@misc{BendixFriesZarateetal., author = {Bendix, J{\"o}rg and Fries, Andreas and Z{\´a}rate, Jorge and Trachte, Katja and Rollenbeck, R{\"u}tger and Pucha-Cofrep, Franz and Paladines, Renzo and Palacios, Ivan and Orellana-Alvear, Johanna and O{\~n}ate-Valdivieso, Fernando and Naranjo, Carlos and Mendoza, Leonardo and Mejia, Diego and Guallpa, Mario and Gordillo, Francisco and Gonzalez-Jaramillo, Victor and Dobbermann, Maik and C{\´e}lleri, Rolando and Carrillo, Carlos and Araque, Augusto and Achilles, Sebastian}, title = {RadarNet-Sur First Weather Radar Network in Tropical High Mountains}, series = {Bulletin of the American Meteorological Society}, volume = {98}, journal = {Bulletin of the American Meteorological Society}, number = {6}, doi = {10.1175/BAMS-D-15-00178.1}, pages = {1235 -- 1254}, abstract = {Weather radar networks are indispensable tools for forecasting and disaster prevention in industrialized countries. However, they are far less common in the countries of South America, which frequently suffer from an underdeveloped network of meteorological stations. To address this problem in southern Ecuador, this article presents a novel radar network using cost-effective, single-polarization, X-band technology: the RadarNet-Sur. The RadarNet-Sur network is based on three scanning X-band weather radar units that cover approximately 87,000 km2 of southern Ecuador. Several instruments, including five optical disdrometers and two vertically aligned K-band Doppler radar profilers, are used to properly (inter) calibrate the radars. Radar signal processing is a major issue in the high mountains of Ecuador because cost-effective radar technologies typically lack Doppler capabilities. Thus, special procedures were developed for clutter detection and beam blockage correction by integrating ground-based and satelliteborne measurements. To demonstrate practical applications, a map of areas frequently affected by intense rainfall is presented, based on a time series of one radar that has been in operation since 2002. Such information is of vital importance to, for example, infrastructure management because rain-driven landslides are a major issue for road maintenance and safety throughout Ecuador. The presented case study of exceptionally strong rain events during the recent El Ni{\~n}o in March 2015 highlights the system's practicality in weather forecasting related to disaster management. For the first time, RadarNet-Sur warrants a spatial-explicit observation of El Ni{\~n}o-related heavy precipitation in a transect from the coast to the highlands in a spatial resolution of 500 m.}, language = {en} } @misc{GrigusovaLimbergerMurkuteetal., author = {Grigusova, Paulina and Limberger, Oliver and Murkute, Charuta and Pucha-Cofrep, Franz and Gonzalez-Jaramillo, Victor Hugo and Fries, Andreas and Windhorst, David and Breuer, Lutz and Dantas de Paula, Mateus and Hickler, Thomas and Trachte, Katja and Bendix, J{\"o}rg}, title = {Radiation partitioning in a cloud-rich tropical mountain rain forest of the S-Ecuadorian Andes for use in plot-based land surface modelling}, series = {Dynamics of atmospheres and oceans : planetary fluid, climatic and biogeochemical systems}, volume = {110}, journal = {Dynamics of atmospheres and oceans : planetary fluid, climatic and biogeochemical systems}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0377-0265}, doi = {10.1016/j.dynatmoce.2025.101553}, pages = {1 -- 16}, abstract = {Understanding the partitioning of downward shortwave radiation into direct and diffuse components is essential for modeling ecosystem energy fluxes. Accurate partitioning functions are critical for land surface models (LSMs) coupled with climate models, yet these functions often depend on regional cloud and aerosol conditions. While data for developing semi-empirical partitioning functions are abundant in mid-latitudes, their performance in tropical regions, particularly in the high Andes, remains poorly understood due to scarce ground-based measurements. This study analyzed a unique dataset of shortwave radiation components from a tropical mountain rainforest (MRF) in southern Ecuador, developing and testing a locally adapted partitioning function using Random Forest Regression. The model achieved high accuracy in predicting the percentage of diffuse radiation (\%Dif; R²=0.95, RMSE = 5.33, MAE = 3.74) and absolute diffuse radiation (R²=0.99, RMSE = 5.30, MAE = 14). When applied to simulate upward shortwave radiation, the model outperformed commonly used partitioning functions achieving the lowest RMSE (8.62) and MAE (5.82) while matching the highest R² (0.97). These results underscore the importance of regionally adapted radiation partitioning functions for improving LSM performance, particularly in complex tropical environments. The adapted LSM will be further utilized for studies on heat fluxes and carbon sequestration.