Assessment of Future Drought Conditions in the Chesapeake Bay Watershed

Impacts of climate change on the severity and intensity of future droughts can be evaluated based on precipitation and temperature projections, multiple hydrological models, simulated hydrometeorological variables, and various drought indices. The objective of this study was to assess climate change impacts on future drought conditions and water resources in the Chesapeake Bay (CB) watershed. In this study, the Soil and Water Assessment Tool (SWAT) and the Variable Infiltration Capacity model were used to simulate a Modified Palmer Drought Severity Index (MPDSI), a Standardized Soil Moisture index (SSI), a Multivariate Standardized Drought Index (MSDI), along with Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models for both historical and future periods (f1: 2020‐2049, f2: 2050‐2079). The results of the SSI suggested that there was a general increase in agricultural droughts in the entire CB watershed because of increases in surface and groundwater flow and evapotranspiration. However, MPDSI and MSDI showed an overall decrease in projected drought occurrences due to the increases in precipitation in the future. The results of this study suggest that it is crucial to use multiple modeling approaches with specific drought indices that combine the effects of both precipitation and temperature changes.     

Consequences of ignoring dispersal variation in network models for landscape connectivity

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.     

Strategic Rat Control for Restoring Populations of Native Species in Forest Fragments

Forest fragments have biodiversity value that may be enhanced through management such as control of non‐native predators. However, such efforts may be ineffective, and research is needed to ensure that predator control is done strategically. We used Bayesian hierarchical modeling to estimate fragment‐specific effects of experimental rat control on a native species targeted for recovery in a New Zealand pastoral landscape. The experiment was a modified BACI (before‐after‐control‐impact) design conducted over 6 years in 19 forest fragments with low‐density subpopulations of North Island Robins (Petroica longipes). The aim was to identify individual fragments that not only showed clear benefits of rat control, but also would have a high probability of subpopulation growth even if they were the only fragment managed. We collected data on fecundity, adult and juvenile survival, and juvenile emigration, and modeled the data in an integrated framework to estimate the expected annual growth rate (λ) of each subpopulation with and without rat control. Without emigration, subpopulation growth was estimated as marginal (λ = 0.95–1.05) or negative (λ = 0.74–0.90) without rat control, but it was estimated as positive in all fragments (λ = 1.4–2.1) if rats were controlled. This reflected a 150% average increase in fecundity and 45% average increase in adult female survival. The probability of a juvenile remaining in its natal fragment was 0.37 on average, but varied with fragment connectivity. With juvenile emigration added, 6 fragments were estimated to have a high (>0.8) probability of being self‐sustaining (λ > 1) with rat control. The key factors affecting subpopulation growth rates under rat control were low connectivity and stock fencing because these factors were associated with lower juvenile emigration and higher fecundity, respectively. However, there was also substantial random variation in adult survival among fragments, illustrating the importance of hierarchical modeling for fragmentation studies. Control Estratégico de Ratas para Restaurar Poblaciones de Especies Nativas en Fragmentos de Bosque     

基于AHP-FUZZY与SVM理论电网雷害风险评估

为了对电网遭受雷击灾害风险进行研究,以高压输电线路多个雷害影响因子为出发点对其进行归类,解决了以单因子雷击跳闸率作为高压输电线路遭受雷害评价指标的不足。采用AHP(层次分析法)-FUZZY(模糊数学理论)对电网雷害风险展开评估分级,引入某地500 k V电网工程实例,成功将该地电网雷害风险等级定为Ⅲ级中等雷害风险。为验证AHP-FUZZY法对电网雷害风险进行评估的准确性及可靠度,采用SVM(支持向量机)理论对工程实例进行分析计算,结果相关性系数R2为0.932,预测错误率为0,表明本次风险评估是可靠的,能够为电网雷害风险研究提供较大的理论与实际支撑。     

Probabilistic multiple model neural network based leak detection system: Experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.     

Street canyon ventilation and atmospheric turbulence

Operational models for pollutant dispersion in urban areas require an estimate of the turbulent transfer between the street canyons and the overlying atmospheric flow. To date, the mechanisms that govern this process remain poorly understood. We have studied the mass exchange between a street canyon and the atmospheric flow above it by means of wind tunnel experiments. Fluid velocities were measured with a Particle Image Velocimetry system and passive scalar concentrations were measured using a Flame Ionisation Detector. The mass-transfer velocity between the canyon and the external flow has been estimated by measuring the cavity wash-out time. A two-box model, used to estimate the transfer velocity for varying dynamical conditions of the external flow, has been used to interpret the experimental data. This study sheds new light on the mechanisms which drive the ventilation of a street canyon and illustrates the influence of the external turbulence on the transfer process.     

