Sensitivity analysis of the two dimensional application of the Generic Ecological Model (GEM) to algal bloom prediction in the North Sea

Harmful algae can cause damage to co-existing organisms, tourism and farmers. Accurate predictions of algal future composition and abundance as well as when and where algal blooms may occur could help early warning and mitigating. The Generic Ecological Model is an instrument that can be applied to any water system (fresh, transitional or coastal) to calculate the primary production, chlorophyll-a concentration and phytoplankton species composition. It consists of physical, chemical and ecological model components which are coupled together to build one generic and flexible modelling tool. In this paper the model has been analyzed to assess sensitivity of the simulated chlorophyll-a concentration to a subset of ecologically significant input factors. Only a small number of approaches could be considered as suitable for several reasons including the model complexity, engagement of numerous interacting parameters and relatively long time of a single simulation. Thus, sensitivity analysis has been carried out with the use of the Morris method and later enriched by the computation of the correlation ratios of the selected parameters on the model response at more than a few locations in the modelled area. The obtained results are in agreement with expert knowledge of the ecological processes in the North Sea and correspond well with local characteristics.     

Policy relevance of three integrated assessment tools—A comparison with specific reference to agricultural policies

The Common Agricultural Policy (CAP), a system of market support instruments, direct income transfers, and rural development measures, has been put through an ongoing reform process in recent decades. This paper introduces three policy impact assessment tools (SIAT, SEAMLESS-IF, MEA-Scope tool) and analyses how these tools have responded to a number of challenges for integrated assessment modelling as reported in the international literature. Significant progress has been made with regard to modelling linkages whereas other challenges, particularly those related to issues of scale and uncertainty management, require further efforts. It is also analysed which CAP instruments are represented and what kinds of effects can be analysed at different scales. Market instruments and direct payments are comparatively well represented, while the ability to model rural development measures is mostly beyond the scope of these tools. Because each tool has found a different solution for coping with the common challenges of integrated assessment modelling, the choice of one of the tools for a particular application depends strongly on the policy questions being asked. The SIAT provides the big picture via its ability to represent broad changes in policy instruments with EU-wide cross-sector impacts. The most comprehensive analysis of agricultural policy instruments can be obtained with SEAMLESS-IF. The MEA-Scope tool complements the other two approaches with detailed regional profiles.     

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.     

Enhancing generic ecological model for short-term prediction of Southern North Sea algal dynamics with remote sensing images

Physically based numerical modelling follows from the basic understanding of the underlying mechanisms and is often represented by a set of (partial differential) equations. It is one of the main approaches in population dynamics modelling. The emphasis of the model introduced in this paper is on the simulation of short-term spatial and temporal dynamics of harmful algal bloom (HAB) events. Total suspended matter (TSM) concentration is one of the dominant factors for harmful algal bloom (HAB) prediction in North Sea. However, the modelling of suspended matter contains a high degree of uncertainty in this area. Therefore, this research aims to achieve a better estimation for the short-term prediction of harmful algal bloom development in both space and time by using spatially distributed TSM retrieved from remotely sensed images as physically based model inputs. In order to supply complete spatially covered datasets for the physically based model instrument: generic ecological model (GEM), this research retrieves TSM information from MERIS images by means of proper estimation techniques including biharmonic splines and self-learning cellular automata. A better estimation of HAB spatial pattern development is achieved by adding spatially distributed TSM data as inputs to original GEM model, and it proved that chlorophyll-a concentration in this area is very sensitive to TSM concentration.     

Role of behavioural factors in green supply chain management implementation in Indian mining industries

Green supply chain management (GSCM) integrates ecological concepts with those of supply chain management in order to minimize energy and material usage and to reduce adverse impacts of supply chain activities on the environment. GSCM implementation in mining industries depends largely upon certain factors which are influenced by human behaviours. Human behaviour is dynamic in nature and the relationships between them continuously evolve and change. In this ever-changing context, therefore, identifying and ranking the behavioural factors that affect GSCM implementation becomes essential. This can be taken as a reference by the decision makers while deciding the hierarchy of action necessary for effective implementation of green practices in mining supply chains. The present research attempts to explore various behavioural factors affecting GCSM practices and their interactions which help to attain green-enabled needs. Interpretive structural modelling (ISM) is employed in this research to extract the interrelationships among the identified behavioural factors.     

A new approach to assessment and management of the impact from medical liquid radioactive waste

The Swedish regulations concerning disposal of clinical radioactive waste are currently under revision and a graded approach is proposed for risk limitation purposes. To assist the revision procedures, a screening study was performed to estimate public exposures from liquid releases from hospitals to public sewers. The results showed that doses to sewage workers were above the dose constraint of 100muSva(-1) especially for (131)I and (99m)Tc. Hence, a dynamic model, LUCIA, was developed for realistic assessments in which radionuclide transportation in sewers was modelled. Probabilistic simulations were performed to obtain probability distributions of radionuclide concentrations in sludge. Concurrently, estimates of the effective doses to sewage workers decreased significantly and were below 10muSva(-1) except for (111)In and (131)I. However, the Kd-coefficients representing the partition of radioactivity between water and sludge in sewers are highly uncertain for (111)In. As shown by sensitivity studies, these values are the major determinant of the exposures in sewers.     

Modelling spills of water-reactive chemicals

This paper describes part of a programme of work undertaken at the Health and Safety Laboratory (HSL) to investigate the behaviour of selected water-reactive chemicals. Following an accidental release, such substances react exothermically with any water present, generating acidic vapours. The STAWaRS (Source Term Assessment of Water Reactive Substances) software was developed for the Health and Safety Executive (HSE) by ESR Technology to model this complex process. The aims of the study described here were to provide experimental validation of the heats of hydrolysis used within STAWaRS, and to perform sensitivity studies on selected STAWaRS input parameters.The heat of hydrolysis of acetyl chloride was measured and showed good correlation with the value used within STAWaRS. Some of the variables that influence the severity of acetyl chloride spills are examined, with reference to predictions made by the STAWaRS model. The heats of hydrolysis of titanium tetrachloride previously measured at HSL are also discussed, and the effect of adopting these experimentally derived values for modelling spills is shown for a hypothetical land use planning case. This study demonstrates the importance of using experimentally validated values for STAWaRS input parameters.     

Temporal limits to simulating the future spread pattern of invasive species: Buddleja davidii in Europe and New Zealand

The spread of invasive species is a major ecological and economic problem. Dynamic spread modelling is a potentially valuable tool to assist regional and central government authorities to monitor and control invasive species. To date a lack of suitable data has meant that most broad scale dispersal models have not been validated with independent datasets, and so their predictive ability and reliability has remained unscrutinised. A dynamic, stochastic dispersal model of the widely invasive plant Buddleja davidii was calibrated on European spread data and then used to project the temporal progression of B. davidii's distribution in New Zealand, starting from several different historical distributions. To assess the model's performance, we constructed an occupancy map based on the average number of simulation realisations that have a population present. The application of Receiver Operating Characteristic (ROC) curves to occupancy maps is introduced, but with specificity substituted by the proportion of available area used in a realisation. A derivative measure, the partial area under these curves when assessed through time (pAUC), is introduced and used to assess overall performance of the spread model. The model was able to attain a high level of model sensitivity, encompassing all of the known locations within the occupancy envelope. However, attempting to simulate the spread of this invasive species beyond a decade had very low model specificity. This is due to several factors, including the exponential process of spread (the further a population spreads the more sites exist from which it can spread stochastically), and the Markovian chain property of the stochastic system whereby differences between realisations compound through time. These features are seen in many reports of spread models, without being explicitly acknowledged. Our measure of pAUC through time allows a model's temporal performance and its specificity to be simultaneously assessed. While the rapid deterioration in model performance limits the utility of this type of modelling for forecasting long-term broad-scale strategic management of biological invasions, it does not necessarily limit its attractiveness for informing smaller scale and shorter term invasion management activities such as surveillance, containment and local eradication.     

Modelling assessment of PM10 exposure control policies in Northern Italy

To assess the impact of three different emission reduction scenarios on PM10 concentrations in Northern Italy, the TCAM multiphase model has been applied in the framework of the CityDelta III-CAFE EU project. The considered domain, that is characterized by high urban and industrial emissions and a dense road traffic, due to frequently stagnating meteorological conditions is often affected by severe PM10 levels, far from the European standard regulations. The impact evaluation has been performed in terms of both yearly mean values and 50 g/m³ exceedance days for the 2004 Base Case simulation. The results show that the three selected emission reduction scenarios up to 2020 improve air quality all over the domain, in particular, in the area with higher emission density.