Analysis of the forest fires in the Antalya region of Turkey using the Keetch–Byram drought index

Varol, Tuğrul; Ertuğrul, Mertol

doi:10.1007/s11676-016-0235-0

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…When these results are assessed on the basis of countries, the explained variance is lower because large fires occur intensely in some regions like in Turkey. While a strong relationship between the amount of burned areas and FWI, BUI, ISI and SSR values in Balıkesir region was stated by Ertuğrul and Varol [6], in Muğla region was found a relationship only between SSR and the amount of burned area. Similarly, the regression equation found by Balshi et al [59] for the west of Canada explains 80% of the variance, whereas the one for the east of Canada is able to explain only 43% of the variance.…”

Section: Resultsmentioning

confidence: 90%

“…Normal or extreme values of KBDI and SPEI could not take place in the equations obtained as a result of step-wise regression. In a study in Antalya carried out by Varol and Ertuğrul [6], it was found that there was a significant relationship between KBDI values and the burned area only in the years when big fires arose but there was no relationship between KBDI values and the number of fires. As a result of step-wise regression analysis, the extreme ones of FWI, DSR, T components for both the burned area and the number of fires were selected as significant variables.…”

Section: Resultsmentioning

confidence: 98%

“…Second, therefore, the relationships were examined by developing several indexes such as KBDI (Keetch-Byram Drought Index), SPEI (The Standardized Precipitation-Evapotranspiration Index), FWI (Fire Weather Index) and PDSI (Palmer Drought Severity Index). In the studies conducted across the world, various results were obtained in the explanation of the variance depending on the study areas and variables utilized such as 12, 35, and 66% [2][3][4][5][6][7][8][9]. In the third phase, descriptive regression models were used.…”

Section: Introductionmentioning

confidence: 99%

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Drought-Forest Fire Relationships

Varol¹,

Ertuğrul²,

Özel³

2017

Mediterranean Identities - Environment, Society, Culture

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This study was carried out to determine the methods that bear the most realistic results in predicting the number of fires and burned area under the climate conditions in future. Different indices and statistical methods were used in predicting the burned area and the number of fires. With this aim, in addition to the indices used in estimating the climate, Machine Learning and multivariate adaptive regression spline (MARS) models are also used in predicting these factors. According to the results obtained in several studies, the relationship between the drought and fire indices burned area and the number of fires changes from region to region. While better results are obtained in predicting the burned area and the number of fires via the drought indices being used in this study and the MARS models that the combinations of these indices use, it is seen that a 30-39% success was achieved for predicting the amount of burned area via Machine Learning methods (Kernel Nearest Neighbor (kNN), Recursive Partitioning and Regression Trees (RPART), Support Vector Machine (SVM) and RF), and this success ranges widely from 8 to 41% in terms of the number of fires. RPART, of these four algorithms, performed the best in fire prediction, but kNN was the worst.

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Section: Resultsmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Drought-Forest Fire Relationships

Varol¹,

Ertuğrul²,

Özel³

2017

Mediterranean Identities - Environment, Society, Culture

Self Cite

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“…The low-NDVI grid cells identified in this study typically represented summers with a heat-and/or drought-induced loss of forest greenness (Anyamba and Tucker, 2012;Orth et al, 2016;Buras et al, 2020). Known drought and heat events were identified as low-NDVI events, e.g., the Iberian drought in 2005 (Gouveia et al, 2009), a 2-year-long drought in 2007-2008 in Turkey (Varol and Ertugrul, 2016), the 2011-2013 drought in the Balkans (Cindrić et al, 2016), the hot summer of 2017 in Italy (Rita et al, 2020), and the central European hot drought in 2018 (Schuldt et al, 2020;Senf and Seidl, 2021b). Additionally, we identify 2022 as a recordbreaking year of the most widespread low-NDVI events covering 37 % of the Mediterranean and temperate forest biome each.…”