}, language = {en} } @misc{BendixLimburgerBreueretal., author = {Bendix, J{\"o}rg and Limburger, Oliver and Breuer, Lutz and Dantas de Paula, Mateus and Fries, Andreas and Gonzalez-Jaramillo, Victor Hugo and Grigusova, Paulina and Hickler, Thomas and Murkute, Charuta and Pucha-Cofrep, Franz and Trachte, Katja and Windhorst, David}, title = {Simulation of latent heat flux over a high altitude pasture in the tropical Andes with a coupled land surface framework}, series = {The science of the total environment}, volume = {981}, journal = {The science of the total environment}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1879-1026}, doi = {10.1016/j.scitotenv.2025.179510}, pages = {1 -- 21}, abstract = {Latent heat flux is a central element of land-atmosphere interactions under climate change. Knowledge is particularly poor in the biodiversity hotspot of the Andes, where heat flux measurements using eddy covariance stations are scarce and land surface models (LSMs) often oversimplify the complexity of the ecosystems. The main objective of this study is to perform latent heat flux simulations for the tropical South Eastern (SE) Ecuadorian Andes using a coupled LSM framework, and to test the performance with heat flux and soil moisture data collected from a tropical high-altitude pasture. Prior to testing, we applied multi-criteria model calibration of sensitive model parameters, focusing on improving simulated soil water conditions and radiation fluxes as a prerequisite for proper heat flux simulations. The most sensitive parameters to improve soil moisture and radiation flux simulations were soil porosity, saturated hydraulic conductivity, leaf area index, soil colour and NIR (Near Infrared) leaf optical properties. The best calibrated model run showed a very good performance for half-hourly latent heat flux simulations with an R² of 0.8 and an RMSE of 34.0 W m⁻², outperforming simulations with uncalibrated and uncoupled LSM simulations in comparable areas. The slight overall overestimation in the simulated latent heat flux can be related to (i) simulation uncertainties in the canopy heat budget, (ii) an imbalance in the observed flux data and (iii) slight overestimations in the simulated soil moisture. Although our study focuses on latent heat fluxes and their relation to simulated radiation fluxes and soil moisture, model outputs of sensible heat fluxes were also discussed. The systematic overestimation of sensible heat flux in the model seems to be mainly a result of overestimated canopy temperatures. The improved simulation for latent heat flux has a high translational potential to support land use strategies in the tropical Andes under climate change.}, language = {en} } @misc{LimbergerHomeierGonzalezJaramilloetal., author = {Limberger, Oliver and Homeier, J{\"u}rgen and Gonzalez-Jaramillo, Victor and Fries, Andreas and Murkute, Charuta and Trachte, Katja and Bendix, J{\"o}rg}, title = {Foliar trait retrieval models based on hyperspectral satellite imagery perform well in a biodiversity hotspot of the SE Ecuadorian Andes}, series = {International journal of remote sensing}, journal = {International journal of remote sensing}, publisher = {Taylor \& Francis}, address = {London}, issn = {1366-5901}, doi = {10.1080/01431161.2025.2511211}, pages = {1 -- 19}, abstract = {The determination of the spatial distribution of canopy traits is crucial for understanding the spatio-temporal dynamics of ecosystem functions, such as carbon sequestration, water and energy fluxes in tropical montane forests. Especially in remote areas such as the Andes of south-east Ecuador, remote sensing using satellites has been proven to provide valuable information on canopy traits. However, the performance of the multispectral models to date is limited. Here, we analyse the potential of DESIS (DLR Earth Sensing Imaging Spectrometer) hyperspectral surface reflectance data for the prediction of specific leaf area (SLA), foliar toughness, nitrogen (N) and phosphorus (P) content by calibrating a PLSR model for each foliar trait. Model validation showed a high explanation of variance (R2 : 0.78-0.92) and a low model error (mean-normalized RMSE: 8-13\%) for all traits, showing an improved performance compared to models based on multispectral imagery and ancillary data within the same forest type. Hyperspectral models predicting foliar traits from individual tree crowns showed a lower performance for most traits, probably related to the difference in spatial resolution compared to this study. Given the high species richness of the Andean biodiversity hotspot and the complex topography, the high quality of the DESIS models is remarkable, considering that the multispectral models from the region require topographic predictors in addition to reflectances to achieve a good performance. The spatial trait maps generated by our models based on the reflectances only show clear relationships with terrain elevation. Thus, the hyperspectral trait maps from satellite orbit are capable of supporting large-scale ecological analyses between features, diversity and ecosystem functions.}, language = {en} }