Environment-specific elasticity and sensitivity analysis of the stochastic growth rate

For matrix population models, analyses of how sensitive the population growth rate is to changes in vital rates (i.e. perturbations) are important for studies of life history evolution as well as for management and conservation of threatened species. There are two types of sensitivity analyses corresponding to absolute (sensitivity) or relative (elasticity) changes in the vital rates and both types can be applied to both deterministic and stochastic matrix population models. To date, most empirical studies of elasticity and sensitivity of the stochastic growth rate have examined the response to perturbations in the vital rates in a complete set of possible environments. However, it is often of interest to examine the response to perturbations occurring in only a subset of the possible environments. This has been done for periodic time-varying models elsewhere, but here we describe a recently published method for calculating the environment-specific sensitivity and elasticity of the stochastic growth rate and apply this method to data. These environment-specific perturbation analyses provide a logical way of dividing the sensitivity and elasticity among the environments. They give important insight into the selection regime in different environments and can provide valuable information for making management decisions and management evaluations in stochastic environments.     

Uncertainty evaluation in air quality planning decisions: a case study for Northern Italy

In recent years, evaluating the robustness of environmental models results has become essential in order to effectively support decision makers to define suitable emission control strategies. This evaluation is performed in literature through uncertainty and sensitivity analyses. Therefore, the application of such methodologies to air quality Integrated Assessment Models (IAMs) is extremely challenging. In fact, in this case uncertainty and sensitivity analyses should be assessed not only for each single component of the system, but also for the overall IAM. In the paper, an attempt is made to extend and systematize the information available on uncertainty/sensitivity analysis, at first considering environmental models in general, and then focusing on air quality IAMs. The study aims to offer a tentative framework addressed to modelers and decision makers in the implementation of IAM and evaluation of its results. The framework has been tested on Lombardy region (Northern Italy). The results show how the uncertainty on Drivers of emissions propagates on the whole modelling chain characterizing an integrated assessment study.     

Effects of Harvest of Nontimber Forest Products and Ecological Differences between Sites on the Demography of African Mahogany

Abstract: The demographic impacts of harvesting nontimber forest products (NTFP) have been increasingly studied because of reports of potentially unsustainable harvest. Nevertheless, our understanding of how plant demographic response to harvest is altered by variation in ecological conditions, which is critical for developing realistic sustainable‐use plans, is limited. We built matrix population models to test whether and how variation in ecological conditions affects population responses to harvest. In particular, we examined the effect of bark and foliage harvest on the demography of populations of African mahogany (Khaya senegalensis) in two contrasting ecological regions of Benin, West Africa. K. senegalensis bark and foliage harvest significantly reduced its stochastic population growth rates, but ecological differences between regions had a greater effect on population growth rates than did harvest. The effect of harvest on population growth rates (Δλ) was slightly stronger in the moist than in the drier region. Life‐table response experiments revealed that the mechanism by which harvesting reduced λ differed between ecological regions. Lowered stasis (persistence) of larger life stages lead to a reduction in λ in the drier region, whereas lowered growth of all life stages lowered λ in moist region. Potential strategies to increase population growth rates should include decreasing the proportion of individuals harvested, promoting harvester‐owned plantations of African mahogany, and increasing survival and growth by promoting no‐fire zones in gallery forests. Our results show how population responses to harvest of NTFP may be altered by ecological differences across sites and emphasize the importance of monitoring populations over the climatic range in which they occur to develop more realistic recommendations for conservation.     

Measuring impacts on species with models and metrics of varying ecological and computational complexity

Approaches to assess the impacts of landscape disturbance scenarios on species range from metrics based on patterns of occurrence or habitat to comprehensive models that explicitly include ecological processes. The choice of metrics and models affects how impacts are interpreted and conservation decisions. We explored the impacts of 3 realistic disturbance scenarios on 4 species with different ecological and taxonomic traits. We used progressively more complex models and metrics to evaluate relative impact and rank of scenarios on the species. Models ranged from species distribution models that relied on implicit assumptions about environmental factors and species presence to highly parameterized spatially explicit population models that explicitly included ecological processes and stochasticity. Metrics performed consistently in ranking different scenarios in order of severity primarily when variation in impact was driven by habitat amount. However, they differed in rank for cases where dispersal dynamics were critical in influencing metapopulation persistence. Impacts of scenarios on species with low dispersal ability were better characterized using models that explicitly captured these processes. Metapopulation capacity provided rank orders that most consistently correlated with those from highly parameterized and data-rich models and incorporated information about dispersal with little additional computational and data cost. Our results highlight the importance of explicitly considering species’ ecology, spatial configuration of habitat, and disturbance when choosing indicators of species persistence. We suggest using hybrid approaches that are a mixture of simple and complex models to improve multispecies assessments.