Sunxun

Dr. Xun Sun has studied in the following universities for at least one university year, and achieved his Becholar in Mathematics and Computer Sciences, Master in Mathematics and Ph.D in Environmental Sciences.

Université Pierre-et-Marie-Curie (now Sorbonne Université), France. 

ARWU Rank 2020: 39

Université de Grenoble (now Université Grenoble Alpes), France.

ARWU Rank 2020: 99

National University of Singapore. 

ARWU Rank 2020: 80

The University of Adalide, Australia. 

ARWU Rank 2020: 150

Columbia University, USA. 2014-now

ARWU Rank 2020: 7

East China Normal University, China. 2016-now

ARWU Rank 2020: 

Adjunct Associate Research Scientist, Columbia Water Center, Earth Institute Columbia University, New York NY 10027,USA

Journal reviewer tasks:

Water Resources Research, Journal of Hydrology, Hydrology and Earth System Sciences, Advances in Water Resources, Journal of Geophysical Research (Atmospheres), etc.

Statistical modeling, flood and drought, the impact of climate change on hydrology and water resources, uncertainty analysis, extreme events and extreme value theory

Book chapter:

·Renard, B., Sun, X., and Lang, M. (2013), Bayesian Methods for Non-stationary Extreme Value Analysis, in Extremes in a Changing Climate: Detection, Analysis and Uncertainty, edited by A. AghaKouchak, D. Easterling, K. Hsu, S. Schubert and S. Sorooshian, pp. 39-95, Springer Netherlands.

Journal paper:

1.Ma, Y., Sun, X.*, Chen, H., Hong, Y., Zhang, Y. (2021) A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau, Hydrol. Earth Syst. Sci., 25, 359-374. https://doi.org/10.5194/hess-25-359-2021 (IF=4.94)

2.Qin, Y., Sun, X.*, Li, B., Merz, B. (2021) A nonlinear hybrid model to assess the impacts of climate variability and human activities on runoff at different time scales. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-021-01984-4 (IF=2.35)

3.Zeng, P., Sun, X.*, Farnham, D.J. (2020) Skillful statistical models to predict seasonal wind speed and solar radiation in a Yangtze River estuary case study. Sci Rep 10, 8597. https://doi.org/10.1038/s41598-020-65281-w ((IF=4.01)

4.Su, Z., Sun, X.*, Devineni, N., Lall, U., Hao, Z., Chen, X. (2020) The effects of pre-season high flows, climate, and the Three Gorges Dam on low flow at the Three Gorges Region, China. Hydrological Processes. 34: 2088-2100. https://doi.org/10.1002/hyp.13714 (IF=3.19)

5.Su, Z., Ho, M., Hao, Z., Lall, U., Sun, X., Chen, X., Yan, L. (2020) The impact of the three gorges dam on summer streamflow in the yangtze river basin. Hydrological Processes.34: 705– 717. https://doi.org/10.1002/hyp.13619 (IF=3.19)

6.Huang, Y., Sun, X., Liu, M., Zhu, J., Yang, J., Du, W., Zhang, X., Gao, D., Qadeer, A., Xie, Y., Nie, N. (2019) A multimedia fugacity model to estimate the fate and transport of polycyclic aromatic hydrocarbons (PAHs) in a largely urbanized area, Shanghai, China. Chemosphere. Feb;217:298-307. https://doi.org/10.1016/j.chemosphere.2018.10.172 (IF=5.11)

7.Yuan, X.*, Sun, X. (2019). Climate change impacts on socioeconomic damages from weather-related events in china. Natural Hazards.99, 1197–1213. https://doi.org/10.1007/s11069-019-03588-2 (IF=2.32)

8.Steirou, E.*, Gerlitz, L., Apel, H., Sun, X.*, and Merz, B. (2019) Climate influences on flood probabilities across Europe, Hydrol. Earth Syst. Sci., 23, 1305-1322. https://doi.org/10.5194/hess-23-1305-2019 (IF=4.94)

9.Qin, Y., Li, B.*, Sun, X, Chen, Y., Shi, X. (2019) Nonlinear response of runoff to atmospheric freezing level height variation based on hybrid prediction models. Hydrological Sciences Journal, 64(13):1556–1572. https://doi.org/10.1080/02626667.2019.1662023 (IF=2.43)

10.Wei, X., Liu, M.*, Yang, J., Du, W., Sun, X., Huang, Y., Zhang, X., Khalila, S., Luo, D., Zhou, Y. (2019) Characterization of PM2.5-bound PAHs and carbonaceous aerosols during three-month severe haze episode in Shanghai, China: Chemical composition, source apportionment and long-range transportation. Atmospheric Environment, 203: 1-9. https://doi.org/10.1016/j.atmosenv.2019.01.046 (IF=4.04)

11.Wang, S., Sun, X.*, Lall, U., (2017) Residential electricity demand prediction during summer season across USA, Energy. https://doi.org/10.1016/j.energy.2017.08.076 (IF=4.520)

12.Morón, S., Amos, K., Edmonds, D.A., Payenberg, T., Sun, X., Thyer, M. (2017), Avulsion triggering by El Niño-Southern Oscillation and tectonic forcing on the tropical Magdalena River, Colombia, The Geological Society of America Bulletin. https://doi.org/10.1130/B31580.1 (IF=4.212)

