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3. Monitoring of carbon-water fluxes at Eurasian meteorological stations using random forest and remote sensing

Author

Xie, Mingjuan, Ma, Xiaofei, Wang, Yuangang, Li, Chaofan, Shi, Haiyang, Yuan, Xiuliang, Hellwich, Olaf, Chen, Chunbo, Zhang, Wenqiang, Zhang, Chen, Ling, Qing, Gao, Ruixiang, Zhang, Yu, Ochege, Friday Uchenna, Frankl, Amaury, De Maeyer, Philippe, Buchmann, Nina, Feigenwinter, Iris, Olesen, Jørgen E., Juszczak, Radoslaw, Jacotot, Adrien, Korrensalo, Aino, Pitacco, Andrea, Varlagin, Andrej, Shekhar, Ankit, Lohila, Annalea, Carrara, Arnaud, Brut, Aurore, Kruijt, Bart, Loubet, Benjamin, Heinesch, Bernard, Chojnicki, Bogdan, Helfter, Carole, Vincke, Caroline, Shao, Changliang, Bernhofer, Christian, Brümmer, Christian, Wille, Christian, Tuittila, Eeva-Stiina, Nemitz, Eiko, Meggio, Franco, Dong, Gang, Lanigan, Gary, Niedrist, Georg, Wohlfahrt, Georg, Zhou, Guoyi, Goded, Ignacio, Gruenwald, Thomas, Olejnik, Janusz, Jansen, Joachim, Neirynck, Johan, Tuovinen, Juha-Pekka, Zhang, Junhui, Klumpp, Katja, Pilegaard, Kim, Šigut, Ladislav, Klemedtsson, Leif, Tezza, Luca, Hörtnagl, Lukas, Urbaniak, Marek, Roland, Marilyn, Schmidt, Marius, Sutton, Mark A., Hehn, Markus, Saunders, Matthew, Mauder, Matthias, Aurela, Mika, Korkiakoski, Mika, Du, Mingyuan, Vendrame, Nadia, Kowalska, Natalia, Leahy, Paul G., Alekseychik, Pavel, Shi, Peili, Weslien, Per, Chen, Shiping, Fares, Silvano, Friborg, Thomas, Tallec, Tiphaine, Kato, Tomomichi, Sachs, Torsten, Maximov, Trofim, di Cella, Umberto Morra, Moderow, Uta, Li, Yingnian, He, Yongtao, Kosugi, Yoshiko, and Luo, Geping

Published
2023
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4. ReSurveyEurope: A database of resurveyed vegetation plots in Europe

