Worldwide and likewise in Ecuador, the 1982–1983 and 1997–1998 El Niño Southern Oscillation (ENSO) events had devastating effects in the economic and human dimension. Thus, scientists and decision markers look for a deeper knowledge about ENSO and its phases El Niño (EN)/La Niña (LN). Recent research highlights the changing nature of ENSO under opposite conditions of the Pacific Decadal Oscillation (PDO), making the assessment of the ENSO–PDO relation in Ecuador urgent. This study explores the time‐frequency characteristics of rainfall in the coast of Ecuador from January to April (PC‐JA) and evaluates the influence of PDO in the relation of ENSO with PC‐JA. For this, wavelet analysis was used to asses this nonstationary problem, Five long‐term (1964–2014) ground stations were used. The main results indicate that during the warm PDO period 1980–2000, the high wavelet coherence (ca. 0.9) implies a strong coupling between ENSO and PC‐JA. For cold PDO periods, prior to 1980 and after 2005, such coupling weakens with coherence about 0.5. This might indicate that PDO influence the relation between ENSO and rainfall in the coast of Ecuador. This coupling, during warm PDO, enhances high rainfall when in phase with EN, and drought conditions in LN events. The weak coupling of ENSO‐PC‐JA during cold PDO produces high rainfall amounts in Niño Neutral conditions and droughts during Neutral and LN. To account for ENSO flavours variability, the wavelet coherence between PC‐JA and the two ENSO uncorrelated indices E and C from Takahashi et al. was studied. Interestingly, we show that PDO warm phase influences the relation of Eastern Pacific related E index with PC‐JA from 2 to 8 years periods, and that the orthogonal Central Pacific related C index is not affected. These results raise questions about the validity of ENSO indices for contrasting PDO phases.

Monitoring precipitation in mountainous areas using traditional tipping-bucket rain gauges (TPB) has become challenging in sites with strong variations of air temperature and wind speed (Ws). The drop size distributions (DSD), amount, and precipitation-type of a Parsivel OTT2 disdrometer installed at 4730 m above sea level (close to the 0 °C isotherm) in the glacier foreland of the Antisana volcano in Ecuador are used to analyze the precipitation type. To correct the DSDs, we removed spurious particles and shifted fall velocities such that the mean value matches with the fall velocity–diameter relationship of rain, snow, graupel, and hail. Solid (SP) and liquid precipitation (LP) were identified through −1 and 3 °C thresholds and then grouped into low, medium, and high Ws categories by k-means approach. Changes in DSDs were tracked using concentration spectra and particle’s contribution by diameter and fall velocity. Thus, variations of concentration/dispersion and removed hydrometeors were linked with Ws changes. Corrected precipitation, assuming constant density (1 g cm−3), gives reliable results for LP with respect to measurements at TPB and overestimates SP measured in disdrometer. Therefore, corrected precipitation varying density models achieved fewer differences. These results are the first insight toward the understating of precipitation microphysics in a high-altitude site of the tropical Andes.

The precipitation phase (PP) affects the hydrologic cycle which in turn affects the climate system. A lower ratio of snow to rain due to climate change affects timing and duration of the stream flow. Thus, more knowledge about the PP occurrence and drivers is necessary and especially important in cities dependent on water coming from glaciers, such as Quito, the capital of Ecuador (2.5 million inhabitants), depending in part on the Antisana glacier. The logistic models (LM) of PP rely only on air temperature and relative humidity to predict PP. However, the processes related to PP are far more complex. The aims of this study were threefold: (i) to compare the performance of random forest (RF) and artificial neural networks (ANN) to derive PP in relation to LM; (ii) to identify the main drivers of PP occurrence using RF; and (iii) to develop LM using meteorological drivers derived from RF. The results show that RF and ANN outperformed LM in predicting PP in 8 out of 10 metrics. RF indicated that temperature, dew point temperature, and specific humidity are more important than wind or radiation for PP occurrence. With these predictors, parsimonious and efficient models were developed showing that data mining may help in understanding complex processes and complements expert knowledge.

In this two-phase study, it was shown that a mixture with equal parts of manure and resulting animal blood was the optimal combination for obtaining biogas and biol. A quadratic growth trend in variable gas pressure over time—as well as its behavior—was confirmed for pHs around the neutral value for the substrate used in both the pilot phase and in the microplant, which had a mechanical implementation and mechatronic system for the control of variables that intervene in the anaerobic digestion process; this allowed for the confirmation of the results found in the first phase of research—without concerns that a lack of control over the process variables would cause—in such a way that it constituted a path for the industrialization of the waste treatment process in slaughterhouses that could be optimized by the use of the optimal combination that produces the greatest cm3 amount of gas. Anaerobic digestion in biodigesters is carried out at different constant temperature values within the mesophilic range, with a hydraulic retention time of 25 days. A direct relationship was found between temperature, biogas production and pH behavior in the buffer(s). The pH remained close to neutral and the gas pressure increased from 15 to 20. The findings indicated that the value for the C/N ratio of the blood of four was compensated for by its buffer system, composed of bicarbonate, hemoglobin, proteins and phosphates.