Detection and attribution of vegetation greening trend in China over the last 30 years
The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening tr...
Saved in:
| Published in | Global change biology Vol. 21; no. 4; pp. 1601 - 1609 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Blackwell Science
01.04.2015
Blackwell Publishing Ltd |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite‐derived Leaf Area Index (LAI) datasets for detection as well as five different process‐based ecosystem models for attribution. Rising atmospheric CO₂concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing‐season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr⁻¹, ranging from 0.0035 yr⁻¹to 0.0127 yr⁻¹), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGSat the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai–Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS(P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGStrend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO₂concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. |
|---|---|
| AbstractList | The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite‐derived Leaf Area Index (LAI) datasets for detection as well as five different process‐based ecosystem models for attribution. Rising atmospheric CO₂concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing‐season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr⁻¹, ranging from 0.0035 yr⁻¹to 0.0127 yr⁻¹), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGSat the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai–Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS(P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGStrend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO₂concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr(-1), ranging from 0.0035 yr(-1) to 0.0127 yr(-1)), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr(-1), ranging from 0.0035 yr(-1) to 0.0127 yr(-1)), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite‐derived Leaf Area Index (LAI) datasets for detection as well as five different process‐based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing‐season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr−1, ranging from 0.0035 yr−1 to 0.0127 yr−1), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai–Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr(-1), ranging from 0.0035 yr(-1) to 0.0127 yr(-1)), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite‐derived Leaf Area Index ( LAI ) datasets for detection as well as five different process‐based ecosystem models for attribution. Rising atmospheric CO 2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing‐season LAI trend ( LAI GS ) estimated by satellite datasets (average trend of 0.0070 yr −1 , ranging from 0.0035 yr −1 to 0.0127 yr −1 ), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI GS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai–Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI GS ( P < 0.05), and marginally significantly ( P = 0.07) correlated with the residual of LAI GS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO 2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite‐derived Leaf Area Index (LAI) datasets for detection as well as five different process‐based ecosystem models for attribution. Rising atmospheric CO₂concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing‐season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr⁻¹, ranging from 0.0035 yr⁻¹to 0.0127 yr⁻¹), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGSat the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai–Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS(P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGStrend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO₂concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO sub(2) concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAI sub(GS)) estimated by satellite datasets (average trend of 0.0070 yr super(-1), ranging from 0.0035 yr super(-1) to 0.0127 yr super(-1)), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI sub(GS) at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI sub(GS) (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAI sub(GS) trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO sub(2) concentration and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. |
| Author | Yin, Guodong Shi, Xiaoying Wang, Yingping Myneni, Ranga B. Cheng, Lei Zeng, ZhenZhong Huang, Mengtian Poulter, Ben Xiao, Zhiqiang Peng, Shushi Liu, Ronggao Li, Yue Mao, Jiafu Piao, Shilong Zeng, Ning Tan, Jianguang |
| Author_xml | – sequence: 1 fullname: Piao, Shilong – sequence: 2 fullname: Yin, Guodong – sequence: 3 fullname: Tan, Jianguang – sequence: 4 fullname: Cheng, Lei – sequence: 5 fullname: Huang, Mengtian – sequence: 6 fullname: Li, Yue – sequence: 7 fullname: Liu, Ronggao – sequence: 8 fullname: Mao, Jiafu – sequence: 9 fullname: Myneni, Ranga B – sequence: 10 fullname: Peng, Shushi – sequence: 11 fullname: Poulter, Ben – sequence: 12 fullname: Shi, Xiaoying – sequence: 13 fullname: Xiao, Zhiqiang – sequence: 14 fullname: Zeng, Ning – sequence: 15 fullname: Zeng, ZhenZhong – sequence: 16 fullname: Wang, Yingping |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25369401$$D View this record in MEDLINE/PubMed |
| BookMark | eNqN0c1u1DAQB3ALFdF24cALQCQu5ZDW33GOdOkuoIoPQdWj5WTHW5esU2ynsG_Ds_BkeDdtD5VA9cUe6Tdjjf77aMf3HhB6TvAhyedo2TaHhFa1eIT2CJOipFzJnc1b8JJgwnbRfoyXGGNGsXyCdqlgsuaY7KHzt5CgTa73hfGLwqQUXDNs694W17CEZLbVMgB455dFCpCh88X0wnlT9NcQinQBRWdiKhj-83sNJsSn6LE1XYRnN_cEnc1Ovk3flaef5u-nb07LVgoqSm4babixQgrV2AZTRbmtwcoKMwymBW6wWHDTSiWkVFLUpGmsghYbtZAE2AQdjHOvQv9jgJj0ysUWus546IeoSd6TKyJx9QAqFRU1pfwhlEpWSckyfXWPXvZD8HnnjcofE5x3maAXN2poVrDQV8GtTFjr2yAyeD2CNvQxBrB3hGC9CVnnkPU25GyP7tnWjSmlYFz3v46froP1v0fr-fT4tqMcO1xM8Ouuw4TvWlasEvr841x_-TD7jOfHSs-yfzl6a3ptlsFFffaVYiIwJnVOU7C_oq_OFA |
| CitedBy_id | crossref_primary_10_1016_j_agrformet_2017_06_016 crossref_primary_10_3390_w16172440 crossref_primary_10_1111_1365_2435_13088 crossref_primary_10_3390_rs11232766 crossref_primary_10_5194_hess_20_935_2016 crossref_primary_10_1016_j_agrformet_2022_108842 crossref_primary_10_1007_s10980_015_0319_9 crossref_primary_10_3390_rs14122947 crossref_primary_10_1016_j_ecolind_2022_108835 crossref_primary_10_3390_rs10101525 crossref_primary_10_1016_j_scitotenv_2017_02_156 crossref_primary_10_3389_fpls_2023_1178485 crossref_primary_10_1002_ldr_4269 crossref_primary_10_1007_s10661_020_08593_8 crossref_primary_10_1016_j_rama_2019_06_001 crossref_primary_10_3390_rs13173531 crossref_primary_10_3390_rs9050425 crossref_primary_10_1016_j_scitotenv_2023_165288 crossref_primary_10_1016_j_scitotenv_2023_166133 crossref_primary_10_1088_1748_9326_ad107f crossref_primary_10_3390_w12061692 crossref_primary_10_1007_s11356_022_21138_5 crossref_primary_10_3390_f14091880 crossref_primary_10_1002_ldr_4381 crossref_primary_10_1016_j_agrformet_2024_110124 crossref_primary_10_1080_15230430_2019_1605798 crossref_primary_10_1016_j_aosl_2022_100297 crossref_primary_10_1016_j_ecolind_2019_105892 crossref_primary_10_1371_journal_pone_0130143 crossref_primary_10_3390_f12060733 crossref_primary_10_3390_rs15194785 crossref_primary_10_1111_gcb_13787 crossref_primary_10_3390_f10010039 crossref_primary_10_3390_rs14205091 crossref_primary_10_1016_j_scitotenv_2020_142553 crossref_primary_10_1016_j_jenvman_2021_114304 crossref_primary_10_1109_TGRS_2017_2691542 crossref_primary_10_3390_rs15184621 crossref_primary_10_1016_j_jag_2021_102537 crossref_primary_10_3390_land12020413 crossref_primary_10_34133_remotesensing_0122 crossref_primary_10_1016_j_ecolind_2023_111039 crossref_primary_10_1002_eco_2420 crossref_primary_10_1038_s41597_022_01204_w crossref_primary_10_3390_rs15194670 crossref_primary_10_1002_joc_8633 crossref_primary_10_1007_s11707_022_1075_1 crossref_primary_10_1109_ACCESS_2023_3325485 crossref_primary_10_1002_ldr_4129 crossref_primary_10_3390_land11081179 crossref_primary_10_3390_rs14122929 crossref_primary_10_1016_j_ecolind_2021_108336 crossref_primary_10_1038_srep26958 crossref_primary_10_1080_10095020_2024_2446293 crossref_primary_10_3390_rs11141656 crossref_primary_10_1016_j_palaeo_2023_111745 crossref_primary_10_3390_f16030460 crossref_primary_10_3390_rs8121032 crossref_primary_10_1016_j_ecoinf_2022_101776 crossref_primary_10_3390_rs14061328 crossref_primary_10_1007_s13201_020_01332_x crossref_primary_10_3390_f14020425 crossref_primary_10_1088_1748_9326_abbde9 crossref_primary_10_1080_10106049_2022_2136257 crossref_primary_10_3390_rs12213567 crossref_primary_10_1016_j_jag_2024_104009 crossref_primary_10_1016_j_ecolind_2024_111553 crossref_primary_10_1038_s41598_024_61200_5 crossref_primary_10_1080_01431161_2020_1714781 crossref_primary_10_1093_forsci_fxac054 crossref_primary_10_3390_su14148402 crossref_primary_10_1016_j_ecolind_2021_108323 crossref_primary_10_1126_science_aaa9933 crossref_primary_10_1016_j_ecolind_2021_107479 crossref_primary_10_1016_j_forpol_2021_102516 crossref_primary_10_1016_j_pce_2022_103148 crossref_primary_10_1080_15324982_2023_2269883 crossref_primary_10_3390_f9070412 crossref_primary_10_1016_j_scitotenv_2020_140395 crossref_primary_10_1016_j_scitotenv_2023_165255 crossref_primary_10_5194_essd_15_4877_2023 crossref_primary_10_1016_j_agrformet_2020_108183 crossref_primary_10_1016_j_catena_2022_106530 crossref_primary_10_1111_bor_12589 crossref_primary_10_1093_jpe_rtab082 crossref_primary_10_1016_j_agee_2021_107719 crossref_primary_10_1080_15481603_2021_1872244 crossref_primary_10_3389_fpls_2023_1280126 crossref_primary_10_1016_j_ecolind_2023_111088 crossref_primary_10_3390_rs17030487 crossref_primary_10_1016_j_scitotenv_2024_176013 crossref_primary_10_1016_j_srs_2021_100027 crossref_primary_10_1080_10106049_2021_2017016 crossref_primary_10_1002_2016JG003640 crossref_primary_10_1016_j_isprsjprs_2023_10_017 crossref_primary_10_1016_j_jhydrol_2024_131731 crossref_primary_10_1093_nsr_nwz132 crossref_primary_10_3390_land11060894 crossref_primary_10_3390_rs15102675 crossref_primary_10_1029_2018JG004804 crossref_primary_10_1080_10106049_2022_2129820 crossref_primary_10_1007_s12665_022_10245_8 crossref_primary_10_1093_nsr_nwz021 crossref_primary_10_1016_j_gecco_2021_e01685 crossref_primary_10_1016_j_agrformet_2022_108809 crossref_primary_10_1016_j_biocon_2023_110424 crossref_primary_10_1007_s11430_023_1345_9 crossref_primary_10_3389_fenvs_2022_819277 crossref_primary_10_3390_ijerph20031874 