Startseite Potential influence of meteorological variables on forest fire risk in Serbia during the period 2000-2017
Artikel Open Access

Potential influence of meteorological variables on forest fire risk in Serbia during the period 2000-2017

  • I. Tošić EMAIL logo , D. Mladjan , M. B. Gavrilov , S. Živanović , M. G. Radaković , S. Putniković , P. Petrović , I. Krstić Mistridželović und S. B. Marković
Veröffentlicht/Copyright: 29. August 2019
Artikel downloaden (PDF)
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Open Geosciences
Aus der Zeitschrift Open Geosciences Band 11 Heft 1

Abstract

To examine potential relationships between meteorological variables and forest fires in Serbia, daily temperature, precipitation, relative humidity and wind speed data for 15 meteorological stations across Serbia were used to construct fire indices. The daily values of the Ångström and Nesterov indices were calculated for the period 2000–2017. A high number of forest fires occurred in 2007 and 2012 in Serbia, during a period of extremely high air temperatures in 2007, followed by the longest heat wave and the worst drought in 2012.

In order to identify the ideal weather conditions for fire break outs, different combinations of input variables, e.g., meteorological variables (mean temperature, precipitation, relative humidity, maximum temperature, minimum temperature and wind speed), fire danger indices or a combination of both, for the Belgrade area during the period 1986–2017, were tested. It was found that using relative humidity or precipitation as a predictor only generates a satisfactory model for forecasting of number of forest fires.

Keywords: meteorological variables; forest fire risk; fire indices; stepwise regression; Serbia

1 Introduction

Based on the definition and classification of natural disasters that are listed in the annual statistical examination by the Centre for Research on the Epidemiology of Disasters (CRED) [1] with its database EM-DAT (http://www.cred.be/em-dat) forest fires are classified into a group of natural climatological disasters [2]. The CRED and the classification of natural disasters of Munich Reinsurance Company [3] with its database NatCat-SERVICE (https://www.munichre.com/en/reinsurance/business/non-life/natcatservice/index.html) [4] also consider wildfires as climatological disasters but divide them into two categories: forest fires and land fires.

The danger of fire is a complex topic, influenced not only by weather but also by a number of factors. The intensity and size of the area affected by forest fires, to a large extent, depends on the values of meteorological elements. The importance of weather conditions to the behaviour and rate of spread of a wildfire is well documented [5, 6, 7, 8, 9]. Weather elements are recognized as major determinants of the exposure to fire risk and the spread of fire [10, 11, 12]. The most important climatic factors that influence the degree of risk of forest fires are air temperature, precipitation, relative humidity, wind and droughts. Based on these values and the variability of weather, different models have been constructed to determine the index of fire risk. Fire-danger indices usually combine information about weather and drought. For several decades, fire-weather indices have been used all over the world as proxies to estimate the dryness of the vegetation [13, 14, 15]. Some include very simple algorithms combining temperature and humidity [16, 17], while others are sophisticated tools which can be used to estimate not only the probability of a fire but also its rate of spread and severity [18, 19]. In general, ignition of a fire is related to both the prevailing atmospheric conditions and the local geomorphological structure, among any number of other anthropogenic factors that are not easily quantified [20].

The annual number of deaths due to fire hazards exceeds 10,000 worldwide, according to the United Nations World Fire Statistics Centre [21, 22]. The territory of Serbia is vulnerable to fires in nature, the number of which is increasing. These fires, depending on their intensity and duration, often have unpredictable and far-reaching consequences. In Serbia, there is a more common threat to people, the environment and property from catastrophic fires in open spaces, especially in forests. Particularly disturbing is the number of mortalities (53 persons) in fires in open spaces from 2009 to 2015 in the territory of Serbia according to the Sector for Emergency Situations of the Ministry of Internal Affairs of the Republic Serbia (MUP RS). This institution was in charge of recording all fire events in Serbia.

The total forested area in Serbia amounts to 2,252,400 ha, i.e. 29.1% of its territory [23]. Three main climate types are recognized in Serbia: continental, moderate continental and modified Mediterranean climate [24]. Northern Serbia is characterized by arid climate [25, 26]. The western part of Serbia has humid climate characteristics [27], as well as areas with higher altitude [28]. The combination of climate and forest coverage leads to a high potential danger for fire in southern and eastern Serbia, especially in the warmest and driest months of the year, which are July and August. The significance of the impact of climate change on forest-fire risk is greatest for Belgrade and minimal for the area of Kopaonik and Zlatibor [29].

Consideration of the impact of climate conditions on the occurrence of forest fires in Serbia is becoming more and more important. Previous studies have faced the impact of climate conditions on the occurrence of forest fires in certain areas [28, 30]. The examinations did not cover all input variables of importance to the creation of favourable conditions for the occurrence of forest fires, and it is necessary to identify the meteorological variables that affect forest fires in Serbia.

The aim of this study is to examine potential relationships between meteorological variables and wildfire occurrence in Serbia. Identifying the most important meteorological fire drivers is fundamental to the management of forest-fire risk in Serbia.

2 Materials and methods

2.1 Study area and data

The republic of Serbia covers 88,361 km2,with an average altitude of 470 m. Serbia is located between central and southern Europe and is characterized by a complex topography. Forest coverage in Serbia is around 29.1% of the territory, with a predominance of deciduous forest, of which 660,400 ha (29.3%) is occupied by beech forests [31].

The average daily air temperature, precipitation, relative humidity, maximum temperature, minimum temperature and wind speed data from 15 meteorological stations (Table 1) in Serbia were used. The location of the 15 stations are presented in Fig. 1. These stations are operated by the Republic Hydrometeorological Service of Serbia (http://www.hidmet.gov.rs/) Data series are complete (i.e. no missing values). Annual mean temperature (◦C), precipitation (mm) and relative humidity (%) for the 15 stations during the period 2000–2017 are shown in Table 1. Mean temperatures in Serbia are around 11◦C, with the highest average annual air temperature observed in Belgrade (13.4◦C), and the lowest average annual air temperature in Zlatibor (8.5◦C), the mountainous range in south-western Serbia. The annual mean precipitation increases from north (~600.0 mm) to southwest, with the maximum in Zlatibor (1049.6 mm). Relative humidity is around 70% in Serbia (Table 1).

Figure 1 Regional position of Serbia, and CORINE Land Cover with overlaid climatic stations used in this study.
Figure 1

Regional position of Serbia, and CORINE Land Cover with overlaid climatic stations used in this study.

Table 1

Abbreviation (Abb) of meteorological stations with their latitude, longitude and altitude (m), and annual mean values of temperature (◦C), precipitation (mm) and relative humidity (%) during the period 2000-2017.

AltitudeTPRH
AbbStationLatitudeLongitude(m)(◦C)(mm)(%)
SOSombor45◦47’19◦05’8811.8639.471.8
ZRZrenjanin45◦24’20◦21’8012.3593.173.6
NSNovi Sad45◦20’19◦51’8411.2761.975.0
SMSremska Mitrovica44◦58’19◦38’8211.9617.776.6
BGBelgrade44◦48’20◦28’13213.4710.167.1
VGVeliko Gradište44◦45’21◦31’8212.0678.773.8
LOLoznica44◦33’19◦14’12112.4884.774.5
SPSmederevska Palanka44◦22’20◦57’12112.2687.772.3
KGKragujevac44◦02’20◦56’18512.3667.572.1
CUĆuprija43◦56’21◦23’12311.8717.974.2
ZAZaječar43◦53’22◦17’14411.5632.973.4
KVKraljevo43◦44’20◦41’21512.1753.072.9
ZLZlatibor43◦39’19◦41’10288.51049.675.3
NINiš43◦20’21◦54’20212.6641.070.7
KUKuršumlija43◦08’21◦16’38411.0688.178.3

For this study, we used statistical data from the Sector for Analytics, Telecommunications and Information Technology (Sector ATIT) and the Sector for Emergency Situations of the MUP RS about a registered number of fires in open space and burnt areas in the territory of the Republic of Serbia. Statistical data of the number of fires in open space are classified into six groups (forest fires, grain fires in meadows and pastures, orchard fires, fires in garbage dumps (waste) and other fires in open space). Fire hazards (total number of all types of fires) occurred more than 503,870 times in Serbia during the period 2000–2017, leading to 1,574 fatalities, with more than 6,000 people injured and economic losses of about a million euros.

The vegetation cover of Serbia was determined by CORINE Land Cover 2012, which is a free access map of 44 land classes in over 39 countries, with a minimum mapping unit of 25 ha. For the purpose of this study, 16 major land classes in Serbia are represented in Fig. 1, by using the software ArcGIS 10.1.

2.2 Daily fire-weather index

Selected fire danger indices are based on standard meteorological observations.

2.2.1 The Ångström index

The Ångström index, I, is calculated [32] using Eq. (1), where RH is relative humidity in percent and T is temperature in ◦C

(1)I=RH20+(27−T10)

Its values are high in times of low danger/ flammability and low in times of high danger/ flammability [33]. The use of the index for risk categorization is shown in Table 2.

Table 2

Values of the Ångström index (I) translated into fire-risk probability [34]

Ångström index (I)Probability of fire
I>4.0Unlikely
4.0<I<3.0Unfavourable
3.0<I<2.5Favourable
2.5<I<2.0More favourable
I<2.0Very likely

2.2.2 The Nesterov index

Nesterov proposed a fire-risk rating index, NI, in 1949 [14]. This index establishes a range of discrete fire-risk levels.

The Nesterov Index is calculated as follows:

(2)NI=∑i=1w(Ti−Tidew)Ti

where, w is the number of days since the last rainfall exceeding 3 mm per day, is the temperature (◦C) on a given day i and Tidewis the dew-point temperature (◦C) on the same day. NI is reset to zero when the daily rainfall exceeds 3 mmper day [34]. The original risk levels proposed by Nesterov are shown in Table 3.

Table 3

Values of the Nesterov index (NI) translated into fire-risk probability [34]

Nesterov index (NI)Probability of fire
NI<300No risk
301<NI<1000Low risk
1001<NI<4000Medium risk
4001<NI<10000High risk
NI>10000Extremely high risk

2.2.3 The Lang precipitation factor

Recognizing temperature as the major factor in evaporation, Lang [35] used a coefficient of humidity, defined as the ratio of precipitation to the mean temperatures

(3)L=PT

where P is the annual mean precipitation (mm), and T is the annual mean temperature (◦C).

2.3 Stepwise regression

Stepwise regression (SR) is a systematic method for adding and removing terms from a multilinear model based on their statistical significance in a regression [36]. A stepwise regression analysis generates a linear equation that predicts a dependent (predicted) variable as a function of several independent (predictor) variables. Each variable is entered in sequence and its value assessed. If adding the variable contributes to the model, it is retained, and all other variables in the model are then re-tested to see if they still contribute to the success of the model. If they no longer contribute significantly, they are removed. In this paper the number of forest fires was selected as the dependent variable, while the independent variables were the annual mean values of temperature, precipitation, relative humidity, maximum temperature, minimum temperature and wind speed.

The model efficiency coefficient (MEF) [37], Pearson’s correlation coefficient (r) and coefficient of determination (R2) are used to assess the predictive power of the regression model. The Nash–Sutcliffe efficiency can range from -∞ to 1. The model is perfect when the efficiency is 1 [38]. An efficiency of 0 indicates that the model predictions are as accurate as the mean of the observed data, whereas an efficiency lower than 0 occurs when the observed mean is a better predictor than the model. The coefficient of determination ranges from 0 to 1, with a perfect fit being equal to 1. The lower the root mean square error (RMSE), the better the performance of the model.

3 Results

3.1 Analysis of fires in Serbia

The number of outdoor fires in Serbia during the period 2000–2017 is presented in Fig. 2. The maximum number of fires was observed in 2012, followed by 2007, 2011 and 2017. Figure 3 shows the number of forest fires and size of the burnt area (ha) in Serbia. It can be seen that 2007 was the year with the highest number of forest fires (1627) and the largest burnt area (22161 ha) in Serbia. Table 4 shows the total number of fires in open space and the number of fires by group during the period 2000–2017. Fires in open space comprised approximately 46.34% of all fires. This analysis shows that the highest proportion of fires (50.67%) occurred in the group ‘other fires in open space’.

Figure 2 Number of open-space fires in Serbia during the period 2000-2017.
Figure 2

Number of open-space fires in Serbia during the period 2000-2017.

Figure 3 Number of forest fires (upper panel) and burnt area (ha, lower panel) in Serbia during the period 2000-2017.
Figure 3

Number of forest fires (upper panel) and burnt area (ha, lower panel) in Serbia during the period 2000-2017.

Table 4

Total number of fires in open space and fire by groups during the period 2000-2017.

Category of fire20002001200220032004200520062007200820092010
Outdoor fires15,6635,9618,90310,7458,1268,3147,74922,58417,72012,1418,315
Forest fires/3856435952642598371,627552408254
Cereals fires/189221175213626214720028698
Grass and meadows fires/1,8772,9193,8202,3111,9362,83110,2736,3394,1592,789
Orchard fires/51919055329229914012970
Garbage dump (waste) fires/8111,2732,0311,7972,2153,0734,0604,5541,212755
Other fires in open space/2,8373,7564,0343,4863,8104,7216,1785,935,5,9474,349
Category of fire2011201220132014201520162017MeanSum
Outdoor fires21,93125,45512,966/15,95810,12920,85412,973233,514
Forest fires7341,3213841654012157605449,804
Cereals fires721416228651121181791943,492
Grass and meadows fires9,81411,6654,5962,0494,8353,0799,2814,69884,573
Orchard fires33234910924103422281242,236
Garbage dump (waste) fires1,6711,6639482,7179975843,8941,90334,255
Other fires in open space8,65910,0416,7012,7287,1826,0916,5125,16492,967

Monthly number of forest fires in Serbia during the period 2009-2017 is presented in Fig. 4. About 70% of all fires is registered in four months; the highest number of fires is observed in August (980), followed by April (891), March (812), and September (725). The dynamics of forest fires in Serbia indicates that most frequently forest fires occurred in early spring and during the summer months. Forest fires usually occur in the season of agricultural works in spring, and in summer due to high temperatures and drought. Annual number of forest fires for selected municipalities is presented in Table 5. The highest number of forest fires is recorded in all areas in 2012.

Figure 4 Monthly number of forest fires in Serbia during the period 2009-2017.
Figure 4

Monthly number of forest fires in Serbia during the period 2009-2017.

Table 5

Annual number of forest fires in: a) Novi Sad (NS), b) Belgrade (BG), c) Kragujevac (KG), d) Zaječar (ZA), e) Kuršumlija (KU) and f) Niš (NI) during the period 2009-2017.

