Abstract:
One goal of the LIFE project “Del.Ta.” (NAT/IT/000163) was the preparation of an Action Plan to protect the bottlenose dolphin community in the Pelagie Archipelago (Sicily, Italy). It stressed the importance of regular monitoring of the spatial and temporal distribution of dolphins in order to evaluate the impact of local activities. This study assesses whether land-based surveys could be an effective alternative to vessel-based surveys. During the summer of 2006, both surveys’ methodologies were used at Lampedusa, with 35 sightings recorded from land and 31 from a boat. Comparison was based on the assessment of the type of information they provided in relation to the presence of the animals and their behavior. Both methodologies were applicable, but there were differences in their requirements, potential information generated, costs, and sensitivity to weather conditions. Vessel-based surveys require well trained observers and enable photo-identification and observation of social interaction and morphology. Animal movements, interactions with anthropogenic elements and group dynamics are better collected from land but spatial data can be documented up to 1 nautical mile from the coast. Weather conditions have a significant platform specific effect on sighting frequencies. The high sighting frequency during land surveys provides support for the development of zero-impact land-based dolphins watching activity.

Abstract:
the volume of boat traffic and its potential connection to the coastal distribution of the common bottlenose dolphin (tursiops truncatus) was evaluated off lampedusa island (strait of sicily). from july to september 2006 daily surveys were carried out at eight sites along the coast, three times a day, to assess the number, type, and size of boats moving, fishing, or stationed in lampedusa waters. the study area was divided into four geographic areas: northwest, northeast, southwest, and southeast. data were analyzed to determine the difference in the number of boats among the areas, sampling months, and times of day. the presence of dolphins was monitored by standardized land-based observations. dolphins (n = 139) from 38 sightings were observed throughout the study period (90 days). in order to compare the presence of dolphins among areas, a relative abundance index was used: a-eh (number of sighted specimens per effort hour). common bottlenose dolphins appeared to be broadly distributed around lampedusa, although this study highlighted a possible overlap between their habitat, boat traffic, and fishery, especially in the southwest.

Abstract:
Eddy covariance data are increasingly used to estimate parameters of ecosystem models. For proper maximum likelihood parameter estimates the error structure in the observed data has to be fully characterized. In this study we propose a method to characterize the random error of the eddy covariance flux data, and analyse error distribution, standard deviation, cross- and autocorrelation of CO2 and H2O flux errors at four different European eddy covariance flux sites. Moreover, we examine how the treatment of those errors and additional systematic errors influence statistical estimates of parameters and their associated uncertainties with three models of increasing complexity – a hyperbolic light response curve, a light response curve coupled to water fluxes and the SVAT scheme BETHY. In agreement with previous studies we find that the error standard deviation scales with the flux magnitude. The previously found strongly leptokurtic error distribution is revealed to be largely due to a superposition of almost Gaussian distributions with standard deviations varying by flux magnitude. The crosscorrelations of CO2 and H2O fluxes were in all cases negligible (R2 below 0.2), while the autocorrelation is usually below 0.6 at a lag of 0.5 h and decays rapidly at larger time lags. This implies that in these cases the weighted least squares criterion yields maximum likelihood estimates. To study the influence of the observation errors on model parameter estimates we used synthetic datasets, based on observations of two different sites. We first fitted the respective models to observations and then added the random error estimates described above and the systematic error, respectively, to the model output. This strategy enables us to compare the estimated parameters with true parameters. We illustrate that the correct implementation of the random error standard deviation scaling with flux magnitude significantly reduces the parameter uncertainty and often yields parameter retrievals that are closer to the true value, than by using ordinary least squares. The systematic error leads to systematically biased parameter estimates, but its impact varies by parameter. The parameter uncertainty slightly increases, but the true parameter is not within the uncertainty range of the estimate. This means that the uncertainty is underestimated with current approaches that neglect selective systematic errors in flux data. Hence, we conclude that potential systematic errors in flux data need to be addressed more thoroughly in data assimilation approaches since otherwise uncertainties will be vastly underestimated.

Abstract:
Stochastic Quantization (SQ) is a method for the approximation of a continuous probability distribution with a discrete one. The proposal made in this paper is to apply this technique to reduce the number of numerical simulations for systems with uncertain inputs, when estimates of the output distribution are needed. This question is relevant in volcanology, where realistic simulations are very expensive and uncertainty is always present. We show the results of a benchmark test based on a one-dimensional steady model of magma flow in a volcanic conduit.

Abstract:
In the period from January to June 2000 Mt. Etna exhibited an exceptional explosive activity characterized by a succession of 64 Strombolian and fire-fountaining episodes from the summit South-East Crater. Textural analysis of the eruptive products reveals that the magma associated with the Strombolian phases had a much larger crystal content (>55 vol%) with respect to the magma discharged during the fire-fountain phases (~35 vol%). Rheological modelling shows that the crystal-rich magma falls in a region beyond a critical crystal content where small addition of solid particles causes an exponential increase of the effective magma viscosity. When implemented into the modeling of steady magma ascent dynamics (as assumed for the fire-fountain activity), a large crystal content as the one found for products of Strombolian eruption phases results in a one order of magnitude decrease of mass flow-rate, and in the onset of conditions where small heterogeneities in the solid fraction carried by the magma translate into highly unsteady eruption dynamics. We argue that crystallization on top of the magmatic column during the intermediate phases when magma was not discharged favoured conditions corresponding to Strombolian activity, with fire-fountain activity resuming after removal of the highly crystalline top. The numerical simulations also provide a consistent interpretation of the association between fire-fountain activity and emergence of lava flows from the crater flanks.

