caa a22 4500 445300809 CHVBK 20180317142557.0 cr unu---uuuuu 170323e20100801xx s 000 0 eng 10.1007/s11430-010-3061-9 doi (NATIONALLICENCE)springer-10.1007/s11430-010-3061-9 Spatial patterns of ecosystem carbon residence time in Chinese forests [Elektronische Daten] [Tao Zhou, PeiJun Shi, GenSuo Jia, XiuJuan Li, YiQi Luo] Capacity of carbon sequestration in forest ecosystem largely depends on the trend of net primary production (NPP) and the length of ecosystem carbon residence time. Retrieving spatial patterns of ecosystem carbon residence time is important and necessary for accurately predicting regional carbon cycles in the future. In this study, a data-model fusion method that combined a process-based regional carbon model (TECO-R) with various ground-based ecosystem observations (NPP, biomass, and soil organic carbon) and auxiliary data sets (NDVI, meteorological data, and maps of vegetation and soil texture) was applied to estimate spatial patterns of ecosystem carbon residence time in Chinese forests at steady state. In the data-model fusion, the genetic algorithm was used to estimate the optimal model parameters related with the ecosystem carbon residence time by minimizing total deviation between modeled and observed values. The results indicated that data-model fusion technology could effectively retrieve model parameters and simulate carbon cycling processes for Chinese forest ecosystems. The estimated carbon residence times were highly heterogenous over China, with most of regions having values between 24 and 70 years. The deciduous needleleaf forest and the evergreen needleleaf forest had the highest averaged carbon residence times (73.8 and 71.3 years, respectively), the mixed forest and the deciduous broadleaf forest had moderate values (38.1 and 37.3 years, respectively), and the evergreen broadleaf forest had the lowest value (31.7 years). The averaged carbon residence time of forest ecosystems in China was 57.8 years. Science China Press and Springer-Verlag Berlin Heidelberg, 2010 carbon residence time nationallicence carbon cycle nationallicence forest ecosystem nationallicence data-model fusion nationallicence inverse modeling nationallicence genetic algorithm nationallicence Zhou Tao State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875, Beijing, China aut Shi PeiJun State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875, Beijing, China aut Jia GenSuo Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Chinese Academy of Sciences, 100029, Beijing, China aut Li XiuJuan State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875, Beijing, China aut Luo YiQi Department of Botany and Microbiology, University of Oklahoma, 73019, Norman, OK, USA aut Science China Earth Sciences SP Science China Press 53/8(2010-08-01), 1229-1240 1674-7313 53:8<1229 2010 53 11430 https://doi.org/10.1007/s11430-010-3061-9 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11430-010-3061-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhou Tao State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875, Beijing, China aut NATIONALLICENCE 700 1- Shi PeiJun State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875, Beijing, China aut NATIONALLICENCE 700 1- Jia GenSuo Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Chinese Academy of Sciences, 100029, Beijing, China aut NATIONALLICENCE 700 1- Li XiuJuan State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875, Beijing, China aut NATIONALLICENCE 700 1- Luo YiQi Department of Botany and Microbiology, University of Oklahoma, 73019, Norman, OK, USA aut NATIONALLICENCE 773 0- Science China Earth Sciences SP Science China Press 53/8(2010-08-01), 1229-1240 1674-7313 53:8<1229 2010 53 11430 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer