major disturbing agent in Boreal forests, which is “influenced by climate, weather, topography, vegetation, fuel deposits and human activities. In return, fires affect the climate through emission of gases and aerosols, changes in surface albedo, soil processes and vegetation dynamics”. Furthermore, the effects of these factors are not well investigated (Oris, Asselin, Ali, Finsinger, & Bergeron, 2014). The future increases of Boreal fires predicted by climate models will possibly exacerbate a global warming.

The interactions between boreal forest fires and climate change have been extensively investigated for last 30 years, and scientists have developed the descriptors of boreal fire activity (Table 1.2). Quantifying the importance of these factors is critical for better understanding of climate change impacts on boreal fires and their regimes; primarily through assessment of changes in meteorological parameters describing the fire weather such as changes in temperature and precipitation (Barrett et al., 2016).

5 Table 1.2. Boreal fires characteristics and measurement approaches. Fire activity metrics What is measured Measurement approaches Annual area burned How much area is burned on an Large fire databases, fire annual basis, for a specific region statistics, satellite imagery Long-term fire How often fires occur in a Paleo data, dendroecological frequency specific region or site data Fire seasonality The seasonal patterns of fire Fire reports, satellite imagery activity Fire occurrence Where fire occurs on the Large fire databases, satellite landscape imagery Fire size How large are fires in a specific Large fire databases, satellite region imagery Data Source: From Barrett et al 2016. Static and dynamic controls on fire activity at moderate spatial and temporal scales in the Alaskan boreal forest.

The combination of fire activity metrics from Table 1.1 and Table 1.2 help build fire activity descriptors for this study. Yet, these metrics need some adjustment. First, by adding the measure of the timing of changes in fire activity. The simple description of generic fire parameters such as number of fires and burned area are not enough to find the causes of changes, because there is no clue about when the changes occurred. The other limitation is the absence of important fire activity descriptor such as fires causality. The identified limitations will be addressed in this study (see Chapter 5) in order to investigate the spatiotemporal evolution of fire regimes.

1.5 Boreal forest fires as a growing natural disaster in the Sakha Republic of Russia

Russia is a forested country comprising approximately 25% of the world’s forests (Karpachevsky, 2004). “The diverse climate, soil and vegetation structures along with a great diversity of anthropogenic impacts, is inherent in the vast territories of the country. The main factors influencing distribution, species composition, structure and productivity of the Russian forests and their fire regimes are temperature, precipitation, continentality and dryness of climate, and land use changes” (FAO, 2001). In turn, forest fires are the major disturbance “that impacts the structure, sustainability, and biodiversity of the Russian forests” (Kukavskaya, Buryak, Shvetsov, Conard, & Kalenskaya, 2016) due to set of reasons, including: - “the majority of the Russian forests are boreal forests (about 95%), which is mostly dominated by open coniferous stands of high fire hazard” (FAO, 2001); - a big part of Russian forests is still unprotected from fires due to their remote location (FAO 2011), especially in Siberia and in the Far East of the country;

6 - the presence of large amounts of accumulated organic material due to slow pace of a decomposition process; - the boreal forest regions usually have limited amounts of precipitation and overall dry climate with frequent occurrence of droughts during the peak fire period (A. Shvidenko & Goldammer, 2001).

The annual burned area in the Russian forests according to the satellite estimates is approximately 4-20 Mha (Ponomarev, Kharuk, & Ranson, 2016) with majority of burns occurring (up 80% from overall country burned area) in the Asian part of the country such as Siberia and the Far East (Kharuk, Kasischke, & Yakubailik, 2007; A. Z. Shvidenko & Schepaschenko, 2013; Vivchar, 2011), including the study area, the Sakha Republic. “This trend is elaborated by a general tendency toward an enhancement of the summer forest fire risk and extension of the duration of the fire season, which is evident under the light of present and projected anthropogenic climate change” (Mokhov & Chernokulsky, 2010). In addition, the Russian fire regimes are becoming more intense: with approximately 90% of burned area in the country being caused by extra-large fires (> 2000 ha). During the last two decades, both burned area and fire severity have been intensifying. Burned area increased by 29% compared to 1980s and by 19% in comparison with the 1990s, which was attributed to warming and drying (Schaphoff, Reyer, Schepachenko, Gerten, & Shvidenko, 2015).

However, it is a mistake to think that the increase of fire activity happens solely due to the effect of warming. Scientific literature already gives evidence of an increase in anthropogenic fire activity in the country (Conard and Ivanova 1997). The study by Kondrashov (Kondrashov, 2004) shows, majority of fires in the country occur due to anthropogenic factors, which is crucial for the Asian part of Russia; where large areas of forested land are leased for a long-term use by big mining corporations (Karjalainen, Jutila, Leinonen , & Gerasimov, 2013; Tornianinen & Saastamoinen, 2007). The research done by Kharuk et al. (Kharuk, Ranson, & Dvinskaya, 2008) discusses the increase of frequency of fires as result of increase of industrial activities on the example of impact of gold mining industry on fire activity in the Central Siberia. However, despite the existence of some evidence on the impact of industrial activities on fire activity, the problem is not fully investigated in Russia. In this regard, this study advocates for inclusion of anthropogenic activities into fire regime characterization approaches. This is specifically applicable for regions with forest-based industries and developing economies, such as our study area, the Sakha Republic in the Russian Far East.

7 The Sakha Republic is the largest region of the country, solely occupying 40% of the territory of Eastern Siberia. The climate is sharply continental, which accounts for extreme temperature changes and low precipitation. It is characterized by few snows during winter, fast snow melting, and dry spring seasons. These contribute to the natural high forest fire risk in the region (Solovyev & Kozlov, 2005). As result, the Republic suffers from persistent fire events (Protopopova & Gabysheva, 2016) and exhibits one of the largest areas of burning in the country (Rosstat 2018). “Forest fires pollute the atmosphere with significant amount of combustion products as mixture of various gases, aerosol and smoke particles” (Tomshin & Solovyev, 2014a), these emissions possibly contribute to further climate change because of the large scale of the regional fire activity. In turn, humans also negatively affect fire problem. That is; the increasing use of forested lands for industrial activities and new development projects increase future risk of fires. This system is analyzed further in the analytical parts of this study (Chapters 5 and 6).

The factors, analyzed here, including strong regional climate warming (Fedorov, Ivanova, Park, Hiyama, & Iijima, 2014; K. S. Kirillina & Lobanov, 2015; K. S. Kirillina, Lobanov, & Serditova, 2015) and the warming potential of the recent trend of rapid industrial development in the region (K. Kirillina, Shvetsov, Protopopova, Thiesmeyer, & Yan, 2020) make this study highly relevant.

8 CHAPTER 2 – IDENTIFIED RESEARCH GAPS IN UNDERSTANDING CONTEMPORARY BOREAL FIRE REGIMES AS A KEY CONCEPT FOR BOREAL FOREST FIRE ECOLOGY AND FORESTRY MANAGEMENT 2.1 State of knowledge on fire regimes: fire regime as a key concept in Fire Ecology and Forestry Management

The term “regime” is widely distributed in all areas of science, and differs profoundly from its usage in socio-political sciences, where the term “regime” may suggest various types of political systems and switches between them (Teti & Abbott, 2016). In natural sciences, particularly in climatology and fire ecology, the term “regime” means variations of ecosystems, called regimes (Handorf & Dethloff, 2009; Rodionov, 2004).

The concept of fire regime is a key concept in forest fire ecology used for quantitative description of fire activity (Rolstad et al., 2017). For this study, the concept of fire regime is a starting point for statistical description of fire activity in the region of interest, the Sakha Republic. Review of the current state of knowledge on fire regimes by Krebs et al. (Krebs, Pezzatti, Mazzoleni, Talbot, & Conedera, 2010) refers to a fire regime as a set of fire parameters describing fire activity including the frequency, size, seasonality and causes of forest fires.

This study uses the term “regime” for definition of two particular natural phenomena such as forest fire and climate variability. Here, the term “regime” is understood as a continuous relatively stable state. However, the regime can be changed as illustrated by shifts, which are so-called regime shifts and represented by rapid reorganization from one relatively stable state to another (see for details Rodionov and Overland 2005). This research uses abrupt change in the mean as descriptor of regime shift. Further, the study analyzes fire regime shifts illustrated by burned area changes and climate change-driven shifts in fire weather, which is shown by changes in temperature and precipitation.

In this research, I develop my own definition of fire regime and approach for their characterization depending on the aims of this study. Hence literature review was conducted on approaches on the characterization of contemporary boreal fire regimes found that the existing studies have considerable research gaps such as: 1) lack in studies considering an assessment of a temporal evolution of fire regimes as a key part for fire regimes characterization;

9 2) existing fire-climate studies do not consider impact of short-term climate changes on fire weather variability, even though fire weather is the key driver of fire activity; 3) today many Boreal forest regions are the areas of large industrial development projects, especially in the countries with extractive industry-based economies such as Canada and Russia. However, there is little work on possible correlations between increase of fires and impact of anthropogenic factors.

The identified research gaps (lack of studies on anthropogenic causes of fire ignition, assessment of the temporal evolution of fire regime and analysis of impact of short-term climate changes on fire activity) is included in the suggested approach for characterization of contemporary boreal fire regimes as affected by both warming and anthropogenic factors. It is also reflected in the definition of the contemporary boreal fire regimes by this study such as a term statistically describing the spatiotemporal distribution, temporal evolution, seasonality and causes of fires, which are dynamic varying system in a response to certain drivers such as climate change, and direct anthropogenic activities in forests (Figure 2.1). In accordance with the suggested definition of the fire regime, I constructed the set of fire parameters, which is used for contemporary boreal fire regimes characterization in Chapter 5.

Figure 2.1. The definition of fire regime suggested in this research. Data Source: Compiled by author from Krebs et al. 2010. Fire regime: history and definition of a key concept in disturbance ecology.

This study is aimed to include anthropogenic activities as an essential component for characterization of contemporary (i.e., modern) fire regimes, because human-induced changes can modify fire regimes and drive them outside of their natural historical range (Chuvieco, Giglio, & Justice, 2008; Pechony & Shindell, 2010; Torres, 2013). The anthropogenic component here

10 includes forestry policy and legislation, regulation of the economic use of forests and forested lands, the regional plans and strategies on economic development and list of direct industrial activities in the forest areas. Here, the key component is a forestry legislation, as it allows the economic activities in forests, which might increase the risk of fires. Hence, forestry legislation changes regulating overall forestry management and economic use of forests can be seen in this light in similar direction with policy science as related with political regime changes affecting fire activity. Here, changes in forestry legislation and related changes in forestry management will be related with changes in fire activity and included in the suggested approach for contemporary boreal fire regimes definition. The logic of inclusion of policy factor into suggested approach for fire regimes characterization is shown by two case studies: 1) changes in forestry sector and fire activity before and after the year 1990, because at that time Russia had undergone significant changes in political regime, and 2) changes related with recent forestry legislation implementation (for details see Chapter 4, Section 4.3 Recent changes in forest laws and regulation in Russia and Sakha Republic: the increasing anthropogenic pressure on forests and their suspected relation to the changes in the regional fire activity) when the Russian forestry policy changed the paradigm to on the increase of an investment attractiveness of forest sector.

Regarding the first case study. Forest management in the Soviet Union has followed a centralized decision-making. However, “since the disintegration of the Soviet Union in 1991, the country experienced various political, institutional, economic and legal reforms. Even though, the federal government had a considerable power, and the responsibility over regional authorities, their functions were started to be shared between the federal bodies (responsible for general policy and decision-making) and the regional administrations (policy adaptation and implementation)” (Mokhnacheva, 2011). The same considerable changes were happened in the Russian forestry sector.

“Since 1992, political and economic reforms in Russia have illustrated the slow transition of forestry sector to adapt to a market economy framework. In 1990s forest management was decentralized to local government authorities two times, in 1993 and 1997” (FLI, 2016). That changes caused a significant misunderstanding between the state and regional government bodies regarding functions to be performed by each authority. The result was seen by Yakutian forestry practitioners in the form of “continuous decentralization and chronic underfunding of the regional forestry service and authorities” (Ammosova, Artemyeva, & Nikoforov, 2014). In the 2000s the decentralization trend was continued and took place twice, in 2004 and 2006. Since then “the instability of the legal regulation governing forest relations became a continuous challenge for the

11 Russian forestry sector” (FLI, 2016). The mentioned changes can be seen from Figure 2.2, illustrating the fire activity dynamics in the Sakha Republic, when fire activity illustrated by burned area changes considerably increased during this transitioning period, before and after 1990.

600000

500000

400000

300000

200000 Burned area, ha

100000

0 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

FFigure 2.2. Historical trends of fire activity illustrated by burned area dynamics in the Sakha Republic of Russia, 1970-2000. Visualization of total burned area from Russian National Fire Statistics.

Further, I will discuss the main fire regime descriptors chosen to this study. That is; fire weather and anthropogenic conditions related to fire activity.

Two main groups of fire regime characteristics are used in this study such as fire drivers and fire characteristics (see Table 2.1). The selection of descriptors was based on unified character of fire activity descriptors and possibility to use them on different spatiotemporal scales and study regions.

Table 2.1. Approaches to characterization of contemporary boreal forest fire regimes. Boreal Fire Regime The Study Approach Originality Characteristics Suggested Fire Drivers Fire Characteristics by Existing Studies (when, where, which) Fire weather, seasonality in 1 Meteorology 4 Temporal Distribution Boreal Canada (Fire Weather and Climate, (temporal evolution and shifts, (Kochtubajda et al. 2006) Future Projections) fire seasonality and related changes in fire activity)

12 Continued

Fire weather, occurrence 2 Causes of fires 5 Spatial distribution in Boreal Russia (anthropogenic fire ignition (burned area extent) (Malevsky-Malevich et al. 2008) sources) Burned area and fire occurrence 3 Anthropogenic conditions 6 Fire Effects and Synergisms in (forestry policy and legislation, (impact on Forestry Management Boreal Canada and USA economic development plans and and fire suppression strategies, (Balshi et al. 2008) strategies, anthropogenic interactions and impacts) activities, economic use of forests / forested lands) Burned Area in Derived Fire Parameters for Development of the Own Boreal United States Definition of Fire Regime (Bachelet et al. 2005)

In the first group “Fire Drivers”, the descriptors such as fire meteorology and fire causes have a unified character and can be applied in any forest region. The last descriptor from this group “Anthropogenic conditions”, which includes forestry policy and legislation, economic development plans, anthropogenic activities in the forests and economic use of forests and forested lands, differ from region to region. However, the logic of research still will be the same – to define how forestry legislation and management practices affect changes in fire activity and which source of direct anthropogenic activities in the forests may affect a fire activity the most.

Descriptors from the second group “Fire Characteristics” such as temporal and spatial distributions of fire activity also have a unified character. However, descriptors such as fire seasonality and peak fire occurrence depend from local geographic and climate conditions of the particular region of interest and needed some customization, although they will retain the research logic suggested by this study and research approach.

The final descriptor from this group “Fire Effects and Synergisms” is completely different for each region. However, the future research can use the research logic of this study as starting point for building their own research approach.

2.2 Fire regime descriptors: weather and climate

Fire weather is known as “a key enabler of fire regime” (Abatzoglou & Brown, 2012). Future climate is expected to be characterized by increase in temperatures and existence of some trend

13 towards dryness according to projections from climate models (Y. Liu, Goodrick, & Stanturf, 2013). However, fire potential is defined not only by fire weather, but also by “climatology, low- frequency climate variability, the integrated sequence of daily meteorological conditions in the days to months prior to ignition, and meteorological conditions during active burning” (Abatzoglou & Brown, 2012). In turn, fire weather is defined by temperature, precipitation, humidity and wind conditions” (Y. Liu et al., 2013).

In this study, the impact of weather and climate on fire activity is investigated in two directions: (1) to assess the impact of fast, short-term climate changes on fire weather variability and on the related changes in fire seasonality and fire activity using the main fire weather parameters, such as air temperature and atmospheric precipitation; (2) to make projections of future fire weather under impact of climate change. The following analyses will be shown in analytical chapters (see Chapters 6 and 7).

2.3 Fire regime descriptors: anthropogenic activities

Climate and land use changes have altered forest fires and their regimes across the world. Deviations from natural fire regimes to anthropogenic factors is already evidenced by changes in fire attributes such as frequency, size, seasonality, severity and return interval of fires (Freeborn, Jolly, & Cochrane, 2016). Understanding the influence of anthropogenic activities on fire ignition is a key point to mitigating forest fire risk, especially in the presence of rapid climate and land use changes (Z. Liu, Yang, Chang, Weisberg, & Hong, 2012). Liu and colleagues further suggest that their study shows the evidence of strong impact of human factors on fire distribution attributed to highly populated areas and areas with developed road density as it was stated as “human development and activity patterns are beginning to override the biophysical factors controlling the fire regimes in Boreal Northeast China”.

This part of the study focuses on finding a relation between anthropogenic activities and increased fire activity in the Sakha Republic. As first step, the study analyzes the changes in the state and regional forestry legislation regulating the economic use of forests and forested lands as well as strategies for economic development of the Sakha Republic. As a second step, the study looks for a spatial correlation among areas with increased fire activity and the areas of industrial development. Finally, climate change effects accompanying industrialization is analyzed.

14 Hence, in order to suggest effective fire suppression tactics, it is extremely important to identify key anthropogenic activities altering the natural fire regimes and to take the appropriate actions in order to mitigate impacts of industrialization, road network expansion, and other development processes on fire activity, what will be discussed in the upcoming chapters.

2.4 Contemporary boreal fire regimes model

This study suggests developing a new model of contemporary boreal fire regimes. Unlike the existing conventional model, which is based on the three main fire regime enablers such as fire climate, causes of fires, fuel and vegetation composition, this study suggests including two additional parameters for fire regime characterization procedure. First is climate change and the second being anthropogenic factor.

The suggested model understands “climate component” as short-term climate change and its impact on fire weather changes. The “anthropogenic component” is anthropogenic sources of fire ignition including impacts of industrial activities on fire activity and climate change effects accompanying the industrial development.

The updated model of contemporary boreal fire regimes is presented on Figure 2.3. The suggested model of contemporary boreal fire regimes also known as fire regimes triangle has a unified character and can be applied to any forest region that is of interest.

Figure 2.3. The suggested model of contemporary boreal fire regimes.

15 CHAPTER 3 – RESEARCH APPROACH AND METHODOLOGY 3.1 General background and the significance of the research

Increasing forest fires has become a major disturbance agent in Yakutian forests (Nikolaev, Isaev, & Gabysheva, 2012), at the same, many industries are based in these forested areas. There is a further possibility of an increasing fire activity in the Sakha Republic due industrialization.

In the Republic, severe fire regimes were frequently noted over the entire fire history of the region. However, the relative impact of anthropogenic activities and regional climate variability to explain the regional fire statistics are not adequately investigated, although, the region is the most affected by fire in the Russian Far East, regarding both the number of fires and extent of the burned area (Rosstat).

Forest fires in the Sakha Republic are the major natural hazard that cause high annual ecological and economic losses, as well as the degradation of forest ecosystems and their environmental services accompanied by strong air pollution trend (Tomshin & Solovyev, 2014b) and health issues from smog caused by fires (REGNUM, 2018).

In the Sakha Republic, despite the existence of the long-term, extensive studies on forest fires (see for details Chapter 5, Section 5.1), yet, there is limited understanding of the nature and concept of contemporary fire regimes, especially in the light of recent climate change and increasing human alteration to forests. Furthermore, there is a vital necessity for assessment of the impact of the regional climate warming on fire weather and related changes in fire seasonality and fire activity. The current fire research and management are insufficient for fire monitoring and also lack credible long-term records of fires. Through addressing these challenges, this research can help the Republic’s authorities to understand the nature of changing contemporary fire regimes, in order to maintain an effective fire suppression and prevention policies. As ignitions related to anthropogenic causes will likely keep occurring in the future according to the economic development strategy of the Sakha Republic, this research also made an attempt to correlate anthropogenic activities to the changes in the fire activity in Yakutian forests.

Therefore, this study further develops a systems analytic approach for identifying characteristics of contemporary fire regimes for the region recently affected by warming and rapid industrialization to determine how these trends are related to fire activity between 1996-2018.

16 3.2 Definition of terms Table 3.1. Definition of terms related with fire activity in the Sakha Republic of Russia. Term/ Fire Parameter Definition 1 Number of fires – total number of forest fires, ignited by both lightning and anthropogenic factors 2 Burned area – total area affected by forest fires, including areas covered by forests and forested land 3 Number of anthropogenic fires – all fires, ignited by both human and industry 4 Number of lightning fires – all fires, ignited by lightning 5 Fire season – the period of the year when fires occurred, in days 6 Peak fire month – month with the highest number of registered fires/ largest extent of burned area Data Source: From Kirillina K., Shvetsov E.G., Protopopova V.V., Thiesmeyer L., & Yan W. Consideration of anthropogenic factors in boreal forest fire regime changes during rapid socio-economic development: case study of forestry districts with increasing burnt area in the Sakha Republic, Russia, Environmental Research Letters, 15 (2020) 035009 (published under CC 3.0 license).

Table 3.2. Definition of terms related with detection of climate changes signals in fire weather variability. Term Definition 1 Climate change – a statistically significant variation in the mean state of the climate or in its variability, persistent during an extended period (decades or longer) 2 Climate variability – deviations of climatic statistics over a given period of time (e.g. a month, season or year) when compared to long-term statistics for the same calendar period 3 Short-term climate variability – the “short term” are those fluctuations of the climate, whose scales range from interannual (1000 days~3 years) to interdecadal (10000 days~27 years) 4 Fire weather – weather elements that may have direct impacts on fire activity 5 Climate change signal* – the specific term developed for this study to find the impact of short-term climate variability on development of fire weather Data Sources: 1 WMO Official Climate Definitions - http://www.wmo.int/pages/prog/wcp/ccl/faqs.php https://www.who.int/globalchange/publications/climvariab.pdf?ua=1 2 (Sommers, Coloff, & Conard, 2011).

