Variance-based sensitivity analysis of a wind risk model - Model behaviour and lessons for forest modelling

We submitted the semi-empirical, process-based wind-risk model ForestGALES to a variance-based sensitivity analysis using the method of Sobol for correlated variables proposed by Kucherenko et al. (2012). Our results show that ForestGALES is able to simulate very effectively the dynamics of wind damage to forest stands, as the model architecture reflects the significant influence of tree height, stocking density, dbh, and size of an upwind gap, on the calculations of the critical wind speeds of damage. These results highlight the importance of accurate knowledge of the values of these variables when calculating the risk of wind damage with ForestGALES. Conversely, rooting depth and soil type, i.e. the model input variables on which the empirical component of ForestGALES that describes the resistance to overturning is based, contribute only marginally to the variation in the outputs. We show that these two variables can confidently be fixed at a nominal value without significantly affecting the model's predictions. The variance-based method used in this study is equally sensitive to the accurate description of the probability distribution functions of the scrutinised variables, as it is to their correlation structure.JRC.C.3-Energy Security, Distribution and Market

The prevalence of complexity in flammable ecosystems and the application of complex systems theory to the simulation of fire spread

Les forêts sont une ressource naturelle importante sur le plan écologique, culturel et économique, et sont confrontées à des défis croissants en raison des changements climatiques. Ces défis sont difficiles à prédire en raison de la nature complexe des interactions entre le climat et la végétation, dont une le feu. Compte tenu de l’importance des écosystèmes forestiers, des dangers potentiels des feux de forêt et de la complexité de leurs interactions, il est primordial d'acquérir une compréhension de ces systèmes à travers le prisme de la science des systèmes complexes. La science des systèmes complexes et ses techniques de modélisation associées peuvent fournir des informations sur de tels systèmes que les techniques de modélisation traditionnelles ne peuvent pas. Là où les techniques statistiques et basées sur équations cherchent à contourner la dynamique non-linéaire, auto-organisée et émergente des systèmes complexes, les approches de modélisation telles que les automates cellulaires et les modèles à base d'agents (MBA) embrassent cette complexité en cherchant à reproduire les interactions clés de ces systèmes. Bien qu'il existe de nombreux modèles de comportement du feu qui tiennent compte de la complexité, les MBA offrent un terrain d'entente entre les modèles de simulation empiriques et physiques qui peut fournir de nouvelles informations sur le comportement et la simulation du feu. Cette étude vise à améliorer notre compréhension du feu dans le contexte de la science des systèmes complexes en développant un tel MBA de propagation du feu. Le modèle utilise des données de type de carburant, de terrain et de météo pour créer l'environnement des agents. Le modèle est évalué à l'aide d’une étude de cas d'un incendie naturel qui s'est produit en 2001 dans le sud-ouest de l'Alberta, au Canada. Les résultats de cette étude confirment la valeur de la prise en compte de la complexité lors de la simulation d'incendies de forêt et démontrent l'utilité de la modélisation à base d'agents pour une telle tâche.Forests are an ecologically, culturally, and economically important natural resource that face growing challenges due to climate change. These challenges are difficult to predict due to the complex nature of the interactions between climate and vegetation. Furthermore, fire is intrinsically linked to both climate and vegetation and is, itself, complex. Given the importance of forest ecosystems, the potential dangers of forest fires, and the complexity of their interactions, it is paramount to gain an understanding of these systems through the lens of complex systems science. Complex systems science and its attendant modeling techniques can provide insights on such systems that traditional modelling techniques cannot. Where statistical and equation-based techniques seek to work around the non-linear, self-organized, and emergent dynamics of complex systems, modelling approaches such as Cellular Automata and Agent-Based Models (ABM) embrace this complexity by seeking to reproduce the key interactions of these systems. While there exist numerous models of fire behaviour that account for complexity, ABM offers a middle ground between empirical and physical simulation models that may provide new insights into fire behaviour and simulation. This study seeks to add to our understanding of fire within the context of complex systems science by developing such an ABM of fire spread. The model uses fuel-type, terrain, and weather data to create the agent environment. The model is evaluated with a case study of a natural fire that occurred in 2001 in southwestern Alberta, Canada. Results of this study support the value of considering complexity when simulating forest fires and demonstrate the utility of ABM for such a task