crossref_primary_10_5194_essd_12_1037_2020 crossref_primary_10_1002_2017JD027597 crossref_primary_10_1002_2016JG003417 crossref_primary_10_1016_j_cosust_2020_11_006 crossref_primary_10_1016_j_ecolind_2024_112465 crossref_primary_10_1016_j_ecolind_2024_112101 crossref_primary_10_1016_j_scitotenv_2023_168464 crossref_primary_10_1016_j_agrformet_2020_108197 crossref_primary_10_3390_f9010024 crossref_primary_10_1007_s42452_020_2290_6 crossref_primary_10_1016_j_agrformet_2019_107701 crossref_primary_10_1080_19479832_2019_1601642 crossref_primary_10_1016_j_scitotenv_2020_141537 crossref_primary_10_3390_rs14040839 crossref_primary_10_1016_j_atmosres_2022_106308 crossref_primary_10_3390_su162210122 crossref_primary_10_1016_j_agrformet_2021_108384 crossref_primary_10_1088_1748_9326_ad5f43 crossref_primary_10_3390_atmos14091419 crossref_primary_10_3390_rs14246327 crossref_primary_10_1360_SSTe_2022_0011 crossref_primary_10_1016_j_ecolind_2020_106392 crossref_primary_10_1016_j_foreco_2019_05_065 crossref_primary_10_3390_rs14143320 crossref_primary_10_3390_rs15194736 crossref_primary_10_3390_su16031351 crossref_primary_10_3390_rs13183756 crossref_primary_10_1016_j_jhydrol_2025_133181 crossref_primary_10_1016_j_scitotenv_2021_147380 crossref_primary_10_1016_j_jenvman_2024_122967 crossref_primary_10_1016_j_ecolind_2023_111186 crossref_primary_10_1029_2018EF000890 crossref_primary_10_1038_srep35105 crossref_primary_10_3390_rs15235582 crossref_primary_10_26833_ijeg_1583206 crossref_primary_10_3390_rs13204063 crossref_primary_10_1016_j_ecolind_2018_08_031 crossref_primary_10_1002_ldr_3351 crossref_primary_10_1007_s00484_020_01913_0 crossref_primary_10_1175_JCLI_D_17_0848_1 crossref_primary_10_1016_j_envres_2021_111787 crossref_primary_10_3390_rs14143312 crossref_primary_10_3390_w15224010 crossref_primary_10_1002_eco_1748 crossref_primary_10_1002_ldr_4689 crossref_primary_10_1016_j_agrformet_2024_110229 crossref_primary_10_1016_j_accre_2024_08_003 crossref_primary_10_1016_j_ecolind_2024_112317 crossref_primary_10_1002_hyp_15055 crossref_primary_10_3389_fevo_2023_1177849 crossref_primary_10_1016_j_atmosres_2022_106450 crossref_primary_10_1007_s11442_024_2298_8 crossref_primary_10_1016_j_jclepro_2020_122487 crossref_primary_10_1016_j_jenvman_2024_120535 crossref_primary_10_2166_hydro_2019_003 crossref_primary_10_1002_2017GB005714 crossref_primary_10_1111_gcb_13762 crossref_primary_10_1016_j_geosus_2022_04_002 crossref_primary_10_1007_s10531_024_02789_x crossref_primary_10_3390_rs11182110 crossref_primary_10_1016_j_ecolind_2023_111291 crossref_primary_10_1002_joc_7639 crossref_primary_10_1002_ldr_5408 crossref_primary_10_1016_j_scitotenv_2022_155086 crossref_primary_10_3390_f15071207 crossref_primary_10_1002_2016GL071743 crossref_primary_10_1175_JCLI_D_18_0772_1 crossref_primary_10_1016_j_foreco_2023_121668 crossref_primary_10_1002_ldr_3587 crossref_primary_10_1002_ldr_4675 crossref_primary_10_1002_ecm_70005 crossref_primary_10_1007_s11676_019_00999_6 crossref_primary_10_1016_j_jhydrol_2020_125689 crossref_primary_10_1080_01431161_2016_1142688 crossref_primary_10_1016_j_foreco_2022_120392 crossref_primary_10_1016_j_agrformet_2019_02_039 crossref_primary_10_1016_j_scitotenv_2021_147574 crossref_primary_10_1111_gcb_14369 crossref_primary_10_1111_gcb_15217 crossref_primary_10_1007_s10021_016_0068_x crossref_primary_10_1016_j_scitotenv_2022_160943 crossref_primary_10_3390_rs15051238 crossref_primary_10_5194_acp_21_4825_2021 crossref_primary_10_1016_j_ecoinf_2022_101838 crossref_primary_10_1038_s41467_019_11035_w crossref_primary_10_1038_s41598_017_07732_5 crossref_primary_10_1071_RJ18041 crossref_primary_10_1186_s13021_023_00239_9 crossref_primary_10_3390_f14081563 crossref_primary_10_1007_s11629_021_7110_y crossref_primary_10_1016_j_envres_2025_121120 crossref_primary_10_1016_j_agwat_2023_108649 crossref_primary_10_1038_s41586_018_0411_9 crossref_primary_10_3390_rs13214326 crossref_primary_10_1016_j_ecolind_2015_09_041 crossref_primary_10_1007_s10661_015_4922_7 crossref_primary_10_1016_j_agrformet_2018_02_007 crossref_primary_10_1016_j_scitotenv_2021_145160 crossref_primary_10_3390_f9060329 crossref_primary_10_1029_2024GL111209 crossref_primary_10_1016_j_jenvman_2023_119963 crossref_primary_10_3390_rs71115517 crossref_primary_10_1016_j_ecolind_2024_112141 crossref_primary_10_3389_fevo_2023_1105832 crossref_primary_10_1016_j_ecolind_2024_112140 crossref_primary_10_1016_j_landusepol_2020_105084 crossref_primary_10_1016_j_scitotenv_2020_140649 crossref_primary_10_3390_atmos12121576 crossref_primary_10_1007_s00382_020_05563_1 crossref_primary_10_1038_s41598_024_69276_9 crossref_primary_10_1029_2019WR027019 crossref_primary_10_3390_rs15051233 crossref_primary_10_3390_rs16071296 crossref_primary_10_3390_rs13030496 crossref_primary_10_1016_j_gecco_2024_e02904 crossref_primary_10_1016_j_jhydrol_2022_128817 crossref_primary_10_1029_2022EF003057 crossref_primary_10_1016_j_scitotenv_2024_170055 crossref_primary_10_1126_sciadv_aar4182 crossref_primary_10_1175_BAMS_D_18_0341_1 crossref_primary_10_1016_j_scitotenv_2024_170053 crossref_primary_10_5194_hess_26_5291_2022 crossref_primary_10_1111_gcb_16569 crossref_primary_10_1016_j_agrformet_2023_109478 crossref_primary_10_3390_s19214693 crossref_primary_10_3390_ijerph19106239 crossref_primary_10_3390_rs12203355 crossref_primary_10_1016_j_catena_2019_104182 crossref_primary_10_1016_j_ecolind_2022_109429 crossref_primary_10_1016_j_scitotenv_2022_153270 crossref_primary_10_3390_w12071996 crossref_primary_10_1016_j_foreco_2021_119463 crossref_primary_10_1016_j_jag_2021_102378 crossref_primary_10_3390_rs13245081 crossref_primary_10_1007_s11104_021_05054_0 crossref_primary_10_1038_s41467_020_15881_x crossref_primary_10_1016_j_rse_2023_113481 crossref_primary_10_3390_rs14194735 crossref_primary_10_3390_rs16214024 crossref_primary_10_3390_rs10030424 crossref_primary_10_1029_2020JG005735 crossref_primary_10_1007_s00484_023_02489_1 crossref_primary_10_1016_j_jhydrol_2022_128707 crossref_primary_10_34133_2021_9842830 crossref_primary_10_1111_gcb_16561 crossref_primary_10_1007_s11769_024_1477_y crossref_primary_10_1016_j_jhydrol_2021_126231 crossref_primary_10_3390_land9100388 crossref_primary_10_1029_2023GL105489 crossref_primary_10_1080_15481603_2020_1751406 crossref_primary_10_3390_f14071415 crossref_primary_10_1029_2022WR032415 crossref_primary_10_3390_land12010235 crossref_primary_10_3390_su151410878 crossref_primary_10_1007_s40808_020_00896_6 crossref_primary_10_1016_j_ecoleng_2021_106504 crossref_primary_10_1088_1748_9326_ad122b crossref_primary_10_1016_j_scitotenv_2022_159995 crossref_primary_10_1007_s11356_021_16664_7 crossref_primary_10_1016_j_ese_2020_100025 crossref_primary_10_1029_2024GL113403 crossref_primary_10_1016_j_jhydrol_2022_128714 crossref_primary_10_1016_j_catena_2024_108354 crossref_primary_10_1029_2018GB005922 crossref_primary_10_1016_j_habitatint_2022_102733 crossref_primary_10_1016_j_agrformet_2020_107950 crossref_primary_10_3389_fevo_2023_1067209 crossref_primary_10_3390_rs14174198 crossref_primary_10_1371_journal_pone_0202966 crossref_primary_10_1007_s12237_023_01216_8 crossref_primary_10_1016_j_ecolind_2018_12_039 crossref_primary_10_1007_s00484_024_02794_3 crossref_primary_10_1016_j_energy_2019_116215 crossref_primary_10_1038_s41467_025_57548_5 crossref_primary_10_1029_2023GL102809 crossref_primary_10_3390_ijgi5090158 crossref_primary_10_3390_ijerph192113916 crossref_primary_10_1002_ldr_4744 crossref_primary_10_1007_s12040_023_02200_3 crossref_primary_10_1016_j_jag_2022_102747 crossref_primary_10_1016_j_jhydrol_2023_130087 crossref_primary_10_1016_j_scitotenv_2023_167664 crossref_primary_10_3390_atmos14081217 crossref_primary_10_3390_rs70810243 crossref_primary_10_1016_j_jag_2020_102240 crossref_primary_10_1038_srep40092 crossref_primary_10_1007_s11356_023_31335_5 crossref_primary_10_1016_j_ecolind_2022_108883 crossref_primary_10_1111_geb_12411 crossref_primary_10_1029_2023JD039914 crossref_primary_10_1038_s41893_017_0004_x crossref_primary_10_3390_rs12030353 crossref_primary_10_1038_nclimate3004 crossref_primary_10_1029_2018RG000608 crossref_primary_10_1016_j_jenvman_2021_112562 crossref_primary_10_1186_s13021_021_00186_3 crossref_primary_10_1029_2024GL110312 crossref_primary_10_1071_RJ18007 crossref_primary_10_1080_20964471_2023_2268322 crossref_primary_10_1007_s13157_020_01271_y crossref_primary_10_1016_j_ecolind_2020_107081 crossref_primary_10_1016_j_envres_2022_113085 crossref_primary_10_1016_j_habitatint_2025_103286 crossref_primary_10_1175_JCLI_D_17_0236_1 crossref_primary_10_3389_feart_2023_1168384 crossref_primary_10_1002_joc_6137 crossref_primary_10_3390_rs13214380 crossref_primary_10_1007_s11430_024_1530_7 crossref_primary_10_1016_j_ecolind_2019_105607 crossref_primary_10_3390_su8040326 crossref_primary_10_1016_j_jhydrol_2024_131436 crossref_primary_10_1016_j_scitotenv_2018_10_058 crossref_primary_10_1029_2022JD036919 crossref_primary_10_3390_rs14102408 crossref_primary_10_1007_s11104_023_06430_8 crossref_primary_10_3390_f12010083 crossref_primary_10_1016_j_scitotenv_2021_148408 crossref_primary_10_1016_j_scitotenv_2022_155490 crossref_primary_10_1002_2015GL067162 crossref_primary_10_1071_RJ16032 crossref_primary_10_3390_f13050708 crossref_primary_10_1016_j_agrformet_2023_109561 crossref_primary_10_1016_j_envpol_2017_11_052 crossref_primary_10_1016_j_foreco_2021_120000 crossref_primary_10_3390_f12121710 crossref_primary_10_1029_2021JG006320 crossref_primary_10_1016_j_scib_2018_07_015 crossref_primary_10_1002_clt2_12116 crossref_primary_10_1016_j_scitotenv_2023_168842 crossref_primary_10_1016_j_ecolind_2021_108004 crossref_primary_10_1016_j_scitotenv_2020_140625 crossref_primary_10_1016_j_scitotenv_2017_02_073 crossref_primary_10_3390_rs11101224 crossref_primary_10_1007_s11356_018_2340_4 crossref_primary_10_5194_hess_21_295_2017 crossref_primary_10_3390_atmos13050738 crossref_primary_10_1016_j_agrformet_2019_107771 crossref_primary_10_3390_rs10030477 crossref_primary_10_3390_su16083251 crossref_primary_10_1016_j_agrformet_2019_107652 crossref_primary_10_1038_srep28435 crossref_primary_10_1016_j_envdev_2024_100991 crossref_primary_10_3390_rs10081270 crossref_primary_10_1038_s41893_019_0220_7 crossref_primary_10_1016_j_agrformet_2023_109653 crossref_primary_10_3390_su14127262 crossref_primary_10_3390_su11174531 crossref_primary_10_3390_rs16030475 crossref_primary_10_1007_s11356_021_16440_7 crossref_primary_10_1016_j_rse_2016_03_036 crossref_primary_10_1111_rec_13760 crossref_primary_10_1007_s11442_022_1948_y