陈铁喜,男,1983年,黑龙江省牡丹江市人,博士,教授,博士生导师,南京信息工程大学地理科学学院副院长,青海理工大学(筹)生态与环境科学学院院长、青海师范大学地理科学学院副院长。2006年6月本科毕业于南京大学大气科学系,大气科学专业(基地班)。2006年9月南京大学大气科学学院攻读硕士,导师为陈星教授,受国家留学基金委(CSC)“国家建设高水平大学公派研究生项目”资助,2009年由南京大学公派至荷兰阿姆斯特丹自由大学地球科学系,于2014年5月获得理学博士学位,荷方导师为Han Dolman教授。2013年作为访问科学家在NASA短期研究。2014年6月起入职南京信息工程大学从事教学与科研工作,2017年7月晋升教授,2019年10月–2021年9月,任自然地理系系主任,2021年3–12月任雷丁学院副院长(挂职),2021年12月参与教育援青工作至今。
主要研究领域为陆地生态系统碳循环,重点研究大尺度农田生态系统、短期极端事件与长期气候干湿变化对植被生长的影响。发表中英文学术论文多篇,被引超过2000次H指数17(Google Scholar数据 https://scholar.google.com/citations?user=EDaPFJsAAAAJ&hl=zh-CN)。
主持国家自然科学基金面上项目、重大研发计划子课题以及横向课题多项,参研重点基金、UNEP国际合作项目各一项。2019年获批江苏省双创团队(排名3),青海省“昆仑英才.高端创新创业人才”计划杰出人才。主讲课程包括《气象与气候学》、《灾害遥感监测》等。现任青海省高原气候变化及其生态环境效应重点实验室主任,国际水文科学协会中国委员会遥感专业委员会委员(CNC-IAHS-CRS),南京市气象学会理事,青海省遥感学会理事,国际数字地球学会中国国家委员会数字水圈专业委员会委员,《应用生态学报》、《干旱气象》编委等。(txchen@nuist.edu.cn)
严抓学生高质量培养,培养学术技能型人才为主。毕业生去向:
2023届:郭仁杰,北京师范大学,博士研究生。
2022届:蔡江涛,荷兰屯特大学,博士研究生;陈鑫,南京信息工程大学,博士研究生。
2021届:张林林,南京立人学校,教师;梁传壮,南京师范大学,博士研究生;周圣杰,南京信息工程大学,科研助理,博士研究生。
2020届:马海云,塘桥高级中学,教师。
发表论文目录(*通讯作者,#指导学生)
57. Chen, X.#, Chen, T.*, Liu, Y. Y., He, B., Liu, S., Guo, R., & Dolman, H. (2024). Emergent constraints on historical and future global gross primary productivity. Global Change Biology, 30, e17479. https://doi.org/10.1111/gcb.17479
56. Xiao, Y.#, Chen, T.*, Chen, X., Yang, Y., Wang, S., & Zhou, S. (2024). CMIP6 ESMs overestimate greening and the photosynthesis trends in Dryland East Asia. Science of The Total Environment, 173432. https://doi.org/10.1016/j.scitotenv.2024.173432
55. Wang, S., Chen, T., Luo, J. J., Gao, M., Zuo, H., Ling, F., ... & Yamagata, T. (2024). Warming climate is helping human beings run faster, jump higher and throw farther through less dense air. npj Climate and Atmospheric Science, 7(1), 94.
