TY  - JOUR
A1  - Wuttke, Matthias
A1  - Li, Yong
A1  - Li, Man
A1  - Sieber, Karsten B.
A1  - Feitosa, Mary F.
A1  - Gorski, Mathias
A1  - Tin, Adrienne
A1  - Wang, Lihua
A1  - Chu, Audrey Y.
A1  - Hoppmann, Anselm
A1  - Kirsten, Holger
A1  - Giri, Ayush
A1  - Chai, Jin-Fang
A1  - Sveinbjornsson, Gardar
A1  - Tayo, Bamidele O.
A1  - Nutile, Teresa
A1  - Fuchsberger, Christian
A1  - Marten, Jonathan
A1  - Cocca, Massimiliano
A1  - Ghasemi, Sahar
A1  - Xu, Yizhe
A1  - Horn, Katrin
A1  - Noce, Damia
A1  - Van der Most, Peter J.
A1  - Sedaghat, Sanaz
A1  - Yu, Zhi
A1  - Akiyama, Masato
A1  - Afaq, Saima
A1  - Ahluwalia, Tarunveer Singh
A1  - Almgren, Peter
A1  - Amin, Najaf
A1  - Arnlov, Johan
A1  - Bakker, Stephan J. L.
A1  - Bansal, Nisha
A1  - Baptista, Daniela
A1  - Bergmann, Sven
A1  - Biggs, Mary L.
A1  - Biino, Ginevra
A1  - Boehnke, Michael
A1  - Boerwinkle, Eric
A1  - Boissel, Mathilde
A1  - Böttinger, Erwin
A1  - Boutin, Thibaud S.
A1  - Brenner, Hermann
A1  - Brumat, Marco
A1  - Burkhardt, Ralph
A1  - Butterworth, Adam S.
A1  - Campana, Eric
A1  - Campbell, Archie
A1  - Campbell, Harry
A1  - Canouil, Mickael
A1  - Carroll, Robert J.
A1  - Catamo, Eulalia
A1  - Chambers, John C.
A1  - Chee, Miao-Ling
A1  - Chee, Miao-Li
A1  - Chen, Xu
A1  - Cheng, Ching-Yu
A1  - Cheng, Yurong
A1  - Christensen, Kaare
A1  - Cifkova, Renata
A1  - Ciullo, Marina
A1  - Concas, Maria Pina
A1  - Cook, James P.
A1  - Coresh, Josef
A1  - Corre, Tanguy
A1  - Sala, Cinzia Felicita
A1  - Cusi, Daniele
A1  - Danesh, John
A1  - Daw, E. Warwick
A1  - De Borst, Martin H.
A1  - De Grandi, Alessandro
A1  - De Mutsert, Renee
A1  - De Vries, Aiko P. J.
A1  - Degenhardt, Frauke
A1  - Delgado, Graciela
A1  - Demirkan, Ayse
A1  - Di Angelantonio, Emanuele
A1  - Dittrich, Katalin
A1  - Divers, Jasmin
A1  - Dorajoo, Rajkumar
A1  - Eckardt, Kai-Uwe
A1  - Ehret, Georg
A1  - Elliott, Paul
A1  - Endlich, Karlhans
A1  - Evans, Michele K.
A1  - Felix, Janine F.
A1  - Foo, Valencia Hui Xian
A1  - Franco, Oscar H.
A1  - Franke, Andre
A1  - Freedman, Barry I.
A1  - Freitag-Wolf, Sandra
A1  - Friedlander, Yechiel
A1  - Froguel, Philippe
A1  - Gansevoort, Ron T.
A1  - Gao, He
A1  - Gasparini, Paolo
A1  - Gaziano, J. Michael
A1  - Giedraitis, Vilmantas
A1  - Gieger, Christian
A1  - Girotto, Giorgia
A1  - Giulianini, Franco
A1  - Gogele, Martin
A1  - Gordon, Scott D.
A1  - Gudbjartsson, Daniel F.
A1  - Gudnason, Vilmundur
A1  - Haller, Toomas
A1  - Hamet, Pavel
A1  - Harris, Tamara B.
A1  - Hartman, Catharina A.
A1  - Hayward, Caroline
A1  - Hellwege, Jacklyn N.
A1  - Heng, Chew-Kiat
A1  - Hicks, Andrew A.
A1  - Hofer, Edith
A1  - Huang, Wei
A1  - Hutri-Kahonen, Nina
A1  - Hwang, Shih-Jen
A1  - Ikram, M. Arfan
A1  - Indridason, Olafur S.
A1  - Ingelsson, Erik
A1  - Ising, Marcus
A1  - Jaddoe, Vincent W. V.
A1  - Jakobsdottir, Johanna
A1  - Jonas, Jost B.
A1  - Joshi, Peter K.
A1  - Josyula, Navya Shilpa
A1  - Jung, Bettina
A1  - Kahonen, Mika
A1  - Kamatani, Yoichiro
A1  - Kammerer, Candace M.
A1  - Kanai, Masahiro
A1  - Kastarinen, Mika
A1  - Kerr, Shona M.
A1  - Khor, Chiea-Chuen
A1  - Kiess, Wieland
A1  - Kleber, Marcus E.
A1  - Koenig, Wolfgang
A1  - Kooner, Jaspal S.
A1  - Korner, Antje
A1  - Kovacs, Peter
A1  - Kraja, Aldi T.
A1  - Krajcoviechova, Alena
A1  - Kramer, Holly
A1  - Kramer, Bernhard K.
A1  - Kronenberg, Florian
A1  - Kubo, Michiaki
A1  - Kuhnel, Brigitte
A1  - Kuokkanen, Mikko
A1  - Kuusisto, Johanna
A1  - La Bianca, Martina
A1  - Laakso, Markku
A1  - Lange, Leslie A.
A1  - Langefeld, Carl D.
A1  - Lee, Jeannette Jen-Mai
A1  - Lehne, Benjamin
A1  - Lehtimaki, Terho
A1  - Lieb, Wolfgang
A1  - Lim, Su-Chi
A1  - Lind, Lars
A1  - Lindgren, Cecilia M.
A1  - Liu, Jun
A1  - Liu, Jianjun
A1  - Loeffler, Markus
A1  - Loos, Ruth J. F.
A1  - Lucae, Susanne
A1  - Lukas, Mary Ann
A1  - Lyytikainen, Leo-Pekka
A1  - Magi, Reedik
A1  - Magnusson, Patrik K. E.
A1  - Mahajan, Anubha
A1  - Martin, Nicholas G.
A1  - Martins, Jade
A1  - Marz, Winfried
A1  - Mascalzoni, Deborah
A1  - Matsuda, Koichi
A1  - Meisinger, Christa
A1  - Meitinger, Thomas
A1  - Melander, Olle
A1  - Metspalu, Andres
A1  - Mikaelsdottir, Evgenia K.
A1  - Milaneschi, Yuri
A1  - Miliku, Kozeta
A1  - Mishra, Pashupati P.
A1  - Program, V. A. Million Veteran
A1  - Mohlke, Karen L.
A1  - Mononen, Nina
A1  - Montgomery, Grant W.
A1  - Mook-Kanamori, Dennis O.
A1  - Mychaleckyj, Josyf C.
A1  - Nadkarni, Girish N.
A1  - Nalls, Mike A.
A1  - Nauck, Matthias
A1  - Nikus, Kjell
A1  - Ning, Boting
A1  - Nolte, Ilja M.
A1  - Noordam, Raymond
A1  - Olafsson, Isleifur
A1  - Oldehinkel, Albertine J.
A1  - Orho-Melander, Marju
A1  - Ouwehand, Willem H.
A1  - Padmanabhan, Sandosh
A1  - Palmer, Nicholette D.
A1  - Palsson, Runolfur
A1  - Penninx, Brenda W. J. H.
A1  - Perls, Thomas
A1  - Perola, Markus
A1  - Pirastu, Mario
A1  - Pirastu, Nicola
A1  - Pistis, Giorgio
A1  - Podgornaia, Anna I.
A1  - Polasek, Ozren
A1  - Ponte, Belen
A1  - Porteous, David J.
A1  - Poulain, Tanja
A1  - Pramstaller, Peter P.
A1  - Preuss, Michael H.
A1  - Prins, Bram P.
A1  - Province, Michael A.
A1  - Rabelink, Ton J.
