桑燕芳

中国科学院地理科学与资源研究所

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  • 桑燕芳
  • 研究员

个人简况

男,博士,研究员,博士生导师。中国科学院青年创新促进会会员和中国科学院地理资源所“秉维”优秀青年人才计划获得者。长期致力于水文系统变异研究。围绕水文系统变异诊断与评估、物理成因分析与模拟等科学问题,开展系统性的基础理论方法及应用研究工作。主持参与国家自然科学基金重点和面上项目、科技部重点研发计划、中国科学院战略性先导项目;参与全国水利现代化规划、科技部与水利部水利公益项目、西藏水情预报系统研发等多项部委和地区重大生产课题。发表第一作者学术论文60余篇,包括Journal of Hydrology, Remote Sensing of Environment, Journal of Climate, Journal of Geophysical Research-Atmospheres, Hydrological Processes等刊物40余篇SCI论文,被他引400多次;发表地理学报、科学通报、水利学报、水科学进展、地理科学进展等刊物50余篇中文论文;出版专著2本;获省部级科技进步二等奖1项;获7项国家发明专利和3项软件著作权。

教育、工作经历

2017.12-至今中国科学院地理科学与资源研究所,研究员

2017.5-2018.10 华盛顿大学,访问学者

2014.12-2017.11 中国科学院地理科学与资源研究所,副研究员

2011.7-2014.11 中国科学院地理科学与资源研究所,助理研究员

2006.9-2011.7 南京大学地球科学与工程学院获水文学及水资源博士学位(硕博连读)

研究领域

水文水资源

主要研究方向:水文变异、气候变化、水文模拟预报、水文不确定性及风险分析

主要科研项目

主持项目

1.国家自然科学基金重大研究计划培育项目《雅鲁藏布江流域径流过程非平稳性形成机制研究》(91647110)

2.国家自然科学基金青年项目《气候变化下流域径流过程复杂性的测度与辨识》(41021036)

3.中国科学院地理资源所优秀青年人才计划基金《流域降雨—径流响应程度定量表征研究》

参与项目

1.科技部重点研发计划《全球气候-陆面-水文过程及极端水文事件风险与中国适应研究》(2017YFA0603702)

2.国家自然科学基金重点项目《中国湿润-干旱过渡带水循环过程对气候变化的响应机制》(41330529)

3.中国科学院战略性先导项目《青藏高原现代水体在不同圈层间的相态转化过程及其影响》(XDB03030202)

代表性成果

[1]Sang Y.F., P. Xie, W.M. Cheng, Y.C. Zhang, L. Guo, Y.D. Sun, 2019. Mountain torrent control in China: Hydrologic nonstationarity is a challenging problem. Journal of Hydrologic Engineering, 24(3): 02519001.

[2]Sang Y.F., V.P. Singh, Z. Hu, P. Xie, X. Li, 2018. Entropy-aided evaluation of meteorological droughts over China. Journal of Geophysical Research-Atmospheres, 123. DOI: 10.1002/2017JD026956.

[3]Sang Y.F., F. Sun, V.P. Singh, P. Xie, J. Sun, 2018. A discrete wavelet spectrum approach to identifying non-monotonic trend pattern of hydroclimate data. Hydrology and Earth System Sciences, 22, 757-766.

[4]Xie P., Z. Wu, Y.F. Sang*, H. Gu, Y. Zhao, Vijay P. Singh, 2018. Evaluation of the significance of abrupt changes in precipitation and runoff process in China. Journal of Hydrology, 560, 451-460

[5]Sang Y.F., V.P. Singh, K. Xu, 2018. Evolution of the IOD-ENSO relationship on multi-time scales. Theoretical and Applied Climatology, DOI: 10.1007/s00704-018-2557-7

[6]Sang, Y.F., V.P. Singh, T. Gong, K. Xu, F. Sun, C. Liu, W. Liu, R. Chen, 2016. Precipitation variability and response to changing climatic condition in the Yarlung Tsangpo River basin, China. Journal of Geophysical Research-Atmospheres, 121, doi:10.1002/2016JD025370.

[7]Sang, Y.F., M. Yang, 2016. Urban waterlogs control in China: more effective strategies and actions are needed. Natural Hazards, doi: 10.1007/s11069-016-2614-4.

[8]Sang Y.F., V.P. Singh, F. Sun, Y. Chen, Y. Liu, M. Yang, 2016. Wavelet-based hydrological time series forecasting. Journal of Hydrologic Engineering, DOI:10.1061/(ASCE)HE.1943-5584.0001347.