54. Wang, X., Zhang, N., Chen, K.*, Chen, T.*, Qi, D., Ma, Y., 2024.Response mechanism of soil microorganisms to simulated precipitation in the source wetland of Qinghai Lake. Ecological Process 13, 25. https://doi.org/10.1186/s13717-024-00502-y
53. Wang, S., Chen, T., Xie, Y. and Luo, J.J., 2024. Different impacts of the variations of western and eastern portions of the East Asian westerly jet stream on southern China rainfalls in Meiyu season. Atmospheric Research, 300, p.107229. https://doi.org/10.1016/j.atmosres.2024.107229
52. Chen, X.#, Chen, T.*, Liu, S., Chai, Y., Guo, R., Dai, J., Wang, S., Zhang, L. and Wei, X., 2024. Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity. Journal of Geophysical Research: Biogeosciences, 129(1), p.e2023JG007499. https://doi.org/10.1029/2023JG007499
(提出仅用植被指数估算GPP的问题,无法有效估算极端事件的影响)
51. Chen, T.*, Dai, J., Chen, X., Liang, C., Shi, T., Lyu, Y., Zhao, F., Wu, X., Gao, M., Huang, J. and Zhou, S., 2024. Agricultural land management extends the duration of the impacts of extreme climate events on vegetation in double–cropping systems in the Yangtze–Huai plain China. Ecological Indicators, 158, p.111488. https://doi.org/10.1016/j.ecolind.2023.111488
(首次指出土地管理会影响极端事件对植被影响的时间尺度)
50. Chen, X.#, Chen, T.*, He, B., Liu, S., Zhou, S. and Shi, T., 2024. The global greening continues despite increased drought stress since 2000. Global Ecology and Conservation, 49, p.e02791. https://doi.org/10.1016/j.gecco.2023.e02791
49. Li, S., Wang, G., Chai, Y., Miao, L., Hagan, D.F.T., Sun, S., Huang, J., Su, B., Jiang, T., Chen, T. and Lu, C., 2023. Increasing vapor pressure deficit accelerates land drying. Journal of Hydrology, 625, p.130062. https://doi.org/10.1016/j.jhydrol.2023.130062
48. Wang, S., He, B., Chen, H.W., Chen, D., Chen, Y., Yuan, W., Shi, F., Duan, J., Wu, W., Chen, T. and Guo, L., 2023. Fire carbon emissions over Equatorial Asia reduced by shortened dry seasons. npj Climate and Atmospheric Science, 6(1), p.129. https://doi.org/10.1038/s41612-023-00455-7
47. Guo, R.#, Chen, T.*, Chen, X., Yuan, W., Liu, S., He, B., Li, L., Wang, S., Hu, T., Yan, Q. and Wei, X., 2023. Estimating global GPP from the plant functional type perspective using a machine learning approach. Journal of Geophysical Research: Biogeosciences, 128(4), p.e2022JG007100. https://doi.org/10.1029/2022JG007100
(建立全新的基于机器学习算法的GPP数据集,欢迎下载使用:https://datadryad.org/stash/dataset/doi:10.5061/dryad.dncjsxm2v)
46. Yan, Q., Chen, Y., Jin, S., Liu, S., Jia, Y., Zhen, Y., Chen, T.*, Huang, W., 2022. Inland Water Mapping Based on GA-LinkNet from CyGNSS Data. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2022.3227596
45. Hu, T., Wang, T., Yan, Q., Chen, T., Jin, S. and Hu, J., 2022. Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS. Applied Energy, 322, p.119473. https://doi.org/10.1016/j.apenergy.2022.119473
44. Chai, Y., Yue, Y.*, Slater, L. J., Yin, J., Borthwick, A. G., Chen, T., and Wang, G., 2022. Constrained CMIP6 projections indicate less warming and a slower increase in water availability across Asia. Nature communications, 13(1), 1-9. https://doi.org/10.1038/s41467-022-31782-7
43. Chen, T.*, Dolman, H., Sun, Z., Gao, H., Miao, L., Wei, X., Li, C., Han, Q., Shi, T., Wang. G., Zhou. S., Liang, C., and Chen. X., 2022. Land management explains the contrasting greening pattern across China-Russia border based on Paired Land Use Experiment approach, JGR-Biogeosciences, 127, e2021JG006659. https://doi.org/10.1029/2021JG006659
(首次建立PLUE方法,识别土地管理对植被变化的影响)
42. Zhou, S.#, Chen, T.*, Zeng, N., Cai, Q.*, Fang Zhao, F., Han, P., and Yan. Q., 2022. The Impact of Cropland Abandonment of Post-Soviet Countries on the Terrestrial Carbon Cycle Based on Optimizing the Cropland Distribution Map, Biology, 11, no. 5: 620. https://doi.org/10.3390/biology11050620
41. Chen, T.*, Guo, R., Yan, Q., Xin Chen, Zhou, S., Chen, X., Liang, C., Wei, X., and Dolman, H., 2022. Land Management Contributes significantly to observed Vegetation Browning in Syria during 2001–2018, Biogeosciences, 19, 1515–1525. https://doi.org/10.5194/bg-19-1515-2022
(叙利亚社会动荡引发的土地管理能力退化导致了褐变现象)
40. Wei, X., Ye, Y., Li, B. and Chen, T., 2022. Reconstructing cropland change since 1650 AD in Shaanxi province, central China. Quaternary International, 641, pp.74-86.