A1  - Raffield, Laura M.
A1  - Raitakari, Olli T.
A1  - Reilly, Dermot F.
A1  - Rettig, Rainer
A1  - Rheinberger, Myriam
A1  - Rice, Kenneth M.
A1  - Ridker, Paul M.
A1  - Rivadeneira, Fernando
A1  - Rizzi, Federica
A1  - Roberts, David J.
A1  - Robino, Antonietta
A1  - Rossing, Peter
A1  - Rudan, Igor
A1  - Rueedi, Rico
A1  - Ruggiero, Daniela
A1  - Ryan, Kathleen A.
A1  - Saba, Yasaman
A1  - Sabanayagam, Charumathi
A1  - Salomaa, Veikko
A1  - Salvi, Erika
A1  - Saum, Kai-Uwe
A1  - Schmidt, Helena
A1  - Schmidt, Reinhold
A1  - Ben Schottker, 
A1  - Schulz, Christina-Alexandra
A1  - Schupf, Nicole
A1  - Shaffer, Christian M.
A1  - Shi, Yuan
A1  - Smith, Albert V.
A1  - Smith, Blair H.
A1  - Soranzo, Nicole
A1  - Spracklen, Cassandra N.
A1  - Strauch, Konstantin
A1  - Stringham, Heather M.
A1  - Stumvoll, Michael
A1  - Svensson, Per O.
A1  - Szymczak, Silke
A1  - Tai, E-Shyong
A1  - Tajuddin, Salman M.
A1  - Tan, Nicholas Y. Q.
A1  - Taylor, Kent D.
A1  - Teren, Andrej
A1  - Tham, Yih-Chung
A1  - Thiery, Joachim
A1  - Thio, Chris H. L.
A1  - Thomsen, Hauke
A1  - Thorleifsson, Gudmar
A1  - Toniolo, Daniela
A1  - Tonjes, Anke
A1  - Tremblay, Johanne
A1  - Tzoulaki, Ioanna
A1  - Uitterlinden, Andre G.
A1  - Vaccargiu, Simona
A1  - Van Dam, Rob M.
A1  - Van der Harst, Pim
A1  - Van Duijn, Cornelia M.
A1  - Edward, Digna R. Velez
A1  - Verweij, Niek
A1  - Vogelezang, Suzanne
A1  - Volker, Uwe
A1  - Vollenweider, Peter
A1  - Waeber, Gerard
A1  - Waldenberger, Melanie
A1  - Wallentin, Lars
A1  - Wang, Ya Xing
A1  - Wang, Chaolong
A1  - Waterworth, Dawn M.
A1  - Bin Wei, Wen
A1  - White, Harvey
A1  - Whitfield, John B.
A1  - Wild, Sarah H.
A1  - Wilson, James F.
A1  - Wojczynski, Mary K.
A1  - Wong, Charlene
A1  - Wong, Tien-Yin
A1  - Xu, Liang
A1  - Yang, Qiong
A1  - Yasuda, Masayuki
A1  - Yerges-Armstrong, Laura M.
A1  - Zhang, Weihua
A1  - Zonderman, Alan B.
A1  - Rotter, Jerome I.
A1  - Bochud, Murielle
A1  - Psaty, Bruce M.
A1  - Vitart, Veronique
A1  - Wilson, James G.
A1  - Dehghan, Abbas
A1  - Parsa, Afshin
A1  - Chasman, Daniel I.
A1  - Ho, Kevin
A1  - Morris, Andrew P.
A1  - Devuyst, Olivier
A1  - Akilesh, Shreeram
A1  - Pendergrass, Sarah A.
A1  - Sim, Xueling
A1  - Boger, Carsten A.
A1  - Okada, Yukinori
A1  - Edwards, Todd L.
A1  - Snieder, Harold
A1  - Stefansson, Kari
A1  - Hung, Adriana M.
A1  - Heid, Iris M.
A1  - Scholz, Markus
A1  - Teumer, Alexander
A1  - Kottgen, Anna
A1  - Pattaro, Cristian
T1  - A catalog of genetic loci associated with kidney function from analyses of a million individuals
JF  - Nature genetics
N2  - Chronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through transancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these,147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
Y1  - 2019
U6  - https://doi.org/10.1038/s41588-019-0407-x
SN  - 1061-4036
SN  - 1546-1718
VL  - 51
IS  - 6
SP  - 957
EP  - +
PB  - Nature Publ. Group
CY  - New York
ER  - 
TY  - JOUR
A1  - Middeldorp, Christel M.