Growth allocation and stand structure in Norway spruce stands

This thesis is based on analyses of permanent sample plot data gathered over periods of 10-34 years from an experiment in which a wide range of thinning regimes, and thinnings combined with N-fertilisation, were applied to 25 even-aged Norway spruce (Picea abies (L.) Karst.) stands in southern Sweden (56-63 No). At the start of the experiment, before the first thinning, the dominant height was 12-18 m. The overall objective was to evaluate the extent to which growth allocation along the bole and the stand structure of Picea abies stands can be controlled by different silvicultural regimes. To do this the data were used in four studies to evaluate the impact of: thinning and N-fertilisation on stem form and taper (Study I); different thinning regimes on the removal and growth in the diameter at breast height (DBH) of individual stems (Studies II and III); and the thinning regimes on the growth in mean DBH of four classes of the largest stems by DBH ha 1 (Study IV). The studies (ii) and (iii) form a growth model. In stands subjected to different thinning regimes, one model predicts which individual trees will remain at future points in time and an associated model predicts the future DBH of the remaining stems. Separate models were developed for stands thinned from below, stands thinned from above and unthinned stands. In Study IV the actual and genuine increases in the arithmetic mean DBH of the 100, 200, 300 and 400 largest stems by DBH ha 1 associated with six different thinning regimes in periods up to 35 years were compared to the corresponding stems in unthinned stands. The goals of achieving rapid diameter growth and low stem tapering cannot be attained simultaneously as heavy thinnings cause increased tapering, and thus silvicultural regimes must reflect a compromise between these and other production objectives. Trees in thinned and N-fertilised stands had the same taper as trees in equally thinned, unfertilised stands. Heavy thinnings from below promote high frequencies of thick stems and extra heavy thinnings promote high frequencies of extra thick stems. Thinning from above (or no thinnngs) may be an alternative to thinning from below in situations where a main crop consisting of moderately thick stems would be regarded as a satisfactory outcome. The actual mean DBH of larger stems can be increased, compared to the corresponding stems in unthinned stands, by up to 2.6 mm per year if extra heavy thinnings are carried out. The biological response to thinning of thick stems is influenced by the thinning intensity but not by the thinning method. The variation in DBH increases over time but increases more in stands thinned from above and unthinned stands than in stands thinned from below

On the merits of sparse surrogates for global sensitivity analysis of multi-scale nonlinear problems: application to turbulence and fire-spotting model in wildland fire simulators

Many nonlinear phenomena, whose numerical simulation is not straightforward, depend on a set of parameters in a way which is not easy to predict beforehand. Wildland fires in presence of strong winds fall into this category, also due to the occurrence of firespotting. We present a global sensitivity analysis of a new sub-model for turbulence and fire-spotting included in a wildfire spread model based on a stochastic representation of the fireline. To limit the number of model evaluations, fast surrogate models based on generalized Polynomial Chaos (gPC) and Gaussian Process are used to identify the key parameters affecting topology and size of burnt area. This study investigates the application of these surrogates to compute Sobol' sensitivity indices in an idealized test case. The performances of the surrogates for varying size and type of training sets as well as for varying parameterization and choice of algorithms have been compared. In particular, different types of truncation and projection strategies are tested for gPC surrogates. The best performance was achieved using a gPC strategy based on a sparse least-angle regression (LAR) and a low-discrepancy Halton's sequence. Still, the LAR-based gPC surrogate tends to filter out the information coming from parameters with large length-scale, which is not the case of the cleaning-based gPC surrogate. The wind is known to drive the fire propagation. The results show that it is a more general leading factor that governs the generation of secondary fires. Using a sparse surrogate is thus a promising strategy to analyze new models and its dependency on input parameters in wildfire applications.This research is supported by the Basque Government through the BERC 2014–2017 and BERC 2018–2021 programs, by the Spanish Ministry of Economy and Competitiveness MINECO through BCAM Severo Ochoa accreditations SEV-2013-0323 and SEV-2017-0718 and through project MTM2016-76016-R “MIP”, and by the PhD grant “La Caixa2014”. The authors acknowledge EDF R&D for their support on the OpenTURNS library. They also acknowledge Pamphile Roy and Matthias De Lozzo at CERFACS for helpful discussions on batman and scikit-learn tools

