26 results on '"Langerwisch, Fanny"'
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2. D3.5 Farming System Archetypes for each CS
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Langerwisch, Fanny, Václavík, Tomáš, Bednář, Marek, Ziv, Guy, Gunning, Jodi, Gosal, Arjan, Paulus, Anne, Brdar, Sanja, Lugonja, Predrag, Stojković, Stefanija, Roilo, Stephanie, Cord, Anna, Langerwisch, Fanny, Václavík, Tomáš, Bednář, Marek, Ziv, Guy, Gunning, Jodi, Gosal, Arjan, Paulus, Anne, Brdar, Sanja, Lugonja, Predrag, Stojković, Stefanija, Roilo, Stephanie, and Cord, Anna
- Abstract
This deliverable provides an overview of the methods and data used for developing the Farming System Archetypes (FSAs) in the five case studies - Humber, Mulde, SouthMoravia, Bačka and Catalonia. Additionally, it discusses limitations as well as problems and presents solutions. The FSAs are a generalized typology of farming systems that are assumed to have similar response to policy change. FSAs are a major component of the BESTMAP modelling architecture because they provide linkages between many aspects of the project, especially connecting the biophysical and agent-based modelling in the case studies (CS), based on local data (e.g. IACS/LPIS, for explanation see Methodology), with the modelling of policy effects at the EU level, based on FADN micro-data within the FADN regions. The FSA framework defines the main farm characteristics determined by two main dimensions: firstly farm specialization and secondly economic size, both calculated and mapped for each farm in the CSs. ‘Farmer agents’ who belong to the same FSA are then assumed to have similar decision patterns regarding the adoption of agri-environmental schemes, based on the relationships revealed in the CS agent-based models.
- Published
- 2023
3. Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges
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Reyer, Christopher P. O., Brouwers, Niels, Rammig, Anja, Brook, Barry W., Epila, Jackie, Grant, Robert F., Holmgren, Milena, Langerwisch, Fanny, Leuzinger, Sebastian, Lucht, Wolfgang, Medlyn, Belinda, Pfeifer, Marion, Steinkamp, Jörg, Vanderwel, Mark C., Verbeeck, Hans, and Villela, Dora M.
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- 2015
4. Forest resilience, tipping points and global change processes
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Reyer, Christopher P.O., Rammig, Anja, Brouwers, Niels, and Langerwisch, Fanny
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- 2015
5. D1.3 Guidelines and protocols harmonizing activities across case studies
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Václavík, Tomáš, Langerwisch, Fanny, Ziv, Guy, Gunning, Jodi, Gosal, Arjan, Beckmann, Michael, Paulus, Anne, Wittstock, Felix, Cord, Anna, Roilo, Stephanie, Domingo-Marimon, Cristina, Sanchez, Anabel, Broekman, Annelies, and Vujaklija, Dajana
- Subjects
case study ,geodata ,guidelines ,Stakeholder engagement ,management ,policy - Abstract
This document is the first version of the Guidelines and protocols harmonizing activities across case studies of the H2020 BESTMAP project. It is intended to be updated in month 40 (D1.8).
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- 2022
- Full Text
- View/download PDF
6. Variable tree rooting strategies are key for modelling the distribution, productivity and evapotranspiration of tropical evergreen forests
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Sakschewski, Boris, primary, von Bloh, Werner, additional, Drüke, Markus, additional, Sörensson, Anna Amelia, additional, Ruscica, Romina, additional, Langerwisch, Fanny, additional, Billing, Maik, additional, Bereswill, Sarah, additional, Hirota, Marina, additional, Oliveira, Rafael Silva, additional, Heinke, Jens, additional, and Thonicke, Kirsten, additional
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- 2021
- Full Text
- View/download PDF
7. Tackling unresolved questions in forest ecology: The past and future role of simulation models
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Maréchaux, Isabelle, primary, Langerwisch, Fanny, additional, Huth, Andreas, additional, Bugmann, Harald, additional, Morin, Xavier, additional, Reyer, Christopher P.O., additional, Seidl, Rupert, additional, Collalti, Alessio, additional, Dantas de Paula, Mateus, additional, Fischer, Rico, additional, Gutsch, Martin, additional, Lexer, Manfred J., additional, Lischke, Heike, additional, Rammig, Anja, additional, Rödig, Edna, additional, Sakschewski, Boris, additional, Taubert, Franziska, additional, Thonicke, Kirsten, additional, Vacchiano, Giorgio, additional, and Bohn, Friedrich J., additional
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- 2021
- Full Text
- View/download PDF
8. LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description
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Schaphoff, Sibyll, Bloh, Werner, Rammig, Anja, Thonicke, Kirsten, Biemans, Hester, Forkel, Matthias, Gerten, Dieter, Heinke, Jens, Jägermeyr, Jonas, Knauer, Jürgen, Langerwisch, Fanny, Lucht, Wolfgang, Müller, Christoph, Rolinski, Susanne, and Waha, Katharina
- Abstract
This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL, we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process.
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- 2018
9. Make EU trade with Brazil sustainable
