Empirical Model Predictions Research Articles

Validating the clustering predictions of empirical models with the FLAMINGO simulations

Context. Mock galaxy catalogues are essential for correctly interpreting current and future generations of galaxy surveys. Despite their significance in galaxy formation and cosmology, little to no work has been done to validate the predictions of these mocks for high-order clustering statistics. Aims. We compare the predicting power of the latest generation of empirical models used in the creation of mock galaxy catalogues: a 13-parameter halo occupation distribution (HOD) and an extension of the SubHalo Abundance Matching technique (SHAMe). Methos. We built GalaxyEmu-Planck, an emulator that makes precise predictions for the two-point correlation function, galaxy-galaxy lensing (restricted to distances greater than 1 h−1 Mpc in order to avoid baryonic effects), and other high-order statistics resulting from the evaluation of SHAMe and HOD models. Results. We evaluated the precision of GalaxyEmu-Planck using two galaxy samples extracted from the FLAMINGO hydrodynamical simulation that mimic the properties of DESI-BGS and BOSS galaxies, finding that the emulator reproduces all the predicted statistics precisely. The HOD shows a comparable performance when fitting galaxy clustering and galaxy-galaxy lensing. In contrast, the SHAMe model shows better predictions for higher-order statistics, especially regarding the galaxy assembly bias level. We also tested the performance of the models after removing some of their extensions, finding that we can withdraw two (out of 13) of the HOD parameters without a significant loss of performance. Conclusions. The results of this paper validate the current generation of empirical models as a way to reproduce galaxy clustering, galaxy-galaxy lensing, and other high-order statistics. The excellent performance of the SHAMe model with a small number of free parameters suggests that it is a valid method to extract cosmological constraints from galaxy clustering.

Multi-Product Exporters: Verifying Stylized Facts

Abstract In this paper, we propose a statistical model to characterize the null hypothesis of the random product size distribution for multi-product exporters. It serves as a benchmark to test the empirical facts and model predictions. We ask which stylized facts that characterize multi-product exporters and motivate such theories as core competencies, productivity being a driver of both scope and scale, and economies of scope among others, are spurious, and which are informative. The benchmark model also provides alternative moments to test the validity of these theories.

Uncertainty-Based Validation Methodology and Experimental Analysis for External Control Room Human Performance Simulation: Application to Fire Probabilistic Risk Assessment of Nuclear Power Plants

In probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation. To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model. Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions. The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs.

Phase inversion detection in immiscible binary polymer blends via zero‐shear viscosity measurements

AbstractIn the present work, we demonstrate that zero‐shear viscosity is a sensitive rheological function to detect phase inversion in immiscible binary polymer blends characterized by a viscosity ratio larger than one. The phase inversion of poly(propylene) (PP)/low‐density poly(ethylene) (LDPE) and poly(styrene) (PS)/LDPE, at various compositions, was assessed via our novel approach. For both blends, three distinctive regions could be determined through zero‐shear viscosity measurements; the LDPE matrix, the co‐continuous phase, and the PS or PP matrix. For PP/LDPE blends, the co‐continuous structure was between 50 and 75 wt.% PP, and for PS/LDPE blends the co‐continuous structure was between 45 and 75 wt.% PS, in agreement with scanning electron microscopy analysis, empirical model predictions, and literature data.Highlights Phase inversion revealed via viscosity measurements. Limitations of linear viscoelastic models for immiscible blends assessed.

How realistic features affect the stability of an Arctic marine food web model.

Rapid sea-ice decline and warmer waters are threatening the stability of Arctic ecosystems and potentially forcing their restructuring. Mathematical models that support observational evidence are becoming increasingly important. We develop a food web model for the Southern Beaufort Sea based on species with high ecological significance. Generalized modeling is applied to study the impact of realistic characteristics on food web stability; a powerful method that provides a linear stability analysis for systems with uncertainty in data and underlying physical processes. We find that including predator-specific foraging traits, weighted predator-prey interactions, and habitat constraints increase food-web stability. The absence of a fierce top predator (killer whale, polar bear, etc.) also significantly increases the portion of stable webs. Adding ecosystem background noise in terms of a collective impact of latent, minor ecosystem members shows a peak in stability at an optimum, relatively small background pressure. These results indicate that refining models with more realistic detail to account for the complexity of the ecological system may be key to bridge the gap between empirical observations and model predictions in ecosystem stability.

