TY - GEN A1 - Bendix, Jörg A1 - Fries, Andreas A1 - Zárate, Jorge A1 - Trachte, Katja A1 - Rollenbeck, Rütger A1 - Pucha-Cofrep, Franz A1 - Paladines, Renzo A1 - Palacios, Ivan A1 - Orellana-Alvear, Johanna A1 - Oñate-Valdivieso, Fernando A1 - Naranjo, Carlos A1 - Mendoza, Leonardo A1 - Mejia, Diego A1 - Guallpa, Mario A1 - Gordillo, Francisco A1 - Gonzalez-Jaramillo, Victor A1 - Dobbermann, Maik A1 - Célleri, Rolando A1 - Carrillo, Carlos A1 - Araque, Augusto A1 - Achilles, Sebastian T1 - RadarNet-Sur First Weather Radar Network in Tropical High Mountains T2 - Bulletin of the American Meteorological Society N2 - 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 Niño 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 Niño-related heavy precipitation in a transect from the coast to the highlands in a spatial resolution of 500 m. Y1 - 2017 U6 - https://doi.org/10.1175/BAMS-D-15-00178.1 VL - 98 IS - 6 SP - 1235 EP - 1254 ER - TY - GEN A1 - Limberger, Oliver A1 - Homeier, Jürgen A1 - Farwig, Nina A1 - Pucha-Cofrep, Franz A1 - Fries, Andreas A1 - Leuschner, Christoph A1 - Trachte, Katja A1 - Bendix, Jörg T1 - Classification of Tree Functional Types in a Megadiverse Tropical Mountain Forest from Leaf Optical Metrics and Functional Traits for Two Related Ecosystem Functions T2 - Forests N2 - Few plant functional types (PFTs) with fixed average traits are used in land surface models (LSMs) to consider feedback between vegetation and the changing atmosphere. It is uncertain if highly diverse vegetation requires more local PFTs. Here, we analyzed how 52 tree species of a megadiverse mountain rain forest separate into local tree functional types (TFTs) for two functions: biomass production and solar radiation partitioning. We derived optical trait indicators (OTIs) by relating leaf optical metrics and functional traits through factor analysis. We distinguished four OTIs explaining 38%, 21%, 15%, and 12% of the variance, of which two were considered important for biomass production and four for solar radiation partitioning. The clustering of species-specific OTI values resulted in seven and eight TFTs for the two functions, respectively. The first TFT ensemble (P-TFTs) represented a transition from low to high productive types. The P-TFT were separated with a fair average silhouette width of 0.41 and differed markedly in their main trait related to productivity, Specific Leaf Area (SLA), in a range between 43.6 to 128.2 (cm2/g). The second delineates low and high reflective types (E-TFTs), were subdivided by different levels of visible (VIS) and near-infrared (NIR) albedo. The E-TFTs were separated with an average silhouette width of 0.28 and primarily defined by their VIS/NIR albedo. The eight TFT revealed an especially pronounced range in NIR reflectance of 5.9% (VIS 2.8%), which is important for ecosystem radiation partitioning. Both TFT sets were grouped along elevation, modified by local edaphic gradients and species-specific traits. The VIS and NIR albedo were related to altitude and structural leaf traits (SLA), with NIR albedo showing more complex associations with biochemical traits and leaf water. The TFTs will support LSM simulations used to analyze the functioning of mountain rainforests under climate change. KW - ecosystem productivity KW - energy fluxes KW - leaf hyperspectra KW - functional traits KW - tree functional types KW - tropical forest Y1 - 2021 U6 - https://doi.org/10.3390/f12050649 SN - 1999-4907 VL - 12 IS - 5 ER -