Detection of surface change in complex topography using terrestrial laser scanning: application to the Illgraben debris‐flow channel

Detection of surface change is a fundamental task in geomorphology. Terrestrial laser scanners are increasingly used for monitoring surface change resulting from a variety of geomorphic processes, as they allow the rapid generation of high‐resolution digital elevation models. Irrespective of instrument specifics, survey design or data processing, such data are subject to a finite level of ambiguity in position measurement, a consideration of which must be taken into account when deriving change. The propagation of errors is crucial in change detection because even very small uncertainties in elevation can produce large uncertainties in volume when extrapolated over an area of interest. In this study we propose a methodology to detect surface change and to quantify the resultant volumetric errors in areas of complex topography such as channels, where data from multiple scan stations must be combined. We find that a commonly proposed source of error – laser point elongation at low incidence angles – has a negligible effect on the quality of the final registered point cloud. Instead, ambiguities in elevation inherent to registered datasets have a strong effect on our ability to detect and measure surface change. Similarly, we find that changes in surface roughness between surveys also reduce our ability to detect change. Explicit consideration of these ambiguities, when propagated through to volume calculations, allows us to detect volume change of 87 ± 5 m³, over an area of ～ ?4900 m², due to passage of a debris flow down a 300 m reach of the Illgraben channel in Switzerland. Copyright © 2011 John Wiley & Sons, Ltd.     

Traffic ‐and Industry‐Related Air Pollution Exposure Assessment in an Asian Megacity

Like other Asian countries, Pakistan is facing the issue of air pollution due to rapid urbanization, enormous transportation increases, and other related human activities. Moreover, continuously increasing emission sources have not only raised pollutant concentrations but also their types, thus damaging both human health and the environment. Faisalabad is the third largest megacity of Pakistan and its state of air quality is getting worse due to factors such as industrialization, high traffic volumes, and extensive fossil‐fuel‐burning activities. This review article aims to highlight the present status of air pollution in this city with special reference to particulate matter, elemental profiles, gaseous pollutants, organic–inorganic particulate contents as well as their sources. The concentration levels of these entities were also compared with other national and international cities, and related environmental standards. It is found that current levels of these pollutants are beyond safety limits as specified by various environment protection agencies and organizations. Several weak aspects and gaps are also identified along with suggestions for improvements of the present situation and directions for future research.     

Assessing environmental risks through fuzzy parameterized probabilistic analysis

A fuzzy parameterized probabilistic analysis (FPPA) method was developed in this study to assess risks associated with environmental pollution-control problems. FPPA integrated environmental transport modeling, fuzzy transformation, probabilistic risk assessment, fuzzy risk quantification into a general risk assessment framework, and was capable of handling uncertainties expressed as fuzzy-parameterized stochastic distributions. The proposed method was applied to two environmental pollution problems, with one being about the point-source pollution in a river system with uncertain water quality parameters and the other being concerned with groundwater contaminant plume from waste landfill site with poorly known contaminant physical properties. The study results indicated that the complex uncertain features had significant impacts on modeling and risk-assessment outputs; the degree of impacts of modeling parameters were highly dependent on the level of imprecision of these parameters. The results also implied that FPPA was capable of addressing vagueness or imprecision associated with probabilistic risk evaluation, and help generate risk outputs that could be elucidated under different possibilistic levels. The proposed method could be used by environmental managers to evaluate trade-offs involving risks and costs, as well as identify management solutions that sufficiently hedge against dual uncertainties.     

Sensitivity,responsivity, stability and irreversibility as multiple objectives in civil systems

This paper recommends the consideration of sensitivity, stability, risk, and irreversibility as objective functions in water resource management models within the framework of multiobjective analysis. Six major sources of uncertainties and errors in systems modeling are identified. They are associated with the following model characteristics: model structure (topology), model parameters, model scope or focus, data, optimization technique, and human subjectivity. In particular, the major objective of this paper is to set the stage for the development of an analytical and operational multiobjective framework which will provide decision-makers and plamers with alternatives that consider systems' sensitivity, responsivity, stability and irreversibility along with cost and other performance indices as multiple objectives. This type of a framework should have a very wide spectrum of applications in water and related land resources, environmental studies, energy, and others. The Surrogate Worth Trade-off method is proposed for the solution of the resulting multiobjective optimization problem.     

Attributing runoff changes to climate variability and human activities: uncertainty analysis using four monthly water balance models

Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging of four monthly water balance models was proposed. The method was applied to the Weihe River Basin, the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956–1990) and human-impacted period (1991–2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.     