39. 张林林,梁传壮,马海云,陈鑫,蔡江涛,郭仁杰,陈铁喜*. 2022. 土地管理对植被变绿的潜在贡献——以中国东北农业区为例[J]. 生态学报, 42(02):720-731. http://dx.doi.org/10.5846/stxb202011192977
38. Chen, S., Yan, Q., Jin, S., Huang, W., Chen, T., Jia, Y., Liu, S. and Cao, Q., 2022. Soil Moisture Retrieval from the CyGNSS Data Based on a Bilinear Regression. Remote Sensing, 14(9), p.1961. https://doi.org/10.3390/rs14091961
37. Cai, J., Chen, T.*, Yan, Q., Chen, X., and Guo, R., 2022. The Spatial-Temporal Characteristics of Soil Moisture and Its Persistence over Australia in the Last 20 Years. Water, 14(4), 598. https://doi.org/10.3390/w14040598
36. Xie, Y., Su, Y., Gu, X., Chen, T., Shao, W., and Hu, Q., 2022. Columnar Aerosol Optical Property Characterization and Aerosol Typing Based on Ground-Based Observations in a Rural Site in the Central Yangtze River Delta Region. Remote Sensing, 14(2), 406. https://doi.org/10.3390/rs14020406
35. Chen, X.#, Chen, T.*, Shu, Y.*, Yan, Q., Han, Q., Wei, X., Li, C., Wang, G. and Xie, Y., 2021. A framework to assess the potential uncertainties of three FPAR products. Journal of Geophysical Research: Biogeosciences, 126(10), p.e2021JG006320. https://doi.org/10.1029/2021JG006320
34. Wei, X., Li, Y., Guo, Y., Chen, T., and Li, B., 2021. Spatio-temporal analysis of cropland change in the Guanzhong area, China, from 1650 to 2016. Journal of Geographical Sciences, 31(9), 1381-1400. https://doi.org/10.1007/s11442-021-1902-4
33. 蔡江涛,陈铁喜,姜建武,胡垚,潘文宇. 2021. 基于空间点模式的桂林市星级酒店空间分布特征研究[J].福建师范大学学报(自然科学版), 37(04):77-86. https://doi.org/10.12046/j.issn.1000-5277.2021.04.011
32. Wei, X., Widgren, M., Li, B., Ye, Y., Fang, X., Zhang, C., Chen, T., 2021. Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia. Earth System Science Data, 13(6), 3035-3056. https://doi.org/10.5194/essd-13-3035-2021
31. Wang, J., Wang, M., Kim, J.-S., Joiner, J., Zeng, N., Jiang, F., Wang, H., He, W., Wu. M., Chen, T., Ju. W., Chen. J., 2021. Modulation of land photosynthesis by the Indian Ocean Dipole: Satellite-based observations and CMIP6 future projections. Earth's Future, 9, e2020EF001942. https://doi.org/10.1029/2020EF001942
30. Shi, X., Wang, G., Chen, T., Li, S., Lu, J., & Hagan, D. F. T., 2021. Long‐term changes in layered soil temperature based on ground measurements in Jiangsu Province, China. International Journal of Climatology, 41(5), 2996-3009. https://doi.org/10.1002/joc.7001
29. Li, S., Wang, G., Sun, S., Hagan, D.F.T., Chen, T., Dolman, H. and Liu, Y., 2021. Long-term changes in evapotranspiration over China and attribution to climatic drivers during 1980–2010. Journal of Hydrology, 595, p.126037.