YearNSBGKGZAKUNI
200961299464
2010212245109
201163456186510
201213511081711124
201309143404
201412350288
2015110156227
20160286205
201723545193619

The daily values of the Ångström index for six selected stations (Novi Sad, Belgrade, Kragujevac, Zaječar, Kuršumlija and Niš) during the period 2000–2017 are presented in Fig. 5. It can be seen that values of I < 2.0, indicating a high likelihood of occurrence of fire, appeared in 2000, 2003, 2007 and 2012 at all selected stations, except in Kuršumlija. In Belgrade (Fig. 5b), all years after 2007 appeared (except 2014) to have a high probability of fire occurrence. According to the Ångström index (Fig. 5c), 2001 and 2008 were years of probable fire occurrence in Kragujevac (central Serbia). Zaječar (Fig. 5d) is the only station in eastern Serbia where the values of the Ångström index were not lower than 2 in 2012. In Kuršumlija (Fig. 5e) in southern Serbia, 2007 and 2012 were years of very probable fire occurrence. In Niš (Fig. 5f) in southern Serbia, all years after 2010 were determined to have a very high likelihood of fire.

Figure 5 Daily values of the Ångström index for: a) Novi Sad, b) Belgrade, c) Kragujevac, d) Zaječar, e) Kuršumlija and f) Niš during the period 2000-2017.
Figure 5

Daily values of the Ångström index for: a) Novi Sad, b) Belgrade, c) Kragujevac, d) Zaječar, e) Kuršumlija and f) Niš during the period 2000-2017.

Large-scale forest fires are recorded at 8 locations in July 2007 and at 20 locations in August 2012 in Serbia. Favourable conditions for wildfires have also obtained by simulation using the Ångström index (except for Zaječar in 2012). A good agreement existed between number of fires (Table 5) and values of the Ångström index for Novi Sad (Fig. 5a). The only exception appeared in 2017, when 2 forest fires are registered. Good results are obtained for Belgrade (Fig. 5b) and Kragujevac (Fig. 5c) for 2011, 2012 and 2017, but not for 2013 and 2015. The highest discrepancy between observed and simulated data is noted for Zaječar (Fig. 5d). Comparing data about forest fires (Table 5) and values of the Ångström index, a good agreement is obtained for Kuršumlija (Fig. 5e) and Niš (Fig. 5f).

Figure 6 shows daily values of the Nesterov index for six selected stations. In Novi Sad (Fig. 6a), Belgrade (Fig. 6b), Kragujevac (Fig. 6c), Zaječar (Fig. 6d), Kuršumlija (Fig. 6e) and Niš (Fig. 6f), 2012 appeared as the year with the highest probability of fires, because the values of the Nesterov index were higher than 10,000. In addition, 2000 in Novi Sad (Fig. 6a), 2003 and 2013 in Belgrade (Fig. 6b), 2000, 2013 and 2015 in Zaječar (Fig. 6d) and 2007 and 2013 in Niš (Fig. 6f) were years with fires most likely to occur.

Figure 6 Daily values of the Nesterov index for: a) Novi Sad, b) Belgrade, c) Kragujevac, d) Zaječar, e) Kuršumlija and f) Niš during the period 2000-2017.
Figure 6

Daily values of the Nesterov index for: a) Novi Sad, b) Belgrade, c) Kragujevac, d) Zaječar, e) Kuršumlija and f) Niš during the period 2000-2017.

Comparing data about number of forest fires (Table 5) and values of the Nesterov index (Fig. 6), it can be seen that a good agreement existed for Novi Sad (Fig. 6a). Using the Nesterov index, fires in 2011 and 2017 are not reproduced for Belgrade (Fig. 6b) and Kragujevac (Fig. 6c). A wrong number of fires is obtained for Zaječar in 2013, 2015 and 2017 (Fig. 6d). Applying the Nesterov index, good results are obtained for Kuršumlija (Fig. 6e) and Niš (Fig. 6f), except for Niš in 2013.

3.2 Stepwise regression models

Stepwise regression models (SRMs) were applied to Belgrade, for which data on forest fires, fire indices and meteorological variables were available from 1986 to 2017. The models were calibrated for the first 25 years (15 years in the second case and 10 years in the third case) and validated for the remaining 7 years (17 and 22 years).

Table 6 shows the results of regression models for the number of forest fires in Belgrade. The annual mean values of temperature, precipitation, relative humidity, maximumtemperature, minimum temperature and wind speed were designated as independent variables. Three SRMs for three periods were evaluated using four goodness-of-fit estimates. Results are presented for three periods: 2001–2017, 2006–2017 and 2011–2017. From Table 6, it can be seen that the highest coefficients of correlation and determination (r = 0.6556, R2 = 0.4298) were for the period 2001–2017. This indicates that about 43%of the forest-fire variability in Belgrade could be explained by relative humidity alone. The MEF values were positive for the first two periods and negative for the period 2011–2017. According to the estimates (Table 6), the model for the period 2006–2017 was more efficient, because the RMSE was lower, although MEF was closer to 1 in the period 2001–2017.

Table 6

Results from stepwise regression models for number of forest fires in Belgrade with retained predictor (meteorological variables: relative humidity-RH, precipitation-P)

Evaluation periodrR2RMSEMEFPredictor
2001-20170.65560.42981.18910.2681RH (%)
2006-20170.62720.39330.89840.1703RH (%)
2011-20170.36870.13590.8054-0.3577P (mm)

Table 7 shows results from stepwise regression models for the number of forest fires for Belgrade with the Ångström index (I), Nesterov index (NI) and Lang factor (L) included as predictors. For the first two periods, the Ångström index was retained only for a significance level (p) of 0.05, while for the evaluation period 2011–2017, L was only retained (Fig. 6b). According to the evaluation estimates, very similar results were obtained for the evaluation periods 2001–2017 and 2006–2017 when the Ångström index was retained as predictor.

Table 7

Results from stepwise regression models for number of forest fires in Belgrade with the Ångström index (I) and the Lang index (L) retained as predictor

Evaluation periodrR2RMSEMEFPredictor
2001-20170.54420.29621.29290.0297I
2006-20170.55140.30400.86330.0019I
2011-20170.39120.15300.7012-0.3073L

Table 8 presents results of the regression models obtained when a combination of meteorological variables and fire-weather index were used as inputs. For two periods, 2006–2017 and 2011–2017, the same results were obtained as when only meteorological variables were used. For the evaluation period 2001–2017, three predictors were retained for p values of 0.05: relative humidity, wind speed and the Ångström index. In this case, estimates were somewhat worse compared with the results obtained for the same period when relative humidity (Table 6) and Ångström index were retained (Table 7). It should be noted that all retained predictors had negative coefficients, except wind speed, which had a positive regression coefficient.

Table 8

Results from stepwise regression models for number of forest fires in Belgrade with retained predictor (meteorological variables: relative humidity-RH, precipitation-P and wind speed-V, and fire index)

Evaluation periodrR2RMSEMEFPredictor
2001-20170.51870.26900.5797-0.0046RH (%), V (m/s), I
2006-20170.62720.39330.89840.1703RH (%)
2011-20170.36870.13590.8054-0.3577P (mm)

The time series of the modelled (with different combinations of input variables) and observed annual number of forest fires in Belgrade for the evaluation period 2011–2017 are presented in Fig. 7. Figure 7a shows the model for forest fire occurrence with precipitation (P) retained as a predictor; no other single predictor contributed to the success of the model at a significance level of 0.05. A model with fire indices as predictors, from which the Lang factor (L) was retained, is presented in Fig. 7b. Results show that the modelled number of forest fires followed the observed number of forest fires during the period 2011–2017, and the model captured the maximum value in 2012 well, but not the secondary maximum in 2014. Difference between the model and observations could be explained by the small number of data points.

