scholarly journals Simulating the effects of weather and climate on large wildfires in France

2019 ◽  
Vol 19 (2) ◽  
pp. 441-454 ◽  
Author(s):  
Renaud Barbero ◽  
Thomas Curt ◽  
Anne Ganteaume ◽  
Eric Maillé ◽  
Marielle Jappiot ◽  
...  
Keyword(s):  
Linear Models ◽  
Weather Conditions ◽  
Historical Period ◽  
Natural Ecosystem ◽  
Atmospheric Variability ◽  
Modeling Framework ◽  
Spatial And Temporal Scales ◽  
Weather And Climate ◽  
Change Context ◽  
Wildfire Spread

Abstract. Large wildfires across parts of France can cause devastating damage which puts lives, infrastructure, and the natural ecosystem at risk. In the climate change context, it is essential to better understand how these large wildfires relate to weather and climate and how they might change in a warmer world. Such projections rely on the development of a robust modeling framework linking large wildfires to present-day atmospheric variability. Drawing from a MODIS product and a gridded meteorological dataset, we derived a suite of biophysical and fire danger indices and developed generalized linear models simulating the probability of large wildfires (>100 ha) at 8 km spatial and daily temporal resolutions across the entire country over the last two decades. The models were able to reproduce large-wildfire activity across a range of spatial and temporal scales. Different sensitivities to weather and climate were detected across different environmental regions. Long-term drought was found to be a significant predictor of large wildfires in flammability-limited systems such as the Alpine and southwestern regions. In the Mediterranean, large wildfires were found to be associated with both short-term fire weather conditions and longer-term soil moisture deficits, collectively facilitating the occurrence of large wildfires. Simulated probabilities on days with large wildfires were on average 2–3 times higher than normal with respect to the mean seasonal cycle, highlighting the key role of atmospheric variability in wildfire spread. The model has wide applications, including improving our understanding of the drivers of large wildfires over the historical period and providing a basis on which to estimate future changes to large wildfires from climate scenarios.

scholarly journals Simulating the effects of weather and climate on large wildfires in France

2018 ◽  
Author(s):  
Renaud Barbero ◽  
Thomas Curt ◽  
Anne Ganteaume ◽  
Eric Maillé ◽  
Marielle Jappiot ◽  
...  
Keyword(s):  
Linear Models ◽  
Weather Conditions ◽  
Historical Period ◽  
Natural Ecosystem ◽  
Atmospheric Variability ◽  
Modeling Framework ◽  
Weather And Climate ◽  
Change Context ◽  
Spatio Temporal

Abstract. Large wildfires across parts of France can cause devastating damages which put lives, infrastructures, and natural ecosystem at risk. One of the most challenging questions in the climate change context is how these large wildfires relate to weather and climate and how they might change in a warmer world. Such projections rely on the development of a robust modeling framework linking wildfires to atmospheric variability. Drawing from a MODIS product and a gridded meteorological dataset, we derived a suite of biophysical and fire danger indices and developed generalized linear models simulating the probability of large wildfires (> 100 ha) at 8-km spatial and daily temporal resolutions across the entire country over the MODIS period. The models were skillful in reproducing the main spatio-temporal patterns of large wildfires across different environmental regions. Long-term drought was found to be a significant predictor of large wildfires in flammability-limited systems such as the Alpine and Southwest regions. In the Mediterranean, large wildfires were found to be associated with both short-term fire weather conditions and longer-term soil moisture deficits, collectively facilitating the occurrence of large wildfires. Simulated probabilities during the day of large wildfires were on average 2–3 times higher than normal with respect to the mean seasonal cycle. The model has wide applications, including improving our understanding of the drivers of large wildfires over the historical period and providing a basis to estimate future changes to large wildfire from climate scenarios.

Car Ownership and the Built Environment: A Spatial Modeling Approach

2021 ◽  
pp. 036119812110494
Author(s):  
Jerome Laviolette ◽  
Catherine Morency ◽  
Owen D. Waygood ◽  
Konstadinos G. Goulias
Keyword(s):  
Built Environment ◽  
Spatial Dependence ◽  
Linear Models ◽  
Census Data ◽  
Model Specification ◽  
Car Ownership ◽  
Grocery Stores ◽  
Modeling Framework ◽  
Explanatory Variables ◽  
Environment Variables

Car ownership is linked to higher car use, which leads to important environmental, social and health consequences. As car ownership keeps increasing in most countries, it remains relevant to examine what factors and policies can help contain this growth. This paper uses an advanced spatial econometric modeling framework to investigate spatial dependences in household car ownership rates measured at fine geographical scales using administrative data of registered vehicles and census data of household counts for the Island of Montreal, Canada. The use of a finer level of spatial resolution allows for the use of more explanatory variables than previous aggregate models of car ownership. Theoretical considerations and formal testing suggested the choice of the Spatial Durbin Error Model (SDEM) as an appropriate modeling option. The final model specification includes sociodemographic and built environment variables supported by theory and achieves a Nagelkerke pseudo-R2 of 0.93. Despite the inclusion of those variables the spatial linear models with and without lagged explanatory variables still exhibit residual spatial dependence. This indicates the presence of unobserved autocorrelated factors influencing car ownership rates. Model results indicate that sociodemographic variables explain much of the variance, but that built environment characteristics, including transit level of service and local commercial accessibility (e.g., to grocery stores) are strongly and negatively associated with neighborhood car ownership rates. Comparison of estimates between the SDEM and a non-spatial model indicates that failing to control for spatial dependence leads to an overestimation of the strength of the direct influence of built environment variables.

Projected changes in pyroconvective conditions in the Iberian Peninsula at the end of the 21st century

2021 ◽  
Author(s):  
Martín Senande-Rivera ◽  
Gonzalo Miguez-Macho
Keyword(s):  
Iberian Peninsula ◽  
21St Century ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Weather Conditions ◽  
Fire Season ◽  
Fire Plume ◽  
Near Surface ◽  
Wildfire Spread ◽  
Projected Changes

<p>Extreme wildfire events associated with strong pyroconvection have gained the attention of the scientific community and the society in recent years. Strong convection in the fire plume can influence fire behaviour, as downdrafts can cause abrupt variations in surface wind direction and speed that can result in bursts of unexpected fire propagation. Climate change is expected to increase the length of the fire season and the extreme wildfire potential, so the risk of pyroconvection occurence might be also altered. Here, we analyse atmospheric stability and near-surface fire weather conditions in the Iberian Peninsula at the end of the 21st century to assess the projected changes in pyroconvective risk during favourable weather conditions for wildfire spread.  </p>

Model predictive control of a CSTR: A hybrid modeling approach

2010 ◽  
Vol 64 (3) ◽  
Author(s):  
Michal Kvasnica ◽  
Martin Herceg ◽  
Ľuboš Čirka ◽  
Miroslav Fikar
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Nonlinear Behavior ◽  
Continuous Stirred Tank Reactor ◽  
Modeling Framework ◽  
Future Evolution ◽  
Continuous Stirred Tank ◽  
Traditional Approaches

AbstractThis paper presents a case study of model predictive control (MPC) applied to a continuous stirred tank reactor (CSTR). It is proposed to approximate nonlinear behavior of a plant by several local linear models, enabling a piecewise affine (PWA) description of the model used to predict and optimize future evolution of the reactor behavior. Main advantage of the PWA model over traditional approaches based on single linearization is a significant increase of model accuracy which leads to a better control quality. It is also illustrated that, by adopting the PWA modeling framework, MPC strategy can be implemented using significantly less computational power compared to nonlinear MPC setups.

scholarly journals Models and applications for risk assessment and prediction of Asian soybean rust epidemics

2006 ◽  
Vol 31 (6) ◽  
pp. 533-544 ◽  
Author(s):  
Emerson M. Del Ponte ◽  
Cláudia V. Godoy ◽  
Marcelo G. Canteri ◽  
Erlei M. Reis ◽  
X.B. Yang
Keyword(s):  
Disease Modeling ◽  
Weather Conditions ◽  
Epidemiological Studies ◽  
Soybean Rust ◽  
Phakopsora Pachyrhizi ◽  
Spatial And Temporal Scales ◽  
Asian Soybean Rust ◽  
New Development ◽  
Glycine Max L ◽  
Favorable Weather

Asian rust of soybean [Glycine max (L.) Merril] is one of the most important fungal diseases of this crop worldwide. The recent introduction of Phakopsora pachyrhizi Syd. & P. Syd in the Americas represents a major threat to soybean production in the main growing regions, and significant losses have already been reported. P. pachyrhizi is extremely aggressive under favorable weather conditions, causing rapid plant defoliation. Epidemiological studies, under both controlled and natural environmental conditions, have been done for several decades with the aim of elucidating factors that affect the disease cycle as a basis for disease modeling. The recent spread of Asian soybean rust to major production regions in the world has promoted new development, testing and application of mathematical models to assess the risk and predict the disease. These efforts have included the integration of new data, epidemiological knowledge, statistical methods, and advances in computer simulation to develop models and systems with different spatial and temporal scales, objectives and audience. In this review, we present a comprehensive discussion on the models and systems that have been tested to predict and assess the risk of Asian soybean rust. Limitations, uncertainties and challenges for modelers are also discussed.

