Spatial wildfire hazard patterns in the Eastern Mediterranean: perspectives from a harmonised approach

Trucchia, Andrea; Meschi, Giorgio; Fiorucci, Paolo; Provenzale, A.; Tonini, Marj; Pernice, Umberto

doi:10.14195/978-989-26-2298-9_198

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…In the Mediterranean basin during the drought event of 2017, Italy, Greece, Portugal and Spain were interested in a total of 43,733 fires, over a total area of 894,244 ha (San‐Miguel‐Ayanz et al, 2018). For forest fire hazards, in particular, the spatial continuity of vegetation types was shown to play a relevant role in Mediterranean ecosystems (Trucchia et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Vegetation response to droughts: The case of northern Italy

Baronetti,

Menichini,

Provenzale

2023

Intl Journal of Climatology

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Drought is a normal and recurrent climate feature, but one of the most important climate hazards that can produce negative effects on natural and socioeconomic systems. The northern part of Italy is historically rich in water resources and a very fertile and productive area. Recently, however, drought events increased their influence on the hydrological behaviour of the main rivers in this region. In this work, we analyse the spatial distribution of drought events and their effects on vegetation greenness in northern Italy during the 2000–2020 period, using MODIS images at 1 km spatial resolution. Correlation maps between fields of bi‐weekly vegetation indices (normalized difference vegetation index [NDVI] and enhanced vegetation index [EVI]) and drought indices (standardized precipitation index [SPI] and standardized precipitation evapotranspiration index [SPEI]) were estimated. To generate the SPEI index, 150 daily precipitation and temperature ground station series were collected, aggregated at bi‐weekly scale, reconstructed, homogenized and spatialized at 1 km resolution by mean of Universal Kriging with auxiliary variables. The NDVI and EVI indices (MOD13A2) and the land surface temperature (LST; MOD11A2) were collected from MODIS images and quality checked, removing pixels with clouds and low‐quality data. Drought events were detected by means of the SPEI and SPI at medium (12‐months) and long (24 and 36 months) timescales. The events were characterized by drought intensity, duration and percentage of affected area. For each pixel, the Pearson's correlation between NDVI/EVI and SPEI/SPI at different timescales was estimated. On the basis of the correlation maps and the CORINE Land Cover 2020 (MCD12Q1), we estimated the drought impact on different vegetation types as the percentage of vegetation undergoing drought stress. The investigation on drought indices has revealed nine main summer and spring events in the study period. The events of summer 2003 and 2017 were especially remarkable, with respectively 50% and 40% of the total area under drought according to the SPEI index at 12 months. The correlation between drought and vegetation indices revealed that in the Po Valley and Tuscan hills, the vegetation communities perturbed by agriculture are those that are most affected by droughts, with 13% of vegetation under drought on long timescales. This study also indicated that in the last two decades, the main trigger of droughts in northern Italy was the lack of precipitation rather than pure temperature increase.

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Section: Discussionmentioning

confidence: 99%

Vegetation response to droughts: The case of northern Italy

Baronetti,

Menichini,

Provenzale

2023

Intl Journal of Climatology

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“…To promote prevention, mitigation and Wildfire management activities, proper understanding of environmental factors that immensely contribute to severe Wildfires especially in the TCA is necessary (Trucchia et al 2023). The findings promote understanding on the patterns of wildfire for monitoring of drought indices and Wildfire risk condition for successful wildfire management (Sadat Razavi et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Interplay of weather patterns and Wildfire in Tsavo Conservation Area, Kenya.

kariuki,

Chiawo,

Kairu

et al. 2024

Preprint

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Background: Wildfires, in recent years have caused devastating destructions in many parts of the world due to extreme weather conditions. Wildfire trend in Tsavo Conservation Area in Kenya is not well understood and published articles on wildfires in the region are limited. Our study aimed at establishing the relationship between weather and Wildfire pattern in Tsavo Conservation Area, which is in southeastern side of Kenya. Such information could be useful in predicting the likelihood of Wildfire occurrence. Hence making Wildfire management easier and sustainable. Results: We obtained secondary data for Wildfire occurrence and climatic factors to establish: Relationship between Wildfires and weather patterns, Wildfire seasons, Wildfire predictive models and Wildfire indicators combination matrix model. Our study obtained remote sensed Wildfires data for 19-year period from 2002 – 2020 from National Aeronautical and Space Administration website. We also obtained weather data for the same period from the Kenya Meteorological Department. The data provided by Kenya Meteorological Department included temperature, rainfall, wind speed, and humidity. Our study used Generalized Additive Model with poison distribution and Spearman Correlation to analyze the relationship between Wildfire occurrence and weather variables. The year 2020 recorded the highest number of Wildfire of 481 during the study period. We established the wildfire season for the Tsavo Conservation Area to be from June to October. September recorded the highest average number of Wildfires of 690 within the 19-year period. Humidity is the best predictor for wildfires in Tsavo Conservation Area. Our study also came up with a Matrix Model that could be used to identify months of the year that are likely to experience high Wildfire occurrence. Conclusion: The findings would be very useful in Wildfire management as they provide new knowledge on Wildfire occurrences in the area. Humidity factors being the best in predicting Wildfires and June-October being the Wildfire season for Tsavo Conservation Area forms such knowledge. The Matrix Model provide the basis for further studies in Wildfire forecast.

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Spatial wildfire hazard patterns in the Eastern Mediterranean: perspectives from a harmonised approach

Cited by 2 publications

References 7 publications

Vegetation response to droughts: The case of northern Italy

Vegetation response to droughts: The case of northern Italy

Interplay of weather patterns and Wildfire in Tsavo Conservation Area, Kenya.

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