Figure 7 Modeled (dashed line) and observed (solid line) annual number of forest fires in Belgrade for the evaluation period 2011-2017: a) using meteorological variables (retained precipitation – P (mm) as predictor, and b) using fire indices (retained L as predictor).
Figure 7

Modeled (dashed line) and observed (solid line) annual number of forest fires in Belgrade for the evaluation period 2011-2017: a) using meteorological variables (retained precipitation – P (mm) as predictor, and b) using fire indices (retained L as predictor).

4 Discussion

The aims of this study were to examine possible relationships between forest fires and meteorological variables and to model fire occurrences in Serbia using meteorological variables, fire indices or a combination of both. Our results confirm that interannual variability in climate has impact on fire activity.

The number of forest fires increased in Serbia after 2000. Lukić et al. [30] found a positive trend in the number of forest fires in Serbia during the period 2000–2012. Our results agree with Moriondo et al. [40], who observed both an increase in the length of the fire season and an increase in the number of extreme events in Mediterranean countries. They found that the Alps region in Italy, the Pyrenees in Spain and mountains of the Balkan region, where forest cover is very high (>50%), were principally affected.

Monthly analysis indicated that the highest number of fires is observed in August, followed by April, March, and September. Our results are in accordance with Tabaković-Tošić et al. [40], who found three critical periods of forest fires, based on the fifty-year monitoring: the early spring (from March to mid-April), summer (from mid-July to late August), and autumn (from the early September to mid-October). They indicated that agricultural producers clean the fields prior to spring sowing by burning, causing the spread of fires in spring. There is a real risk of large forest fires during the period of high air temperatures and lack of precipitation over an extended period of time in summer. Tourists and hikers (wildfire initiators) usually visit forests in summer and from the early September to mid-October, contributing to spread of fires [40].

The rate of fire occurrence in Serbia was particularly high in two years, 2007 and 2012. Air temperatures were extremely high in 2007 in Serbia. Record high values of maximum temperature affected the territory of Serbia during the summer of 2007, and the previous absolute maximum temperature records dating back to the middle of the twentieth century were exceeded at almost all meteorological stations [41]. According to Šorak and Rvović [42], who analysed the period 2010–2014, the highest number of fires in Serbia was recorded in 2012, characterized by the greatest damage (7,460 ha of burned area and 63,118 m3 of damaged wood mass). The year 2012 had the longest heat waves and the worst drought since the beginning of observations in Serbia [43].

The Ångström and Nesterov indices, as fire occurrence likelihood measures, were used to study number of forest fires in several cities in Serbia. Applying the Ångström and Nesterov indices, we pointed out that the risk of fire was very high after 2011 in Serbia. Good results are obtained for forest fires occurrences using the Ångström index, which includes air temperature and relative humidity. According to Arpaci et al. [44], when looking at fires >1,000 m2, the simple indices like the Angström index, showed a better performance than complex ones, since larger fires occur under conditions that are fire prone.

A stepwise regression model was used to study how much of the variability in the number of forest fires can be explained by meteorological variables. Several models using meteorological variables, fire indices or a combination of both as inputs (predictors) were examined. Among the following meteorological variables: temperature, relative humidity, precipitation, maximum temperature, minimum temperature and wind speed, only precipitation and relative humidity were retained as predictors with a negative coefficient, and wind speed as a predictor with a positive regression coefficient. The Ångström and Lung indices were retained with a negative regression coefficient. The model’s performance for Belgrade was found to be reasonable, including only precipitation or relative humidity as predictors. Our results are in accordance with the findings of De Angelis et al. [45], who demonstrated that, surprisingly, even using meteorological variables only allows a similar or better performance than using the complex Canadian Fire Weather Index (FWI).

5 Conclusions

In this study, the potential influence of meteorological variables on forest fire risk in Serbia was examined. The obtained results indicated that forest fires interact with climate dynamics. The most favourable conditions for the occurrence of the wildfires are high air temperatures, low relative humidity, and lack of precipitation. Monthly analysis indicated that most frequently, forest fires in Serbia occurred in August, March-April, and September. A particularly high number of wildfires occurred in Serbia in 2007 and 2012. The maximum number of forest fires was 1627, while the burnt area was 22161 ha in 2007 in Serbia. Air temperatures were extremely high in 2007, while the longest heat waves and the worst drought since the beginning of observations were recorded in 2012 in Serbia.

The Ångström and Nesterov indices were used to estimate a risk of fires. Better results were obtained using the Ångström than the Nesterov index, since the Ångström index includes relative humidity and temperature, while the Nesterov index includes only temperature and dew-point temperature. Further investigation which of fire indices are best suited for forest fire risk analysis is necessary.

In order to identify the meteorological variables responsible for forest fires, different combinations of input variables (meteorological variables, fire danger indices or a combination of both) were tested. The performance of the stepwise regression model for Belgrade was found to be reasonable, including only precipitation or relative humidity as predictors. Our results are in accordance with those obtained by other researchers.

Monitoring climatic conditions in a given area is increasingly recognized as a useful tool for the successful prediction and management of forest resources. Values of climate elements and their variability indicate when and to what extent there is a risk of the emergence and spread of fire in the forest. Understanding the effects of climate elements on forest fire is essential to preparing for climate change impacts on future forest fires, when an increase in temperature is expected.

This examination of fires and meteorological variables serves as a starting point for understanding the role of fire in Serbia. Further understanding of how climate variability affects wildfire activity is needed to guide managers and policy makers as they face difficult decisions regarding issues such as fuel management, firefighting, and post-fire rehabilitation practices under varying scenarios of climate and land-use changes.

tel. +381665185766

Acknowledgements

This study was supported by the Serbian Ministry of Science, Education and Technological Development, under Grants No. 176013 and 176020. The authors would like to thank the Republic Hydrometeorological Service of Serbia, which provided the data necessary for this study. The authors also wish to thank anonymous reviewers for helpful comments and suggestions.

  1. Conflict of interest: The authors of this manuscript declare no conflict of interest.

References

[1] Guha-Sapir D, Hoyois P, Wallemacq P., Below R, Annual Disaster Statistical Review, The numbers and trends, Centre for Research on the Epidemiology of Disasters (CRED), 2016Suche in Google Scholar

[2] Mladjan D., Kekic D., Emergency situation - Contribution to conceptual definition of security, J. Criminal. Law, 2007, 12, 61–83 (in Serbian)Suche in Google Scholar

[3] Below R., Wirtz A., Guha-Sapir D., Disaster Category Classification and peril Terminology for Operational Purposes, 2009. http://ipcc-wg2.gov/njlite_download.php?id=5089Suche in Google Scholar

[4] Lukić T., Gavrilov M.B., Marković S.B., Zorn M., Komac B., Mladjan D., Djordjević J., Milanović M., Vasiljević Dj.A., Vujićič M.D., Kuzmanović B., Prentović R., Classification of the natural disasters between the legislation and application: experience of the Republic of Serbia, Acta Geogr. Slov., 2013, 53-1, 149–16410.3986/AGS53301Suche in Google Scholar

[5] Flannigan M.D., Harrington J.B., A study of the relation of meteorological variables to monthly provincial area burned by wildfire in Canada (1953–80). J. Appl. Meteorol., 1988, 27, 331–45210.1175/1520-0450(1988)027<0441:ASOTRO>2.0.CO;2Suche in Google Scholar