Data-driven wake model parameter estimation to analyze effects of wake superposition

Low-fidelity wake models are used for wind farm design and control optimization. To generalize to a wind farm model, individually modeled wakes are commonly superimposed using approximate superposition models. Wake models parameterize atmospheric and wake turbulence, introducing unknown model parameters that historically are tuned with idealized simulation or experimental data and neglect uncertainty. We calibrate and estimate the uncertainty of the parameters in a Gaussian wake model using Markov chain Monte Carlo (MCMC) for various wake superposition methods. Posterior distributions of the uncertain parameters are generated using power production data from large eddy simulations and a utility-scale wake steering field experiment. The posteriors for the wake expansion coefficient are sensitive to the choice of superposition method, with relative differences in the means and standard deviations on the order of 100%. This sensitivity illustrates the role of superposition methods in wake modeling error. We compare these data-driven parameter estimates to estimates derived from a standard turbulence-intensity based model as a baseline. To assess predictive accuracy, we calibrate the data-driven parameter estimates with a training dataset for yaw-aligned operation. Using a Monte Carlo approach, we then generate predicted distributions of turbine power production and evaluate against a hold-out test dataset for yaw-misaligned operation. For the cases tested, the MCMC-calibrated parameters reduce the total error of the power predictions by roughly 50% compared to the deterministic empirical model predictions. An additional benefit of the data-driven parameter estimation is the quantification of uncertainty, which enables physically quantified confidence intervals of wake model predictions.

A two-process model of Drosophila sleep reveals an inter-dependence between circadian clock speed and the rate of sleep pressure decay.

Sleep is controlled by two processes-a circadian clock that regulates its timing and a homeostat that regulates the drive to sleep. Drosophila has been an insightful model for understanding both processes. For four decades, Borbély and Daan's two-process model has provided a powerful framework for understanding sleep regulation. However, the field of fly sleep has not employed such a model as a framework for the investigation of sleep. To this end, we have adapted the two-process model to the fly and established its utility by showing that it can provide empirically testable predictions regarding the circadian and homeostatic control of fly sleep. We show that the ultradian rhythms previously reported for loss-of-function clock mutants in the fly are robustly detectable and a predictable consequence of a functional sleep homeostat in the absence of a functioning circadian system. We find that a model in which the circadian clock speed and homeostatic rates act without influencing each other provides imprecise predictions regarding how clock speed influences the strength of sleep rhythms and the amount of daily sleep. We also find that quantitatively good fits between empirical values and model predictions were achieved only when clock speeds were positively correlated with rates of decay of sleep pressure. Our results indicate that longer sleep bouts better reflect the homeostatic process than the current definition of sleep as any inactivity lasting 5 minutes or more. This two-process model represents a powerful framework for work on the molecular and physiological regulation of fly sleep.

The small-bat-in-summer paradigm: Energetics and adaptive behavioural routines of bats investigated through a stochastic dynamic model.

