Evolution of spit morphology: a case study using a remote sensing and statistical based approach

The dynamics and factors responsible for morphological changes of spits viz., Uliyargoli-Padukere, Oddu Bengre and Kodi Bengre, southern Karnataka, India, are investigated using multi-dated satellite images and topographic maps during the last 95-years (1910–2005). Variations and overall rate of changes in length, area and coefficient of determination (R²) of each spit are calculated separately for two periods (1910 and 1967 as base years) to find out whether there is any significant trend in the case of change in length and area in all the three spits that are under study. Linear trend lines are fitted using a least squares method and the statistical significance is considered at 80 % level of confidence. The results recorded significant changes in spit morphology, especially in length and area if 1910 and 1967 are considered separately as base years, are may be due to non-availability of data set between 1910 and 1967 period. The study reveals that coastal processes, such as SW-monsoon influenced strong currents and longshore drifts are the main process for formation and growth of spits, whereas rivers influence/drift also plays significant role. The statistical uncertainty estimation in spits morphology is prevalent wherever the coast is affected by human interventions. The study demonstrates that combined use of satellite imagery and statistical techniques can be effectively used to understand the evolution of spits morphology.     

Modelling dependence structures of extreme wind speed using bivariate distribution: a Bayesian approach

When investigating extremes of weather variables, it is seldom that a single weather station determines the damage, and extremes may be caused from the combined behaviour of several weather stations. To investigate joint dependence of extreme wind speed, a bivariate generalised extreme value distribution (BGEVD) was considered from frequentist and Bayesian approaches to analyse the extremes of component-wise monthly maximum wind speed at selected weather stations in South Africa. In the frequentist approach, the parameters of extreme value distributions (EVDs) were estimated with maximum likelihood, whereas in the Bayesian approach the Markov Chain Monte Carlo (MCMC) technique was used with the Metropolis–Hastings algorithm. The results showed that when fitted to component-wise maxima of extreme weather variables, the BGEVD provided apparent benefits over the univariate method, which allowed information to be pooled across stations and resulted in improved precision of the estimates for the parameters and return levels of the distributions. The paper also discusses a method to construct informative priors empirically using historical data of the underlying process from weather characteristics of four pairs of surrounding weather stations at various distances. The results from the Bayesian analysis showed that posterior inference might be affected by the choice of priors that were used to formulate the informative priors. From the results, it could be inferred that the Bayesian approach provides a satisfactory estimation strategy in terms of precision, compared with the frequentist approach, because it accounts for uncertainty in parameters and return level estimations.     

Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach

Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model.     

Impact of the quality of climate models for modelling species occurrences in countries with poor climatic documentation: a case study from Bolivia

The quality of climate models has largely been overlooked as a possible source of uncertainty that may affect the outcomes of species distribution models, especially in the tropics, where comparatively few climatic stations are available. We compared the geographical discrepancies and potential conservation implications of using two different climate models (Saga and Worldclim) in combination with the species modelling approach Maxent in Bolivia. We estimated ranges of selected bird and fern species biogeographically restricted to either humid montane forest of the northern Bolivian Andes or seasonal dry tropical forests (in the Andes and southern lowlands). Saga and Worldclim predicted roughly similar climate patterns of temperature that were significantly correlated. Precipitation layers of both climate models were also roughly similar, but showed important differences. Species ranges estimated with Worldclim and Saga likewise produced different results. Ranges of species endemic to humid montane forests estimated with Saga had higher AUC (Area under the curve) values than those estimated with Worldclim, which for example predicted the occurrence of humid montane forest bird species near Lake Titicaca, an area that is clearly unsuitable for these species. Likewise, Worldclim overpredicted the occurrence of fern and bird species in the lowlands of the Chapare region and well south of the Andean Elbow, where more seasonal biomes occur. By contrast, Saga predictions were coherent with the known distribution of humid montane forests in the northern Bolivian Andes. Estimated ranges of species endemic to seasonal dry tropical forests predicted with Saga and Worldclim were not statistically different in most cases. However, detailed comparisons revealed that Saga was able to distinguish fragments of seasonal dry tropical forests in rain-shadow valleys of the northern Bolivian Andes, whereas Worldclim was not. These differences highlight the neglected influence of climate layers on modelling results and the importance of using the most accurate climate data available when modelling species distributions.     

Bayesian hierarchical spatio-temporal modelling of trends and future projections in the ocean wave climate with a \text{ CO }_2 regression component

Bad weather and rough seas continue to be a major cause for ship losses and is thus a significant contributor to the risk to maritime transportation. This stresses the importance of taking severe sea state conditions adequately into account, with due treatment of the uncertainties involved, in ship design and operation in order to enhance safety. Hence, there is a need for appropriate stochastic models describing the variability of sea states. These should also incorporate realistic projections of future return levels of extreme sea states, taking into account long-term trends related to climate change and inherent uncertainties. The stochastic ocean wave model presented in this paper exploits the flexible framework of Bayesian hierarchical space-time models. It allows modelling of complex dependence structures in space and time and incorporation of physical features and prior knowledge, yet at the same time remains intuitive and easily interpreted. Furthermore, by taking a Bayesian approach, the uncertainties of the model parameters are also taken into account. A regression component with $\text{ CO }_2$ as an explanatory variable has been introduced in order to extract long-term trends in the data. The model has been fitted by monthly maximum significant wave height data for an area in the North Atlantic ocean. The different components of the model will be outlined in the paper, and the results will be discussed. Furthermore, a discussion of possible extensions to the model will be given.     

Metal contamination of surface soils of industrial city Sialkot,Pakistan: a multivariate and GIS approach

In this study concentrations of selected metals viz., Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Na, Ni, Pb and Zn in surface soils of Sialkot city known worldwide for tanneries and pharmaceutical industries were measured to assess the status of urban soil pollution and to identify sources of contamination. Hierarchical cluster analysis (HACA) indicated concentrations of Mg and Ca related to parent rock material, Cd, Co, and Pb with traffic related activities, Cr, Cu, Ni and Zn either associated with automobiles activities or industrial pollution and Fe, K and Na related with anthropogenic activities or lithogenous materials. Correlation analyses and principal component analysis based on factor analysis confirmed the results of HACA. Spatial distribution maps exhibited relatively higher concentrations of Cd, Co, Cu, Ni, Pb, Cr and Zn along traffic routes in the city and streams. The results highlighted concentration of Cd, Ni, Cr, Zn, and Pb measured in urban soil exceeded the permissible limit of surface soils and advocated an imperative need for detailed baseline investigations of spatial distribution of heavy metals and other contaminants for the formulation of geochemical database that should be made available to stakeholder involved in monitoring, assessment and conservation of soil contamination for future planning and management of the Sialkot city.     

From local sustainable development towards climate change adaptation: a case study of Serbia

The recent 2014 floods in Serbia highlighted the potential extent of climate-related risks in the region, resulting in more than 30 deaths and more than 30,000 displaced people, with the overall cost of the damage close to €1.5 billion. By the end of this century, Serbia will experience an increase in temperature of up to 4°C and a decrease in summer precipitation of up to 50%. Adaptation to those changes calls for the redefinition of a strategic approach and the integration of climate change challenges into the processes of local sustainable development planning. The objective of this study was to assess current local sustainable development strategies (LSDSs) and highlight some of the threats and opportunities which may help or hinder the adaptation process, the evaluation of development priorities, coordination mechanisms at the local level and risk management practice. Based on climate trends and vulnerability level, 20 of the potentially most vulnerable municipalities in Serbia were selected. The analysis shows that although local sustainable development planning still lacks cross-sectorial planning practice and risk management plans and measures, and does not yet recognize vulnerable sectors as climate sensitive, it can still serve as a platform for adaptation planning. Based on the assessed LSDS framework, the most suitable approach to the development of an adaption strategy is the vulnerability-based approach. At the same time, the potential for initiating adaptation planning is recognized in the utilization of already established networks with external and international donors and planning experts, joined county or regional adaptation planning, and cross-border collaboration.     

Mountain environment and population in Georgia: case study of Upper Svaneti

The purpose of the presented research is to identify local community opinions toward ecological problems in one of the mountainous region of Georgia (Upper Svaneti), to analyze how population ‘perceive, experience, and interpret’ the social, and ecological issues.

The paper is based on the results of Upper Svanet ipopulation’s survey which was carried out by the authors of presented paper during the August of 2015 and 2016.The questionnaire was elaborated taking into account the peculiarities of the region. Stratified-cluster selection method was used for selection of respondents and database compilation. For statistical analysis of polling data the SPSS package was used.

Based on the statistical analysis of survey results, the following issues have been studied: the most important ecological problems, environmental problems with respect to socio-economic ones, information sources on ecological problems, people’s trust in environmental issues, awareness level of the population on environmental policy, their personal contribution in environmental protection, ways to solve ecological problems, implemented environmental measures.

