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The 2015-2016 El Niño event ranks as one of the most severe on record in terms of the magnitude and extent of sea surface temperature (SST) anomalies generated in the tropical Pacific Ocean. Corresponding global impacts on the climate were expected to rival, or even surpass, those of the 1997-1998 severe El Niño event, which had SST anomalies that were similar in size. However, the 2015-2016 event failed to meet expectations for hydrologic change in many areas, including those expected to receive well above normal precipitation. To better understand how climate anomalies during an El Niño event impact soil moisture, we investigate changes in soil moisture in the humid tropics (between ±25ĝˆ ) during the three most recent super El Niño events of 1982-1983, 1997-1998 and 2015-2016, using data from the Global Land Data Assimilation System (GLDAS). First, we use in situ soil moisture observations obtained from 16 sites across five continents to validate and bias-correct estimates from GLDAS (r2Combining double low line0.54). Next, we apply a k-means cluster analysis to the soil moisture estimates during the El Niño mature phase, resulting in four groups of clustered data. The strongest and most consistent decreases in soil moisture occur in the Amazon basin and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. In addition, we compare changes in soil moisture to both precipitation and evapotranspiration, which showed a lack of agreement in the direction of change between these variables and soil moisture most prominently in the southern Amazon basin, the Sahel and mainland southeastern Asia. Our results can be used to improve estimates of spatiotemporal differences in El Niño impacts on soil moisture in tropical hydrology and ecosystem models at multiple scales..

High spatio‐temporal variability is a characteristic of extreme rainfall. In mountainous regions like the Tropical Andes, where intricate orography and mesoscale atmospheric dynamics greatly impact rainfall systems, this particularly holds for mountain areas like the Tropical Andes. Thus, the absence of operational rainfall monitoring networks with high spatio‐temporal resolution has imposed difficulties for a proper analysis of extreme rainfall events in the Ecuadorian Andes. Nowhere, we present our improved knowledge on rainfall extremes based on newly available rainfall radar data of this region. In our study we employ a clustering approach to identify types of extreme rainfall events and analyze their spatio‐temporal characteristics. Based on 3 years of data obtained from an X‐band scanning weather radar data, the study was conducted in the southern Ecuadorian Tropical Andes at 4450 m a.s.l. By applying a rainfall threshold, 67 extreme rainfall events were selected. The rainfall characteristics of each extreme rainfall event, such as the amount of rain, its duration, its hour, and month of occurrence were determined and used as input variables of a k‐means clustering analysis to group the events into different classes. The result revealed three main classes of extreme rainfall events. The first class is characterized by highest rain intensity and lowest duration. The second class is characterized by its month of occurrence, during the first 5 months of the year. The third class showed lowest rain intensity and highest duration mainly occurred at higher elevations. The typology of events advances our understanding of the spatio‐temporal characteristics of extreme rainfall in the Tropical Andes. [ABSTRACT FROM AUTHOR]

This article discusses the use of spatial and temporal visualization techniques to understand the production and transport of dissolved organic carbon (DOC) in a high-elevation Andean catchment. The authors introduce a new model called STARR-DOC, which simulates the dynamics of DOC alongside water movement. The study focuses on the Zhurucay catchment and provides visualizations of water movement and DOC production in different soil types. The findings suggest that the production of DOC is influenced by soil water content, with the valley bottom areas exhibiting faster response times compared to the hillslopes. The visualization helps to identify areas within the catchment that have higher or lower DOC production and transport under different meteorological conditions. [Extracted from the article]

Tropical mountains such as the páramos of the Andes, which serve as 'water towers' for local communities and downstream cities, are important areas for early detection of climate change. Here, fog and low‐intensity rainfall are very common and play a key role in ecohydrological processes. Although evapotranspiration (ET) represents an important part of the water cycle, how ET and fog processes interact and how they affect páramo vegetation and water resources availability are poorly understood. This study investigated the effects of foggy (fog only) and mixed (fog and rainfall) conditions on ET. To determine whether fog significantly reduces ET, we compared ET and meteorological data under these two conditions with those during dry days. We found that on foggy days, when fog was most prevalent in the early morning, ET declined on average by 4% and net radiation (Rn) by 9.2%. Under mixed conditions, daily ET declined by 42% and Rn by 33%. In the páramo, where mean annual precipitation and ET are 1210 and 635 mm, respectively, the estimated annual reduction in ET due to fog and rainfall combined is between 77 and 174 mm. We found that during fog and rainfall mixed conditions, solar radiation was reduced, consequently constraining the energy available for ET while sustaining high relative humidity, ultimately reducing water loss. Our findings, which suggest that the presence of fog and low‐intensity rainfall restricts water losses by evaporative demand, contribute to a better understanding of the ecohydrological importance of these water inputs in the Andes. [ABSTRACT FROM AUTHOR]