Strong seasonality at high latitudes represents a major challenge for many endotherms as they must balance survival and reproduction in an environment that varies widely in food availability and temperature. To avoid energetic mismatches caused by limited foraging time and stochastic weather conditions, bats employ the energy-saving state of torpor during summer to save accumulated energy reserves. However, at high-latitude small-bats-in-summer face a particular challenge: as nocturnal foragers, they rely on the darkness at night to avoid predators and/or interspecific competition, but live in an environment with short, light summer nights, and even a lack of true night at the northernmost distributions of some bat species. To predict optimal behaviour in relation to latitudinal variation in diurnal cycles, we constructed a stochastic dynamic programming model of bats living at high latitudes. Using a stochastic dynamic programming framework with values that are representative for our study system, we show that individual energetic reserves are a strong driver of daytime use of torpor and night-time foraging behaviour alike, with these linked effects being both temperature- and photoperiod-dependent. We further used the model to predict survival probabilities at five locations across a latitudinal gradient (60.1° N to 70.9° N), finding that combinations of photoperiod and temperature conditions limited population distributions in the model. To verify our model results, we compared predictions for optimal decisions with our own empirical data collected on northern bats (Eptesicus nilssonii) from two latitudes in Norway. The similarities between our predictions and observations provide strong evidence that this model framework incorporates the most important drivers of diurnal decision-making in bat physiology and behaviour. Comparing empirical data and model predictions also revealed that bats facing lighter night conditions further north restrict their mass gain, which strengthens the hypothesis that predation threat is a main driver of bat nocturnality. Our model findings regarding state-dependent decisions in bats should contribute to the understanding of how bats cope with the summer challenges at high latitudes.

Machine learning based prediction model for plastic hinge length calculation of reinforced concrete structural walls

Reinforced concrete structural walls (RCSWs) are integral part of buildings and other structures and are used to carry in-plane and out-plane loads. For assessment purposes and to ensure safety and resilience of the structure, the curvature, capacity, and strain demands of RCSWs need to be estimated. Nonlinear numerical models are increasingly used in earthquake engineering design and assessment, where it is critical to develop high fidelity simulation tools to precisely forecast the global and local behavior of RCSWs. Plastic deformations concentrated in small regions, i.e. plastic hinges, and characterized through the concept of plastic hinge length (PHL), are conventionally used to define the inelastic response of RCSWs. In this study machine learning (ML) algorithms were leveraged in evaluating the PHL of RCSWs. A database containing 721 planar and nonplanar RCSW samples were utilized for training and testing ML models. Four different algorithms were employed, namely: XGBoost, CatBoost, Random Forest (RF), and genetic programming. The RF model outperformed other ML counterparts. The results of the best ML model were also compared against empirical equations available in literature and nonlinear regression analysis. The proposed ML model provided much better predictions than the existing relationships. Predictions of most empirical models were found to be extremely over (or under) conservative. The proposed ML model was also presented via an online tool where users can obtain the PHL of any RCSW simply by inputting wall parameters and clicking a run button.

A Linear Velocity Gradient in the Subsolar Magnetosheath

AbstractTracking the location and motion of the bow shock and magnetopause can reveal many aspects concerning the nature of the solar wind‐magnetosphere interaction. In the absence of missions that image the magnetopause, we propose to detect and track the magnetopause and bow shock motion using multipoint in situ observations of the magnetosheath. Making theoretical assumptions corroborated by numerical simulations, Sibeck et al. (2022, https://doi.org/10.1029/2022JA030704) proposed a method to predict the subsolar magnetopause and the bow shock from the linear variation in magnetosheath plasma velocity expected from global magnetohydrodynamic simulations along the Sun‐Earth line. This paper presents a case study that employs the velocity gradient method using multi‐point THEMIS plasma observations from 21 to 22 June 2007. Using the five THEMIS spacecraft we demonstrate that the radial gradient in plasma velocity remains nearly linear across the subsolar magnetosheath, except when the magnetosheath lies behind the quasi‐parallel bow shock and conditions are very turbulent. The bow shock and magnetopause move outward in response to a gradual decrease in solar wind dynamic pressure. We show that results from the velocity gradient method agree well with predictions of global magnetohydrodynamic and empirical models for this time interval. The velocity gradient method can therefore be used to remotely determine the location of the magnetopause and bow shock, solely based on two‐spacecraft measurements at the subsolar magnetosheath when not downstream of the quasi‐parallel shock.

What is the optimal country for minimum COVID-19 morbidity and mortality rates?