[6] Bessie W.C., Johnson E.A., The relative importance of fuels and weather on fire behavior in Sub-Alpine forests. Ecology, 1995, 76, 747–76210.2307/1939341Suche in Google Scholar

[7] Burgan R.E., Andrews P.L., Bradshaw L.S., Chase C.H., Hartford R.A., Latham D.J., WFAS: Wildland fire assessment system. Fire Management Notes, 1997, 57, 14–17Suche in Google Scholar

[8] Flannigan M.D., Wotton B.M., Climate, weather, and area burned. In: Johnson EA, Miyanishi K (eds), Forest Fires: Behavior and Ecological Effects, Academic Press, San Deigo, CA, 2001, 351–37310.1016/B978-012386660-8/50012-XSuche in Google Scholar

[9] Taylor A.H., Trouet V., Skinner C.N., Climatic influences on fire regimes in montane forests of the southern Cascades, California, USA. Int. J. Wildland Fire, 2008, 17, 60–7110.1071/WF07033Suche in Google Scholar

[10] Pausas J.G., Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin). Clim Change, 2004, 63, 337–35010.1023/B:CLIM.0000018508.94901.9cSuche in Google Scholar

[11] Koutsias N, Xanthopoulos G, Founda D, Xystrakis F, Nioti F, Pleniou M, Mallinis G, Arianoutsou M, On the relationships between forest fires and weather conditions in Greece from long-term national observations (1894–2010). Int. J. Wildland Fire, 2013, 22, 493–50710.1071/WF12003Suche in Google Scholar

[12] Michetti M., Pinar M., Forest Fires Across Italian Regions and Implications for Climate Change: A Panel Data Analysis. Environmental and Resource Economics, 2018, https://doi.org/10.1007/s10640-018-0279-z10.1007/s10640-018-0279-zSuche in Google Scholar

[13] Thornthwaite C.W., An approach toward a rational classification of climate. Geogr. Rev., 1948, 38, 55–9410.2307/210739Suche in Google Scholar

[14] Nesterov V., Forest fires and methods of fire risk determination. Russian. Goslesbumizdat, Moscow, 1949Suche in Google Scholar

[15] Käse H., Ein Vorschlag für eine Methode zur Bestimmung und Vorhersage der Waldbrandgefährdung mit Hilfe komplexer Kennziffern. Akademie Verlag, Berlin, 1969Suche in Google Scholar

[16] Sharples J.J., McRae R.H.D., Weber R.O., Gill A.M., A simple index for assessing fuel moisture content. Environ Modell Softw, 2009, 24, 637–64610.1016/j.envsoft.2008.10.012Suche in Google Scholar

[17] Sharples J.J, McRae R.H.D., Weber R.O., Gill A.M., A simple index for assessing fire danger rating. Environmental Modelling and Software, 2009, 24, 764–77410.1016/j.envsoft.2008.11.004Suche in Google Scholar

[18] Van Wagner C.E., Pickett, T.L., Equations and FORTRAN Program for the Canadian Forest Fire Weather Index System, Canadian Forestry Service, Forestry Technical Report 33, Ottawa, 1985Suche in Google Scholar

[19] Willis C., van Wilgen B., Tolhurst K., Everson C., D‘Abreton P., Pero L., Fleming G., Development of a national fire danger rating system for South Africa. Department of Water Affairs and Forestry, Pretoria, 2001Suche in Google Scholar

[20] Kambezidis HD, Kalliampakos GK, Fire-Risk Assessment in Northern Greece Using a Modified Fosberg Fire-Weather Index That Includes Forest Coverage. International Journal of Atmospheric Sciences, 2016, Article ID 8108691, http://dx.doi.org/10.1155/2016/810869110.1155/2016/8108691Suche in Google Scholar

[21] Budnick E.K., Quantitative fire hazards analysis-an overview of needs, methods and limitations. Fire Saf. J., 2012, 11, 3–1410.1016/0379-7112(86)90049-4Suche in Google Scholar

[22] Xie D.W., Research on analysis modeling of fire accidents and its applications using data mining technology. Central South University, Changsha (in Chinese), 2014Suche in Google Scholar

[23] Banković S., Medarević M., Pantić D., Petrović N., National Forest Inventory of the Republic of Serbia - Forest Fund of the Republic of Serbia. Ministry of Agriculture, Forestry and Water Management of the Republic of Serbia, Planeta print Belgrade, 2009Suche in Google Scholar

[24] Bajat B., Blagojević D., Kilibarda M., Luković J., Tošić I., Spatial analysis of the temperature trends in Serbia during the period 1961–2010. Theor. Appl. Climatol., 2015, 121, 289–30110.1007/s00704-014-1243-7Suche in Google Scholar

[25] Živanović S., Zigar D., Zdravković M., Meteorological monitoring for wildfire protection. Ecologica, 2013, 20, 63-66Suche in Google Scholar

[26] Hrnjak I., Lukić T., Gavrilov M.B., Marković, S.B., Unkašević M., Tošić I., Aridity in Vojvodina, Serbia. Theor. Appl. Climatol., 2014, 115, 323–33210.1007/s00704-013-0893-1Suche in Google Scholar

[27] Radaković M.G., Tošić I., Bačević N., Mladjan D., Gavrilov M.B., Marković S.B. The analysis of aridity in Central Serbia from 1949 to 2015. Theor. Appl. Climatol., 2017, 1-12.10.1007/s00704-017-2220-8Suche in Google Scholar

[28] Živanović S., Impact of drought in Serbia on fire vulnerability of forests. Int J Bioautomation, 2017, 21, 217–226Suche in Google Scholar

[29] Živanović S., Evaluating the impact of climate on forest vulnerability to fires. Acta Agriculturae Serbica XX, 2015, 39, 17–2810.5937/AASer1539017ZSuche in Google Scholar

[30] Lukić T., Marić P., Hrnjak I., Gavrilov M.B., Mlađan D., Zorn M., Komac B., Milošević Z., Marković S.B., Sakulski D., Jordan A., Ðorđević J., Pavić D., Stojsavljević R., Forest fire analysis and classification based on a Serbian case study. Acta Geogr Slov, 2017, 57, 1–1310.3986/AGS.918Suche in Google Scholar

[31] RZS (Republic Institute for Statistics), 2018: Forestry in the Republic of Serbia, 70 ppSuche in Google Scholar

[32] Chandler C., Cheney P., Thomas P., Trabaud L., Williams D., Fire in Forestry, Vol. 1: Forest Fire Behaviour and Effects. John Wiley and Sons, New York, 1983Suche in Google Scholar

[33] Schunk C.,Wastl C., Leuchner M., Schuster C., Menzel A., Forest fire danger rating in complex topography – results from a case study in the Bavarian Alps in autumn 2011. Nat. Hazards Earth Syst. Sci., 2013, 13, 2157–216710.5194/nhess-13-2157-2013Suche in Google Scholar

[34] Shetinsky E.A., Protection of forests and forest pyrology. Moscow, 1994Suche in Google Scholar

[35] Lang R., Verwitterrung und Bodenbildung als Eifuhrung die Bodenkunde, Stuttgart, 1920Suche in Google Scholar

[36] Draper N.R., Smith H., Applied Regression Analysis. John Wiley and Sons, New York, 199810.1002/9781118625590Suche in Google Scholar

[37] Nash J., Sutcliffe J., River flow forecasting through conceptual models part I—a discussion of principles. J. Hydrol, 1970, 10, 282–29010.1016/0022-1694(70)90255-6Suche in Google Scholar