Author

Knollová, Ilona, primary, Chytrý, Milan, additional, Bruelheide, Helge, additional, Dullinger, Stefan, additional, Jandt, Ute, additional, Bernhardt‐Römermann, Markus, additional, Biurrun, Idoia, additional, de Bello, Francesco, additional, Glaser, Michael, additional, Hennekens, Stephan, additional, Jansen, Florian, additional, Jiménez‐Alfaro, Borja, additional, Kadaš, Daniel, additional, Kaplan, Ekin, additional, Klinkovská, Klára, additional, Lenzner, Bernd, additional, Pauli, Harald, additional, Sperandii, Marta Gaia, additional, Verheyen, Kris, additional, Winkler, Manuela, additional, Abdaladze, Otar, additional, Aćić, Svetlana, additional, Acosta, Alicia T. R., additional, Alignier, Audrey, additional, Andrews, Christopher, additional, Arlettaz, Raphaël, additional, Attorre, Fabio, additional, Axmanová, Irena, additional, Babbi, Manuel, additional, Baeten, Lander, additional, Baran, Jakub, additional, Barni, Elena, additional, Benito‐Alonso, José‐Luis, additional, Berg, Christian, additional, Bergamini, Ariel, additional, Berki, Imre, additional, Boch, Steffen, additional, Bock, Barbara, additional, Bode, Frank, additional, Bonari, Gianmaria, additional, Boublík, Karel, additional, Britton, Andrea J., additional, Brunet, Jörg, additional, Bruzzaniti, Vanessa, additional, Buholzer, Serge, additional, Burrascano, Sabina, additional, Campos, Juan A., additional, Carlsson, Bengt‐Göran, additional, Carranza, Maria Laura, additional, Černý, Tomáš, additional, Charmillot, Kévin, additional, Chiarucci, Alessandro, additional, Choler, Philippe, additional, Chytrý, Kryštof, additional, Corcket, Emmanuel, additional, Csecserits, Anikó, additional, Cutini, Maurizio, additional, Czarniecka‐Wiera, Marta, additional, Danihelka, Jiří, additional, de Francesco, Maria Carla, additional, De Frenne, Pieter, additional, Di Musciano, Michele, additional, De Sanctis, Michele, additional, Deák, Balázs, additional, Decocq, Guillaume, additional, Dembicz, Iwona, additional, Dengler, Jürgen, additional, Di Cecco, Valter, additional, Dick, Jan, additional, Diekmann, Martin, additional, Dierschke, Hartmut, additional, Dirnböck, Thomas, additional, Doerfler, Inken, additional, Doležal, Jiří, additional, Döring, Ute, additional, Durak, Tomasz, additional, Dwyer, Ciara, additional, Ejrnæs, Rasmus, additional, Ermakova, Inna, additional, Erschbamer, Brigitta, additional, Fanelli, Giuliano, additional, Fernández‐Calzado, María‐Rosa, additional, Fickert, Thomas, additional, Fischer, Andrea, additional, Fischer, Markus, additional, Foremnik, Kacper, additional, Frouz, Jan, additional, García‐González, Ricardo, additional, García‐Magro, Daniel, additional, García‐Mijangos, Itziar, additional, Gavilán, Rosario G., additional, Germ, Mateja, additional, Ghosn, Dany, additional, Gigauri, Khatuna, additional, Gizela, Jaroslav, additional, Golob, Aleksandra, additional, Golub, Valentin, additional, Gómez‐García, Daniel, additional, Gowing, David, additional, Grytnes, John‐Arvid, additional, Güler, Behlül, additional, Gutiérrez‐Girón, Alba, additional, Haase, Peter, additional, Haider, Sylvia, additional, Hájek, Michal, additional, Halassy, Melinda, additional, Harásek, Martin, additional, Härdtle, Werner, additional, Heinken, Thilo, additional, Hester, Alison, additional, Humbert, Jean‐Yves, additional, Ibáñez, Ricardo, additional, Illa, Estela, additional, Jaroszewicz, Bogdan, additional, Jensen, Kai, additional, Jentsch, Anke, additional, Jiroušek, Martin, additional, Kalníková, Veronika, additional, Kanka, Róbert, additional, Kapfer, Jutta, additional, Kazakis, George, additional, Kermavnar, Janez, additional, Kesting, Stefan, additional, Khanina, Larisa, additional, Kindermann, Elisabeth, additional, Kotrík, Marek, additional, Koutecký, Tomáš, additional, Kozub, Łukasz, additional, Kuhn, Gisbert, additional, Kutnar, Lado, additional, La Montagna, Dario, additional, Lamprecht, Andrea, additional, Lenoir, Jonathan, additional, Lepš, Jan, additional, Leuschner, Christoph, additional, Lorite, Juan, additional, Madsen, Bjarke, additional, Ugarte, Rosina Magaña, additional, Malicki, Marek, additional, Maliniemi, Tuija, additional, Máliš, František, additional, Maringer, Alexander, additional, Marrs, Robert, additional, Matesanz, Silvia, additional, Metze, Katrin, additional, Meyer, Stefan, additional, Millett, Jonathan, additional, Mitchell, Ruth J., additional, Moeslund, Jesper Erenskjold, additional, Moiseev, Pavel, additional, di Cella, Umberto Morra, additional, Mudrák, Ondřej, additional, Müller, Frank, additional, Müller, Norbert, additional, Naaf, Tobias, additional, Nagy, Laszlo, additional, Napoleone, Francesca, additional, Nascimbene, Juri, additional, Navrátilová, Jana, additional, Ninot, Josep M., additional, Niu, Yujie, additional, Normand, Signe, additional, Ogaya, Romá, additional, Onipchenko, Vladimir, additional, Orczewska, Anna, additional, Ortmann‐Ajkai, Adrienne, additional, Pakeman, Robin J., additional, Pardo, Iker, additional, Pätsch, Ricarda, additional, Peet, Robert K., additional, Penuelas, Josep, additional, Peppler‐Lisbach, Cord, additional, Pérez‐Hernández, Javier, additional, Pérez‐Haase, Aaron, additional, Petraglia, Alessandro, additional, Petřík, Petr, additional, Pielech, Remigiusz, additional, Piórkowski, Hubert, additional, Pladevall‐Izard, Eulàlia, additional, Poschlod, Peter, additional, Prach, Karel, additional, Praleskouskaya, Safiya, additional, Prokhorov, Vadim, additional, Provoost, Sam, additional, Pușcaș, Mihai, additional, Pustková, Štěpánka, additional, Randin, Christophe François, additional, Rašomavičius, Valerijus, additional, Reczyńska, Kamila, additional, Rédei, Tamás, additional, Řehounková, Klára, additional, Richner, Nina, additional, Risch, Anita C., additional, Rixen, Christian, additional, Rosbakh, Sergey, additional, Roscher, Christiane, additional, Rosenthal, Gert, additional, Rossi, Graziano, additional, Rötzer, Harald, additional, Roux, Camille, additional, Rumpf, Sabine B., additional, Ruprecht, Eszter, additional, Rūsiņa, Solvita, additional, Sanz‐Zubizarreta, Irati, additional, Schindler, Meret, additional, Schmidt, Wolfgang, additional, Schories, Dirk, additional, Schrautzer, Joachim, additional, Schubert, Hendrik, additional, Schuetz, Martin, additional, Schwabe, Angelika, additional, Schwaiger, Helena, additional, Schwartze, Peter, additional, Šebesta, Jan, additional, Seiler, Hallie, additional, Šilc, Urban, additional, Silva, Vasco, additional, Šmilauer, Petr, additional, Šmilauerová, Marie, additional, Sperle, Thomas, additional, Stachurska‐Swakoń, Alina, additional, Stanik, Nils, additional, Stanisci, Angela, additional, Steffen, Kristina, additional, Storm, Christian, additional, Stroh, Hans Georg, additional, Sugorkina, Nadezhda, additional, Świerkosz, Krzysztof, additional, Świerszcz, Sebastian, additional, Szymura, Magdalena, additional, Teleki, Balázs, additional, Thébaud, Gilles, additional, Theurillat, Jean‐Paul, additional, Tichý, Lubomír, additional, Treier, Urs A., additional, Turtureanu, Pavel Dan, additional, Ujházy, Karol, additional, Ujházyová, Mariana, additional, Ursu, Tudor Mihai, additional, Uziębło, Aldona K., additional, Valkó, Orsolya, additional, Van Calster, Hans, additional, Van Meerbeek, Koenraad, additional, Vandevoorde, Bart, additional, Vandvik, Vigdis, additional, Varricchione, Marco, additional, Vassilev, Kiril, additional, Villar, Luis, additional, Virtanen, Risto, additional, Vittoz, Pascal, additional, Voigt, Winfried, additional, von Hessberg, Andreas, additional, von Oheimb, Goddert, additional, Wagner, Eva, additional, Walther, Gian‐Reto, additional, Wellstein, Camilla, additional, Wesche, Karsten, additional, Wilhelm, Markus, additional, Willner, Wolfgang, additional, Wipf, Sonja, additional, Wittig, Burghard, additional, Wohlgemuth, Thomas, additional, Woodcock, Ben A., additional, Wulf, Monika, additional, and Essl, Franz, additional