28. Lu, J., Wang, G., Chen, T., Li, S., Hagan, D. F. T., Kattel, G., Peng, J., Jiang, T., and Su, B., 2021. A harmonized global land evaporation dataset from model-based products covering 1980–2017, Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, 2021. https://doi.org/10.5194/essd-13-5879-2021
27. Chai, Y., Martins, G., Nobre, C., von Randow, C., Chen, T., Dolman, H., 2021. Constraining Amazonian land surface temperature sensitivity to precipitation and the probability of forest dieback. npj Climate and Atmospheric Science, 4(1), 1-7. https://doi.org/10.1038/s41612-021-00162-1
26. 马海云,张林林,魏学琼,施婷婷,陈铁喜* 2021. 2000—2015年西南地区土地利用与植被覆盖的时空变化[J].应用生态学报, 32(02):618-628. https://doi.org/10.13287/j.1001-9332.202102.017
25. 张林林, 陈铁喜* 2021. 1989—2018年京津冀地区人体舒适度的变化特征[J].天津师范大学学报(自然科学版), 41(04):53-60. https://doi.org/10.19638/j.issn1671-1114.20210408
24. Chen, X., Chen, T*., Yan, Q.., Cai, J.., Guo, R.., Gao, M.., Wei, X.., Zhou, S.., Li, C.., Xie, Y., 2021. The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends. Remote Sensing, 13, 3374. https://doi.org/10.3390/rs13173374
23. Yan, Q., Hu, T., Jin, S., Huang, W., Jia, Y., Chen, T., Wang, J., 2021. Improving CyGNSS-Based Land Remote Sensing: Track-Wise Data Calibration Schemes. Remote Sensing, 13(14), 2844. https://doi.org/10.3390/rs13142844
22. 蔡江涛,付波霖,陈铁喜,耿仁方,李颖,何宏昌,范冬林,邓腾芳, 2020.基于Sentinel-2卫星多光谱数据的会仙喀斯特湿地植物理化参数反演研究[J].湿地科学,18(06):693-705. https://doi.org/10.13248/j.cnki.wetlandsci.2020.06.008
21. Yue, S., Chen, M., Song, J., Yuan, W., Chen, T., Lü, G., Shen, C., Ma, Z., Xu, K., Wen, Y. and Song, H., 2020. Participatory intercomparison strategy for terrestrial carbon cycle models based on a service-oriented architecture. Future Generation Computer Systems, 112, pp.449-466.https://doi.org/10.1016/j.future.2020.05.044
20. Miao, L., Li, S., Zhang, F., Chen, T., Shan, Y., Zhang, Y., 2020. Future drought in the drylands of Asia under the 1.5° C and 2.0° C warming scenarios. Earth's Future, e2019EF001337. https://doi.org/10.1029/2019EF001337
19. Liang, C., Chen, T.*, Dolman, H., Shi, T.*, Wei, X., Xu, J., & Hagan, D. F. T., 2020. Drying and Wetting Trends and Vegetation Covariations in the Drylands of China. Water, 12(4), 933. https://doi.org/10.3390/w12040933
18. 朱浩朋,伍玉梅,陈铁喜* 2020. 基于MODIS数据的洞庭湖水域面积变动研究[J].渔业信息与战略,35(02):147-153. https://doi.org/10.13233/j.cnki.fishis.2020.02.009