The SARS-CoV-2 is a deceptive virus. Despite the remarkable progress in genetic sequencing and subsequent vaccine development, the world continues to grapple with the ominous threats of rapidly appearing SARS-CoV-2 variants. The objective of this manuscript is to rank world countries based on the anticipated scope of COVID-19 morbidity and mortality, measured in terms of prevalence per 1 million persons, from the lowest to the highest. The ranking of 162 countries is based on predictions of empirical models, which include three explanatory variables: hospital beds per thousand persons, population density, and the median age of the country's population. Referring to the COVID-19 scope of morbidity, the lowest likelihood of infection is obtained in Niger and Mali, where the dominant characteristic is the young median age (15.1-16.4years). Referring to the COVID-19 scope of mortality, the lowest likelihood is obtained in Singapore. For Singapore, the dominant feature is the high population density. The optimal solution is intensive vaccination campaigns in the initial phase of the pandemic, particularly among countries with low GDP per capita. Yet, vaccinations may work only where the personal immune system is healthy and thus respond by creating antibodies to the SARS-CoV2 virus. Referring to populations that lack the natural protection of the healthy immune system and thus cannot be vaccinated (e.g., old people, cancer patients undergoing chemotherapy treatments), a complementary solution might be coordination between countries and the establishment of field hospitals, testing laboratories, isolation of areas, humanitarian aid-in the same manner of treatment in other disasters like earthquakes.

Effects of embedded distance measurements interacting with modeling approaches on empirical dynamical model predictions

Empirical Dynamic Modeling (EDM) has been a powerful tool for complex ecosystem prediction by providing an equation-free modelling framework. Theoretically, it allows future ecosystem behavior to be predicted by connecting current state to the similar, adjacent and future state on the attractor manifold which is reconstructed by single or multiple time series observed from natural systems. However, the Euclidean distance metric used in these algorithms could bias the true distance on the attractor manifold and consequently decrease the prediction performance. This could become worse if the dimension of the ecosystem is much higher and the system behavior is much complicated so that the reconstructed attractor manifold is more intricate. Therefore, manifold distance metric for both Simplex Projection and S-map was proposed. Our results clearly showed that the prediction accuracy of EDM had a general improvement after manifold distance metric was adopted. Experiments conducted on both synthetic and empirical data proved this advancement. Interestingly, these improvements were unequal for different implementations and the number of variables for embedding. Analysis demonstrated that S-map under multivariate embedding achieved the best prediction performance when manifold distance metric was applied. This suggested that the proposed manifold distance metric can work particularly well for predicting high dimensional ecosystem with complex behaviors. The main contribution of this research is that a new ecological indicator has been developed to more accurately estimate the similarity between ecological states in a reconstructed manifold and therefore provide higher prediction accuracy for EDM framework.

Extending optical flare models to the UV: results from comparing of TESS and GALEX flare observations for M Dwarfs

ABSTRACT The ultraviolet (UV) emission of stellar flares may have a pivotal role in the habitability of rocky exoplanets around low-mass stars. Previous studies have used white-light observations to calibrate empirical models which describe the optical and UV flare emission. However, the accuracy of the UV predictions of models has previously not been tested. We combined TESS optical and GALEX UV observations to test the UV predictions of empirical flare models calibrated using optical flare rates of M stars. We find that the canonical 9000-K black-body model used by flare studies underestimates the GALEX near-ultraviolet (NUV) energies of field age M stars by up to a factor of 6.5 ± 0.7 and the GALEX far-ultraviolet energies of fully convective field age M stars by 30.6 ± 10.0. We calculated energy correction factors that can be used to bring the UV predictions of flare models closer in line with observations. We calculated pseudo-continuum flare temperatures that describe both the white-light and GALEX NUV emission. We measured a temperature of 10 700 K for flares from fully convective M stars after accounting for the contribution from UV line emission. We also applied our correction factors to the results of previous studies of the role of flares in abiogenesis. Our results show that M stars do not need to be as active as previously thought in order to provide the NUV flux required for prebiotic chemistry, however, we note that flares will also provide more FUV flux than previously modelled.