Published
2024
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6. Accelerated increase in plant species richness on mountain summits is linked to warming

Author

Steinbauer, Manuel J., Grytnes, John-Arvid, Jurasinski, Gerald, Kulonen, Aino, Lenoir, Jonathan, Pauli, Harald, Rixen, Christian, Winkler, Manuela, Bardy-Durchhalter, Manfred, Barni, Elena, Bjorkman, Anne D., Breiner, Frank T., Burg, Sarah, Czortek, Patryk, Dawes, Melissa A., Delimat, Anna, Dullinger, Stefan, Erschbamer, Brigitta, Felde, Vivian A., Fernández-Arberas, Olatz, Fossheim, Kjetil F., Gómez-García, Daniel, Georges, Damien, Grindrud, Erlend T., Haider, Sylvia, Haugum, Siri V., Henriksen, Hanne, Herreros, María J., Jaroszewicz, Bogdan, Jaroszynska, Francesca, Kanka, Robert, Kapfer, Jutta, Klanderud, Kari, Kühn, Ingolf, Lamprecht, Andrea, Matteodo, Magali, di Cella, Umberto Morra, Normand, Signe, Odland, Arvid, Olsen, Siri L., Palacio, Sara, Petey, Martina, Piscová, Veronika, Sedlakova, Blazena, Steinbauer, Klaus, Stöckli, Veronika, Svenning, Jens-Christian, Teppa, Guido, Theurillat, Jean-Paul, Vittoz, Pascal, Woodin, Sarah J., Zimmermann, Niklaus E., and Wipf, Sonja

Published
2018
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9. Monitoring Climatic Change Impacts on Protection Forests in Aosta Valley (Italy) and in Drôme (France) Using Medium and High Resolution Remote Sensing and Mateloscopes Plots

Author

Borgniet, Laurent, primary, Toe, David, additional, Berger, Frdric, additional, Galvagno, Marta, additional, Panigada, Cinzia, additional, Colombo, Roberto, additional, di Cella, Umberto Morra, additional, Gottardelli, Simone, additional, Rollet, Ivan, additional, Negro, Mario, additional, Vertui, Flavio, additional, and Fermont, Cdric, additional

Published
2013
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10. S3M 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt.