crossref_primary_10_1029_2020JD032404 crossref_primary_10_1029_2023GL107182 crossref_primary_10_1016_j_rsase_2021_100466 crossref_primary_10_1109_TGRS_2024_3434366 crossref_primary_10_1029_2021EF002634 crossref_primary_10_1016_j_ecolind_2022_109373 crossref_primary_10_1016_j_jclepro_2024_143702 crossref_primary_10_3390_ijerph18020416 crossref_primary_10_3390_rs11202421 crossref_primary_10_3390_rs14010208 crossref_primary_10_1016_j_agrformet_2023_109546 crossref_primary_10_3390_su16083303 crossref_primary_10_1016_j_agrformet_2017_10_001 crossref_primary_10_1029_2019JD031467 crossref_primary_10_1029_2020GL089681 crossref_primary_10_1029_2022JG007300 crossref_primary_10_1016_j_agrformet_2018_04_020 crossref_primary_10_1080_01431161_2018_1519286 crossref_primary_10_1038_s41598_018_35108_w crossref_primary_10_3390_f13101583 crossref_primary_10_1016_j_envres_2022_113976 crossref_primary_10_3390_su162310453 crossref_primary_10_1007_s10980_023_01679_x crossref_primary_10_1088_1748_9326_11_10_104006 crossref_primary_10_1360_SSTe_2023_0106 crossref_primary_10_3390_f14071485 crossref_primary_10_1016_j_jhazmat_2020_124996 crossref_primary_10_1088_1748_9326_ac44c5 crossref_primary_10_1088_1748_9326_ac69fb crossref_primary_10_1002_ldr_2985 crossref_primary_10_1007_s00704_018_2476_7 crossref_primary_10_1016_j_horiz_2022_100007 crossref_primary_10_1016_j_scitotenv_2021_147756 crossref_primary_10_1016_j_ecolmodel_2015_12_019 crossref_primary_10_1175_JHM_D_18_0259_1 crossref_primary_10_1016_j_agrformet_2023_109758 crossref_primary_10_1186_s12302_024_00852_6 crossref_primary_10_1016_j_agrformet_2022_109118 crossref_primary_10_1016_j_ecolind_2020_106545 crossref_primary_10_3390_rs12193150 crossref_primary_10_3390_rs14051092 crossref_primary_10_3390_su9101853 crossref_primary_10_1016_j_compag_2020_105238 crossref_primary_10_1080_15481603_2022_2139387 crossref_primary_10_1016_j_rama_2024_05_005 crossref_primary_10_1016_j_gloplacha_2024_104440 crossref_primary_10_1016_j_scitotenv_2022_157729 crossref_primary_10_1177_2754124X241240164 crossref_primary_10_3390_rs14163849 crossref_primary_10_1016_j_jenvman_2022_116576 crossref_primary_10_3390_rs15030727 crossref_primary_10_3390_rs16122121 crossref_primary_10_1016_j_rse_2017_12_024 crossref_primary_10_1016_j_scitotenv_2023_163626 crossref_primary_10_1016_j_geosus_2023_11_008 crossref_primary_10_5194_acp_22_2351_2022 crossref_primary_10_1016_j_ecoleng_2022_106768 crossref_primary_10_1111_gcb_15061 crossref_primary_10_3389_fpls_2023_1129665 crossref_primary_10_1016_j_gloplacha_2016_10_014 crossref_primary_10_1007_s00382_024_07317_9 crossref_primary_10_1016_j_rse_2024_114589 crossref_primary_10_3390_w14132019 crossref_primary_10_1111_gcb_16274 crossref_primary_10_1007_s11442_023_2093_y crossref_primary_10_1016_j_scitotenv_2024_172758 crossref_primary_10_1038_s41598_017_08477_x crossref_primary_10_1007_s11430_022_9926_6 crossref_primary_10_1016_j_scitotenv_2024_175845 crossref_primary_10_1029_2023EF004028 crossref_primary_10_3390_rs10010032 crossref_primary_10_1038_nclimate3092 crossref_primary_10_1029_2020JG005902 crossref_primary_10_1002_esp_5752 crossref_primary_10_1016_j_scitotenv_2022_154222 crossref_primary_10_1080_16742834_2020_1695515 crossref_primary_10_1016_j_oneear_2019_12_015 crossref_primary_10_1016_j_agrformet_2018_03_009 crossref_primary_10_1109_TGRS_2023_3343876 crossref_primary_10_3390_rs14133082 crossref_primary_10_1007_s11769_023_1355_z crossref_primary_10_1016_j_jhydrol_2022_128968 crossref_primary_10_1029_2018JG004489 crossref_primary_10_1016_j_ecolind_2018_04_027 crossref_primary_10_1029_2023EF004395 crossref_primary_10_1360_N072024_0239 crossref_primary_10_1016_j_gecco_2022_e02016 crossref_primary_10_1016_j_ecolind_2018_04_041 crossref_primary_10_1016_j_rse_2022_112985 crossref_primary_10_3390_f8030060 crossref_primary_10_3390_rs10010149 crossref_primary_10_1007_s41976_023_00091_y crossref_primary_10_1080_07038992_2018_1526064 crossref_primary_10_3390_land12010013 crossref_primary_10_3390_rs13061209 crossref_primary_10_3390_rs14215417 crossref_primary_10_1038_s43247_023_00814_5 crossref_primary_10_3390_f13091404 crossref_primary_10_1111_gcb_13180 crossref_primary_10_1016_j_rse_2016_12_018 crossref_primary_10_1016_j_agrformet_2018_05_025 crossref_primary_10_1016_j_jenvman_2020_110321 crossref_primary_10_1016_j_uclim_2022_101230 crossref_primary_10_3390_rs12193247 crossref_primary_10_1016_j_agwat_2021_106798 crossref_primary_10_1016_j_jclepro_2020_122705 crossref_primary_10_1016_j_geosus_2020_12_002 crossref_primary_10_1016_j_scs_2024_105977 crossref_primary_10_3390_f10050372 crossref_primary_10_34133_ehs_0284 crossref_primary_10_3390_agronomy14102235 crossref_primary_10_1109_TGRS_2020_2993641 crossref_primary_10_7717_peerj_7735 crossref_primary_10_3390_w15183305 crossref_primary_10_3390_rs14102485 crossref_primary_10_1016_j_rse_2021_112678 crossref_primary_10_1177_2754124X231204715 crossref_primary_10_2139_ssrn_3998978 crossref_primary_10_1088_1748_9326_ac6002 crossref_primary_10_1016_j_scitotenv_2024_172551 crossref_primary_10_1038_s41467_019_13798_8 crossref_primary_10_1016_j_ecolind_2022_109342 crossref_primary_10_1016_j_ecolind_2022_109101 crossref_primary_10_1016_j_agrformet_2020_108309 crossref_primary_10_1088_1748_9326_11_3_034021 crossref_primary_10_1016_j_ecoinf_2021_101413 crossref_primary_10_3390_rs13132446 crossref_primary_10_1007_s11629_019_5649_7 crossref_primary_10_3390_land9080274 crossref_primary_10_1002_fee_2077 crossref_primary_10_3390_w14182923 crossref_primary_10_1029_2020JD033637 crossref_primary_10_3390_rs14051298 crossref_primary_10_1016_j_agwat_2023_108662 crossref_primary_10_1016_j_rse_2020_112247 crossref_primary_10_1007_s11069_024_06540_1 crossref_primary_10_1111_csp2_13309 crossref_primary_10_1002_ird_2740 crossref_primary_10_3390_f15020231 crossref_primary_10_1002_joc_8046 crossref_primary_10_1016_j_jhydrol_2021_126037 crossref_primary_10_1016_j_rse_2021_112428 crossref_primary_10_3390_rs15122975 crossref_primary_10_3390_rs16203875 crossref_primary_10_1175_JCLI_D_18_0587_1 crossref_primary_10_1016_j_foreco_2020_117990 crossref_primary_10_1007_s00704_018_2519_0 crossref_primary_10_1016_j_catena_2018_12_007 crossref_primary_10_1016_j_scitotenv_2023_163792 crossref_primary_10_1016_j_catena_2021_105501 crossref_primary_10_1016_j_ecolind_2021_108091 crossref_primary_10_1088_1748_9326_ace83d crossref_primary_10_1016_j_catena_2021_105500 crossref_primary_10_3389_fenvs_2022_892747 crossref_primary_10_3390_rs14102386 crossref_primary_10_3390_land12091692 crossref_primary_10_1016_j_scitotenv_2021_151153 crossref_primary_10_1016_j_ecolind_2018_03_029 crossref_primary_10_1016_j_envres_2024_118233 crossref_primary_10_1016_j_agrformet_2020_108207 crossref_primary_10_1016_j_agrformet_2020_108208 crossref_primary_10_1038_s43247_024_01962_y crossref_primary_10_3390_land12061235 crossref_primary_10_1016_j_scitotenv_2022_161246 crossref_primary_10_5194_esd_10_9_2019 crossref_primary_10_1016_j_envdev_2019_100464 crossref_primary_10_3390_f15040668 crossref_primary_10_1088_2515_7620_ab9525 crossref_primary_10_3390_su151713089 crossref_primary_10_1134_S0001433821120148 crossref_primary_10_3390_s20143839 crossref_primary_10_1080_16742834_2020_1696650 crossref_primary_10_1029_2023EF004119 crossref_primary_10_1016_j_jhydrol_2022_128018 crossref_primary_10_3390_land9030090 crossref_primary_10_1016_j_resconrec_2023_106878 crossref_primary_10_1007_s11769_018_0952_8 crossref_primary_10_1007_s00484_016_1277_x crossref_primary_10_1007_s00484_020_01978_x crossref_primary_10_1016_j_jenvman_2024_122617 crossref_primary_10_3390_rs16203813 crossref_primary_10_1029_2022WR034142 crossref_primary_10_1029_2023JD040421 crossref_primary_10_1016_j_ecolind_2024_111739 crossref_primary_10_1016_j_scitotenv_2022_161250 crossref_primary_10_1016_j_rsase_2022_100695 crossref_primary_10_3390_s19020430 crossref_primary_10_3390_rs15092324 crossref_primary_10_1007_s11356_021_14988_y crossref_primary_10_1088_1748_9326_ac37d2 crossref_primary_10_1177_0309133320979501 crossref_primary_10_3390_land13121980 crossref_primary_10_3390_su12051955 crossref_primary_10_5194_hess_24_515_2020 crossref_primary_10_3390_rs14174332 crossref_primary_10_3390_su17010306 crossref_primary_10_1007_s11356_022_19711_z crossref_primary_10_1016_j_ecolind_2021_107648 crossref_primary_10_1016_j_srs_2022_100054 crossref_primary_10_1155_2017_8282353 crossref_primary_10_1007_s10661_022_10530_w crossref_primary_10_1088_1748_9326_ab3012 crossref_primary_10_1007_s00267_020_01276_7 crossref_primary_10_1016_j_scitotenv_2024_171816 crossref_primary_10_3390_rs14010061 crossref_primary_10_1016_j_scitotenv_2020_137836 crossref_primary_10_1029_2022EF002674 crossref_primary_10_1016_j_catena_2024_107831 crossref_primary_10_1016_j_jenvman_2022_115509 crossref_primary_10_1111_gcb_13723 crossref_primary_10_1088_1748_9326_ac58a9 crossref_primary_10_3390_rs16234558 crossref_primary_10_1016_j_catena_2024_107819 crossref_primary_10_1002_joc_6620 crossref_primary_10_1007_s10661_022_10685_6 crossref_primary_10_3390_land11081349 crossref_primary_10_3390_rs15010221 crossref_primary_10_1007_s11442_025_2313_8 crossref_primary_10_1007_s12665_018_7252_6 crossref_primary_10_1109_TGRS_2024_3462099 crossref_primary_10_3390_rs12040718 crossref_primary_10_3390_f14030509 crossref_primary_10_1029_2020JG006154 crossref_primary_10_1016_j_agsy_2019_102679 crossref_primary_10_1016_j_rse_2024_114066 crossref_primary_10_3390_rs15184489 crossref_primary_10_1038_s41598_024_52863_1 crossref_primary_10_3390_rs13071305 crossref_primary_10_1016_j_catena_2020_104796 crossref_primary_10_3390_s19081832 crossref_primary_10_1016_j_agrformet_2021_108767 crossref_primary_10_1016_j_ecolind_2023_110277 crossref_primary_10_1016_j_jenvman_2024_123632 crossref_primary_10_1016_j_jhydrol_2022_128565 crossref_primary_10_1016_j_jhydrol_2022_128680 crossref_primary_10_3390_rs15071727 crossref_primary_10_1029_2023EF004309 crossref_primary_10_1007_s10021_023_00819_3 crossref_primary_10_1080_15481603_2021_2022426 crossref_primary_10_1016_j_scitotenv_2023_162771 crossref_primary_10_1016_j_jclepro_2022_130713 crossref_primary_10_1007_s10531_016_1214_7 crossref_primary_10_1016_j_gloplacha_2024_104586 crossref_primary_10_3390_su15043073 crossref_primary_10_1016_j_ecoinf_2022_101591 crossref_primary_10_1029_2021EF002553 crossref_primary_10_31497_zrzyxb_20210717 crossref_primary_10_3390_rs16234417 crossref_primary_10_1002_ece3_11467 crossref_primary_10_1029_2024WR038065 