A1  - Mahajan, Anubha
A1  - Horikoshi, Momoko
A1  - Robertson, Neil R.
A1  - Beaumont, Robin N.
A1  - Bradfield, Jonathan P.
A1  - Bustamante, Mariona
A1  - Cousminer, Diana L.
A1  - Day, Felix R.
A1  - De Silva, N. Maneka
A1  - Guxens, Monica
A1  - Mook-Kanamori, Dennis O.
A1  - St Pourcain, Beate
A1  - Warrington, Nicole M.
A1  - Adair, Linda S.
A1  - Ahlqvist, Emma
A1  - Ahluwalia, Tarunveer Singh
A1  - Almgren, Peter
A1  - Ang, Wei
A1  - Atalay, Mustafa
A1  - Auvinen, Juha
A1  - Bartels, Meike
A1  - Beckmann, Jacques S.
A1  - Bilbao, Jose Ramon
A1  - Bond, Tom
A1  - Borja, Judith B.
A1  - Cavadino, Alana
A1  - Charoen, Pimphen
A1  - Chen, Zhanghua
A1  - Coin, Lachlan
A1  - Cooper, Cyrus
A1  - Curtin, John A.
A1  - Custovic, Adnan
A1  - Das, Shikta
A1  - Davies, Gareth E.
A1  - Dedoussis, George V.
A1  - Duijts, Liesbeth
A1  - Eastwood, Peter R.
A1  - Eliasen, Anders U.
A1  - Elliott, Paul
A1  - Eriksson, Johan G.
A1  - Estivill, Xavier
A1  - Fadista, Joao
A1  - Fedko, Iryna O.
A1  - Frayling, Timothy M.
A1  - Gaillard, Romy
A1  - Gauderman, W. James
A1  - Geller, Frank
A1  - Gilliland, Frank
A1  - Gilsanz, Vincente
A1  - Granell, Raquel
A1  - Grarup, Niels
A1  - Groop, Leif
A1  - Hadley, Dexter
A1  - Hakonarson, Hakon
A1  - Hansen, Torben
A1  - Hartman, Catharina A.
A1  - Hattersley, Andrew T.
A1  - Hayes, M. Geoffrey
A1  - Hebebrand, Johannes
A1  - Heinrich, Joachim
A1  - Helgeland, Oyvind
A1  - Henders, Anjali K.
A1  - Henderson, John
A1  - Henriksen, Tine B.
A1  - Hirschhorn, Joel N.
A1  - Hivert, Marie-France
A1  - Hocher, Berthold
A1  - Holloway, John W.
A1  - Holt, Patrick
A1  - Hottenga, Jouke-Jan
A1  - Hypponen, Elina
A1  - Iniguez, Carmen
A1  - Johansson, Stefan
A1  - Jugessur, Astanand
A1  - Kahonen, Mika
A1  - Kalkwarf, Heidi J.
A1  - Kaprio, Jaakko
A1  - Karhunen, Ville
A1  - Kemp, John P.
A1  - Kerkhof, Marjan
A1  - Koppelman, Gerard H.
A1  - Korner, Antje
A1  - Kotecha, Sailesh
A1  - Kreiner-Moller, Eskil
A1  - Kulohoma, Benard
A1  - Kumar, Ashish
A1  - Kutalik, Zoltan
A1  - Lahti, Jari
A1  - Lappe, Joan M.
A1  - Larsson, Henrik
A1  - Lehtimaki, Terho
A1  - Lewin, Alexandra M.
A1  - Li, Jin
A1  - Lichtenstein, Paul
A1  - Lindgren, Cecilia M.
A1  - Lindi, Virpi
A1  - Linneberg, Allan
A1  - Liu, Xueping
A1  - Liu, Jun
A1  - Lowe, William L.
A1  - Lundstrom, Sebastian
A1  - Lyytikainen, Leo-Pekka
A1  - Ma, Ronald C. W.