Author

Avanzi, Francesco, Gabellani, Simone, Delogu, Fabio, Silvestro, Francesco, Cremonese, Edoardo, di Cella, Umberto Morra, Ratto, Sara, and Stevenin, Hervé

Subjects
MASS budget (Geophysics), FLOOD forecasting, HYDROLOGIC models, GLACIERS, WATER supply, CRYOSPHERE, PREDICTION models
Abstract

By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this shifting mechanism to climate, as well as the role played by the cryosphere in the Earth energy budget, has motivated the development of a broad spectrum of predictive models. Such models rarely combine a high degree of physical realism in both the seasonal snow and glaciers, and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present S3M v5.1, a spatially explicit and hydrology-oriented cryospheric model that successfully reconstructs seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier energy and mass balances, snow rheology and hydraulics, and a data-assimilation protocol. Comparatively novel aspects of S3M with respect to the existing literature are an explicit representation of the spatial patterns of snow liquid-water content, an hybrid approach to snowmelt that decouples the radiation- and temperature-driven contributions, the implementation of the ∆h parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the Italian north-western Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises an user manual as well as resources to prepare input data and set up computational environments and libraries. [ABSTRACT FROM AUTHOR]

Published
2021
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11. Learning about precipitation orographic enhancement from snow-course data improves water-balance modeling.

Author

Avanzi, Francesco, Ercolani, Giulia, Gabellani, Simone, Cremonese, Edoardo, Pogliotti, Paolo, Filippa, Gianluca, di Cella, Umberto Morra, Ratto, Sara, Stevenin, Hervè, Cauduro, Marco, and Juglair, Stefano

Abstract

Precipitation orographic enhancement depends on both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic-forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged 11,000+ snow-course data upstream two reservoirs in the Western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and consequently improve predictions of a snow-hydrologic modeling chain (Flood- PROOFS). We found that Snow Water Equivalent (SWE) above 3000 m ASL was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m ASL, with gradients up to 1000 mm w.e. km-1. Enhancement factors estimated by blending precipitation-gauge and snow-course data were quite consistent between the two hydropower headwaters (median values above 3000 m ASL between 4.1 and 4.8). Including blended gauge-course lapse rates in an iterative precipitation-spatialization procedure allowed Flood-PROOFS to remedy underestimations of both SWE above 3000 m ASL (up to 50 %) and importantly precipitation vs. observed streamflow. Runoff coefficients based on blended lapse rates were also more consistent from year to year that those based on precipitation gauges alone (standard deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to their essential role in supporting water security and ecosystem services worldwide. [ABSTRACT FROM AUTHOR]

Published
2020
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12. Sensitivity of snow models to the accuracy of meteorological forcings in mountain environment.

Author

Terzago, Silvia, Andreoli, Valentina, Arduini, Gabriele, Balsamo, Gianpaolo, Campo, Lorenzo, Cassardo, Claudio, Cremonese, Edoardo, Dolia, Daniele, Gabellani, Simone, von Hardenberg, Jost, di Cella, Umberto Morra, Palazzi, Elisa, Piazzi, Gaia, Pogliotti, Paolo, and Provenzale, Antonello

Abstract

Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing the model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing to drive snow models is typically derived from spatial interpolation of the available in-situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolutions, obtained (i) by sampling the original Torgnon 30-minute time series at 3, 6, and 12 hours, (ii) by spatially interpolating neighboring in-situ station measurements and (iii) by extracting information from GLDAS, ERA5, ERA-Interim reanalyses at the gridpoint closest to the Torgnon station. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. Results show that when forced by accurate 30-min resolution weather station data the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills as the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills as the control run while with 6- and 12-hourly temporal resolution forcings we generally observe a reduction in model performances, except for the SMASH model which shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighboring stations and reanalyses result to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. A simple bias-adjustment technique applied to ERA-Interim temperatures, however, allowed all models to achieve similar performances as in the control run. All models irrespectively of their complexity show weaknesses in the representation of the snow density. [ABSTRACT FROM AUTHOR]

Published
2020
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13. Early stage litter decomposition across biomes