[38] Putniković S., Tošić I., Relationship between atmospheric circulation weather types and seasonal precipitation in Serbia. Meteor. Atmos. Phys., 2018, 130, 393–40310.1007/s00703-017-0524-ySuche in Google Scholar

[39] Moriondo M., Good P., Durao R., Bindi M., Giannakopoulos C., Corte-Real J., Potential impact of climate change on fire risk in the Mediterranean area. Climate Res., 2006, 31, 85–9510.3354/cr031085Suche in Google Scholar

[40] Tabaković-Tošić, M., Marković, M., Rajković, S., Veselinović, M., Wildfires in Serbia – chance or frequent phenomenon. Sustainable forestry, Proceedings 59-60, Institute for forestry, Belgrade, 2009, 97-125Suche in Google Scholar

[41] Unkašević M., Tošić I., The maximum temperatures and heat waves in Serbia during the summer of 2007. Clim. Change, 2011, 108, 207–22310.1007/s10584-010-0006-4Suche in Google Scholar

[42] Šorak R, Rvović I. A damage analysis of wildfires in the Republic of Serbia for the 2010-2014 period. Researches review of the Department of Geography, Tourism and Hotel Management, 2016, 45, 1-10Suche in Google Scholar

[43] Unkašević M., Tošić I., Seasonal analysis of cold and heatwaves in Serbia during the period 1949–2012. Theor. Appl. Climatol., 2015, 120, 29–4010.1007/s00704-014-1154-7Suche in Google Scholar

[44] Arpaci A., Eastaugh C. S., Vacik H., Selecting the best performing fire weather indices for Austrian ecoregions. Theor. Appl. Climatol., 2013, 114, 393–40610.1007/s00704-013-0839-7Suche in Google Scholar PubMed PubMed Central

[45] De Angelis A., Ricotta C., Conedera M., Pezzatti G.B., Modelling the Meteorological Forest Fire Niche in Heterogeneous Pyro- logic Conditions. PLoSONE, 2015, 10, e0116875, doi:10.1371/journal.pone.011687510.1371/journal.pone.0116875Suche in Google Scholar PubMed PubMed Central

Received: 2018-12-24
Accepted: 2019-05-10
Published Online: 2019-08-29

© 2019 I. Tošić et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Artikel in diesem Heft

