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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]

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]

Andean ecosystems provide important hydrological services for downstream communities. Due to this importance, several hydrological studies have been carried out in recent years, with emphasis on hydrological processes identification and land use change impacts. In several studies, but also for the operation of small-scale irrigation and drinking water projects, small streams have been equipped with compound, sharp-crested weirs for discharge estimation. To transform the water level (stage) into a discharge (water rate), weir equations use theoretical discharge coefficients, which do not necessarily apply under the actual field conditions, mainly site fluviomorphology and weir construction aspects, introducing uncertainty in their measurements. Therefore, this study analyzes the effect of using theoretical coefficients instead of adjusted coefficients in field. The study was conducted on 9 micro-catchments (0;2-7;53km²) located in the Zhurucay Ecohydrological Observatory in the paramo of southern Ecuador. To calibrate the coefficients, discharge curves were generated by mechanical and salt-dilution gauging methods. Results revealed that the discharge coefficients differed from their theoretical value by up to 15% for triangular (V-notch) weir section (DCvn) and by up to 41% for rectangular weir section (DCr). The DCvn affects 4 times more in low and medium discharges estimation than DCvn in high discharges. On the other hand, salt-dilution method is more precise for medium and high discharges, but at very low discharges, it overestimates discharge up to 10%. Overall, results suggest that it is essential to calibrate the discharge coefficients in the field to avoid errors in hydrological studies. [ABSTRACT FROM AUTHOR]

Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile. [ABSTRACT FROM AUTHOR]

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A successful EC experiment was conducted on a 3765 m a.s.l. tropical grassland. • The páramo is found to be a source of carbon in the study period. • NEE and GPP light-response models for this alpine-tundra site are presented. • ETa and Kc for páramo tussock grasslands are determined via H 2 O vapor detection. Abstract Atmospheric carbon (CO 2) exchange, evapotranspiration (ET) processes, and their interactions with climatic drivers across tropical alpine grasslands are poorly understood. This lack of understanding is particularly evident for the páramo, the highest vegetated frontier in the northern Andes, the main source of water for inter-Andean cities, and a large carbon storage area. Studies of CO 2 and ET fluxes via the standard Eddy Covariance (EC) technique have never been applied to this region, limiting the understanding of diurnal / nocturnal exchanges and budget estimations. In this paper, we report the first EC analysis conducted on the Andean páramo (3765 m a.s.l.); this analysis measured CO 2 , ET, and micrometeorological variables over two years (2016–2018) to understand their interactions with climatic / biophysical controls. The páramo was found to be a source of CO 2 and exhibited a net positive exchange (mean = +99 ± 30 gC m−2 per year). The light-responses of net CO 2 exchange and the primary productivity were correlated and model-parameterized. Evapotranspiration was 635 ± 9 mm per year (51% of the annual rainfall total), and we obtained crop coefficients for the dominant vegetation (Tussock grass) based on reference-ET models FAO56 and ASCE-ERWI (0.90 and 0.78, respectively). We also compared our results to those from other high-altitude (alpine) and high-latitude grasslands (tundra). Finally, we demonstrate that our measurement period is representative of the páramo's longer-term climate dynamics. Our investigation contributes to the body of knowledge on the land surface-atmosphere processes of the tropical Andes and supports decision-making about ecosystem services management and the preservation of this vulnerable biome. [ABSTRACT FROM AUTHOR]

Extreme peak runoff forecasting is still a challenge in hydrology. In fact, the use of traditional physically-based models is limited by the lack of sufficient data and the complexity of the inner hydrological processes. Here, we employ a Machine Learning technique, the Random Forest (RF) together with a combination of Feature Engineering (FE) strategies for adding physical knowledge to RF models and improving their forecasting performances. The FE strategies include precipitation-event classification according to hydrometeorological criteria and separation of flows into baseflow and directflow. We used ∼ 3.5 years of hourly precipitation information retrieved from two near-real-time satellite precipitation databases (PERSIANN-CCS and IMERG-ER), and runoff data at the outlet of a 3391-km2 basin located in the tropical Andes of Ecuador. The developed models obtained Nash-Sutcliffe efficiencies varying from 0.86 to 0.59 for lead times between 1 and 6 h. The best performances were obtained for peak runoffs triggered by short-extension precipitation events (<50 km2) where infiltration- or saturation-excess runoff responses are well learned by the RF models. Conversely, the forecasting difficulty is associated with extensive precipitation events. For such conditions, a deeper characterization of the biophysical characteristics of the basin is encouraged for capturing the dynamic of directflow across multiple runoff responses. All in all, the potential to employ near-real-time satellite precipitation and the use of FE strategies for improving RF forecasting provides hydrologists with new tools for real-time runoff forecasting in remote or complex regions. • Near-real-time satellite-precipitation products were tested for peak runoff forecasting. • The addition of feature engineering strategies to machine learning models improved their forecasting efficiencies. • The methods proposed in this study can be used for runoff forecasting in remote or complex areas. [ABSTRACT FROM AUTHOR]

• Site-specific parameterization of the Community Land Model for páramo catchments. • The CLM was evaluated with remote sensing, water balance and eddy covariance methods. • This land-surface model proven useful for the prediction of actual evapotranspiration. Interest in atmosphere-surface flux modeling over the mountainous regions of the globe has increased recently, with a major focus on the prediction of water, carbon and other functional indicators in natural and disturbed conditions. However, less research has been centered on exploring energy fluxes (net radiation; sensible, latent and soil heat) and actual evapotranspiration (ETa) over the Neotropical Andean biome of the páramo. The present study assesses the implementation and parameterization of a state-of-art Land-Surface Model (LSM) for simulation of these fluxes over two representative páramo catchments of southern Ecuador. We evaluated the outputs of the LSM Community Land Model (CLM ver. 4.0) with (i) ground-level flux observations from the first (and highest) Eddy Covariance (EC) tower of the Northern Andean páramos; (ii) spatial ETa estimates from the energy balance-based model METRIC (based on Landsat imagery); and (iii) derived ETa from the closure of the water balance (WB). CLM's energy predictions revealed a significant underestimation on net radiation, which impacts the sensible and soil heat fluxes (underestimation), and delivers a slight overestimation on latent heat flux. Modeled CLM ETa showed acceptable goodness-of-fit (Pearson R = 0.82) comparable to ETa from METRIC (R = 0.83). Contrarily, a poor performance of ETa WB was observed (R = 0.46). These findings provide solid evidence on the CLM's accuracy for the ETa modeling, and give insights in the selection of other ETa methods. The study contributes to a better understanding of ecosystem functioning in terms of water loss through evaporative processes, and might help in the development of future LSMs' implementations focused on climate / land use change scenarios for the páramo. [ABSTRACT FROM AUTHOR]