Author

Djukic, Ika, primary, Kepfer-Rojas, Sebastian, additional, Schmidt, Inger Kappel, additional, Larsen, Klaus Steenberg, additional, Beier, Claus, additional, Berg, Björn, additional, Verheyen, Kris, additional, Caliman, Adriano, additional, Paquette, Alain, additional, Gutiérrez-Girón, Alba, additional, Humber, Alberto, additional, Valdecantos, Alejandro, additional, Petraglia, Alessandro, additional, Alexander, Heather, additional, Augustaitis, Algirdas, additional, Saillard, Amélie, additional, Fernández, Ana Carolina Ruiz, additional, Sousa, Ana I., additional, Lillebø, Ana I., additional, da Rocha Gripp, Anderson, additional, Francez, André-Jean, additional, Fischer, Andrea, additional, Bohner, Andreas, additional, Malyshev, Andrey, additional, Andrić, Andrijana, additional, Smith, Andy, additional, Stanisci, Angela, additional, Seres, Anikó, additional, Schmidt, Anja, additional, Avila, Anna, additional, Probst, Anne, additional, Ouin, Annie, additional, Khuroo, Anzar A., additional, Verstraeten, Arne, additional, Palabral-Aguilera, Arely N., additional, Stefanski, Artur, additional, Gaxiola, Aurora, additional, Muys, Bart, additional, Bosman, Bernard, additional, Ahrends, Bernd, additional, Parker, Bill, additional, Sattler, Birgit, additional, Yang, Bo, additional, Juráni, Bohdan, additional, Erschbamer, Brigitta, additional, Ortiz, Carmen Eugenia Rodriguez, additional, Christiansen, Casper T., additional, Carol Adair, E., additional, Meredieu, Céline, additional, Mony, Cendrine, additional, Nock, Charles A., additional, Chen, Chi-Ling, additional, Wang, Chiao-Ping, additional, Baum, Christel, additional, Rixen, Christian, additional, Delire, Christine, additional, Piscart, Christophe, additional, Andrews, Christopher, additional, Rebmann, Corinna, additional, Branquinho, Cristina, additional, Polyanskaya, Dana, additional, Delgado, David Fuentes, additional, Wundram, Dirk, additional, Radeideh, Diyaa, additional, Ordóñez-Regil, Eduardo, additional, Crawford, Edward, additional, Preda, Elena, additional, Tropina, Elena, additional, Groner, Elli, additional, Lucot, Eric, additional, Hornung, Erzsébet, additional, Gacia, Esperança, additional, Lévesque, Esther, additional, Benedito, Evanilde, additional, Davydov, Evgeny A., additional, Ampoorter, Evy, additional, Bolzan, Fabio Padilha, additional, Varela, Felipe, additional, Kristöfel, Ferdinand, additional, Maestre, Fernando T., additional, Maunoury-Danger, Florence, additional, Hofhansl, Florian, additional, Kitz, Florian, additional, Sutter, Flurin, additional, Cuesta, Francisco, additional, de Almeida Lobo, Francisco, additional, de Souza, Franco Leandro, additional, Berninger, Frank, additional, Zehetner, Franz, additional, Wohlfahrt, Georg, additional, Vourlitis, George, additional, Carreño-Rocabado, Geovana, additional, Arena, Gina, additional, Pinha, Gisele Daiane, additional, González, Grizelle, additional, Canut, Guylaine, additional, Lee, Hanna, additional, Verbeeck, Hans, additional, Auge, Harald, additional, Pauli, Harald, additional, Nacro, Hassan Bismarck, additional, Bahamonde, Héctor A., additional, Feldhaar, Heike, additional, Jäger, Heinke, additional, Serrano, Helena C., additional, Verheyden, Hélène, additional, Bruelheide, Helge, additional, Meesenburg, Henning, additional, Jungkunst, Hermann, additional, Jactel, Hervé, additional, Shibata, Hideaki, additional, Kurokawa, Hiroko, additional, Rosas, Hugo López, additional, Rojas Villalobos, Hugo L., additional, Yesilonis, Ian, additional, Melece, Inara, additional, Van Halder, Inge, additional, Quirós, Inmaculada García, additional, Makelele, Isaac, additional, Senou, Issaka, additional, Fekete, István, additional, Mihal, Ivan, additional, Ostonen, Ivika, additional, Borovská, Jana, additional, Roales, Javier, additional, Shoqeir, Jawad, additional, Lata, Jean-Christophe, additional, Theurillat, Jean-Paul, additional, Probst, Jean-Luc, additional, Zimmerman, Jess, additional, Vijayanathan, Jeyanny, additional, Tang, Jianwu, additional, Thompson, Jill, additional, Doležal, Jiří, additional, Sanchez-Cabeza, Joan-Albert, additional, Merlet, Joël, additional, Henschel, Joh, additional, Neirynck, Johan, additional, Knops, Johannes, additional, Loehr, John, additional, von Oppen, Jonathan, additional, Þorláksdóttir, Jónína Sigríður, additional, Löffler, Jörg, additional, Cardoso-Mohedano, José-Gilberto, additional, Benito-Alonso, José-Luis, additional, Torezan, Jose Marcelo, additional, Morina, Joseph C., additional, Jiménez, Juan J., additional, Quinde, Juan