The Spatiotemporal Pattern of Fires in Northern Taiga Larch Forests of Central Siberia

The periodicity of fires in larch forests of Evenkia and their relationship with landscape elements have been studied. Cross-sections with “burns” in them caused by past fires have been analyzed in 72 test plots; the fire chronology encompassed the period from the 15th to the 20th century. The between-fire intervals (BFIs) have been calculated by two methods: (I) on the basis of burns alone and (II) on the basis of burns and the start of growth of the new generation of larch after the earliest fire. The BFI depends on local orographic features; it is 86 ± 11 (105 ± 12), 61 ± 8 (73 ± 8), 139 ± 17 (138 ± 18), and 68 ± 14 (70 ± 13) years for northeastern slopes, southwestern slopes, bogs, and flatlands, respectively. The mean BFIs calculated by methods I and II are 82 ± 7 and 95 ± 7 years, respectively. The permafrost horizon rises at a mean rate of 0.3 cm per year after a forest fire. It has been shown that the number of fires regularly peaks at periods of 36 and 82 years. There is also a temporal trend in fire frequency: the mean BFI was approximately 100 years in the 19th century and 65 years in the 20th century.     

Using fuzzy set theory to address the uncertainty of susceptibility to drought

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.