Watershed Dynamics and Hydrological Extremes

Saale River during winter flood (February 2021). Photo: Larisa Tarasova

Research Focus


In the WaterExtremes group we develop effective process-informed data-driven methods grounded in comprehensive perceptual representation of dominant hydrological dynamics and catchment organization to leverage the increasing sample sizes of hydrometeorological observations from small- to mesoscales. With these methods we uncover regularities in complex hydrological systems to understand better the emergence of extremes and provide more reliable predictions at large scales.


The following Master thesis/Internships are currently available in the group:

  • Improving parameter regionalization of the distributed catchment model (contact: Dr. Zhenyu Wang)
  • Drought and post-drought impacts on pollutant transport from catchments to streams (contact: Felipe Saavedra)
  • Effect of extreme events on nutrient concentrations and ratios in the Elbe River catchment (contact: Felipe Saavedra)

We also looking to host Doctoral/Postdoctoral research stays supported by the HIDA network on various topics concerning explainable machine learning and deep learning (contact: Dr. Larisa Tarasova).


Felipe Saavedra

Doctoral researcher (PhD Cohort DYNAMO; TRACER Resarch School)

Topic: Effects of hydrological events on solute mobilization and delivery in German river catchments


Dr. Zhenyu Wang

Postdoctoral researcher

Topic: Event-type-based diagnostics of hydrological models and groundwater droughts


Daniela Peña-Guerrero

Postdoctoral researcher

Topic: Propagation and impacts of hydrological droughts


Dr. Hsing-Jui Wang

Postdoctoral researcher

Topic: Emergence of unprecedented floods


Jiarui Yu

Visiting doctoral researcher

Topic: Damage footprint of different flood generation processes


Ying Zhang

Visiting doctoral researcher

Topic: Forecasting widespread floods using deep learning

Chahinaz Ziani

Research Assistant

Topic: Development of spatially-differentiated catchment descriptors


Uncovering generation processes of complex hazards using data evidences supported by simulations

  • changes in flood generation processes
  • archetypes of drought propagation through catchment compartments
  • spatial extremes
  • impact of extremes on nutrient mobilization

Improving process foundation of conceptual, statistical, machine and deep learning models for more reliable predictions under global change

  • event-type-based diagnostics of hydrological models
  • explainable AI
  • sparse neural networks for S2S forecasting
  • physically-enhanced statistical approaches for estimating extremes

Developing methods to account for inherent spatial organization within hydrological catchments



Propensity of rivers to extreme floods: climate-landscape controls and early detection

(DFG Research Project, grant 421396820, 2019-2022)



Space-time dynamics of extreme floods

(DFG Research Group, FOR 2416, 2017-2023)



Events as dynamic drivers of pollutant transport, turnover and export in catchments – from monitoring to models

Subproject 3: Effects of hydrological events on solute mobilization and delivery in German river catchments

(Helmholtz Centre for Environmental Research - UFZ PhD college, 2020-2023)


You could use our publication index for further requests.

2024 (4)

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2023 (11)

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2022 (11)

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2021 (3)

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2020 (8)

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2019 (1)

  • Tarasova, L., Merz, R., Kiss, A., Basso, S., Blöschl, G., Merz, B., Viglione, A., Plötner, S., Guse, B., Schumann, A., Fischer, S., Ahrens, B., Anwar, F., Bárdossy, A., Bühler, P., Haberlandt, U., Kreibich, H., Krug, A., Lun, D., Müller‐Thomy, H., Pidoto, R., Primo, C., Seidel, J., Vorogushyn, S., Wietzke, L. (2019):
    Causative classification of river flood events
    Wiley Interdiscip. Rev.-Water 6 (4), e1353 10.1002/wat2.1353
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2018 (3)

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2016 (1)

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