17. Chen, T.*, Zhou, S., Liang, C., Hagan, D.F.T., Zeng, N., Wang, J., Shi, T., Chen, X., Dolman, A., 2020. The Greening and Wetting of the Sahel Have Leveled off since about 1999 in Relation to SST. Remote Sensing, 12, 2723. https://doi.org/10.3390/rs12172723
16. Wei, X., Wang, G., Chen, T., Hagan, D.F.T., Ullah, W., 2020. A Spatio-Temporal Analysis of Active Fires over China during 2003–2016. Remote Sens., 12, 1787. https://doi.org/10.3390/rs12111787
15. Wang, G., Gong, T., Lu, J., Lou, D., Hagan, D. F. T., Chen, T., 2018. On the long‐term changes of drought over China (1948–2012) from different methods of potential evapotranspiration estimations. International Journal of Climatology, 38(7), 2954-2966. https://doi.org/10.1002/joc.5475
14. Chen, T.*, Zhang, H., Chen, X., Hagan, D.F., Wang, G., Gao, Z., Shi, T., 2017. Robust drying and wetting trends found in regions over China based on Köppen climate classifications. Journal of Geophysical Research: Atmospheres, 122, 4228-4237, https://doi.org/10.1002/2016JD026168
(建立基于多干旱指数的区域干湿变化确定性方法)
13. Wang, G., Hagan, D.F.T., Lou, D., Chen, T., 2016. Evaluation of soil moisture derived from FY3B microwave brightness temperature over the Tibetan Plateau. Remote Sensing Letters, 7, 817-826. https://doi.org/10.1080/2150704X.2016.1192303
12. Chen, T.*, McVicar, T.R., Wang, G., Chen, X., de Jeu, R.A., Liu, Y.Y., Shen, H., Zhang, F., & Dolman, A.J., 2016. Advantages of Using Microwave Satellite Soil Moisture over Gridded Precipitation Products and Land Surface Model Output in Assessing Regional Vegetation Water Availability and Growth Dynamics for a Lateral Inflow Receiving Landscape. Remote Sensing, 8, 428. https://doi.org/10.3390/rs8050428
(证明了表层土壤湿度在侧向流地区研究生态水文过程中相比降水的优势)
11. Chen, T., Wang, G.*, Yuan, W., Li, A., & Liu, Y.Y.. 2016. Asymmetric NDVI trends of the two cropping seasons in the Huai River basin. Remote Sensing Letters, 7, 61-70. https://doi.org/10.1080/2150704X.2015.1109156
10. Shen, L., Wu, H., Gao, Z., Xu, X., Chen, T., Liu, S., & Cheng, H., 2015. Occurrence and importance of anaerobic ammonium-oxidising bacteria in vegetable soils. Applied Microbiology and Biotechnology, 99, 5709-5718. https://doi.org/10.1007/s00253-015-6454-z
9. Chen, T.*, van der Werf, G., Gobron, N., Moors, E., & Dolman, A., 2014. Global cropland monthly gross primary production in the year 2000. Biogeosciences, 11, 3871-3880. https://doi.org/10.5194/bg-11-3871-2014
(建立了全球26种农田的初级生产力数据集)