17 3.3 Research questions and hypothesis

On the basis of a conducted literature review and identified research gaps in existing research, I formulated the following research hypothesis: - this study assumes that increased fire activity in the Sakha Republic of Russia was due to the combined effect of the regional climate warming and industrialization of the regional economy: (1) the significant warming trend made the fire season longer and more severe (the extension of the fire season and related cumulative increases of the burned area in the Sakha Republic were due to the impact of regional climate change, namely earlier coming of Spring and later coming of Fall, i.e., significant warming trends during Spring and Fall months); (2) industrialization of Yakutian economy primarily based on mining operations and large agro- industries, which involved more forested lands into industrial use and accompanied by subsequent deforestation, which made the regional climate hotter and drier, and hence more vulnerable to forest fires.

Proceeding from this formulated research hypothesis, the list of detailed research questions was prepared: 1. How should we understand forest fires as both natural and anthropogenic phenomena? 2. How should we define contemporary fire regimes and choose parameters to characterize them under a changing climate and increased anthropogenic impact in Boreal forest? 3. How should we find a broader relation of fire activity changes to the impact of climate change and how should we find a more specific means for understanding this phenomenon through recent meteorological statistics and their analyses? 4. How should we statistically describe the impact of climate change on fire weather and related changes in fire seasonality and fire activity? 5. How should we model future fire activity in response to projected climate change? 6. How can findings of this study have used for implementation of effective fire suppression tactics and forestry management strategies for contemporary Boreal forests?

3.4 Research objectives

The formulated research questions helped to define the main research objective of this study, which is to develop a systems analytic approach for identifying characteristics of contemporary fire regimes for the region recently affected by warming and rapid industrialization to determine

18 how these trends were related with fire activity on the example of the most fire-hazardous region of Boreal Russia, the Sakha Republic for 1996-2018.

The main research objective can be divided into three specific objectives: (1) to find a relation between anthropogenic activities and increased fire activity in the Sakha Republic; (2) to find the impact of the rapid, short-term climate changes on fire weather variability and related changes in fire seasonality and fire activity; (3) to prepare projections of future fire season weather conditions under impact of climate change; (4) to suggest measures for effective fire suppression tactics and forestry management strategies based on the findings of this research.

In turn, that main objectives have the specific objectives are as follows. Specific objective 1: (1.1) to develop a set of parameters for analysis of recent changes in fire activity; (1.2) to analyze the temporal evolution of fire activity in order to identify the underlying causes of changes in fire activity; (1.3) to investigate a possible relation between the potential impact of anthropogenic activities on fire regime changes leading to a greater extent of the burned area. Specific objective 2: (2.1) to develop an approach for detecting signals indicating changes in fire weather due to climate change to express recent fire weather variability and related changes in fire seasonality and activity; (2.2) to find both a broader relation of fire weather changes and related changes in fire seasonality and fire activity to the climate change and a more specific means for understanding this phenomenon through recent meteorological statistics and their analyses Specific objective 3: (3.1) to find the best fitting climate model, which is based in ability of climate model to simulate the current climate; (3.2) to prepare projections of future weather conditions for the entire fire season.

3.5 Research framework and methodology

The research framework of this study based on the assessment of complex interactions between climate change, forest fires and anthropogenic activities as presented in Figure 3.1.

19 Characterization of contemporary boreal fire regimes and analysis of relation of anthropogenic factor to fire activity will be done for seasonal scale (whole fire season) for study period, 1996- 2018. However, fire seasonality analysis will consider changes in fire activity illustrated by burned area for each fire season month from April to October. Analysis of impact of fire weather changes on fire activity will consider monthly, seasonal and annual changes of selected meteorological parameters and monthly burned area changes for finding relation between changes in fire weather and burned area. Future climate projections will relate to weather conditions during entire fire season.

Figure 3.1. The research framework of the study.

The development of methodology for this study was based on this research framework. The suggested methodological approaches are shown below (Figure 3.2 and Figure 3.3).

Figure 3.2. Methodological approach for fire regimes characterization.

Figure 3.3. Methodological approaches for assessment of the impact of climate change on fire weather variability (left) and preparation of predictions of future fire weather under projected impact of climate change (right).

Detailed description of proposed methodological approaches and their application will be shown in analytical chapters:

21 - Chapter 5. Consideration of anthropogenic factors for characterization of contemporary boreal fire regimes during rapid socio-economic development: The case study of the forestry districts with increased burned area in the Sakha Republic. - Chapter 6. Identifying possible climate change signals in short-term fire weather variability and the related changes in fire seasonality and fire activity for Yakutian boreal forest. Chapter 7. Future projections of fire weather in Yakutian boreal forest under impact of climate change.

3.6 Organization of thesis

This thesis consists of 8 chapters. Chapters 1-2 are introductory. Chapter 3 presents the research organization process in order to achieve the proposed research objectives. Chapter 4 describes in detail the study area and gives the initial data description. More specific data parametrization into research indicators are presented in the following analytical chapters, 5-7. Analytical chapters, 5- 7, are organized around 3 publications reflecting the progressive orientation of the research objectives in the light of the findings achieved throughout this research. Chapter 8 presents original findings and final results of the research and provides recommendations for sustainable forest fire management and effective forest fire suppression for the study area as well as other Boreal world regions with some applied customization.

Chapter 1 introduces the theoretical context, background and motivation of the research on climate change impact on forest fires and changing contemporary fire regimes with focus on Boreal forest region.

Chapter 2, “Identified Research Gaps in Understanding Contemporary Boreal Fire Regimes as a Key Concept for Boreal Fire Ecology and Forestry Management” provides the literature review on current state of knowledge on contemporary boreal fire regimes as a key concept in a boreal forestry and fire management. Presented the identified research gaps on contemporary boreal fire regimes as affected by climate change and increasing anthropogenic pressure, making a basis for development of the research framework and methodology.

Chapter 3, “Research Approach and Methodology” gives the details of this research, including development of research questions, hypotheses and objectives, building the research framework and methodology, definition of key terms and analytical indicators.

22 Chapter 4, “Study Area and Initial Data Description” provides detailed description of Yakutian forest composition, Forestry Service organization and functions and overview of recent changes in the Russian forestry legislation and its impact on functioning of Yakutian Forestry Service and related involvement of forested lands into economic activities.

Chapter 5, “Consideration of anthropogenic factors for characterization of contemporary boreal fire regimes during rapid socio-economic development: The case study of the forestry districts with increased burned area in the Sakha Republic” suggests an approach to find a relation between anthropogenic activities and recent increase of fire activity as illustrated by Yakutian forestry districts with the increased burned areas.

Chapter 6, “Identifying possible climate change signals in short-term fire weather variability and the related changes in fire seasonality and fire activity for Yakutian boreal forest” suggests an approach for detecting signals indicating climate-induced changes in fire weather to express the recent fire weather variability and its impact on fire seasonality and fire activity.

Chapter 7, “Future projections of fire weather in Yakutian boreal forest under impact of climate change” suggests the approach for preparation of fire weather projections under impact of climate change based on the long-term observational meteorological data and climate modeling outputs.

Chapter 8 provides synthesis and recommendations for implementing effective fire monitoring and suppression tools for sustainable Boreal forest management.

23 CHAPTER 4 – STUDY AREA AND INITIAL DATA DESCRIPTION 4.1 Study area: The Republic of Sakha (Yakutia) of Russia

The study area, the Sakha Republic, is the largest region of the Russian Federation located on the north-east part of the country (see Figure 4.1). The Sakha Republic is characterized by severe continental climate with very low temperatures in the winter (between -50°C and -60°C), and very high summer temperatures (as high as 29°C-38°C (Sofronova, Ivanova, Mikhaleva, & Poryadina, 2014)). The climate is characterized also by being very dry. The amount of annual precipitation, especially in the central part of the Republic, is almost equal to semi-arid territories and characterized by frequent drought conditions in summer (Korzhuev, 1965). Precipitation is as low as 150-250 mm, which along with frequent droughts create the favorable fuel conditions for fires (Tomshin & Solovyev, 2018).

In recent decades, the Republic had the largest area of fire disturbance in the Russian Far East. Chen et al. (see (Chen, Loboda, Channan, & Hoffmann-Hall, 2014)), using analyses of Landsat data, showed that the Eastern Siberia (the federal district in which the Sakha Republic belongs) had the largest area of fire disturbance in Russia during 1985-2000. The work of De Groot et al. on Russian boreal fire regimes (in (De Groot et al., 2013)) of Central Siberia (including the Western part of the Sakha Republic), used remote sensing to arrive at a total of 39.67 million hectares of burned area during 2001-2007.

Forests occupy 82% of the territory of the Republic, which equals to 254.7 million hectares (Minprirody, 2018). The territory of the Republic is crossed by four vegetation zones: boreal forests (taiga), tundra, forest-tundra and arctic deserts. The arctic vegetation occupies 26%, and the boreal – 74%, where this study is focused on. The open coniferous tree stands prevail. Larch species (Larix cajanderi, L. gmelinii, L. sibirica, L. czekanowskii) comprise 90.5%, pine (Pinus sylvestris) 7.3% and Siberian spruce (Picea obovata) 0.6% of the tree cover (Protopopova & Gabysheva, 2016).

Forest inventories in Russia define all Russian forests by five classes of fire hazard based on landscape/ecosystem indicators from Class 1 (the highest fire danger), to Class 5 (A. Shvidenko & Goldammer, 2001). The average natural fire hazard risk class of Yakutian forest is 2.9, which is about the median. However, forests with the highest fire risk (1-2 classes) account for 33.7% of Yakutian forests according to the Forest Plan of Sakha (The Forest Plan of the Sakha Republic, 2018).

Figure 4.1. Study area.

4.2 Information on Yakutian forestry service and its administrative division

The study assesses a fire activity in the Sakha Republic using forestry district as a unit for analysis. The Russian forestry legislation defines “forestry district” as the main territorial unit of management in the field of use, protection, and reproduction of forests and forested areas in the country. At the same time, the forestry district is a key instrument of the forest management system in the country. A total 19 forestry districts are located on the territory of the Sakha Republic. Information on them is presented in Table 4.1. For further analysis, the research used 5 forestry districts with increased burned area, including Amginsky, Gorny, Khangalassky, Verkhnevilyuisky and Vilyuisky.

25 Table 4.1. Information on the Forestry Service of the Sakha Republic. Forestry Size, ha Forest Percentage Class cover, of of % flammable natural land per fire forestry, % hazard 1 Aldansky 15 565 881 82.4 6.9 2.5 2 Amginsky 2 811 838 86.1 21.9 2.3 3 Gorny 4 371 772 87.8 15.0 2.1 4 Indigirsky* 49 021 981 22.2 0.8 3.2 5 Khangalassky 2 818 473 84.9 8.7 2.5 6 Lensky 7 462 539 88.9 8.2 2.0 7 Mirninsky 16 034 899 67.8 3.8 3.0 8 Megino- 3 693 131 79.5 14.4 3.0 Khangalassky* 9 Neryungrinsky 11 687 352 91.7 2.5 2.6 10 Nyurbinsky 4 894 650 84.2 16.4 1.8 11 Olekminsky 12 119 286 77.4 8.9 1.6 12 Suntarsky 5 296 463 87.0 16.8 1.0 13 Tomponsky* 45 405 090 27.8 2.7 3.3 14 Ust-Aldansky 1 616 768 79.2 7.5 3.0 15 Ust-Maisky 9 331 916 76.4 14.3 3.3 16 Verkhnevilyuisky 3 803 452 72.7 13.5 2.4 17 Vilyusky 5 114 216 62.6 24.9 1.8 18 Yakutskiy* 1 196 037 62.7 23.0 2.1 19 Zhigansky* 52 506 859 45.6 1.2 3.1 * One forestry district encompasses the territory of several districts (the unit of administrative division in the Republic). Underlined forestry districts names are those forestry districts I focused on here. Data Source: The Forest Plan of the Sakha Republic, The Republic Act No 48, in Russian.

4.3 Recent changes in forest laws and regulation in Russia and Sakha Republic: the increasing anthropogenic pressure on forests and their suspected relation to the changes in the regional fire activity

For Russia as a country with the largest forested area of the world, the appropriate legally established norms and rules of forest management are of particular importance (Zainutdinov,

26 2009). However, the most recent changes in the forestry legislation might result in a threat to sustainable forestry practices.

The newly approved “Foundations of the State Policy in the Field of Forests Use, Conservation, Protection and Regeneration of Russia till 2030” are primarily focused on the increase of an investment attractiveness of the Forest Sector. According to the opinion of some Russian scientists and legal experts, the new Forestry Code helped to bring the forest legislation in accordance with civil legislation which facilitates economic use of land, that can include forests and forested lands (Vasilyeva, 2007). It makes possible the conversion of forested lands into lands for industrial development. Some Russian forests are already privatized or leased for the long-term to big mining companies (Dolinina, 2012; Gladun, 2011) who deforest the areas around the mining operations. Hence, these forests do not provide their important ecological services, including temperature moderation and moisture retention which reduce flammability. The study described the above- mentioned relationships in the scheme, which is presented in Figure 4.2.

Figure 4.2. Impact of recent forest legislation changes on forest fire risk.

The highest number of incidents of forest land conversion reported for the exploration and mining operations, mostly in the Russian Far East (Maryin & Glazachev, 2007), where Sakha Republic belongs. As result, the economic use of forests and forested lands has risen rapidly; leading to further deforestation. This increases heat, dryness, and risk of forest fires in the region.

In particular, the “Strategy of socio-economic development 2030 of the Sakha Republic” defines the region’s development to further focus on potential extraction and processing of mineral and fossil resources, establishment of refineries, pipelines, roads and other supporting infrastructure for industry. In this regard, the area under development of new industrial sites and building of new roads and pipelines keeps actively growing. Indicating a more engagement of forests and forested

27 lands into economic activities. There were 3929 contracts leases of forest plots as at 01/01/2018, representing an increase of 17% from the previous year. The main uses are geological exploration of mineral resources and development of mineral deposits (55.3%); building, construction, and operation of linear infrastructure such as pipelines, power lines and roads (25.1%) and logging for building, construction and operation of facilities for industrial use, linear objects, hydro-technical installations, and wood processing industries (10.0%) (Minprirody, 2018). All of these require deforestation and result in drying and warming and possibly overlap with the larger burned area. These activities are represented in the scheme as shown in Figure 4.3. However, the described relationships have a more complex character, especially in a way how to connect the regional and climate change patterns and their possible impact for different spatial scales including regional and global.

The suggested correlations is investigated further.

Figure 4.3. Interrelations between climate change, forest fires and industrialization.

In the following chapters (5-7), I will discuss the specifics of these interrelations.

28 CHAPTER 5 – CONSIDERATION OF ANTHROPOGENIC FACTORS FOR CHARACTERIZATION OF CONTEMPORARY BOREAL FIRE REGIMES DURING RAPID SOCIO-ECONOMIC DEVELOPMENT: THE CASE STUDY OF THE FORESTRY DISTRICTS WITH INCREASING BURNED AREA IN THE SAKHA REPUBLIC 5.1 Introduction “Characterization of contemporary fire regimes under impact of climate change and increasing human alterations to forests”

The increase of forest fires is potentially a major hazard across forest ecosystems worldwide and has been linked to climate change and increasing human impacts (Zumbrunnen et al., 2011). Fire activity is a complex natural phenomenon (Wastl & Menzel, 2012), because each fire has its own spatiotemporal characteristics and fire effects. To capture this diversity, ecologists in the early 1960s formulated the term “fire regime” (Bowman et al., 2009) to indicate the characteristics, effects, and variability of fire disturbance patterns (Schoennagel, Veblen, & Romme, 2004). Specifically, “fire regime” refers to the different time windows, spatial units, fire characteristics, and conditions determining fire occurrence and its impacts (Conedera et al., 2009). However, these characterizations may possess limitations.

Human-caused changes can modify fire regimes and drive them outside of their natural historical range (Torres, 2013). Many studies (Chuvieco et al., 2008; Pechony & Shindell, 2010) report shifts from natural fire regimes due to human impacts. From this point of view, this research advocates for inclusion of socioeconomic aspects into the fire regime characterization, specifically in regions with forest-based industries and developing economies, such as the Sakha Republic in the Russian Far East. In relation to that, this study suggests following definition of contemporary fire regimes as term statistically describing the spatiotemporal distribution, temporal evolution, seasonality and causes of fires, which are dynamic varying system in a response to certain drivers such as climate change, and direct anthropogenic activities in forests.

The other challenge in fire regime characterization is where there is a lack of documented historical fire data. This is crucial for the regions having a long history of persistent fire events. Eastern Siberia and the Far East of Russia possess these characteristics as the area lacks adequate data on past fire occurrences (Vivchar, 2011). The region is quite critical as the Republic of Sakha (Yakutia) is one of the areas with the highest annual burned area. In certain periods, the Republic alone accounts for up to 75-93% of burning forest fires based in the Russian Far East (Rosstat).

29 At the same time, the Sakha Republic was undergoing an economic transformation through the State economic development policy. As a result, since 1996 the Republic has experienced rapid industrialization, primarily in mining exploration, exploitation, and refining, along with large agro- industries ("Catalogue of investment projects of Sakha Republic (Yakutia)," 2019; "Investment guide book of the Sakha Republic (Yakutia)," 2011; Nikiforova, 2019). Since 1996 the new wave of industrialization in the Sakha Republic is due in part to the recent changes in forestry legislation oriented to increasing the economic use of forested lands. It brought forestry legislation, which was supposed to protect forests, into accord with civil legislation, managing businesses and services; converted some forested lands into lands for industrial use; and granted easy access and long-term leases of forested lands to large industries (Karjalainen et al., 2013; Tornianinen & Saastamoinen, 2007). The highest number of incidents of forest land conversion are those for exploration and mining operations, particularly in the Far East of the country, of which the Sakha Republic is the largest part. As a result, economic uses of forest and forested lands in the Republic have risen rapidly leading to further deforestation, which increases heat, dryness, and risk of forest fires. The yearly changes in the law that resulted in increased economic activity since 1996 are shown in the Russian original of the Forest code (see articles 7-9, 12, 21, 24, 43, 70-71, 79-80, 87, 91 of the Forest Code of Russia (The Forest Code of the Russian Federation, 2006) and its English version (The Forest Code of the Russian Federation, in English, 2006). These changes, coupled with other anthropogenic interventions such as deforestation, can alter forests and can cause changes in natural fire regimes. The deforestation can make the regional climate even drier and hotter (Bonan, 2008; Lejeune, Davin, Gudmundsson, Winckler, & Seneviratne, 2018), and hence more vulnerable to forest fires during peak fire period.

Sakha Republic has a long history of persistent fire events. Published research on fire phenomena began from the 1960s when various scientific surveys were conducted (see (Utkin, 1965); also the overviews by (Scherbakov, Zabelin, & Karpel, 1979) on surveys starting from 1960 and by (Tsvetkov & Buryak, 2014)). Later surveys covered a wide range of fire research topics such as the “development of local fire danger scales in accordance with regional forest composition” (Protopopova & Gabysheva, 2016), “analysis of spatiotemporal distribution of lightning and lightning-ignited forest fires” (Kozlov, Mullayarov, Grigoriev, & Tarabukina, 2014; Tarabukina, Innokentiev, & Kozlov, 2018), and “fire-climate studies” (Hayasaka, 2011; K. S. Kirillina, Goumehei, & Yan, 2016).

Despite this extensive research, fire studies in Yakutia can be argued to contain three main limitations. First is a lack of regional fire studies, as most of the research was focused in Central

30 Yakutia which historically was a fire hot spot area, whereas now fires occur on the whole territory of the Republic. Secondly, these studies did not address how the fire regime in the republic might change due to a strong warming trend which began in the 1960s (Fedorov et al., 2014; Skachkov, 2005). This is because the weather station data for the 96 meteorological stations in the Sakha Republic dates back to 1953 and up to 2013. The record shows a trend of gradual warming, and a cumulative trend of warming of over 1°C since the year 2004. Particularly, from 2011 we see an obvious and continual warming trend (see Appendix 1), occurring at the same time as new industrial projects began, including: • Launching of timber logging and processing facility, Amginsky forestry district (2011- 2012). • Launching of timber logging and processing facility, Gorny forestry district (2011-2014). • Expansion of cement production, and gasification of rural facilities, Khangalassky forestry district (2012-2016). • Construction of road service facilities at public motorways, Verkhnevilyuisky forestry district (2012-2016). • Development of Srednetyungskoe gas condensate field (2011-2013), and construction of gas condensate processing complex (2011-2012), Vilyuisky forestry district ("Investment guide book of the Sakha Republic (Yakutia)," 2011).

This warming trend is especially strong in the transitioning spring season (K. S. Kirillina & Lobanov, 2015), which might affect the length of fire season. The third limitation is the omission of a human-induced or anthropogenic aspect to the fire regime characterization.

Regional climate change combined with intensive human alteration of forests and conversion of forested lands into lands for industrial use have the potential to shift the regional fire regimes. To build on the previous research and account for the identified limitations, this research analyzes historical trends of fire activity in the Sakha Republic. It assesses trends in the fire activity for Sakha Republic from 1996 to 2018 and analyzes the spatiotemporal evolution of fire regime, the underlying causes of changes identified as described by annual burned area. A possible correlation between the potential impact of anthropogenic factors on fire regimes leading to greater burned area was investigated.