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Kehoe, Laura, Reis, Tiago, Virah-Sawmy, Malika, Balmford, Andrew, Kuemmerle, Tobias, Knohl, Alexander, Antonelli, Alexandre, Hochkirch, Axel, Vira, Bhaskar, Massa, Bruno, Peres, Carlos A., Ammer, Christian, Goerg, Christoph, Schneider, Christoph, Curtis, David, de la Pena, Eduardo, Tello, Enric, Sperfeld, Erik, Corbera, Esteve, Morelli, Federico, Valladares, Fernando, Peterson, Garry, Hide, Geoff, Mace, Georgina, Kallis, Giorgos, Olsson, Gunilla Almered, Brumelis, Guntis, Alexanderson, Helena, Haberl, Helmut, Nuissl, Henning, Kreft, Holger, Ghazoul, Jaboury, Piotrowski, Jan A., Macdiarmid, Jennie, Newig, Jens, Fischer, Joern, Altringham, John, Gledhill, John, Nielsen, Jonas O., Mueller, Joerg, Palmeirim, Jorge, Barlow, Jos, Alonso, Juan C., Presa Asencio, Juan Jose, Steinberger, Julia K., Jones, Julia Patricia Gordon, Cabral, Juliano Sarmento, Dengler, Juergen, Stibral, Karel, Erb, Karlheinz, Rothhaupt, Karl-Otto, Wiegand, Kerstin, Cassar, Louis F., Lens, Luc, Rosalino, Luis Miguel, Wassen, M. J., Stenseke, Marie, Fischer-Kowalski, Marina, Diaz, Mario, Rounsevell, Mark, van Kleunen, Mark, Junginger, Martin, Kaltenpoth, Martin, Zobel, Martin, Weigend, Maximilian, Partel, Meelis, Schilthuizen, Menno, Bastos Araujo, Miguel, Haklay, Muki, Eisenhauer, Nico, Selva, Nuria, Mertz, Ole, Meyfroidt, Patrick, Borges, Paulo A. V., Kovar, Pavel, Smith, Pete, Verburg, Peter, Pysek, Petr, Seppelt, Ralf, Valentini, Riccardo, Whittaker, Robert J., Henrique Faria, Sergio, Ulgiati, Sergio, Loetters, Stefan, Bjorck, Svante, Larson, Sven Ake, Tscharntke, Teja, Domingos, Tiago, Krueger, Tobias, Pascual, Unai, Olsson, Urban, Kati, Vassiliki, Winiwarter, Verena, Reyes-Garcia, Victoria, Vajda, Vivi, Sutherland, William J., de Waroux, Yann le Polain, Buckley, Yvonne, Rammig, Anja, Kasimir, Asa, Crona, Beatrice, Sindicic, Magda, Persson, Martin, Lapka, Miloslav, Di Gregorio, Monica, Hahn, Thomas, Boonstra, Wiebren, Lipsky, Zdenek, Zucaro, A., Roeder, Achim, Lopez Baucells, Adria, Danet, Alain, Franco, Aldina, Nieto Roman, Alejandra, Lehikoinen, Aleksi, Collalti, Alessio, Keller, Alexander, Strugariu, Alexandru, Perrigo, Allison, Fernandez-Llamazares, Alvaro, Salaseviciene, Alvija, Hinsley, Amy, Santos, Ana M. C., Novoa, Ana, Rodrigues, Ana, Mascarenhas, Andre, Martins, Andrea Damacena, Holzschuh, Andrea, Meseguer, Andrea S., Hadjichambis, Andreas, Mayer, Andreas, Hacket-Pain, Andrew, Ringsmuth, Andrew, de Frutos, Angel, Stein, Anke, Heikkinen, Anna, Smith, Annabel, Bjoersne, Anna-Karin, Bagneres, Anne-Genevieve, Machordom, Annie, Kristin, Anton, Ghoddousi, Arash, Staal, Arie, Martin, Arnaud, Taylor, Astrid, Borrell, Asuncion, Marescaux, Audrey, Torres, Aurora, Helm, Aveliina, Bauer, Barbara, Smetschka, Barbara, Rodriguez-Labajos, Beatriz, Peco, Begona, Gambin, Belinda, Celine, Bellard, Phalan, Ben, Cotta, Benedetta, Rugani, Benedetto, Jarcuska, Benjamin, Leroy, Boris, Nikolov, Boris Petrov, Milchev, Boyan Petrov, Brown, Calum, Ritter, Camila Duarte, Gomes, Carmen Bessa, Meyer, Carsten, Munteanu, Catalina, Penone, Caterina, Friis, Cecilie, Teplitsky, Celine, Roemer, Charlotte, Orland, Chloe, Voigt, Christian C., Levers, Christian, Zang, Christian, Bacon, Christine D., Meyer, Christoph, Wordley, Claire, Grilo, Clara, Cattaneo, Claudio, Battisti, Corrado, Banks-Leite, Cristina, Zurell, Damaris, Challender, Dan, Mueller, Daniel, Matenaar, Daniela, Silvestro, Daniele, McKay, David Armstrong, Buckley, David, Frantz, David, Gremillet, David, Mateos, David Moreno, Sanchez-Fernandez, David, Vieites, David, Ascoli, Davide, Arlt, Debora, Louis, Deharveng, Zemp, Delphine Clara, Strubbe, Diederik, Gil, Diego, Llusia, Diego, Bennett, Dominic J., Chobanov, Dragan Petrov, Aguilera, Eduardo, Oliveira, Eduardo, Pynegar, Edwin L., Granda, Elena, Grieco, Elisa, Conrad, Elisabeth, Revilla, Eloy, Lindkvist, Emilie, Caprio, Enrico, zu Ermgassen, Erasmus, Berenguer, Erika, Ochu, Erinma, Polaina, Ester, Nuernberger, Fabian, Esculier, Fabien, de Castro, Fabio, Albanito, Fabrizio, Langerwisch, Fanny, Batsleer, Femke, Ascensao, Fernando, Moyano, Fernando Esteban, Sayol, Ferran, Buzzetti, Filippo Maria, Eiro, Flavio, Volaire, Florence, Gollnow, Florian, Menzel, Florian, Pilo, Francesca, Moreira, Francisco, Briens, Francois, Essl, Franz, Vlahos, George, Billen, Gilles, Vacchiano, Giorgio, Wong, Grace, Gruychev, Gradimir Valentinov, Fandos, Guillermo, Petter, Gunnar, Sinare, Hanna, Mumby, Hannah S., Cottyn, Hanne, Seebens, Hanno, Bjorklund, Heidi, Schroeder, Heike, Lopez Hernandez, Heriberto D., Rebelo, Hugo, Chenet, Hugues, De la Riva, Ignacio, Torre, Ignasi, Aalders, Inge, Grass, Ingo, Chuine, Isabelle, Goepel, Jan, Wieringa, Jan J., Engler, Jan O., Pergl, Jan, Schnitzler, Jan, Vavra, Jan, Medvedovic, Jasna, Cabello, Javier, Martin, Jean-Louis, Mutke, Jens, Lewis, Jerome, da Silva, Jessica Fonseca, Marull, Joan, Carvalho, Joana, Carnicer, Jofre, Enqvist, Johan, Simaika, John P., Noguera, Jose C., Blanco Moreno, Jose M., Bruna, Josef, Garnier, Josette, Fargallo, Juan A., Rocha, Juan Carlos, Carrillo, Juan D., Infante-Amate, Juan, Traba Diaz, Juan, Schleicher, Judith, Simon, Judy, Noe, Julia Le, Gerlach, Justin, Eriksson, K. Martin, Prince, Karine, Ostapowicz, Katarzyna, Stajerova, Katerina, Farrell, Katharine N., Snell, Katherine, Yates, Katherine, Fleischer, Katrin, Darras, Kevin, Schumacher, Kim, Orach, Kirill, Thonicke, Kirsten, Riede, Klaus, Heller, Klaus-Gerhard, Wang-Erlandsson, Lan, Pereira, Laura, Riggi, Laura, Florez, Laura V., Emperaire, Laure, Durieux, Laurent, Tatin, Laurent, Rozylowicz, Laurentiu, Latella, Leonardo, Andresen, Louise C., Cahen-Fourot, Louison, de Agua, Luis Borda, Boto, Luis, Lassaletta, Luis, Amo, Luisa, Sekerka, Lukas, Morales, Manuel B., Macia, Manuel J., Suarez, Manuela Gonzalez, Cabeza, Mar, Londo, Marc, Pollet, Marc, Schwieder, Marcel, Peters, Marcell K., D'Amico, Marcello, Casazza, Marco, Florencio, Margarita, Felipe-Lucia, Maria, Gebara, Maria Fernanda, Johansson, Maria, Garcia, Maria Mancilla, Piquer-Rodriguez, Maria, Tengo, Maria, Elias, Marianne, Leve, Marine, Conde, Marta, Winter, Marten, Koster, Martijn, Mayer, Martin, Salek, Martin, Schlerf, Martin, Sullivan, Martin, Baumann, Matthias, Pichler, Melanie, Marselle, Melissa, Oddie, Melissa, Razanajatovo, Mialy, Borregaard, Michael Krabbe, Theurl, Michaela C., Hernandez, Miguel, Krofel, Miha, Kechev, Mihail Ognianov, Clark, Mike, Rands, Mike, Antal, Miklos, Pucetaite, Milda, Islar, Mine, Truong, Minh-Xuan A., Vighi, Morgana, Johanisova, Nadia, Prat, Narcis, Escobar, Neus, Deguines, Nicolas, Rust, Niki, Zafra-Calvo, Noelia, Maurel, Noelie, Wagner, Norman, Fitton, Nuala, Ostermann, Ole, Panferov, Oleg, Ange, Olivia, Canals, Oriol, Englund, Oskar, De