Harmful algal bloom (HAB) events in front of Pisco River, inside Paracas Bay and Lagunillas inlet on the southern coast of Peru was identified from a satellite index (IOPifa) generated with daily high-resolution satellite data of phytoplankton absorption (aphy,GIOP) and non-algal detrital material plus CDOM (adCDOM,GIOP) from the Generalized Inherent Optical Properties (GIOP) model of Modis-Aqua, Viirs-Snpp and Viirs-Jpss1 satellites were used. Phytoplankton density field data sampling from HAB's monitoring programs of IMARPE of 2018 and 2019 were used to validate and identify the extent and spatio-temporal variability of these events. The satellite index (IOPifa) identified for Modis-Aqua 9 active HABs, 8 events in final conditions and 6 events that do not represent HAB conditions, while for Viirs-Snpp found 14 active HABs, 7 events in decaying bloom conditions and 13 events that do not represent HABs; and for Viirs-Jpss1 the index identified 7 active events, 14 in final bloom conditions and 6 that do not represent HABs conditions. The one-factor anova model was applied (p-value = 0.32 > 0.05), indicating that there is no evidence of a difference in the population means of the indices for each sensor. Subsequently, the pairwise multiple comparisons analysis with a 95 % confidence level of Tukey's test confirmed that there are no significant differences in the satellite index value, the differences could be associated with the spectral characteristics of the cell density of the species community and the oceanographic and environmental conditions. The spatial overlap between the in situ harmful algal blooms areas and the calculated satellite index, shows the capacity of the IOP satellite data for the HABs detection. However, it was also evidenced that some HAB events with high phytoplankton cell density had low IOPifa values, while other events with lower cell density were easily identified by the satellite index. This would indicate the ability of the ocean inherent optical properties to differentiate the phytoplankton types that cause algal blooms.

A study of the “El Nino Costero 2017” event is presented through the analysis of satellite information on Sea Surface Temperature (SST), its anomalies (SSTA), and surface winds of the Peruvian sea from December 2016 to April 2017 as well as the extraordinary El Nino events of 1982-1983 and 1997-1998. The analysis of the variables showed that from December 2016 until early February 2017 the thermal conditions of the sea did not present conditions for the development of an El Nino event; however, in March 2017, SST reached historical values >28 °C at 10 nm off Chimbote, temperatures that did not occur during the El Nino events of 1982-83 and 1997-98. El Nino Costero was characterized by a rapid increase in SST in a short period of time (approximately one month), which was influenced by the high variability of surface winds. SSTA recorded values of +4 °C in the entire northern coastal region from Chimbote to Paita up to more than 100 nm and +5 °C off Trujillo in March, which demonstrates the scale of the event. These values were associated to the decrease in the intensity of the surface wind that presented values <4 m/s between 0° and 11°S from January to March 2017, which favored the approximation of Surface Equatorial Waters (SEW) masses towards the coast and the subsequent anomalous sea surface warming.

Se analizó el nivel hídrico (1914-2016) y las precipitaciones (1981-2016) en el Lago Titicaca para determinar patrones de variabilidad interanual, decenal y multidecenal. A las variables de nivel del lago y de precipitación, se les aplicó análisis estadísticos y la técnica de ondeletas (wavelets), encontrándose que la precipitación tiene una periodicidad anual y que el nivel del lago obedece, principalmente, a períodos decenales. El tiempo que demora (retardo) el lago para mostrar variación del nivel hídrico, por efecto de las precipitaciones, es de 2 a 3 meses. Así mismo, la data fue correlacionada con el Índice de Oscilación del Sur (IOS), la Oscilación Decenal del Pacífico (ODP) y el Índice Multivariado ENSO (EMI), determinándose que la evolución tiempo-frecuencia de los datos de nivel hídrico del lago y precipitación respecto del Índice Multivariado ENOS, muestran una asociación de fase alternada o de signo opuesto entre sí.