31 5.2 Conceptual framework for characterizing fire regimes

Fire data obtained from the Sukachev Institute of Forest wildfire database (satellite burned area estimates and number of fires) and the Yakutian Branch of Aerial Forest Protection Service of Russia (fire cause and seasonality) were aggregated to regional (Sakha Republic) and local (for selected forestry districts) levels and were compiled into the fire variables for analysis, including: • Number of fires – total number of forest fires, ignited by both lightning and anthropogenic factors. • Burned area – total area affected by forest fires, including areas covered by forests and forested land. • Number of anthropogenic fires – all fires, ignited by both human and industry. • Fire season – the period of the year when fires occurred, in days. • Peak fire month – month with the highest number of registered fires/largest extent of burned area.

Characterization of fire regime was done in four aspects: (1) spatiotemporal distribution, (2) interannual variability, (3) seasonality and (4) fire causes.

First, to analyze the historical fire activity we derived historical trends of annual number of fires and burned area for Sakha Republic. Standard errors were calculated for all applicable averages. To make a more robust analysis of burned area extent we used satellite burned area estimates obtained by Shvetsov E.G. for the entire Republic and five selected forestries with increased burned area for 1996-2018 from the Sukachev Institute of Forest wildfire database, which contains active fire detections from NOAA AVHRR (1996-2007) and TERRA/AQUA MODIS (2007- 2018). This wildfire database was generated using a multistep process including: (1) contextual active-fire detection; (2) creation of fire polygons from adjacent fire pixels and (3) correction of resulting polygons. The detection procedure for AVHRR-data was generally similar to the HANDS algorithm (Fraser, Li, & Cihlar, 2000). The processing chain for MODIS data was based on the approach of Giglio (Giglio, Descloitres, Justice, & Kaufman, 2003) considering several adjustments in background characterization and detection probability estimation (Shvetsov, 2012). The algorithm used in the Institute of Forest differs mainly by aggregating fire detections into fire polygons and subsequently using a correction procedure. This correction procedure was based on comparison between the burned areas measured using low resolution data (AVHRR/ MODIS) and moderate resolution data (Landsat) for the test sample of fires (~5% of all annually detected fires) and obtaining the linear regression equations for several fire size classes. Then these equations

32 were used to correct fire polygons for the rest of the fires detected using low resolution data (Ponomarev & Shvetsov, 2015).

Second, changes in the temporal evolution of fire regime features were investigated using the Rodionov regime shift detection method (Rodionov, 2004). Rodionov’s method helps to find inflection points indicating regime shift, i.e. years when shifts from one phase to another occurred. To detect increases in fire activity, the total annual burned area data were analyzed for abrupt positive shifts in the mean, where shift is a statistically significant deviation from the mean value of the current regime, in this case that year becomes a potential change point (more in (Rodionov & Overland, 2005)). We used Rodionov’s regime shift detection software (Version 6.2), in which the following parameters could be adjusted: probability level, cut-off length, outliers weight parameter (Huber parameter) and subsample size (see relevant parameters in (Meyn et al., 2010)). All parameters were dependent on the original fire data.

To detect long-term trends in selected fire features during the study period (1996-2016), we used the Mann–Kendall trend test. The slopes of trends were estimated by Sen’s slope estimator. The trend analysis was completed for burned area only as our analysis was centered on burned area. The trend analysis was carried out using XLSTAT Software. We compared conducted trend analysis using the MAKESENS program for annual time series data developed by the Finnish Meteorological Institute (Salmi, Maatta, Anttila, Ruoho-Airola, & Amnell, 2002).

Both Rodionov’s regime shift detection method and the Mann-Kendall trend test were chosen due to their robustness to autocorrelation.

Finally, we assessed fire seasonality and causes based on descriptive analysis and literature review. Information on the length of fire season and peak fire months (month with the highest number of registered fires) were obtained from the Yakutian Branch of Aerial Forest Protection Service of Russia. Information on peak fire period depending on when the largest burned area extent was obtained from Sukachev Institute of Forest wildfire database for selected fire seasons (years).

5.3 Compilation of fire features for fire regime characterization

To assess historical fire occurrence in the Sakha Republic we used fire records from 1996 to 2018, obtained from the Sukachev Institute of Forest wildfire database (general details of this database can be found in (Ponomarev et al., 2016)). Fire causes and seasonality data were obtained from

33 annual reports of the Yakutian branch of the Aerial Forest Protection Service of Russia which are based on satellite (ISDM-Rosleskhoz, available online on https://nffc.aviales.ru/main_pages/index.shtml, website requires authorization), aerial and ground observations of fires (collected by Kirillina, K. in August 2017). There are 19 forestry districts, which are the main territorial unit of management in the field of use, protection, and reproduction of forested areas in the country. For analysis we chose five forestry districts having high fire activity including Amginsky, Gorny, Khangalassky (in Central Sakha Republic), Verkhnevilyuisky and Vilyuisky (in western Sakha Republic).

As supporting research data, we used: • GIS data for preparation of the regional fire maps from the Russian Open GIS Portal ‘GIS- Lab’ (http://gis-lab.info), in Russian. • Climate data (air temperature) obtained from NOAA climate database (https://7.ncdc.noaa.gov/CDO/cdoselect.cmd?datasetabbv=GSOD&countryabbv=RS&ge oregionabbv=&resolution=40) and the Russian Institute of Hydrometeorological Information climate database (http://aisori-m.meteo.ru/waisori/index0.xhtml, in Russian). • Information on economic use of forests and forested land collected from regional forest policies and legislation, Government reports on environmental protection, and plans and strategies on socioeconomic development of the Sakha Republic.

5.4 Data analysis and discussion 5.4.1 Assessment of recent fire occurrences and burned area extent, 1996-2018

This 22-year period was chosen because during this period of time the Sakha Republic underwent rapid industrialization, which we are proposing impacted the fire activity. Further, as mentioned above we have satellite data from AVHRR and MODIS for the period starting from 1996.

There were 13620 fires and 39 120 600 ha burned area from 1996, the year from which we had satellite data, to 2016 in the Sakha Republic; with average annual values being 592±85.92 fires and 1700 895.7±359 792.57 ha burned (Figure 5.1).

34 7000 1800

1600 6000 1400 5000 1200

4000 1000

3000 800

600 2000

400 Number of fires 1000 200 Total burned area, ha (x1000) 0 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Total burned area (x1000) ha Number of fires

Figure 5.1. Long-term trends of the number of fires and burned area in the Sakha Republic, Russia. Visualization of total burned area and number of fires from raw data.

These findings represent a departure from the results of previous historical fire studies for Sakha Republic (Nikolaev et al., 2012): there is a simultaneous growth in the number of fires, which is used for fire seasonality analysis below, and in the extent of annual burned area.

5.4.2 Analysis of interannual variability and trends in fire regime

Burned area on both regional (Sakha Republic) and local (five selected forestry districts) levels became the subject for this analysis. The results of the trend analysis of burned area are presented in Table 5.1.

Table 5.1. Burned area trend detection analysis for Sakha Republic and five selected forestry districts, 1996–2016 (the latest year for which forestry district data were available). Time series Test Z Significance Q, for the forestry statistic Sen’s slope district estimate Sakha Republic 2.14 α=0.05 68.244 Verkhnevilyuisky 1.99 α=0.05 1.600 Vilyuisky 2.48 α=0.05 2.008 Gorny 2.24 α=0.05 3.396 Khangalassky 1.45 - 0.450 Amginsky 0.88 - 0.182 Data source: Raw data compiled by Shvetsov, E.G., 11.01.2019.

35 Significant increases in the burned area were detected for the Sakha Republic overall, and three out of five researched forestry districts, which were located in the central (Gorny) and western parts of the Republic.

These changes might be explained by the existence of a strong warming trend and increasing anthropogenic impact. From year 2000, the Sakha Republic became much more rapidly industrialized. The central part has been impacted by agro-industry and population growth. The western part has been impacted by rapid industrial development.

Burned area on a regional scale showed significant abrupt increase in 2012 (Figure 5.2). One forestry district (Verkhnevilyuisky, Western part of the Republic) out of five showed abrupt significant increase of burned area observed starting from 2013 (Figure 5.3).

Shifts in the mean for burned area in the Sakha Republic, 1996-2018 Target p = 0.05, cutoff length = 3, tuning constant = 2 7000

6000

5000

4000

3000

2000

Burned area (x 1000 ha) 1000

0 1996 2006 2016

Figure 5.2. Temporal evolution of burned area in the Sakha Republic, 1996–2018. Regime shift analysis for Sakha Republic (based on Rodionov regime shift detection method). Data source: Raw data compiled by Shvetsov, E.G., 10.24.2019. Where blue line – the actual time series, red line –series weighted, and black line – trend line.

36 Shifts in the mean for burned area in Verkhnevilyuisky forestry district, 1996-2016 (the latest year for which forestry district data were available). Target p = 0.05, cutoff length = 3, tuning constant = 2 600

500

400

300

200

Burned area (x1000 ha) 100

0 1996 2006 2016

Figure 5.3. Temporal evolution of burned area in Verkhnevilyuisky forestry district, 1996–2016. Regime shift analysis for Verkhnevilyuisky forestry district (based on Rodionov regime shift detection method).

In the following we aim to show a correlation between anthropogenic factors involving new, rapid developments in mining, refining, and agroindustry.

5.4.3 Analysis of changes in fire seasonality and related changes in burned area

Duration of fire season in the Sakha Republic is a period between the first and last registered fires in the protected forest areas. Historically the start of the fire season was usually the first week of May and the fire season lasted until mid-September. The length of fire season varies among forestry districts; and might vary depending on the weather conditions of a particular year. The seasonal distribution of fire activity also varies. Fires at the beginning and the end of the fire season usually have shorter duration, with smaller burned area due to more moderate fuels and fire weather conditions for large and long-lasting fires. The peak of fire occurrence falls in July-early August (Solovyev, Kozlov, & Mullayarov, 2009).

Analysis of historical fire records showed that the average duration of the fire season shifts to a longer fire season, from 115±3.81 days from 2005 up to 128±2.78 days starting from 2011 (summarized in Table 5.2). Moreover, in the 2010s the extension of peak fire period was detected. Previously, the month with the peak (maximum) number of fires had been July, but in the latest decade (since 2010) the peak fire period was recorded as being throughout a three-month period from May to July. In the 2000s, peak fires were recorded only in July (besides 2009); however,

37 since 2010 the peak fire month has varied: May (2011, 2013), June (2012, 2016), and July (2010, 2014, 2015).

Table 5.2. Significant changes in fire seasonality in the Sakha Republic up to 2016 (the latest year for which data were available).

Year Duration of fire season, days Peak fire month (Mean) (measured by number of fires) 2005-2010 115 ±3.81 July

2011-2016 128±2.78 May (2011, 2013)/ June (2012, 2016)/ July (2010, 2014, 2015) Data source: The annual reports of the Aerial Forest Protection Service of Russia in the Sakha Republic based on satellite, aerial and ground fire observation data.

Table 5.3 presents the analysis of peak fire period based on the largest extent of burned area.

Table 5.3. Peak fire months in the Sakha Republic from 2000-2018 correlated with burned area (ha x 1000), with largest burned areas shown in bold face. Year April May June July August September October Cumulative burned area, (ha x 1000) 2000 0 0 8.4 17.1 16.2 1.4 1.5 44.6 2001 0 0 95.8 1281.4 679.6 41 0 2097.8 2002 0 209.4 442.5 1605.7 1598.9 661.7 15.2 4533.5 2003 0 8.8 52.9 246.7 448.1 141.4 2.3 900.3 2004 0 0.9 1.4 42.9 0.9 0.1 0.7 46.9 2005 0 4.8 15.5 780.5 0.3 45.6 0.1 846.8 2006 0 0.8 23.6 248.4 60.9 6.3 1.8 341.8 2007 0 0 3.9 12.4 31.4 2.8 0 50.5 2008 0 13.9 489.5 528.4 31.6 0.9 0.1 1064.4 2009 0.2 12.9 101 518.8 46.2 58.9 0.7 738.8 2010 2.8 89.8 3.9 577.3 194 26.1 30.8 924.7 2011* 138.5 1334.6 196.6 1179.4 409.1 NA NA 3258.2 2012 0.3 81.1 1421.4 2520.6 1178.6 610.3 2.9 5812.2 2013 39.8 394.2 711.3 1062.8 1569.6 60.1 13.1 3850.9 2014 8.5 65.7 127.9 2135.8 1570.6 94.3 9.9 4012.7 2015 25.6 108.2 22.7 243.6 299.8 384.8 159.4 1244.1 2016 37.4 36.6 64.2 107.1 21 479.9 12.4 758.6 2017 10.1 20.3 269.5 1007.9 779.6 17.5 2.2 2107.1 2018 6.5 146.1 714.5 1855 1232.5 445.7 12.1 4412.4 With averages: for 2000-2009 – 1066.5±433.44 (ha x 1000); for 2010-2018 – 2931± 587.94 (ha x 1000). *2011 fire season – burned area data was available only until end of August. NA – not available. Data source: Raw data compiled by Shvetsov, E.G., 10.24.2019.

38 From Table 5.3 we can see a marked increase in burned area almost steadily starting from 2011. Also, the fire season from 2009 starts in April and lasts until October. Notably there were also significant amounts of burned area in the new onset month of April and the ending month of October, which impacts not only the length of fire season, but also increases total burned area extent. July, however, was still mostly the month with the largest burned area. Also, there exists a second (post-2016) rapid and intensive increase of cumulative burned area during the most recent fire seasons (2017 and 2018).

5.4.4 Analysis of fire causes

According to the regional fire statistics, during the study period 1996–2016 (the latest year in which the binary breakdown into lightning – anthropogenic was used), the majority of forest fires in the Sakha Republic had an anthropogenic character – 47.5%. The second main cause of fires was lightning – 43.3% (Figure 5.4). The decreasing ratio of lightning fires during the study period is consistent with similar trend in lightning frequency, which did not show a consistent increasing trend, just some periodical oscillations as presented by research from Tarabukina et al. 2018 (Tarabukina et al. 2018).

80 70 60 50 40 30

Fire causes, % 20 10 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year Lightning, % Anthropogenic, % Fire of unknown reason, % Figure 5.4. The fire causes in the Sakha Republic, 2000–2016. Data Source: the annual reports of the Aerial Forest Protection Service of Russia in the Sakha Republic based on satellite, aerial and ground fire observation data. These do not specify which kind of anthropogenic causes but show a general prevalence of them from 2006.

As in the other boreal regions of the world, the lightning-ignited fires in the Sakha Republic historically have been responsible for more burned area than anthropogenic fires. However, this situation started to change in recent decades. In our case, the determination of causality is

39 complicated by the fact that the regional forestry legislation does not require the on-site observation or suppression of fires if they are located in remote areas and do not make a threat to human settlements (Government Degree N177 from 25.05.2016 “On the approval of forest fire control zones in the territory of the forest fund of the Sakha Republic Yakutia”). Thus, the causes of ‘remote fires’ are not thoroughly assessed; and by default, lightning is designated as their main possible cause, especially if they occur in the areas with high lightning activity (Solovyev et al., 2009).

Lightning fires are typically most likely to occur during June-August following the seasonal dynamics of lightning activity (Solovyev, Kozlov, Karimov, & Vasiliev, 2010). Spring (May fires) and Fall (September fires) fires were typically called anthropogenic. Spring fires were thought mainly occur due to traditional agricultural burning practice needed for renewal of agricultural fields. This procedure was part of Yakutian cattle breeding and grazing culture. Fall fires were usually blamed on local livelihood and recreational activities such as hunting, picnicking, berry and mushroom picking (Protopopova & Gabysheva, 2015).

Within the historical prevalence of lightning fires on the territory of Sakha Republic, since the early 2000s the ratio of anthropogenic fires has begun to increase, as shown in Figure 5.5. Further, from the mid-2000s anthropogenic fires started to prevail. To test this assertion, we performed a test for a statistically significant difference in cause attribution for 2000-2016 study period, which was found to be statistically significant (P=0.03). At this point we might suggest that new sources of anthropogenic fires may be attributed not only to farms, hunting and recreational areas, but also to increasingly populated and industrialized areas (Central and Western forestry districts). The same goes with new industrial sites and development areas. This was shown in Figure 6.5, which gives the spatial correlation between the location of industrial and agricultural areas and fire activity.

Figure 5.5. Map of current industrial and agro-industrial areas with burned area for five forestry districts in the Sakha Republic, 2000-2016. Map key: Cross-hatched areas – agricultural districts, bold striped areas – industrial district, striped areas – new industrial site, and red circles – burned area, in (x 1000 ha). Visualization of total burned area from raw data compiled by Shvetsov, E.G.

Figure 5.5 shows that the distribution of burned area extent is higher in developed industrial and agricultural areas. The growth of burned area extent also can be observed near new industrial sites. The high burned area extent in agro-industrial areas such as most of the forestry districts in Central Yakutia (Amginsky, Gorny and Khangalassky) can be explained by the vulnerability of the dry climate of this region to the agro-industrial practice of large-scale burning, especially before new planting, which occurs at the beginning of fire season in May. In regard to the percentage of anthropogenic fires, the increase was detected in five forestry districts and mostly attributed to the zones of new industrial and agro-industrial development, which might be related to involvement of more forested areas into industrial, agro-industrial and infrastructural development in the Sakha Republic.

5.5 Conclusion

We characterize historical fire regimes in the Sakha Republic, Russia - an area with limited fire data with statistical analysis of spatiotemporal changes of historical fire trends by supplementing regional fire statistics with satellite burned area estimates.

41 There is evidence on long-term increase of fire activity during the period 1996 to 2018 in the Sakha Republic, illustrated by Figure 5.1 (to 2018) and Table 5.3 (to 2018): 1. There were 13 620 fires and 39 120 600 ha burned area from 1996 to 2018; with average annual values being 592±85.92 fires and 1700 895.7± 359 792.57 ha burned. 2. Significant increase in the burned area detected in the Republic and its central and western forestry districts as seen in table 5.1. 3. The shift to a longer fire season from 115±3.81 days in the mid-2000s up to 128±2.78 days starting from 2011, as well as an earlier beginning of the fire season (from April) from 2009 until 2018 (Tables 5.2 and 5.3). 4. The peak fire occurrence period is now extending across a three-month period (from May to July). 5. Were registered positive shifts in the temporal evolution of burned area after 2010, on both regional and local (forestry district) levels. 6. The study articulated potential shift of the causes in recent Boreal fire regimes as the effects of anthropogenic activity. As result, fire regimes were affected by anthropogenic factors shown by significant increases of the ratio of anthropogenic fires and the increases of the burned area in the forestry districts affected by recent industrialization illustrated by the increased burned area after 2000. The timing of significant upward shifts in the evolution of fire regimes matched with periods of climatic warming, which accompanied the industrial development projects in the same forestry districts.

We believe that it should be possible to use the proposed approach for assessment of historical fire activity in other regions with limited data on fire, but where knowledge about risks of fire is vitally needed.

The Chapter 5 is a slightly modified version of the paper by Kirillina K., Shvetsov E.G., Protopopova V.V., Thiesmeyer L., & Yan W. Consideration of anthropogenic factors in boreal forest fire regime changes during rapid socio-economic development: case study of forestry districts with increasing burnt area in the Sakha Republic, Russia that first appeared in Environmental Research Letters 15 (2020) 035009 (https://doi.org/10.1088/1748- 9326/ab6c6e) under the terms of the Creative Commons Attribution 3.0 license and has been reproduced here with the permission of the copyright holder.

42 CHAPTER 6 – IDENTIFYING POSSIBLE CLIMATE CHANGE SIGNALS IN SHORT- TERM FIRE WEATHER VARIABILITY AND THE RELATED CHANGES IN FIRE SEASONALITY AND FIRE ACTIVITY FOR YAKUTIAN BOREAL FOREST 6.1 Introduction “Climate change signals in fire weather and related changes in fire seasonality and fire activity”

In the past century the global climate has changed by an increase of mean temperature. For boreal areas, the Asian Development Bank notes that warming is higher over higher latitudes in Asia, and projects that temperatures may rise by 2-8°C in this century (ADB, 2017). Because this increase has been noticeable since the 1970s, it has been possible to examine whether the changing climate has already affected the frequency and magnitude of recent forest fires. Due to this, scholars could establish relationships between meteorological conditions and fire occurrence (Pinol, Terradas, & Lloret, 1998). However, “fire potential and fire behavior of individual fires during specific days or months are identified by fire weather” (Y. Liu et al., 2013), which can be highly variable.

In this study, we aimed to develop an approach for detecting signals indicating changes in fire weather due to short-time climate changes to express recent fire weather variability and related changes in fire seasonality and activity. In doing so, we would like to cover three important fire ecology problems. The first is the necessity for research devoted to the assessment of the impact of short-term and rapid climate changes on fire weather variability. It is related to the fact that recent climate changes became faster and more abrupt (NOAA 2019). Also, most of the existing research (Dupire, Curt, & Bigot, 2017; Freeborn et al., 2016; Jolly et al., 2015; Z. Liu et al., 2012) investigated the impact of climate change on fire weather variability using long-term ranks of meteorological parameters with durations of more than thirty years. However, long-term averages can conceal climate changes appeared in recent decades and may not detect the initial signal of rapid climate changes (Bateman et al., 2016; Farinotti, 2013). So, what do we know about the impact of more rapid climate changes on fire weather variability and how can we extract that climate change signals through widely used statistics and analytical techniques? The second issue to be addressed is the examination of the fire weather changes during the entire fire season, including the onset, the peak fire period and the end of the fire season. And, third, how should these changes be explained in the areas most affected by modern climate change such as boreal forest ecosystems? To address these research questions, we selected as our study area one of the most fire-prone regions of boreal Russia, the Republic of Sakha (Yakutia) situated in the Far East of the country.

