Miguel Ángel Sáez García

Hacia un cártel perfecto. Los acuerdos colusivos en el sector siderúrgico español (1871-1907)

El objetivo de este trabajo es analizar el proceso de cartelizacion de la siderurgia moderna espanola desde los pimeros acuerdos de precios de 1871 hasta la creacion de la Central Siderurgica en 1907. A partir de documentacion primaria obtenida de archivos de empresas siderurgicas, el articulo estudia el funcionamiento interno de los carteles y las dificultdes a las que se enfrentaban para detectar a los transgresores de los acuerdos y para evitar la competencia de las fabricas libres. Por otra parte, se pretende contribuir al debate sobre la responsabilidad de la oferta en la estrcutura del mercado siderurgico espanol y su influencia en el proceso de industrializacion espanol.The aim of this work is to analyse the process of cartelisation in the modern Spanish iron and steel industry from the first pricing agreements in 1871 to the creation of the Central Siderurgica in 1907. Using documentation from archives of iron and steel companies, the article studies the internal operation of the cartels and the difficulties faced in detecting cheating and in avoiding competition from free riders. The work also aims to contribute to the debate on the responsibility of supply within the structure of the Spanish iron and steel market and its influence on the Spanish industrialization.

Detection and mapping of burnt areas from time series of MODIS-derived NDVI data in a Mediterranean region

Moderate resolution remote sensing data, as provided by MODIS, can be used to detect and map active or past wildfires from daily records of suitable combinations of reflectance bands. The objective of the present work was to develop and test simple algorithms and variations for automatic or semiautomatic detection of burnt areas from time series data of MODIS biweekly vegetation indices for a Mediterranean region. MODIS-derived NDVI 250m time series data for the Valencia region, East Spain, were subjected to a two-step process for the detection of candidate burnt areas, and the results compared with available fire event records from the Valencia Regional Government. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Combining drops between two consecutive points and 1-year average drops, we used discrepancies or jumps between the pre and post models to identify seed pixels, and then delimitated fire scars for each potential wildfire using an extension algorithm from the seed pixels. The resulting maps of the detected burnt areas showed a very good agreement with the perimeters registered in the database of fire records used as reference. Overall accuracies and indices of agreement were very high, and omission and commission errors were similar or lower than in previous studies that used automatic or semiautomatic fire scar detection based on remote sensing. This supports the effectiveness of the method for detecting and mapping burnt areas in the Mediterranean region.

Likely effects of climate change on groundwater availability in a Mediterranean region of Southeastern Spain

Groundwater resources are typically the main fresh water source in arid and semi-arid regions. Natural recharge of aquifers is mainly based on precipitation; however, only heavy precipitation events (HPEs) are expected to produce appreciable aquifer recharge in these environments. In this work, we used daily precipitation and monthly water level time series from different locations over a Mediterranean region of Southeastern Spain to identify the critical threshold value to define HPEs that lead to appreciable aquifer recharge in this region. Wavelet and trend analyses were used to study the changes in the temporal distribution of the chosen HPEs (=20?mm?day-1) over the observed period 1953–2012 and its projected evolution by using 18 downscaled climate projections over the projected period 2040–2099. The used precipitation time series were grouped in 10 clusters according to similarities between them assessed by using Pearson correlations. Results showed that the critical HPE threshold for the study area is 20?mm?day-1. Wavelet analysis showed that observed significant seasonal and annual peaks in global wavelet spectrum in the first sub-period (1953–1982) are no longer significant in the second sub-period (1983–2012) in the major part of the ten clusters. This change is because of the reduction of the mean HPEs number, which showed a negative trend over the observed period in nine clusters and was significant in five of them. However, the mean size of HPEs showed a positive trend in six clusters. A similar tendency of change is expected over the projected period. The expected reduction of the mean HPEs number is two times higher under the high climate scenario (RCP8.5) than under the moderate scenario (RCP4.5). The mean size of these events is expected to increase under the two scenarios. The groundwater availability will be affected by the reduction of HPE number which will increase the length of no aquifer recharge periods (NARP) accentuating the groundwater drought in the region. Copyright

¿Proyecto faraónico o chivo expiatorio?: la IV Planta Siderúrgica Integral de Sagunto (1966-1977)

The aim of this article is to analyse the reasons for the creation of the Fourth Integrated Steel Plant in Sagunto. Using primary sources, we demonstrate that it was a rational decision that imitated the experience of other developed countries. This decision was based on a market study that failed in its steel consumption forecast. But such error was not due to the imperial dreams of Franco’s regime, but to the unforeseeable character of the 1970’s economic crisis and its effects on the steel industry.

Determination of phenological parameters from MODIS derived NDVI data using hidden Markov models

The phenological characteristics of the vegetation are key elements for understanding vegetation responses in different climate change scenarios, as well as indicators of ongoing processes of increasing aridity. Determination of phenological parameters for different types of vegetation in large areas help evaluate current and future impacts of climate change in ecosystems, specially in those more vulnerable. Moderate resolution remote sensing data, as provided by MODIS, has already been used to extract phenological characteristics from time series data of vegetation indices, most usually by data smoothing and fitting of polynomial models. In this work, we use hidden Markov models (HMMs) to define phenological parameters from MODIS derived NDVI time series data in a semiarid Mediterranean region. Different types of HMMs are applied in selected areas with well-defined vegetation communities, and their potentials for automatic phenological analysis at large scale are discussed.

Analysis of MODIS NDVI time series using quasi-periodic components

NDVI time series data exhibit cyclic behaviors derived from the phenological characteristics of the vegetation. Different forms of Fourier analysis have been effectively used to analyze remote sensed NDVI data, allowing the simultaneous fitting of secular and cyclic components. However, variations in frequencies and/or phases of the cyclic components may exist, that are not considered in typical spectral analysis. In this work, MODIS derived NDVI biweekly time series data are analyzed considering secular and quasi-periodic components, fitted to the data using a smoothing algorithm based on a spectral time-frequency analysis. The algorithm works well with equispaced data, and allows the analysis of temporal variations in cyclic frequencies and phases. Examples of applications in different types of vegetation and conditions in Southeast Spain are shown.

Detection and analysis of burnt areas from MODIS derived NDVI time series data

Burnt areas as a result of wildfires can be readily detected from high resolution aerial photographs or satellite imagery of the zone that includes the wildfire. Moderate resolution remote sensing data, as provided by MODIS, can also be used to detect active or past wildfires, most usually from daily records of a suitable combination of reflectance bands. The objective of the present work was to test some simple algorithms and variations for automatic blind detection of burnt areas from MODIS biweekly vegetation indices time series data. MODIS derived NDVI 250m time series data for the Valencia region, Southeast Spain, were subjected to a two-steps process for the detection of candidate burnt areas, and the results compared with the record of wildfires with affected area greater than 100 hectares. For each pixel and date in the data series, a model was fitted to both the previous and posterior time series data. Discrepancies or jumps between the pre- and post- models exceeding a certain threshold were used as seeds to define cluster of pixels, the candidate burnt areas, with similarities between pixels either from their extreme discrepancy dates or from their parameters in the fitted models. Results using a simple combination of a constant fitted model and pixel similarity from jump dates were in good agreement with the perimeters of the actual burnt areas. A computationally efficient implementation of the method was developed using a digital filter type approach.