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As a consequence of the remote location of the Andean páramo, knowledge on their hydrologic functioning is limited; notwithstanding, these alpine tundra ecosystems act as water towers for a large fraction of the society. Given the harsh environmental conditions in this region, year-round monitoring is cumbersome, and it would be beneficial if the monitoring needed for the understanding of the rainfall-runoff response could be limited in time. To identify the hydrological response and the effect of temporal monitoring, a nested ( n = 7) hydrological monitoring network was set up in the Zhurucay catchment (7.53 km2), south Ecuador. The research questions were as follows: (1) Can event sampling provide similar information in comparison with continuous monitoring, and (2) if so, how many events are needed to achieve a similar degree of information? A subset of 34 rainfall-runoff events was compared with monthly values derived from a continuous monitoring scheme from December 2010 to November 2013. Land cover and physiographic characteristics were correlated with 11 hydrological indices. Results show that despite some distinct differences between event and continuous sampling, both data sets reveal similar information; more in particular, the monitoring of a single event in the rainy season provides the same information as continuous monitoring, while during the dry season, ten events ought to be monitored. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Quantitative precipitation estimation (QPE) from weather radar data is crucial forhydrological applications, which benefit of the high spatio-temporal resolution of X-bandradar imagery. Nonetheless, radar rainfall retrieval process involves a challenging correctionof reflectivity, which is particularly difficult for single polarized technology. In addition, highmountain topography as the Andean cordillera deeply increases the complexity of radar QPE.This study explores a Random Forest (RF), state-of-the-art data-driven model, for optimizingthe radar rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by meansof single Plan Position Indicator (PPI) scans. Several features were derived from rawreflectivity to account for the evolution of rainfall along the beam. Performance of the RFmodel was evaluated in comparison with the well-known step-wise approach. Forthis, the Marshall-Palmer and a site-specific Z-R relationship were used. The biasadjustment step was omitted from the step-wise correction chain due to the lack ofevenly distributed gauge networks in mountain regions. Results showed that thestep-wise approach slightly improved when using the site-specific instead of theMarshall-Palmer Z-R relationship. However, both models performed poorly for radarQPE (R2 < 0.1). Conversely, the RF model remarkably outperformed the othermodels in all different testing locations and rainfall events (R2 up to 0.85). This is thefirst time a QPE random forest model is applied on single polarized X-band radarimagery. The results are outstanding and provide a simplified model to overcome thecomplexity of deriving QPE from single polarized radars. Further work will focus onblending techniques to enhance the radar rainfall maps obtained using this RF model. [ABSTRACT FROM AUTHOR]

Efforts to correct precipitation measurements have been ongoing for decades, but are scarce for tropical highlands. Four tipping-bucket (TB) rain gauges with different resolution that are commonly used in the Andean mountain region were compared-one DAVIS-RC-II, one HOBO-RG3-M, and two TE525MM TB gauges (with and without an Alter-Type wind screen). The relative performance of these rain gauges, installed side-by-side in the Zhurucay Ecohydrological Observatory, south Ecuador, at 3780 m a.s.l., was assessed using the TB with the highest resolution (0.1 mm) as reference, i.e. the TE525MM. The effect of rain intensity and wind conditions on gauge performance was estimated as well. Using 2 years of data, results reveal that (i) the precipitation amount for the reference TB is on average 5.6 to 7.2% higher than the rain gauges having a resolution of 0.2 mm and 0.254 mm respectively; (ii) relative underestimation of precipitation from the gauges with coarser resolution is higher during low-intensity rainfall mounting to a maximum deviation of 11% was observed for rain intensities ≤1 mm h-1; (iii) precipitation intensities of 2 mm h-1 or less that occur 75% of the time cannot be determined accurately for timescales shorter than 30 minutes because of the gauges' resolution, e.g. the absolute bias is >10%; and (iv) wind has a similar effect on all sensors. This analysis contributes to increase the accuracy and homogeneity of precipitation measurements throughout the Andean highlands, by quantifying the key role of rain-gauge resolution. [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]

Abstract We demonstrated the great value of spatially distributed and temporally high-resolution hydro-chemical data to enhance knowledge about the intra-catchment variability of flow processes and the runoff composition of individual storms in a tropical alpine (Páramo) ecosystem. In this study, water sources (rainfall, spring water, and water from soil layers of Histosols and Andosols) and nested streams were sampled bi-weekly (2013–2014), including three storm high-resolution events (5–240 min). Water samples were analyzed for 14 tracers including electrical conductivity (EC) and rare earth trace elements and used as input to perform End-Member Mixing Analysis (EMMA). End-members identified for the outlet could explain the hydrological behavior of four out of the five tributaries, indicating similar hydro-geochemical processes and geomorphic features within the catchments. The runoff source contributions of the individual sub-catchments varied among (e.g. Andosols ~40% in tributaries and ~25% at the outlet) and within storm events (e.g. Histosols 15% higher in small peak discharge event), indicating a time-variable composition of streamflows. The latter was also reflected by the interaction of different sources and the chronology of flow paths in EMMA-space, evidencing a faster connectivity with hillslopes in the upper sub-catchments compared to the lower sub-catchments. We found counter-clockwise hysteresis patterns of storms in the lower catchments and clockwise hysteresis loops in the upper catchments. The latter bi-directionality can be related to lower slopes, wider riparian areas and the higher proportion of Histosols in the lower catchments compared to the upper sites. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

The article focuses on Radarnet-Sur first weather radar network in tropical high mountains. Weather radar networks deliver high-resolution spatiotemporal information, which could offer a viable solution to the problems. Despite their indispensable utility for weather forecasting and disaster prevention with regard to spatiotemporal rainfall assessments, radar networks are less developed in South America.

• Comparison of multiple-models (4 to 18 calibrated parameters) with stream flow and tracer (stable isotopes, DOC) data. • Identification of the simplest model to represent a high-elevation páramo catchment. • DOC, streamflow and stable isotope dynamics originated almost exclusively from soil water fluxes. • A shallow fractured rock was necessary to simulate streamflow and tracer transport. How simple or complex catchment models need to be is still unclear particularly for tracer-aided models that go beyond hydrograph fitting. Here, we take advantage of a sub-daily hydrometric and tracer data set from a tropical montane (páramo) experimental catchment to fill this knowledge gap. We evaluated six conceptual rainfall-runoff model structures with different complexity that represent the perceptual understanding of the catchment's hydrological functioning. The models solved conservative and reactive tracer mass balances to simulate streamflow, stable isotopes, and DOC concentrations and were assessed for performance and parsimony using three calibration targets (discharge only, discharge and stable isotopes, and discharge and DOC), resulting in 18 model configurations. Although all models satisfied the discharge calibration (KGE > 0.8), differences arose when considering tracer transport. The more complex models outperformed the simpler ones in terms of goodness-of-fit and parsimony, indicating that the catchment streamflow response consisted of quick near-surface flow and tracer contributions, more mixed flow through the two main soil types (Andosols and Histosols), as well as flow from the well-mixed shallow fractured rock (up to 20 m depth). The bedrock flow pathway contributed up to ∼25% of total flow during baseflow conditions. This study provides a benchmark experiment to identify hydrological and biogeochemical behavior in tropical montane catchments using relatively parsimonious tracer-aided hydrological models. [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]