Landsat images were processed from 1985 to 2015 between the Rimac and Chillon rivers, in order to monitor and identify the suspended solids originated by the waste collectors and landfills located on the aforementioned rivers. An increase in temperature of up to 2 °C was observed in areas with a high concentration of waste through the visible and thermal band of the satellite. It was identified that after the construction of the submarine collector, differences of coloration and temperature appeared. In addition, by means of a graph of spectral signatures it was possible to verify that the residues caused an increase of reflectance between the ranges of 600-700 nanometers. A high correlation (R = 0.951) was found between the in situ and satellite temperatures, which indicates that by using the measurements of the satellite, the real values of the sea surface temperature can be accurately estimated.

We analyzed the temporal variability of the fishing fleet of Dosidicus gigas, located outside the exclusive economic zone of Peru (EEZ), with a spatial luminosity index. The nighttime satellite images were provided by the Operational Linescan System (DMSP-OLS) from 2004 to 2015. 2995 images were processed, selecting pixels in the range of 30-63 Digital Number (DN), to identify the presence of vessels in the image.The time series showed an extensive latitudinal distribution of the fishing fleet from 6 degrees S to 32 degrees S, with years of low (2005-2009) and high (2004, 2010-2015) presence, describing a recurrent seasonal pattern of latitudinal displacement measured from its center of gravity (CG). The CG reaches its southernmost position between February to April and its northernmost position between August to October. Some vessels were also detected within the Peruvian EEZ.The latitudinal inertia presented values of 0.3-1 indicating high fleet concentration between 12 degrees S to 17 degrees S and the longitudinal inertia presented values >2, showing the wide distribution of the resource. Luminous pixels showed high fishing occurrence (>18 times) on a single pixel, in front of Chimbote around 9 degrees 51'S-82 degrees 31'W from 2004 to 2011. From 2012 to 2015 areas of high fishing occurrence increased in front of Huarmey (10 degrees 36'S/82 degrees 41'W) and San Juan de Marcona (15 degrees 53'S/80 degrees 6'W).For both periods, high intensity pixels (DN >60) show extensive areas of fishing operation between 9 degrees S to 20 degrees S along 200 nautical miles from the coast, while values between 30 and 45 DN could be mostly associated with the search for fishing zones. Since 2012, pixels with DN >58 have increased, indicating a greater fishing activity likely related to a higher availability of the resource or a better knowledge of the fishing zones, associated with an increase of the fishing effort and a possible higher pressure on the resource. (C) 2017 Elsevier B.V. All rights reserved.

The Peruvian Coastal Upwelling System (PCUS) is one of the most productive fisheries in the world. Upwelling events are associated with changes in the magnitude and location of frontal structures. SST gradients from four different data sets, NCDC, REMSS, OSTIA, and MUR are compared in two test areas off the PCUS: Païta (5°S) and Pisco (14°S). In both areas gradients derived from the MUR data set show greater magnitudes, as well as larger seasonal cycles. Off Pisco, the magnitude of the seasonal cycle of 2.2 °C/100 km in MUR is larger than the one derived from the lower resolution data sets. All data sets at Pisco exhibit a seasonal cycle that peaks in late Austral summer and early fall. Hovmöller diagrams calculated at 5.5°S, 10.5°S, and 14.5°S show clearly defined offshore maxima in the cross-shore gradients for all the data sets. Upwelling scales determined by the distance to the first maxima vary depending on the data set used. At 5.5°S upwelling scales vary from 10 km for MUR to 50 km for NCDC. At 14.5°S the scales vary from 20 km for MUR to 40 km for OSTIA. All four data sets show similar large-scale structures associated with the Peruvian upwelling. However, MUR shows finer scale structures that are most likely due to submesoscale to mesoscale eddies. Sub-sampled MUR 1 km data at the 25 km, 9 km, and 4 km resolutions compare well in magnitude and phase with the lower resolution products. Agreement in gradient magnitude between the lower resolution data sets and the MUR sub-sampled at their respective resolutions implies that the pixel-to-pixel analysis noise in MUR is at a similar level as the other data sets.