On the significance and utilization of secondary magnetizations in red beds

We document three cases of observed Quaternary or much older secondary magnetizations in red beds. A better than usual knowledge about past and present temperature conditions enables us to compare these secondary magnetizations with theoretical relationships between relaxation time and the temperature of acquisition of viscous partial thermo remanent magnetizations (VpTRM's). Arguments can be made for a viscous-thermal origin of the secondary magnetizations in two of the red bed collections, involving Beltian argillites from Montana and Late Precambrian red beds from the Michigan basin. In the third cases, involving Upper Keweenawan sediments from the Upper Peninsula of Michigan, a chemical origin must be assigned to the secondary magnetizations, because thermal and viscous cuases can be ruled out. Stability, blocking-temperature ranges, and discreteness of secondary and characteristics magnetizations are very similar for all three of the red bed collections, so that apparently no magnetic criteria exist to distinguish between partial remagnetizations of thermal or chemical origin in red beds. However, when thermal causes can be documented the theory of VpTRM acquisition offers possibilities for palaeo-heat-flow determinations.     

Polygenic eruptions on Alba Patera,Mars

A combination of photogeologic mapping, analysis of Viking Orbiter thermal inertia data, and numerical modelling of eruption conditions has permitted us to construct a new model for the evolution of the martian volcano Alba Patera. Numerous digitate channel networks on the flanks of the volcano are interpreted to be carved by sapping due to the release of non-juvenile water from unconsolidated flank deposits. Using the thermal inertia measurements, we estimate the particle size of these deposits to be 3–10 µm, which, together with theoretical modelling of the disperison of explosively derived volcanic materials, leads us to conclude that the flank deposits on Alba Patera are low-relief pyroclastic flows. The recognition of numerous late-stage summit and sub-terminal lava flows thus makes Alba Patera a unique martian volcano that is transitional between the older pyroclastic-dominated highland paterae and the more recent effusive central-vent volcanoes such as the Tharsis Montes.     

Seismic Tomography by Monte Carlo Sampling

The paper discusses the performance and robustness of the Bayesian (probabilistic) approach to seismic tomography enhanced by the numerical Monte Carlo sampling technique. The approach is compared with two other popular techniques, namely the damped least-squares (LSQR) method and the general optimization approach. The theoretical considerations are illustrated by an analysis of seismic data from the Rudna (Poland) copper mine. Contrary to the LSQR and optimization techniques the Bayesian approach allows for construction of not only the “best-fitting” model of the sought velocity distribution but also other estimators, for example the average model which is often expected to be a more robust estimator than the maximum likelihood solution. We demonstrate that using the Markov Chain Monte Carlo sampling technique within the Bayesian approach opens up the possibility of analyzing tomography imaging uncertainties with minimal additional computational effort compared to the robust optimization approach. On the basis of the considered example it is concluded that the Monte Carlo based Bayesian approach offers new possibilities of robust and reliable tomography imaging.     

Greenhouse Gases, Aerosols and Reducing Future Climate Uncertainties

The past record of global earth surface temperatures is unable to offer quantitative evidence about the amplitude of climate sensitivity, due to the competing effects of long-lived greenhouse gases and short-lived aerosols. This factor constitutes one of the reasons why uncertainties about climate sensitivity have remained almost unchanged for more than 30 years, and it is also limiting our current capacity to propose reliable climate projections for the coming century. This paper offers a short review of the studies that have dealt with this issue. A number of approaches aim at a process-oriented diagnostic of current models. These studies are in constant progress since the launch of remote-sensing instruments, such as those from the A-train satellite constellation. Past climate fluctuations may also offer some limited possibilities to discriminate the effects of greenhouse gases and aerosols. There is therefore a real hope that climate projections may eventually become more accurate, which would be extremely useful in monitoring global warming during the next decades.     

Sensitivity of acoustic propagation to uncertainties in the marine environment as characterized by various rapid environmental assessment methods