A1  - Mace, Aurelien
A1  - Magi, Reedik
A1  - Magnus, Per
A1  - Mamun, Abdullah A.
A1  - Mannikko, Minna
A1  - Martin, Nicholas G.
A1  - Mbarek, Hamdi
A1  - McCarthy, Nina S.
A1  - Medland, Sarah E.
A1  - Melbye, Mads
A1  - Melen, Erik
A1  - Mohlke, Karen L.
A1  - Monnereau, Claire
A1  - Morgen, Camilla S.
A1  - Morris, Andrew P.
A1  - Murray, Jeffrey C.
A1  - Myhre, Ronny
A1  - Najman, Jackob M.
A1  - Nivard, Michel G.
A1  - Nohr, Ellen A.
A1  - Nolte, Ilja M.
A1  - Ntalla, Ioanna
A1  - Oberfield, Sharon E.
A1  - Oken, Emily
A1  - Oldehinkel, Albertine J.
A1  - Pahkala, Katja
A1  - Palviainen, Teemu
A1  - Panoutsopoulou, Kalliope
A1  - Pedersen, Oluf
A1  - Pennell, Craig E.
A1  - Pershagen, Goran
A1  - Pitkanen, Niina
A1  - Plomin, Robert
A1  - Power, Christine
A1  - Prasad, Rashmi B.
A1  - Prokopenko, Inga
A1  - Pulkkinen, Lea
A1  - Raikkonen, Katri
A1  - Raitakari, Olli T.
A1  - Reynolds, Rebecca M.
A1  - Richmond, Rebecca C.
A1  - Rivadeneira, Fernando
A1  - Rodriguez, Alina
A1  - Rose, Richard J.
A1  - Salem, Rany
A1  - Santa-Marina, Loreto
A1  - Saw, Seang-Mei
A1  - Schnurr, Theresia M.
A1  - Scott, James G.
A1  - Selzam, Saskia
A1  - Shepherd, John A.
A1  - Simpson, Angela
A1  - Skotte, Line
A1  - Sleiman, Patrick M. A.
A1  - Snieder, Harold
A1  - Sorensen, Thorkild I. A.
A1  - Standl, Marie
A1  - Steegers, Eric A. P.
A1  - Strachan, David P.
A1  - Straker, Leon
A1  - Strandberg, Timo
A1  - Taylor, Michelle
A1  - Teo, Yik-Ying
A1  - Thiering, Elisabeth
A1  - Torrent, Maties
A1  - Tyrrell, Jessica
A1  - Uitterlinden, Andre G.
A1  - van Beijsterveldt, Toos
A1  - van der Most, Peter J.
A1  - van Duijn, Cornelia M.
A1  - Viikari, Jorma
A1  - Vilor-Tejedor, Natalia
A1  - Vogelezang, Suzanne
A1  - Vonk, Judith M.
A1  - Vrijkotte, Tanja G. M.
A1  - Vuoksimaa, Eero
A1  - Wang, Carol A.
A1  - Watkins, William J.
A1  - Wichmann, H-Erich
A1  - Willemsen, Gonneke
A1  - Williams, Gail M.
A1  - Wilson, James F.
A1  - Wray, Naomi R.
A1  - Xu, Shujing
A1  - Xu, Cheng-Jian
A1  - Yaghootkar, Hanieh
A1  - Yi, Lu
A1  - Zafarmand, Mohammad Hadi
A1  - Zeggini, Eleftheria
A1  - Zemel, Babette S.
A1  - Hinney, Anke
A1  - Lakka, Timo A.
A1  - Whitehouse, Andrew J. O.
A1  - Sunyer, Jordi
A1  - Widen, Elisabeth E.
A1  - Feenstra, Bjarke
A1  - Sebert, Sylvain
A1  - Jacobsson, Bo
A1  - Njolstad, Pal R.
A1  - Stoltenberg, Camilla
A1  - Smith, George Davey
A1  - Lawlor, Debbie A.
A1  - Paternoster, Lavinia
A1  - Timpson, Nicholas J.
A1  - Ong, Ken K.
A1  - Bisgaard, Hans
A1  - Bonnelykke, Klaus
A1  - Jaddoe, Vincent W. V.
A1  - Tiemeier, Henning
A1  - Jarvelin, Marjo-Riitta
A1  - Evans, David M.
