Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

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

View through CrossRef
Abstract 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.
Title: Interplay of weather patterns and Wildfire in Tsavo Conservation Area, Kenya.
Description:
Abstract 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.

Related Results

Weather pattern and wildfire interplay in Tsavo Conservation Area, Kenya
Weather pattern and wildfire interplay in Tsavo Conservation Area, Kenya
Abstract Background In recent years, wildfires have caused devastating destruction in many protected areas globally. The ...
Status and trends of the elephant population in the Tsavo–Mkomazi ecosystem
Status and trends of the elephant population in the Tsavo–Mkomazi ecosystem
This paper updates the data on the population status of elephants in the Tsavo–Mkomazi ecosystem. Data were acquired through aerial census of elephants in the ecosystem, from 7 to ...
Wildfire Risk Assessment Considering Seasonal Differences: A Case Study of Nanning, China
Wildfire Risk Assessment Considering Seasonal Differences: A Case Study of Nanning, China
Wildfire disasters pose a significant threat to the stability and sustainability of ecosystems. The assessment of wildfire risk based on a seasonal dimension has contributed to imp...
Modeling Wildfire Dynamics in Latin America Using the FLAM Framework
Modeling Wildfire Dynamics in Latin America Using the FLAM Framework
The increasing frequency of wildfires caused by climate change poses a significant threat globally, particularly in Latin America – a region known for its critical ecosys...
Registering small-scale wildfires in Belgium using satellite data
Registering small-scale wildfires in Belgium using satellite data
For a long time, wildfires in Belgium were not considered a major risk. However, climate change is causing more frequent and longer periods of droughts, and when combined with high...
A Probabilistic Wildfire Risk Model for Canada: Insights for Data, Science and Policy
A Probabilistic Wildfire Risk Model for Canada: Insights for Data, Science and Policy
Wildfires are increasing in intensity globally, causing death, displacement, elevated health risks due to smoke inhalation, and billions of dollars in damages. In Canada, ...
Future Property Risk Estimation For Wildfire In Louisiana, USA
Future Property Risk Estimation For Wildfire In Louisiana, USA
Abstract Background: Wildfire is an important but understudied natural hazard. As with other natural hazards, wildfire research is all too often conducted at too broad a sp...
Efficiently Estimating Patterns in Wildfire Burn Probability
Efficiently Estimating Patterns in Wildfire Burn Probability
Wildfires can be dangerous phenomena, creating risks for communities that are likely to be exposed to wildfire. The likelihood of community exposure to a wildfire is influenced by ...

Back to Top