8. Chen, T.*, de Jeu, R., Liu, Y., van der Werf, G., & Dolman, A., 2014. Using satellite based soil moisture to quantify the water driven variability in NDVI: A case study over mainland Australia. Remote Sensing of Environment, 140, 330-338. https://doi.org/10.1016/j.rse.2013.08.022
(首次证明表层土壤湿度在植被对水分响应过程研究的有效性)
7. Chen, T., Werf, G., Jeu, R., Wang, G., & Dolman, A.*, 2013. A global analysis of the impact of drought on net primary productivity. Hydrology and Earth System Sciences, 17, 3885-3894. https://doi.org/10.5194/hess-17-3885-2013
6. Dolman, A., Shvidenko, A., Schepaschenko, D., Ciais, P., Tchebakova, N., Chen, T., Van Der Molen, M., Belelli Marchesini, L., Maximov, T., & Maksyutov, S., 2012. An estimate of the terrestrial carbon budget of Russia using inventory-based, eddy covariance and inversion methods. Biogeosciences, 9, 5323-5340. https://doi.org/10.5194/bg-9-5323-2012
5. van der Molen, M.K., Dolman, A.J., Ciais, P., Eglin, T., Gobron, N., Law, B.E., Meir, P., Peters, W., Phillips, O.L., Reichstein, M., Chen, T., Dekker, S.C., Doubková, M., Friedl, M.A., Jung, M., van den Hurk, B.J.J.M., de Jeu, R.A.M., Kruijt, B., Ohta, T., Rebel, K.T., Plummer, S., Seneviratne, S.I., Sitch, S., Teuling, A.J., van der Werf, G.R., & Wang, G., 2011. Drought and ecosystem carbon cycling. Agricultural and Forest Meteorology, 151, 765-773. https://doi.org/10.1016/j.agrformet.2011.01.018
4. Dong, Q., Chen, X., & Chen, T., 2011. Characteristics and Changes of Extreme Precipitation in the Yellow-Huaihe and Yangtze-Huaihe Rivers Basins, China. Journal of Climate, 24, 3781-3795. https://doi.org/10.1175/2010JCLI3653.1
3. Chen, T.*, van der Werf, G.R., Dolman, A., Groenendijk, M., 2011. Evaluation of cropland maximum light use efficiency using eddy flux measurements in North America and Europe. Geophysical Research Letters, 38. https://doi.org/10.1029/2011GL047533
2. 董全, 陈星, 陈铁喜, 程兴无, 2009. 淮河流域极端降水与极端流量关系的研究. 南京大学学报自然科学, 45, 790-801.
1. 陈铁喜, 陈星*, 2007. 近 50 年中国气温日较差的变化趋势分析. 高原气象, 26, 150-157.
【主要代表性成果】
一、建立配对土地利用试验(PLUE)方法
植被变化同时受到气候环境变化和人类活动的影响,基于FACE试验和模拟都表明CO2施肥效应对全球植被绿度增加起到主导作用,但农田与生态工程区的绿度增加并不支持此结论。当前困境在于动态植被模式(DGVM)缺乏土地管理变化(LMC)的过程,而统计方法难以明确归因。为解决这一问题,申请者与荷兰Han Dolman教授等多个国内外学者合作,创建了配对土地利用实验(Paired Land Use Experiment,PLUE)方法。如图1所示,PLUE方法通过在对照区“自然控制”气候变化,从而将差异归因到非气候要素。两个区域气候平均态与气候变化基本一致,但植被变化趋势又存在着显著性的差异。进而基于现实的土地覆盖变化和土地管理变化来具体量化人类活动的形式与类型。PLUE方法被成功应用于中国-俄罗斯边境的三江平原变绿现象与叙利亚褐变现象分析中。
PLUE方法理论框架图
代表论文
Chen, T.*,et al., 2022, Land management explains the contrasting greening pattern across China-Russia border based on Paired Land Use Experiment approach, JGR-Biogeosciences.