A1  - Perry, John R. B.
A1  - Grant, Struan F. A.
A1  - Boomsma, Dorret I.
A1  - Freathy, Rachel M.
A1  - McCarthy, Mark I.
A1  - Felix, Janine F.
T1  - The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia
BT  - design, results and future prospects
JF  - European journal of epidemiology
N2  - The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.
KW  - Genetics
KW  - Consortium
KW  - Childhood traits and disorders
KW  - Longitudinal
Y1  - 2019
U6  - https://doi.org/10.1007/s10654-019-00502-9
SN  - 0393-2990
SN  - 1573-7284
VL  - 34
IS  - 3
SP  - 279
EP  - 300
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - GEN
A1  - Smith, Taylor
A1  - Zotta, Ruxandra-Maria
A1  - Boulton, Chris A.
A1  - Lenton, Timothy M.
A1  - Dorigo, Wouter
A1  - Boers, Niklas
T1  - Reliability of resilience estimation based on multi-instrument time series
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1322 
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-589120
SN  - 1866-8372
IS  - 1322
SP  - 173
EP  - 183
ER  - 
TY  - JOUR
A1  - Smith, Taylor
A1  - Zotta, Ruxandra-Maria
A1  - Boulton, Chris A.
A1  - Lenton, Timothy M.
A1  - Dorigo, Wouter
A1  - Boers, Niklas
T1  - Reliability of resilience estimation based on multi-instrument time series
JF  - Earth System Dynamics
N2  - Many widely used observational data sets are comprised of several overlapping instrument records. While data inter-calibration techniques often yield continuous and reliable data for trend analysis, less attention is generally paid to maintaining higher-order statistics such as variance and autocorrelation. A growing body of work uses these metrics to quantify the stability or resilience of a system under study and potentially to anticipate an approaching critical transition in the system. Exploring the degree to which changes in resilience indicators such as the variance or autocorrelation can be attributed to non-stationary characteristics of the measurement process – rather than actual changes in the dynamical properties of the system – is important in this context. In this work we use both synthetic and empirical data to explore how changes in the noise structure of a data set are propagated into the commonly used resilience metrics lag-one autocorrelation and variance. We focus on examples from remotely sensed vegetation indicators such as vegetation optical depth and the normalized difference vegetation index from different satellite sources. We find that time series resulting from mixing signals from sensors with varied uncertainties and covering overlapping time spans can lead to biases in inferred resilience changes. These biases are typically more pronounced when resilience metrics are aggregated (for example, by land-cover type or region), whereas estimates for individual time series remain reliable at reasonable sensor signal-to-noise ratios. Our work provides guidelines for the treatment and aggregation of multi-instrument data in studies of critical transitions and resilience.
Y1  - 2023
U6  - https://doi.org/10.5194/esd-14-173-2023
SN  - 2190-4987
VL  - 14
SP  - 173
EP  - 183
PB  - Copernicus Publications
CY  - Göttingen
ER  - 
TY  - GEN
A1  - Atmani, Farid
A1  - Bookhagen, Bodo
A1  - Smith, Taylor
T1  - Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1275 
KW  - ICESat-2
KW  - GEDI
KW  - canopy height
KW  - lidar
KW  - savanna
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-569915
SN  - 1866-8372
IS  - 1275
ER  - 
TY  - JOUR
A1  - Smith, Taylor
A1  - Rheinwalt, Aljoscha
A1  - Bookhagen, Bodo
T1  - Topography and climate in the upper Indus Basin
BT  - Mapping elevation-snow cover relationships
JF  - The science of the total environment : an international journal for scientific research into the environment and its relationship with man
N2  - The Upper Indus Basin (UIB), which covers a wide range of climatic and topographic settings, provides an ideal venue to explore the relationship between climate and topography. While the distribution of snow and glaciers is spatially and temporally heterogeneous, there exist regions with similar elevation-snow relationships. In this work, we construct elevation-binned snow-cover statistics to analyze 3415 watersheds and 7357 glaciers in the UIB region. We group both glaciers and watersheds using a hierarchical clustering approach and find that (1) watershed clusters mirror large-scale moisture transport patterns and (2) are highly dependent on median watershed elevation. (3) Glacier clusters are spatially heterogeneous and are less strongly controlled by elevation, but rather by local topographic parameters that modify solar insolation. Our clustering approach allows us to clearly define self-similar snow-topographic regions. Eastern watersheds in the UIB show a steep snow cover-elevation relationship whereas watersheds in the central and western UIB have moderately sloped relationships, but cluster in distinct groups. We highlight this snow-cover-topographic transition zone and argue that these watersheds have different hydrologic responses than other regions. Our hierarchical clustering approach provides a potential new framework to use in defining climatic zones in the cyrosphere based on empirical data.
