Figure 2 - uploaded by Rene Parra
Content may be subject to copyright.
Location of stations and their nomenclature. Cotopaxi volcano (left): 1 Mariscal (Mar), 2 Machachi 1 (Ma1), 3 Jambelí (Jam), 4 Machachi 2 (Ma2), 5 Obelisco (Obe), 6 Aloag (Alo), 7 Santa Ana (San), 8 Gualilagua (Gua), 9 Tiopullo (Tio), 10 Progreso (Pro), 11 Entrada Sur (Ent), 12 Instituto Geofísico (Ins), 13 Agualongo (Agu), 14 BNAS. Tungurahua volcano (right): 1 Choglontus (Cho), 2 Palictahua (Pal), 3 Pillate (Pil), 4 Runtun (Run)

Location of stations and their nomenclature. Cotopaxi volcano (left): 1 Mariscal (Mar), 2 Machachi 1 (Ma1), 3 Jambelí (Jam), 4 Machachi 2 (Ma2), 5 Obelisco (Obe), 6 Aloag (Alo), 7 Santa Ana (San), 8 Gualilagua (Gua), 9 Tiopullo (Tio), 10 Progreso (Pro), 11 Entrada Sur (Ent), 12 Instituto Geofísico (Ins), 13 Agualongo (Agu), 14 BNAS. Tungurahua volcano (right): 1 Choglontus (Cho), 2 Palictahua (Pal), 3 Pillate (Pil), 4 Runtun (Run)

Source publication
Preprint
Full-text available
Volcanic ash produces air pollution and other impacts. Regions potentially affected require information about the possible ash dispersion trajectories and affected zones by ash fallout. In the last 19 years, five volcanoes in Ecuador have produced moderate to large explosive eruptions. So, information about the volcanic ash dispersion in forecastin...

Context in source publication

Context 1
... above mean sea level when the pressure at sea level is 1013.2 mb (e.g., FL300 = 30 000 feet, ≈ 9.1 km). We compared the modeled ash fallout results with records from ash meters around these volcanoes (4 stations for Tungurahua, 14 stations for Cotopaxi), which are operated by the Instituto Geofísico de la Escuela Politécnica Nacional [4,11] (Fig. 2). 3 RESULTS Although with differences, for all the spatial resolutions, the direction of modeled ash clouds was consistent with the course of the detected clouds. As an example, Figs. 3 and 4 show the detected the corresponding computed ash clouds, for the eruptions at Tungurahua on 01-Feb-2014, and Cotopaxi on 14-Aug-2015 ...

Similar publications

Article
Full-text available
Vegetation coverage variation may influence watershed water balance and water resource availability. Yarlung Zangbo River, the longest river on the Tibetan Plateau, has high spatial heterogeneity in vegetation coverage and is the main freshwater resource of local residents and downstream countries. In this study, we proposed a model based on random...
Article
Full-text available
Background: The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether...
Article
Full-text available
Cognitive radio (CR) environment helps in solving the spectrum scarcity by predicting the available channels through the cooperative spectrum sensing. Spectrum sensing is considered to be one of the prime tasks in CR environment and many researchers have contributed for the same. In this paper, a cooperative spectrum sensing (CSS) environment for t...
Article
Full-text available
State incarceration rates have been a topic of much policy discussion. One notable issue missing from much of the literature concerns the various determinants that account for state differences in incarceration rates. Why are some states more or less likely to incarcerate their citizenry than other states? The article examines the 50 states in a cr...
Article
Full-text available
Predicting future climatic conditions at high spatial resolution is essential for many applications and impact studies in science. Here, we present monthly time series data on precipitation, minimum- and maximum temperature for four downscaled global circulation models. We used model output statistics in combination with mechanistic downscaling (th...