[9]Sang Y.F., V.P. Singh, J. Wen, C.M. Liu, 2015. Gradation of complexity and predictability of hydrological processes. Journal of Geophysical Research – Atmospheres, DOI:10.1002/2014JD022844. —SCI

[10] Sang Y.F., Z.G. Wang, C.M. Liu, 2015. Wavelet neural modeling for hydrologic time series forecasting with uncertainty evaluation. Water Resources Management, 29(6), 1789-1801.—SCI

[11] Sang Y.F., Wang Z.G., Liu C.M., 2014. Comparison of the MK test and EMD method for trend identification in hydrologic time series. Journal of Hydrology, 510, 293-298. —SCI

[12] Sang Y.F., Wang Z.G., Liu C.M., 2014. Spatial and temporal variability of precipitation extrema in the Haihe River Basin, China. Hydrological Processes, 28(3), 926-932.—SCI

[13] Sang Y.F., Wang Z.G., Liu C.M., Gong T.L., 2013. Investigation into the climate variability in the headwater regions of the Yangtze River and Yellow River, China. Journal of Climate, 26(14), 5061-5071.—SCI

[14] Sang Y.F., 2013. A review on the applications of wavelet transform in hydrologic time series analysis. Atmospheric Research, 122, 8-15.—SCI

[15] Sang Y.F., 2013, Improved wavelet modeling framework for hydrologic time series forecasting. Water Resources Management, 27(8), 2807-2821.—SCI

[16] Sang Y.F., Wang Z.G., Liu C.M., 2013. Discrete wavelet-based trend identification in hydrologic time series. Hydrological Processes, 27(14), 2021-2031. —SCI

[17] Sang Y.F., Wang D., Wu J.C., Zhu Q.P., Wang L., 2013. Improved continuous wavelet analysis on the variation of hydrologic time series’ dominant period. Hydrological Sciences Journal, 58(1), 118-132.—SCI

[18] Sang Y.F., Wang Z.G., Li Z.L., Liu C.M., Liu X.J., 2013. Investigation into the daily precipitation variability in the Yangtze River Delta, China. Hydrological Processes, 27(2), 175-185.—SCI

[19] Sang Y.F., Wang Z.G., Liu C.M., 2012. What factors are responsibille for the Beijing storm? Natural Hazards, 65(3), 2399-2400.—SCI

[20] Sang Y.F., 2012. A practical guide to discrete wavelet decomposition of hydrologic time series. Water Resources Management, 26(11), 3345-3365.—SCI

[21] Sang Y.F., Wang Z.G., Liu C.M., 2012. Period identification in hydrologic time series using empirical mode decomposition and maximum entropy spectral analysis. Journal of Hydrology, 424, 154-164.—SCI

[22] Sang Y.F., Wang D., Wu J.C., 2010. Probabilistic Forecast and Uncertainty Assessment of Hydrologic Design Values Using Bayesian Theories. Human and Ecological Risk Assessment, 16(5), 1184-1207.—SCI

[23] Sang Y.F., Wang D., Wu J.C., Zhu Q.P., Wang L., 2009. The relation between periods' identification and noises in hydrologic series data. Journal of Hydrology, 368(1-4), 165-177. —SCI

[24] 朱艳欣, 桑燕芳*. 青藏高原降水季节分配的空间变化特征. 地理科学进展, 2018, 37(11): 1533-1544.

[25] 桑燕芳, 李鑫鑫, 谢平, 刘勇. 水文时间序列概率预报方法的通用架构. 湖泊科学, 2018, 30(3): 611-618

[26] 桑燕芳, 谢平, 顾海挺, 李鑫鑫. 水文过程非平稳性研究若干问题探讨. 科学通报, 2017, doi: 10.1360/N972016-00736—EI

[27] 桑燕芳, 王中根, 刘昌明. 小波分析方法在水文学中的研究应用现状及展望. 地理科学进展, 2013, 32(9), 1413-1422.

[28] 桑燕芳, 王中根, 刘昌明. 水文时间序列分析方法研究进展. 地理科学进展, 2013, 32(1), 20-30.

[29] 桑燕芳, 王栋, 吴吉春, 朱庆平, 2010. 水文时间序列小波互相关分析方法. 水利学报, 41(11), 172-1179.— EI

[30] 桑燕芳, 王栋, 吴吉春, 朱庆平, 2009. 水文序列分析中基于信息熵理论的消噪方法. 水利学报, 40(8), 919-926. — EI

[31] 桑燕芳, 王栋, 2008. 水文序列小波分析中小波函数选择方法. 水利学报, 39(3), 296-300, 306. — EI

[32] 桑燕芳, 王栋, 2008. 水文时间序列周期识别的新思路与两种新方法. 水科学进展, 19(3), 412-417.— EI