Fine-resolution mapping of wildfire fuel types for Canada: Fuzzy logic modeling for an Alberta pilot area

The Canadian Forest Fire Danger Rating System (CFFDRS) is used daily across Canada for evaluating forest fire danger. Fuel-type information is one of the inputs required by the models used in the CFFDRS. In this project, three fuel-type maps with a 25 m resolution were produced for a pilot study area located in Alberta using land cover only; land cover and biomass; and, land cover, biomass and leaf area index data derived from satellite imagery. The relationships between inputs and fuel types were determined mainly by the opinions of forest fire scientists and incorporated into a computer program using fuzzy set methodology. Not all the CFFDRS fuel types could be distinguished using these inputs; three of the coniferous types had to be grouped into one common fuel type. Overall accuracy was between 74 and 83% based on ground-truth comparisons. The most accurate map resulted from land cover and biomass data. Detailed accuracy assessment indicated that the overall accuracy increased up to 86% if ambiguous fuel type identification was considered. No combination of inputs was able to define a fuel type with absolute certainty, which is a reflection of differing expert opinions and the small number of inputs used to produce the maps.     

A Simulation Study to Assess the Sensitivity of a Forest Health Monitoring Network to Outbreaks of Defoliating Insects

The sensitivity of the United States Forest Health Monitoring network to outbreaks of defoliating insects was examined by means of a simulation study. A model constructed specifically for the study was used to generate a wide variety of defoliation patterns in forested landscapes. Forest configuration was that of Minnesota, USA, as expressed by the GAP land cover classification. Combinations of model parameters were based on a Latin Hypercube sample. The relationship between the average number of plots defoliated and outbreak characteristics was then examined via multiple regression. Both theoretical and model results pointed to a strong, linear relationship between the average number of plots defoliated and outbreak size. Model results provided additional insight, suggesting a significant relationship between the average number of plots defoliated and other outbreak characteristics after outbreak size was taken into account.     

Supervisor expediency to employee expediency: The moderating role of leader–member exchange and the mediating role of employee unethical tolerance

We utilize social learning theory to test the role‐modeling effect of supervisor expediency (i.e., a supervisor's use of unethical practices to expedite work for self‐serving purposes). In particular, we examine the relationship between supervisor expediency and employee expediency, as moderated by leader–member exchange (LMX) and mediated by employee unethical tolerance. We predict that employees are more likely to model their supervisors' expedient behaviors when their relationship is characterized by high‐LMX (a high‐quality exchange relationship that is rich in socioemotional support). Furthermore, we argue that supervisor expediency, especially when LMX is high, influences employees' attitudes of unethical tolerance, which then affects employees' expedient behaviors. Across 2 multisource field studies and a third time‐lagged field study, we found general support for our theoretical predictions. Theoretical and practical implications are discussed.     

Field effectiveness evaluation of advanced driver assistance systems

Abstract

Objective: Advanced driver assistance systems (ADAS) are a class of vehicle technologies designed to increase safety by providing drivers with timely warnings and autonomously intervening to avoid hazardous situations. Though laboratory testing suggests that ADAS technologies will greatly impact crash involvement rates, real-world evidence that characterizes their effectiveness is still limited. This study evaluates and quantifies the association of ADAS technologies with the likelihood of a moderate or severe crash for new-model BMWs in the United States.

Methods: Vehicle ADAS option information for the cohort of model year 2014 and later BMW passenger vehicles sold after January 1, 2014 (n?=?1,063,503), was coded using VIN-identified options data. ADAS technologies of interest include frontal collision warning with autonomous emergency braking, lane departure warning, and blind spot detection. BMW Automated Crash Notification system data (from January 2014 to November 2017) were merged with vehicle data by VIN to identify crashed vehicles (n?=?15,507), including date, crash severity (delta V), and area of impact. Using Cox proportional hazards regression modeling, the study calculates the adjusted hazard ratio for crashing among BMW passenger vehicles with versus without ADAS technologies. The adjusted percentage reduction in moderate and severe crashes associated with ADAS is interpreted as one minus the hazard ratio.

Results: Vehicles equipped with both autonomous emergency braking and lane departure warning were 23% less likely to crash than those not equipped (hazard ratio [HR]?=?0.77; 95% confidence interval [CI], 0.73–0.81), controlling for model year, vehicle size and body type. Autonomous emergency braking and lane departure warning generally occur together, making it difficult to tease apart their individual effects. Blind spot detection was associated with a 14% reduction in crashes after controlling for the presence of autonomous emergency braking and lane departure warning (HR =0.86; 95% CI, 0.744–0.99). Differences were observed by vehicle type and crash type. The combined effect of autonomous emergency braking and lane departure warning was greater in newer model vehicles: Equipped vehicles were 13% less likely to crash (HR =0.87; 95% CI, 0.79–0.95) among 2014 model year vehicles versus 34% less likely to crash (HR =0.66; 95% CI, 0.57–0.77) among 2017 model year vehicles.