crossref_primary_10_3389_fpls_2022_1062691 crossref_primary_10_1016_j_scitotenv_2021_150152 crossref_primary_10_1016_j_foreco_2015_04_031 crossref_primary_10_1002_2016RG000550 crossref_primary_10_3390_rs14010015 crossref_primary_10_1002_ldr_5146 crossref_primary_10_1007_s11356_019_06378_2 crossref_primary_10_1038_s41467_023_36835_z crossref_primary_10_3390_land11040535 crossref_primary_10_3390_rs15184544 crossref_primary_10_1016_j_agrformet_2018_07_016 crossref_primary_10_1016_j_wace_2023_100622 crossref_primary_10_1002_eco_1811 crossref_primary_10_3390_rs15184542 crossref_primary_10_3390_su142315941 crossref_primary_10_1016_j_scib_2023_01_014 crossref_primary_10_1007_s11676_024_01818_3 crossref_primary_10_1016_j_ecolind_2023_111101 crossref_primary_10_1016_j_ecolind_2023_111342 crossref_primary_10_1016_j_scitotenv_2021_149055 crossref_primary_10_1016_j_catena_2020_104879 crossref_primary_10_1016_j_jag_2022_103140 crossref_primary_10_1088_1748_9326_aaec95 crossref_primary_10_1016_j_apgeog_2018_03_013 crossref_primary_10_1088_1748_9326_acdaad crossref_primary_10_3390_rs13173357 crossref_primary_10_5194_acp_24_3309_2024 crossref_primary_10_1016_j_resconrec_2022_106748 crossref_primary_10_3389_feart_2021_782287 crossref_primary_10_3390_rs11172044 crossref_primary_10_1002_ldr_5379 crossref_primary_10_1016_j_ancene_2023_100414 crossref_primary_10_3390_su16146188 crossref_primary_10_1016_j_jhydrol_2023_129519 crossref_primary_10_3390_rs12213525 crossref_primary_10_2139_ssrn_3902474 crossref_primary_10_1016_j_jhydrol_2023_129865 crossref_primary_10_1016_j_ecolind_2024_112720 crossref_primary_10_3390_rs13050951 crossref_primary_10_1016_j_ecolind_2019_106009 crossref_primary_10_5194_essd_16_15_2024 crossref_primary_10_1016_j_ecolind_2019_02_014 crossref_primary_10_3390_rs14235982 crossref_primary_10_1088_2515_7620_ad9144 crossref_primary_10_1155_2021_5169913 crossref_primary_10_1016_j_ecolind_2021_107831 crossref_primary_10_1016_j_scitotenv_2020_144669 crossref_primary_10_3390_rs12091355 crossref_primary_10_1002_lno_11968 crossref_primary_10_1016_j_agwat_2021_106854 crossref_primary_10_1007_s11676_022_01491_4 crossref_primary_10_3390_w8100458 crossref_primary_10_1029_2020EF001618 crossref_primary_10_3390_rs16173302 crossref_primary_10_1016_j_agrformet_2017_11_013 |
| Cites_doi | 10.1126/science.1074153 10.1038/nature06444 10.1126/science.1071828 10.1111/j.1365-2486.2008.01626.x 10.1029/2002JD002848 10.1890/11-1408.1 10.1029/2003GB002199 10.1111/j.1744-697X.2007.00085.x 10.1109/TGRS.2013.2237780 10.1073/pnas.0509478102 10.1029/2006GB002888 10.1029/2009JG001062 10.3334/CDIAC/atg.ndp001.2004 10.5194/bg-7-2261-2010 10.1109/36.649788 10.1111/gcb.12217 10.1126/science.1201609 10.1073/pnas.0611338104 10.1002/joc.3701 10.1029/2001JD001516 10.1658/1100-9233(2004)015[0219:VIASVI]2.0.CO;2 10.1038/ngeo230 10.1029/2010GL042430 10.1016/j.agrformet.2014.01.002 10.1029/2005GL024607 10.1046/j.1365-2486.2003.00569.x 10.1016/j.agrformet.2013.02.002 10.1038/srep03763 10.1088/1748-9326/6/4/044027 10.1038/ngeo721 10.1175/JCLI3800.1 10.1127/0941-2948/2005/0022 10.3390/rs5020927 10.5194/bgd-10-20113-2013 10.1029/2009GB003530 10.1029/2011MS000045 10.1029/2012JG002084 10.1038/ncomms6018 10.3390/rs5031484 10.1007/s11120-013-9874-6 10.5194/bgd-11-8861-2014 10.1002/gbc.20026 10.1088/1748-9326/7/1/014010 10.1073/pnas.1315126111 10.1111/gcb.12187 10.1146/annurev.pp.31.060180.002423 10.1038/ngeo844 10.1073/pnas.1317065111 10.1029/2001JD001389 10.1038/nature09364 10.1111/j.1365-2486.2008.01598.x 10.1038/nature07944 10.1029/2000JD000115 10.1890/05-1792 10.1038/nclimate1836 10.1038/nclimate1580 10.1007/s11427-013-4492-2 10.1038/nature10588 10.1126/science.1082750 10.1029/2006GL028205 10.1073/pnas.1006463107 10.1016/j.agrformet.2012.12.006 |
| ContentType | Journal Article |
| Copyright | 2014 John Wiley & Sons Ltd 2014 John Wiley & Sons Ltd. Copyright © 2015 John Wiley & Sons Ltd |
| Copyright_xml | – notice: 2014 John Wiley & Sons Ltd – notice: 2014 John Wiley & Sons Ltd. – notice: Copyright © 2015 John Wiley & Sons Ltd |
| DBID | FBQ BSCLL AAYXX CITATION CGR CUY CVF ECM EIF NPM 7SN 7UA C1K F1W H97 L.G 7X8 7ST 7U6 SOI 7S9 L.6 |
| DOI | 10.1111/gcb.12795 |
| DatabaseName | AGRIS Istex CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Ecology Abstracts Water Resources Abstracts Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality Aquatic Science & Fisheries Abstracts (ASFA) Professional MEDLINE - Academic Environment Abstracts Sustainability Science Abstracts Environment Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Aquatic Science & Fisheries Abstracts (ASFA) Professional Ecology Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality ASFA: Aquatic Sciences and Fisheries Abstracts Water Resources Abstracts Environmental Sciences and Pollution Management MEDLINE - Academic Environment Abstracts Sustainability Science Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA MEDLINE - Academic MEDLINE Aquatic Science & Fisheries Abstracts (ASFA) Professional CrossRef Ecology Abstracts |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: FBQ name: AGRIS url: http://www.fao.org/agris/Centre.asp?Menu_1ID=DB&Menu_2ID=DB1&Language=EN&Content=http://www.fao.org/agris/search?Language=EN sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Meteorology & Climatology Biology Environmental Sciences |
| EISSN | 1365-2486 |
| EndPage | 1609 |
| ExternalDocumentID | 3618861621 25369401 10_1111_gcb_12795 GCB12795 ark_67375_WNG_QJFP0GB8_F US201500196705 |
| Genre | article Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article |
| GeographicLocations | China China, People's Rep., Qinghai-Xizang Plateau China, People's Rep., Inner Mongolia China, People's Rep |
| GeographicLocations_xml | – name: China – name: China, People's Rep., Inner Mongolia – name: China, People's Rep., Qinghai-Xizang Plateau – name: China, People's Rep |
| GrantInformation_xml | – fundername: National Youth Top‐notch Talent Support Program in China funderid: B14001 – fundername: Chinese Ministry of Environmental Protection funderid: 201209031 – fundername: National Basic Research Program of China funderid: 2013CB956303 – fundername: US Department of Energy – fundername: Biological and Environmental Research – fundername: Oak Ridge National Laboratory funderid: DE‐AC05‐00OR22725 – fundername: National Natural Science Foundation of China funderid: 41125004; 31321061 – fundername: US DOE Office of Science |
| GroupedDBID | -DZ .3N .GA .Y3 05W 0R~ 10A 1OB 1OC 29I 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5HH 5LA 5VS 66C 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AANLZ AAONW AASGY AAXRX AAZKR ABCQN ABCUV ABEFU ABEML ABHUG ABJNI ABPTK ABPVW ACAHQ ACBWZ ACCFJ ACCZN ACGFS ACPOU ACPRK ACSCC ACXBN ACXME ACXQS ADAWD ADBBV ADDAD ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN ADZOD AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFEBI AFFPM AFGKR AFPWT AFRAH AFVGU AFZJQ AGJLS AHEFC AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN AMBMR AMYDB ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 C45 CAG COF CS3 D-E D-F DC6 DCZOG DDYGU DPXWK DR2 DRFUL DRSTM DU5 EBS ECGQY EJD ESX F00 F01 F04 FBQ FEDTE FZ0 G-S G.N GODZA H.T H.X HF~ HVGLF HZI HZ~ IHE IX1 J0M K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OVD P2P P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 R.K RIWAO RJQFR ROL RX1 SAMSI SUPJJ TEORI UB1 UQL VOH W8V W99 WBKPD WIH WIK WNSPC WOHZO WQJ WRC WUP WXSBR WYISQ XG1 Y6R ZZTAW ~02 ~IA ~KM ~WT AAHBH AHBTC AITYG BSCLL HGLYW OIG AAHQN AAMNL AANHP AAYCA ACRPL ACYXJ ADNMO AFWVQ ALVPJ AAYXX AEYWJ AGHNM AGQPQ AGYGG CITATION CGR CUY CVF ECM EIF NPM 7SN 7UA AAMMB AEFGJ AGXDD AIDQK AIDYY C1K F1W H97 L.G 7X8 7ST 7U6 SOI 7S9 L.6 |
| ID | FETCH-LOGICAL-c6525-4fb6a4af5658bfb02824f9ef67030eace4a05d4ac6856686591bbf8ec0a8d61e3 |
| IEDL.DBID | DR2 |
| ISSN | 1354-1013 1365-2486 |
| IngestDate | Fri Jul 11 18:31:22 EDT 2025 Thu Jul 10 23:15:07 EDT 2025 Fri Jul 11 04:03:08 EDT 2025 Fri Jul 25 11:01:42 EDT 2025 Wed Feb 19 01:52:39 EST 2025 Tue Jul 01 03:52:52 EDT 2025 Thu Apr 24 23:11:51 EDT 2025 Wed Jan 22 17:10:22 EST 2025 Wed Oct 30 09:51:34 EDT 2024 Wed Dec 27 19:15:08 EST 2023 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | greening trend detection afforestation China nitrogen deposition attribution CO2 fertilization effect |
| Language | English |
| License | http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor 2014 John Wiley & Sons Ltd. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c6525-4fb6a4af5658bfb02824f9ef67030eace4a05d4ac6856686591bbf8ec0a8d61e3 |
| Notes | http://dx.doi.org/10.1111/gcb.12795 US Department of Energy Chinese Ministry of Environmental Protection - No. 201209031 ArticleID:GCB12795 ark:/67375/WNG-QJFP0GB8-F National Natural Science Foundation of China - No. 41125004; No. 31321061 Biological and Environmental Research Office of Science Figure S1. Spatial distribution of province administrations in China. Figure S2. Trend of three different satellite-derived LAIGS across different provinces during the period 1982-2009. (a), GIMMS LAI dataset; (b) GLOBMAP LAI dataset; (c) GLASS LAI dataset. Figure S3. Trend of process model-estimated LAIGS in response to change in climate, rising atmospheric CO2 concentration, and nitrogen deposition, across different provinces during the period 1982-2009. National Youth Top-notch Talent Support Program in China - No. B14001 Oak Ridge National Laboratory - No. DE-AC05-00OR22725 istex:39FBD87BAFD53C9626CADBC2FF8E7F27B0A42F77 National Basic Research Program of China - No. 2013CB956303 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | http://ir.igsnrr.ac.cn/handle/311030/38801 |
| PMID | 25369401 |
| PQID | 1661601070 |
| PQPubID | 30327 |
| PageCount | 9 |
| ParticipantIDs | proquest_miscellaneous_1694481607 proquest_miscellaneous_1668259224 proquest_miscellaneous_1662637663 proquest_journals_1661601070 pubmed_primary_25369401 crossref_primary_10_1111_gcb_12795 crossref_citationtrail_10_1111_gcb_12795 wiley_primary_10_1111_gcb_12795_GCB12795 istex_primary_ark_67375_WNG_QJFP0GB8_F fao_agris_US201500196705 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | April 2015 |
| PublicationDateYYYYMMDD | 2015-04-01 |
| PublicationDate_xml | – month: 04 year: 2015 text: April 2015 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: Oxford |
| PublicationTitle | Global change biology |
| PublicationTitleAlternate | Glob Change Biol |
| PublicationYear | 2015 |