Smedt, Pallieter, Petridis, Panos, Heikkurinen, Pasi, Weigelt, Patrick, Henriksson, Patrik J. G., de Castro, Paula Drummond, Matos-Maravi, Pavel, Duran, Paz, Aragon, Pedro, Cardoso, Pedro, Leitao, Pedro J., Hosner, Peter A., Biedermann, Peter, Keil, Petr, Petrik, Petr, Martin, Philip, Bocquillon, Pierre, Renaud, Pierre-Cyril, Addison, Prue, Antwis, Rachael, Carmenta, Rachel, Barrientos, Rafael, Smith, Rebecca, Rocha, Ricardo, Fuchs, Richard, Felix, Rob, Kanka, Robert, Aguilee, Robin, Padro Caminal, Roc, Libbrecht, Romain, Lorrilliere, Romain, van der Ent, Ruud J., Henders, Sabine, Pueyo, Salvador, Roturier, Samuel, Jacobs, Sander, Lavorel, Sandra, Leonhardt, Sara Diana, Fraixedas, Sara, Villen-Perez, Sara, Cornell, Sarah, Redlich, Sarah, De Smedt, Sebastian, van der Linden, Sebastian, Perez-Ortega, Sergio, Petrovan, Silviu, Cesarz, Simone, Sjoberg, Sissel, Caillon, Sophie, Schindler, Stefan, Trogisch, Stefan, Taiti, Stefano, Oppel, Steffen, Lutter, Stephan, Garnett, Tara, Guedes, Thais, Wanger, Thomas Cherico, Kastner, Thomas, Worthington, Thomas, Daw, Tim, Schmoll, Tim, McPhearson, Timon, Engl, Tobias, Rutting, Tobias, Vaclavik, Tomas, Jucker, Tommaso, Robillard, Tony, Krause, Torsten, Ljubomirov, Toshko, Aavik, Tsipe, Richardson, Vanessa A., Masterson, Vanessa Anne, Seufert, Verena, Cathy, Vet Gibault, Colino Rabanal, Victor, Montade, Vincent, Thieu, Vincent, Sober, Virve, Morin, Xavier, Mehrabi, Zia, Gonzalez, Adriana Trompetero, Sanz-Cobena, Alberto, Christie, Alec Philip, Romero-Munoz, Alfredo, Dauriach, Alice, Queiroz, Allan Souza, Golland, Ami, Evans, Amy Louise, Cordero, Ana Maria Araujo, Dara, Andrey, Rilovic, Andro, Pedersen, Anna Frohn, Csergo, Anna Maria, Lewerentz, Anne, Monserand, Antoine, Valdecasas, Antonio G., Doherty, Anya, Semper-Pascual, Asuncion, Bleyhl, Benjamin, Rutschmann, Benjamin, Bongalov, Boris, Hankerson, Brett, Heylen, Brigitte, Alonso-Alvarez, Carlos, Comandulli, Carolina, Frossard, Carolina M., Mckeon, Caroline, Godde, Cecile, Palm, Celinda, Singh, Chandrakant, Sieger, Charlotte Sophie, Ohrling, Christian, Paitan, Claudia Parra, Cooper, Conor, Edler, Daniel, Roessler, Daniela C., Kessner-Beierlein, Daniela, Garcia del Amo, David, Lopez Bosch, David, Gueldner, Dino, Noll, Domink, Motivans, Elena, Canteri, Elisabetta, Garnett, Emma, Malecore, Eva, Brambach, Fabian, Ruedenauer, Fabian, Yin, Fang, Hurtado, Fernando, Mempel, Finn, de Freitas, Flavio Luiz Mazzaro, Pendrill, Florence, Leijten, Floris, Somma, Francesca, Schug, Franz, De Knijf, Geert, Peterson, Gustaf, Pe'er, Guy, Booth, Hollie, Rhee, Howon, Staude, Ingmar, Gherghel, Iulian, Vila Traver, Jaime, Kerner, Janika, Hinton, Jennifer, Hortal, Joaquin, Persson, Joel, Uddling, Johan, Coenen, Johanna, Geldmann, Jonas, Geschke, Jonas, Juergensen, Jonathan, Lobo, Jorge M., Skejo, Josip, Heinen, Julia Helena, Schuenzel, Julia, Daniel-Ferreira, Juliana, Christophe Piquet, Julien, Murtough, Katie L., Prevel, Leonie, Hissa, Leticia B. V., af Segerstad, Louise Hard, Willemse, Luc, Benavides, Lucia, Sovova, Lucie, Figueiredo, Ludmilla, Leidinger, Ludwig, Piemontese, Luigi, da Fonte, Luis Fernando Marin, Moreta, Lys Sanz, Bhan, Manan, Toledo-Hernandez, Manuel, Engert, Manuela, Davoli, Marco, Mas Navarro, Maria, Voigt, Maria, Zirion, Maria, Wandl, Marie-Theres, Kipson, Marina, Johnson, Mark D., Lukic, Marko, Goula, Marta, Jung, Martin, Nunes, Matheus Henrique, Alvarez, Matheus Rodriguez, van den Burg, Matthijs P., Guerrero, Mayra Daniela Pena, Greenfield, Michael, Lobmann, Michael, Nygren, Michelle, Guth, Miriam Karen, Koh, Niak, Stanek, Nicola, Roux, Nicolas, Karagouni, Niki, Tiralla, Nina, Mairota, Paola, Savaget, Paulo, von Doehren, Peer, Benyei, Petra, Lena, Philippe, Rufin, Philippe, Janke, Rebekka, Santagata, Remo, Motta, Renzo, Battiston, Roberto, Oyanedel, Rodrigo, Bernardo-Madrid, Ruben, Vasconcelos, Sasha, Henriques, Sergio, Bager, Simon L., Qin, Siyu, Ivkovic, Slobodan, Cooke, Sophia, Ernst, Stefan, Schmelzer, Stefan, da Silva, Sven, Faberova, Tamara, Enseroth, Tanja, De Marzo, Teresa, Pienkowski, Thomas, Engel, Thore, Boehnert, Tim, Swinfield, Tom, Kurdikova, Vendula, Chvatalova, Veronika, Lopez-Marquez, Violeta, Arlidge, William, Zhang, Zhijie, Kehoe, Laura, Reis, Tiago, Virah-Sawmy, Malika, Balmford, Andrew, Kuemmerle, Tobias, Knohl, Alexander, Antonelli, Alexandre, Hochkirch, Axel, Vira, Bhaskar, Massa, Bruno, Peres, Carlos A., Ammer, Christian, Goerg, Christoph, Schneider, Christoph, Curtis, David, de la Pena, Eduardo, Tello, Enric, Sperfeld, Erik, Corbera, Esteve, Morelli, Federico, Valladares, Fernando, Peterson, Garry, Hide, Geoff, Mace, Georgina, Kallis, Giorgos, Olsson, Gunilla Almered, Brumelis, Guntis, Alexanderson, Helena, Haberl, Helmut, Nuissl, Henning, Kreft, Holger, Ghazoul, Jaboury, Piotrowski, Jan A., Macdiarmid, Jennie, Newig, Jens, Fischer, Joern, Altringham, John, Gledhill, John, Nielsen, Jonas O., Mueller, Joerg, Palmeirim, Jorge, Barlow, Jos, Alonso, Juan C., Presa Asencio, Juan Jose, Steinberger, Julia K., Jones, Julia Patricia Gordon, Cabral, Juliano Sarmento, Dengler, Juergen, Stibral, Karel, Erb, Karlheinz, Rothhaupt, Karl-Otto, Wiegand, Kerstin, Cassar, Louis F., Lens, Luc, Rosalino, Luis Miguel, Wassen, M. J., Stenseke, Marie, Fischer-Kowalski, Marina, Diaz, Mario, Rounsevell, Mark, van Kleunen, Mark, Junginger, Martin, Kaltenpoth, Martin, Zobel, Martin, Weigend, Maximilian, Partel, Meelis, Schilthuizen, Menno, Bastos Araujo, Miguel, Haklay, Muki, Eisenhauer, Nico, Selva, Nuria, Mertz, Ole, Meyfroidt, Patrick, Borges, Paulo A. V., Kovar, Pavel, Smith, Pete, Verburg, Peter, Pysek, Petr, Seppelt, Ralf, Valentini, Riccardo, Whittaker, Robert J., Henrique Faria, Sergio, Ulgiati, Sergio, Loetters, Stefan, Bjorck, Svante, Larson, Sven Ake, Tscharntke, Teja, Domingos, Tiago, Krueger, Tobias, Pascual, Unai, Olsson, Urban, Kati, Vassiliki, Winiwarter, Verena, Reyes-Garcia, Victoria, Vajda, Vivi, Sutherland, William J., de Waroux, Yann le Polain, Buckley, Yvonne, Rammig, Anja, Kasimir, Asa, Crona, Beatrice, Sindicic, Magda, Persson, Martin, Lapka, Miloslav, Di