Role of the plasma-electrode bias and the transverse magnetic field upon H- ion extraction in the negative ion source

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Seasonal water storage and delayed evapotranspiration across continents: patterns and drivers

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Information footprint of different ecohydrological data sources using multi-objective calibration of a physically-based model as hypothesis testing

Acknowledgments We particularly thank the many people who have been involved in establishing and continuing data collection at the Bruntland Burn, particularly C. Birkel, M. Blumstock, J.J. Dick, J. Geris, K. Piegat, C. Tunaley, and H. Wang.Non peer reviewedPublisher PD

What can we learn from multi-data calibration of a process-based ecohydrological model?

This work was funded by the European Research Council (project GA 335910 VeWa). M. Maneta acknowledges support from the U.S National Science Foundation (project GSS 1461576) and U.S National Science Foundation EPSCoR Cooperative Agreement #EPS1101342. All model runs were performed using the High Performance Computing (HPC) cluster of the University of Aberdeen, and the IT Service is thanked for its help in installing PCRaster and other libraries necessary to run EcH2O and post-processing Python routines on the HPC cluster. Finally, the authors are grateful to the many people who have been involved in establishing and continuing data collection at the Bruntland Burn, particularly Christian Birkel, Maria Blumstock, Jon Dick, Josie Geris, Konrad Piegat, Claire Tunaley, and Hailong Wang.Peer reviewedPostprintPostprin

EcH2O-iso 1.0 : water isotopes and age tracking in a process-based, distributed ecohydrological model

This work was funded by the European Research Council (project GA 335910 VeWa). Marco P. Maneta acknowledges support from the US National Science Foundation (project GSS 1461576) and US National Science Foundation EPSCoR cooperative agreement no. EPS-1101342. We thank the topical editor (Min-Hui Lo), Jr-Chuan Huang, two anonymous reviewers for their suggestions which significantly improved the paper, and Aaron A. Smith for fruitful discussions regarding the model development. We also acknowledge the support of the Maxwell computer cluster funded by the University of Aberdeen. Finally, we are grateful to the many people who have been involved in establishing and continuing data collection at the Bruntland Burn, particularly Christian Birkel, Maria Blumstock, Jon Dick, Josie Geris, Konrad Piegat, Bernhard Scheliga, Matthias Sprenger, Claire Tunaley, and Hailong Wang.Peer reviewedPublisher PD

Long-lasting floods buffer the thermal regime of the Pampas

This work was funded by grants from the National Research Council of Argentina (CONICET), the International Research Development Centre [IDRC-Canada, Project 106601-001], ANPCyT [PRH 27 [PICT 2013-2973; PICT 2014-2790], and the Inter-American Institute for Global Change Research [IAI, CRN II 2031], which is supported by the US National Science Foundation[Grant number 448 GEO-0452325]. We thank Dr. Horacio Zagarese from INTECH for the lagoon temperature dataset provided. We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.Peer reviewedPostprin

Energy and CO2 fluxes in temperate deciduous broadleaf forests: using local add-covariance data for a global optimization of ORCHIDEE model

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What does it take to flood the Pampas?: Lessons from a decade of strong hydrological fluctuations

While most landscapes respond to extreme rainfalls with increased surface water outflows, very flat and poorly drained ones have little capacity to do this and their most common responses include (i) increased water storage leading to rising water tables and floods, (ii) increased evaporative water losses, and, after reaching high levels of storage, (iii) increased liquid water outflows. The relative importance of these pathways was explored in the extensive plains of the Argentine Pampas, where two significant flood episodes (denoted FE1 and FE2) occurred in 2000?2003 and 2012?2013. In two of the most flood-prone areas (Western and Lower Pampa, 60,000 km2 each), surface water cover reached 31 and 19% during FE1 in each subregion, while FE2 covered up to 22 and 10%, respectively. From the spatiotemporal heterogeneity of the flood events, we distinguished slow floods lasting several years when the water table is brought to the surface following sustained precipitation excesses in groundwater-connected systems (Western Pampa), and ?fast? floods triggered by surface water accumulation over the course of weeks to months, typical of poor surface-groundwater connectivity (Lower Pampa) or when exceptionally strong rainfalls overwhelm infiltration capacity. Because of these different hydrological responses, precipitation and evapotranspiration were strongly linked in the Lower Pampa only, while the connection between water fluxes and storage was limited to the Western Pampa. In both regions, evapotranspirative losses were strongly linked to flooded conditions as a regulatory feedback, while liquid water outflows remained negligible.Fil: Kuppel, Sylvain. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis. Instituto de Matemática Aplicada de San Luis; Argentina. Universidad Nacional de San Luis; ArgentinaFil: Houspanossian, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis. Instituto de Matemática Aplicada de San Luis; Argentina. Universidad Nacional de San Luis; ArgentinaFil: Nosetto, Marcelo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis. Instituto de Matemática Aplicada de San Luis; Argentina. Universidad Nacional de San Luis; ArgentinaFil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Luis. Instituto de Matemática Aplicada de San Luis; Argentina. Universidad Nacional de San Luis; Argentin

Floods in the Pampas : Insights from over a decade of satellite observations

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Land surface model parameter optimisation using in situ flux data : Comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)

This work used eddy covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (U.S. Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program; DE-FG02-04ER63917 and DE-FG02-04ER63911), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia and USCCC. We acknowledge the financial support to the eddy covariance data harmonisation provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, Max Planck Institute for Biogeochemistry, National Science Foundation, University of Tuscia, Universiteì Laval, Environment Canada and US Department of Energy and the database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California – Berkeley and the University of Virginia.Peer reviewedPublisher PD