| Publisher | Blackwell Science Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Science – name: Blackwell Publishing Ltd |
| References | Peng S, Piao S, Shen Z et al. (2013) Precipitation amount, seasonality and frequency regulate carbon cycling of a semi-arid grassland ecosystem in Inner Mongolia, China: a modeling analysis. Agricultural and Forest Meteorology, 178, 46-55. Piao S, Ciais P, Huang Y et al. (2010) The impacts of climate change on water resources and agriculture in China. Nature, 467, 43-51. Piao S, Fang J, Zhou L et al. (2003) Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. Journal of Geophysical Research: Atmospheres, 108, 4401. Yu G, Chen Z, Piao S et al. (2014a) High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region. Proceedings of the National Academy of Sciences of the United States of America, 111, 4910-4915. Niemand C, Köstner B, Prasse H , Grünwald T, Bernhofer C (2005) Relating tree phenology with annual carbon fluxes at Tharandt forest. Meteorologische Zeitschrift, 14, 197-202. Friedlingstein P, Cox P, Betts R et al. (2006) Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison. Journal of Climate, 19, 3337-3353. Myneni RB, Yang W, Nemani RR et al. (2007) Large seasonal swings in leaf area of Amazon rainforests. Proceedings of the National Academy of Sciences of the United States of America, 104, 4820-4823. Sitch S, Huntingford C, Gedney N et al. (2008) Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs). Global Change Biology, 14, 2015-2039. Berry J, Bjorkman O (1980) Photosynthetic response and adaptation to temperature in higher plants. Annual Review of Plant Physiology, 31, 491-543. Chen B, Zhang X, Tao J et al. (2014) The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural and Forest Meteorology, 189, 11-18. Krinner G, Viovy N, de Noblet-Ducoudré N et al. (2005) A dynamic global vegetation model for studies of the coupled atmosphere biosphere system. Global Biogeochemical Cycles, 19, GB1015. Kaufmann RK, Zhou L, Tucker CJ, Slayback D, Shabanov NV, Myneni RB. (2002) Reply to Comment on 'Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981-1999' by JR Ahlbeck. Journal of Geophysical Research: Atmospheres (1984-2012), 107, ACL 7-1?ACL 7-3. Thomas RQ, Canham CD, Weathers KC, Goodale CL (2009) Increased tree carbon storage in response to nitrogen deposition in the US. Nature Geoscience, 3, 13-17. Piao SL, Friedlingstein P, Ciais P, Viovy N, Demarty J (2007) Growing season extension and its effects on terrestrial carbon flux over the last two decades. Global Biogeochemical Cycles, 21, GB3018. Lucht W, Prentice IC, Myneni RB et al. (2002) Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296, 1687-1689. Nemani RR, Keeling CD, Hashimoto H et al. (2003) Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300, 1560-1563. Pan Y, Birdsey RA, Fang J et al. (2011) A large and persistent carbon sink in the world's forests. Science, 333, 988-993. Peng S, Piao S, Zeng Z et al. (2014) Afforestation in China cools local land surface temperature. Proceedings of the National Academy of Sciences of the United States of America, 111, 2915-2919. Wang S, Duan J, Xu G et al. (2012) Effects of warming and grazing on soil N availability, species composition, and ANPP in an alpine meadow. Ecology, 93, 2365-2376. Norby RJ, Warren JM, Iversen CM, Medlyn BE, McMurtrie RE (2010) CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proceedings of the National Academy of Sciences of the United States of America, 107, 19368-19373. Yamori W, Hikosaka K, Way DA (2014) Temperature response of photosynthesis in C3, C4, and CAM plants: temperature acclimation and temperature adaptation. Photosynthesis Research, 119, 101-117. Piao SL, Ciais P, Friedlingstein P et al. (2008) Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451, 49-52. Tan K, Ciais P, Piao S et al. (2010) Application of the orchidee global vegetation model to evaluate biomass and soil carbon stocks of Qinghai Tibetan grasslands. Global Biogeochemical Cycles, 24, GB1013. Hegerl GC, Hoegh-Guldberg O, Casassa G et al. (2010) Good Practice Guidance Paper on Detection and Attribution Related to Anthropogenic Climate Change. Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change. IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland. Hickler T, Smith B, Prentice Ic, Mjofors K, Miller P, Arneth A, Sykes Mt. (2008) CO2 fertilization in temperate FACE experiments not representative of boreal and tropical forests. Global Change Biology, 14, 1531-1542. Peng S, Chen A, Xu L et al. (2011) Recent change of vegetation growth trend in China. Environmental Research Letters, 6, 044027. Fleischer K, Rebel KT, Molen MK et al. (2013) The contribution of nitrogen deposition to the photosynthetic capacity of forests. Global Biogeochemical Cycles, 27, 187-199. Ahlbeck JR (2002) Comment on 'Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981-1999' by L. Zhou et al. Journal of Geophysical Research: Atmospheres, (1984-2012) 107, ACH 9-1?ACH 9-2. Mao J, Shi X, Thornton PE, Piao S, Wang X (2012) Causes of spring vegetation growth trends in the northern mid-high latitudes from 1982 to 2004. Environmental Research Letters, 7, 014010. Sitch S, Friedlingstein P, Gruber N et al. (2013) Trends and drivers of regional sources and sinks of carbon dioxide over the past two decades. Biogeosciences Discussion, 10, 20113-20177. Janssens IA, Dieleman W, Luyssaert S et al. (2010) Reduction of forest soil respiration in response to nitrogen deposition. Nature Geoscience, 3, 315-322. Zeng N, Qian H, Roedenbeck C, Heimann M (2005) Impact of 1998-2002 midlatitude drought and warming on terrestrial ecosystem and the global carbon cycle. Geophysical Research Letters, 32, L22709. Piao S, Fang J, Ji W, Guo Q, Ke J, Tao S (2004) Variation in a satellite-based vegetation index in relation to climate in China. Journal of Vegetation Science, 15, 219-226. Norby RJ, DeLucia EH, Gielen B et al. (2005) Forest response to elevated CO2 is conserved across a broad range of productivity. Proceedings of the National Academy of Sciences of the United States of America, 102, 18052-18056. Yao T, Thompson L, Yang W et al. (2012) Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Climate Change, 2, 663-667. Mao J, Shi X, Thornton PE, Hoffman FM, Zhu Z, Myneni RB (2013) Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982-2009. Remote Sensing, 5, 1484-1497. Xiao Z, Liang S, Wang J, Chen P, Yin X, Zhang L, Song J (2014) Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. Geoscience and Remote Sensing, IEEE Transactions on, 52, 209-223. Bonan GB, Levis S (2010) Quantifying carbon-nitrogen feedbacks in the Community Land Model (CLM4). Geophysical Research Letters, 37, L07401. Myneni RB, Ramakrishna R, Nemani R, Running SW (1997) Estimation of global leaf area index and absorbed PAR using radiative transfer models. Geoscience and Remote Sensing, IEEE Transactions on, 35, 1380-1393. Oleson KW, Bonan GB, Feddema J, Vertenstein M, Kluzek E (2010) Technical Description of an Urban Parameterization for the Community Land Model (CLMU). NCAR, Boulder. Melillo JM, Steudler PA, Aber JD et al. (2002) Soil warming and carbon-cycle feedbacks to the climate system. Science, 298, 2173-2176. Piao S, Fang J, Ciais P et al. (2009) The carbon balance of terrestrial ecosystems in China. Nature, 458, 1009-1013. Huang Y, Zhang W, Sun W, Zheng X. (2007) Net primary production of Chinese croplands from 1950 to 1999. Ecological Applications, 17, 692-701. Zhou L, Tucker CJ, Kaufmann RK, Slayback D, shabanov NV, Myneni RB (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres, 106, 20069-20083. Guo ZD, Hu HF, Li P et al. (2013) Spatio-temporal changes in biomass carbon sinks in China's forests from 1977 to 2008. Science China-Life Sciences, 56, 661-671. Liu Y, Liu R, Chen JM (2012) Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data. Journal of Geophysical Research: Biogeosciences, 117, G04003. Jia Y, Yu G, He N et al. (2014) Spatial and decadal variations in inorganic nitrogen wet deposition in China induced by human activity. Scientific Reports, 4, 3763. Sitch S, Smith B, Prentice IC et al. (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9, 161-185. Yu M, Li Q, Hayes MJ, Svoboda MD, Heim RR (2014b) Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951-2010. International Journal of Climatology, 34, 545-558. Wang YP, Law RM, Pak B (2010) A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences, 7, 2261-2282. Poulter B, Pederson N, Liu H et al. (2013) Recent trends in Inner Asian forest dynamics to temperature and precipitation indicate high sensitivity to climate change. Agricultural and Forest Meteorology, 178, 31-45. Piao S, Nan H, Huntingford C, et al. (2014) Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nature communications, 5, 1-7. Xu L, Myneni RB, Chapin FS III et al. (2013) Temperature and vegetation seasonality diminishment over 2011; 479 2007; 104 2013; 3 2013; 27 2010; 107 2006; 33 2010; 467 2008; 1 2013; 5 2001; 106 2013; 19 2014; 5 2014; 4 2010; 24 1980; 31 2013; 10 2013; 56 2005; 102 2010; 115 2003; 9 2002; 107 2005; 32 2010; 3 2014; 52 2007; 21 2010; 7 2014; 11 2007; 17 2014; 119 2011; 333 2010; 37 2002; 296 2012 2010 2002; 298 2008; 14 2006; 19 2014a; 111 2005 2007; 53 2011; 3 2014; 111 2011; 6 2009; 458 2012; 93 2005; 19 2003; 108 2012; 2 2014b; 34 2004; 15 1997; 35 2013; 178 2009; 3 2012; 7 2003; 300 2008; 451 2012; 117 2014; 189 2005; 14 e_1_2_6_51_1 e_1_2_6_53_1 e_1_2_6_30_1 e_1_2_6_19_1 e_1_2_6_13_1 e_1_2_6_36_1 e_1_2_6_59_1 e_1_2_6_11_1 e_1_2_6_34_1 e_1_2_6_17_1 e_1_2_6_55_1 e_1_2_6_15_1 e_1_2_6_38_1 e_1_2_6_57_1 e_1_2_6_62_1 e_1_2_6_64_1 Oleson KW (e_1_2_6_32_1) 2010 e_1_2_6_43_1 e_1_2_6_20_1 e_1_2_6_41_1 e_1_2_6_60_1 e_1_2_6_9_1 e_1_2_6_5_1 e_1_2_6_7_1 e_1_2_6_24_1 e_1_2_6_49_1 e_1_2_6_3_1 e_1_2_6_22_1 e_1_2_6_66_1 e_1_2_6_28_1 e_1_2_6_45_1 e_1_2_6_26_1 e_1_2_6_47_1 e_1_2_6_52_1 e_1_2_6_54_1 e_1_2_6_31_1 e_1_2_6_50_1 e_1_2_6_14_1 e_1_2_6_35_1 e_1_2_6_12_1 e_1_2_6_33_1 Hegerl GC (e_1_2_6_10_1) 2010 e_1_2_6_18_1 e_1_2_6_39_1 e_1_2_6_56_1 e_1_2_6_16_1 e_1_2_6_37_1 e_1_2_6_58_1 e_1_2_6_63_1 e_1_2_6_42_1 e_1_2_6_65_1 e_1_2_6_21_1 e_1_2_6_40_1 e_1_2_6_61_1 e_1_2_6_8_1 e_1_2_6_4_1 e_1_2_6_6_1 e_1_2_6_25_1 e_1_2_6_48_1 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_29_1 e_1_2_6_44_1 e_1_2_6_27_1 e_1_2_6_46_1 |