Gregorio, Monica, Hahn, Thomas, Boonstra, Wiebren, Lipsky, Zdenek, Zucaro, A., Roeder, Achim, Lopez Baucells, Adria, Danet, Alain, Franco, Aldina, Nieto Roman, Alejandra, Lehikoinen, Aleksi, Collalti, Alessio, Keller, Alexander, Strugariu, Alexandru, Perrigo, Allison, Fernandez-Llamazares, Alvaro, Salaseviciene, Alvija, Hinsley, Amy, Santos, Ana M. C., Novoa, Ana, Rodrigues, Ana, Mascarenhas, Andre, Martins, Andrea Damacena, Holzschuh, Andrea, Meseguer, Andrea S., Hadjichambis, Andreas, Mayer, Andreas, Hacket-Pain, Andrew, Ringsmuth, Andrew, de Frutos, Angel, Stein, Anke, Heikkinen, Anna, Smith, Annabel, Bjoersne, Anna-Karin, Bagneres, Anne-Genevieve, Machordom, Annie, Kristin, Anton, Ghoddousi, Arash, Staal, Arie, Martin, Arnaud, Taylor, Astrid, Borrell, Asuncion, Marescaux, Audrey, Torres, Aurora, Helm, Aveliina, Bauer, Barbara, Smetschka, Barbara, Rodriguez-Labajos, Beatriz, Peco, Begona, Gambin, Belinda, Celine, Bellard, Phalan, Ben, Cotta, Benedetta, Rugani, Benedetto, Jarcuska, Benjamin, Leroy, Boris, Nikolov, Boris Petrov, Milchev, Boyan Petrov, Brown, Calum, Ritter, Camila Duarte, Gomes, Carmen Bessa, Meyer, Carsten, Munteanu, Catalina, Penone, Caterina, Friis, Cecilie, Teplitsky, Celine, Roemer, Charlotte, Orland, Chloe, Voigt, Christian C., Levers, Christian, Zang, Christian, Bacon, Christine D., Meyer, Christoph, Wordley, Claire, Grilo, Clara, Cattaneo, Claudio, Battisti, Corrado, Banks-Leite, Cristina, Zurell, Damaris, Challender, Dan, Mueller, Daniel, Matenaar, Daniela, Silvestro, Daniele, McKay, David Armstrong, Buckley, David, Frantz, David, Gremillet, David, Mateos, David Moreno, Sanchez-Fernandez, David, Vieites, David, Ascoli, Davide, Arlt, Debora, Louis, Deharveng, Zemp, Delphine Clara, Strubbe, Diederik, Gil, Diego, Llusia, Diego, Bennett, Dominic J., Chobanov, Dragan Petrov, Aguilera, Eduardo, Oliveira, Eduardo, Pynegar, Edwin L., Granda, Elena, Grieco, Elisa, Conrad, Elisabeth, Revilla, Eloy, Lindkvist, Emilie, Caprio, Enrico, zu Ermgassen, Erasmus, Berenguer, Erika, Ochu, Erinma, Polaina, Ester, Nuernberger, Fabian, Esculier, Fabien, de Castro, Fabio, Albanito, Fabrizio, Langerwisch, Fanny, Batsleer, Femke, Ascensao, Fernando, Moyano, Fernando Esteban, Sayol, Ferran, Buzzetti, Filippo Maria, Eiro, Flavio, Volaire, Florence, Gollnow, Florian, Menzel, Florian, Pilo, Francesca, Moreira, Francisco, Briens, Francois, Essl, Franz, Vlahos, George, Billen, Gilles, Vacchiano, Giorgio, Wong, Grace, Gruychev, Gradimir Valentinov, Fandos, Guillermo, Petter, Gunnar, Sinare, Hanna, Mumby, Hannah S., Cottyn, Hanne, Seebens, Hanno, Bjorklund, Heidi, Schroeder, Heike, Lopez Hernandez, Heriberto D., Rebelo, Hugo, Chenet, Hugues, De la Riva, Ignacio, Torre, Ignasi, Aalders, Inge, Grass, Ingo, Chuine, Isabelle, Goepel, Jan, Wieringa, Jan J., Engler, Jan O., Pergl, Jan, Schnitzler, Jan, Vavra, Jan, Medvedovic, Jasna, Cabello, Javier, Martin, Jean-Louis, Mutke, Jens, Lewis, Jerome, da Silva, Jessica Fonseca, Marull, Joan, Carvalho, Joana, Carnicer, Jofre, Enqvist, Johan, Simaika, John P., Noguera, Jose C., Blanco Moreno, Jose M., Bruna, Josef, Garnier, Josette, Fargallo, Juan A., Rocha, Juan Carlos, Carrillo, Juan D., Infante-Amate, Juan, Traba Diaz, Juan, Schleicher, Judith, Simon, Judy, Noe, Julia Le, Gerlach, Justin, Eriksson, K. Martin, Prince, Karine, Ostapowicz, Katarzyna, Stajerova, Katerina, Farrell, Katharine N., Snell, Katherine, Yates, Katherine, Fleischer, Katrin, Darras, Kevin, Schumacher, Kim, Orach, Kirill, Thonicke, Kirsten, Riede, Klaus, Heller, Klaus-Gerhard, Wang-Erlandsson, Lan, Pereira, Laura, Riggi, Laura, Florez, Laura V., Emperaire, Laure, Durieux, Laurent, Tatin, Laurent, Rozylowicz, Laurentiu, Latella, Leonardo, Andresen, Louise C., Cahen-Fourot, Louison, de Agua, Luis Borda, Boto, Luis, Lassaletta, Luis, Amo, Luisa, Sekerka, Lukas, Morales, Manuel B., Macia, Manuel J., Suarez, Manuela Gonzalez, Cabeza, Mar, Londo, Marc, Pollet, Marc, Schwieder, Marcel, Peters, Marcell K., D'Amico, Marcello, Casazza, Marco, Florencio, Margarita, Felipe-Lucia, Maria, Gebara, Maria Fernanda, Johansson, Maria, Garcia, Maria Mancilla, Piquer-Rodriguez, Maria, Tengo, Maria, Elias, Marianne, Leve, Marine, Conde, Marta, Winter, Marten, Koster, Martijn, Mayer, Martin, Salek, Martin, Schlerf, Martin, Sullivan, Martin, Baumann, Matthias, Pichler, Melanie, Marselle, Melissa, Oddie, Melissa, Razanajatovo, Mialy, Borregaard, Michael Krabbe, Theurl, Michaela C., Hernandez, Miguel, Krofel, Miha, Kechev, Mihail Ognianov, Clark, Mike, Rands, Mike, Antal, Miklos, Pucetaite, Milda, Islar, Mine, Truong, Minh-Xuan A., Vighi, Morgana, Johanisova, Nadia, Prat, Narcis, Escobar, Neus, Deguines, Nicolas, Rust, Niki, Zafra-Calvo, Noelia, Maurel, Noelie, Wagner, Norman, Fitton, Nuala, Ostermann, Ole, Panferov, Oleg, Ange, Olivia, Canals, Oriol, Englund, Oskar, De Smedt, Pallieter, Petridis, Panos, Heikkurinen, Pasi, Weigelt, Patrick, Henriksson, Patrik J. G., de Castro, Paula Drummond, Matos-Maravi, Pavel, Duran, Paz, Aragon, Pedro, Cardoso, Pedro, Leitao, Pedro J., Hosner, Peter A., Biedermann, Peter, Keil, Petr, Petrik, Petr, Martin, Philip, Bocquillon, Pierre, Renaud, Pierre-Cyril, Addison, Prue, Antwis, Rachael, Carmenta, Rachel, Barrientos, Rafael, Smith, Rebecca, Rocha, Ricardo, Fuchs, Richard, Felix, Rob, Kanka, Robert, Aguilee, Robin, Padro Caminal, Roc, Libbrecht, Romain, Lorrilliere, Romain, van der Ent, Ruud J., Henders, Sabine, Pueyo, Salvador, Roturier, Samuel, Jacobs, Sander, Lavorel, Sandra, Leonhardt, Sara Diana, Fraixedas, Sara, Villen-Perez, Sara, Cornell, Sarah, Redlich, Sarah, De Smedt, Sebastian, van der Linden, Sebastian, Perez-Ortega, Sergio, Petrovan, Silviu, Cesarz, Simone, Sjoberg, Sissel, Caillon, Sophie, Schindler, Stefan, Trogisch, Stefan, Taiti, Stefano, Oppel, Steffen, Lutter, Stephan, Garnett, Tara, Guedes, Thais, Wanger, Thomas Cherico, Kastner, Thomas, Worthington, Thomas, Daw, Tim, Schmoll, Tim, McPhearson, Timon, Engl, Tobias, Rutting, Tobias, Vaclavik, Tomas, Jucker, Tommaso, Robillard, Tony, Krause, Torsten, Ljubomirov, Toshko, Aavik, Tsipe, Richardson, Vanessa