Dario, additional, Alatalo, Juha, additional, Seeber, Julia, additional, Stadler, Jutta, additional, Kriiska, Kaie, additional, Coulibaly, Kalifa, additional, Fukuzawa, Karibu, additional, Szlavecz, Katalin, additional, Gerhátová, Katarína, additional, Lajtha, Kate, additional, Käppeler, Kathrin, additional, Jennings, Katie A., additional, Tielbörger, Katja, additional, Hoshizaki, Kazuhiko, additional, Green, Ken, additional, Yé, Lambiénou, additional, Pazianoto, Laryssa Helena Ribeiro, additional, Dienstbach, Laura, additional, Williams, Laura, additional, Yahdjian, Laura, additional, Brigham, Laurel M., additional, van den Brink, Liesbeth, additional, Rustad, Lindsey, additional, Zhang, Lipeng, additional, Morillas, Lourdes, additional, Xiankai, Lu, additional, Carneiro, Luciana Silva, additional, Di Martino, Luciano, additional, Villar, Luis, additional, Bader, Maaike Y., additional, Morley, Madison, additional, Lebouvier, Marc, additional, Tomaselli, Marcello, additional, Sternberg, Marcelo, additional, Schaub, Marcus, additional, Santos-Reis, Margarida, additional, Glushkova, Maria, additional, Torres, María Guadalupe Almazán, additional, Giroux, Marie-Andrée, additional, de Graaff, Marie-Anne, additional, Pons, Marie-Noëlle, additional, Bauters, Marijn, additional, Mazón, Marina, additional, Frenzel, Mark, additional, Didion, Markus, additional, Wagner, Markus, additional, Hamid, Maroof, additional, Lopes, Marta L., additional, Apple, Martha, additional, Schädler, Martin, additional, Weih, Martin, additional, Gualmini, Matteo, additional, Vadeboncoeur, Matthew A., additional, Bierbaumer, Michael, additional, Danger, Michael, additional, Liddell, Michael, additional, Mirtl, Michael, additional, Scherer-Lorenzen, Michael, additional, Růžek, Michal, additional, Carbognani, Michele, additional, Di Musciano, Michele, additional, Matsushita, Michinari, additional, Zhiyanski, Miglena, additional, Pușcaș, Mihai, additional, Barna, Milan, additional, Ataka, Mioko, additional, Jiangming, Mo, additional, Alsafran, Mohammed, additional, Carnol, Monique, additional, Barsoum, Nadia, additional, Tokuchi, Naoko, additional, Eisenhauer, Nico, additional, Lecomte, Nicolas, additional, Filippova, Nina, additional, Hölzel, Norbert, additional, Ferlian, Olga, additional, Romero, Oscar, additional, Pinto, Osvaldo B., additional, Peri, Pablo, additional, Weber, Paige, additional, Vittoz, Pascal, additional, Turtureanu, Pavel Dan, additional, Fleischer, Peter, additional, Macreadie, Peter, additional, Haase, Peter, additional, Reich, Peter, additional, Petřík, Petr, additional, Choler, Philippe, additional, Marmonier, Pierre, additional, Muriel, Priscilla, additional, Ponette, Quentin, additional, Guariento, Rafael Dettogni, additional, Canessa, Rafaella, additional, Kiese, Ralf, additional, Hewitt, Rebecca, additional, Rønn, Regin, additional, Adrian, Rita, additional, Kanka, Róbert, additional, Weigel, Robert, additional, Gatti, Roberto Cazzolla, additional, Martins, Rodrigo Lemes, additional, Georges, Romain, additional, Meneses, Rosa Isela, additional, Gavilán, Rosario G., additional, Dasgupta, Sabyasachi, additional, Wittlinger, Sally, additional, Puijalon, Sara, additional, Freda, Sarah, additional, Suzuki, Satoshi, additional, Charles, Sean, additional, Gogo, Sébastien, additional, Drollinger, Simon, additional, Mereu, Simone, additional, Wipf, Sonja, additional, Trevathan-Tackett, Stacey, additional, Löfgren, Stefan, additional, Stoll, Stefan, additional, Trogisch, Stefan, additional, Hoeber, Stefanie, additional, Seitz, Steffen, additional, Glatzel, Stephan, additional, Milton, Sue J., additional, Dousset, Sylvie, additional, Mori, Taiki, additional, Sato, Takanori, additional, Ise, Takeshi, additional, Hishi, Takuo, additional, Kenta, Tanaka, additional, Nakaji, Tatsuro, additional, Michelan, Thaisa Sala, additional, Camboulive, Thierry, additional, Mozdzer, Thomas J., additional, Scholten, Thomas, additional, Spiegelberger, Thomas, additional, Zechmeister, Thomas, additional, Kleinebecker, Till, additional, Hiura, Tsutom, additional, Enoki, Tsutomu, additional, Ursu, Tudor-Mihai, additional, di Cella, Umberto Morra, additional, Hamer, Ute, additional, Klaus, Valentin H., additional, Rêgo, Vanessa Mendes, additional, Di Cecco, Valter, additional, Busch, Verena, additional, Fontana, Veronika, additional, Piscová, Veronika, additional, Carbonell, Victoria, additional, Ochoa, Victoria, additional, Bretagnolle, Vincent, additional, Maire, Vincent, additional, Farjalla, Vinicius, additional, Zhou, Wenjun, additional, Luo, Wentao, additional, McDowell, William H., additional, Hu, Yalin, additional, Utsumi, Yasuhiro, additional, Kominami, Yuji, additional, Zaika, Yulia, additional, Rozhkov, Yury, additional, Kotroczó, Zsolt, additional, and Tóth, Zsolt, additional