A handbook of wildfire engineering: Guidance for wildfire suppression and resilient urban design

A Handbook of Wildfire Engineering (the Handbook) provides firefighters, engineers and town planners with detailed technical approaches and analysis to enhance the resilience of communities in areas prone to wildfire impacts, and enhance the safety and effectiveness of wildfire suppression at the urban interface during catastrophic wildfire condition

An acoustic view of ocean mixing

Knowledge of the parameter K (turbulent diffusivity/"mixing intensity") is a key to understand transport processes of matter and energy in the ocean. Especially the almost vertical component of K across the ocean stratification (diapycnal diffusivity) is vital for research on biogeochemical cycles or greenhouse gas budgets. Recent boost in precision of water velocity data that can be obtained from vessel-mounted acoustic instruments (vmADCP) allows identifying ocean regions of elevated diapycnal diffusivity during research cruises - in high horizontal resolution and without extra ship time needed. This contribution relates acoustic data from two cruises in the Tropical North East Atlantic Oxygen Minimum Zone to simultaneous field observations of diapycnal diffusivity: pointwise measurements by a microstructure profiler as well as one integrative value from a large scale Tracer Release Experiment

Scale and abstraction : the sensitivity of fire regime simulation to nuisance parameters

Fire plays a key role in ecosystem dynamics and its impact on environmental, social and economic assets is increasingly a critical area of research. Fire regime simulation models are one of many approaches that provide insights into the relative importance of factors driving the dynamics of fire-vegetation systems. Fire propagates as a contagious process and simulation is an approach that captures this behaviour explicitly, integrating spatial and temporal data to produce auto-correlated patterns of fire regimes. However, when formulating these models, time and many aspects of space must be made discrete. These parameters are 'nuisance parameters': parameters necessary for the model formulation but not otherwise of interest. Fire growth simulations are therefore discrete approximations of continuous non-linear systems, and it might be expected that the values chosen for these nuisance parameters will be important. While it is well known that discrete geometries have consequences for the shape and area of simulated fires, no research has investigated the consequence this may have for estimates of the relative importance of the various drivers of fire regimes. I argue that nuisance parameters can be demonstrated to be unimportant for this class of model. I use the idea of 'importance' to underline the need for context with such an assertion. With sufficient replication, any parameter can be found statistically significant. A parameter is important, on the other hand, if different values produce qualitatively different outcomes. Models are commonly either re-parameterised to account for changes in resolution or scaling-up methods applied if such exist. I will further argue that such differences as there are in model outputs due to spatial resolution, cannot be accounted for by either re-parameterising or using a common approach that allows resolution to vary over the spatial extent. A set of experiments were devised using a published fire regime simulation model, modified, verified and validated, to isolate just those aspects of the model's sensitivity to resolution and discrete geometries that are unavoidable or intrinsic to these choices. This new model was used to test the above hypotheses, using peer-reviewed treatments that stand as yardsticks by which formal estimates of the importance of nuisance parameters can be made. As estimated by the model, neither spatio-temporal resolution nor any of the various choices available for discrete geometries, altered the model predictions. As expected, it is spatial resolution that has the greatest impact on running times for the model but this study finds that neither calibration, nor taking an approach that allows resolution to vary over the spatial extent, can account for differences in model outputs that arise from running simulations at coarser resolutions. All models are abstractions and a good model should ideally hold over levels of abstraction. This is rarely the case, but this study shows that results obtained through simulation in estimating the drivers of fire frequency in large landscapes, are robust with regard to these aspects of abstraction. This adds considerable confidence to a significant body of work that has used this approach over the last two decades