Qing Zhan MSc
PhD StudentAquatic Ecology |
Research themes
Research expertiseEcological modelling Lake restoration
Page last updated: 09-11-2021
Qing Zhan is the ESR-10 for MANTEL Project, with the title "Mitigating negative impacts of extreme events on the sustained provision of lake ecosystem service". We aim at combining laboratory experiment (Mesocosm) with field data to determine the impacts of extreme events (e.g. heatwave, storm) on eutrophication in shallow lakes, and developing a compromising mitigation technique for sustainable lake management in a changing world. Qing is interested in data mining, time series analysis, and lake physical and ecological modeling.
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Qing has a background in mathematics and studied Ecological Engineering at the Magdeburg-Stendal University of Applied Sciences for his master, where he focused on water quality modelling and reservoir management. In his second year, he worked as a student assistant handling high-frequency monitoring data of a reservoir observatory system (RRO) at the Helmholtz Central for Environmental Research, Magdeburg (UFZ). Later on, he continued his master thesis at UFZ investigating the dissolved organic carbon (DOC) dynamics in a drinking water reservoir in the Harz Mountains, Central Germany. For mitigating the DOC loading from this reservoir, he has constructed an optimization model including a decision-making module and a DOC dynamics simulation module, and then interpolated the high-frequency data into this model. Qing's reservoir operation model has been recognized with great interest in a presentation at the responsible reservoir authority (Talsperrenbetrieb Sachsen-Anhalt), and he has been awarded Otto-von-Guericke Scholarship during the last year of his master program (see news). Since July 10, 2020, Qing started to work at the AqE department of NIOO as a PhD candidate on the project titled "management of extreme events in lakes and catchments" (MANTEL-project), supervised by Dr. Lisette De Sernopont Domis of the working group "Aquatic Knowledge Centre Wageningen" (AKWA).
Links
- Zhan, Q., Teurlincx, S., van Herpen, F., Raman, N. V., Lürling, M., Waajen, G., & de Senerpont Domis, L. N. (2022). Towards climate-robust water quality management: Testing the efficacy of different eutrophication control measures during a heatwave in an urban canal. Science of the Total Environment, 828, [154421]. https://doi.org/10.1016/j.scitotenv.2022.154421
- Reinl, K. L., Brookes, J. D., Carey, C. C., Harris, T. D., Ibelings, B., Morales-Williams, A. M., de Senerpont Domis, L., Atkins, K. S., Isles, P. D. F., Mesman, J. P., North, R. L., Rudstam, L. G., Stelzer, J. A. A., Venkiteswaran, J. J., Yokota, K., & Zhan, Q. (2021). Cyanobacterial blooms in oligotrophic lakes: Shifting the high-nutrient paradigm. Freshwater Biology, 66(9), 1846-1859. https://doi.org/10.1111/fwb.13791
- Zhan, Q., Stratmann, C. N., van der Geest, H. G., Veraart, A. J., Brenzinger, K., Lürling, M., & de Senerpont Domis, L. N. (2021). Effectiveness of phosphorus control under extreme heatwaves: implications for sediment nutrient releases and greenhouse gas emissions. Biogeochemistry, 156, 421-436. https://doi.org/10.1007/s10533-021-00854-z
- Zhan, Q., Kong, X., & Rinke, K. (2021). High frequency monitoring enables operational opportunities for reducing dissolved organic carbon (DOC) load of Germany's largest drinking water reservoir. Inland Waters, In press. https://doi.org/10.1080/20442041.2021.1987796
- Zhan, Q., Kong, X., & Rinke, K. (2021). High-frequency monitoring enables operational opportunities to reduce the dissolved organic carbon (DOC) load in Germany’s largest drinking water reservoir. Inland Waters, In press. https://doi.org/10.1080/20442041.2021.1987796
- Musolff, A., Zhan, Q., Dupas, R., Minaudo, C., Fleckenstein, J. H., Rode, M., Dehaspe, J., & Rinke, K. (2021). Spatial and Temporal Variability in Concentration-Discharge Relationships at the Event Scale. Water Resources Research, 57(10), [e2020WR029442]. https://doi.org/10.1029/2020WR029442
- Kong, X., Zhan, Q., Boehrer, B., & Rinke, K. (2019). High frequency data provide new insights into evaluating and modeling nitrogen retention in reservoirs. Water Research, 166, [115017]. https://doi.org/10.1016/j.watres.2019.115017
2022
2021
2019
- 2019 NIOO open day
Contact
+31 (0)317 473 534
Droevendaalsesteeg 10
6708 PB Wageningen
+31 (0)317 47 34 00
Postbus 50
6700 AB Wageningen