Published
2018
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14. Trade-offs between global warming and day length on the start of the carbon uptake period in seasonally cold ecosystems

Author

Wohlfahrt, Georg, Cremonese, Edoardo, Hammerle, Albin, Hörtnagl, Lukas, Galvagno, Marta, Gianelle, Damiano, Marcolla, Barbara, and di Cella, Umberto Morra

Subjects
photosynthesis, snowmelt, net ecosystem exchange, Settore BIO/07 - ECOLOGIA, carbon gain, grassland, respiration, Regular Articles
Abstract

[1] It is well established that warming leads to longer growing seasons in seasonally cold ecosystems. Whether this goes along with an increase in the net ecosystem carbon dioxide (CO2) uptake is much more controversial. We studied the effects of warming on the start of the carbon uptake period (CUP) of three mountain grasslands situated along an elevational gradient in the Alps. To this end, we used a simple empirical model of the net ecosystem CO2 exchange, calibrated, and forced with multiyear empirical data from each site. We show that reductions in the quantity and duration of daylight associated with earlier snowmelts were responsible for diminishing returns, in terms of carbon gain, from longer growing seasons caused by reductions in daytime photosynthetic uptake and increases in nighttime losses of CO2. This effect was less pronounced at high, compared to low, elevations, where the start of the CUP occurred closer to the summer solstice when changes in day length and incident radiation are minimal.

Published
2013

16. Testing Accuracy and Repeatability of UAV Blocks Oriented with GNSS-Supported Aerial Triangulation.

Author

Benassi, Francesco, Dall'Asta, Elisa, Diotri, Fabrizio, Forlani, Gianfranco, di Cella, Umberto Morra, Roncella, Riccardo, and Santise, Marina

Subjects
DRONE aircraft, AERIAL triangulation, PHOTOGRAMMETRY, GLOBAL Positioning System, GROUND controlled approach
Abstract

UAV Photogrammetry today already enjoys a largely automated and efficient data processing pipeline. However, the goal of dispensing with Ground Control Points looks closer, as dual-frequency GNSS receivers are put on board. This paper reports on the accuracy in object space obtained by GNSS-supported orientation of four photogrammetric blocks, acquired by a senseFly eBee RTK and all flown according to the same flight plan at 80 m above ground over a test field. Differential corrections were sent to the eBee from a nearby ground station. Block orientation has been performed with three software packages: PhotoScan, Pix4D and MicMac. The influence on the checkpoint errors of the precision given to the projection centers has been studied: in most cases, values in Z are critical. Without GCP, the RTK solution consistently achieves a RMSE of about 2-3 cm on the horizontal coordinates of checkpoints. In elevation, the RMSE varies from flight to flight, from 2 to 10 cm. Using at least one GCP, with all packages and all test flights, the geocoding accuracy of GNSS-supported orientation is almost as good as that of a traditional GCP orientation in XY and only slightly worse in Z. [ABSTRACT FROM AUTHOR]

Published
2017
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19. Phenological monitoring of grassland and larch in the Alps from Terra and Aqua MODIS images.

