The annual occurrence of harmful algal blooms (HABs) of the dinoflagellate Gymnodinium catenatum in the northern Gulf of California (NGC) during winter and spring has negative ecological, economic, and social impacts on the local coastal population. G. catenatum produces paralytic shellfish toxins, and a robust monitoring program of the species is necessary to sustain mitigation actions against their detrimental effects. Here, we applied the maximum-likelihood classification (MLC) method to classify satellite images from MODIS and Sentinel-3 to evaluate their effectiveness to detect G. catenatum. Different classes associated with the presence of the species were developed from data of two HABs that occurred in 2015 and 2017. Two classes derived from Sentinel-3 data from the 2017 HAB allowed the detection of this species. These Sentinel-3 classes adequately represented the temporal and geographical distribution of G. catenatum in the region and the no-bloom condition during the summer. The concordance between the detection of the Sentinel-3 classes on the west coast of the NGC and the recorded presence of G. catenatum (75% of concordance) in the area indicates that the MLC method could be applied for early detection of the species in the NGC, using Sentinel-3 full resolution images.

The Gulf of Mexico is an ocean basin with high environmental and economic importance where, inevitably, natural and anthropic disasters will take place. Therefore it is relevant to assess and regionalize its main biological, environmental and socioeconomic characteristics, for both management and theoretical purposes. In order to characterize the variation of this system, we divided the Gulf of Mexico’s exclusive economic zone of Mexico into 2682, 256.2 km ² hexagonal cells grouped within four zones: oceanic, coastal, insular, and inland. In each cell, we assessed 32 biological, ecosystem, and socioeconomic variables. We made a principal component analysis (PCA) for each zone using the standardized variables to detect cell clusters. We found heterogeneity wtihin the four zones, each having significantly different regions. The coastal zone was the most complex because its regions combine environmental and socioeconomic attributes. Using network analysis with PCA results we identified groups of synergistic and antagonistic variables in each zone. In general, we observed that the synergistic variables are proportionally more connected than the antagonistic ones. However, in the oceanic zone, the connectivity of the antagonistic variables was slightly higher than in the other three zones. This study offers a new integrative view of a complex region with high biological and socioeconomic relevance in a global context. These findings can be useful both for applied and academic aims. The link between PCA and network analysis offers a novel approach for identifying the relative importance of regions and finding not obvious connections between variables. This approach can be used in any socioecological system, whether marine or terrestrial, large or small.

Las simulaciones numéricas de escenarios y pronósticos de trayectorias de derrames de hidrocarburos juegan un papel preponderante en la atención de estos incidentes. Uno de los principales retos es obtener estas simulaciones en corto tiempo. En este trabajo, se aborda la paralelización de simulaciones de derrames por medio del desarrollo de scripts en Bash y Python, lo cual permite aprovechar los múltiples nodos del cluster Ometéotl del Grupo Interacción Océano-Atmósfera del Instituto de Ciencias de la Atmósfera y Cambio Climático (ICAyCC) de la UNAM. Los resultados de la implementación de cómputo paralelo muestran una reducción del 77.8% en el tiempo de pared respecto a la versión secuencial, lo que permitió obtener 66 430 simulaciones de derrames en 36 horas. Por otro lado, las simulaciones de un solo pozo, considerando un derrame diario durante 26 años, tardaron 3 horas y 20 minutos en ejecutarse.

Accurate wind resource assessment in regions with complex meteorology, such as the Isthmus of Tehuantepec, requires modeling approaches capable of capturing non-linear transitions and bimodal distributions. This study develops a robust hybrid statistical framework that integrates a Gaussian Mixture Model (GMM) for regime classification with a stochastic blending approach using sigmoid-weighted linear regressions. A rigorous data-cleaning process, employing quantile-based filtering and data-specific sigmoid power curve fitting, was implemented to isolate genuine operational states and define the effective performance envelope of the turbine. The model ensures a physically sound and continuous transition between wind regimes, demonstrating high predictive capacity for energy yield estimation. Results show a predicted Capacity Factor (CF) of 0.47 compared to an observed value of 0.41, representing a minimal margin of only 6%. These findings validate the integration of regime-specific