Chen, T.*,et al., 2022, Land Management Contributes significantly to observed Vegetation Browning in Syria during 2001–2018, Biogeosciences
二、区域尺度生态气候学分析--气候干湿变化对植被的影响
理论上根系层土壤湿度与植被生理关系密切,然而土壤的水分剖面数据在大尺度上难以直接测量,完全依赖于模拟。基于遥感的表层土壤湿度(remote sensing based surface soil moisture)也能够较好的描述旱区生态水文过程,并且在侧向流区域的典型案例中,其时空特征优于目前的再分析土壤湿度产品(Chen et al., RSE 2014, RS 2016)。
气候干湿变化对植被和生产力都会产生显著影响。净初级生产力NPP与干湿波动的相关性在全球的分布规律与气候态相关,主要分为三类:在干旱地区,NPP随干湿变化始终显著正相关波动;在湿润地区,与干湿变化一般没有关系,但是极端干旱事件会导致NPP明显下降;在高纬度地区,NPP与干湿变化成负相关波动,原因是温度作为控制因子而非水分(Chen et al., HESS 2013)。一些有趣的区域特征为:在非洲Sahel地区,从气候到生态的过程为 海温-->降水-->植被(Chen et al., 2020, RS);而我国西南地区,尽管在西南大旱期间(2009-2012)对植被产生严重破坏作用,但是该地区的变绿(greening)趋势(2001-2019)并未受到显著影响,意味着该地区植被在干旱后迅速恢复。同时,该地区呈现出显著干旱化趋势,即出现了变绿与变干的共存现象(Chen et al., 2021, RS)。
三、 陆地生态系统碳循环监测与模拟
农田生态系统是陆地生态系统重要组成部分,特点是作为类型多样。一般模式中仅设定为单一植被类型,造成较大误差。本团队研究发现,农田中最大光能利用率参数设定较小,造成农田生产力低估严重。首次在全球估算了26类农作物的初级生产力数据(Chen et al., 2011 GRL, 2014 BG)。
2023年4月,完成了基于机器学习的全球初级生产力GPP数据集ECGC_GPP,部分解决了FLUXCOM缺乏年际波动和趋势问题,特别把农田C4、C3进行了区分,优化空间分布。欢迎批评指正和下载使用。https://datadryad.org/stash/dataset/doi:10.5061/dryad.dncjsxm2v
四、 叙利亚褐变(browning)现象机制解析:社会动荡引起土地管理水平退化导致褐变
全球-区域尺度的植被变化的机制是什么?近些年有关变绿Greening讨论很多,我们发现叙利亚出现了褐变(Browning)现象,直觉上应该与土地管理(land management, LM)有关。经过一年的讨论我们选定了三种方法来论证:去除气候影响的残差趋势法,水分利用效率趋势法和自然配对分析,惊喜的发现这三者给出的结论一致性非常高,因此确定LM是主要的驱动要素。
该地区气候干燥,植被对气候和人为活动都十分敏感。社会动荡加之偶发的强自然灾害,使得该地区土地管理能力持续性下降,进一步造成了植被褐变。(Chen et al., 2022, BG)
【业余爱好--羽毛球】 南京信息工程大学教职工羽毛球赛 2019男双冠军、2020男双冠军、混双冠军、2021双打亚军、团体亚军。
【业余爱好--对联】酒香久香九乡酒,园名原名圆明园 (本人创作于2013年,“园名原名圆明园”一直被冠以绝对)
【政策建议】
1、关于解决清明节烧纸与消防的矛盾:建议增设消防法规或者管理条例内容,规定要求上坟烧纸需携带灭火器,否则违法。这样可以兼顾传统节日风俗和消防需要,火灾初期最容易扑灭,灭火器一般足以应付,而且有利于推广家庭常备灭火设备。
2、小区周边自种蔬菜并不卫生:很多人以为小区周边一些“开荒”地(甚至有人破坏小区绿化地开荒)的自种蔬菜很健康,其实远不如超市售卖的蔬菜基地的产品,因为城镇小区周边的土不适合作为农业用途,这些地方往往是垃圾土,而且用水也经常就近水沟取水,加上常年受到到交通排放甚至周边工厂排放的烟尘污染物的沉降污染,造成土、水、气三项都不健康,种出来的蔬菜可能有一定毒性,食用这类蔬菜致病的报道也屡见不鲜。
Name of Research Group:生态气候团队
Description of Research Group: