The Folklore Society

Recurrent disasters such as wildfires are increasingly attributed to anthropogenic climate change, but this broad explanation often obscures the political choices that shape how disasters are recognised, governed, and instrumentalised. Focusing on the Uttarakhand Himalaya in India, where forest fires have become a near-annual phenomenon, this article examines how disaster management is deeply entangled with state-making practices in political forests. Drawing on interviews, policy documents, and media articles, it highlights how the 2016 Uttarakhand fires marked a turning point in the formal inclusion of forest fires within India's disaster policy. Since then, the spectacle of fire, amplified through media coverage, remote sensing technologies, and smoke spreading to distant urban centres, has helped normalise the framing of fire as a disaster. This spectacle is often disconnected from the social and ecological realities of fire in the region. It also enables the Uttarakhand Forest Department to access emergency resources, distribute blame and responsibility, and consolidate territorial control. Yet rather than reducing fire risk, disaster management interventions reinforce a suppression-oriented approach and lock the region into a firefighting trap. The study argues that disaster management in Uttarakhand is not simply a response to environmental crisis, but a political project that governs through crisis, produces risk, and deepens the state's authority over forests. It suggests that managing wildfires as disasters can, in certain contexts, serve as a renewed site of state-making in political forests, complicating accounts of state retreat advanced in scholarship on green neoliberalism and disaster capitalism.

Abstract. Assessing the impact of biomass burning (BB) emissions on tropospheric ozone is critical for understanding air pollution and climate interactions. BB emission inventories like Global Fire Emissions Database and Global Fire Assimilation System, typically based on sun-synchronous satellite observations, report emissions on daily, weekly or longer timescales with empirically derived factors generally used to overlay diurnal variations. To explore the sensitivity of tropospheric ozone to diurnal variability, we incorporated day-specific hourly BB variations inferred from geostationary satellite data into the GEOS-Chem atmospheric chemistry transport model. The simulations were compared with those using established inventories and evaluated against in situ and satellite observations. Simulations with real hourly-resolved emissions produce comparable surface ozone biases (−1.54 to +9.09 ppbv vs. −1.58 to +9.13 ppbv) and marginally higher correlations with TROPOMI nitrogen dioxide (r=0.80–0.89) and OMI ozone (r=0.80–0.94). Although the statistical improvements are limited, the geostationary-driven approach reveals pronounced regional ozone differences and mechanistic insights into the role of diurnal fire variability. Data-driven diurnal BB variations across Africa cause significant surface ozone changes (−8.57 to +21.88 ppbv) and alter tropospheric ozone columns by −0.41 to 1.09 DU, particularly in regions with intense fire activity like Angola and Zambia. These changes propagate globally, shifting regional OH concentrations by −4.4 % to +51.7 %. These findings emphasize the critical role of accurately describing diurnal BB variations in atmospheric models to better quantify its impacts on atmospheric composition, providing insights for Earth system model development and the use of geostationary-derived BB emissions datasets.

<strong class="journal-contentHeaderColor">Abstract.</strong> Wildfire management strategies increasingly demand accurate predictive models that integrate real-time intervention measures. Despite advances in machine learning (ML) for wildfire modelling, existing approaches largely overlook the role of firebreak placement. In this work, we present the first deep learning-based predictive model for simulating spatio-temporal wildfire propagation with dynamic firebreaks. Utilizing a Convolutional Long Short-Term Memory (ConvLSTM) architecture, the model captures both the spatial and temporal complexities of wildfire spread while incorporating data on firebreak positioning and effectiveness. Our training dataset, derived from Cellular Automata (CA) simulations, integrates key geophysical parameters and human intervention strategies, including temporary and permanent firebreaks. Model validation across three major wildfire events in California demonstrates robust performance, with significant accuracy gains in scenarios involving strategic firebreak placement. This integration of movable firebreak placement into a wildfire spread model provides a tool for improving real-time wildfire management efforts.

"The Devil and the Victorians: Supernatural Evil in Nineteenth-Century English Culture." Folklore, ahead-of-print(ahead-of-print), pp. 1–2

Abstract. We present the first top-down CO fire emissions inventory for Africa based on the direct relation between geostationary satellite-based Fire Radiative Power (FRP) measures and satellite observations of Total Column Carbon Monoxide (TCCO). This work extends significantly the previous Fire Radiative Energy Emissions (FREM) approach that derived Total Particulate Matter (TPM) emission coefficients from FRP measures and Aerosol Optical Depth (AOD) observations. The use of satellite-based CO observations to derive CO emission coefficients, CeCO, addresses key uncertainties in the use of AOD measures to estimate fire-generated CO emissions including; the requirement for a smoke mass extinction coefficient in the AOD to TPM conversion; and the large variation in TPM emission factors – which are used to convert TPM emissions to CO emissions. We use the FREM-derived CO emission coefficients to produce a Pan-African CO fire emission inventory spanning 16 years. Regional CO emissions are in close agreement with the most recent version of GFED(v4.1s), despite the two inventories using completely different satellite datasets and methodologies to derive CO emissions. Dry Matter Consumed (DMC) and DMC per unit area values are generated from our CO emission inventory – the latter using the 20 m resolution Sentinal-2 FireCCISFD burnt area (BA) product for 2019. We carry out an evaluation of our FREM-based CO emissions by using them as input in the WRF-CMAQ chemical transport model and comparing simulated TCCO fields to independent Sentinal-5P TROPOMI TCCO observations. The results of this validation show FREM CO emissions to generally be in good agreement with these independent measures – particularly in the case of individual fire-generated CO plumes where modelled in-plume CO was within 5 % of satellite observations with a coefficient of determination of 0.80. Modelled and observed CO, averaged over the full model domain, are within 4 % of each other, though localised regions show an overestimation of modelled CO by up to 50 %. However, when compared to other evaluations of current state-of-the-art fire emissions inventories, the FREM CO emission inventory derived in this work shows some of the best agreement with independent measures. Updates to the previously published FREM TPM emissions coefficients are also provided in the Appendix of this article, along with a satellite and ground-based validation of this FREM TPM emissions inventory. The methodology and resulting CO fire inventory described in this work will form the basis of an upcoming operational LSASAF CO fire emissions product for Africa.

&amp;lt;p&amp;gt;Wildfires are fundamental for maintaining ecosystem structure and functioning, and thus it is important to know how projected climate and land use changes will affect wildfire regimes globally. Fire-enabled vegetation models can be used to predict changes in fire regime but are still far from perfect since many of the processes that control different aspects of the fire regime are still relatively poorly understood. In this work, we investigated the underlying relationships between potential controls of different fire properties, including fire size, intensity and burnt area, at a global scale. We fitted three generalized linear models (GLM) to monthly data from 2010 to 2015 for fractional burnt area from the Global Fire Emissions Database version 4.1 (GFEDv4), fire size from the Global Fire Atlas database and median fire radiative power divided by square root of median fire size (as a proxy for fire intensity) from the MODIS MCD14ML dataset. We used partial residual plots between each predictor and each response variable to show the underlying linear relationships fitted by each model. We show that there are different controls on burnt area, on fire size and on fire intensity. Specifically, whilst burnt area is driven mainly by fuel availability and dryness, fire size is driven primarily by wind speed and fire intensity by tree cover and road density. Land fragmentation was highly limiting for fire size and burnt area whereas dryness was limiting for fire intensity. These findings suggest that it is possible to develop empirical models of multiple aspects of fire regimes which could be used to predict how these will change in the future. Furthermore, they highlight the importance of including landscape fragmentation as a control on fire explicitly within process-based fire models. Additionally, the limiting nature of dryness on fire intensity could be due to antecedent vegetation conditions and highlights the need for better representation of these conditions and their effect on fuel load. Finally, these results also suggest that the current treatment of ignition sources as an important driver in these models is unnecessary.&amp;lt;/p&amp;gt;

The Arctic has recently taken centre-stage in broader global challenges that cross disciplinary, as well as national, boundaries. Current debates on climate change, resource extraction, and geopoli...

&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;It is now commonly-understood that improved understanding of global fire regimes demands better representation of anthropogenic fire in dynamic global vegetation models (DGVMs). However, currently there is no clear agreement on how human activity should be incorporated into fire-enabled DGVMs and existing models exhibit large differences in the sensitivities of socio-economic variables. Furthermore, existing approaches are limited to empirical statistical relations between fire regime variables and globally available socio-economic indicators such as population density or GDP. Although there has been some limited representation in global models of the contrasting ways in which different classes of actors use or manage fires, we argue that fruitful progress in advancing representation of anthropogenic fire in DGVMs will come by building on agent-based modelling approaches. Here, we report on our progress developing a global agent-based representation of anthropogenic fire and its coupling with the JULES-INFERNO fire-enabled DGVM.&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;Our modelling of anthropogenic fire adopts an approach that classifies &amp;amp;#8216;agent functional types&amp;amp;#8217; (AFTs) to represent human fire activity based on land use/cover and Stephen Pyne&amp;amp;#8217;s fire development stages. For example, the &amp;amp;#8216;swidden&amp;amp;#8217; AFT represents shifting cultivation farmers managing cropland and secondary vegetation in a pre-industrial development setting. This approach is based on the assumption that anthropogenic fire use and management is primarily a function of land use but influenced by socio-economic context, leading different AFTs to produce qualitatively distinct fire regimes. The literature empirically supports this assumption, however data on human fire interactions are fragmented across many academic fields (including anthropology, geography, land economics). Therefore, we developed a Database of Anthropogenic Fire Impacts (DAFI) containing 1798 case studies of fire use/management from 519 publications, covering more than 100 countries and all major biomes (except Arctic/Antarctic). We discuss DAFI development, patterns in the resulting data, and possible applications. Specifically, DAFI is used with ancillary data (e.g. biophysical, socio-economic indicators), classification and regression methods to test and refine our initial AFT classification, characterise AFT fire variables, and distribute AFTs spatially. Our model will then simulate AFT distributions for alternative scenarios of change (e.g. specified by the Shared Socioeconomic Pathways).&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;Coupling distinct models can be achieved in a variety of ways, but broadly we can distinguish between &amp;amp;#8216;loose&amp;amp;#8217; coupling in which information flow is uni-directional, and &amp;amp;#8216;tight&amp;amp;#8217; coupling in which information flows are integrated with feedbacks and dynamic updating. Our intention is to tightly couple our AFT model with JULES-INFERNO, such that fire use and suppression behaviours from the former influence simulated fire ignitions and burned area in the latter. Reciprocally, total burned area simulated by JULES-INFERNO will feedback to influence spatial distribution of AFTs in the next time step, modifying anthropogenic fire patterns for the next step of DGVM simulation. We discuss the potential for this tight model coupling to capture socio-ecological feedbacks in fire regimes, as well as&amp;amp;#160; possible pitfalls and steps needed to test and verify model outputs. These are early steps in an important journey to improve representation of anthropogenic fire in DGVMs.&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;