43 Our previous research (K. Kirillina et al., 2020) showed the extension of the fire season in the Sakha Republic and related it to cumulative increases of the burned area in the region, during the period 1996-2018, showing the most significant, rapid increase in fire activity. Here, the study period 1996-2018, based on credible satellite burned area observations for the Republic (NOAA AVHRR and MODIS). We hypothesized that the reported changes were associated with the earlier coming of spring and the lengthening of fall which extended the duration of the fire season in the Republic. The extension of the fire season was correlated with the exponential growth in the extent of the burned area.

Moving from these two previous findings, the lengthening of the fire season and the increase of fire activity, in this current research we aimed to find both a broader relation to climate change and a more specific means of understanding this phenomenon through recent meteorological statistics and their analyses. Despite existing research describing both regional climate change (see Study Area description for details) and the impact of climate and weather changes on fire activity in the Sakha Republic, particularly historical changes in temperature and precipitation (Hayasaka, 2011; K. S. Kirillina et al., 2016; Protopopova & Gabysheva, 2015), comprehensive research statistically demonstrating this impact still does not exist. Our selected study area is five forestry districts (these are the main administrative division of the Russian Forestry Service, on the territory of the Sakha Republic which has a total of nineteen forestry districts) that show significant increases in burned area. Each forestry district has one meteorological station. Due to the relatively small size of these forestry districts, their latitudes and longitudes as well as elevation and terrain do not vary much, so we used the data from each existing station per district to represent climate conditions of the forestry district where this station is located. However, our further purpose is for the presented methodology to be used for wider areas with more developed networks of hydrometeorological stations.

6.2 Conceptual framework for detection of climate change signals in fire weather data

We hypothesized that the recent extension of the fire season and related cumulative increases of the burned area in five forestry districts of the Sakha Republic were related to recent and more abrupt climate changes as observable through two fundamental meteorological parameters, temperature and precipitation. We further hypothesized that the earlier coming of spring and later coming of fall, i.e., significant warming and drying trends during spring (April-May) and fall months (September-October) could be correlated to the increased fire activity.

44 To test hypotheses, we developed an approach to search for possible climate change signals in short-term fire weather ranks through widely used statistical techniques (see Appendix 3).

We first collected data for the two most relevant climate phenomena, temperature and precipitation. A preliminary examination of the data for 1996-2018 showed four conspicuous trends in the recent period. We then constructed four indicators from these trends suggesting the existence of a climate change signal: 1) the magnitude of changes; 2) the pace of changes; 3) inconsistency; and 4) significance of changes. For detection of climate change signals, all of the following conditions were respected: 1. the magnitude of changes has to be high; 2. the pace of changes has to be fast; 3. the changes have to be inconsistent; 4. detected changes have to be statistically significant.

The magnitude of changes we assessed by using differences from historical normals (for temperature) and differences from historical means (for precipitation). The pace of changes we analyzed using the variability analysis including calculation of standard deviations, coefficients of variation, departures from historical normals (for temperature) and departures from historical means (for precipitation). Inconsistency was analyzed through analysis of homogeneity. The homogeneity analysis allowed us to find the timing of abrupt changes in the temporal evolution of selected meteorological parameters and direction of that changes, upward or downward. Finally, we employed the trend test to assess the significance of detected changes.

6.3 Data organization

Air temperature and atmospheric precipitation data have been obtained from the Global Summary of the Day Product of the U.S. National Oceanic and Atmospheric Administration (further NOAA GSOD) for the period of 1996-2018 (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd?datasetabbv=GSOD&resolution=40). As the reference stations we used meteorological stations located in the five Yakutian forestry districts with significant increases of burned area (Table 5.1). One meteorological station represents one forestry district (Table 6.1).

The monthly means of the air temperature from April to October, the annual averages, seasonal (May-October) and peak fire period (June-August) means were analyzed. As for atmospheric

45 precipitation data, we used the monthly total precipitation for months from April to October, the annual, seasonal and peak fire period means of total atmospheric precipitation. Further, we computed historical normals for 1961-1990 and for the most recent 30-year period (1989 to 2018) for comparison with climatological means for study period, 1996-2018.

GIS data comprising files of the region boundaries has been downloaded from the website of the Russian open GIS Portal “GIS-Lab” (see URL in the References).

Table 6.1 Characteristics of meteorological stations. Station name/ WMO Code Forestry District Latitude Longitude

Amga / 24962 Amginsky 131°97’ 60°90’ Berdigestyakh / 24758 Gorny 126°70’ 62°10’ Pokrovsk / 24856 Khangalassky 129°14’ 61°48’ Verkhnevilyuisk / 24644 Verkhnevilyuisky 120°31’ 63°45’ Vilyuisk/ 24641 Vilyuisky 121°63’ 63°75’ Data Source: All-Russia Research Institute of Hydrometeorological Information - World Data Center (RIHMI-WDC). List of the Russian Meteorological Stations (see URL in the References).

6.4 Methodology

The search for detectable climate change signals in the short-term ranks of fire weather parameters was carried out in four steps (see Appendix 2). The initial descriptive statistical analysis was carried out in terms of comparison of recent fire weather means for 1996-2018 to historical normals (for 1961-1990 and 1989-2018) and calculation of their actual differences to see the magnitude of changes. With the assessment of variability of the fire weather held to find out how fast and inconsistent the changes were. Homogeneity analysis was used to find the timing of abrupt changes and their consistency or otherwise around selected sites in the study area. Finally, we employed the trend test to assess the significance of detected trends.

For the first step, we computed basic descriptive statistics (means and standard deviations (SD)), historical normals for the most recent 30-year period, 1989-2018, and the reference WMO period, 1961-1990, for comparison. I prepared graphs of trends in fire weather parameters (temperature and precipitation) to visualize the changes (see Appendix 3). SDs were calculated for initial assessment of variability in the fire weather.

For the second step, we performed analysis of fire weather variability using coefficients of variation (CV), percentage departures from historical normals (for temperature, (for the method 46 see (WMO, 2017)), percentage departures from the mean (for atmospheric precipitation (for the method see (Kant, Meshram, & Sahu, 2014)) and precipitation concentration index (PCI, for the method see (Jaswal, Kumar, & Khare, 2014)).

The third step was devoted to the analysis of inconsistency and abrupt changes in the evolution of fire weather using homogeneity tests. Before the third step, an autocorrelation test was applied to the data series as the measure of lag-1 serial correlation. It was applied on the data to observe the presence of any significant correlation. Homogeneity was assessed by Pettitt’s, Standard Normal Homogeneity (SNHT) and Buishand’s tests. Significance of detected trends was assessed by Mann-Kendall trend test as a final step of our analyses. Data analyses were undertaken using MS Excel, XLSTAT Software, and MAKESENS Program for Trends Detection.

6.5 Data analysis and discussion “Detecting climate change signals in fire weather” 6.5.1 Assessment of recent fire weather variability

In this study, we first used descriptive statistics to grasp monthly, annual, fire season and peak fire period characteristics and trends of main fire weather parameters such as air temperature and atmospheric precipitation. We computed historical normals for 1961-1990 and the most recent 30- year period, 1989-2018, for comparison with current means, 1996-2018 (Tables 6.2 and 6.4). Descriptive statistics such as means, and standard deviations were computed (Tables 6.3 and 6.5). Also, we prepared graphs of temperature trends and precipitation trends during study period, 1996- 2018, for each of the fire season month, annual, fire season and peak fire period for initial visual assessment of changes (see Appendix 3). Further identified trends were assessed for significance using the Mann-Kendall trend test.

Table 6.2. Historical normals for monthly, annual, seasonal and peak fire period means of temperature, in °C.

24962 – Amga Meteorological Station, Amginsky Forestry District April May June July August September October Annual Fire Peak season fire period 1989-2018 -4.7 7.9 16.1 18.9 14.7 5.8 -7.9 -9.5 9.2 16.6 1961-1990 -6.8 6.5 14.6 17.7 13.8 5.1 -9.3 -11.0 8.1 15.4 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District April May June July August September October Annual Fire Peak season fire period 1989-2018 -6.0 6.3 15.0 17.6 13.4 4.4 -8.1 -9.1 8.1 15.3 1961-1990 -7.5 5.2 13.5 16.3 12.5 3.9 -9.1 -10.6 7.1 14.1

47 Continued

24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District April May June July August September October Annual Fire Peak season fire period 1989-2018 -4.8 7.4 15.9 18.7 14.7 5.7 -7.2 -8.9 9.2 16.4 1961-1990 -6.3 6.3 14.5 17.8 14.1 5.3 -8.4 -10.1 8.3 15.5 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period 1989-2018 -4.7 6.4 16.0 18.9 14.8 5.6 -6.9 -7.7 9.1 16.6 1961-1990 -7.0 5.2 14.5 18.1 14.1 5.2 -8.0 -9.2 8.2 15.5 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period 1989-2018 -5.4 6.1 15.3 18.1 14.0 5.2 -6.9 -8.1 8.6 15.8 1961-1990 -7.7 5.0 13.8 17.1 13.2 4.6 -7.9 -9.4 7.6 14.7 Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

Table 6.3. Basic descriptive statistics for the monthly, annual, seasonal and peak fire period means of temperature for the period 1996-2018 and their differences from historical normals (DFHN), in ºC.

24962 – Amga Meteorological Station, Amginsky Forestry District April May June July August September October Annual Fire Peak season fire period Current -4.4 8.1 16.2 19.1 15.0 5.8 -8.2 -9.4 9.3 16.8 mean SD 1.86 1.50 1.65 1.42 1.58 1.40 2.30 0.76 0.80 1.03 DFHN 2.4 1.6 1.6 1.4 1.2 0.7 1.1 1.6 1.2 1.4 (1961-1990) DFHN 0.3 0.2 0.1 0.2 0.3 0 -0.3 0.1 0.1 0.2 (1989-2018) 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District April May June July August September October Annual Fire Peak season fire period Current -5.6 6.4 15.2 17.9 13.6 4.4 -8.6 -9.1 8.2 15.6 mean SD 2.78 1.61 1.59 1.49 1.48 1.36 2.42 0.90 0.84 1.05 DFHN 1.9 1.2 1.7 1.6 1.1 0.5 0.5 1.5 1.1 1.5 (1961-1990) DFHN 0.4 0.1 0.2 0.3 0.2 0 -0.5 0 0.1 0.3 (1989-2018) 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District April May June July August September October Annual Fire Peak season fire period Current -4.3 7.4 16.1 19.0 14.9 5.6 -7.7 -8.8 9.2 16.6 mean SD 1.99 1.41 1.60 1.46 1.52 1.30 1.99 0.74 0.74 1.07 DFHN 2 1.1 1.6 1.2 0.8 0.3 0.7 1.3 0.9 1.1 (1961-1990) DFHN 0.5 0 0.2 0.3 0.2 -0.1 -0.5 0.1 0 0.2 (1989-2018)

48 Continued

24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period Current -4.1 6.6 16.4 19.2 14.8 5.7 -7.2 -7.6 9.2 16.8 mean SD 2.41 1.91 1.69 1.55 1.45 1.46 2.43 0.96 0.90 1.00 DFHN 2.9 1.4 1.9 1.1 0.7 0.5 0.8 1.6 1 1.3 (1961-1990) DFHN 0.6 0.2 0.4 0.3 0 0.1 -0.3 0.1 0.1 0.2 (1989-2018) 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period Current -4.8 6.2 15.6 18.3 14.1 5.3 -7.3 -8.0 8.7 16.0 mean SD 2.62 1.85 1.55 1.38 1.34 1.35 2.62 1.07 0.86 0.90 DFHN 2.9 1.2 1.8 1.2 0.9 0.7 0.6 1.4 1.1 1.3 (1961-1990) DFHN 0.6 0.1 0.3 0.2 0.1 0.1 -0.4 0.1 0.1 0.2 (1989-2018) Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

Table 6.4. Historical means for monthly, annual, seasonal and peak fire period total precipitation, in mm.

24962 – Amga Meteorological Station, Amginsky Forestry District April May June July August September October Annual Fire Peak season fire period 1961-1990 7.6 19.0 36.5 42.1 50.1 31.5 18.7 21.4 33.0 42.9 1989-2018 8.2 21.3 39.2 50.6 55.8 39.4 27.7 24.2 39.0 48.5 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District April May June July August September October Annual Fire Peak season fire period 1961-1990 10.1 19.3 38.5 42.8 36.2 28.9 25.1 21.0 31.8 39.2 1989-2018 10.2 27.0 34.1 44.0 51.4 38.4 24.1 24.0 36.5 43.2 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District April May June July August September October Annual Fire Peak season fire period 1961-1990 9.1 19.7 40.0 42.8 39.7 30.1 18.4 20.8 31.8 40.8 1989-2018 14.2 23.8 32.5 53.2 44.3 32.8 24.5 22.9 35.2 43.3 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period 1961-1990 11.4 18.9 33.2 46.1 37.3 27.2 23.9 21.2 31.1 38.9 1989-2018 10.8 27.6 33.8 49.1 41.1 32.4 26.2 23.8 35.0 41.3 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period 1961-1990 9.5 18.5 33.5 43.1 35.5 27.5 19.4 19.7 29.6 37.4 1989-2018 8.4 21.7 34.6 43.9 41.9 33.0 19.9 21.0 32.5 40.1 Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

49 Table 6.5. Basic descriptive statistics for the monthly, annual, seasonal and peak fire period means of total precipitation for the period 1996-2018 and their differences from historical means (DFHM), in mm.

24962 – Amga Meteorological Station, Amginsky Forestry District April May June July August September October Annual Fire Peak season fire period Current 9.9 23.5 42.5 50.0 61.2 42.3 31.7 25.8 41.9 51.2 mean Standard 9.1 16.9 29.8 34.7 40.9 22.6 31.8 6.7 12.9 20.1 deviation DFHM 2.3 4.5 6 7.9 11.1 10.8 13 4.4 8.9 8.3 (1961-1990) DFHM 1.7 2.2 3.3 -0.6 5.4 2.9 4 1.6 2.9 2.7 (1989-2018) 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District April May June July August September October Annual Fire Peak season fire period Current 10.8 26.2 33.5 45.1 59.3 40.8 23.5 25.0 38.1 46.0 mean Standard 6.3 27.6 19.1 27.5 37.4 23.2 12.8 5.6 10.6 17.5 deviation DFHM 0.7 6.9 -5 2.3 23.1 11.9 -1.6 4 6.3 6.8 (1961-1990) DFHM 0.6 -0.8 -0.6 1.1 7.9 2.4 -0.6 1 1.6 2.8 (1989-2018) 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District April May June July August September October Annual Fire Peak season fire period Current 17.1 22.7 28.1 55.6 48.6 35.0 26.6 23.7 36.1 44.1 mean Standard 29.6 14.8 21.2 38.9 29.3 24.3 27.9 7.9 12.0 17.6 deviation DFHM 8 3 -11.9 12.8 8.9 4.9 8.2 2.9 4.3 3.3 (1961-1990) DFHM 2.9 -1.1 -4.4 2.4 4.3 2.2 2.1 0.8 0.9 0.8 (1989-2018) 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period Current 31.0 33.9 50.5 44.9 34.0 24.4 36.5 43.1 11.2 24.5 mean Standard 25.6 17.6 28.6 23.9 19.5 11.5 8.4 12.8 7.6 4.6 deviation DFHM -0.2 12.1 0.7 4.4 7.6 6.8 0.5 3.3 5.4 4.2 (1961-1990) DFHM 0.4 3.4 0.1 1.4 3.8 1.6 -1.8 0.7 1.5 1.8 (1989-2018) 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period Current 8.5 23.2 35.1 46.5 45.1 34.5 18.6 21.7 33.8 42.2 mean Standard 5.3 16.7 18.2 26.9 22.4 22.2 7.0 4.1 8.2 10.6 deviation DFHM -1 4.7 1.6 3.4 9.6 7 -0.8 2 4.2 4.8 (1961-1990) DFHM 0.1 1.5 0.5 2.6 3.2 1.5 -1.3 0.7 1.3 2.1 (1989-2018) Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

Tables 6.2 and 6.3 show that temperature is increasing throughout the year in all stations, especially in April which is now the onset month of the fire season in the Sakha Republic. The

50 temperature increases in April and October has been shown to correlate with the extension of the fire season itself; it also makes the fire activity more intense and increases the extent of the burned area due to cumulative increase of burned area from early spring and throughout the entire fire season (see (K. Kirillina et al., 2020)). Similarly, the results of our previous research reporting about the earlier beginnings of the fire season in April and cumulative increase of burned area in the Sakha Republic strongly support this finding. We found a substantial increase of temperature up to 1.9°C in the onset month of the peak fire period, June, too. Also, a significant increasing trend was found in the peak fire month July ((the month with the highest extent of burned area) as shown in the article mentioned above)) as high as up to 1.6°C, which as we have seen intensifies the fire weather itself and fire activity. Identified increases in the monthly means in turn increased the annual, seasonal and peak fire temperatures.

The onset (April-May) and the ending (October) months of the fire season showed the highest variability in temperature indicated by high values of standard deviation. This might be a signal of climate change, because temperatures in these months were not just significantly increased, but were highly dispersed, inconsistent and changed very fast, as shown in Table 6.6.

In turn, precipitation changes are inconsistent and differ from station to station. However, in some stations we found significant decreases of precipitation at the beginning of the peak fire period in June. The highest inconsistency and dispersion in precipitation illustrated by high standard deviations were shown during peak fire period and the ending month of the fire season in October.

As the second step, we performed analysis of fire weather variability using coefficients of variation, percentage departures from historical normals (for temperature), percentage departures from the mean (for precipitation) and precipitation concentration index to identify possible changes in temperature and precipitation patterns (Tables 6.6 and 6.7).

Table 6.6. Variability analysis for temperature, 1996-2018.

24962 – Amga Meteorological Station, Amginsky Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient 42.69 18.48 10.19 7.41 10.55 24.17 28.02 8.14 8.62 6.15 of vitiation Percentage departure from 35.9 25.1 10.8 8.2 8.7 14.0 11.5 14.7 15.3 8.9 historical normal (1961-1990)

51 Continued

Percentage departure from 7.2 2.9 0.5 1.8 1.3 2.0 -2.8 1.2 1.5 1.7 historical normal (1989-2018) 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient 49.81 24.95 10.46 8.34 10.86 31.09 9.94 10.21 10.26 6.72 of variation Percentage departure from 25.5 24.0 12.4 9.5 9.0 12.0 5.9 14.3 14.8 10.3 historical normal (1961-1990) Percentage departure from 5.3 2.3 1.2 1.5 0.9 1.6 3.2 0.4 0.6 1.7 historical normal (1989-2018) 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient 46.51 18.95 9.99 7.65 10.24 23.06 25.83 8.43 8.02 6.40 of variation Percentage departure from historical 32.4 -0.9 7.6 3.0 0.2 -0.9 13.4 12.7 11.3 7.3 normal (1961-1990) Percentage departure from 11.2 -14.5 -1.2 -2.0 -4.6 -6.2 0.4 0.4 1.4 1.0 historical normal (1989-2018) 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient 59.21 28.96 10.34 8.08 9.77 25.73 33.89 12.56 9.74 5.95 of variation Percentage departure from 41.9 26.9 13.0 6.0 5.1 8.8 10.2 16.9 12.7 8.3 historical normal (1961-1990) Percentage departure from 13.5 3.1 2.4 1.5 0.1 1.0 -4.1 0.7 1.6 1.2 historical normal (1989-2018)

52 Continued

24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient 54.34 29.59 9.95 7.51 9.46 25.76 36.03 13.31 9.86 5.64 of variation Percentage departure from 37.3 24.9 13.1 7.2 7.0 14.2 7.9 14.4 14.7 9.0 historical normal (1961-1990) Percentage departure from 10.5 2.4 2.0 1.3 0.2 1.0 -3.9 0.7 1.3 1.4 historical normal (1989-2018) Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

As mentioned above, considerable variation of temperature was observed at the beginning (April- May) and at the end of the fire season (September-October), ranging as high as up to 59% at the onset of the fire season in April and up to 36% at the end of the season. This again clearly indicates significant changes of temperature at the onset and the ending months of the fire season.

Table 6.6 also reports that the highest departures in temperature again were exhibited at the beginning (April-May) and at the end of the fire season (September-October).

This indication of continuing significant changes of temperature at the onset and ending months of the fire season is often correlated to more intense fire activity, as mentioned above, but the above comparison of the historical normals with the current study period showed definable periods of change, which provided an important step toward identifying detectable climate change signals.

Table 6.7. Variability analysis for precipitation, 1996-2018.