The obtained results are important to foster mountain population’s active participation in processes that contribute to decision-making processes, planning and implementing programs for sustainable development.     

Rising sea and threatened mangroves: a case study on stakeholders,engagement in climate change communication and non-formal education

Scientific consensus shows that the changes related to climate change are already occurring and will intensify in the future. This will likely result in significant alterations to coastal ecosystems such as mangroves, increase coastal hazards and affect lifestyles of coastal communities. There is increasing speculation that mangrove, a socio-economically important ecosystem, will become more fragile and sensitive to uncertain climate variability such as sea level rise. As a result, mangrove-dependent societies may find themselves trapped in a downward spiral of ecological degradation in terms of their livelihoods and life security. Strengthening the resilience capacity of coastal communities to help them cope with this additional threat from climate change and to ensure sustainability calls for immediate action. In this context, this paper critically examines the regional implications of expected sea level rise and threats to mangrove-dependent communities through a case study approach. The main objective is to highlight the requirement for climate change communication and education to impart information that will fulfil three expectations: (1) confer understanding; (2) assess local inference on climate change through a participatory approach; and (3) construct a framework for climate change awareness among mangrove-dependent communities through community-based non-formal climate change education. This scale of approach is attracting increasing attention from policymakers to achieve climate change adaptation and derive policies from a social perspective.     

Ecofriendly approach of urban solid waste management: a case study of Jalgaon city, Maharashtra

In India, the traditional methods are used for urban solid waste management. These practices are associated with degradation of the urban environment Hence, an environmentally sound garbage management system is required for the urban waste management. The concept of waste minimization needs to be adopted in such systems. It is possible to implement certain corrective measures at collection, storage, transport and disposal of urban solid waste to minimize the adverse impacts on the environment The present investigations are emphasized on generation, characterzation and ecofriendly disposal of solid waste generated in Jalgaon city, Maharashtra. In the present study the waste generation at source was studied in the various income groups of the city. The per capita waste generation was studied by sampling the waste directly from the source of generation. The study reveals that the waste generation is large in the high-income group followed by middle and low-income group public. Further the physicochemical characterization of the waste was also studied. The data generated will help to develop environmentally sound and economically feasible solid waste management system for Ihe city.     

Distribution,enrichment mechanism and risk assessment for fluoride in groundwater: a case study of Mihe-Weihe River Basin,China

● High fluorine is mainly HCO₃·Cl-Na and HCO₃-Na type. ● F⁻ decreases with the increase of depth to water table. ● High fluoride is mainly affected by fluorine-containing minerals and weak alkaline. ● Fluorine pollution is mainly in the north near Laizhou Bay (wet season > dry season). ● Groundwater samples have a high F⁻ health risk (children > adults). Due to the unclear distribution characteristics and causes of fluoride in groundwater of Mihe-Weihe River Basin (China), there is a higher risk for the future development and utilization of groundwater. Therefore, based on the systematic sampling and analysis, the distribution features and enrichment mechanism for fluoride in groundwater were studied by the graphic method, hydrogeochemical modeling, the proportionality factor between conventional ions and factor analysis. The results show that the fluorine content in groundwater is generally on the high side, with a large area of medium-fluorine water (0.5–1.0 mg/L), and high-fluorine water is chiefly in the interfluvial lowlands and alluvial-marine plain, which mainly contains HCO₃·Cl-Na- and HCO₃-Na-type water. The vertical zonation characteristics of the fluorine content decrease with increasing depth to the water table. The high flouride groundwater during the wet season is chiefly controlled by the weathering and dissolution of fluorine-containing minerals, as well as the influence of rock weathering, evaporation and concentration. The weak alkaline environment that is rich in sodium and poor in calcium during the dry season is the main reason for the enrichment of fluorine. Finally, an integrated assessment model is established using rough set theory and an improved matter element extension model, and the level of groundwater pollution caused by fluoride in the Mihe-Weihe River Basin during the wet and dry seasons in the Shandong Peninsula is defined to show the necessity for local management measures to reduce the potential risks caused by groundwater quality.     

