Tools from mathematical ecology in a combat model with humanitarian aid agencies Conflict models have a long history of taking inspiration from mathematical ecology. In “A mathematical model of humanitarian aid agencies in attritional conflict environments,” McLennan-Smith et al. seek to enrich counterinsurgency (COIN) warfare models to account for modern and future complexities by incorporating nontrophic effects and the functional response from mathematical ecology. The authors consider the application of these ideas in a COIN scenario in which a humanitarian aid agency is present in the conflict environment to support the local population. In this scenario, the aid agency plays the unwilling role of a “hospital shield” whereby it is forced to, or inadvertently, shield combatants or weapons. In contrast to the typical behavior seen in the classic Lanchester system, this model gives rise to limit cycles and bifurcations that the authors interpret through a warfighting application. Finally, through a case study, the authors highlight the importance of the agility of an intervention force in achieving victory when humanitarian aid agencies are present.

<p>The incidence and magnitude of extreme wildfires have increased markedly in Australia. In some extreme wildfires, violent pyroconvection can manifest as pyrocumulonimbus clouds (pyroCb), which impact not only the surface but also the atmosphere, leading to dangerous escalation of wildfire risk and severe stratospheric pollution. Climate change could amplify the conditions associated with pyroCb development, so a better understanding of pyroCb drivers and their impact mechanisms is crucial for the prediction and management of pyroCb development.</p><p>Previous studies have shown that atmospheric instability, surface fire weather conditions, vegetation type, and topography are important conditions affecting pyroCb development, but the synergy and extent of the contribution of these factors have not been systematically analyzed. Also, previous studies have focused more on individual events or a certain region, lacking analysis of the drivers and occurrence patterns of pyroCb over larger spatial and temporal scales.</p><p>This study therefore examined atmospheric, surface weather, topographic and vegetation conditions associated with pyroCbs and standard wildfires occurring over southeast Australia, including the mainland of New South Wales (NSW), Australian Capital Territory (ACT) and Victoria (Vic) from 1980-2020. Then we used logistic models to analyze the relative importance    and regional differences of pyroCb drivers. We found that for the whole southeast Australia, atmospheric variables are the most important pyroCb drivers, followed by topographic and surface weather variables.  Fuel moisture content was found to be the most important among the given surface weather variables. However, the importance of pyroCb drivers varied from region to region. In NSW (including ACT), atmospheric and surface weather variables were more important than topographic variables, while in Vic, topographic variables were the most important, followed by atmospheric and vegetation variables, with surface weather variables having less influence on pyroCb incidence.</p><p>The results provide insights that can be drawn upon to better predict the likelihood of pyroCb occurrence and the risk of extreme wildfires over southeastern Australia.</p>

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The incidence of pyro-cumulonimbus (pyroCb) caused by extreme wildfires has increased markedly in Australia over the last several decades. This increase can be associated with a dangerous escalation of wildfire risk and severe stratospheric pollution events. Atmospheric and fuel conditions are important influences on pyroCb occurrence, but the exact causal relationships are still not well understood. We used the Continuous Haines Index (C-Haines) to represent atmospheric instability and the Fuel Moisture Index (FMI) to represent fuel moisture to provide better insight into the effects of atmospheric and fuel conditions on pyroCb occurrence over southeast Australia. C-Haines and FMI were related to the probability of pyroCb occurrence by employing a logistic regression on data gathered between 1980 and 2020. Emphasis is placed on investigating the independent effects and combined effects of FMI and C-Haines, as well as assessing their potential to predict whether a pyroCb develops over a fire. The main findings of this study are: (1) high C-Haines and low FMI values are representative of favorable conditions for pyroCb occurrence, but C-Haines can offset the effect of FMI—the addition of C-Haines to the logistic model muted the significance of FMI; (2) among the components of C-Haines, air temperature lapse rate (CA) is a better predictor of pyroCb occurrence than the dryness component (CB); (3) there are important regional differences in the effect of C-Haines and FMI on pyroCb occurrence, as they have better predictive potential in New South Wales than in Victoria.