24962 – Amga Meteorological Station, Amginsky Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient of 91.7 72.2 70.3 69.3 66.8 53.5 100.2 25.9 30.9 39.2 variation Percentage departure from 30.7 23.5 16.3 18.9 22.2 34.1 69.7 20.7 26.9 19.4 the mean (1961-90) Percentage departure from 21.2 10.2 8.3 -1.1 9.7 7.2 14.6 6.7 7.3 5.7 the mean (1989-2018) Precipitation concentration 3.2 7.6 13.7 16.2 19.8 13.7 10.3 - - - index

53 Continued 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient of 58.4 105.5 57.1 61.1 63.0 56.8 54.3 22.4 27.9 38.1 variation Percentage departure from 6.8 35.8 -13.0 5.3 63.9 41.0 -6.3 18.9 19.7 17.3 the mean (1961-90) Percentage departure from 5.7 -2.9 -1.7 2.4 15.4 6.1 -2.4 4.0 4.3 6.4 the mean (1989-2018) Precipitation concentration 3.6 8.8 11.2 15.0 19.8 13.6 - - - - index 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient of 172.5 65.3 75.7 70.0 60.3 69.4 104.7 33.3 33.1 39.8 variation Percentage departure from 88.3 15.0 -29.8 29.9 22.3 16.4 44.8 13.7 13.5 8.0 the mean (1961-90) Percentage departure from 20.7 -4.8 -13.6 4.5 9.6 6.8 8.7 3.3 2.5 1.8 the mean (1989-2018) Precipitation concentration 6.0 8.0 9.9 19.6 17.1 12.3 9.4 - - - index 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient of 82.6 52.0 56.6 53.2 57.3 47.0 23.0 29.7 68.2 18.6 variation Percentage departure from -1.7 63.8 2.1 9.6 20.5 25.1 2.3 17.2 17.2 10.8 the mean (1961-90) Percentage departure from 3.8 12.1 0.3 2.9 9.3 5.0 -6.7 3.1 4.2 4.4 the mean (1989-2018) Precipitation concentration 3.8 10.5 11.5 17.2 15.3 11.6 8.3 - - - index 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District April May June July August September October Annual Fire Peak season fire period Coefficient of 62.2 72.1 52.0 57.9 49.6 64.4 37.5 18.8 24.3 25.0 variation Percentage departure from -10.4 25.5 4.6 7.9 27.0 25.6 -4.2 10.2 14.3 12.9 the mean (1961-90) Percentage departure from 1.3 7.0 1.3 5.9 7.6 4.6 -6.6 3.4 4.1 5.3 the mean (1989-2018)

54 Continued

Precipitation 3.3 8.9 13.5 17.9 17.3 13.3 7.1 concentration - - - index Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

Precipitation shows considerable variation throughout the year with the highest values of coefficient of variation at the beginning (April-May) and at the end of the season (September- October).

Similarly, the highest departures from historical means were also found at the beginning and end of the fire season.

Also, we detected negative departures in some stations at the beginning of the peak fire period in June, which might intensify a drought situation and make the fire season more severe. One station (24856 – Pokrovsk, Khangalassky forestry district) showed moderate drought conditions in June (with the percentage departure from historical mean – -29.8%).

The highest precipitation concentration index in the current period was detected in July and August in all five districts; though the monthly precipitation distribution in July and August months is irregular, cumulative precipitation is higher these two months. If there is precipitation decrease in any months it may severely affect the fire season and intensify fire activity, especially if it is accompanied by significant increases in temperature as may be happening in Berdigestyakh and Pokrovsk. The significance of these two as potential climate change signals is discussed below.

6.5.2 Analysis of inconsistency and abrupt changes in the evolution of fire weather

For the analysis of inconsistency and abrupt changes in the evolution of fire weather we applied homogeneity tests in order to detect significant inhomogeneity that could signal change points. Prior to the analysis of homogeneity, we applied an autocorrelation test. None of the temperature data series show significant autocorrelation. The same results were found for precipitation. Then we assessed the homogeneity of the weather data series using the Pettitt’s, SNHT’s and Buishand’s tests in order to detect specific change points towards prolonged trends, and their directions – upward or downward (see Tables 6.8 and 6.9, Figures 6.1.1-6.5.2 and Figures 6.6.1-6.10.2).

Figures 6.1.1-6.5.2 below and the following Table 6.8 show several possible change points for temperature which occurred abruptly and were also shown by variability analyses.

55 24962 – Amga Meteorological Station, Amginsky Forestry District.

April October

0 0 -11996 2001 2006 2011 2016 -21996 2001 2006 2011 2016 -2 -4 -3 -6 -4 -8

April -5

October -10 -6 -7 -12 -8 -14 -9 -16 Year Year

April October mu1 = -4.847 mu1 = -9.409 mu2 = -2.233 mu2 = -7.142

Figure 6.1.1. Months with significant change points for upward shifts in the temperature, for 1996- 2018.

24962 – Amga Meteorological Station, Amginsky Forestry District.

Annual average Fire season

0 12 1996 2001 2006 2011 2016 -2 10 8 -4 6 -6 4 -8 season Fire 2 Annual average Annual -10 0 -12 1996 2001 2006 2011 2016 Year Year

Annual average Fire season mu1 = -9.892 mu1 = 8.939 mu2 = -9.020 mu2 = 9.707

Figure 6.1.2. Annual and seasonal upward shifts in the temperature, for 1996-2018.

56 24758 - Berdigestyakh Meteorological Station, Gorny Forestry District.

April

0 1996 2001 2006 2011 2016 -2

-4

-6

-8 April -10

-12

-14

-16 Year

April mu1 = -6.300 mu2 = -3.463

Figure 6.2.1. Month with significant change points for upward shifts in the temperature, for 1996- 2018.

24758 - Berdigestyakh Meteorological Station, Gorny Forestry District.

September October

8 0 1996 2001 2006 2011 2016 7 -2 6 -4 5 -6 4 -8 3

October -10 September 2 -12 1 -14 0 -16 1996 2001 2006 2011 2016 Year Year September October mu1 = 3.250 mu1 = -9.900 mu2 = 4.765 mu2 = -7.342

Figure 6.2.2. Months with significant change points for upward shifts in the temperature, for 1996- 2018.

57 24758 - Berdigestyakh Meteorological Station, Gorny Forestry District.

Annual average Fire season

0 12 1996 2001 2006 2011 2016 -2 10

-4 8

-6 6 4

-8 season Fire

Annual average Annual 2 -10 0 -12 Year 1996 2001 2006 2011 2016 Year Annual average Fire season mu1 = -9.664 mu1 = 7.695 mu2 = -8.629 mu2 = 8.571

Figure 6.2.3. Annual and seasonal upward shifts in the temperature, for 1996-2018.

24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District.

April

0 1996 2001 2006 2011 2016 -1 -2 -3 -4

April -5 -6 -7 -8 -9 Year

April mu1 = -4.650 mu2 = -1.660

Figure 6.3.1. Month with significant change points for upward shifts in the temperature, for 1996- 2018.

58 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District.

May October

10 0 9 1996 2001 2006 2011 2016 8 -2 7 -4 6 5 -6 May 4 3 October -8 2 1 -10 0 -12 1996 2001 2006 2011 2016 Year Year

May October mu1 = 6.736 mu1 = -8.673 mu2 = 8.183 mu2 = -6.558

Figure 6.3.2. Months with significant change points for upward shifts in the temperature, for 1996- 2018.

24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District.

Annual average Fire season

0 12 1996 2001 2006 2011 2016 -2 10

-4 8

-6 6

-8 4 Fire season Fire

Annual average Annual -10 2 0 -12 1996 2001 2006 2011 2016 Year Year

Annual average Fire season mu1 = -9.265 mu1 = 8.844 mu2 = -8.475 mu2 = 9.597

Figure 6.3.3. Annual and seasonal upward shifts in the temperature, for 1996-2018.

59 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District.

May September

12 10

10 8 8 6 6

May 4 4 September 2 2 0 0 1996 2001 2006 2011 2016 1996 2001 2006 2011 2016 Year Year

May September mu1 = 5.211 mu1 = 4.414 mu2 = 7.493 mu2 = 6.200

Figure 6.4.1. Months with significant change points for upward shifts in the temperature, for 1996- 2018.

24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District.

October

0 1996 2001 2006 2011 2016 -2

-4

-6 October -8

-10

-12 Year

October mu1 = -8.455 mu2 = -6.017

Figure 6.4.2. Month with significant change points for upward shifts in the temperature, for 1996- 2018.

60 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District.

Annual average Fire season

0 12 1996 2001 2006 2011 2016 -2 10

-4 8

-6 6

-8 4 Fire season Fire Annual average Annual -10 2

-12 0 Year 1996 2001 2006 2011 2016 Year Annual average Fire season mu1 = -8.183 mu1 = 8.718 mu2 = -7.153 mu2 = 9.814

Figure 6.4.3. Annual and seasonal upward shifts in the temperature, for 1996-2018.

24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District.

September May

10 10 9 8 8 7 6 6 5

May 4 4 3 September 2 2 1 0 0 1996 2001 2006 2011 2016 1996 2001 2006 2011 2016 Year Year

September May mu1 = 4.967 mu1 = 4.243 mu2 = 7.064 mu2 = 5.694

Figure 6.5.1. Months with significant change points for upward shifts in the temperature, for 1996- 2018.

61 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District.

Annual average Fire season

10 12 10 5 8 0 6 1996 2001 2006 2011 2016 -5 4 Fire season Fire 2 Annual average Annual -10 0 -15 1996 2001 2006 2011 2016 Year Year

Annual average Fire season mu1 = -8.586 mu1 = 8.106 mu2 = -6.355 mu2 = 9.105

Figure 6.5.2. Annual and seasonal shifts in temperature, for 1996-2018.

Table 6.8. The results of homogeneity tests for temperature.

24962 – Amga Meteorological Station, Amginsky Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point April 0.023 2012 0.016 2012 0.016 2012 October 0.045 2006 “-“ “-“ 0.025 2006 Annual average 0.001 2006 0.031 2006 0.001 2006 Fire season 0.043 2006 “-“ “-“ 0.034 2006 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point April 0.027 2010 “-“ “-“ 0.042 2010 September 0.033 2001 “-“ “-“ “-“ “-“ October 0.015 2006 “-“ “-“ 0.014 2006 Annual average 0.003 2006 0.013 2006 0.003 2006 Fire season 0.014 2006 “-“ “-“ 0.017 2006 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point April 0.040 2013 0.017 2013 0.025 2013 May 0.025 2006 “-“ “-“ 0.021 2006 October 0.014 2006 “-“ “-“ 0.017 2006 Annual average 0.008 2006 0.040 2006 0.005 2006 Fire season 0.019 2006 “-“ “-“ 0.019 2006 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point May 0.007 2004 0.030 2004 0.010 2004 September 0.007 2002 0.049 2002 0.016 2002 October 0.042 2006 “-“ “-“ 0.022 2006 Annual average 0.012 2006 “-“ “-“ 0.016 2006 Fire season 0.001 2004 0.016 2004 0.002 2004 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point May 0.008 2004 0.049 2004 0.014 2004 September 0.033 2002 “-“ “-“ 0.043 2002 October “-“ “-“ “-“ “-“ 0.042 2006 Annual average 0.029 2006 “-“ “-“ 0.032 2006 Fire season 0.007 2004 “-“ “-“ 0.008 2004 “-“ – inhomogeneity was not detected. Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

62 Results for the homogeneity tests for temperature show statistically significant inhomogeneity at the beginning (April-May) and at the end of the fire season (September-October), and also inhomogeneity at the annual and seasonal scales. In the five districts, significant shifts in temperature during the fire season and at the annual scale occurred in 2006. In these districts, then, the change point can plausibly be 2006.

Next we show the results of homogeneity tests for precipitation (Table 6.9 and Figures 6.6.1- 6.10.2).

24962 – Amga Meteorological Station, Amginsky Forestry District.

April October 120 140 120 100 100 80 80 60

April 60 October 40 40 20 20 0 0 1996 2006 2016 1996 2006 2016 Year Year

April October mu = 17.135 mu = 31.735

Figure 6.6.1 Monthly shifts in precipitation, for 1996-2018 (inhomogeneity was not found).

Annual average

50 45 40 35 30 25 20 15 Annual average Annual 10 5 0 1996 2001 2006 2011 2016 Year

Average ann mu1 = 22.759 mu2 = 32.849

Figure 6.6.2 Annual shifts in precipitation, for 1996-2018.

63 24962 – Amga Meteorological Station, Amginsky Forestry District.

Fire season Peak fire period

100 120 100 80 80 60 60 40 40 Fire season Fire

20 period fire Peak 20 0 0 1996 2001 2006 2011 2016 1996 2001 2006 2011 2016 Year Year

Seasonal Peak mu1 = 35.907 mu1 = 43.667 mu2 = 55.474 mu2 = 68.552

Figure 6.6.3. Seasonal and peak fire period shifts in precipitation, for 1996-2018.

24758 – Berdigestyakh Meteorological Station, Gorny Forestry District.

April October 30 60 25 50 20 40 15 30 April October 10 20 5 10 0 0 1996 2006 2016 1996 2006 2016 Year Year

April October

mu = 10.783 mu = 23.522

Figure 6.7.1. Monthly shifts in precipitation, for 1996-2018 (inhomogeneity was not found).

64 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District.

Annual average Peak fire period

40 90 35 80 30 70 25 60 50 20 40 15 30 10 20 Annual average Annual 5 period fire Peak 10 0 0 1996 2001 2006 2011 2016 1996 2001 2006 2011 2016 Year Year

Average ann Peak mu1 = 21.421 mu1 = 35.263 mu2 = 27.241 mu2 = 52.857

Figure 6.7.2. Annual and peak fire period shifts in precipitation, for 1996-2018.

Fire season 70 60 50 40 30

Fire season Fire 20 10 0 1996 2001 2006 2011 2016

Year

Seasonal mu = 38.068

Figure 6.7.3. Seasonal shifts in precipitation, for 1996-2018 (inhomogeneity was not found).

65 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District.

April October

120 120 100 100 80 80 60 60 April

40 October 40 20 20 0 0 1996 2001 2006 2011 2016 1996 2001 2006 2011 2016 Year Year

April October mu1 = 8.400 mu1 = 18.875 mu2 = 108.850 mu2 = 78.433

Figure 6.8.1. Monthly shifts in precipitation, for 1996-2018.

24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District.

Annual average Fire season

45 70 40 60 35 50 30 25 40 20 30 15 20 10 Fire season 5 10 0 0

Annual average Annual 1996 2001 2006 2011 2016 1996 2006 2016 Year Year

Average ann Seasonal mu1 = 21.482 mu = 36.096 mu2 = 38.178

Figure 6.8.2. Annual and seasonal shifts in precipitation, for 1996-2018 (seasonal shift was not found).

66 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District.

April September

100 35 30 80 25 60 20 40

April 15 September 10 20 5 0 0 1996 2001 2006 2011 2016 1996 2006 2016 Year Year September April mu1 = 50.171 mu = 11.209 mu2 = 26.963

Figure 6.9.1. Monthly shifts in precipitation, for 1996-2018 (shift in April was not found).

24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District.

Annual average Peak fire period 40 80 35 70 30 60 25 50 20 40 15 30

Annual average Annual 10 Peak fire period fire Peak 20 5 10 0 0 1996 2006 2016 1996 2006 2016 Year Year

Average ann Peak mu = 24.531 mu = 43.117

Figure 6.9.2. Annual and peak fire period shifts in precipitation, for 1996-2018 (inhomogeneity was not found).

67 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District.

May September

80 120 70 100 60 50 80 40 60 May 30 40 20 September 10 20 0 0 1996 2001 2006 2011 2016 1996 2001 2006 2011 2016 Year Year

May September mu1 = 38.317 mu1 = 56.867 mu2 = 17.888 mu2 = 26.647

Figure 6.10.1. Monthly shifts in precipitation, for 1996-2018.

24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District.

Annual average Fire season

35 60 30 50 25 40 20 30 15 20 10 Fire season Fire

Annual average Annual 5 10 0 0 1996 2001 2006 2011 2016 1996 2001 2006 2011 2016 Year Year

Average ann Seasonal mu1 = 25.401 mu1 = 41.725 mu2 = 20.400 mu2 = 31.039

Figure 6.10.2. Annual and seasonal shifts in precipitation, for 1996-2018.

68 Table 6.9. The results of the homogeneity tests for precipitation.

24962 – Amga Meteorological Station, Amginsky Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point Increase Annual average 0.003 2011 0.011 2011 0.000 2011 Increase Fire season 0.002 2011 0.012 2011 0.012 2011 Increase Peak fire period 0.013 2011 “-“ “-“ 0.011 2011 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point Increase Annual average 0.017 2004 “-“ “-“ 0.027 2004 Increase Peak fire period 0.032 2004 “-“ “-“ 0.031 2004 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point Increase April “-“ “-“ 0.004 2016 0.004 2016 Increase October “-“ “-“ 0.017 2015 0.026 2015 Increase Annual average “-“ “-“ 0.001 2015 0.030 2015 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point Decrease September 0.018 2002 “-“ “-“ 0.016 2002 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District Variable Pettitt’s test p Change SNHT’s test p Change Buishand’s test p Change point point point Decrease May “-“ “-“ “-“ “-“ 0.033 2001 Decrease September 0.020 2001 0.010 2001 0.013 2001 Decrease Annual average “-“ “-“ “-“ “-“ 0.041 2001 Decrease Fire season 0.036 2001 0.044 2001 0.025 2001 “-“ – inhomogeneity was not detected. Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

The results of the homogeneity test for precipitation show downward shifts at the beginning (April- May) and the end of the fire season (September-October) as well as in the annual scale for Vilyuisk and Verkhnevilyuisk. Three stations in Central Yakutia (Amga, Berdigestyakh and Pokrovsk) show upward shifts in annual and peak fire precipitation. One station (Pokrovsk) shows upward shifts at the beginning (April) and at the end of the fire season (October).

Although drying trends are not totally synchronous with warming trends, they do show some correlation to burned area, as shown in the list of districts burned area below. Precipitation shows a general decrease in two stations (Vilyuisk and Verkhnevilyuisk). Precipitation shows an area- wide decrease in all five stations in the specific years 2001-2002 (Amga – 2001, Berdigestyakh – 2001 and 2002, Pokrovsk – 2001). The years with downward shifts in precipitation (2001 and 2002) were the years with most burned area during the 2000s in all forestry districts. It seems to 69 be a change signal less significant than temperature during the selected study period but should be worth further examination with longer-period and more location specific data.

The data for burned area in 2001-2002 below: • Amga, Amginsky forestry district – with cumulative burned area 8.1 x 1000 ha in 2001 – and 14.7 x 1000 ha – in 2002 (with average for 2000-2009 10.8 ± 6.16 (standard error – SE) x 1000 ha). • Berdigestyakh, Gorny forestry district – with cumulative burned area 182.1 x 1000 ha in 2001 – and 1227.1 x 1000ha – in 2002 (with average for 2000-2009 146.3 ± 121.38 (SE) x 1000 ha). • Pokrovsk, Khangalassky forestry district – with cumulative burned area 29.7 x 1000 ha in 2001 – and 621.1 x 1000 ha – in 2002 (with average for 2000-2009 66.7 ± 61.67 (SE) x 1000 ha). • Vilyuisk, Vilyuisky forestry district – with cumulative burned area 88.2 x 1000 ha in 2001 – and 473.0 x 1000 ha – in 2002 (with average for 2000-2009 73.1 ± 45.98 (SE) x 1000 ha). • Verkhnevilyuisk, Verkhnevilyuisky forestry district – with cumulative burned area 56.8 x 1000 ha in 2001 – and 172.1 x 1000 ha – in 2002 (with average for 2000-2009 26.4 ± 17.10 (SE) x 1000 ha).

However, these are not the years with much of the high temperature shifts. The spatial inconsistency in precipitation trends, and their occasional lack of correlation to temperature trends, will need to be explained in further research incorporating more variables such as evapotranspiration and other mitigating factors.

6.5.3 Trend analysis of fire weather

In order to identify the significance of detected trends and calculate the magnitude of trends in the fire weather, we performed the Mann-Kendall’s trend test and the Sen’s slope estimation (Tables 6.10-6.11).

These tests confirmed that temperatures showed significant increases in the onset (April-May) and in the ending months of the fire season (September-October). Also, significant upward trends were found for annual and seasonal scales and during the peak fire period, which can be used in formulating a climate signal.

70 With regard to precipitation, we found some significant upward and downward trends. Upward trends in precipitation were found at the annual and seasonal scales. Significant downwards trends were detected at the end of the fire season in September in two districts. The previous step of analysis, the homogeneity tests, showed the time of the most significant decrease in the study period, 2001-2002, which were years of greater burned area. Decreasing precipitation trends in September indicate a recent rainfall shortage (regarding to Solovyev (Solovyev & Kozlov, 2005)); historically, heavy rainfalls in August and September closed the fire season), which now extend the fire season, and in turn may increase cumulative extent of burned area.

Table 6.10. Mann-Kendall test results for temperature, 1996-2018.

24962 – Amga Meteorological Station, Amginsky Forestry District Test Z statistic Q, Sen’s slope estimate Significance Annual average 3.06 0.066 0.01 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District October 1.74 0.141 0.1 Annual average 2.72 0.076 0.01 Fire season 1.80 0.049 0.1 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District Annual average 2.40 0.053 0.05 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District May 2.06 0.167 0.05 June 2.04 0.100 0.05 September 2.06 0.089 0.05 Annual 2.85 0.075 0.01 Season 2.72 0.072 0.01 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District April 2.26 0.076 0.05 May 2.06 0.150 0.05 June 1.80 0.085 0.1 September 1.88 0.076 0.1 Annual average 2.26 0.076 0.05 Peak fire period 2.22 0.058 0.05 Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

Table 6.11. Mann-Kendall test results for precipitation, 1996-2018.

24962 – Amga Meteorological Station, Amginsky Forestry District Test Z statistic Q, Sen’s slope estimate Significance July 3.09 3.013 0.01 Annual average 2.59 0.464 0.01 Fire season 2.59 0.850 0.01 24758 – Berdigestyakh Meteorological Station, Gorny Forestry District April 1.74 0.333 0.1 September -1.74 -1.000 0.1 24856 – Pokrovsk Meteorological Station, Khangalassky Forestry District September -1.77 -1.064 0.1 October 2.06 0.863 0.05 24641 – Vilyuisk Meteorological Station, Vilyuisky Forestry District September -2.67 -1.292 0.01 24644 – Verkhnevilyuisk Meteorological Station, Verkhnevilyuisky Forestry District September -2.43 -1.457 0.05 Fire season -2.11 -0.512 0.05 Data Source: generated from NOAA GSOD dataset (https://www7.ncdc.noaa.gov/CDO/cdoselect.cmd).