scholarly journals Regional assessment of climate risks using GIS technologies (on the example of the Belgorod Region)

2020 ◽  
Vol 26 (1) ◽  
pp. 271-278
Author(s):  
Yuri Chendev ◽  
Maria Lebedeva ◽  
Olga Krymskaya ◽  
Maria Petina
Keyword(s):  
Climate Change ◽  
Quantitative Assessment ◽  
Weather Conditions ◽  
Climatic Conditions ◽  
Winter Period ◽  
Adaptation Measures ◽  
Climate Services ◽  
Climate Risks ◽  
Weather And Climate ◽  
The Impact

The ongoing climate change requires a quantitative assessment of the impact of weather conditions on the nature and livelihoods of the population. However, to date, the concept of “climate risk” has not been finally defined, and the corresponding terminology is not universally recognized. One manifestation of climate change is an increase in climate variability and extremeness in many regions. At the same time, modern statistics indicate growing worldwide damage from dangerous weather and climate events. The most widely used in climate services is the concept of “Vulnerability index”, which reflects a combination (with or without weighing) of several indicators that indicate the potential damage that climate change can cause to a particular sector of the economy. development of adaptation measures to ensure sustainable development of territories. The main criterion for the vulnerability of the territory from the point of view of meteorological parameters is the extremeness of the basic values: daily air temperature, daily precipitation, maximum wind speed. To fully take into account the possible impacts of extreme climatic conditions on the region’s economy, it is necessary to detail the weather and climate risks taking into account the entire observation network, since significant differences in quantitative assessment are possible. The obtained average regional values of the climate vulnerability indices for the Belgorod Region of the Russian Federation provide 150 points for the winter period, 330 points for the summer season, which indicates the prevalence of extreme weather conditions in the warm season. Most of the territory has a relative influence on climatic phenomena, with the exception of the East and the Southeast Region. Moreover, the eastern part of the region is the most vulnerable in climatic terms.

scholarly journals Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria

2020 ◽  
Vol 17 (10) ◽  
pp. 3474 ◽  
Author(s):  
Oguntade Emmanuel Segun ◽  
Shamarina Shohaimi ◽  
Meenakshii Nallapan ◽  
Alaba Ajibola Lamidi-Sarumoh ◽  
Nader Salari
Keyword(s):  
Linear Models ◽  
Negative Binomial ◽  
Malaria Incidence ◽  
Pearson Correlation ◽  
Ecological Factors ◽  
Weather Conditions ◽  
Statistical Modelling ◽  
Weather Variables ◽  
Weather Factors ◽  
Unit Increase

Background: despite the increase in malaria control and elimination efforts, weather patterns and ecological factors continue to serve as important drivers of malaria transmission dynamics. This study examined the statistical relationship between weather variables and malaria incidence in Abuja, Nigeria. Methodology/Principal Findings: monthly data on malaria incidence and weather variables were collected in Abuja from the year 2000 to 2013. The analysis of count outcomes was based on generalized linear models, while Pearson correlation analysis was undertaken at the bivariate level. The results showed more malaria incidence in the months with the highest rainfall recorded (June–August). Based on the negative binomial model, every unit increase in humidity corresponds to about 1.010 (95% confidence interval (CI), 1.005–1.015) times increase in malaria cases while the odds of having malaria decreases by 5.8% for every extra unit increase in temperature: 0.942 (95% CI, 0.928–0.956). At lag 1 month, there was a significant positive effect of rainfall on malaria incidence while at lag 4, temperature and humidity had significant influences. Conclusions: malaria remains a widespread infectious disease among the local subjects in the study area. Relative humidity was identified as one of the factors that influence a malaria epidemic at lag 0 while the biggest significant influence of temperature was observed at lag 4. Therefore, emphasis should be given to vector control activities and to create public health awareness on the proper usage of intervention measures such as indoor residual sprays to reduce the epidemic especially during peak periods with suitable weather conditions.

Retrospective space–time analysis methods to support West Nile virus surveillance activities

2014 ◽  
Vol 143 (1) ◽  
pp. 202-213 ◽  
Author(s):  
P. MULATTI ◽  
M. MAZZUCATO ◽  
F. MONTARSI ◽  
S. CIOCCHETTA ◽  
G. CAPELLI ◽  
...  
Keyword(s):  
West Nile Virus ◽  
Linear Models ◽  
Space Time ◽  
Scan Statistics ◽  
Limited Area ◽  
Entomological Surveillance ◽  
West Nile ◽  
Spatial And Temporal Scales ◽  
Virus Surveillance ◽  
Human West Nile Virus

SUMMARYThe steep increase in human West Nile virus (WNV) infections in 2011–2012 in north-eastern Italy prompted a refinement of the surveillance plan. Data from the 2010–2012 surveillance activities on mosquitoes, equines, and humans were analysed through Bernoulli space–time scan statistics, to detect the presence of recurrent WNV infection hotspots. Linear models were fit to detect the possible relationships between WNV occurrence in humans and its activity in mosquitoes. Clusters were detected for all of the hosts, defining a limited area on which to focus surveillance and promptly identify WNV reactivation. Positive relationships were identified between WNV in humans and in mosquitoes; although it was not possible to define precise spatial and temporal scales at which entomological surveillance could predict the increasing risk of human infections. This stresses the necessity to improve entomological surveillance by increasing both the density of trapping sites and the frequency of captures.

A typology of compound weather and climate events

2020 ◽  
Author(s):  
Jakob Zscheischler ◽  
Olivia Martius ◽  
Seth Westra ◽  
Emanuele Bevacqua ◽  
Colin Raymond ◽  
...  
Keyword(s):  
Heavy Precipitation ◽  
Future Research ◽  
Event Analysis ◽  
Climate Conditions ◽  
Spatial And Temporal Scales ◽  
Compound Event ◽  
Weather And Climate ◽  
Compound Events ◽  
Climate Events ◽  
Analytical Approaches

<p>Weather- and climate-related extreme events such as droughts, heatwaves and storms arise from interactions between complex sets of physical processes across multiple spatial and temporal scales, often overwhelming the capacity of natural and/or human systems to cope. In many cases, the greatest impacts arise through the ‘compounding’ effect of weather and climate-related drivers and/or hazards, where the scale of the impacts can be much greater than if any of the drivers or hazards occur in isolation; for instance, when a heavy precipitation falls on an already saturated soil causing a devastating flood. Compounding in this context refers to the amplification of an impact due to the occurrence of multiple drivers and/or hazards either because multiple hazards occur at the same time, previous climate conditions or weather events have increased a system’s vulnerability to a successive event, or spatially concurrent hazards lead to a regionally or globally integrated impact. More generally, compound weather and climate events refer to a combination of multiple climate drivers and/or hazards that contributes to societal or environmental risk.</p><p>Although many climate-related disasters are caused by compound events, our ability to understand, analyse and project these events and interactions between their drivers is still in its infancy. Here we review the current state of knowledge on compound events and propose a typology to synthesize the available literature and guide future research. We organize the highly diverse event types broadly along four main themes, namely preconditioned, multivariate, temporally compounding, and spatially compounding events. We highlight promising analytical approaches tailored to the different event types, which will aid future research and pave the way to a coherent framework for compound event analysis. We further illustrate how human-induced climate change affects different aspects of compound events, such as their frequency and intensity through variations in the mean, variability, and the dependence between their climatic drivers. Finally, we discuss the emergence of new types of events that may become highly relevant in a warmer climate.</p>

scholarly journals A Probabilistic Multispectral Pattern Recognition Method for Detection of Overshooting Cloud Tops Using Passive Satellite Imager Observations

2016 ◽  
Vol 55 (9) ◽  
pp. 1983-2005 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Konstantin Khlopenkov
Keyword(s):  
Pattern Recognition ◽  
Detection Rate ◽  
Earth Science ◽  
Weather Conditions ◽  
The United States ◽  
Detection Methods ◽  
False Detection Rate ◽  
False Detection ◽  
Weather And Climate ◽  
Climate Analysis

AbstractDeep convective updrafts often penetrate through the surrounding cirrus anvil and into the lower stratosphere. Cross-tropopause transport of ice, water vapor, and chemicals occurs within these “overshooting tops” (OTs) along with a variety of hazardous weather conditions. OTs are readily apparent in satellite imagery, and, given the importance of OTs for weather and climate, a number of automated satellite-based detection methods have been developed. Some of these methods have proven to be relatively reliable, and their products are used in diverse Earth science applications. Nevertheless, analysis of these methods and feedback from product users indicate that use of fixed infrared temperature–based detection criteria often induces biases that can limit their utility for weather and climate analysis. This paper describes a new multispectral OT detection approach that improves upon those previously developed by minimizing use of fixed criteria and incorporating pattern recognition analyses to arrive at an OT probability product. The product is developed and validated using OT and non-OT anvil regions identified by a human within MODIS imagery. The product offered high skill for discriminating between OTs and anvils and matched 69% of human OT identifications for a particular probability threshold with a false-detection rate of 18%, outperforming previously existing methods. The false-detection rate drops to 1% when OT-induced texture detected within visible imagery is used to constrain the IR-based OT probability product. The OT probability product is also shown to improve severe-storm detection over the United States by 20% relative to the best existing method.

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