KW  - Snow-cover
KW  - Hierarchical clustering
KW  - Glaciers
KW  - Upper Indus Basin
Y1  - 2021
U6  - https://doi.org/10.1016/j.scitotenv.2021.147363
SN  - 0048-9697
SN  - 1879-1026
VL  - 786
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Smith, Taylor
A1  - Bookhagen, Bodo
T1  - Climatic and biotic controls on topographic asymmetry at the global scale
JF  - Journal of geophysical research : JGR, Earth surface
N2  - Insolation differences play a primary role in controlling microclimate and vegetation cover, which together influence the development of topography. Topographic asymmetry (TA), or slope differences between terrain aspects, has been well documented in small-scale, field-based, and modeling studies. Here we combine a suite of environmental (e.g., vegetation, temperature, solar insolation) and topographic (e.g., elevation, drainage network) data to explore the driving mechanisms and markers of TA on a global scale. Using a novel empirical TA analysis method, we find that (1) steeper terrain has higher TA magnitudes, (2) globally, pole-facing terrain is on average steeper than equator-facing terrain, especially in mid-latitude, tectonically quiescent, and vegetated landscapes, and (3) high-elevation and low-temperature regions tend to have terrain steepened toward the equator. We further show that there are distinct differences in climate and vegetation cover across terrain aspects, and that TA is reflected in the size and form of fluvial drainage networks. Our work supports the argument that insolation asymmetries engender differences in local microclimates and vegetation on opposing terrain aspects, which broadly encourage the development of asymmetric topography across a range of lithologic, tectonic, geomorphic, and climatic settings.
KW  - erosion
KW  - freeze-thaw cycling
KW  - solar radiation
KW  - topographic asymmetry
KW  - topography
KW  - vegetation cover
Y1  - 2021
U6  - https://doi.org/10.1029/2020JF005692
SN  - 2169-9003
SN  - 2169-9011
VL  - 126
IS  - 1
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - JOUR
A1  - Atmani, Farid
A1  - Bookhagen, Bodo
A1  - Smith, Taylor
T1  - Measuring vegetation heights and their seasonal changes in the Western Namibian Savanna using spaceborne lidars
JF  - Remote sensing / Molecular Diversity Preservation International (MDPI)
N2  - The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
KW  - ICESat-2
KW  - GEDI
KW  - canopy height
KW  - lidar
KW  - savanna
Y1  - 2022
U6  - https://doi.org/10.3390/rs14122928
SN  - 2072-4292
VL  - 14
IS  - 12
SP  - 1
EP  - 20
PB  - MDPI
CY  - Basel, Schweiz
ET  - 12
ER  - 
TY  - GEN
A1  - Smith, Taylor
A1  - Bookhagen, Bodo
T1  - Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data
T2  - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1020 
KW  - snow
KW  - glacier
KW  - climate change
KW  - passive microwave
KW  - special sensor microwave imager
KW  - special sensor microwave imager/sounder
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-484176
SN  - 1866-8372
IS  - 1020
ER  - 
TY  - JOUR
A1  - Smith, Taylor
A1  - Bookhagen, Bodo
T1  - Assessing Multi-Temporal Snow-Volume Trends in High Mountain Asia From 1987 to 2016 Using High-Resolution Passive Microwave Data
JF  - Frontiers in Earth Science
N2  - High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.
KW  - snow
KW  - glacier
KW  - climate change
KW  - passive microwave
KW  - special sensor microwave imager
KW  - special sensor microwave imager/sounder
Y1  - 2020
U6  - https://doi.org/10.3389/feart.2020.559175
SN  - 2296-6463
VL  - 8
PB  - Frontiers Media
CY  - Lausanne
ER  -