| References_xml | – reference: Tao F, Zhang Z (2010) Dynamic responses of terrestrial ecosystems structure and function to climate change in China. Journal of Geophysical Research: Biogeosciences, 115, G03003. – reference: Xie Y, Becker U, Wittig R (2007) Vegetation of the Stipa loess steppe in Ningxia (northern China) in relation to grazing intensity. Grassland Science, 53, 143-154. – reference: Mao J, Shi X, Thornton PE, Hoffman FM, Zhu Z, Myneni RB (2013) Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982-2009. Remote Sensing, 5, 1484-1497. – reference: Wang YP, Law RM, Pak B (2010) A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere. Biogeosciences, 7, 2261-2282. – reference: Hegerl GC, Hoegh-Guldberg O, Casassa G et al. (2010) Good Practice Guidance Paper on Detection and Attribution Related to Anthropogenic Climate Change. Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change. IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland. – reference: Kaufmann RK, Zhou L, Tucker CJ, Slayback D, Shabanov NV, Myneni RB. (2002) Reply to Comment on 'Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981-1999' by JR Ahlbeck. Journal of Geophysical Research: Atmospheres (1984-2012), 107, ACL 7-1?ACL 7-3. – reference: Sitch S, Friedlingstein P, Gruber N et al. (2013) Trends and drivers of regional sources and sinks of carbon dioxide over the past two decades. Biogeosciences Discussion, 10, 20113-20177. – reference: Piao S, Friedlingstein P, Ciais P, Zhou L, Chen A (2006) Effect of climate and CO2 changes on the greening of the Northern Hemisphere over the past two decades. Geophysical Research Letters, 33, L23402. – reference: Xu L, Myneni RB, Chapin FS III et al. (2013) Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, 3, 581-586. – reference: Yu G, Chen Z, Piao S et al. (2014a) High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region. Proceedings of the National Academy of Sciences of the United States of America, 111, 4910-4915. – reference: Piao SL, Ciais P, Friedlingstein P et al. (2008) Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 451, 49-52. – reference: Zeng N, Qian H, Roedenbeck C, Heimann M (2005) Impact of 1998-2002 midlatitude drought and warming on terrestrial ecosystem and the global carbon cycle. Geophysical Research Letters, 32, L22709. – reference: Piao S, Sitch S, Ciais P et al. (2013) Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Global Change Biology, 19, 2117-2132. – reference: Sitch S, Huntingford C, Gedney N et al. (2008) Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs). Global Change Biology, 14, 2015-2039. – reference: Berry J, Bjorkman O (1980) Photosynthetic response and adaptation to temperature in higher plants. Annual Review of Plant Physiology, 31, 491-543. – reference: Sitch S, Smith B, Prentice IC et al. (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9, 161-185. – reference: Piao SL, Friedlingstein P, Ciais P, Viovy N, Demarty J (2007) Growing season extension and its effects on terrestrial carbon flux over the last two decades. Global Biogeochemical Cycles, 21, GB3018. – reference: Peng S, Chen A, Xu L et al. (2011) Recent change of vegetation growth trend in China. Environmental Research Letters, 6, 044027. – reference: Friedlingstein P, Cox P, Betts R et al. (2006) Climate-carbon cycle feedback analysis: results from the C4MIP model intercomparison. Journal of Climate, 19, 3337-3353. – reference: Liu H, Park Williams A, Allen CD et al. (2013) Rapid warming accelerates tree growth decline in semi-arid forests of Inner Asia. Global Change Biology, 19, 2500-2510. – reference: Piao S, Fang J, Ciais P et al. (2009) The carbon balance of terrestrial ecosystems in China. Nature, 458, 1009-1013. – reference: Peng S, Piao S, Zeng Z et al. (2014) Afforestation in China cools local land surface temperature. Proceedings of the National Academy of Sciences of the United States of America, 111, 2915-2919. – reference: Yao T, Thompson L, Yang W et al. (2012) Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Climate Change, 2, 663-667. – reference: Tan K, Ciais P, Piao S et al. (2010) Application of the orchidee global vegetation model to evaluate biomass and soil carbon stocks of Qinghai Tibetan grasslands. Global Biogeochemical Cycles, 24, GB1013. – reference: Yu M, Li Q, Hayes MJ, Svoboda MD, Heim RR (2014b) Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951-2010. International Journal of Climatology, 34, 545-558. – reference: Nemani RR, Keeling CD, Hashimoto H et al. (2003) Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300, 1560-1563. – reference: Yamori W, Hikosaka K, Way DA (2014) Temperature response of photosynthesis in C3, C4, and CAM plants: temperature acclimation and temperature adaptation. Photosynthesis Research, 119, 101-117. – reference: Oleson KW, Bonan GB, Feddema J, Vertenstein M, Kluzek E (2010) Technical Description of an Urban Parameterization for the Community Land Model (CLMU). NCAR, Boulder. – reference: Huang Y, Zhang W, Sun W, Zheng X. (2007) Net primary production of Chinese croplands from 1950 to 1999. Ecological Applications, 17, 692-701. – reference: Niemand C, Köstner B, Prasse H , Grünwald T, Bernhofer C (2005) Relating tree phenology with annual carbon fluxes at Tharandt forest. Meteorologische Zeitschrift, 14, 197-202. – reference: Liu Y, Liu R, Chen JM (2012) Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data. Journal of Geophysical Research: Biogeosciences, 117, G04003. – reference: Peng S, Piao S, Shen Z et al. (2013) Precipitation amount, seasonality and frequency regulate carbon cycling of a semi-arid grassland ecosystem in Inner Mongolia, China: a modeling analysis. Agricultural and Forest Meteorology, 178, 46-55. – reference: Reay DS, Dentener F, Smith P, Grace J, Feely RA (2008) Global nitrogen deposition and carbon sinks. Nature Geoscience, 1, 430-437. – reference: Fleischer K, Rebel KT, Molen MK et al. (2013) The contribution of nitrogen deposition to the photosynthetic capacity of forests. Global Biogeochemical Cycles, 27, 187-199. – reference: Norby RJ, Warren JM, Iversen CM, Medlyn BE, McMurtrie RE (2010) CO2 enhancement of forest productivity constrained by limited nitrogen availability. Proceedings of the National Academy of Sciences of the United States of America, 107, 19368-19373. – reference: Lee X, Goulden ML, Hollinger DY et al. (2011) Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479, 384-387. – reference: Lucht W, Prentice IC, Myneni RB et al. (2002) Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296, 1687-1689. – reference: Piao S, Fang J, Ji W, Guo Q, Ke J, Tao S (2004) Variation in a satellite-based vegetation index in relation to climate in China. Journal of Vegetation Science, 15, 219-226. – reference: Pan Y, Birdsey RA, Fang J et al. (2011) A large and persistent carbon sink in the world's forests. Science, 333, 988-993. – reference: Zhu Z, Bi J, Pan Y et al. (2013) Global data sets of vegetation leaf area index (LAI) 3 g and Fraction of Photosynthetically Active Radiation (FPAR) 3 g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3 g) for the period 1981 to 2011. Remote Sensing, 5, 927-948. – reference: Chen B, Zhang X, Tao J et al. (2014) The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural and Forest Meteorology, 189, 11-18. – reference: Lawrence DM, Oleson KW, Flanner MG et al. (2011) Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. Journal of Advances in Modeling Earth Systems, 3, M03001. – reference: Mao J, Shi X, Thornton PE, Piao S, Wang X (2012) Causes of spring vegetation growth trends in the northern mid-high latitudes from 1982 to 2004. Environmental Research Letters, 7, 014010. – reference: Melillo JM, Steudler PA, Aber JD et al. (2002) Soil warming and carbon-cycle feedbacks to the climate system. Science, 298, 2173-2176. – reference: Wang S, Duan J, Xu G et al. (2012) Effects of warming and grazing on soil N availability, species composition, and ANPP in an alpine meadow. Ecology, 93, 2365-2376. – reference: Ahlbeck JR (2002) Comment on 'Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981-1999' by L. Zhou et al. Journal of Geophysical Research: Atmospheres, (1984-2012) 107, ACH 9-1?ACH 9-2. – reference: Jia Y, Yu G, He N et al. (2014) Spatial and decadal variations in inorganic nitrogen wet deposition in China induced by human activity. Scientific Reports, 4, 3763. – reference: Bonan GB, Levis S (2010) Quantifying carbon-nitrogen feedbacks in the Community Land Model (CLM4). Geophysical Research Letters, 37, L07401. – reference: Hickler T, Smith B, Prentice Ic, Mjofors K, Miller P, Arneth A, Sykes Mt. (2008) CO2 fertilization in temperate FACE experiments not representative of boreal and tropical forests. Global Change Biology, 14, 1531-1542. – reference: Piao S, Ciais P, Huang Y et al. (2010) The impacts of climate change on water resources and agriculture in China. Nature, 467, 43-51. – reference: Poulter B, Pederson N, Liu H et al. (2013) Recent trends in Inner Asian forest dynamics to temperature and precipitation indicate high sensitivity to climate change. Agricultural and Forest Meteorology, 178, 31-45. – reference: Janssens IA, Dieleman W, Luyssaert S et al. (2010) Reduction of forest soil respiration in response to nitrogen deposition. Nature Geoscience, 3, 315-322. – reference: Zhou L, Tucker CJ, Kaufmann RK, Slayback D, shabanov NV, Myneni RB (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres, 106, 20069-20083. – reference: Norby RJ, DeLucia EH, Gielen B et al. (2005) Forest response to elevated CO2 is conserved across a broad range of productivity. Proceedings of the National Academy of Sciences of the United States of America, 102, 18052-18056. – reference: Thomas RQ, Canham CD, Weathers KC, Goodale CL (2009) Increased tree carbon storage in response to nitrogen deposition in the US. Nature Geoscience, 3, 13-17. – reference: Guo ZD, Hu HF, Li P et al. (2013) Spatio-temporal changes in biomass carbon sinks in China's forests from 1977 to 2008. Science China-Life Sciences, 56, 661-671. – reference: Piao S, Fang J, Zhou L et al. (2003) Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. Journal of Geophysical Research: Atmospheres, 108, 4401. – reference: Krinner G, Viovy N, de Noblet-Ducoudré N et al. (2005) A dynamic global vegetation model for studies of the coupled atmosphere biosphere system. Global Biogeochemical Cycles, 19, GB1015. – reference: Myneni RB, Ramakrishna R, Nemani R, Running SW (1997) Estimation of global leaf area index and absorbed PAR using radiative transfer models. Geoscience and Remote Sensing, IEEE Transactions on, 35, 1380-1393. – reference: Babel W, Biermann