New method for Earth neutral atmospheric density retrieval based on energy spectrum fitting during occultation with LE/Insight-HXMT

We propose a new method for retrieving the atmospheric number density profile in the lower thermosphere, based on the X-ray Earth occultation of the Crab Nebula with the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite. The absorption and scattering of X-rays by the atmosphere result in changes in the X-ray energy, and the Earth’s neutral atmospheric number density can be directly retrieved by fitting the observed spectrum and spectrum model at different altitude ranges during the occultation process. The pointing observations from LE/Insight-HXMT on 16 November 2017 are analyzed to obtain high-level data products such as lightcurve, energy spectrum and detector response matrix. The results show that the retrieved results based on the spectrum fitting in the altitude range of 90–200 km are significantly lower than the atmospheric density obtained by the NRLMSISE-00 model, especially in the altitude range of 110–120 km, where the retrieved results are 34.4% lower than the model values. The atmospheric density retrieved by the new method is qualitatively consistent with previous independent X-ray occultation results (Determan et al., 2007; Katsuda et al., 2021), which are also lower than empirical model predictions. In addition, the accuracy of atmospheric density retrieved results decreases with the increase of altitude in the altitude range of 150–200 km, and the accurate quantitative description will be further analyzed after analyzing a large number of X-ray occultation data in the future.

A full physics algorithm to retrieve nighttime sea surface temperature with IASI: Toward an independent homogeneous long time-series for climate studies

A fully physically-based algorithm is presented and used to retrieve nighttime sea-surface (skin) temperatures (SSTs) from spectra recorded by the Infrared Atmospheric Sounder Interferometer (IASI). The main advantage of the proposed approach, based on radiative transfer calculations, is to provide a dataset totally independent of any in-situ measurement or model. The SSTs are retrieved at the IASI spot resolution (clear sky) for the three Metop platforms, today leading to a continuous record over more than 13 years, which is planned to be extended for at least another decade. Five atmospheric transparency windows centered at 3.8, 4.0, 4.7, 9.0 and 11 μm have been used in the retrieval. Results are compared with in-situ temperatures provided by the buoys network as well as with other satellite datasets. This first analysis enables to draw the following three main conclusions. First, we confirm the interest of using short wavelengths around 4 μm which enables accurate estimates characterized by a reduced bias with respect to those around 9 and 11 μm, thanks to significantly weaker sensitivities to errors in the modeling of the water vapor contribution to the atmospheric absorption and emission. Second, we show that the observed dependences of the skin-depth temperature difference on various observed-scene parameters (wind speed, air-sea temperature difference, humidity) are consistent with predictions of theoretical and empirical models, as well as with those from other SST products. Finally, the overall averaged bias between our retrieved values converted to depth temperatures and those provided by in-situ measurements is −0.04 K (median: −0.024 K), with a standard deviation (SD) of 0.38 K (Robust SD = 0.25 K). The bias is thus well below the 0.1 K required for a SST product usable for climate studies and extremely stable over more than a decade. Then, we investigate the consistency between the three generations of IASI on-board Metop-A, -B, and -C over their overlap periods (2013–2020 for Metop-A and Metop-B, and 2019–2020 for Metop-C) by comparing the associated retrieved monthly-averaged SSTs over 1°x1° grids. The mean differences obtained are lower than 0.02 K, with a SD of 0.3 K consistent with the natural variation of the SST within a month. This demonstrates the high stability of the SSTs retrieved using spectra recorded from the IASI suite and opens the possibility to generate long time series for climate studies. This is tested in a preliminary investigation of the SST anomalies between 2008 and 2020, with the successful detection of short-term (El Niño and La Niña) changes as well as of a global warming trend of about +0.3 K/decade quantitatively consistent with other observations.