A., Masterson, Vanessa Anne, Seufert, Verena, Cathy, Vet Gibault, Colino Rabanal, Victor, Montade, Vincent, Thieu, Vincent, Sober, Virve, Morin, Xavier, Mehrabi, Zia, Gonzalez, Adriana Trompetero, Sanz-Cobena, Alberto, Christie, Alec Philip, Romero-Munoz, Alfredo, Dauriach, Alice, Queiroz, Allan Souza, Golland, Ami, Evans, Amy Louise, Cordero, Ana Maria Araujo, Dara, Andrey, Rilovic, Andro, Pedersen, Anna Frohn, Csergo, Anna Maria, Lewerentz, Anne, Monserand, Antoine, Valdecasas, Antonio G., Doherty, Anya, Semper-Pascual, Asuncion, Bleyhl, Benjamin, Rutschmann, Benjamin, Bongalov, Boris, Hankerson, Brett, Heylen, Brigitte, Alonso-Alvarez, Carlos, Comandulli, Carolina, Frossard, Carolina M., Mckeon, Caroline, Godde, Cecile, Palm, Celinda, Singh, Chandrakant, Sieger, Charlotte Sophie, Ohrling, Christian, Paitan, Claudia Parra, Cooper, Conor, Edler, Daniel, Roessler, Daniela C., Kessner-Beierlein, Daniela, Garcia del Amo, David, Lopez Bosch, David, Gueldner, Dino, Noll, Domink, Motivans, Elena, Canteri, Elisabetta, Garnett, Emma, Malecore, Eva, Brambach, Fabian, Ruedenauer, Fabian, Yin, Fang, Hurtado, Fernando, Mempel, Finn, de Freitas, Flavio Luiz Mazzaro, Pendrill, Florence, Leijten, Floris, Somma, Francesca, Schug, Franz, De Knijf, Geert, Peterson, Gustaf, Pe'er, Guy, Booth, Hollie, Rhee, Howon, Staude, Ingmar, Gherghel, Iulian, Vila Traver, Jaime, Kerner, Janika, Hinton, Jennifer, Hortal, Joaquin, Persson, Joel, Uddling, Johan, Coenen, Johanna, Geldmann, Jonas, Geschke, Jonas, Juergensen, Jonathan, Lobo, Jorge M., Skejo, Josip, Heinen, Julia Helena, Schuenzel, Julia, Daniel-Ferreira, Juliana, Christophe Piquet, Julien, Murtough, Katie L., Prevel, Leonie, Hissa, Leticia B. V., af Segerstad, Louise Hard, Willemse, Luc, Benavides, Lucia, Sovova, Lucie, Figueiredo, Ludmilla, Leidinger, Ludwig, Piemontese, Luigi, da Fonte, Luis Fernando Marin, Moreta, Lys Sanz, Bhan, Manan, Toledo-Hernandez, Manuel, Engert, Manuela, Davoli, Marco, Mas Navarro, Maria, Voigt, Maria, Zirion, Maria, Wandl, Marie-Theres, Kipson, Marina, Johnson, Mark D., Lukic, Marko, Goula, Marta, Jung, Martin, Nunes, Matheus Henrique, Alvarez, Matheus Rodriguez, van den Burg, Matthijs P., Guerrero, Mayra Daniela Pena, Greenfield, Michael, Lobmann, Michael, Nygren, Michelle, Guth, Miriam Karen, Koh, Niak, Stanek, Nicola, Roux, Nicolas, Karagouni, Niki, Tiralla, Nina, Mairota, Paola, Savaget, Paulo, von Doehren, Peer, Benyei, Petra, Lena, Philippe, Rufin, Philippe, Janke, Rebekka, Santagata, Remo, Motta, Renzo, Battiston, Roberto, Oyanedel, Rodrigo, Bernardo-Madrid, Ruben, Vasconcelos, Sasha, Henriques, Sergio, Bager, Simon L., Qin, Siyu, Ivkovic, Slobodan, Cooke, Sophia, Ernst, Stefan, Schmelzer, Stefan, da Silva, Sven, Faberova, Tamara, Enseroth, Tanja, De Marzo, Teresa, Pienkowski, Thomas, Engel, Thore, Boehnert, Tim, Swinfield, Tom, Kurdikova, Vendula, Chvatalova, Veronika, Lopez-Marquez, Violeta, Arlidge, William, and Zhang, Zhijie
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- 2019
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10. A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region
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Rammig, Anja, Heinke, Jens, Hofhansl, Florian, Verbeeck, Hans, Baker, Timothy R., Christoffersen, Bradley, Ciais, Philippe, De Deurwaerder, Hannes, Fleischer, Katrin, Galbraith, David, Guimberteau, Matthieu, Huth, Andreas, Johnson, Michelle, Krujit, Bart, Langerwisch, Fanny, Meir, Patrick, Papastefanou, Phillip, Sampaio, Gilvan, Thonicke, Kirsten, von Randow, Celso, Zang, Christian, and Rödig, Edna
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ddc - Published
- 2017
11. Variable tree rooting strategies improve tropical productivity and evapotranspiration in a dynamic global vegetation model.
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Sakschewski, Boris, Bloh, Werner von, Drüke, Markus, Sörensson, Anna A., Ruscica, Romina, Langerwisch, Fanny, Billing, Maik, Bereswill, Sarah, Hirota, Marina, Oliveira, Rafael S., Heinke, Jens, and Thonicke, Kirsten
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EVAPOTRANSPIRATION ,PLANTS ,TREES - Abstract
Tree water access via roots is crucial for forest functioning and therefore forests have developed a vast variety of rooting strategies across the globe. However, Dynamic Global Vegetation Models (DGVMs), which are increasingly used to simulate forest functioning, often condense this variety of tree rooting strategies into biome-scale averages, potentially under- or overestimating forest response to intra- and inter-annual variability in precipitation. Here we present a new approach of implementing variable rooting strategies and dynamic root growth into the LPJmL4.0 DGVM and apply it to tropical and sub-tropical South-America under contemporary climate conditions. We show how competing rooting strategies which underlie the trade-off between above- and below-ground carbon investment lead to more realistic simulated intra-annual productivity and evapotranspiration, and consequently forest cover and spatial biomass distribution. We find that climate and soil depth determine a spatially heterogeneous pattern of mean rooting depth and belowground biomass across the study region. [ABSTRACT FROM AUTHOR]
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- 2020
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12. A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations: an example from the Amazon region
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Rammig, Anja, primary, Heinke, Jens, additional, Hofhansl, Florian, additional, Verbeeck, Hans, additional, Baker, Timothy R., additional, Christoffersen, Bradley, additional, Ciais, Philippe, additional, De Deurwaerder, Hannes, additional, Fleischer, Katrin, additional, Galbraith, David, additional, Guimberteau, Matthieu, additional, Huth, Andreas, additional, Johnson, Michelle, additional, Krujit, Bart, additional, Langerwisch, Fanny, additional, Meir, Patrick, additional, Papastefanou, Phillip, additional, Sampaio, Gilvan, additional, Thonicke, Kirsten, additional, von Randow, Celso, additional, Zang, Christian, additional, and Rödig, Edna, additional