Author

Colombo, Roberto, Busetto, Lorenzo, Fava, Francesco, Di Mauro, Biagio, Migliavacca, Mirco, Cremonese, Edoardo, Galvagno, Marta, Rossini, Micol, Meroni, Michele, Cogliati, Sergio, Panigada, Cinzia, Siniscalco, Consolata, and di Cella, Umberto Morra

Subjects
GRASSLANDS, LARCHES, CLIMATE change research, MODIS (Spectroradiometer), TEMPERATURE
Abstract

This study compares MODIS NDVI 16-day (250 m) time series, acquired by Terra and Aqua platforms, for monitoring the phenological cycle of larch and grasslands in an alpine environment. The accuracy of MODIS 250 m Terra and Aqua phenological metrics he accuracy of MODIS 250 m Terra and Aqua phenological metrics was evaluated for larch forests through comparison with field data. At regional level it was carried out a correlation analysis between the mean dates of start and end of season detected from MODIS Terra and Aqua in different years. Regional maps of start and end of season were derived from MODIS data and the interannual phenological variability of both ecosystems was evaluated. Annual anomalies of the beginning of the growing season, obtained from satellite data, were related with air temperature anomalies, computed from meteorological stations, to evaluate the effects of recent climate variability on the vegetation phenological cycle. Comparison with field phenological observations showed that the start and the end of phenological cycle can be accurately determined from MODIS Terra and Aqua data and that an increase/decrease of 1°C in spring temperature lead to about 10 days in advance/ delay of the start of the growing season. [ABSTRACT FROM AUTHOR]

Published
2011
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22. On the role of Saharan dust events on snow season duration in the European Alps.

Author

Di Mauro, Biagio, Garzonio, Roberto, Rossini, Micol, Filippa, Gianluca, Pogliotti, Paolo, Galvagno, Marta, di Cella, Umberto Morra, Migliavacca, Mirco, Baccolo, Giovanni, Delmonte, Barbara, Dumont, Marie, Tuzet, François, Lafaysse, Matthieu, Morin, Samuel, Cremonese, Edoardo, and Colombo, Roberto

Published
2018

23. Factors controlling the water quality of rock glacier springs in European and American mountain ranges.

Author

Brighenti S, Colombo N, Wagner T, Pettauer M, Guyennon N, Krainer K, Tolotti M, Rogora M, Paro L, Steingruber SM, Del Siro C, Scapozza C, Sileo NR, Villarroel CD, Hayashi M, Munroe J, Liaudat DT, Cerasino L, Tirler W, Comiti F, Freppaz M, Salerno F, Litaor MI, Cremonese E, di Cella UM, and Winkler G

Abstract

Rock glaciers (RGs) provide significant water resources in mountain areas under climate change. Recent research has highlighted high concentrations of solutes including trace elements in RG-fed waters, with negative implications on water quality. Yet, sparse studies from a few locations hinder conclusions about the main drivers of solute export from RGs. Here, in an unprecedented effort, we collected published and unpublished data on rock glacier hydrochemistry around the globe. We considered 201 RG springs from mountain ranges across Europe, North and South America, using a combination of machine learning, multivariate and univariate analyses, and geochemical modeling. We found that 35 % of springs issuing from intact RGs (containing internal ice) have water quality below drinking water standards, compared to 5 % of springs connected to relict RGs (without internal ice). The interaction of ice and bedrock lithology is responsible for solute concentrations in RG springs. Indeed, we found higher concentrations of sulfate and trace elements in springs sourcing from intact RGs compared to water originating from relict RGs, mostly in specific lithological settings. Enhanced sulfide oxidation in intact RGs is responsible for the elevated trace element concentrations. Challenges for water management may arise in mountain catchments rich in intact RGs, and where the predisposing geology would make these areas geochemical RG hotspots. Our work represents a first comprehensive attempt to identify the main drivers of solute concentrations in RG waters., (Copyright © 2024. Published by Elsevier B.V.)

Published
2024
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24. Tradeoffs between global warming and day length on the start of the carbon uptake period in seasonally cold ecosystems.

Author

Wohlfahrt G, Cremonese E, Hammerle A, Hörtnagl L, Galvagno M, Gianelle D, Marcolla B, and di Cella UM

Abstract

It is well established that warming leads to longer growing seasons in seasonally cold ecosystems. Whether this goes along with an increase in the net ecosystem carbon dioxide (CO 2 ) uptake is much more controversial. We studied the effects of warming on the start of the carbon uptake period (CUP) of three mountain grasslands situated along an elevational gradient in the Alps. To this end we used a simple empirical model of the net ecosystem CO 2 exchange, calibrated and forced with multi-year empirical data from each site. We show that reductions in the quantity and duration of daylight associated with earlier snowmelts were responsible for diminishing returns, in terms of carbon gain, from longer growing seasons caused by reductions in daytime photosynthetic uptake and increases in nighttime losses of CO 2 . This effect was less pronounced at high, compared to low, elevations, where the start of the CUP occurred closer to the summer solstice when changes in day length and incident radiation are minimal.

Published
2013
Full Text
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