Distribution of macrozooplankton at time series station MIGZOO-4

A large population of the colonial pelagic tunicate Pyrosoma atlanticum occurred in April 1991 in offshore waters of the Ligurian Sea (Northwestern Mediterranean). The high numbers of colonies caught allowed their vertical distribution and diel migration in the 0-965 m water column to be described as a function of their size. Daytime depths and amplitudes of the migration were correlated with colony size. The amplitude of the migration ranged from 90 m for 3-mm-length colonies to 760 m for 51-mm-length colonies, with a mean amplitude of 410 m for the whole population, all sizes pooled. The results of horizontal hauls at a given depth around sunrise and sunset showed a marked diurnal symmetry of the migratory cycle relative to noon, and that migration of the population was not cohesive. For example, the larger the colonies, the later after sunset they reached the upper layers during their upward migration.

Dissolved organic carbon measured on water bottle samples at time series station DYFAMED

Dissolved organic carbon (DOC) distribution and dynamics are investigated at the DYFAMED site (central Ligurian Sea, NW Mediterranean) in relation to hydrological and biological contexts, using a 4-year time-series dataset (1991-1994). The DYFAMED site is regarded as a one-dimensional station where simple hydrological mechanisms prevail and where the ecosystem is quite well understood. An average vertical profile of DOC concentration ([DOC]) indicates that maximal concentrations and variability are concentrated in the surface layers. For depths >800 m, the annual variations are on average similar to the analytical standard deviation (~2 ��M). The "composite" [DOC] distribution (average distribution over a typical year, integrating about 40 monthly profiles) for surface waters (0-200 m) is closely related to hydrological and phytoplanktonic forcings. It exhibits summer DOC accumulation in surface waters, due to spring-summer stratification and successive phytoplanktonic events such as spring and summer blooms, and winter DOC removal to deeper waters, due to intense vertical mixing. The analysis of vertical [DOC] gradient at 100-m depth as a function of the integrated DOC content in the 0-100-m layer makes it possible to objectively distinguish three specific periods: the winter vertical mixing period, the period of stratification and spring phytoplankton bloom, and the period of stratification re-inforcement and summer-fall phytoplankton bloom. We recalculate the vertical DOC fluxes to deep waters using a larger original dataset, after the first direct calculation (Deep-Sea Res. 40 (10) (1993) 1963, 1972) that was reproduced for other oceanic areas. The seasonal variations of the "composite" [DOC] distribution in surface waters are significantly correlated to the apparent oxygen utilization distribution, but the biogeochemical significance of such a correlation is still under examination. The global significance of our local findings is presented and the role of the oceanic DOC in the global carbon cycle is emphasized, especially with respect to several current issues, such as the oceanic "missing sink" and the equivalence between new production and exported production.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la din��mica de los procesos medio ambientales est�� considerado como un punto de gran inter��s en el campo medioambiental. La cobertura espacio temporal de los datos de teledetecci��n proporciona informaci��n continua con una alta frecuencia temporal, permitiendo el an��lisis de la evoluci��n de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetecci��n ha sido ampliamente probado, en la actualidad solo existe un n��mero reducido de metodolog��as que permiten su an��lisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetecci��n, basado en la combinaci��n del an��lisis estad��stico de series de tiempo y la fenometr��a. El objetivo principal es demostrar el uso de las series temporales de datos de teledetecci��n para analizar la din��mica de variables medio ambientales de una forma cuantitativa. Los objetivos espec��ficos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos emp��ricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos espec��ficos son: (1) evaluar y cartografiar estados fenol��gicos del cultivo del algod��n mediante an��lisis espectral y fenometr��a, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclim��ticas mediante modelos din��micos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos din��micos y (4) evaluar el funcionamiento de la vegetaci��n en base a la autocorrelaci��n temporal y la fenometr��a. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los ��ndices espectrales AS1 y AS2. Los par��metros fenol��gicos derivados del ajuste de funciones permiten la identificaci��n de distintos estados fenol��gicos del cultivo del algod��n. El an��lisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el ��ndice AS2 y de dos ciclos en el AS1 as�� como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterr��nea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la din��mica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelaci��n temporal para definir y caracterizar el funcionamiento de la vegetaci��n a nivel pixel. Finalmente el concepto ���Optical Functional Type��� ha sido definido, donde se propone que los pixeles deber��an ser considerados como unidades temporales y analizados en funci��n de su din��mica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the ���Optical Functional Types��� concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Detecting and characterising ramp events in wind power time series

In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain

A new algorithm to accelerate harmonic analysis of time series

The Lomb periodogram has been traditionally a tool that allows us to elucidate if a frequency turns out to be important for explaining the behaviour of a given time series. Many linear and nonlinear reiterative harmonic processes that are used for studying the spectral content of a time series take into account this periodogram in order to avoid including spurious frequencies in their models due to the leakage problem of energy from one frequency to others. However, the estimation of the periodogram requires long computation time that makes the harmonic analysis slower when we deal with certain time series. Here we propose an algorithm that accelerates the extraction of the most remarkable frequencies from the periodogram, avoiding its whole estimation of the harmonic process at each iteration. This algorithm allows the user to perform a specific analysis of a given scalar time series. As a result, we obtain a functional model made of (1) a trend component, (2) a linear combination of Fourier terms, and (3) the so-called mixed secular terms by reducing the computation time of the estimation of the periodogram.

A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies

This paper presents a metafrontier production function model for firms in different groups having different technologies. The metafrontier model enables the calculation of comparable technical efficiencies for firms operating under different technologies. The model also enables the technology gaps to be estimated for firms under different technologies relative to the potential technology available to the industry as a whole. The metafrontier model is applied in the analysis of panel data on garment firms in five different regions of Indonesia, assuming that the regional stochastic frontier production function models have technical inefficiency effects with the time-varying structure proposed by Battese and Coelli ( 1992).

Hidden state models for time series

Amongst all the objectives in the study of time series, uncovering the dynamic law of its generation is probably the most important. When the underlying dynamics are not available, time series modelling consists of developing a model which best explains a sequence of observations. In this thesis, we consider hidden space models for analysing and describing time series. We first provide an introduction to the principal concepts of hidden state models and draw an analogy between hidden Markov models and state space models. Central ideas such as hidden state inference or parameter estimation are reviewed in detail. A key part of multivariate time series analysis is identifying the delay between different variables. We present a novel approach for time delay estimating in a non-stationary environment. The technique makes use of hidden Markov models and we demonstrate its application for estimating a crucial parameter in the oil industry. We then focus on hybrid models that we call dynamical local models. These models combine and generalise hidden Markov models and state space models. Probabilistic inference is unfortunately computationally intractable and we show how to make use of variational techniques for approximating the posterior distribution over the hidden state variables. Experimental simulations on synthetic and real-world data demonstrate the application of dynamical local models for segmenting a time series into regimes and providing predictive distributions.