Accurate sonar performance prediction modelling depends on a good knowledge of the local environment, including bathymetry, oceanography and seabed properties. The function of rapid environmental assessment (REA) is to obtain relevant environmental data in a tactically relevant time frame, with REA methods categorized by the nature and immediacy of their application, from historical databases through remotely sensed data to in situ acquisition. However, each REA approach is subject to its own set of uncertainties, which are in turn transferred to uncertainty in sonar performance prediction. An approach to quantify and manage this uncertainty has been developed through the definition of sensitivity metrics and Monte Carlo simulations of acoustic propagation using multiple realizations of the marine environment. This approach can be simplified by using a linearized two-point sensitivity measure based on the statistics of the environmental parameters used by acoustic propagation models. The statistical properties of the environmental parameters may be obtained from compilations of historical data, forecast conditions or in situ measurements. During a field trial off the coast of Nova Scotia, a set of environmental data, including oceanographic and geoacoustic parameters, were collected together with acoustic transmission loss data. At the same time, several numerical models to forecast the oceanographic conditions were run for the area, including 5- and 1-day forecasts as well as nowcasts. Data from the model runs are compared to each other and to in situ environmental sampling, and estimates of the environmental uncertainties are calculated. The forecast and in situ data are used with historical geoacoustic databases and geoacoustic parameters collected using REA techniques, respectively, to perform acoustic transmission loss predictions, which are then compared to measured transmission loss. The progression of uncertainties in the marine environment, within and between different REA categories, and the consequences on acoustic propagation are examined.     

近1800年来云南洱海流域气候变化与人类活动的湖泊沉积记录

对云南洱海湖泊岩芯沉积物进行了多环境指标（年代学、色素、硅藻、有机碳稳定同位素、磁化率、化学元素）的分析,建立了近１８００年来云南洱海流域气候与环境变化的序列,气候演化具有暖干、冷湿交替的组合呈些特征气候阶段如中世纪温暖期、小冰期气候特征在洱海源泊沉积记录中均有反映,洱海湖泊上沉积记录的气候暖干－冷湿交替变化规律,反映了西南委风影响下的气候演化特征,湖泊沉积记录中包含丰富的人类以的信息,磁化率,元     

则木河活动断裂带1∶5万地质填图及综合研究

经过对则木河活动断裂带的1∶5万地质填图及重点地段开挖槽探和剥土,采集了年代样品与构造岩薄片样,实测断错地貌,同时进行了研究区的航、卫片解译,并对前人所作工作成果进行了分析。在此基础上我们对该区活动断裂带的平面展布、几何结构、不同时期的活动习性以及它的形成机制、运动形式等方面作了深入的探讨,包括全新世断裂的研究和它的未来强震危险性估价。本成果的整理发表,对开发西昌地区,振兴西昌经济,促进川、滇两省边界地区经济的发展提供了有利条件,同时也可作为有关方面的参考。     

则木河活动断裂带1∶5万地质填图及综合研究

经过对则木河活动断裂带的 1∶5万地质填图及重点地段开挖槽探和剥土 ,采集了年代样品与构造岩薄片样 ,实测断错地貌 ,同时进行了研究区的航、卫片解译 ,并对前人所作工作成果进行了分析。在此基础上我们对该区活动断裂带的平面展布、几何结构、不同时期的活动习性以及它的形成机制、运动形式等方面作了深入的探讨 ,包括全新世断裂的研究和它的未来强震危险性估价。本成果的整理发表 ,对开发西昌地区 ,振兴西昌经济 ,促进川、滇两省边界地区经济的发展提供了有利条件 ,同时也可作为有关方面的参考。     

On effects of trawling, benthos and sampling design

The evidence for the wider effects of fishing on the marine ecosystem demands that we incorporate these considerations into our management of human activities. The consequences of the direct physical disturbance of the seabed caused by towed bottom-fishing gear have been studied extensively with over 100 manipulations reported in the peer-reviewed literature. The outcome of these studies varies according to the gear used and the habitat in which it was deployed. This variability in the response of different benthic systems concurs with established theoretical models of the response of community metrics to disturbance. Despite this powerful evidence, a recent FAO report wrongly concludes that the variability in the reported responses to fishing disturbance mean that no firm conclusion as to the effects of fishing disturbance can be made. This thesis is further supported (incorrectly) by the supposition that current benthic sampling methodologies are inadequate to demonstrate the effects of fishing disturbance on benthic systems. The present article addresses these two erroneous conclusions which may confuse non-experts and in particular policy-makers.     

A simple approach to constrain the position and the geometry of seismogenic structures: Application to the Karthala volcano (Grande Comores Island, Mozambique Channel)

A simple and quick approach is presented to constrain the position and the geometry of seismogenic structures using hypocentre locations and their associated uncertainties. It assumes that, for a given hypocentre, all other seismic events which are located inside its uncertainty ellipsoid are localised on the same structure. These can then be considered as independent measurements of the same variable which implies, from basic statistics, that the best estimate of the variable is the centroid of all these hypocentres. It is shown that the approach is robust and that it can successfully be applied even when location uncertainties are not well-constrained. When applied to the Karthala volcano (Grande Comores Island), the method, called the Best Estimate Method, constrains the direction of the main active structure. This is a plane oriented N165° and dipping close to the vertical. This result, which is in agreement with recent studies of the Karthala volcano, demonstrates the potential of this technique. It is believed that such a process could advantageously be integrated in preliminary analysis of seismological monitoring.     