Abstract:
Stochastic Quantization (SQ) is a method for the approximation of a continuous probability distribution with a discrete one. The proposal made in this paper is to apply this technique to reduce the number of numerical simulations for systems with uncertain inputs, when estimates of the output distribution are needed. This question is relevant in volcanology, where realistic simulations are very expensive and uncertainty is always present. We show the results of a benchmark test based on a one-dimensional steady model of magma flow in a volcanic conduit.

Abstract:
In the period from January to June 2000 Mt. Etna exhibited an exceptional explosive activity characterised by a succession of 64 Strombolian and fire-fountaining episodes from the summit South-East crater. Textural analysis of the eruptive products reveals that the magma associated with the Strombolian phases had a much larger crystal content >55 vol% with respect to the magma discharged during the fire-fountain phases (~35 vol%). Rheological modelling shows that the crystal-rich magma falls in a region beyond a critical crystal content where the small addition of solid particles causes an exponential increase of the effective magma viscosity. When implemented into the modelling of steady magma ascent dynamics, the large crystal content of the Strombolian eruption phases results in a one order of magnitude decrease of mass flow-rate, and in the onset of conditions where small heterogeneities in the solid fraction carried by the magma translate into highly unsteady eruption dynamics. Therefore, we argue that crystallization on top of the magmatic column during the intermediate phases when magma was not discharged caused the conditions to shift from fire-fountain to Strombolian activity. The numerical simulations also provide a consistent interpretation of the association between fire-fountain activity and emergence of lava flows from the crater flanks.

Abstract:
Eddy covariance data are increasingly used to estimate parameters of ecosystem models and for proper maximum likelihood parameter estimates the error structure in the observed data has to be fully characterized. In this study we propose a method to characterize the random error of the eddy covariance flux data, and analyse error distribution, standard deviation, cross- and autocorrelation of CO2 and H2O flux errors at four different European eddy covariance flux sites. Moreover, we examine how the treatment of those errors and additional systematic errors influence statistical estimates of parameters and their associated uncertainties with three models of increasing complexity – a hyperbolic light response curve, a light response curve coupled to water fluxes and the SVAT scheme BETHY. In agreement with previous studies we find that the error standard deviation scales with the flux magnitude. The previously found strongly leptokurtic error distribution is revealed to be largely due to a superposition of almost Gaussian distributions with standard deviations varying by flux magnitude. The crosscorrelations of CO2 and H2O fluxes were in all cases negligible (R2 below 0.2), while the autocorrelation is usually below 0.6 at a lag of 0.5 hours and decays rapidly at larger time lags. This implies that in these cases the weighted least squares criterion yields maximum likelihood estimates. To study the influence of the observation errors on model parameter estimates we used synthetic datasets, based on observations of two different sites. We first fitted the respective models to observations and then added the random error estimates described above and the systematic error, respectively, to the model output. This strategy enables us to compare the estimated parameters with true parameters. We show that the correct implementation of the random error standard deviation scaling with flux magnitude significantly reduces the parameter uncertainty and often yields parameter retrievals that are closer to the true value, than by using ordinary least squares. The systematic error leads to systematically biased parameter estimates, but its impact varies by parameter. The parameter uncertainty slightly increases, but the true parameter is not within the uncertainty range of the estimate. This means that the uncertainty is underestimated with current approaches that neglect selective systematic errors in flux data. Hence, we conclude that potential systematic errors in flux data need to be addressed more thoroughly in data assimilation approaches since otherwise uncertain

Abstract:
Steppe ecosystems represent an interesting case in which the assessment of carbon balance may be performed through a cross validation of the eddy covariance measurements against ecological inventory estimates of carbon exchanges (Ehman, 2002; Curtis, 2002). Indeed, the widespread presence of ideal conditions for the applicability of the eddy covariance technique, as vast and homogeneous grass vegetation cover over flat terrains (Baldocchi, 2003), make steppes a suitable ground to ensure a constrain to flux estimates with independent methodological approaches. We report about the analysis of the carbon cycle of a true steppe ecosystem in southern Siberia during the growing season of 2004 in the framework of the TCOS-Siberia project activities performed by continuous monitoring of CO2 fluxes at ecosystem scale by the eddy covariance method, fortnightly samplings of phytomass, and ingrowth cores extractions for NPP assessment, and weekly measurements of heterotrophic component of soil CO2 effluxes obtained by an experiment of root exclusion. The carbon balance of the monitored natural steppe was, according to micrometeorological measurements, a sink of carbon of 151.7± 30.1 gC m 2, cumulated during the growing season from May to September. This result was in agreement with the independent estimate through ecological inventory which yielded a sink of 150.1 gC m 2 although this method was characterized by a large uncertainty (±130%) considering the 95% confidence interval of the estimate. Uncertainties in belowground process estimates account for a large part of the error. Thus, in particular efforts to better quantify the dynamics of root biomass (growth and turnover) have to be undertaken in order to reduce the uncertainties in the assessment of NPP. This assessment should be preferably based on the application of multiple methods, each one characterized by its own merits and flaws.