  1. Regular Articles
  2. 2D Seismic Interpretation of the Meyal Area, Northern Potwar Deform Zone, Potwar Basin, Pakistan
  3. A new method of lithologic identification and distribution characteristics of fine - grained sediments: A case study in southwest of Ordos Basin, China
  4. Modified Gompertz sigmoidal model removing fine-ending of grain-size distribution
  5. Diagenesis and its influence on reservoir quality and oil-water relative permeability: A case study in the Yanchang Formation Chang 8 tight sandstone oil reservoir, Ordos Basin, China
  6. Evaluation of AHRS algorithms for Foot-Mounted Inertial-based Indoor Navigation Systems
  7. Identification and evaluation of land use vulnerability in a coal mining area under the coupled human-environment
  8. Hydrocarbon Generation Potential of Chia Gara Formation in Three Selected Wells, Northern Iraq
  9. Source Analysis of Silicon and Uranium in uranium-rich shale in the Xiuwu Basin, Southern China
  10. Lithologic heterogeneity of lacustrine shale and its geological significance for shale hydrocarbon-a case study of Zhangjiatan Shale
  11. Characterization of soil permeability in the former Lake Texcoco, Mexico
  12. Detrital zircon trace elements from the Mesozoic Jiyuan Basin, central China and its implication on tectonic transition of the Qinling Orogenic Belt
  13. Turkey OpenStreetMap Dataset - Spatial Analysis of Development and Growth Proxies
  14. Morphological Changes of the Lower Ping and Chao Phraya Rivers, North and Central Thailand: Flood and Coastal Equilibrium Analyses
  15. Landscape Transformations in Rapidly Developing Peri-urban Areas of Accra, Ghana: Results of 30 years
  16. Division of shale sequences and prediction of the favorable shale gas intervals: an example of the Lower Cambrian of Yangtze Region in Xiuwu Basin
  17. Fractal characteristics of nanopores in lacustrine shales of the Triassic Yanchang Formation, Ordos Basin, NW China
  18. Selected components of geological structures and numerical modelling of slope stability
  19. Spatial data quality and uncertainty publication patterns and trends by bibliometric analysis
  20. Application of microstructure classification for the assessment of the variability of geological-engineering and pore space properties in clay soils
  21. Shear failure modes and AE characteristics of sandstone and marble fractures
  22. Ice Age theory: a correspondence between Milutin Milanković and Vojislav Mišković
  23. Are Serbian tourists worried? The effect of psychological factors on tourists’ behavior based on the perceived risk
  24. Real-Time Map Matching: A New Algorithm Integrating Spatio-Temporal Proximity and Improved Weighted Circle
  25. Characteristics and hysteresis of saturated-unsaturated seepage of soil landslides in the Three Gorges Reservoir Area, China
  26. Petrographical and geophysical investigation of the Ecca Group between Fort Beaufort and Grahamstown, in the Eastern Cape Province, South Africa
  27. Ecological risk assessment of geohazards in Natural World Heritage Sites: an empirical analysis of Bogda, Tianshan
  28. Integrated Subsurface Temperature Modeling beneath Mt. Lawu and Mt. Muriah in The Northeast Java Basin, Indonesia
  29. Go social for your own safety! Review of social networks use on natural disasters – case studies from worldwide
  30. Forestry Aridity Index in Vojvodina, North Serbia
  31. Natural Disasters vs Hotel Industry Resilience: An Exploratory Study among Hotel Managers from Europe
  32. Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters
  33. Potential influence of meteorological variables on forest fire risk in Serbia during the period 2000-2017
  34. Controlling factors on the geochemistry of Al-Shuaiba and Al-Mejarma coastal lagoons, Red Sea, Saudi Arabia
  35. The Influence of Kaolinite - Illite toward mechanical properties of Claystone
  36. Two critical books in the history of loess investigation: ‘Charakteristik der Felsarten’ by Karl Caesar von Leonhard and ‘Principles of Geology’ by Charles Lyell
  37. The Mechanism and Control Technology of Strong Strata Behavior in Extra-Thick Coal Seam Mining Influenced by Overlying Coal Pillar
  38. Shared Aerial Drone Videos — Prospects and Problems for Volunteered Geographic Information Research
  39. Stable isotopes of C and H in methane fermentation of agriculture substrates at different temperature conditions
  40. Prediction of Compression and Swelling Index Parameters of Quaternary Sediments from Index Tests at Mersin District
  41. Detection of old scattered windthrow using low cost resources. The case of Storm Xynthia in the Vosges Mountains, 28 February 2010
  42. Remediation of Copper and Zinc from wastewater by modified clay in Asir region southwest of Saudi Arabia
  43. Sedimentary facies of Paleogene lacustrine dolomicrite and implications for petroleum reservoirs in the southern Qianjiang Depression, China
  44. Correlation between ore particle flow pattern and velocity field through multiple drawpoints under the influence of a flexible barrier
  45. Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm
  46. A geophysical and hydro physico-chemical study of the contaminant impact of a solid waste landfill (swl) in King Williams’ Town, Eastern Cape, South Africa
  47. Landscape characterization using photographs from crowdsourced platforms: content analysis of social media photographs
  48. A Study on Transient Electromagnetic Interpretation Method Based on the Seismic Wave Impedance Inversion Model
  49. Stratigraphy of Architectural Elements of a Buried Monogenetic Volcanic System
  50. Variable secondary porosity modeling of carbonate rocks based on μ-CT images
  51. Traditional versus modern settlement on torrential alluvial fans considering the danger of debris flows: a case study of the Upper Sava Valley (NW Slovenia)
  52. The Influence of Gangue Particle size and Gangue Feeding Rate on Safety and Service Life of the Suspended Buffer’s Spring
  53. Research on the Transition Section Length of the Mixed Workface Using Gangue Backfilling Method and Caving Method
  54. Rainfall erosivity and extreme precipitation in the Pannonian basin
  55. Structure of the Sediment and Crust in the Northeast North China Craton from Improved Sequential H-k Stacking Method
  56. Planning Activities Improvements Responding Local Interests Change through Participatory Approach
  57. GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia
  58. Uncertainty based multi-step seismic analysis for near-surface imaging
  59. Deformation monitoring and prediction for residential areas in the Panji mining area based on an InSAR time series analysis and the GM-SVR model
  60. Statistical and expert-based landslide susceptibility modeling on a national scale applied to North Macedonia
  61. Natural hazards and their impact on rural settlements in NE Romania – A cartographical approach
  62. Rock fracture initiation and propagation by mechanical and hydraulic impact
  63. Influence of Rapid Transit on Accessibility Pattern and Economic Linkage at Urban Agglomeration Scale in China
  64. Near Infrared Spectroscopic Study of Trioctahedral Chlorites and Its Remote Sensing Application
  65. Problems with collapsible soils: Particle types and inter-particle bonding
  66. Unification of data from various seismic catalogues to study seismic activity in the Carpathians Mountain arc
  67. Quality assessment of DEM derived from topographic maps for geomorphometric purposes
  68. Remote Sensing Monitoring of Soil Moisture in the Daliuta Coal Mine Based on SPOT 5/6 and Worldview-2
  69. Utilizing Maximum Entropy Spectral Analysis (MESA) to identify Milankovitch cycles in Lower Member of Miocene Zhujiang Formation in north slope of Baiyun Sag, Pearl River Mouth Basin, South China Sea
  70. Stability Analysis of a Slurry Trench in Cohesive-Frictional Soils
  71. Integrating Landsat 7 and 8 data to improve basalt formation classification: A case study at Buon Ma Thuot region, Central Highland, Vietnam
  72. Assessment of the hydrocarbon potentiality of the Late Jurassic formations of NW Iraq: A case study based on TOC and Rock-Eval pyrolysis in selected oil-wells
  73. Rare earth element geochemistry of sediments from the southern Okinawa Trough since 3 ka: Implications for river-sea processes and sediment source
  74. Effect of gas adsorption-induced pore radius and effective stress on shale gas permeability in slip flow: New Insights
  75. Development of the Narva-Jõesuu beach, mineral composition of beach deposits and destruction of the pier, southeastern coast of the Gulf of Finland
  76. Selecting fracturing interval for the exploitation of tight oil reservoirs from logs: a case study
  77. A comprehensive scheme for lithological mapping using Sentinel-2A and ASTER GDEM in weathered and vegetated coastal zone, Southern China
  78. Sedimentary model of K-Successions Sandstones in H21 Area of Huizhou Depression, Pearl River Mouth Basin, South China Sea
  79. A non-uniform dip slip formula to calculate the coseismic deformation: Case study of Tohoku Mw9.0 Earthquake
  80. Decision trees in environmental justice research — a case study on the floods of 2001 and 2010 in Hungary
  81. The Impacts of Climate Change on Maximum Daily Discharge in the Payab Jamash Watershed, Iran
  82. Mass tourism in protected areas – underestimated threat? Polish National Parks case study
  83. Decadal variations of total organic carbon production in the inner-shelf of the South China Sea and East China Sea
  84. Hydrogeothermal potentials of Rogozna mountain and possibility of their valorization
  85. Postglacial talus slope development imaged by the ERT method: comparison of slopes from SW Spitsbergen, Norway and Tatra Mountains, Poland
  86. Seismotectonics of Malatya Fault, Eastern Turkey
  87. Investigating of soil features and landslide risk in Western-Atakent (İstanbul) using resistivity, MASW, Microtremor and boreholes methods
  88. Assessment of Aquifer Vulnerability Using Integrated Geophysical Approach in Weathered Terrains of South China
  89. An integrated analysis of mineralogical and microstructural characteristics and petrophysical properties of carbonate rocks in the lower Indus Basin, Pakistan
  90. Applicability of Hydrological Models for Flash Flood Simulation in Small Catchments of Hilly Area in China
  91. Heterogeneity analysis of shale reservoir based on multi-stage pumping data
https://doi.org/10.1515/geo-2019-0033
Schlagwörter für diesen Artikel
meteorological variables; forest fire risk; fire indices; stepwise regression; Serbia
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Regular Articles