T, Coners H et al. (2014) Pasture degradation modifies the water and carbon cycles of the Tibetan highlands. Biogeosciences Discuss, 11, 8861-8923. – reference: Piao S, Nan H, Huntingford C, et al. (2014) Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nature communications, 5, 1-7. – reference: Myneni RB, Yang W, Nemani RR et al. (2007) Large seasonal swings in leaf area of Amazon rainforests. Proceedings of the National Academy of Sciences of the United States of America, 104, 4820-4823. – reference: Xiao Z, Liang S, Wang J, Chen P, Yin X, Zhang L, Song J (2014) Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. Geoscience and Remote Sensing, IEEE Transactions on, 52, 209-223. – volume: 21 start-page: GB3018 year: 2007 article-title: Growing season extension and its effects on terrestrial carbon flux over the last two decades publication-title: Global Biogeochemical Cycles – volume: 33 start-page: L23402 year: 2006 article-title: Effect of climate and CO2 changes on the greening of the Northern Hemisphere over the past two decades publication-title: Geophysical Research Letters – volume: 34 start-page: 545 year: 2014b end-page: 558 article-title: Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951–2010 publication-title: International Journal of Climatology – volume: 6 start-page: 044027 year: 2011 article-title: Recent change of vegetation growth trend in China publication-title: Environmental Research Letters – volume: 106 start-page: 20069 year: 2001 end-page: 20083 article-title: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 publication-title: Journal of Geophysical Research: Atmospheres – volume: 7 start-page: 2261 year: 2010 end-page: 2282 article-title: A global model of carbon, nitrogen and phosphorus cycles for the terrestrial biosphere publication-title: Biogeosciences – volume: 111 start-page: 2915 year: 2014 end-page: 2919 article-title: Afforestation in China cools local land surface temperature publication-title: Proceedings of the National Academy of Sciences of the United States of America – volume: 31 start-page: 491 year: 1980 end-page: 543 article-title: Photosynthetic response and adaptation to temperature in higher plants publication-title: Annual Review of Plant Physiology – volume: 178 start-page: 46 year: 2013 end-page: 55 article-title: Precipitation amount, seasonality and frequency regulate carbon cycling of a semi‐arid grassland ecosystem in Inner Mongolia, China: a modeling analysis publication-title: Agricultural and Forest Meteorology – year: 2005 article-title: Atmospheric CO records from sites in the SIO air sampling network. Trends: a compendium of data on global change – volume: 104 start-page: 4820 year: 2007 end-page: 4823 article-title: Large seasonal swings in leaf area of Amazon rainforests publication-title: Proceedings of the National Academy of Sciences of the United States of America – volume: 5 start-page: 1484 year: 2013 end-page: 1497 article-title: Global latitudinal‐asymmetric vegetation growth trends and their driving mechanisms: 1982–2009 publication-title: Remote Sensing – volume: 1 start-page: 430 year: 2008 end-page: 437 article-title: Global nitrogen deposition and carbon sinks publication-title: Nature Geoscience – volume: 93 start-page: 2365 year: 2012 end-page: 2376 article-title: Effects of warming and grazing on soil N availability, species composition, and ANPP in an alpine meadow publication-title: Ecology – volume: 19 start-page: 2117 year: 2013 end-page: 2132 article-title: Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO trends publication-title: Global Change Biology – volume: 9 start-page: 161 year: 2003 end-page: 185 article-title: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model publication-title: Global Change Biology – volume: 102 start-page: 18052 year: 2005 end-page: 18056 article-title: Forest response to elevated CO is conserved across a broad range of productivity publication-title: Proceedings of the National Academy of Sciences of the United States of America – volume: 5 start-page: 927 year: 2013 end-page: 948 article-title: Global data sets of vegetation leaf area index (LAI) 3 g and Fraction of Photosynthetically Active Radiation (FPAR) 3 g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3 g) for the period 1981 to 2011 publication-title: Remote Sensing – volume: 11 start-page: 8861 year: 2014 end-page: 8923 article-title: Pasture degradation modifies the water and carbon cycles of the Tibetan highlands publication-title: Biogeosciences Discuss – volume: 27 start-page: 187 year: 2013 end-page: 199 article-title: The contribution of nitrogen deposition to the photosynthetic capacity of forests publication-title: Global Biogeochemical Cycles – volume: 107 year: 2002 article-title: Reply to Comment on ‘Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981–1999’ by JR Ahlbeck publication-title: Journal of Geophysical Research: Atmospheres – volume: 115 start-page: G03003 year: 2010 article-title: Dynamic responses of terrestrial ecosystems structure and function to climate change in China publication-title: Journal of Geophysical Research: Biogeosciences – volume: 56 start-page: 661 year: 2013 end-page: 671 article-title: Spatio‐temporal changes in biomass carbon sinks in China's forests from 1977 to 2008 publication-title: Science China‐Life Sciences – volume: 108 start-page: 4401 year: 2003 article-title: Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999 publication-title: Journal of Geophysical Research: Atmospheres – volume: 2 start-page: 663 year: 2012 end-page: 667 article-title: Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings publication-title: Nature Climate Change – volume: 14 start-page: 1531 year: 2008 end-page: 1542 article-title: CO fertilization in temperate FACE experiments not representative of boreal and tropical forests publication-title: Global Change Biology – volume: 4 start-page: 3763 year: 2014 article-title: Spatial and decadal variations in inorganic nitrogen wet deposition in China induced by human activity publication-title: Scientific Reports – volume: 296 start-page: 1687 year: 2002 end-page: 1689 article-title: Climatic control of the high‐latitude vegetation greening trend and Pinatubo effect publication-title: Science – volume: 14 start-page: 2015 year: 2008 end-page: 2039 article-title: Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs) publication-title: Global Change Biology – volume: 53 start-page: 143 year: 2007 end-page: 154 article-title: Vegetation of the Stipa loess steppe in Ningxia (northern China) in relation to grazing intensity publication-title: Grassland Science – volume: 111 start-page: 4910 year: 2014a end-page: 4915 article-title: High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region publication-title: Proceedings of the National Academy of Sciences of the United States of America – volume: 178 start-page: 31 year: 2013 end-page: 45 article-title: Recent trends in Inner Asian forest dynamics to temperature and precipitation indicate high sensitivity to climate change publication-title: Agricultural and Forest Meteorology – volume: 107 year: 2002 article-title: Comment on ‘Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981–1999’ by L. Zhou et al. publication-title: Journal of Geophysical Research: Atmospheres – volume: 467 start-page: 43 year: 2010 end-page: 51 article-title: The impacts of climate change on water resources and agriculture in China publication-title: Nature – volume: 5 start-page: 1 year: 2014 end-page: 7 article-title: Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity publication-title: Nature communications – volume: 15 start-page: 219 year: 2004 end-page: 226 article-title: Variation in a satellite‐based vegetation index in relation to climate in China publication-title: Journal of Vegetation Science – volume: 451 start-page: 49 year: 2008 end-page: 52 article-title: Net carbon dioxide losses of northern ecosystems in response to autumn warming publication-title: Nature – volume: 479 start-page: 384 year: 2011 end-page: 387 article-title: Observed increase in local cooling effect of deforestation at higher latitudes publication-title: Nature – volume: 17 start-page: 692 year: 2007 end-page: 701 article-title: Net primary production of Chinese croplands from 1950 to 1999 publication-title: Ecological Applications – volume: 458 start-page: 1009 year: 2009 end-page: 1013 article-title: The carbon balance of terrestrial ecosystems in China publication-title: Nature – volume: 119 start-page: 101 year: 2014 end-page: 117 article-title: Temperature response of photosynthesis in C3, C4, and CAM plants: temperature acclimation and temperature adaptation publication-title: Photosynthesis Research – volume: 3 start-page: M03001 year: 2011 article-title: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model publication-title: Journal of Advances in Modeling Earth Systems – volume: 14 start-page: 197 year: 2005 end-page: 202 article-title: Relating tree phenology with annual carbon fluxes at Tharandt forest publication-title: Meteorologische Zeitschrift – volume: 189 start-page: 11 year: 2014 end-page: 18 article-title: The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai‐Tibet Plateau publication-title: Agricultural and Forest Meteorology – volume: 19 start-page: 3337 year: 2006 end-page: 3353 article-title: Climate‐carbon cycle feedback analysis: results from the C4MIP model intercomparison publication-title: Journal of Climate – volume: 3 start-page: 581 year: 2013 end-page: 586 article-title: Temperature and vegetation seasonality diminishment over northern lands publication-title: Nature Climate Change – volume: 3 start-page: 315 year: 2010 end-page: 322 article-title: Reduction of forest soil respiration in response to nitrogen deposition publication-title: Nature Geoscience – volume: 52 start-page: 209 year: 2014 end-page: 223 article-title: Use of general regression neural networks for generating the GLASS leaf area index product from time‐series MODIS surface reflectance publication-title: Geoscience and Remote Sensing, IEEE Transactions on – volume: 32 start-page: L22709 year: 2005 article-title: Impact of 1998–2002 midlatitude drought and warming on terrestrial ecosystem and the global carbon cycle publication-title: Geophysical Research Letters – year: 2010 – volume: 298 start-page: 2173 year: 2002 end-page: 2176 article-title: Soil warming and carbon‐cycle feedbacks to the climate system