Improved forest fire spread mapping by developing custom fire fuel models in replanted forests in Hyrcanian forests, Iran

Aim of the study: Forest fuel classification and characterization is a critical factor in wildfire management. The main purpose of this study was to develop custom fuel models for accurately mapping wildfire spread compared to standard models. Area of study: The study was conducted at a replanted forest dominated by coniferous species, in the Arabdagh region, Golestan Province, northern Iran. Material and methods: Six custom fuel models were developed to characterize the main vegetation types in the study area. Fuel samples were collected from 49 randomly selected plots. In each plot, the fuel load of 1-hr, 10-hr, 100-hr, 1000-hr, live herbs, live woody plants, surface area volume ratio, and fuel depth were estimated using the Fuel Load (FL) sampling method along three transects. Canopy fuel load was calculated for each fuel model. The performance of the custom fuel models versus standard fuel models on wildfire behavior simulations was compared using the FlamMap MTT simulator. Main results: The results showed that, despite the similarity in the burned area between observed and modeled fires, the custom fuel models produced an increase in simulation accuracy. Compared to the observed fire, simulation results did not give realistic results to the crown fire. The simulation using standard fuel models did not result in crown fire, while the simulation using custom fuel models showed a moderate rate of crown fire with a Kappa coefficient of 0.54. Research highlights: The results demonstrated the importance of developing custom fuel models to simulate wildfire maps with higher accuracy for wildfire risk management.

Fuel Modelling Characterisation Using Low-Density LiDAR in the Mediterranean: An Application to a Natural Protected Area

Fuel structure and characteristics are important to better understand and predict wildfire behaviour. The aim of the present study was to develop a methodology for characterising fuel models using low-density and free LiDAR data that facilitate the work of managers of protected territories. Field inventories were carried out in order to understand the characteristics of the stand and the variables that fuel models must include. This information, together with the use of the intensity and structure provided by LiDAR, was used to perform statistical analyses. The linear regressions obtained to characterise the stand of the mixed Quercus spp.–Pinus ssp.-dominated stand had an R2 value ranging from 0.4393 to 0.66. While working with low-density LiDAR data (which has more difficulties crossing the canopy), in addition to the obtained results, we performed the statistical analysis of the dominant stand to obtain models with R2 values ranging from 0.8201 to 0.8677. The results of this research show that low-density LiDAR data are significant; however, in mixed stands, it is necessary to only use the dominant stratum because other components generate noise, which reduces the predictive capacity of the models. Additionally, by using the decision tree developed in combination, it is possible to update the mapping of fuel models in inaccessible areas, thereby significantly reducing costs.

Cladding Profilometry Analysis of Experimental Breeder Reactor-II Metallic Fuel Pins with HT9, D9, and SS316 Cladding

BISON finite element method fuel performance simulations were conducted using an existing automated process that couples the Fuels Irradiation & Physics Database (FIPD) and the Integral Fast Reactor Materials Information System database by writing input files and comparing the BISON output to post-irradiation fuel pin profilometry measurements contained within the databases. The importance of this work is to demonstrate the ability to benchmark fuel performance metallic fuel models within BISON using Experimental Breeder Reactor-II fuel pin data for a number of similar pins, while building off previous modeling efforts. Changes to the generic BISON input file include implementing pin specific axial power and flux profiles, pin specific fluences, frictional contact, and irradiation-induced volumetric swelling models for cladding. A statistical analysis of irradiation-induced volumetric swelling models for HT9, D9, and SS316 was performed for experiments X421/X421A, X441/X441A, and X486. Between these three experiments, there were 174 post-irradiation examination (PIE) profilometries used for validating the swelling models presented using a standard error of the estimate (SEE) method. Implementation of the volumetric swelling models for D9 and SS316 claddings was found to have a significant impact on the BISON profilometry simulated, where HT9 clad pins had an insignificant change due to low fluence values. BISON profilometry simulated for HT9, D9, and SS316 fuel pins agreed with PIE profilometry measurements, with assembly SEE values being 4.4 × 10−3 for X421A, 2.0 × 10−3 for X441A, and 2.8 × 10−3 for X486. D9 clad pins in X421/X421A had the highest SEE values, which is due to the BISON simulated profilometry being shifted axially. While this work accomplished its purpose to demonstrate the modeling of multiple fuel pins from the databases to help validate models, the results suggest that the continued development of metallic fuel models is necessary for qualifying new metallic fuel systems to better capture some physical performance phenomena, such as the hot pressing of U-Pu-Zr and the fuel cladding chemical interaction.

Using ForeStereo and LIDAR data to assess fire and canopy structure-related risks in relict Abies pinsapo Boiss. forests

In this study we combine information from aerial LIDAR and hemispherical images taken in the field with ForeStereo—a forest inventory device—to assess the vulnerability and to design conservation strategies for endangered Mediterranean fir forests based on the mapping of fire risk and canopy structure spatial variability. We focused on the largest continuous remnant population of the endangered tree species Abies pinsapo Boiss. spanning 252 ha in Sierra de las Nieves National Park (South Andalusia, Spain). We established 49 sampling plots over the study area. Stand structure variables were derived from ForeStereo device, a proximal sensing technology for tree diameter, height and crown dimensions and stand crown cover and basal area retrieval from stereoscopic hemispherical images photogrammetry. With this information, we developed regression models with airborne LIDAR data (spatial resolution of 0.5 points∙m−2). Thereafter, six fuel models were fitted to the plots according to the UCO40 classification criteria, and then the entire area was classified using the Nearest Neighbor algorithm on Sentinel imagery (overall accuracy of 0.56 and a KIA-Kappa Coefficient of 0.46). FlamMap software was used for fire simulation scenarios based on fuel models, stand structure, and terrain data. Besides the fire simulation, we analyzed canopy structure to assess the status and vulnerability of this fir population. The assessment shows a secondary growth forest that has an increasing presence of fuel models with the potential for high fire spread rate fire and burn probability. Our methodological approach has the potential to be integrated as a support tool for the adaptive management and conservation of A. pinsapo across its whole distribution area (<4,000 ha), as well as for other endangered circum-Mediterranean fir forests, as A. numidica de Lannoy and A. pinsapo marocana Trab. in North Africa.

Assessment of Fire Fuel Load Dynamics in Shrubland Ecosystems in the Western United States Using MODIS Products

Assessing fire behavior in shrubland/grassland ecosystems of the western United States has proven especially problematic, in part due to the complex nature of the vegetation and its relationships with prior fire history events. Our goals in this study were (1) to determine if we can effectively leverage the high temporal resolution capabilities of current remote sensing systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve upon shrub and grassland mapping and (2) to determine if these improvements alter and improve fire behavior model results in these grass- and shrub-dominated systems. The study focused on the shrublands and grasslands of the Owyhee Basin, which is located primarily in southern Idaho. Shrubland and grassland fuel load dynamics were characterized using Normalized Difference Vegetation Index (NDVI) and Net Primary Production (NPP) datasets (both derived from MODIS). NDVI shrub and grassland values were converted to biomass, and custom fire behavior fuel models were then developed to evaluate the impacts of surface fuel changes on fire behaviors. Results from the study include the following: (1) high intra- and interannual spectral variability characterized these shrubland/grassland ecosystems, and this spectral variability was highly correlated with climate variables, most notably precipitation; (2) fire activity had a higher likelihood of occurring in areas where the NDVI (and biomass) differential between spring and summer values was especially high; (3) the annual fuel loads estimated from MODIS NPP showed that live herbaceous fuel loads were closely correlated with annual precipitation; (4) estimated fuel load accumulation was higher on shrublands than grasslands with the same vegetation productivity; (5) the total fuel load on shrublands was impacted by shrubland age, and live woody fuel load was over 66% of the total fuel load; and (6) comparisons of simulated fire behavior and spread between dynamic and static fuel loads, the latter estimates being obtained from the operational and nationwide LANDFIRE program, showed clear differences in fire indices and fire burn areas between the dynamic fuel loads and the static fuel loads. Current standard fuel models appear to have bias in underestimating the fire spread and total burnable area.

Multi-Factor Taxonomy of Eco-Routing Models and Future Outlook

Traditionally, routing decisions have been based on minimizing travel time as the associated cost. Eco-routing considers the environmental aspects (e.g., emissions and fuel) as part of the travel cost to mitigate the undesirable impact of transportation systems on the environment. Unlike the existing eco-routing review papers, this research work is aimed at providing a three-factor taxonomy at a more disaggregated level from the optimization perspective and map eco-routing studies to the proposed taxonomy. Furthermore, the strengths and weaknesses of the presented models are summarized. Our main findings include (a) a majority of studies optimized one objective at a time; (b) the microscopic level of aggregation of the flow and emission/fuel models was rarely employed for large case studies, due to the associated complexity; and (c) all of the reviewed studies were applied in a centralized routing system environment. In the near future, when intelligent vehicles will be on the roads, a multi-objective distributed routing framework can be employed with a microscopic level of aggregation for both traffic and emission models, which is capable of operating on largescale networks in real time. Additionally, short-term spatiotemporal prediction of GHG cost is a crucial aspect to be tackled.

Comparison Between Experimental and Simulated Knock Statistics Using an Advanced Fuel Surrogate Model

The statistical tendency of a GDI spark-ignition engine to undergo knocking combustion as a consequence of spark timing variation is numerically investigated. In particular, attention is focused on the importance to match combustion-relevant and knock-relevant fuel properties to ensure consistency with the experimental evidence. An inhouse surrogate formulation methodology is used to emulate real gasoline properties, comparing fuel models of increasing complexity. Knock is investigated using a proprietary statistical knock model (GruMo Knock Model, GK-PDF). The model can infer a log-normal distribution of knock intensity within a RANS formalism, by means of transport equations for variances and turbulence-derived probability density functions (PDFs) for physical quantities. The calculated distributions are compared to measured statistical distributions. The proposed numerical/experimental comparison constitutes an advancement in synthetic chemistry integration into 3D-CFD combustion simulations.