Section: Low-ndvi Eventsmentioning

confidence: 94%

Meteorological history of low-forest-greenness events in Europe in 2002–2022

Hermann¹,

Röthlisberger²,

Geßler

et al. 2023

Biogeosciences

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Abstract. Forest dieback in Europe has recently intensified and has become more extensive. This dieback is strongly influenced by meteorological variations of temperature, T2m, and precipitation, P, and can be monitored with forest greenness. This study quantitatively investigates the 3-year meteorological history preceding events of reduced forest greenness in Europe's temperate and Mediterranean biome with a systematic approach. A specific focus lies in the timing of unusually persistent and unusually strong anomalies of T2m and P, as well as their relation to synoptic weather systems. A pragmatic approach based on remote sensing observations of the normalized difference vegetation index (NDVI) serves to identify low-forest-NDVI events at the 50 km scale in Europe in June to August 2002–2022. We quantify the impact of the hottest summer on record in Europe in 2022, which, according to our criteria, negatively affected 37 % of temperate and Mediterranean forest regions, and thereby reduced forest greenness more extensively than any other summer in 2002–2022. The low-NDVI events occurred in particularly dry and hot summers, but their meteorological histories also featured significant anomalies further in the past, with clear differences between the temperate and Mediterranean biome. A key feature is the anomalous accumulation of dry periods (i.e., periods with a P deficit) over the preceding 26 and 34 months in the temperate and Mediterranean biome, respectively. In the temperate biome only, T2m was anomalously persistent during almost the same 26-month period and featured distinctive peaks late in the past three growing seasons. While anomalously strong hot–dry conditions were characteristic of temperate low-NDVI events already in the previous summer, we find hardly any other systematic meteorological precursor in the Mediterranean prior to the event year. The identified dry periods went along with reduced cyclone activity in the Mediterranean and positive anticyclone frequency in the temperate biome. The occurrence of these two weather systems is locally more nuanced, showing, e.g., consistently increased and decreased cyclone frequency over western and northern Europe, respectively, in all event summers. Finally, the systematic meteorological histories are useful to test whether locally observed meteorological impacts, e.g., structural overshoot, systematically influenced the investigated events. In summary, systematic investigations of the multi-annual meteorological history provided clear evidence of how surface weather and synoptic-scale weather systems over up to 3 years can negatively impact European forest greenness. The observation of the record-extensive low-NDVI event in the summer of 2022 underlines that understanding the forest–meteorology interaction is of particular relevance for forest dieback in a changing climate.

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“…As proxies for biophysical factors we used topographic characteristics of the terrain-slope, elevation, and aspect-which are proved to be important factors for the outbreak and prediction of fire (Maingi and Henry 2007;Dlamini 2010). For climate proxies we used average annual precipitation (since its reduction can lead to more frequent and longer droughts), average annual temperature (as the air temperature has a high correlation with the frequency of wildfires and areas affected by the fire), and forest aridity index (since intensification and the number of fires are linked to drought and aridity) (Alencar et al 2015;MPZS 2015;Varol and Ertugrul 2016).…”

Section: Datamentioning

confidence: 99%

Decision Support for Integrated Management of Local-Level Adaptation to Climate Changes: The Case of Serbia

Vranic

Glišović

Velimirović

2021

Int J Disaster Risk Sci

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Projected climate changes will additionally increase the already significant risk of natural hazard-related disasters in Serbia and the west Balkan region as a whole. Serbia is about to introduce the strategy for climate change adaptation and mitigation. However, a national decision-support system for implementation of the climate change law and strategy is yet to be developed. This study contributes to the implementation of adaptation policies at subnational levels by development of a decision-support model for local-level management of the climate change adaptation process. The study explores the potential for synergetic application of multicriteria decision making analysis and probabilistic reasoning methods by focusing on Bayesian networks, analytical hierarchy processes, and geographic information systems for selection of priority adaptation measures. The study was based on the formation of causal chains, which enable linking management decisions and socioeconomic or biophysical consequences into articulated sequences of conditional relationships. A model was tested in the forestry sector, and it clearly pointed out development of an early warning system and planning of water intake basins as priority adaptation measures. Since the results are shown as a probability distribution for each alternative solutions, the model can assist decision makers with prompt evaluation of various scenarios.

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Analysis of the forest fires in the Antalya region of Turkey using the Keetch–Byram drought index

Cited by 10 publications

References 9 publications

Drought-Forest Fire Relationships

Drought-Forest Fire Relationships

Meteorological history of low-forest-greenness events in Europe in 2002–2022

Decision Support for Integrated Management of Local-Level Adaptation to Climate Changes: The Case of Serbia

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