Conclusion: This robust cohort study contributes to the growing evidence on the effectiveness of ADAS technologies.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.     

Groundwater Modeling in Agricultural Watershed under Different Recharge and Discharge Scenarios for Quaternary Aquifer Eastern Nile Delta,Egypt

Different scenarios of recharge and discharge were assessed for sustainable management of groundwater in Quaternary aquifer east of Nile Delta. MODFLOW was utilized to investigate the effect of land use change and damming construction in the upstream of the Nile River on the current and short-term groundwater management strategies. The interpretive transient simulation was performed between 2004 and 2016 after steady-state calibration in 2004, and transient state from 2004 to 2013 with different irrigation recharges associated with land use change in this period. Sensitivity analysis was performed for hydraulic conductivities, recharge, and conductance parameters. The predictive transient simulation was run till 2023 under three scenarios of increasing pumping rates by 15, 30, and 50% for agriculture expansion and specified head reduction of Port Said Canal by 0.2, 0.4, and 0.6 m associated with the reduction of Nile water levels after Grand Ethiopian Residence Dam, GERD operation in 2017. Results from the in- and out-flow budgets showed that groundwater aquifer is stable at the current rate of pumping till 2023. Groundwater heads decreased by 0.2 and 0.42 m in the southern section, and a slight increase in the northern part was noticed for the first and second scenarios, respectively. When additional pumping stress is applied (50% increase), groundwater head dropped by 0.66 m, and the storage is no longer able to maintain the aquifer capacity after 2020 (worst-case scenario).     

Influence of Clear Fellings on the Bryophyte Component of the Broad-leaved Forests of the Bashkir Cis-Ural Region

The change in the composition of the bryophyte component of succession communities that occur in the process of natural regeneration at the site of clear felling of the indigenous elm—maple–linden forests of the Bashkir Cis-Ural Region has been studied. The vulnerability of species to the consequences of felling is affected by their substrate confinement, ecological amplitude in relation to the factors of temperature, humidity, variability of soil moisture, and type of life strategy. In the secondary aspen forests, the absence or low constancy of nemoral epiphytic and ground mosses was noted.     

Effects of Topographical Heterogeneity and Dispersal Limitation on Species Turnover in a Temperate Mountane Ecosystem: a Case Study in the Henan Province,China

Species turnover patterns can be inconsistent due to differences in the dispersal ability of different growth forms. Here, species of trees, shrubs, herbs, and bryophytes in the Xiaoqinling National Nature Reserve in China were analyzed to determine patterns of species turnover along an elevation and spatial gradient. Variance partitioning was used to assess the relative contribution of topographic heterogeneity and dispersal limitation to species turnover. Our results suggest that the effect of dispersal limitation is more important than topographic heterogeneity on species turnover in temperate mountane ecosystems in the study area. Dispersal limitation has a greater effect on trees species turnover than on shrubs, herbs or bryophytes species turnover.     

Singular Spectrum Analysis of Hydroecological Parameter Dynamics of Lake Naroch’ in the Years 1978–2015

Singular Spectrum Analysis (SSA) has been used to decompose continuous series of data on certain characteristics of Lake Naroch’ ecosystem during the vegetative seasons of years 1978–2015 into a long-time trend, a periodic component, and a residue not amenable to decomposition. The contribution of each component to changes in this variable has been assessed. The trends accounted for 78–97% of parameter variability, and the periodic components accounted for 2.5–15%. The fluctuations of phytoplankton and zooplankton biomass dynamics were the most diverse, and the contribution of these components was the greatest (15 and 8%, respectively). The periodic components in the changes of all parameters could be divided into four groups according to period duration (17–22, 7–15, 4–7, and less than 4 years). Multidimensional factor analysis of seven biotic parameters was performed in order to identify fluctuations in the ecosystem. Five major factors accounting for 93.6% of ecosystem changes together were identified, and each factor variable was subjected to SSA analysis. The period durations were similar for the oscillatory components identified. The first factor was interpreted as a trophic status of the water body, the second was taken to be the geographical location that defines the amount of solar radiation energy available, the third was taken to be the availability of biogenic elements (phosphorus in particular), and the fourth was interpreted as specific developmental cycles of live components of the ecosystem.