Testing the robustness of primary production models in shallow coastal areas: a case study

In this paper we investigate the robustness of a dynamic model, which describes the dynamic of the seagrass Zostera marina, with respect to the inter-annual variability of the two main forcing functions of primary production models in eutrophicated environments. The model was previously applied to simulate the seasonal evolution of this species in the Lagoon of Venice during a specific year and calibrated against time series of field data. In the this paper, we present and discuss the results which were obtained by forcing the model using time series of site-specific daily values concerning the solar radiation intensity and water temperature. The latter was estimated by means of a regression model, whose input variable was a site-specific time series of the air temperature. The regression model was calibrated using a year-long time series of hourly observations. The Z. marina model was first partially recalibrated against the same data set that was used in the original paper. Subsequently, the model was forced using a 7-year-long time series of the driving functions, in order to check the reliability of its long-term predictions. Even though the calibration gave satisfactory results, the multi-annual trends of the output variables were found to be in contrast with the observed evolution of the seagrass biomasses. Since detailed information about the air temperature and solar radiation are often available, these findings suggest that the testing of the ecological consistency of the evolution of primary production models in the long term would provide additional confidence in their results, particularly in those cases in which the scarcity of field data does not allow one to perform a formal corroboration/validation of these models.     

Severity of zinc and iron malnutrition linked to low intake through a staple crop: a case study in east-central Pakistan

Environmental Geochemistry and Health - Micronutrients deficiency in soil–plant and human is well-addressed; however, little is known about their spatial distribution, magnitude of deficiency...     

On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.     

Evaluation of the performance of rural wastewater treatment plants using chemical measurements in combination with statistical techniques: a case study

The performance of a wastewater treatment plant was assessed statistically using multivariate cluster and principle component analysis. This was after measuring some physico-chemical properties in the influent, effluent, downstream, and upstream waters over a 4-month period. The cluster analysis grouped the sampling sites into three clusters: relatively non-polluted (upstream), medium polluted (downstream), and polluted (influent and effluent). The polluted water was further subdivided into very highly (influent) and highly (effluent) polluted. The grouping of influent and effluent into one cluster was due to some water quality parameters such as amount of copper, lead, and phosphates that are not efficiently removed by the plant. Using principal component analysis, samples from the same site taken over a period of 4 months were scattered, indicating inconsistencies in the performance of the plant. This was more pronounced during the rainy season, suggesting that increased water volumes from open sewers make the already poorly performing plant worse. The major loading factors found by principle component analysis were phosphate, lead, iron, zinc, copper, pH, and conductivity. Generally, the wastewater treatment system was found to be efficient in removing heavy metals and these were found in the sludge, but not anions. The mean percentage metal removal could be arranged in the following decreasing order: iron (85%)?>?zinc (57%)?>?copper (40%) and lead (38%) following the concentrations (mg?kg^?1) found in the sludge: iron (11,300)?>?zinc (820)?>?copper (180)?>?lead (20)?>?cadmium (3). Phosphate and iron concentrations in the effluent were found to be above the South African Bureau of Standards (SABS) recommendations. The major cause of poor performance is the high volume of the wastewater, exceeding the capacity of the plant 10 times.     

Comparative analysis using EIA for developed and developing countries: case studies of hydroelectric power plants in Pakistan,Norway and Sweden

Environmental impact assessment (EIA) is an important legislative and scientific tool that may assist and improve the quality assistance for the decision-making process in sustainable development. Here, a comparison of EIAs from three cases of hydropower projects in Pakistan, Norway and Sweden is reported. A huge difference concerning the incorporation of environmental considerations into the decisionmaking process between developed and developing countries is observed. The EIA system of Pakistan appears to be less efficient in the application and review process. In addition, the appraisal of issues, the decision-making process and evaluation through post-monitoring is not as well performed in Pakistan as in cases of hydroelectric power plants in Sweden and Norway. The key reason for this shortcoming is misconceptions about the EIA process, which initially receives intense attention but becomes weakened by the time of implementation. This implies that there is a need to adopt simplified and flexible EIA techniques suitable for the infrastructure and resources of a specific country, taking into account institutional, technical and financial constraints. Improvements are required in public participation, awareness, as well as in environmental control and data system sectors in Pakistan, besides simply enacting legislation to achieve the goals of the EIA system.     

Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and regression tree (CRT), habitat suitability index (HSI), forest structure database (FS), and habitat association database (HA). We focused our comparison on models for five priority forest-breeding species in the Central Hardwoods Bird Conservation Region: Acadian Flycatcher, Cerulean Warbler, Prairie Warbler, Red-headed Woodpecker, and Worm-eating Warbler. Lacking complete knowledge on the distribution and abundance of each species with which we could illuminate differences between approaches and provide strong grounds for recommending one approach over another, we used two approaches to compare models: rank correlations among model outputs and comparison of spatial correspondence. In general, rank correlations were significantly positive among models for each species, indicating general agreement among the models. Worm-eating Warblers had the highest pairwise correlations, all of which were significant (P < 0.05). Red-headed Woodpeckers had the lowest agreement among models, suggesting greater uncertainty in the relative conservation value of areas within the region. We assessed model uncertainty by mapping the spatial congruence in priorities (i.e., top ranks) resulting from each model for each species and calculating the coefficient of variation across model ranks for each location. This allowed identification of areas more likely to be good targets of conservation effort for a species, those areas that were least likely, and those in between where uncertainty is higher and thus conservation action incorporates more risk. Based on our results, models developed independently for the same purpose (conservation planning for a particular species in a particular geography) yield different answers and thus different conservation strategies. We assert that using only one habitat model (even if validated) as the foundation of a conservation plan is risky. Using multiple models (i.e., ensemble prediction) can reduce uncertainty and increase efficacy of conservation action when models corroborate one another and increase understanding of the system when they do not.     

Forest optimisation models including timber production and carbon sequestration values of forest ecosystems: a case study

The integration of carbon sequestration value of forest ecosystems into forest management planning models has become increasingly important in sustainable forest management. This study analyses the economic effects of different minimum cutting ages on timber and carbon sequestration values for a Scots pine forest clumped mainly in older age classes in northeast Turkey. The analysis is performed by formulating three optimisation models. The objective of each model is to maximise net present value (NPV) of harvested timber, net present value of carbon sequestration and the total net present value of timber production and carbon sequestration, respectively. Results showed that increasing the minimum cutting ages by 10 years increased the NPV of timber by 10.5%. However, the current minimum cutting ages were optimal for maximizing the NPV of carbon and the sum of the NPV of timber and carbon benefits. In addition, the model outputs were found to be quite sensitive to unit carbon prices.     

Measuring the benefits of environmental change using a duality approach: The case of ozone and Illinois cash grain farms

The measurement of the economic effects of a changing environment can be complex and costly. This study uses a duality approach to quantify the relationship between ambient levels of ozone and economic damage on Illinois cash grain farms. Econometric estimates from producers' profit functions indicate a significant negative relationship between ozone and the levels of profit and output. These findings are consistent with results from more traditional ecosystem assessment procedures. Given the data and experimentation costs associated with traditional approaches, this study's results point to duality as a potentially powerful tool for assessing the economic effects of various environmental phenomena.     

Energy-economic theory and mathematical models for combining the systems of man and nature, case study: The urban region of Miami, Florida

This paper presents a study of the urban region of Miami, Fla. with consideration of energy flow and the relationship between energy theory and economics. Much of this work is based on the theories and work of Dr Howard T. Odum at the University of Florida. A theory of energy quality is presented which is an attempt to relate energies of different concentrations in their ability to do work. This theory allows comparison of the systems of man and nature. Another theory is proposed which seeks to describe the ability of a region to compete based on its flows of natural and fossil fuel energies.Economic, natural system, and energy data were compiled for the Miami urban region from 1950–1972. Cross-correlation of this data showed significant levels of correlation between the rate of change of fossil fuel use and the rates of change of population, budget, sales tax, income, building structure, and number of telephones. Calculation of several urban indicators for 1972 showed a fossil fuel energy density of 300 kcal/m²/day in the urbanized area, a per capita energy consumption of 53.8 × 10⁶ kcal/capita/year, a ratio of natural to fossil fuel energies of 0.25, a developed area of 260 miles² (673.4 km²), and a rate of development of 6.5 miles² (16.8 km²) per year.An overall model of Miami is presented with flows and storages quantified for 1972. Based on this model a simpler model was simulated on an analog computer. This model consisted of a system of first-order in time, non-linear differential equations which included fossil fuel energy flows, main economic flows, external price functions, building structure, natural energies, and population. This model was simulated for several linearly increasing future price functions and several sets of future energy functions.Natural energies within the region were calculated by determining the land areas associated with various ecosystem types. Estimating the productivities of these systems on a per area basis allowed calculation of total energy flows. The energies associated with winds, tides, waves, and fresh/salt water concentration gradients were also determined. It was found that the ratio of natural to fossil fuel energy changed from 1.77 in 1950 to 0.25 in 1972.