13.Ho, M., Lall, U., Sun, X., Cook, E. (2017), Multiscale temporal variability and regional patterns in 555 year of conterminous US streamflow, Water Resources Research, 53, 3047–3066. https://dx.doi.org/10.1002/2016WR019632 (IF=4.397)

14.Zeng, H., Sun X.*, Lall, U., Fang, P. (2017), Extreme rainfall and flood predictions for Xidayang Reservoir in North China using climate informed Bayesian approaches, International Journal of Climatology, 37: 3810–3820. https://dx.doi.org/10.1002/joc.4955 (IF=3.760) 

15.Yuan, X., Sun, X., Zhao, W., Mi, Z., Wang, B., Wei, Y.* (2017). Forecasting china's regional energy demand by 2030: a bayesian approach. Resources, Conservation and Recycling. https://doi.org/10.1016/j.resconrec.2017.08.016 (IF=8.08)

16.Yuan, X., Sun, X, Lall, U., Mi, Z., He, J., Wei, Y. (2016), China’s socioeconomic damage risk from extreme events in a changing climate: a hierarchical Bayesian model, Climatic Change, 139(2): 169-181. https://doi.org/10.1007/s10584-016-1749-3 (IF=3.496)

17.Sun, X.*, Renard, B., Thyer, M., Westra S., Lang, M. (2015), A global analysis of the asymmetric effect of ENSO on extreme precipitation, Journal of Hydrology, Volume 530, November 2015, Pages 51-65. https://doi.org/10.1016/j.jhydrol.2015.09.016 (IF=3.483)

18.Sun, X.*, Lall, U., Merz, B., Dung, N.V. (2015), Hierarchical Bayesian clustering for nonstationary flood frequency analysis: Application to trends of annual maximum flow in Germany. Water Resources Research, 51(8), 6586–6601. https://doi.org/10.1002/2015WR017117 (IF=4.397)

19.Sun, X.*, Lall, U. (2015), Spatially coherent trends of annual maximum daily precipitation in the United States, Geophysical Research Letters, 42(22), 9781–9789. Featured in EOS Research spotlight. https://dx.doi.org/10.1002/2015GL066483(IF=4.253)

20.Sun, X.*, Thyer, M., Renard, B., Lang, M. (2014), A general regional frequency analysis framework for quantifying local-scale climate effects: A case study of ENSO effects on Southeast Queensland rainfall, Journal of Hydrology, Volume 512, 6 May 2014, Pages 53-68. https://doi.org/10.1016/j.jhydrol.2014.02.025 (IF=3.483)

Conference:

·Sun, X., (2018), How climate affects the dependence of extreme streamflow? American Geophysical Union Fall Meeting 2018, 10-14 Dec 2018, Washington DC, USA. (talk)

·Sun, X., (2017), co-convener of the session NH005. Dams and Reservoirs - Natural Hazards, Risks, and Solutions, American Geophysical Union, 2017 Fall Meeting, 11–15 December 2017, New Orleans, USA.

·Sun, X., (2017), Convener of the session HS5.9/CL2.17/CR6.9/NH1.9, Water infrastructure risks under climate variability and change: role of data analysis, operating approaches, hydro-meteorological and multi-sectoral forecasts. European Geosciences Union General Assembly 2017, 23–28 April 2017, Vienna, Austria.

·Sun, X., Russo, T., Wu, H., Lall, U. (2016), Spatio-temporal variation of the extreme precipitation in California. American Geophysical Union Fall Meeting 2016, 12-16 Dec 2016, San Francisco, USA. (poster)

·Sun, X., Lall, U., (2016), Climate risks to potato yields in Europe. European Geosciences Union General Assembly 2016, 18-22 April 2016, Vienna, Austria. (poster)

·Sun, X., Lall, U., Merz, B., Dung, N.V. (2015), A non-stationary Bayesian clustering framework for Identifying regional hydro-climate trends from large scale data. 26^th IUGG General assembly 2015, 22 June-2 July, 2015, Prague, Czech Republic. (talk)

·Sun, X., Lall, U. (2014), A Bayesian Hierarchical framework for identifying regional hydroclimate trends or climate effects from continental or global data. American Geophysical Union Fall Meeting 2014, 15-19 Dec 2014, San Francisco, USA. (poster)

·Sun, X., Renard, B., Thyer, M., Westra S., Lang, M. (2013), An analysis of ENSO impact on global extreme rainfall using a Bayesian regional model. European Geosciences Union General Assembly 2013, 07-12 April 2013, Vienna, Austria. (poster)

·Sun, X., Thyer, M., Renard, B., Lang, M. (2012), Bayesian methods for non-stationary frequency analysis: impact of ENSO on maximum daily rainfall in Australia. Advanced Methods for Flood Estimation in a Variable and Changing Environment, organized as a Mid-term Conference of COST ES0901 ‘FloodFreq’ Action, 24-26 October 2012, Volos, Greece. (talk)

·Sun, X., Renard, B. and Lang, M. (2011). A Bayesian Analysis of Extreme Precipitation in Mediterranean France Using Non-Stationary GEV Models. 7th Conference on Extreme Value Analysis, Probabilistic and Statistical Models and their Applications, June 27^th -July 1^st, 2011, Lyon, France. (talk)

·Sun, X., Renard, B. and Lang, M. (2011). A Bayesian Analysis of Extreme Precipitation in Mediterranean France Using Non-Stationary GEV Models. Workshop, Environmental Risk and Extreme Events, 10-15 July 2011, Ascona, Switzerland. (talk) 

Updated on 22 March 2021