71 6.5.4 Analysis of fire weather and related changes in fire activity illustrated by burned area: The assessment of 2014 and 2018 fire seasons

The results of this research step show the possibility of finding climate change signals that can be related to changes in recent fire weather and to corresponding changes in fire seasonality and fire activity in the Sakha Republic. However, to analyze directly impact of fire weather changes on fire activity it is necessary to downscale the analysis, moving from assessment of yearly changes to changes during particular fire seasons.

In order to analyze impact of fire weather on fire activity changes made analysis of two recent large fire seasons - 2014 and 2018. Therefore, in the following analysis, a possible relation between fire weather parameters (average monthly air temperature and monthly mean of total amount of atmospheric precipitation) and fire activity, illustrated by burned area changes (total monthly burned area), will be analyzed for five earlier selected forestry districts (Amginsky, Gorny, Khangalassky, Vilyuisky and Verkhnevilyuisky) for two recent large fire seasons 2014 and 2018.

The analysis included three steps: (1) Analysis of the seasonal distribution of burned area. (2) Comparison between weather conditions of selected fire seasons (2014 and 2018) and historical normals (for temperature) / means (for precipitation) for the study 22-year period (1996-2018). (3) Analysis of relation between fire weather parameters (temperature and precipitation) and changes in fire activity, illustrated by burned area.

The mentioned analysis techniques were selected according to their ability to detect particular fire weather anomalies, such as considerable warming or rainfall shortage during the specific month of fire season.

The logic of research was the following. First is to show the seasonal distribution of burned area and to identify the month with the largest extent of burned area. Then, to find the fire weather anomalies such as considerable warming or / and rainfall shortage through comparison of means of temperature and means of precipitation with their historical normals (for temperature) / means (for precipitation) during that specific month. And finally, to assess the relation between the chosen fire weather parameters (temperature and precipitation) and burned area changes to find which parameter is more crucial for forest fire development (or fire activity).

72 The results of analysis presented in the following tables (Tables 6.12-6.21) and figures (Figures 6.11.1-6.11.12). First will be shown analysis for 2014 fire season. Then, analysis of 2018 fire season.

Analysis of 2014 fire season 1) Amginsky Forestry District Table 6.12. Summary Table. Analysis of Seasonal Comparison with Relation of Meteorological Distribution of Burned Historical Normals / Parameters to Changes in Area Means Burned Area The largest extent of burned Warming trends found in Not found. area found in September April and May. (max). The smaller extent of burned area was in April- May and October.

2) Gorny Forestry District Table 6.13. Summary Table. Analysis of Seasonal Comparison with Relation of Meteorological Distribution of Burned Historical Normals / Parameters to Changes in Area Means Burned Area The largest extent of burned Warming trend in Moderate positive correlation area found in July (max) and August. Precipitation with temperature (R2=0.40), August (max)-September. shortage in July (max) see Appendix 4. and September.

20 180 160 15 140

C 10 ° 120 5 100

0 80 April May June July August September October 60

Temperature, -5 40 -10 20 Burned area, (ha x 1000) -15 0

AMT BA

Figure 6.11.1. Relation between temperature and burned area changes

73 70 180 160 60 140 50 120 40 100

30 80 60

Precipitation, mm 20

40 Burned area, (ha x 1000) 10 20 0 0 April May June July August September October

AMP BA

Figure 6.11.2. Relation between precipitation and burned area changes.

3) Khangalassky Forestry District Table 6.14. Summary Table. Analysis of Seasonal Comparison with Historical Normals / Relation of Distribution of Means Meteorological Burned Area Parameters to Changes in Burned Area The largest extent of The increasing temperature trend in April- Not found. burned area found in May. Rainfall shortage during the entire fire April-May (max) and season, especially at the beginning (April- August-September. May) and at the end of the fire season (September-October).

4) Vilyuisky Forestry District Table 6.15. Summary Table. Analysis of Seasonal Comparison with Historical Relation of Distribution of Burned Normals / Meteorological Area Means Parameters to Changes in Burned Area The largest extent of Warming trend in July. Rainfall Moderate positive burned area found in July shortage during the entire fire correlation with (max1) (warming trend + season, especially during the peak temperature (R2=0.44), rainfall shortage)-August fire month, July. see Appendix 4. (max2) and September.

74 25 700

20 600

15 500 C ° 10 400

5 300

0 200

Temperature, April May June July August September October Burned area, (ha x 1000) -5 100

-10 0

AMT Burned area, in (x 1000 ha)

Figure 6.11.3. Relation between temperature and burned area changes.

50 700 45 600 40 35 500

30 400 25 20 300

15 200

Precipitation, mm 10

100 Burned area, (ha x 1000) 5 0 0 April May June July August September October

AMP Burned area, in (x 1000 ha)

Figure 6.11.4. Relation between precipitation and burned area changes.

5) Verkhnevilyuisky Forestry District Table 6.16. Summary Table. Analysis of Seasonal Comparison with Historical Relation of Distribution of Burned Normals / Meteorological Area Means Parameters to Changes in Burned Area The largest extent of burned Warming trend in July. Rainfall Moderate positive area found in July (max1, shortage during the entire fire correlation with warming trend + rainfall season, especially during the temperature (R2=0.42), shortage), August (max2) peak fire month, July, and in see Appendix 4. and September. August.

75 25 400

20 350

300 15 C

° 250 10 200 5 150 0 Temperature, April May June July August September October 100

-5 50 Burned area, (ha x 1000)

-10 0

AMT BA

Figure 6.11.5. Relation between temperature and burned area changes.

90 400

80 350

70 300 60 250 50 200 40 150 30

Precipitation, mm 20 100

10 50 Burned area, (ha x 1000) 0 0 April May June July August September October

AMP BA

Figure 6.11.6. Relation between precipitation and burned area changes.

Analysis of 2018 fire season 1) Amginsky Forestry District Table 6.17. Summary Table. Analysis of Seasonal Comparison with Relation of Meteorological Distribution of Burned Historical Normals / Parameters to Changes in Area Means Burned Area The largest extent of burned Considerable warming Not found. area found in May and trends in May and September (max)-October. September. Considerable rainfall shortage in May and October.

76 2) Gorny Forestry District Table 6.18. Summary Table. Analysis of Seasonal Comparison with Relation of Meteorological Distribution of Burned Historical Normals / Parameters to Changes in Area Means Burned Area The largest extent of burned Significant rainfall Not found. area found in July (max) and shortage in July and September. September.

3) Khangalassky Forestry District Table 6.19. Summary Table. Analysis of Seasonal Comparison with Historical Relation of Distribution of Burned Normals / Meteorological Area Means Parameters to Changes in Burned Area The largest extent of The increasing temperature trend Low negative correlation burned area found in in May. Considerable rainfall with precipitation May, July and September shortage in July and at the end of (R2=0.30), (max). the fire season in September. See Appendix 4.

20 30

25 15

20 C

° 10 15 5 10 Temperature, 0 Burned area, (ha x 1000) April May June July August September October 5

-5 0

AMT BA

Figure 6.11.7. Relation between temperature and burned area changes.

77 120 30

100 25

80 20

60 15

40 10 Precipitation, mm 20 5 Burned area, (hax 1000)

0 0 April May June July August September October

AMP BA

Figure 6.11.8. Relation between precipitation and burned area changes.

4) Vilyuisky Forestry District Table 6.20. Summary Table. Analysis of Seasonal Comparison with Historical Relation of Distribution of Burned Normals / Meteorological Area Means Parameters to Changes in Burned Area The largest extent of burned Warming trend at the beginning Moderate positive area found during summer of the peak fire period, in June, correlation with months (June (max), July, and also in August. Rainfall temperature (R2=0.48), August) and at the end of the shortage during the entire fire see Appendix 4. fire season (September). season, especially at the beginning (May) and at the end of the fire season (September- October).

78 20 200 180 15 160 140 C ° 10 120 100 5 80 60 Temperature,

0 40 Burned area, (ha x 1000) April May June July August September October 20 -5 0

AMT BA

Figure 6.11.9. Relation between temperature and burned area changes.

100 200 90 180 80 160 70 140 60 120 50 100 40 80 30 60

Precipitation, mm 20 40

10 20 Burned area, (ha x 10000) 0 0 April May June July August September October

AMP BA

Figure 6.11.10. Relation between temperature and burned area changes.

5) Verkhnevilyuisky Forestry District Table 6.21. Summary Table. Analysis of Seasonal Comparison with Relation of Meteorological Distribution of Burned Historical Normals / Parameters to Changes in Area Means Burned Area The largest extent of burned Warming trend in July. Low positive correlation area found in July, August Rainfall shortage during the with temperature (R2=0.30), and September. entire fire season, especially see Appendix 4. at the beginning of the peak fire period in June and at the end of the fire season (September-October).

79 20 120

15 100 C ° 10 80

5 60

Temperature, 0 40 April May June July August September October Burned area, (ha x 1000) -5 20

-10 0

AMT BA

Figure 6.11.11. Relation between temperature and burned area changes.

100 120 90 100 80 70 80 60 50 60 40 40 30

Precipitation, mm 20 20 10 Burned area, (ha x 1000) 0 0 April May June July August September October

AMP BA

Figure 6.11.12. Relation between precipitation and burned area changes.

The relation between fire weather parameters (temperature and precipitation) and fire activity illustrated by burned area changes is established by following:

1) The largest extent of the burned area occurred when the temperature increase was combined with considerable rainfall shortage as it shown by examples of Vilyuisky and Verkhnevilyuisky forestry districts in 2014 fire season. 2) Some cases are consistent with the study hypothesis that “significant warming trends in Spring (beginning of the fire season) and Autumn Months (end of the fire season) and significant rainfall shortage at the end of the fire season (September-October) extended the duration of fire season

80 and contributed to the increases of burned area” as it shown by examples of Khangalassky forestry district in 2014 fire season and Amginsky Forestry District in 2018 fire season. 3) The relation between fire weather and fire activity shown by moderate positive correlation between temperature and burned area and low negative correlation between precipitation and burned area. More relation found between temperature and burned area. As it was mentioned before the largest burned area extent was found when temperature increase was combined with considerable rainfall shortage.

Even though, the study found some relatively robust statistical associations between regional climate change and fire activity in the Sakha Republic such as significant warming trends at the beginning and at the end of the fire season combined with significant rainfall shortage trend at the end of the fire season in September, which caused the extension of the fire season and corresponding increases of the burned areas. The interrelationships between climate and fire activity seems to be more complex which is shown by the analysis of fire weather and related changes in fire activity, illustrated by burned area made for a seasonal scale. What was established earlier in the Research Framework of this study (see Section 3.5. Research Framework and Methodology).

Overall, an assessment of changes in fire regimes characteristics still present challenges to understand the impact of recent climate change on fire regime changes. Even though this study could show that changes in fire regimes have been particularly pronounced in the Sakha Republic during the 22-year study period and corresponded with significant warming trend detected in the region during the same study period. It will be difficult to clearly state that the detected fire regime shifts illustrated by increased burned area was solely due to the impact of the regional climate change. Yes, warm spring and fall seasons as well as dryness trend at the end of the fire season in September were influential in determining fire activity illustrated by extension of the fire season and corresponding increases in the burned area. However, the analysis of the temporal evolution of fire regimes shows higher possible relation with impact of anthropogenic activities such as direct industrial activities in forests, because the timing of temporal shifts in fire regimes better corresponds with timing of large industrial development projects in the Republic, which both were after 2010. An open concern here is how to separate an impact of the regional climate change and impact of anthropogenic activities on fires, which can be a perspective direction of future research. However, from existing research we already know that emissions from both anthropogenic activities and forest fires will fuel the warming (M. Flannigan, Stocks, Turetsky, & Wotton, 2009).

81 But how these emissions will alter the regional climate in a large Boreal forest region and possibly impact on global climate system? These questions still remain open.

6.6 Conclusion

In this paper, taking the Republic of Sakha (Yakutia) of Russia as the study area, we aimed to develop an approach for detecting signals indicating changes in fire weather due to climate change, in order to express recent fire weather variability, relating it to fire variability by using short-term ranks of the major meteorological parameters of air temperature and atmospheric precipitation.

In our previous published study, we had also found evidence in temperature trends of their correlation to an extension of the duration of the fire season and significant increase of burned area extent (Kirillina et al. 2020). The results of this current research show the possibility of finding climate change signals that can be related to changes in recent fire weather, illustrated by the following findings:

1. The initial descriptive analysis found that temperature was increasing throughout the year in all stations, especially in the onset month of the fire season, April, and at the beginning of the peak fire period in June. In two stations (Berdigestyakh and Pokrovsk) we also detected notable decreases in precipitation at the beginning of the peak fire period in June and annual decreases during 2001-2002 in all.

Both temperature and precipitation showed high inconsistency and dispersion illustrated by high standard deviations. However, temperature showed the highest variability at the onset (April-May) and the ending (October) months of the fire season. The highest inconsistency and dispersion in precipitation for the current period were found during peak fire period and at the end of the fire season (September-October). Both changes testify to rapid changes and inconsistencies in the selected weather ranks, which might prove useful as signals of climate change when further consistency can be established.

2. The variability analysis also shows considerable variation of temperature again at the beginning (April-May) and at the end of the fire season (September-October), ranging as high as up to 59% at the onset of the fire season in April and up to 36% at the end of the season. In regard to precipitation, it shows the highest values of coefficient of variation at the beginning of the fire season (April-May) and at the end of the season (September-October). The high coefficient of

82 variation can be reporting about fast changes in both temperature and precipitation, which can be early signals of climate change. In regard to temperature, positive departures from historical normals can signal about warming trends. For precipitation, negative departures from the means can signal about rainfall shortage, which will be discussed below in finding 4.

The highest departures in temperature again were exhibited at the beginning (April and May) and at the end of the fire season (September-October) for all five districts. The highest departures from historical means for precipitation were found at the beginning and end of the fire season for all five districts. Also, we detected negative departures in two stations (Berdigestyakh and Pokrovsk) at the beginning of the peak fire period in June, which might intensify the drought situation and make the fire season more severe. One station (24856 – Pokrovsk, Khangalassky forestry district) showed moderate drought conditions in June (with the percentage departure from historical normal – -29.8%).

3. Homogeneity analysis results for temperature consistent with results of two previous steps. Significant inhomogeneity in temperature ranks was found again at the beginning of the fire season (April-May) and at the end of the season (September-October), and we also found inhomogeneity at the annual and seasonal scales. Significant upward shifts in temperature during fire season and at annual scale occurred in 2006. Unlike the consistent trends in temperature, precipitation shows a decreasing trend in two stations (Vilyuisk and Verkhnevilyuisk). However, these are not the years with many of the high temperature shifts.

4. Temperature showed significant upward trends at the onset (April-May) and the ending months of the fire season (September-October). Also, significant upward trends were found for annual and seasonal scales and during peak fire period. With regard to precipitation, we found both significant upward and downward trends. However, important for fire weather analysis, significant downwards trends were detected in Vilyuisky and Verkhnevilyuisky districts at the end of the fire season in September. This decreasing precipitation trend in September may extend the fire season, which in turn can increase the cumulative extent of burned area.

5. The relation between fire weather and fire activity shown by moderate positive correlation between temperature and burned area and low negative correlation between precipitation and burned area. More relation found between temperature and burned area. However, the largest

83 burned area extent was found in the cases when temperature increase was combined with considerable rainfall shortage.

6. The results of this step are consistent with results of previous research of the author on assessment of climate change trends in the Sakha Republic using long-term ranks of climate data since 1960s, which reported about significant warming trends in Spring (April-May) and Fall (October) months (see Kirillina and Lobanov 2015).

Overall, the results of analyses showed the relevance of proposed approach based on measures of magnitude, pace, consistency and significance of changes. Proposed approach can be used in other fire-prone regions of Russia as well as other Boreal areas to detect climate-induced changes in fire weather variability using short-term ranks of meteorological parameters. However, more sophisticated statistical analysis tools will deepen the understanding of climate-induced fire weather changes when more data can be analyzed: we will need a more extended period of data into the future as well as a larger network of reporting stations.

The Chapter 6 is a slightly modified version of the paper by Kirillina K., Yan W., Thiesmeyer L., & Shvetsov E.G. Identifying possible climate change signals using meteorological parameters in short-term fire weather variability for Russian Boreal Forest in the Republic of Sakha (Yakutia)that first appeared in Open Journal of Forestry 10 (2020), pp. 320-359 (https://doi.org/10.4236/ojf.2020.103021) under the terms of the Creative Commons Attribution 4.0 license and has been reproduced here with the permission of the copyright holder.

84 CHAPTER 7 – FUTURE PROJECTIONS OF FIRE WEATHER IN YAKUTIAN BOREAL FOREST UNDER IMPACT OF CLIMATE CHANGE 7.1 Presentation of selected data

This part of the research is aimed to make predictions of future fire weather conditions during the entire fire season under impact of projected climate change.

Two different sources of climate data were used in this study, observational data and data from climate simulations. Observational climate data (air temperature and atmospheric precipitation) were obtained from climate database of this study, developed by the author of this thesis.

Data from climate models was received from CMIP5 Project Database (Coupled Model Intercomparison Project Phase 5) including data from historical project and projections for the period until 2100 for selected climate models (see https://esgf-node.llnl.gov/projects/cmip5/) including Beijing Climate Centre, China, BCC-CM1 Model; Meteo-France, Centre National de Recherches Meteorologiques, CM3 Model; Hadley Centre for Climate Prediction, Met Office, the United Kingdom, HadCM3 Model; Institute for Numerical Mathematics (INM), Russia, INM- CM4 Model; Institute Pierre-Simon Laplace, France, IPSL Model and Max Planck Institute for Meteorology, Germany, ECHAM5 / MPI OM Model.

Region boundary files for preparation of climate maps obtained from the Russian Open GIS Portal ‘GIS-Lab’ (http://gis-lab.info).

7.2 Methodological approach for modeling future climate conditions during flammable season under impact of climate change using different climate scenarios from CMIP5 Project

Approach to modeling of future values of future fire weather included the following stages: 1. choice of the best fitting climate model to describe the current climate using parameter such as deviations of the norms of modelled temperature from observed temperature; 2. prediction of future changes of temperature and precipitation for the period till 2100 using the chosen climate model (for details of the method see (K. S. Kirillina et al., 2015)).

All required procedures were done for the meteorological station level.

85 As final step of the study were prepared maps showing the future conditions of fire season weather under impact of projected climate change. The interpolation of temperature and precipitation data had done using Kriging method, which is geo-statistical procedure that generates an estimated surface from a scattered set of points (from ArcGIS Resources website).

7.3 Projections of future fire weather in Yakutian boreal forest

Choice of the best fitting model for temperature is presented in Figures 7.1 and 7.2. For precipitation in Table 7.1.

January April 14.0 2.0 12.0 10.0 0.0 8.0 BCC HadGEM2 INM IPSL MPI 6.0 -2.0 4.0 2.0 -4.0 0.0 -2.0 BCC HadGEM2 INM IPSL MPI -6.0

1946-1975 1961-1990 1976-2005 1946-1975 1961-1990 1976-2005

July September 2.0 2.0 1.0 1.0 0.0 0.0 -1.0 BCC HadGEM2 INM IPSL MPI -1.0 BCC HadGEM2 INM IPSL MPI -2.0 -2.0 -3.0 -3.0 -4.0 -4.0 -5.0 -5.0 -6.0 -6.0

1946-1975 1961-1990 1976-2005 1946-1975 1961-1990 1976-2005

Figure 7.1. The deviations of the norms of modeled temperature from observed temperature for historical experiment, in °C (for selected meteorological stations, one station represents one region, Central Sakha Republic).

86 January April 8.0 3.0 7.0 2.0 1.0 6.0 0.0 5.0 -1.0 BCC HadGEM2 INM IPSL MPI 4.0 -2.0 3.0 -3.0 2.0 -4.0 -5.0 1.0 -6.0 0.0 -7.0 -1.0 BCC HadGEM2 INM IPSL MPI -8.0

1946-1975 1961-1990 1976-2005 1946-1975 1961-1990 1976-2005

July September 3.0 2.0 2.0 1.0 1.0 0.0 0.0 -1.0 BCC INM MPI BCC HadGEM2 INM IPSL MPI IPSL -1.0 -2.0 HadGEM2 -2.0 -3.0 -3.0 -4.0 -4.0 -5.0

1946-1975 1961-1990 1976-2005 1946-1975 1961-1990 1976-2005

Figure 7.2. The deviations of the norms of modeled temperature from observed temperature for historical experiment, in °C (for selected meteorological stations, one station represents one region, Western Sakha Republic).

87 The deviations of the norms of modeled precipitation from observed one for historical experiment are shown by Table 7.1.

Table 7.1. The deviations of the norms of modeled precipitation from observed precipitation, in mm and %. The central part of the Sakha Republic (on the example of Berdigestyakh Meteostation)

1976–2005 January April July September Observed 8.4 7.0 38.3 31.4 BCC 4.7 29.2 33.6 20.9 HadGEM2 1.7 36.1 23.8 13.0

INM 3.0 21.5 23.6 17.7 IPSL 0.9 31.1 18.7 2.5 MPI 4.2 25.0 38.9 8.9 1976–2005 The western part of the Sakha Republic (on the example of Vilyuisk Meteostation) January April July September Observed 10.1 11.5 47.7 32.6 BCC 2.9 18.0 6.5 5.6 HadGEM2 2.6 33.9 1,8 5.7 INM 2,8 12.0 7.5 11.1 IPSL -2.3 28.4 -5.8 -6.4 MPI 2.4 25.7 16.4 11.5 Data Source: Climate database of this study.

Figures 7.3 and 7.4 show the projected air temperature and atmospheric precipitation changes over Sakha Republic for the fire season. The greatest temperature increase and precipitation decrease will occur in the western part of Sakha Republic, including the area of Vilyuisky and Verkhnevilyuisky forestry districts, which is the area with the greatest risk of the forest fires now and this risk have a tendency to grow further. Maps of projected future atmospheric precipitations changes also show considerable spatial variability of precipitation.