publication-title: Science – volume: 300 start-page: 1560 year: 2003 end-page: 1563 article-title: Climate‐driven increases in global terrestrial net primary production from 1982 to 1999 publication-title: Science – year: 2012 – volume: 19 start-page: 2500 year: 2013 end-page: 2510 article-title: Rapid warming accelerates tree growth decline in semi‐arid forests of Inner Asia publication-title: Global Change Biology – volume: 10 start-page: 20113 year: 2013 end-page: 20177 article-title: Trends and drivers of regional sources and sinks of carbon dioxide over the past two decades publication-title: Biogeosciences Discussion – volume: 24 start-page: GB1013 year: 2010 article-title: Application of the orchidee global vegetation model to evaluate biomass and soil carbon stocks of Qinghai Tibetan grasslands publication-title: Global Biogeochemical Cycles – volume: 37 start-page: L07401 year: 2010 article-title: Quantifying carbon‐nitrogen feedbacks in the Community Land Model (CLM4) publication-title: Geophysical Research Letters – volume: 107 start-page: 19368 year: 2010 end-page: 19373 article-title: CO enhancement of forest productivity constrained by limited nitrogen availability publication-title: Proceedings of the National Academy of Sciences of the United States of America – volume: 19 start-page: GB1015 year: 2005 article-title: A dynamic global vegetation model for studies of the coupled atmosphere biosphere system publication-title: Global Biogeochemical Cycles – volume: 35 start-page: 1380 year: 1997 end-page: 1393 article-title: Estimation of global leaf area index and absorbed PAR using radiative transfer models publication-title: Geoscience and Remote Sensing, IEEE Transactions on – volume: 333 start-page: 988 year: 2011 end-page: 993 article-title: A large and persistent carbon sink in the world's forests publication-title: Science – volume: 7 start-page: 014010 year: 2012 article-title: Causes of spring vegetation growth trends in the northern mid–high latitudes from 1982 to 2004 publication-title: Environmental Research Letters – volume: 3 start-page: 13 year: 2009 end-page: 17 article-title: Increased tree carbon storage in response to nitrogen deposition in the US publication-title: Nature Geoscience – volume: 117 start-page: G04003 year: 2012 article-title: Retrospective retrieval of long‐term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data publication-title: Journal of Geophysical Research: Biogeosciences – ident: e_1_2_6_25_1 doi: 10.1126/science.1074153 – ident: e_1_2_6_42_1 doi: 10.1038/nature06444 – ident: e_1_2_6_22_1 doi: 10.1126/science.1071828 – ident: e_1_2_6_50_1 doi: 10.1111/j.1365-2486.2008.01626.x – ident: e_1_2_6_38_1 doi: 10.1029/2002JD002848 – ident: e_1_2_6_56_1 doi: 10.1890/11-1408.1 – ident: e_1_2_6_17_1 doi: 10.1029/2003GB002199 – ident: e_1_2_6_58_1 doi: 10.1111/j.1744-697X.2007.00085.x – ident: e_1_2_6_57_1 doi: 10.1109/TGRS.2013.2237780 – ident: e_1_2_6_30_1 doi: 10.1073/pnas.0509478102 – ident: e_1_2_6_41_1 doi: 10.1029/2006GB002888 – ident: e_1_2_6_53_1 doi: 10.1029/2009JG001062 – ident: e_1_2_6_16_1 doi: 10.3334/CDIAC/atg.ndp001.2004 – ident: e_1_2_6_55_1 doi: 10.5194/bg-7-2261-2010 – ident: e_1_2_6_26_1 doi: 10.1109/36.649788 – ident: e_1_2_6_21_1 doi: 10.1111/gcb.12217 – ident: e_1_2_6_33_1 doi: 10.1126/science.1201609 – ident: e_1_2_6_27_1 doi: 10.1073/pnas.0611338104 – ident: e_1_2_6_63_1 doi: 10.1002/joc.3701 – ident: e_1_2_6_15_1 doi: 10.1029/2001JD001516 – ident: e_1_2_6_39_1 doi: 10.1658/1100-9233(2004)015[0219:VIASVI]2.0.CO;2 – volume-title: Good Practice Guidance Paper on Detection and Attribution Related to Anthropogenic Climate Change year: 2010 ident: e_1_2_6_10_1 – ident: e_1_2_6_48_1 doi: 10.1038/ngeo230 – ident: e_1_2_6_5_1 doi: 10.1029/2010GL042430 – ident: e_1_2_6_6_1 doi: 10.1016/j.agrformet.2014.01.002 – ident: e_1_2_6_64_1 doi: 10.1029/2005GL024607 – ident: e_1_2_6_49_1 doi: 10.1046/j.1365-2486.2003.00569.x – ident: e_1_2_6_36_1 doi: 10.1016/j.agrformet.2013.02.002 – ident: e_1_2_6_34_1 – ident: e_1_2_6_14_1 doi: 10.1038/srep03763 – ident: e_1_2_6_35_1 doi: 10.1088/1748-9326/6/4/044027 – ident: e_1_2_6_54_1 doi: 10.1038/ngeo721 – ident: e_1_2_6_8_1 doi: 10.1175/JCLI3800.1 – ident: e_1_2_6_29_1 doi: 10.1127/0941-2948/2005/0022 – ident: e_1_2_6_66_1 doi: 10.3390/rs5020927 – ident: e_1_2_6_51_1 doi: 10.5194/bgd-10-20113-2013 – ident: e_1_2_6_52_1 doi: 10.1029/2009GB003530 – ident: e_1_2_6_18_1 doi: 10.1029/2011MS000045 – ident: e_1_2_6_20_1 doi: 10.1029/2012JG002084 – ident: e_1_2_6_46_1 doi: 10.1038/ncomms6018 – ident: e_1_2_6_24_1 doi: 10.3390/rs5031484 – ident: e_1_2_6_60_1 doi: 10.1007/s11120-013-9874-6 – ident: e_1_2_6_3_1 doi: 10.5194/bgd-11-8861-2014 – ident: e_1_2_6_7_1 doi: 10.1002/gbc.20026 – ident: e_1_2_6_23_1 doi: 10.1088/1748-9326/7/1/014010 – ident: e_1_2_6_37_1 doi: 10.1073/pnas.1315126111 – ident: e_1_2_6_45_1 doi: 10.1111/gcb.12187 – ident: e_1_2_6_4_1 doi: 10.1146/annurev.pp.31.060180.002423 – ident: e_1_2_6_13_1 doi: 10.1038/ngeo844 – ident: e_1_2_6_62_1 doi: 10.1073/pnas.1317065111 – ident: e_1_2_6_2_1 doi: 10.1029/2001JD001389 – volume-title: Technical Description of an Urban Parameterization for the Community Land Model (CLMU) year: 2010 ident: e_1_2_6_32_1 – ident: e_1_2_6_44_1 doi: 10.1038/nature09364 – ident: e_1_2_6_11_1 doi: 10.1111/j.1365-2486.2008.01598.x – ident: e_1_2_6_43_1 doi: 10.1038/nature07944 – ident: e_1_2_6_65_1 doi: 10.1029/2000JD000115 – ident: e_1_2_6_12_1 doi: 10.1890/05-1792 – ident: e_1_2_6_59_1 doi: 10.1038/nclimate1836 – ident: e_1_2_6_61_1 doi: 10.1038/nclimate1580 – ident: e_1_2_6_9_1 doi: 10.1007/s11427-013-4492-2 – ident: e_1_2_6_19_1 doi: 10.1038/nature10588 – ident: e_1_2_6_28_1 doi: 10.1126/science.1082750 – ident: e_1_2_6_40_1 doi: 10.1029/2006GL028205 – ident: e_1_2_6_31_1 doi: 10.1073/pnas.1006463107 – ident: e_1_2_6_47_1 doi: 10.1016/j.agrformet.2012.12.006 |
| SSID | ssj0003206 |
| Score | 2.644782 |
| Snippet | The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of... |
| SourceID | proquest pubmed crossref wiley istex fao |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 1601 |
| SubjectTerms | afforestation attribution Biogeochemistry Carbon dioxide Carbon Dioxide - analysis China Climate Change Climate effects CO2 fertilization effect Conservation of Natural Resources data collection detection Development strategies Ecosystem management Ecosystem models ecosystems Environmental impact Environmental Monitoring Forests Greenhouse gases greening trend growing season leaf area index Models, Theoretical Nitrogen Nitrogen - analysis nitrogen deposition Plant Development plateaus Remote Sensing Technology Spacecraft Sustainability management Temperature Vegetation |
| Title | Detection and attribution of vegetation greening trend in China over the last 30 years |
| URI | https://api.istex.fr/ark:/67375/WNG-QJFP0GB8-F/fulltext.pdf https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fgcb.12795 https://www.ncbi.nlm.nih.gov/pubmed/25369401 https://www.proquest.com/docview/1661601070 https://www.proquest.com/docview/1662637663 https://www.proquest.com/docview/1668259224 https://www.proquest.com/docview/1694481607 |
| Volume | 21 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1taxQxEA61IPjF6mntapUoUvyyx74k2Sx-smfvSqHFt6P9IIRkLzlKy67c7Yn11_hb_GXOZF9qpRbx2x2Zhb25mckzu0-eIeQlc1Izq3kYyTwLmY5FmLtChJw7A3AklZrjeefDI7E_ZQcn_GSNvO7OwjT6EP0DN8wMX68xwbVZ_pbk88IM4yTL8YA5crUQEH24lI5KEz9XM045g1ITp62qELJ4-iuv7EW3nK4AoaJzv10HN6-iV7_9jDfI5-7GG9bJ2XBVm2Hx_Q9Nx__8ZffI3RaW0jdNHN0na7YckNvNoMqLAdncuzwPB2ZtQVgOSHAIoLtaeDO6Q0fnp4CA_bcH5PitrT3Rq6S6nFFd98O1aOXoVztvmY50jtwf2EJpjQRdelpSP9WbIruUAkClgPBrmkY_f1xAXi4fkul479NoP2znOISF4AkPmTNCM-0AO0rjDHZ5zOXWCaw2UPgt0xGfMV0ICeBSCp7Hxjhpi0jLmYhtuknWy6q0W4RGmYntLMm0BmyBOkM8Si2AkLzIs0RoGZBX3T-qilbkHGdtnKuu2QHnKu_cgLzoTb80yh7XGW1BWCg9h4qrph8TfD6EikJZBEs7Plb6i_XiDFlyGVfHRxP1_mD8LprsSjUOyHYXTKotEEsVAy7CXjiLAvK8X4bUxvc1urTVytskAjYAkd5oAz1-DkDsJpscmnBUEgzIoyaY-5tOeAqrUQyO8yH5d1eoyWjXf3j876ZPyB10WcN02ibr9WJlnwKIq80zn62_AAvGPjo |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbhMxFLXaIgQbHoHSgQIGQdXNRPOwPZ4FC5o0SR-JeDRqd8YzsaOq1QQlEyB8DR_AV7Dnn7j2PEpRqdh0wS6Rb6LRte_1uZ7jcxF6QTSXREnqejyOXCJ95sY6ZS6lOgE4EnJJzX3n_oD1hmT3iB4toe_VXZhCH6I-cDORYfO1CXBzIP1blI_TpOkHUVxRKvfU4jMUbLNXO22Y3ZdB0Nk-aPXcsqeAmzIaUJfohEkiNeAYnujEVBxEx0ozs_IhCSkiPToiMmUcgA5nNPaTRHOVepKPmK9C-N9ldM10EDdK_e13Z2JVYWA7efohJZDc_LDUMTK8ofpRz-1-y1pOABOb6fxyEcA9j5fthte5jX5Wrip4LifNeZ40069_qEj-L768g26VyBu_LkLlLlpSWQNdL3pxLhpodfvsyh-YlTlv1kBOH-qKydSa4Q3cOj0GkG-_3UOHbZVbLluGZTbCMq_7h-GJxp_UuCRz4rGhNwFKwLnhIOPjDNvG5dgQaDFgcAxFTI5D78e3BXhmdh8Nr8QVq2glm2RqDWEvSnw1CiIpAT4ZKSXqhQpwVpzGUcAkd9BmtYREWuq4m3Yip6Kq52AyhZ1MBz2vTT8W4iUXGa3BOhRyDJuKGL4PzBGYEU2KPBjasIuz_rGcnhgiYETF4aAr3u523njdLS46DlqvVq8oc-BM-AD9TLkfeQ56Vg9D9jKvpGSmJnNrEzDY41h4qQ2HIh2w5mU2MSHciCU66EERPfVDBzSEUc8Hx9kY-LsrRLe1ZT88_HfTp-hG76C_L_Z3BnuP0E3jvoLYtY5W8ulcPQbMmidPbKrA6MNVx9MvtgWdRQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELbaIhAXHgulgQIGQcUlqzxsxzlwoLvd7YOuCnTV3oyT2KuqVbbazQLLr-HOv-DMj2LsPEpRqbj0wC2RJ1E0nhl_43yeQegF0VwSJanr8ThyifSZG-uUuZTqBOBIyCU15513B2xzSLYP6eEC-l6fhSnrQzQbbsYzbLw2Dn6a6d-cfJQmbT-I4ppRuaPmnyFfm77e6sLkvgyC3sZ-Z9OtWgq4KaMBdYlOmCRSA4zhiU5MwkF0rDQzhg8xSBHp0YzIlHHAOZzR2E8SzVXqSZ4xX4Xw3kV0jTAvNn0iuu_PalWFgW3k6YeUQGzzw6qMkaENNZ96bvFb1HIMkNjM5peL8O15uGzXu95t9LPWVElzOW7PiqSdfv2jiOR_oso76FaFu_Gb0lHuogWVt9D1shPnvIWWN84O_IFYFfGmLeTsQlYxnlgxvIY7J0cA8e3dPXTQVYVlsuVY5hmWRdM9DI81_qRGFZUTjwy5CTACLgwDGR_l2LYtx4Y-iwGBY0hhChx6P77NQTPT-2h4JapYRkv5OFcrCHtR4qssiKQE8GQKKVEvVICy4jSOAia5g17VFiTSqoq7aSZyIupsDiZT2Ml00PNG9LQsXXKR0AqYoZAjWFLE8ENgNsBMyaTIg6E1a5vNw3JybGiAERUHg754t93b8_rrXPQctFobr6gi4FT4APxMsh95DnrWDEPsMj-kZK7GMysTMFjhWHipDIcUHZDmZTIxIdyUSnTQg9J5mo8OaAijng-Ksy7wd1WIfmfdXjz8d9Gn6MZetyfebg12HqGbRnslq2sVLRWTmXoMgLVInthAgdHHq3anXy1Lm_Q |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detection+and+attribution+of+vegetation+greening+trend+in+China+over+the+last+30%C2%A0years&rft.jtitle=Global+change+biology&rft.au=Piao%2C+Shilong&rft.au=Yin%2C+Guodong&rft.au=Tan%2C+Jianguang&rft.au=Cheng%2C+Lei&rft.date=2015-04-01&rft.pub=Blackwell+Publishing+Ltd&rft.issn=1354-1013&rft.eissn=1365-2486&rft.volume=21&rft.issue=4&rft.spage=1601&rft.epage=1609&rft_id=info:doi/10.1111%2Fgcb.12795&rft.externalDBID=n%2Fa&rft.externalDocID=ark_67375_WNG_QJFP0GB8_F |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1354-1013&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1354-1013&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1354-1013&client=summon |