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- 2018
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13. LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description
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Schaphoff, Sibyll, primary, von Bloh, Werner, additional, Rammig, Anja, additional, Thonicke, Kirsten, additional, Biemans, Hester, additional, Forkel, Matthias, additional, Gerten, Dieter, additional, Heinke, Jens, additional, Jägermeyr, Jonas, additional, Knauer, Jürgen, additional, Langerwisch, Fanny, additional, Lucht, Wolfgang, additional, Müller, Christoph, additional, Rolinski, Susanne, additional, and Waha, Katharina, additional
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- 2018
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14. Rice ecosystem services in South-east Asia
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Settele, Josef, Heong, Kong Luen, Kuehn, Ingolf, Klotz, Stefan, Spangenberg, Joachim H., Arida, Gertrudo, Beaurepaire, Alexis, Beck, Silke, Bergmeier, Erwin, Burkhard, Benjamin, Brandl, Roland, Bustamante, Jesus Victor, Butler, Adam, Cabbigat, Jimmy, Le, Xuan Canh, Catindig, Josie Lynn A., Ho, Van Chien, Le, Quoc Cuong, Dang, Kinh Bac, Escalada, Monina, Dominik, Christophe, Franzén, Markus, Fried, Oliver, Goerg, Christoph, Grescho, Volker, Grossmann, Sabine, Gurr, Geoff M., Hadi, Buyung A. R., Le, Huu Hai, Harpke, Alexander, Hass, Annika L., Hirneisen, Norbert, Horgan, Finbarr G., Hotes, Stefan, Isoda, Yuzuru, Jahn, Reinhold, Kettle, Helen, Klotzbuecher, Anika, Klotzbuecher, Thimo, Langerwisch, Fanny, Loke, Wai-Hong, Lin, Yu-Pin, Lu, Zhongxian, Lum, Keng-Yeang, Magcale-Macandog, Damasa B., Marion, Glenn, Marquez, Leonardo, Mueller, Felix, Nguyen, Hung Manh, Nguyen, Quynh Anh, Nguyen, Van Sinh, Ott, Juergen, Penev, Lyubomir, Pham, Hong Thai, Radermacher, Nico, Rodriguez-Labajos, Beatriz, Sann, Christina, Sattler, Cornelia, Schaedler, Martin, Scheu, Stefan, Schmidt, Anja, Schrader, Julian, Schweiger, Oliver, Seppelt, Ralf, Soitong, Kukiat, Stoev, Pavel, Stoll-Kleemann, Susanne, Tekken, Vera, Thonicke, Kirsten, Tilliger, Bianca, Tobias, Kai, Trisyono, Y. Andi, Dao, Thanh Truong, Tscharntke, Teja, Le, Quang Tuan, Tuerke, Manfred, Vaclavik, Tomas, Vetterlein, Doris, Villareal, Sylvia 'Bong', Vu, Kim Chi, Vu, Quynh, Weisser, Wolfgang W., Westphal, Catrin, Zhu, Zengrong, Wiemers, Martin, Settele, Josef, Heong, Kong Luen, Kuehn, Ingolf, Klotz, Stefan, Spangenberg, Joachim H., Arida, Gertrudo, Beaurepaire, Alexis, Beck, Silke, Bergmeier, Erwin, Burkhard, Benjamin, Brandl, Roland, Bustamante, Jesus Victor, Butler, Adam, Cabbigat, Jimmy, Le, Xuan Canh, Catindig, Josie Lynn A., Ho, Van Chien, Le, Quoc Cuong, Dang, Kinh Bac, Escalada, Monina, Dominik, Christophe, Franzén, Markus, Fried, Oliver, Goerg, Christoph, Grescho, Volker, Grossmann, Sabine, Gurr, Geoff M., Hadi, Buyung A. R., Le, Huu Hai, Harpke, Alexander, Hass, Annika L., Hirneisen, Norbert, Horgan, Finbarr G., Hotes, Stefan, Isoda, Yuzuru, Jahn, Reinhold, Kettle, Helen, Klotzbuecher, Anika, Klotzbuecher, Thimo, Langerwisch, Fanny, Loke, Wai-Hong, Lin, Yu-Pin, Lu, Zhongxian, Lum, Keng-Yeang, Magcale-Macandog, Damasa B., Marion, Glenn, Marquez, Leonardo, Mueller, Felix, Nguyen, Hung Manh, Nguyen, Quynh Anh, Nguyen, Van Sinh, Ott, Juergen, Penev, Lyubomir, Pham, Hong Thai, Radermacher, Nico, Rodriguez-Labajos, Beatriz, Sann, Christina, Sattler, Cornelia, Schaedler, Martin, Scheu, Stefan, Schmidt, Anja, Schrader, Julian, Schweiger, Oliver, Seppelt, Ralf, Soitong, Kukiat, Stoev, Pavel, Stoll-Kleemann, Susanne, Tekken, Vera, Thonicke, Kirsten, Tilliger, Bianca, Tobias, Kai, Trisyono, Y. Andi, Dao, Thanh Truong, Tscharntke, Teja, Le, Quang Tuan, Tuerke, Manfred, Vaclavik, Tomas, Vetterlein, Doris, Villareal, Sylvia 'Bong', Vu, Kim Chi, Vu, Quynh, Weisser, Wolfgang W., Westphal, Catrin, Zhu, Zengrong, and Wiemers, Martin
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- 2018
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15. LPJmL4 model output for the publications in GMD: LPJmL4 - a dynamic global vegetation model with managed land: Part I – Model description and Part II – Model evaluation
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Schaphoff, Sibyll, von Bloh, Werner, Rammig, Anja, Thonicke, Kirsten, Biemans, H., Forkel, Matthias, Gerten, Dieter, Heinke, Jens, Jägermeyr, Jonas, Knauer, Jürgen, Langerwisch, Fanny, Lucht, Wolfgang, Müller, Christoph, Rolinski, Susanne, Waha, Katharina, Schaphoff, Sibyll, von Bloh, Werner, Rammig, Anja, Thonicke, Kirsten, Biemans, H., Forkel, Matthias, Gerten, Dieter, Heinke, Jens, Jägermeyr, Jonas, Knauer, Jürgen, Langerwisch, Fanny, Lucht, Wolfgang, Müller, Christoph, Rolinski, Susanne, and Waha, Katharina
- Abstract
LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145). The data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2017a,). Additionally, these results are evaluated in the companion paper of Schaphoff et al. (2017b, http://doi.org/10.5194/gmd-2017-146). The data collection includes some key output variables made with different model setups described by Schaphoff et al. (2017b). The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for soil, vegetation, aboveground and litter carbon as well as for vegetation distribution, crop yields, sowing dates, maximum thawing depth, and fire carbon emissions. Vegetation distribution is given for each plant functional type (PFT), crop yields, and sowing dates are given for each crop functional type (CFT), respectively. Monthly data are provided for the carbon fluxes (net primary production, gross primary production, soil respiration) and the water fluxes (transpiration, evaporation, interception, runoff, and discharge) and for absorbed photosynthetically active radiation (FAPAR) and albedo.
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- 2018
16. A generic pixel-to-point comparison for simulated large-scale ecosystem properties and ground-based observations : An example from the Amazon region
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Rammig, Anja, Heinke, Jens, Hofhansl, Florian, Verbeeck, Hans, Baker, Timothy R., Christoffersen, Bradley, Ciais, Philippe, De Deurwaerder, Hannes, Fleischer, Katrin, Galbraith, David, Guimberteau, Matthieu, Huth, Andreas, Johnson, Michelle, Krujit, Bart, Langerwisch, Fanny, Meir, Patrick, Papastefanou, Phillip, Sampaio, Gilvan, Thonicke, Kirsten, von Randow, Celso, Zang, Christian, Rödig, Edna, Rammig, Anja, Heinke, Jens, Hofhansl, Florian, Verbeeck, Hans, Baker, Timothy R., Christoffersen, Bradley, Ciais, Philippe, De Deurwaerder, Hannes, Fleischer, Katrin, Galbraith, David, Guimberteau, Matthieu, Huth, Andreas, Johnson, Michelle, Krujit, Bart, Langerwisch, Fanny, Meir, Patrick, Papastefanou, Phillip, Sampaio, Gilvan, Thonicke, Kirsten, von Randow, Celso, Zang, Christian, and Rödig, Edna
- Abstract
Comparing model output and observed data is an important step for assessing model performance and quality of simulation results. However, such comparisons are often hampered by differences in spatial scales between local point observations and large-scale simulations of grid cells or pixels. In this study, we propose a generic approach for a pixel-to-point comparison and provide statistical measures accounting for the uncertainty resulting from landscape variability and measurement errors in ecosystem variables. The basic concept of our approach is to determine the statistical properties of small-scale (within-pixel) variability and observational errors, and to use this information to correct for their effect when large-scale area averages (pixel) are compared to small-scale point estimates. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity (woody net primary productivity, NPP) and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest, a region with the typical problem of low data availability, potential scale mismatch and thus high model uncertainty. We find that the DGVMs under- and overestimate aboveground biomass by 25% and up to 60%, respectively. Our comparison metrics provide a quantitative measure for model-data agreement and show moderate to good agreement with the region-wide spatial biomass pattern detected by plot observations. However, all four DGVMs overestimate woody productivity and underestimate residence time of woody biomass even when accounting for the large uncertainty range of the observational data. This is because DGVMs do not represent the relation between productivity and residence time of woody biomass correctly. Thus, the DGVMs may simulate the correct large-scale patterns of biomass but for the wrong reasons. We conclude that more information about the underlying processes driving biom
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- 2018
17. Impacts of future deforestation and climate change on the hydrology of the Amazon Basin : A multi-model analysis with a new set of land-cover change scenarios
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Guimberteau, Matthieu, Ciais, Philippe, Pablo Boisier, Juan, Paula Dutra Aguiar, Ana, Biemans, Hester, De Deurwaerder, Hannes, Galbraith, David, Kruijt, Bart, Langerwisch, Fanny, Poveda, German, Rammig, Anja, Andres Rodriguez, Daniel, Tejada, Graciela, Thonicke, Kirsten, Von Randow, Celso, Randow, Rita, Zhang, Ke, and Verbeeck, Hans
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Climate Resilience ,WIMEK ,Water and Food ,Klimaatbestendigheid ,Water en Voedsel ,Life Science - Abstract
Deforestation in Amazon is expected to decrease evapotranspiration (ET) and to increase soil moisture and river discharge under prevailing energy-limited conditions. The magnitude and sign of the response of ET to deforestation depend both on the magnitude and regional patterns of land-cover change (LCC), as well as on climate change and CO2 levels. On the one hand, elevated CO2 decreases leaf-scale transpiration, but this effect could be offset by increased foliar area density. Using three regional LCC scenarios specifically established for the Brazilian and Bolivian Amazon, we investigate the impacts of climate change and deforestation on the surface hydrology of the Amazon Basin for this century, taking 2009 as a reference. For each LCC scenario, three land surface models (LSMs), LPJmL-DGVM, INLAND-DGVM and ORCHIDEE, are forced by bias-corrected climate simulated by three general circulation models (GCMs) of the IPCC 4th Assessment Report (AR4). On average, over the Amazon Basin with no deforestation, the GCM results indicate a temperature increase of 3.3ĝ€°C by 2100 which drives up the evaporative demand, whereby precipitation increases by 8.5 %, with a large uncertainty across GCMs. In the case of no deforestation, we found that ET and runoff increase by 5.0 and 14ĝ€%, respectively. However, in south-east Amazonia, precipitation decreases by 10ĝ€% at the end of the dry season and the three LSMs produce a 6ĝ€% decrease of ET, which is less than precipitation, so that runoff decreases by 22 %. For instance, the minimum river discharge of the Rio Tapajós is reduced by 31ĝ€% in 2100. To study the additional effect of deforestation, we prescribed to the LSMs three contrasted LCC scenarios, with a forest decline going from 7 to 34ĝ€% over this century. All three scenarios partly offset the climate-induced increase of ET, and runoff increases over the entire Amazon. In the south-east, however, deforestation amplifies the decrease of ET at the end of dry season, leading to a large increase of runoff (up to +27ĝ€% in the extreme deforestation case), offsetting the negative effect of climate change, thus balancing the decrease of low flows in the Rio Tapajós. These projections are associated with large uncertainties, which we attribute separately to the differences in LSMs, GCMs and to the uncertain range of deforestation. At the subcatchment scale, the uncertainty range on ET changes is shown to first depend on GCMs, while the uncertainty of runoff projections is predominantly induced by LSM structural differences. By contrast, we found that the uncertainty in both ET and runoff changes attributable to uncertain future deforestation is low.
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- 2017
18. Combined effects of climate and land-use change on the provision of ecosystem services in rice agro-ecosystems
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Langerwisch, Fanny, primary, Václavík, Tomáš, additional, von Bloh, Werner, additional, Vetter, Tobias, additional, and Thonicke, Kirsten, additional
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- 2017
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19. A social-ecological approach to identify and quantify biodiversity tipping points in South America's seasonal dry ecosystems.
- Author
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Thonicke, Kirsten, Langerwisch, Fanny, Baumann, Matthias, Leitão, Pedro J., Václavík, Tomáš, Alencar, Ane, Simões, Margareth, Scheiter, Simon, Langan, Liam, Bustamante, Mercedes, Gasparri, Ignacio, Hirota, Marina, Börner, Jan, Rajao, Raoni, Soares-Filho, Britaldo, Yanosky, Alberto, Ochoa-Quinteiro, José-Manuel, Seghezzo, Lucas, Conti, Georgina, and de la Vega-Leinert, Anne Cristina
- Subjects
TROPICAL dry forests ,ECOLOGICAL integrity ,BIODIVERSITY ,ECOSYSTEMS ,AGRICULTURAL intensification ,ECOLOGICAL resilience - Abstract
Tropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture. Inspired by social-ecological systems theory, our approach helps to explain: (i) how drivers of natural vegetation decline affect the functioning of ecosystems, and thus ecological integrity, (ii) under which conditions, where, and at which scales the loss of ecological integrity may lead to biodiversity tipping points, and (iii) how these biodiversity tipping points may impact human well-being. Implementing such an approach with the greater aim of furthering more sustainable land use in the Dry Diagonal requires a transdisciplinary collaborative network, which in a first step integrates extensive observational data from the field and remote sensing with advanced ecosystem and biodiversity models. Secondly, it integrates knowledge obtained from dialogue processes with local and regional actors as well as meta-models describing the actor network. The co-designed methodological framework can be applied not only to define, detect, and map biodiversity tipping points across spatial and temporal scales, but also to evaluate the effects of tipping points on ES and livelihoods. This framework could be used to inform policy making, enrich planning processes at various levels of governance, and potentially contribute to prevent biodiversity tipping points in the Dry Diagonal and beyond. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
20. Climate change impacts in Latin America and the Caribbean and their implications for development
- Author
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Reyer, Christopher P.O., Adams, Sophie, Albrecht, Torsten, Baarsch, Florent, Boit, Alice, Canales Trujillo, Nella, Cartsburg, Matti, Coumou, Dim, Eden, Alexander, Fernandes, Erick, Langerwisch, Fanny, Marcus, Rachel, Mengel, Matthias, Mira-Salama, Daniel, Perette, Mahé, Pereznieto, Paola, Rammig, Anja, Reinhardt, Julia, Robinson, Alexander, Rocha, Marcia, Sakschewski, Boris, Schaeffer, Michiel, Schleussner, Carl Friedrich, Serdeczny, Olivia, Thonicke, Kirsten, Reyer, Christopher P.O., Adams, Sophie, Albrecht, Torsten, Baarsch, Florent, Boit, Alice, Canales Trujillo, Nella, Cartsburg, Matti, Coumou, Dim, Eden, Alexander, Fernandes, Erick, Langerwisch, Fanny, Marcus, Rachel, Mengel, Matthias, Mira-Salama, Daniel, Perette, Mahé, Pereznieto, Paola, Rammig, Anja, Reinhardt, Julia, Robinson, Alexander, Rocha, Marcia, Sakschewski, Boris, Schaeffer, Michiel, Schleussner, Carl Friedrich, Serdeczny, Olivia, and Thonicke, Kirsten