Figure 7.3. Maps of projected air temperature changes over Sakha Republic for the fire season (according to the RCP 4.5 and 8.5 Scenarios).

Figure 7.4. Maps of projected atmospheric precipitation changes over Sakha Republic for the fire season (according to the RCP 4.5 and 8.5 Scenarios).

90 Tables 7.2 and 7.3 present predictions of future air temperature and atmospheric precipitation.

Table 7.2. Air temperature for future period till 2100. Climate Months Scenario 2011-2040 2041-2070 2071-2100 I IV VII IX I IV VII IX I IV VII IX Western Sakha Republic (on the example of Vilyuisk Meteostation) RCP2.6 1.9 1.4 3.0 1.2 2.5 1.9 2.6 2.9 3.5 1.7 2.6 2.2 RCP4.5 3.4 0.8 3.7 2.3 4.9 1.9 4.7 3.9 6.9 2.5 5.1 4.2 RCP8.5 3.4 1.1 3.0 1.6 5.7 3.2 4.9 4.4 10.4 5.9 8.1 6.9 Central Sakha Republic (on the example of Berdigestyakh Meteostation) RCP2.6 0.6 1.9 - 0.6 1.8 2.2 - 0.5 2.5 2.0 - 0.6 RCP4.5 2.1 1.3 0.2 0.3 4.2 2.6 0.8 0.9 5.3 3.1 1.3 1.7 RCP8.5 1.5 1.4 -0.3 0.1 4.6 3.3 1.4 1.7 8.1 6.1 2.5 4.5 Data Source: Modeled climate data from CMIP5 Project.

Table 7.3. Atmospheric precipitation for future period till 2100. Projections 2011–2040 2041–2070 2071–2100 Scenario I VII IX I VII IX I VII IX Western Yakutia (on the example of Vilyuisk Meteostation) RCP 2.6 2.9/28 1.1/2 -11.0/-34 2.2/22 9.3/19 -17.5/-54 3.7/36 -24.7/-52 -10/-31 RCP 4.5 3.3/32 -1.3/-3 -5.0/-15 3.7/36 3.5/7 -5.5/-17 7.6/75 5.8/12 -2.5/-8 RCP 8.5 3.0/29 1.2/3 – 4.1/40 1.8/4 – 10.1/99 0.7/2 – Central Yakutia (on the example of Berdigestyakh Meteostation) 2011–2040 2041–2070 2071–2100 Scenario I VII IX I VII IX I VII IX RCP 2.6 2.2/26 – 18.6/59 2.7/32 – 17.9/57 2.6/31 – 17.2/55 RCP 4.5 1.8/21 26.2/68 20.6/66 4.3/51 23.8/62 12.6/40 5.1/61 39.5/103 21.5/68 RCP 8.5 2.3/27 31.1/81 – 4.2/49 40.7/106 – 8.9/106 50/131 – Data Source: Modeled climate data from CMIP5 Project.

91 As seen from Tables 7.2 and 7.3 the Republic will have experience an increase temperature. In turn, projection of precipitation shows the rainfall shortage at the end of the fire season in September for Western part of Sakha Republic, including Vilyuisky and Verkhnevilyuisky forestry districts. These districts are already experienced the rainfall shortage at the end of the fire season for investigated study period, 1996-2018. Hence, the Western Sakha Republic will have higher risk of forest fires in the future period.

7.4 Conclusion

Weather plays a significant role in forest fire regime. Yakutian boreal forest are particularly vulnerable as it was shown by recent forest fire history of the Sakha Republic and this risk might increase in future according to the projections from modern climate models.

On the basis of conducted research is possible to make the following conclusion: according to the predictions from modern climate models the greatest temperature increase and significant rainfall shortage will occur in the Western Sakha Republic, which is the area with the greatest risk of forest fires now and this risk will grow further creating more burned area, which requires to take the appropriate measures for mitigation of future wildfires.

The Chapter 7 is a modified version of the conference paper by Kirillina K., Goumehei E., & Yan W. (2016). GIS-mapping of forest fires as climate change indicator on North Russia: Case study of the Republic of Sakha (Yakutia) first published in the Proceedings of the 3rd International Conference on Engineering and Natural Science – ICENS (pp. 647-662), Kyoto, Japan, 12-14 July 2016.

92 CHAPTER 8 – CONCLUSIONS 8.1 General conclusions and recommendations

This study aimed to develop a systems analytic approach for identifying characteristics of contemporary fire regimes for the region recently affected by warming and rapid industrialization and to determine how these trends were related with fire activity on the example of one of the most fire-hazardous regions of Boreal Russia, the Sakha Republic for 1996-2018. The research was conducted in three steps.

The first step was to characterize the fire regimes and their spatiotemporal shifts with original set of fire parameters, and historical trends, evolutionary patterns, fire seasonality and causes. The relationship between anthropogenic activity and forest fires was examined in GIS-environment. The study found high evidence on long-term increase of fire activity in the Sakha Republic during the period 1996- 2018 illustrated by following findings: 1. The Republic and selected for analysis Central and Western forestry districts experienced significant increases of burned area as it can be seen from Table 5.1 (level of significance α=0.05). 2. The shift to longer fire season registered. Fire season extended from 115±3.81 days in 2000s up to 128±2.78 days in 2010s. The fire season since 2009 starts earlier from April. Also, since 2009 fire season considerable burned area increases found at the ending month of fire season, October. 3. The peak fire occurrence period is now extending across a three-month period from May to July. 4. Positive shifts in the temporal evolution of fire regimes illustrated by significant increases of the burned area after 2010s were registered on both regional and local (forestry district) levels, and can signal about shift to a new, more severe fire regimes.

Findings 1-4 with high level of confidence can report about increase of fire activity in the Sakha Republic. However, relation of anthropogenic factor to increase of fire activity is considered to be moderate as shown by following findings: 5. In this compilation of statistics (regional fire data and satellite fire observations) the ratio of anthropogenic fires seems to be increased, 54% of anthropogenic fires versus 47.5% of lightning- induced fires as it was shown by test on statistically significant difference on cause attribution. Overall, the ratio of anthropogenic fires in the Republic prevails since 2006. 6. Significant increases of burned area (with level of significance α=0.05) found in forestry districts affected by either large industrial activities or new large-scale development projects.

93 7. The timing of significant upward shifts in the evolution of fire regimes matched with periods of climatic warming, which accompanied the industrial development projects in the same forestry districts.

The second step of study was to find the connections between the identified changes in fire regimes and regional climate change. Based on meteorological observations, the research used the magnitude, pace, inconsistency and significance of climatic changes as signal descriptors of rapid climate changes in fire weather, and related changes in fire regimes by means of descriptive statistics, variability, homogeneity and trend. The study found that the earlier identified extension of fire season in the Sakha Republic with high possibility was related to significant warming trends detected at the beginning and at the end of the fire season as well as significant rainfall shortage found at the end of the fire season in September. Other findings on relation of impact of regional climate change to increase of fire activity are the following: 1. The initial descriptive analysis found that temperature was increasing throughout the year in all stations, especially in the onset month of the fire season, April, and at the beginning of the peak fire period in June. Both temperature and precipitation showed high inconsistency and dispersion illustrated by high standard deviations. Temperature showed the highest variability at the onset (April-May) and the ending (October) months of the fire season. The highest inconsistency and dispersion in precipitation found during peak fire period and at the end of the fire season (September-October). Both changes testify to rapid changes in the selected weather ranks, which might prove useful as signals of climate change when further consistency can be established. 2. The variability analysis also shows considerable variation of temperature again at the beginning (April-May) and at the end of the fire season (September-October), ranging as high as up to 59% at the onset of the fire season in April and up to 36% at the end of the season. In regard to precipitation, the highest values of coefficient of variation found at the beginning (April-May) and at the end of the season (September-October). Detected high variability can be reporting about fast changes in both temperature and precipitation, which can be early signals of climate change. In regard to temperature, positive departures from historical normals can signal about warming trends. For precipitation, negative departures from historical means can signal about rainfall shortage, which will be discussed in finding 4. 3. Homogeneity analysis results for temperature consistent with results of two previous steps. Significant inhomogeneity in temperature ranks was found again at the beginning of the fire season

94 (April-May) and at the end of the season (September-October) as well as in annual and seasonal scales. Significant upward shifts in temperature during fire season and at annual scale occurred mostly in 2006. Unlike the consistent trends in temperature, precipitation shows a decreasing trend in Western forestry districts (Vilyuisky and Verkhnevilyuisky). 4. Temperature showed significant upward trends at the onset (April-May) and the ending months of the fire season (September-October). With regard to precipitation, significant downward trends were found at the end of the fire season in September. This rainfall shortage trend may extend fire season, which in turn might increase cumulative extent of burned area. 5. The relation between fire weather and fire activity shown by moderate positive correlation between temperature and burned area and low negative correlation between precipitation and burned area. More relation found between temperature and burned area. However, the largest burned area extent was found in the cases when temperature increase was combined with considerable rainfall shortage.

The third step projected the future fire weather based on the datasets of the climate models of CMIP 5 Project for the entire fire season. The results show that current warming and some dryness trends will likely to continue in the future, which may enforce regime shift in fire activity for Western forestry districts, which is already the area of high fire risk. This would result in greater burned area.

Overall, this study found likely a shift of the causes in recent Boreal fire regimes, affected by both anthropogenic activity and short-term climate changes. Yakutian fire regimes were affected by anthropogenic factors shown by significant increases of the ratio of anthropogenic fires and burned area increases in the forestry districts affected by recent industrialization. Timing of significant upward shifts in the evolution of fire regimes matched with periods of climatic warming, which accompanied the industrial development projects in the same forestry districts.

These findings request updated forest management and fire suppression strategies, which will be reviewed in the Discussion Section (8.2. Managing forest and fires in a changing climate and increasing human alteration: Regional implications for Sakha Republic). Further, this study intends to provide scientific evidence on new interrelationships between climate change, anthropogenic activities and forest fires as a complex system, which has a potential to affect global climate. That requires further investigation (discussed in the Section 8.3. Concluding remarks and future work).

95 8.2 Discussion “Managing forest and fires in a changing climate and increasing human alteration: Regional implications for Sakha Republic”

The findings of this study suggest about existence of complex interrelations between climate change, forest fires and anthropogenic activities, which were shown by this study on the example of the Sakha Republic. Better understanding of these complex interrelations requires further investigation. However, findings of this study support the relevance of the suggested approach for analysis of contemporary fire regimes affected by climate change and increasing human alterations. The identified fire weather changes and related changes in fire seasonality, strongly suggest a relation of climate change to the increase of fire activity. On the basis of these findings the study offers two main recommendations in order to maintain sustainable forestry and forest fire suppression practices in the Sakha Republic of Russia.

First of all, it is necessary to start the monitoring of forest fires in April, because in recent years the fire season in the Republic starts earlier; and continue that monitoring until the end of the fire season, in October.

Moreover, for effective monitoring and detection of fires it is necessary to use not only ground and aerial observation as it is already done by Yakutian Forestry Service, but also the satellite monitoring of fires, which is the most comprehensive tool for the early detection of fire hot spots. The other recommended measure is a close collaboration between the Yakutian Forestry and the Hydrometeorological Services to assess both historical and recent fire weather trends and to create effective fire weather and fire risk predictions. The suggested analysis of historical and recent fire weather trends may contribute to finding the regional fire-weather and fire-climate cycles. This analysis also may help to build a forest fire vulnerability system for the Sakha Republic. It should also make use of assessments of fire activity and their relationships to the fire weather. We should then be able to see the regional forest fire vulnerability assessment, including fire risk zoning and mapping.

Finally, a more sophisticated way to maintain effective fire monitoring and suppression practices will need to include a real time hot spot and early fire detection system, for early warning of high fire risk.

96 All recommended measures should be included into updated forest fire monitoring and management strategies and should be made the corresponding changes in the forestry legislation, especially it relates to the legislation regulating the economic use of forests and forested lands.

Research and Government bodies of the Sakha Republic should pay more attention to the forest fire problem, especially in the light of projected climate change and trend on continuing increase of involvement of forested lands into industrial activities. For example, through development of: - a separate fire weather statistics and its database (daily, monthly, seasonal for each forestry district) extended by tool for their spatial description, mapping and analysis using GIS-tools; - better monitoring of drought situations; - a monitoring and research on inter-annual and decadal variability in fire regimes; - an establishment of the regional early-warning and rapid-response systems for forest fire risk using electronics, social media and traditional communication tools.

The findings and developed research approaches for this study can be further used for other Boreal forest regions. With some customization the developed approaches can be used for characterization of contemporary fire regimes for any world region suffering from wildfires. The same applicable for developed approaches for finding relation between anthropogenic activities in the forests and fire activity and for detection of climate change signals in fire weather variability. However, this can require development of specific tools, such as the computational systems for assessment of fire risk and vulnerability, extreme fire weather or separate system for characterization of fire regimes, based on the fire parameters suggested in this study. What will promote a better deployment of this research findings to the society and science.

The study suggests an inclusion of developed approach into current Disaster Risk Reduction Framework and adoption of international measures to mitigate the emerging problem of forest fires.

Overall, the findings of this study suggest making substantial changes in forestry legislation and forestry management practices in the Sakha Republic. The changes have to be made in: (1) forestry legislation regulating a) fire monitoring and suppression, and b) economic use of forests / forested lands. (2) forestry legislation regulating a) an involvement of local people to state and regional environmental impact assessment of new industrial projects / development project and b) rights of local people, which area of inhabitance will be affected by new industrial projects / new industrial

97 development zones, especially if it is related with indigenous people of the Russian North living in the Sakha Republic. (3) forestry legislation regulating participation in international protocols.

Recommendation 1. (1.1) To update the state and regional forestry legislation related to regulation of fire monitoring and suppression. - For the first task, effective fire monitoring and suppression, a unified set of fire activity descriptors have to be created including the clear definitions of each fire descriptor taking into account the quantitative aspects too. Fire descriptors developed for this study such as fire drivers and fire characteristics descriptors can be used as an example. - The period of forest fire monitoring has to be updated taking into account impact of regional climate change on the fire season, including extension of fire season itself and extension of duration of peak fire occurrence period. According to this the new period of fire monitoring has to start from the beginning of April and continue until the end of the fire season in October. The corresponding changes have to be made in the regional legislative acts regulating forest fire monitoring and its time frames. - In relation to that the expensive aerial fire observation practices have to be replaced by more effective satellite fire monitoring. It is recommended to use not only the Government satellite fire monitoring system such as ISDM Roslesxoz, but also independent fire monitoring by Institutions or Organizations having a long-term expertise in satellite observations of fires such as the Sukachev Forest Institute for Sakha Republic’s case. This action will help to avoid any biases related with intentional understatement of burned areas, which is common issue for the Russian national fire statistics on both country and regional levels. This measure also will help to better estimate and distribute Government’s funds for fire suppression in the region and country levels. - The suggested approach for reading climate change signal in fire weather variability can be used for building fire-climate cycles as well as for prediction of future fire risk in combination with results of future climate modeling for the Republic provided by this study. - For regional Forestry and Meteorological Authorities recommended to closely collaborate, especially in the areas of data collection and analysis in order to make better predictions of fire weather risk.

(1.2) For the second task as an effective regulation of economic use of forests / forested lands, an updated definition of forest protection and economic use of forests / forested lands is needed through deriving economic benefits from forests. On a current stage, both terms lack a clear

98 definition and might be misinterpreted, especially if there exist any economic interests from stakeholders such as forestry staff and representatives of mining corporations who make a long- term lease of forested lands for industrial activities. The other problem in the Russian forestry legislation is that forest protection and economic use of forests are considered as separate actions, however in a reality they are closely interrelated. Effective protection of forests is not achievable without adequate regulation of economic use of forests as well as effective regulation of economic use of forest resources is impossible without protection of ecological functions provided by forests. The clear definitions of forests protection (including forested lands) and economic use of forests / forested lands can reduce risk of loss of environmental functions of forests as well as can reduce a forest fire risk related with anthropogenic sources of fire activity such as direct industrial activities in the forests. The study suggests following definitions: - forest protection refers to the creation of areas of legal protection of forests / forested lands from any unauthorized activities and to help preserve healthy ecosystems. - economic use of forests / forested lands is regulated norms (legally established) of economic use of forest resources or involvement of forested lands into any type of industrial activities, which will keep the ecological functions of forests including future generations based on environmental impact assessments. The requested changes have to be applied first of all in forestry regulations as documents which directly regulate economic activities in forestry districts for all Russia forests. Then, after discussion with all stakeholders including local people, which area of inhabitance will be affected by future industrial activities, the changes have to be applied in environmental impact assessment and any other related legislative acts.

Recommendation 2. Moving from previous recommendation, rights and opinion of local people related with decisions, maintenance and sustainable production of benefits from forests resources in the Sakha Republic have to be established by law. In both forestry regulations and legislation regarding environmental impact assessment of new industrial projects. The corresponding changes have to be made also in federal laws and regulations, first of all in the Russian Forest Code. Which is especially in line with recent trend on industrial development of the Russian north territories including the territory of the Sakha Republic and relates to indigenous minority groups living in the Northern part of the Sakha Republic, because their settlement areas might be affected by new industrial projects and areas of new industrial development. This issue requests a further investigation.

99 Recommendation 3. There two major actions should be taken. First is to define international protocols, participation in which can help to maintain effective fire suppression and forestry management in the Sakha Republic. Here, also have to be developed indicators which will assess the efficiency of the taken actions.

Second is involvement of this study findings into international protocols and collaborative international research projects in the field. For example, suggested approaches for fire regime characterization can be involved for development of unified descriptors of fire activity for IPCC report related with climate change adaptation using forest fires as one of the indicators of climate change. Here, also can the used the suggested approach for detection of climate change signals in fire weather variability. Both developed research approaches can be applied for other regions starting from Boreal forest region to see their relevance and applicability.

The suggested approaches for fire regime characterization and detection of climate change signals in fire weather variability can be also implemented into the DRR Framework through creation of a separate forest fire risk reduction framework. However, there will be needed collective efforts from scientists, forestry practitioners and other key stakeholders to build the unified assessment framework for reduction of forest fire risk. For example, through the following actions: - To generate a common knowledge, tools, capacity and guidance to interpret statistically fire activity in any world forest region with focus on maintaining forest management practices taking into account all interactions during all phases of forest fire management (forest fire prevention, detection and suppression). - To analyze changes in fire regimes under various climate, and land use change scenarios with focus on anthropogenic fire ignition sources and spatiotemporal evolution of fire activity as was suggested by this study. - To investigate high fire seasons and large individual fires as well as their causes and impacts on air quality, greenhouse gas emissions, and the human society. - Participatory approaches with national agencies dealing with wildfire management and local people who are affected are vitally needed.

8.3 Concluding remarks and future work

Overall, this study was aimed to provide a scientific evidence on new interrelationships between climate change, anthropogenic activities and forest fires as a complex system, which has potential

100 to affect global climate. Even though, the study found that evidence, complex interrelationships between climate change, anthropogenic activities and forest fires require further investigation.

First of all, it is necessary to learn how to recognize drivers of regional climate change. Is it induced solely by anthropogenic activities? Is it implication of global or regional climate change? Or is it related with GHG emissions from large-scale forest fires?

Secondly, it is necessary to investigate how industrial activities affect regional climate by means of amount of GHG emissions from industries and climate change effects accompanying industrialization process through large-scale industrial and development projects, which requires considerable deforestation. It is undisputable fact that the deforested areas will become hotter and will show the increasing temperature trends. However, how deforestation will affect regional precipitation patterns and climate overall?

The third concern is defining optimal level of economic development, which will not negatively affect regional climate and ecosystems. How to make that analysis and which parameters should be taken into account?

The fourth concern is changing character of contemporary fire regimes affected by both climate change and anthropogenic activities in the forests. How can we monitor that fire regime changes and maintain the sustainable forestry management and effective fire suppression practices?

The final issue to be solved is development of computational models assessing proposed interrelations interrelationships between climate change, anthropogenic activities and forest fires for determining future forest fire risk. I hope that findings of this study will be used for building mechanism of contemporary boreal forest fires and their regimes and constructing unified descriptors of fire activity, which can be used in IPCC reports where forest fire will be shown as one of the indicators of climate change and for development of separate framework for reducing forest fire risk under umbrella of DRR framework.

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111 Appendix 1 – Air temperature changes for study area (five selected forestry districts).

Table 1. Amginsky forestry district (based on Amga meteorological station data).