Toward Zero-Strain Mixed Conductors: Anomalously Low Redox Coefficients of Chemical Expansion in Praseodymium-Oxide Perovskites

Zero-strain materials are desired for high chemo-mechanical stability in energy conversion/storage devices, where operational stoichiometry changes can cause large chemical stresses. Here, we demonstrate near-zero redox coefficients of chemical expansion (CCEs) for mixed- and triple-conducting Pr-oxide perovskites. PrGa0.9Mg0.1O3 – δ (PGM) and BaPr0.9Y0.1O3 – δ (BPY), having Pr on the A- and B-site, respectively, were synthesized and characterized with in situ high temperature, variable atmosphere X-ray diffraction, dilatometry, and thermogravimetric analysis to obtain isothermal stoichiometry changes, chemical strains, and CCEs. Despite empirical model predictions of smaller CCEs for Pr on the A-site, both compositions yielded unprecedented low average CCEs (0.004–0.011), 2–5× lower than the lowest reported perovskite redox CCEs. Simple empirical models assume pseudo-cubic structures and full charge localization on multivalent cations like Pr. To evaluate actual charge distribution, in situ impedance spectroscopy and density functional theory calculations were performed. Results indicate that the anomalously low CCEs in these compositions likely derive from a combination of decreased crystal symmetry (vs cubic), partial charge delocalization through hybridization of Pr-4f and O-2p orbitals, and redox/multivalence on O rather than just Pr (with or without hybridization). On this basis, we suggest band structure design principles for near-zero redox-strain perovskites, highlighting the benefit of locating holes partially or fully on oxygen.

Prediction of an environmental impact of tunnel blasting using ordinary artificial neural network, particle swarm and Dragonfly optimized artificial neural networks

Blasting is an intrinsic component of surface and underground excavation but it is associated with adverse environmental effects that can threaten the safety of lives and property. This study, therefore, proposed artificial intelligence (AI) based models for predicting the air overpressure (Aop) in the tunnel blasting. Three AI models which are ordinary artificial neural network (ANN), particle swarm optimized artificial neural network (PSO-ANN) and Dragonfly optimized artificial neural network (DA-ANN) are proposed. The input parameters into the models are the charge per delay (Cd), the number of holes (Nh), distance from the measuring station to the blasting point (Dm), and the rock mass rating (RMR) while the Aop is targeted output. The model parameters were obtained through field measurements and laboratory experiment. The performance of the models was evaluated using the coefficient of determination (R2), mean-squared error (MSE), mean absolute percentage error (MAPE), and the variance accounted for (VAF). Out of the different model simulations, the PSO-ANN model with 4-15-15-1 architecture performed best with R2 of 1, 0.984, 1, 0.9985, MSE of 0.0004, 0.125, 5.5E-0.6, 0.018, MAPE of 0.004, 0.152, 0.002, 0.025, and VAF of 99.996, 98.29, 1, 98.85 for the respective training, testing, validation and overall datasets. The selected model was compared with the MLR model and empirical model predictions. The proposed model outperformed them. Hence, the proposed model can predict Aop with a high degree of accuracy.

Homeostatic response to sleep restriction in adolescents.

The high prevalence of chronic sleep restriction in adolescents underscores the importance of understanding how adolescent sleep is regulated under such conditions. One component of sleep regulation is a homeostatic process: if sleep is restricted, then sleep intensity increases. Our knowledge of this process is primarily informed by total sleep deprivation studies and has been incorporated in mathematical models of human sleep regulation. Several animal studies, however, suggest that adaptation occurs in chronic sleep restriction conditions, showing an attenuated or even decreased homeostatic response. We investigated the homeostatic response of adolescents to different sleep opportunities. Thirty-four participants were allocated to one of three groups with 5, 7.5, or 10 h of sleep opportunity per night for five nights. Each group underwent a protocol of nine nights designed to mimic a school week between two weekends: two baseline nights (10 h sleep opportunity), five condition nights (5, 7.5, or 10 h), and two recovery nights (10 h). Measures of sleep homeostasis (slow-wave activity and slow-wave energy) were calculated from frontal and central EEG derivations and compared to predictions derived from simulations of the homeostatic process of the two-process model of sleep regulation. Only minor differences were found between empirical data and model predictions, indicating that sleep homeostasis is preserved under chronic sleep restriction in adolescents. These findings improve our understanding of effects of repetitive short sleep in adolescents.

Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations

Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important resources in field campaigns for LFMC monitoring, often focusing on indicator species to reduce sampling time and costs. This paper compares different remote sensing approaches to provide LFMC prediction of Cistus ladanifer, a fire-prone shrub species commonly found in Mediterranean areas and used by fire management services as an indicator species for wildfire risk assessment. Spectral indices (SI) were derived from satellite imagery of different spectral, spatial, and temporal resolution, including Sentinel-2 and two different reflectance products of the Moderate Resolution Imaging Spectrometer (MODIS); MCD43A4 and MOD09GA. The SI were used to calibrate empirical models for LFMC estimation using on ground field LFMC measurements from a monospecific shrubland area located in Madrid (Spain). The empirical models were fitted with different statistical methods: simple (LR) and multiple linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs). MCD43A4 images were also used to estimate LFMC from the inversion of radiative transfer models (RTM). Empirical model predictions and RTM simulations of LFMC were validated and compared using an independent sample of LFMC values observed in the field. Empirical models derived from MODIS products and Sentinel-2 data showed R2 between estimated and observed LFMC from 0.72 to 0.75 and mean absolute errors ranging from 11% to 13%. GAMs outperformed regression methods in model calibration, but NLR had better results in model validation. LFMC derived from RTM simulations had a weaker correlation with field data (R2 = 0.49) than the best empirical model fitted with MCD43A4 images (R2 = 0.75). R2 between observations and LFMC derived from RTM ranged from 0.56 to 0.85 when the validation was performed for each year independently. However, these values were still lower than the equivalent statistics using the empirical models (R2 from 0.65 to 0.94) and the mean absolute errors per year for RTM were still high (ranging from 25% to 38%) compared to the empirical model (ranging 7% to 15%). Our results showed that spectral information derived from Sentinel-2 and different MODIS products provide valuable information for LFMC estimation in C. ladanifer shrubland. However, both empirical and RTM approaches tended to overestimate the lowest LFMC values, and therefore further work is needed to improve predictions, especially below the critical LFMC threshold used by fire management services to indicate higher flammability (<80%). Although lower extreme LFMC values are still difficult to estimate, the proposed empirical models may be useful to identify when the critical threshold for high fire risk has been reached with reasonable accuracy. This study demonstrates that remote sensing data is a promising source of information to derive reliable and cost-effective LFMC estimation models that can be used in operational wildfire risk systems.

Towards better representations of carbon allocation in vegetation: a conceptual framework and mathematical tool

The representation of carbon allocation (CA) in ecosystem differs tremendously among models, resulting in diverse responses of carbon cycling and storage to global change. Several studies have highlighted discrepancies between empirical observations and model predictions, attributing these differences to problems of model structure. We analyzed the mathematical representation of CA in models using concepts from dynamical systems theory; we reviewed a representative sample of models of CA in vegetation and developed a model database within the Python package bgc-md. We asked whether these representations can be generalized as a linear system, or whether a more general framework is needed to accommodate nonlinearities. Some of the vegetation systems simulated with the reviewed models have a fixed partitioning of photosynthetic products, independent of environmental forcing. Vegetation is often represented as a linear system without storage compartments. Yet, other structures with nonlinearities have also been proposed, with important consequences on the temporal trajectories of ecosystem carbon compartments. The proposed mathematical framework unifies the representation of alternative CA schemes, facilitating their classification according to mathematical properties as well as their potential temporal behaviour. It can represent complex processes in a compact form, which can potentially facilitate dialog among empiricists, theoreticians, and modellers.