  2. 2D Seismic Interpretation of the Meyal Area, Northern Potwar Deform Zone, Potwar Basin, Pakistan
  3. A new method of lithologic identification and distribution characteristics of fine - grained sediments: A case study in southwest of Ordos Basin, China
  4. Modified Gompertz sigmoidal model removing fine-ending of grain-size distribution
  5. Diagenesis and its influence on reservoir quality and oil-water relative permeability: A case study in the Yanchang Formation Chang 8 tight sandstone oil reservoir, Ordos Basin, China
  6. Evaluation of AHRS algorithms for Foot-Mounted Inertial-based Indoor Navigation Systems
  7. Identification and evaluation of land use vulnerability in a coal mining area under the coupled human-environment
  8. Hydrocarbon Generation Potential of Chia Gara Formation in Three Selected Wells, Northern Iraq
  9. Source Analysis of Silicon and Uranium in uranium-rich shale in the Xiuwu Basin, Southern China
  10. Lithologic heterogeneity of lacustrine shale and its geological significance for shale hydrocarbon-a case study of Zhangjiatan Shale
  11. Characterization of soil permeability in the former Lake Texcoco, Mexico
  12. Detrital zircon trace elements from the Mesozoic Jiyuan Basin, central China and its implication on tectonic transition of the Qinling Orogenic Belt
  13. Turkey OpenStreetMap Dataset - Spatial Analysis of Development and Growth Proxies
  14. Morphological Changes of the Lower Ping and Chao Phraya Rivers, North and Central Thailand: Flood and Coastal Equilibrium Analyses
  15. Landscape Transformations in Rapidly Developing Peri-urban Areas of Accra, Ghana: Results of 30 years
  16. Division of shale sequences and prediction of the favorable shale gas intervals: an example of the Lower Cambrian of Yangtze Region in Xiuwu Basin
  17. Fractal characteristics of nanopores in lacustrine shales of the Triassic Yanchang Formation, Ordos Basin, NW China
  18. Selected components of geological structures and numerical modelling of slope stability
  19. Spatial data quality and uncertainty publication patterns and trends by bibliometric analysis
  20. Application of microstructure classification for the assessment of the variability of geological-engineering and pore space properties in clay soils
  21. Shear failure modes and AE characteristics of sandstone and marble fractures
  22. Ice Age theory: a correspondence between Milutin Milanković and Vojislav Mišković
  23. Are Serbian tourists worried? The effect of psychological factors on tourists’ behavior based on the perceived risk
  24. Real-Time Map Matching: A New Algorithm Integrating Spatio-Temporal Proximity and Improved Weighted Circle
  25. Characteristics and hysteresis of saturated-unsaturated seepage of soil landslides in the Three Gorges Reservoir Area, China
  26. Petrographical and geophysical investigation of the Ecca Group between Fort Beaufort and Grahamstown, in the Eastern Cape Province, South Africa
  27. Ecological risk assessment of geohazards in Natural World Heritage Sites: an empirical analysis of Bogda, Tianshan
  28. Integrated Subsurface Temperature Modeling beneath Mt. Lawu and Mt. Muriah in The Northeast Java Basin, Indonesia
  29. Go social for your own safety! Review of social networks use on natural disasters – case studies from worldwide
  30. Forestry Aridity Index in Vojvodina, North Serbia
  31. Natural Disasters vs Hotel Industry Resilience: An Exploratory Study among Hotel Managers from Europe
  32. Using Monarch Butterfly Optimization to Solve the Emergency Vehicle Routing Problem with Relief Materials in Sudden Disasters
  33. Potential influence of meteorological variables on forest fire risk in Serbia during the period 2000-2017
  34. Controlling factors on the geochemistry of Al-Shuaiba and Al-Mejarma coastal lagoons, Red Sea, Saudi Arabia
  35. The Influence of Kaolinite - Illite toward mechanical properties of Claystone
  36. Two critical books in the history of loess investigation: ‘Charakteristik der Felsarten’ by Karl Caesar von Leonhard and ‘Principles of Geology’ by Charles Lyell
  37. The Mechanism and Control Technology of Strong Strata Behavior in Extra-Thick Coal Seam Mining Influenced by Overlying Coal Pillar
  38. Shared Aerial Drone Videos — Prospects and Problems for Volunteered Geographic Information Research
  39. Stable isotopes of C and H in methane fermentation of agriculture substrates at different temperature conditions
  40. Prediction of Compression and Swelling Index Parameters of Quaternary Sediments from Index Tests at Mersin District
  41. Detection of old scattered windthrow using low cost resources. The case of Storm Xynthia in the Vosges Mountains, 28 February 2010
  42. Remediation of Copper and Zinc from wastewater by modified clay in Asir region southwest of Saudi Arabia
  43. Sedimentary facies of Paleogene lacustrine dolomicrite and implications for petroleum reservoirs in the southern Qianjiang Depression, China
  44. Correlation between ore particle flow pattern and velocity field through multiple drawpoints under the influence of a flexible barrier
  45. Atmospheric refractivity estimation from AIS signal power using the quantum-behaved particle swarm optimization algorithm
  46. A geophysical and hydro physico-chemical study of the contaminant impact of a solid waste landfill (swl) in King Williams’ Town, Eastern Cape, South Africa
  47. Landscape characterization using photographs from crowdsourced platforms: content analysis of social media photographs
  48. A Study on Transient Electromagnetic Interpretation Method Based on the Seismic Wave Impedance Inversion Model
  49. Stratigraphy of Architectural Elements of a Buried Monogenetic Volcanic System
  50. Variable secondary porosity modeling of carbonate rocks based on μ-CT images
  51. Traditional versus modern settlement on torrential alluvial fans considering the danger of debris flows: a case study of the Upper Sava Valley (NW Slovenia)
  52. The Influence of Gangue Particle size and Gangue Feeding Rate on Safety and Service Life of the Suspended Buffer’s Spring
  53. Research on the Transition Section Length of the Mixed Workface Using Gangue Backfilling Method and Caving Method
  54. Rainfall erosivity and extreme precipitation in the Pannonian basin
  55. Structure of the Sediment and Crust in the Northeast North China Craton from Improved Sequential H-k Stacking Method
  56. Planning Activities Improvements Responding Local Interests Change through Participatory Approach
  57. GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia
  58. Uncertainty based multi-step seismic analysis for near-surface imaging
  59. Deformation monitoring and prediction for residential areas in the Panji mining area based on an InSAR time series analysis and the GM-SVR model
  60. Statistical and expert-based landslide susceptibility modeling on a national scale applied to North Macedonia
  61. Natural hazards and their impact on rural settlements in NE Romania – A cartographical approach
  62. Rock fracture initiation and propagation by mechanical and hydraulic impact
  63. Influence of Rapid Transit on Accessibility Pattern and Economic Linkage at Urban Agglomeration Scale in China
  64. Near Infrared Spectroscopic Study of Trioctahedral Chlorites and Its Remote Sensing Application
  65. Problems with collapsible soils: Particle types and inter-particle bonding
  66. Unification of data from various seismic catalogues to study seismic activity in the Carpathians Mountain arc
  67. Quality assessment of DEM derived from topographic maps for geomorphometric purposes
  68. Remote Sensing Monitoring of Soil Moisture in the Daliuta Coal Mine Based on SPOT 5/6 and Worldview-2
  69. Utilizing Maximum Entropy Spectral Analysis (MESA) to identify Milankovitch cycles in Lower Member of Miocene Zhujiang Formation in north slope of Baiyun Sag, Pearl River Mouth Basin, South China Sea
  70. Stability Analysis of a Slurry Trench in Cohesive-Frictional Soils
  71. Integrating Landsat 7 and 8 data to improve basalt formation classification: A case study at Buon Ma Thuot region, Central Highland, Vietnam
  72. Assessment of the hydrocarbon potentiality of the Late Jurassic formations of NW Iraq: A case study based on TOC and Rock-Eval pyrolysis in selected oil-wells
  73. Rare earth element geochemistry of sediments from the southern Okinawa Trough since 3 ka: Implications for river-sea processes and sediment source
  74. Effect of gas adsorption-induced pore radius and effective stress on shale gas permeability in slip flow: New Insights
  75. Development of the Narva-Jõesuu beach, mineral composition of beach deposits and destruction of the pier, southeastern coast of the Gulf of Finland
  76. Selecting fracturing interval for the exploitation of tight oil reservoirs from logs: a case study
  77. A comprehensive scheme for lithological mapping using Sentinel-2A and ASTER GDEM in weathered and vegetated coastal zone, Southern China
  78. Sedimentary model of K-Successions Sandstones in H21 Area of Huizhou Depression, Pearl River Mouth Basin, South China Sea
  79. A non-uniform dip slip formula to calculate the coseismic deformation: Case study of Tohoku Mw9.0 Earthquake
  80. Decision trees in environmental justice research — a case study on the floods of 2001 and 2010 in Hungary
  81. The Impacts of Climate Change on Maximum Daily Discharge in the Payab Jamash Watershed, Iran
  82. Mass tourism in protected areas – underestimated threat? Polish National Parks case study
  83. Decadal variations of total organic carbon production in the inner-shelf of the South China Sea and East China Sea
  84. Hydrogeothermal potentials of Rogozna mountain and possibility of their valorization
  85. Postglacial talus slope development imaged by the ERT method: comparison of slopes from SW Spitsbergen, Norway and Tatra Mountains, Poland
  86. Seismotectonics of Malatya Fault, Eastern Turkey
  87. Investigating of soil features and landslide risk in Western-Atakent (İstanbul) using resistivity, MASW, Microtremor and boreholes methods
  88. Assessment of Aquifer Vulnerability Using Integrated Geophysical Approach in Weathered Terrains of South China
  89. An integrated analysis of mineralogical and microstructural characteristics and petrophysical properties of carbonate rocks in the lower Indus Basin, Pakistan
  90. Applicability of Hydrological Models for Flash Flood Simulation in Small Catchments of Hilly Area in China
  91. Heterogeneity analysis of shale reservoir based on multi-stage pumping data
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 1.10.2025 von https://www.degruyterbrill.com/document/doi/10.1515/geo-2019-0033/html
Button zum nach oben scrollen