Year 1 2 3 4 5 6 7 8 9 10 11 12 AA* 1996 -40.1 -32.4 -24.3 -5.1 8.1 17.8 18.0 13.1 5.6 -6.7 -28.8 -42.1 -9.7 1997 -39.8 -35.6 -25.0 -3.8 9.5 14.8 19.2 17.0 4.6 -6.6 -26.3 -43.7 -9.6 1998 -41.3 -32.7 -22.5 -6.7 5.3 19.4 22.0 17.0 5.6 -12.0 -30.5 -43.4 - 10.0 1999 -36.6 -37.0 -28.2 -6.6 7.6 16.0 19.1 12.5 5.8 -9.4 -29.9 -37.0 - 10.3 2000 -38.9 -35.3 -23.3 -3.2 8.0 15.4 18.8 12.0 5.7 -12.0 -32.6 -41.9 - 10.6 2001 -41.6 -40.0 -19.5 -5.4 8.6 15.3 21.5 14.2 2.9 -8.5 -24.7 -36.1 -9.4 2002 -39.7 -31.0 -20.9 -3.2 7.7 17.6 18.0 15.9 5.9 -8.0 -27.6 -43.1 -9.0 2003 -41.4 -37.9 -18.0 -4.7 4.9 13.4 20.7 15.5 7.7 -7.9 -30.8 -36.3 -9.6 2004 -41.9 -39.6 -21.8 -6.3 5.7 12.8 18.1 13.3 6.1 -13.6 -22.6 -47.1 - 11.4 2005 -41.3 -38.3 -24.3 -1.9 8.2 16.0 18.2 13.5 8.3 -8.7 -20.1 -37.3 -9.0 2006 -44.4 -37.4 -24.2 -4.1 7.4 17.3 17.7 16.2 7.0 -10.1 -27.6 -39.0 - 10.1 2007 -36.1 -39.1 -22.0 -3.3 9.1 15.6 16.8 15.4 6.6 -6.5 -28.8 -34.0 -8.9 2008 -39.1 -33.3 -14.9 -8.0 8.3 18.5 19.8 16.4 5.2 -5.1 -28.1 -43.0 -8.6 2009 -37.1 -41.3 -20.2 -5.3 7.9 18.2 19.5 13.9 7.7 -5.4 -30.1 -39.2 -9.3 2010 -39.6 -37.5 -23.9 -5.9 9.8 15.5 20.8 15.7 4.7 -8.3 -24.6 -38.6 -9.3 2011 -39.2 -37.9 -18.3 -3.2 8.8 15.3 21.9 16.8 3.2 -6.6 -28.7 -40.1 -9.0 2012 -36.1 -36.8 -25.3 -5.7 9.6 18.7 19.6 13.4 7.4 -6.9 -31.6 -38.8 -9.4 2013 -44.4 -37.7 -23.3 -3.7 9.8 16.5 18.3 15.3 5.1 -7.2 -24.5 -32.1 -9.0 2014 -43.3 -34.2 -19.0 -1.5 9.4 15.7 17.7 15.5 5.4 -10.4 -27.6 -41.7 -9.5 2015 -36.2 -29.4 -18.0 -5.9 7.1 15.2 18.8 16.2 4.6 -8.0 -27.3 -35.3 -8.2 2016 -38.4 -37.7 -17.1 -1.2 7.7 15.3 17.8 13.2 5.5 -8.6 -29.0 -39.1 -9.3

AA* - Annual Average. Data Source: From Kirillina K., Shvetsov E.G., Protopopova V.V., Thiesmeyer L., & Yan W. Consideration of anthropogenic factors in boreal forest fire regime changes during rapid socio-economic development: case study of forestry districts with increasing burnt area in the Sakha Republic, Russia, Environmental Research Letters, 15 (2020) 035009 (published under CC 3.0 license).

112 Table 2. Gorny forestry district (based on Berdigestyakh meteorological station data).

Year 1 2 3 4 5 6 7 8 9 10 11 12 AA 1996 -39.7 -29.5 -22.6 -7.9 5.9 15.4 16.9 11.9 3.8 -7.1 -24.0 -40.6 -9.8 1997 -37.7 -29.9 -22.6 -3.5 7.5 13.4 18.1 14.9 3.4 -5.4 -25.8 -38.1 -8.8 1998 -39.4 -28.7 -21.9 -7.6 3.7 18.4 20.0 16.2 3.3 -11.0 -31.6 -40.8 -10.0 1999 -34.2 -32.2 -28.8 -6.8 4.6 14.2 18.4 10.8 3.9 -11.6 -28.5 -35.4 -10.5 2000 -35.7 -30.9 -21.3 -4.0 6.1 15.1 17.8 12.9 3.5 -11.3 -31.3 -41.0 -10.0 2001 -38.4 -35.6 -21.4 -9.1 6.9 14.5 20.8 12.6 1.6 -14.4 -21.6 -34.6 -9.9 2002 -34.7 -27.0 -18.6 -4.2 7.5 16.4 18.0 15.5 4.8 -10.0 -23.7 -42.5 -8.2 2003 -38.5 -36.5 -15.1 -4.7 3.3 13.3 19.0 14.4 5.6 -8.4 -28.1 -31.1 -8.9 2004 -40.9 -37.7 -23.0 -5.8 4.6 11.5 16.5 11.6 4.5 -11.4 -19.5 -43.0 -11.1 2005 -37.1 -33.5 -21.8 -14.0 6.5 15.1 17.2 11.7 7.0 -8.7 -22.7 -33.7 -9.5 2006 -43.4 -33.0 -22.5 -4.9 6.1 14.8 16.6 13.3 5.5 -9.6 -26.4 -33.3 -9.7 2007 -28.0 -36.6 -20.6 -3.3 6.7 15.0 14.7 14.6 5.6 -6.4 -25.1 -33.0 -8.0 2008 -34.5 -26.5 -12.1 -6.9 8.2 17.6 18.1 14.7 3.7 -4.7 -27.2 -42.5 -7.7 2009 -34.9 -40.1 -20.4 -4.6 5.9 17.1 17.4 12.7 6.1 -7.1 -29.0 -37.8 -9.6 2010 -36.9 -33.9 -22.9 -7.2 8.7 14.7 19.4 13.8 4.1 -8.3 -21.2 -40.2 -9.2 2011 -33.3 -34.1 -15.8 -2.9 6.9 14.3 20.3 15.2 1.6 -6.0 -27.6 -35.6 -8.1 2012 -35.7 -31.4 -26.0 -6.3 7.7 16.5 19.2 12.2 6.2 -9.3 -30.2 -35.5 -9.4 2013 -41.7 -33.6 -23.0 -5.7 8.9 16.3 16.0 13.9 4.7 -6.3 -24.6 -30.4 -8.8 2014 -43.7 -33.6 -16.0 -0.2 8.7 14.9 17.6 14.6 3.9 -9.7 -25.3 -38.0 -8.9 2015 -33.3 -27.9 -16.2 -7.8 5.7 13.6 17.7 14.9 3.1 -8.4 -24.0 -32.6 -7.9 2016 -32.1 -34.4 -15.0 -2.8 5.5 14.2 15.9 11.8 4.7 -9.2 -29.2 -36.3 -8.9

Data Source: From Kirillina K., Shvetsov E.G., Protopopova V.V., Thiesmeyer L., & Yan W. Consideration of anthropogenic factors in boreal forest fire regime changes during rapid socio-economic development: case study of forestry districts with increasing burnt area in the Sakha Republic, Russia, Environmental Research Letters, 15 (2020) 035009 (published under CC 3.0 license).

113 Table 3. Khangalassky forestry district (based on Pokrovsk meteorological station data).

Year 1 2 3 4 5 6 7 8 9 10 11 12 AA 1996 -39.4 -30.2 -23.0 -6.6 7.0 16.1 18.0 12.8 5.5 -6.5 -24.6 -40.9 -9.3 1997 -38.1 -31.7 -22.5 -3.3 8.5 14.1 19.0 16.5 4.8 -5.6 -25.2 -41.3 -8.7 1998 -40.1 -30.1 -21.5 -6.3 4.7 19.1 21.2 17.8 5.0 -9.6 -30.1 -41.2 -9.3 1999 -33.6 -34.4 -27.6 -5.8 6.6 15.2 18.8 12.1 5.3 -9.2 -29.5 -35.6 -9.8 2000 -37.1 -32.0 -21.9 -3.5 7.4 15.8 18.5 14.3 5.1 -11.2 -31.9 -41.5 -9.8 2001 -40.1 -38.1 -21.0 -8.1 7.7 15.4 22.0 13.7 3.1 -8.1 -23.3 -35.9 -9.4 2002 -37.5 -29.7 -19.7 -3.8 7.4 17.5 19.0 16.3 6.6 -8.5 -26.2 -42.4 -8.4 2003 -39.5 -34.3 -16.1 -3.5 4.3 13.7 20.2 15.6 6.2 -7.8 -28.8 -34.0 -8.7 2004 -40.9 -39.0 -21.2 -4.9 5.9 12.6 17.8 12.7 6.2 -10.8 -21.5 -44.8 -10.7 2005 -39.5 -34.8 -22.3 -1.4 7.7 16.1 17.8 13.3 8.0 -9.0 -19.9 -35.5 -8.3 2006 -44.9 -34.9 -22.7 -4.3 6.9 16.2 18.0 15.0 7.0 -9.1 -26.3 -35.4 -9.5 2007 -32.6 -38.0 -20.7 -2.6 8.6 15.5 15.9 16.0 6.6 -5.2 -25.7 -33.7 -8.0 2008 -37.1 -30.5 -13.5 -6.4 9.2 19.0 19.2 16.1 4.7 -3.9 -26.9 -41.6 -7.6 2009 -38.5 -39.6 -19.7 -4.3 7.0 18.3 20.1 14.2 7.5 -5.5 -30.0 -37.2 -9.0 2010 -38.1 -35.7 -22.8 -6.1 9.0 15.6 20.7 15.4 4.6 -8.6 -23.2 -38.7 -9.0 2011 -36.1 -36.7 -16.6 -3.1 8.4 15.3 21.1 15.7 2.9 -5.1 -26.8 -36.5 -8.1 2012 -35.5 -34.2 -24.8 -5.5 9.0 18.1 20.0 13.4 7.2 -8.2 -31.6 -36.7 -9.1 2013 -43.2 -35.6 -22.2 -4.2 9.1 16.5 17.4 14.8 5.8 -5.8 -25.2 -30.8 -8.6 2014 -41.2 -33.8 -17.3 -0.5 9.2 16.0 18.4 15.5 5.0 -9.2 -26.5 -40.4 -8.7 2015 -34.5 -28.0 -17.7 -6.8 6.6 14.7 19.5 16.6 4.2 -7.5 -25.5 -35.0 -7.8 2016 -35.3 -36.5 -16.0 -1.1 7.0 15.4 17.4 12.8 5.7 -7.8 -28.2 -37.6 -8.7

114 Table 4. Verkhnevilyuisky forestry district (based on Verkhnevilyuisk meteorological station data).

Year 1 2 3 4 5 6 7 8 9 10 11 12 AA 1996 -35.9 -26.4 -20.7 -6.5 5.2 13.7 18.6 12.3 3.8 -7.4 -20.3 -39.2 -8.6 1997 -34.0 -29.8 -21.4 -0.2 6.4 14.3 18.2 14.3 4.2 -4.2 -24.7 -32.1 -7.4 1998 -38.2 -26.3 -20.0 -7.3 3.1 17.5 18.5 17.2 3.6 -10.1 -33.0 -37.8 -9.4 1999 -35.6 -29.5 -29.7 -6.6 4.7 14.5 19.9 12.4 5.1 -11.7 -25.9 -32.7 -9.6 2000 -33.7 -31.0 -21.6 -4.1 5.9 16.0 17.0 14.1 4.6 -10.6 -29.4 -40.1 -9.4 2001 -36.8 -35.9 -22.8 -9.1 6.1 16.6 21.5 13.5 3.5 -7.4 -19.0 -32.3 -8.5 2002 -33.5 -26.7 -17.3 -5.8 6.7 16.1 19.1 15.7 4.9 -8.0 -23.2 -38.6 -7.6 2003 -33.7 -28.9 -13.5 -5.0 2.9 13.6 19.9 13.8 6.2 -6.7 -27.3 -26.1 -7.1 2004 -37.8 -38.2 -22.9 -7.3 3.7 11.4 16.8 12.9 5.4 -11.6 -18.6 -40.1 -10.5 2005 -35.3 -31.9 -19.9 -2.2 7.7 16.3 18.5 12.0 8.8 -6.7 -24.3 -29.9 -7.2 2006 -41.3 -30.1 -22.2 -8.6 6.8 15.1 17.6 13.1 6.5 -9.5 -27.6 -29.9 -9.2 2007 -25.1 -38.1 -21.3 -1.8 6.9 15.0 16.1 14.5 6.3 -5.3 -22.0 -31.2 -7.2 2008 -32.1 -22.8 -12.1 -7.2 7.4 16.8 17.3 14.9 4.0 -3.5 -27.6 -39.0 -7.0 2009 -30.9 -40.1 -22.1 -3.6 5.2 17.1 17.4 14.3 6.5 -4.8 -27.0 -35.8 -8.7 2010 -33.1 -33.2 -21.5 -6.5 8.4 15.4 19.8 14.2 4.3 -7.4 -20.0 -38.8 -8.2 2011 -29.1 -32.9 -15.3 -2 8.2 15 19.8 15.4 3.2 -5.1 -25.3 -33.6 -6.8 2012 -33.7 -29.2 -24.7 -6.5 7.9 16.2 19.5 12.4 7.1 -9.7 -26.7 -34.1 -8.5 2013 -39.0 -33.8 -23.3 -6.0 9.4 17.4 16.7 14.4 4.9 -5.6 -21.9 -29.3 -8.0 2014 -42.5 -35.5 -14.3 0.4 9.1 15.3 19.4 14.2 4.8 -7.6 -25.5 -35.0 -8.1 2015 -32.3 -26.0 -14.4 -5.7 6.3 15.5 18.7 15.4 5.3 -7.0 -22.6 -29.7 -6.4 2016 -27.8 -32.0 -15.6 -2.9 4.7 14.5 17.2 12.7 6.8 -8.7 -26.1 -35.5 -7.7

115 Table 5. Vilyuisky forestry district (based on Vilyuisk meteorological station data).

Year 1 2 3 4 5 6 7 8 9 10 11 12 AA 1996 -37.4 -27.4 -20.3 -5.7 4.9 14.1 19.2 12.7 4.1 -7.6 -21.2 -39.7 -8.7 1997 -34.6 -29.7 -20.4 -0.4 6.9 15.2 18.9 15.2 4.4 -4.4 -25.0 -33.7 -7.3 1998 -38.1 -26.6 -19.5 -6.2 3.3 18.5 19.6 18.0 3.9 -10.5 -31.0 -37.4 -8.8 1999 -34.3 -27.3 -25.8 -5.3 5.1 15.4 20.9 12.7 5.6 -10.6 -26.8 -33.1 -8.6 2000 -34.0 -29.7 -19.6 -3.0 6.0 16.3 17.4 14.8 4.5 -10.5 -29.0 -40.1 -8.9 2001 -37.0 -35.3 -20.4 -8.5 6.8 16.8 22.5 13.7 3.5 -7.1 -19.9 -32.3 -8.1 2002 -33.9 -27.4 -15.7 -4.9 6.5 16.9 20.1 16.0 4.9 -7.8 -22.9 -38.9 -7.3 2003 -35.3 -29.2 -12.5 -4.5 3.3 13.9 20.8 14.5 6.9 -6.9 -27.1 -27.9 -7.0 2004 -37.1 -35.4 -20.9 -5.6 4.1 11.7 17.5 13.2 5.7 -11.3 -19.4 -40.3 -9.8 2005 -35.5 -30.9 -19.0 -1.2 8.1 17.4 19.1 12.5 9.2 -7.0 -23.9 -30.2 -6.8 2006 -41.0 -30.9 -20.5 -7.7 6.9 15.8 18.2 14.1 6.7 -9.3 -26.3 -30.4 -8.7 2007 -25.0 -36.1 -19.0 -1.2 7.4 15.8 16.4 15.1 6.5 -5.4 -22.8 -31.5 -6.7 2008 -32.8 -23.8 -11.5 -6.8 8.0 17.3 18.5 16.0 4.9 -3.5 -27.6 -39.6 -6.7 2009 -31.8 -38.2 -20.9 -3.1 5.5 17.9 18.2 14.8 7.0 -5.2 -27.7 -36.0 -8.3 2010 -33.8 -32.4 -19.4 -5.5 9.1 16.5 21.1 15.0 4.7 -7.7 -20.5 -39.2 -7.7 2011 -30.3 -33.1 -14.2 -1.5 8.6 15.5 21.1 16.5 3.6 -5.4 -25.2 -34.4 -6.6 2012 -34.1 -29.1 -22.8 -5.2 8.5 17.9 20.2 13.6 7.5 -9.4 -27.1 -33.9 -7.8 2013 -39.1 -33.9 -21.7 -4.7 9.7 18.7 17.5 15.3 5.4 -5.3 -22.9 -29.9 -7.6 2014 -43 -34.5 -13.3 0.4 9.6 16.4 20.5 15.1 5.3 -7.4 -26.3 -37.1 -7.9 2015 -32.8 -26 -14.7 -5.7 6.5 16.2 19.9 15.9 6.3 -6.6 -23.3 -30.8 -6.3 2016 -29 -31.9 -14.2 -2.1 5.5 15.7 18.3 13.1 8 -7.8 -25.7 -35.8 -7.2

116 Appendix 2 – Research Design.

* 1-4 – steps of analyses: 1 – descriptive statistics (calculation of historical normals/ means and calculation of means for study period, 1996-2018); 2 – variability analysis; 3 – homogeneity analysis; 4 – trend analysis. Figure 1. Research Design.

Data Source: From Kirillina K., Yan, W., Thiesmeyer, L., & Shvetsov, E. G. (2020). Identifying Possible Climate Change Signals Using Meteorological Parameters in Short-Term Fire Weather Variability for Russian Boreal Forest in the Republic of Sakha (Yakutia). Open Journal of Forestry, 10, 320-359 (published under CC 4.0 license).

117 Appendix 3 - Trends of temperature and precipitation across Sakha Republic, 1996-2018.

Mothly mean temperature trend for April (ºC) 2 0

-2 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 -4 -6 -8 -10 -12 -14

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

* Berdigestyakh Station, Gorny forestry district – extreme temperature -14°C (2005) was verified with station data.

Monthly mean temperature trend for May (ºC) 12 11 10 9 8 7 6 5 4 3 2

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 2. Trends of temperature across Sakha Republic, 1996-2018 (in ºC).

118 Monthly mean temperature trend for June (°C) 20 19 18 17 16 15 14 13 12 11 10

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Monthly mean temperature trend for July (°C) 23 22 21 20 19 18 17 16 15 14

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 2. (continued).

119 Monthy mean temperature trend for August (°C) 18 17 16 15 14 13 12 11 10

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Monthly mean temperature trend for September (°C) 10 9 8 7 6 5 4 3 2 1 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 2. (continued).

120 Monthly mean temperature trend for October (°C) 0

-2 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 -4 -6 -8 -10 -12 -14 -16

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Trend of annual average temperature (°C) -5

-6 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

-7

-8

-9

-10

-11

-12

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 2. (continued).

121 Temperature trend for fire season (°C) 12

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Temperature trend for peak fire period (°C) 20 19 18 17 16 15 14 13

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 2. (continued).

122 Monthly total precipitation trend for April (mm) 120

100

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Monthly total precipitation trend for May (mm) 140 120 100 80 60 40 20 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 3. Trends of precipitation across Sakha Republic, 1996-2018 (in mm).

123 Monthly total precipitation trend for June (mm) 160 140 120 100 80 60 40 20 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Monthly total precipitation trend for July (mm) 180 160 140 120 100 80 60 40 20 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 3. (continued).

124 Monthly total precipitation trend for August (mm) 200 180 160 140 120 100 80 60 40 20 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Montly total precipitation trend for September (mm) 120

100

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 3. (continued).

125 Monthly total precipitation trend for October (mm) 140 120 100 80 60 40 20 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Trend of annual average precipitation (mm) 50 45 40 35 30 25 20 15 10 5 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 3. (continued).

126 Fire season precipitation trend (mm) 90 80 70 60 50 40 30 20 10 0

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Peak fire period precipitation trend (mm) 120

100

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Amga Berdigestyakh Vilyuisk Verkhnevilyuisk Pokrovsk

Figure 3. (continued). Data Source: From Kirillina K., Yan, W., Thiesmeyer, L., & Shvetsov, E. G. (2020). Identifying Possible Climate Change Signals Using Meteorological Parameters in Short-Term Fire Weather Variability for Russian Boreal Forest in the Republic of Sakha (Yakutia). Open Journal of Forestry, 10, 320-359 (published under CC 4.0 license).

127 Appendix 4 – Relation between fire weather and burned area changes.

Gorny Forestry District, 2014 fire season.

Relation between average monthly temperature and burned area 180 y = 3.7377x + 12.28 160 R² = 0.40 140 120 100 80

Burned Area 60 40 20 0 -5.0 0.0 5.0 10.0 15.0 20.0 The average monthly temperature

Figure 1. Relation between temperature and burned area.

Vilyuisky Forestry District, 2014 fire season.

Relation between average monthly temperature and burned area 700 y = 17.634x + 5.1479 R² = 0.44 600

500

400

300

200 Burned Area 100

0 -10 -5 0 5 10 15 20 25 The average monthly temperature

Figure 2. Relation between temperature and burned area.

128 Verkhnevilyuisky Forestry District, 2014 fire season.

Relation between average monthly temperature and burned area 400 y = 9.0728x + 1.0786 R² = 0.42 350 300 250 200 150

Burned Area 100 50 0 -10 -5 0 5 10 15 20 25 The average monthly temperature

Figure 3. Relation between temperature and burned area.

Khangalassky Forestry District, 2018 fire season.

Relation between monthly mean of total precipitation and burned area 30 y = -0.1529x + 12.563 R² = 0.30 25

Burned Area 10

0 0 20 40 60 80 100 120 The monthly mean of total precipitation

Figure 4. Relation between precipitation and burned area.

129 Vilyuisky Forestry District, 2018 fire season.

Relation between average monthly temperature and burned area 200 y = 6.1713x + 22.04 180 R² = 0.48 160 140 120 100 80

Burned Area 60 40 20 0 -5 0 5 10 15 20 The average monthly temperature

Figure 5. Relation between temperature and burned area.

Verkhnevilyuisky Forestry District, 2018 fire season.

Relation between average monthly temperature and burned area 120 y = 2.8104x + 14.845 R² = 0.30 100

40 Burned Area

0 -10 -5 0 5 10 15 20 The average monthly temperature

Figure 6. Relation between temperature and burned area.

130