- Abstract
This paper synthesizes what is known about the physical and biophysical impacts of climate change and their consequences for societies and development under different levels of global warming in Latin America and the Caribbean (LAC). Projections show increasing mean temperatures by up to 4.5 °C compared to pre-industrial by the end of this century across LAC. Associated physical impacts include altered precipitation regimes, a strong increase in heat extremes, higher risks of droughts and increasing aridity. Moreover, the mean intensity of tropical cyclones, as well as the frequency of the most intense storms, is projected to increase while sea levels are expected to rise by ~0.2–1.1 mm depending on warming level and region. Tropical glacier volume is found to decrease substantially, with almost complete deglaciation under high warming levels. The much larger glaciers in the southern Andes are less sensitive to warming and shrink on slower timescales. Runoff is projected to be reduced in Central America, the southern Amazon basin and southernmost South America, while river discharge may increase in the western Amazon basin and in the Andes in the wet season. However, in many regions, there is uncertainty in the direction of these changes as a result of uncertain precipitation projections and differences in hydrological models. Climate change will also reduce agricultural yields, livestock and fisheries, although there may be opportunities such as increasing rice yield in several LAC countries or higher fish catch potential in the southernmost South American waters. Species range shifts threaten terrestrial biodiversity, and there is a substantial risk of Amazon rainforest degradation with continuing warming. Coral reefs are at increasing risk of annual bleaching events from 2040 to 2050 onwards irrespective of the climate scenario. These physical and biophysical climate change impacts challenge human livelihoods through, e.g., decreasing income from fisheries, ag
- Published
- 2017
- Full Text
- View/download PDF
21. Climate change impacts in Latin America and the Caribbean and their implications for development
- Author
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World Bank Group, Reyer, Christopher, Adams, Sophie, Albrecht, Torsten, Baarsch, Florent, Boit, Alice, Canales Trujillo, Nella, Cartsburg, Matti, Coumou, Dim, Eden, Alexander, Fernandes, Erick, Langerwisch, Fanny, Marcus, Rachel, Mengel, Mathias, Mira-Salam, Daniel, Perette, Mahé, Pereznieto, Paola, Rammig, Anja, Reinhardt, Julia, Robinson, Alexander, Rocha, Marcia, Sakschewski, Boris, Schaeffer, Michiel, Schleussner, Carl-Friedrich, Serdeczny, Olivia, Thonick, Kirsten, World Bank Group, Reyer, Christopher, Adams, Sophie, Albrecht, Torsten, Baarsch, Florent, Boit, Alice, Canales Trujillo, Nella, Cartsburg, Matti, Coumou, Dim, Eden, Alexander, Fernandes, Erick, Langerwisch, Fanny, Marcus, Rachel, Mengel, Mathias, Mira-Salam, Daniel, Perette, Mahé, Pereznieto, Paola, Rammig, Anja, Reinhardt, Julia, Robinson, Alexander, Rocha, Marcia, Sakschewski, Boris, Schaeffer, Michiel, Schleussner, Carl-Friedrich, Serdeczny, Olivia, and Thonick, Kirsten
- Abstract
This paper synthesizes what is known about the physical and biophysical impacts of climate change and their consequences for societies and development under different levels of global warming in Latin America and the Caribbean (LAC). Projections show increasing mean temperatures by up to 4.5 °C compared to pre-industrial by the end of this century across LAC. Associated physical impacts include altered precipitation regimes, a strong increase in heat extremes, higher risks of droughts and increasing aridity. Moreover, the mean intensity of tropical cyclones, as well as the frequency of the most intense storms, is projected to increase while sea levels are expected to rise by ~0.2–1.1 mm depending on warming level and region. Tropical glacier volume is found to decrease substantially, with almost complete deglaciation under high warming levels. The much larger glaciers in the southern Andes are less sensitive to warming and shrink on slower timescales. Runoff is projected to be reduced in Central America, the southern Amazon basin and southernmost South America, while river discharge may increase in the western Amazon basin and in the Andes in the wet season. However, in many regions, there is uncertainty in the direction of these changes as a result of uncertain precipitation projections and differences in hydrological models. Climate change will also reduce agricultural yields, livestock and fisheries, although there may be opportunities such as increasing rice yield in several LAC countries or higher fish catch potential in the southernmost South American waters. Species range shifts threaten terrestrial biodiversity, and there is a substantial risk of Amazon rainforest degradation with continuing warming. Coral reefs are at increasing risk of annual bleaching events from 2040 to 2050 onwards irrespective of the climate scenario. These physical and biophysical climate change impacts challenge human livelihoods through, e.g., decreasing income from fisheries, agric
- Published
- 2017
22. Deforestation in Amazonia impacts riverine carbon dynamics
- Author
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Langerwisch, Fanny, primary, Walz, Ariane, additional, Rammig, Anja, additional, Tietjen, Britta, additional, Thonicke, Kirsten, additional, and Cramer, Wolfgang, additional
- Published
- 2016
- Full Text
- View/download PDF
23. Investigating potential transferability of place-based research in land system science
- Author
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Václavík, Tomáš, primary, Langerwisch, Fanny, additional, Cotter, Marc, additional, Fick, Johanna, additional, Häuser, Inga, additional, Hotes, Stefan, additional, Kamp, Johannes, additional, Settele, Josef, additional, Spangenberg, Joachim H, additional, and Seppelt, Ralf, additional
- Published
- 2016
- Full Text
- View/download PDF
24. LPJmL4 - a dynamic global vegetation model with managed land: Part I - Model description.
- Author
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Schaphoff, Sibyll, von Bloh, Werner, Rammig, Anja, Thonicke, Kirsten, Biemans, Hester, Forkel, Matthias, Gerten, Dieter, Heinke, Jens, Jägermeyr, Jonas, Knauer, Jürgen, Langerwisch, Fanny, Lucht, Wolfgang, Müller, Christoph, Rolinski, Susanne, and Waha, Katharina
- Subjects
MATHEMATICAL models of agricultural productivity ,VEGETATION & climate ,ECOSYSTEMS - Abstract
This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates - internally consistently - the growth and productivity of both natural and agricultural vegetation in direct coupling with water and carbon fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within, and impacts upon, the terrestrial biosphere as increasingly shaped by human activities such as climate change and land-use change. Here we describe the core model structure including recently eveloped modules now unified in LPJmL4. Thereby we also summarize LPJmL model developments and evaluations (based on 34 earlier publications focused e.g. on improved representations of crop types, human and ecological water demand, and permafrost) and model applications (82 papers, e.g. on historical and future climate change impacts) since its first description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at a Gitlab server, we hope to stimulate the application and further development of LPJmL4 across scientific communities, not least in support of major activities such as the IPCC and SDG process. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
25. Including small-scale topographic effects on local climate substantially affects modelled vegetation distribution in a central European mountain area.
- Author
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Langerwisch, Fanny, Macek, Martin, Wild, Jan, Thonicke, Kirsten, and Kopecký, Martin
- Subjects
- *
TEMPERATURE lapse rate , *CLIMATOLOGY , *TOPOGRAPHY , *PLANTS , *CLIMATE change , *TIMBERLINE - Abstract
Understanding the effect of climate on the distribution and composition of vegetation is crucial to investigate its response to climate change. However, process-based vegetation models usually use comparably coarse-grain climatic data, which underestimate topographic effects on local climate.To evaluate potential effects of topoclimate on vegetation redistribution predicted by the process-based vegetation model LPJmL, we compared modelled vegetation based on different climate inputs, namely coarse-grained climatic data, data corrected for temperature lapse rate according to local elevation and data incorporating topographic effects responsible for cool-air pooling and anisotropic heat load in a hilly region in the Czech Republic.The results showed the pronounced effect of terrain topography as well the large discrepancy between coarse-scale climate and locally adapted climate. Our results thus give a hint on how sensitive modelled vegetation distribution is to climate input correction. We found that despite future climate change, including higher temperatures and less precipitation, the current vegetation might be able to remain in some local refuges, which are however not captured by coarse-scale climatic dataset. Therefore, the inclusion of topographic effects is crucial for realistic estimates of the vegetation redistribution in the response to climate change. [ABSTRACT FROM AUTHOR]
- Published
- 2019
26. Variable rooting strategies stabilize biome productivity.
- Author
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Sakschewski, Boris, Bloh, Werner von, Bereswill, Sarah, Sorensson, Anna, Ruscica, Romina, Drüke, Markus, Langerwisch, Fanny, Billing, Maik, Oliveira, Rafael, Hirota, Marina, Schaphoff, Sibyll, and Thonicke, Kirsten
- Published
- 2019
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