Probability-credibility health risk assessment under uncertain environment

The ability to describe variables in a health risk model through probability theory enables us to estimate human health risk. These types of risk assessment are interpreted as probabilistic risk assessment (PRA). Generally, PRA requires specific estimate of the parameters of the probability density of the input variables. In all circumstances, such estimates of the parameters may not be available due to the lack of knowledge or information. Such types of variables are treated as uncertain variables. These types of information are often termed uncertainty which are interpreted through fuzzy theory. The ability to describe uncertainty through fuzzy set theory enables us to process both random variable and fuzzy variable in a single framework. The method of processing aleatory and epistemic uncertainties into a same framework is coined as hybrid method. In this paper, we are going to talk about such type of hybrid methodology for human health risk assessment. Risk assessment on human health through different pathways of exposure has been attempted many a times combining Monte Carlo analysis and extension principle of fuzzy set theory. The emergence of credibility theory enables transforming fuzzy variable into credibility distribution function which can be used in those hybrid analyses. Hence, an attempt, for the first time, has been made to combine probability theory and credibility theory to estimate risk in human health exposure. This method of risk assessment in the presence of credibility theory and probability theory is identified as probabilistic-credibility method (PCM). The results obtained are then interpreted through probability theory, unlike the other hybrid methodology where the results are interpreted in terms of possibility theory. The results obtained are then compared with probability-fuzzy risk assessment (PFRA) method. Generally, decision under hybrid methodology is made on the index of optimism. An optimistic decision maker estimates from the \(\alpha\)-cut at 1, whereas a pessimistic decision maker estimates from the \(\alpha\)-cut at 0. The PCM is an optimistic approach as the decision is always made at \(\alpha\)=1.     

Sur lesStaurastrum du lac Léman

Among the Desmids which have been observed in the plankton of the lake of Geneva, organisms having 4 processes have been discovered. Their frequency in the water samples and the results of in vitro studies on their progeny when cultivated in several environmental conditions, allow us to consider these organisms as a form ofStaurastrum sebaldi. The triradiate organisms have been incorrectly named and one must now consider three different species.      

Scales and uncertainties in using models and GIS for volcano hazard prediction

Practical decision-making in civil protection based on predicting volcano hazards often involves using process models linked with Geographic Information Systems (GIS). Optimum use of these techniques for such decision-support requires careful and coordinated consideration of process, data and model scales and their related uncertainties. To avoid wasting resources and time on inappropriate data collection, improper model use, and resultant poor decision-making, there is a pressing need for a scientific and functional framework within which to examine implementation and use of geo-spatial assessment tools. To be useful for researchers and decision-makers, volcano hazard simulation approaches must consider the spatial and temporal variability in volcano processes and the data collected representing those. The successful application and implementation of a geo-spatial distributed volcano hazard model at variable scales requires explicit or implicit use of some form of scaling theory applied to the tasks of selection and transformation of appropriate data, and use of results. In general, there are five consecutive scaling steps that demonstrate how data and model scale, as well as the methods for information transformation between these, play key roles in controlling whether prediction results have been produced efficiently and are appropriate at the scale of interest for a civil protection manager's decision-making process. This new scaling theory can be used as a framework to construct practical procedures for applying GIS-Model-based volcano models that allow effective model application based on realistic data availability and environmental settings.     

Decision Support Modeling: Data Assimilation,Uncertainty Quantification,and Strategic Abstraction

We present a framework for design and deployment of decision support modeling based on metrics which have their roots in the scientific method. Application of these metrics to decision support modeling requires recognition of the importance of data assimilation and predictive uncertainty quantification in this type of modeling. The difficulties of implementing these procedures depend on the relationship between data that is available for assimilation and the nature of the prediction(s) that a decision support model is required to make. Three different data/prediction contexts are identified. Unfortunately, groundwater modeling is generally aligned with the most difficult of these. It is suggested that these difficulties can generally be ameliorated through appropriate model design. This design requires strategic abstraction of parameters and processes in a way that is optimal for the making of one particular prediction but is not necessarily optimal for the making of another. It is further suggested that the focus of decision support modeling should be on the ability of a model to provide receptacles for decision-pertinent information rather than on its purported ability to simulate environmental processes. While models are compromised in both of these roles, this view makes it clear that simulation should serve data assimilation and not the other way around. Data assimilation enables the uncertainties of decision-critical model predictions to be quantified and maybe reduced. Decision support modeling requires this.