Karan Mahajan

Scientist

Department of Remote Sensing (RS)
Helmholtz Centre for Environmental Research (UFZ)
Permoserstraße 15, 04318 Leipzig

Tel.: ++49 0341 60254040
email: karan.mahajan@ufz.de

Career details

July 2026 - Present	Scientist, Visual Analytics for Earth System and Geospatial Data, UFZ
Apr 2025 - Oct 2025	Tutor in Remote Sensing and Student Research Assistant, Chair of Hydrology and River Basin Management, Technical University of Munich
Mar 2024 - Sep 2024	Student Research Assistant, Chair of Hydrology and River Basin Management, Technical University of Munich
Oct 2022 - Oct 2025	Student Research Assistant, Chair of Hydraulic Engineering, Technical University of Munich
Aug 2020 - Apr 2021	Research Intern, Centre for Environment, Energy and Natural Resource Governance, University of Cambridge
Feb 2019 - Jul 2019	Research Intern, Centre for Water Research, National University of Singapore

Education

Dec 2023	Indo-German Partnership Winter School, Indian Institute of Technology Bombay
Oct 2021 - Jan 2026	Master of Science in Environmental Engineering, Technical University of Munich Final grade: 1.0 (High distinction) Thesis: Large Scale Transpiration Estimation Using a Hybrid Approach and it's Spatial Temporal Trends in Climate Extremes Study Project: Developing a Regionalized Machine Learning Model for Nitrate Concentration Prediction in Rivers and Streams in Bavaria
Aug 2016 - Jun 2020	Bachelor of Technology in Civil Engineering, Punjab Engineering College, Chandigarh

Research interests

(Eco) Hydrological modeling
Urban flood modeling
Water quality
Machine learning (Hybrid modeling)
Large scale gridded datasets

My research focuses on improving our understanding of water systems using computer models, both process based and machine learning algorithms. In the past few years I have worked on various topics related to water resources, like urban flood modeling in Berlin and Wuerzburg using Telemac2D, predicting nitrate concentrations in rivers and streams in Bavaria using machine learning, tutoring master's students in remote sensing, and creating gridded radiation and transpiration datasets for Europe at high spatial-temporal resolution using hybrid approaches. At the UFZ my work will focus on developing visual analytics methods for large-scale geospatial datasets (e.g. remote sensing and Earth system model simulations), to analyse spatial-temporal patterns, variability, and extremes, and contributing to data-centric workflows within the Helmholtz Imaging Data Curation Unit.

Awards

Deutschlandstipendium 2023, 2024
2nd Runner's Up, InnovateTheAlps Hackathon, Alpioneers, Traunstein
Certificate of Excellence for Exemplary Contribution to Debate Club, Punjab Engineering College

Publications / Abstracts

De Vos L. F., Mahajan K., Caviedes-Voullième D., and Rüther N.: Culvert Blockages in 2D-Hydrodynamic Flood Modeling: Quantifying the Impact on Flood Dynamics and Designing Mitigation Strategies. Natural Hazards and Earth System Sciences. 2026, https://doi.org/10.5194/nhess-26-2319-2026
Mahajan, K., Tuo, Y., and Peng, J.: High-resolution net-shortwave and net-radiation products for Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21227, https://doi.org/10.5194/egusphere-egu26-21227, 2026. (PICO presentation)
De Vos, L. F., Mahajan, K., Caviedes-Voullième, D., Rohmat, F., Adiprayoga, M. F., and Rüther, N.: Efficient and Automated Mesh Generation for Refined Flood Modeling in Complex Urban Environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11573, https://doi.org/10.5194/egusphere-egu26-11573, 2026. (Poster session)
De Vos L. F., Mahajan K., Caviedes-Voullième D., and Rüther N.: The Impact and Benefits of Local Mesh
Refinements at Buildings in Hydrodynamic Modeling. Advances in Water Resources. 2026 [preprint],
https://dx.doi.org/10.2139/ssrn.6218998
Mahajan, K. and De Vos, L. F.: Assessment of different building representations in numerical urban flood
modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15865 , https://doi.org/10.5194/egusphere-egu24-15865 (PICO presentation)
De Vos, L. F., Mahajan, K., and Rüther, N.: Can we quantify the impact of the modeler on the model?, EGU
General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15853, https://doi.org/10.5194/egusphere-egu24-15853 ,(Poster session)
De Vos L. F., Rüther N., Mahajan K., Dallmeier A., and Broich, K.: Establishing Improved Modeling Practices of
Segment-Tailored Boundary Conditions for Pluvial Urban Floods. Water. 2024; 16(17):2448. https://doi.org/10.3390/w16172448

Contact

Head
Prof. Dr. Jian Peng
Department of Remote Sensing
jian.peng@ufz.de

Assistant
Catharina Reul
Department Remote Sensing and
Remote Sensing Centre for Earth System Research
catharina.reul-jimenez@ufz.de
Tel.: +49 341 6025-2335

Permoserstraße 15 | 04318 Leipzig | Germany

News / Press

We’re hiring: Research Scientist for Soil–Vegetation–Atmosphere Interactions under Climate Extremes

The Terrestrial Water Dynamics team successfully attended the EGU General Assembly 2024 in Vienna! Presenting cutting-edge research and fostering collaborations, we're already looking forward to next year's EGU.

The UFZ Forest Condition Monitor is now live. It provides spatially explicit information about the condition of Germany's forests from 2016 to 2022.

Two of our MoDEV PhD candidates published their latest research in Remote Sensing of Environment. Antonia Ludwig assessed the spectral variation hypothesis doi and Toni Schmidt compared soil moisture products with cosmic-ray neutron sensing observations doi.

UFZ press release of a study led by Almudena García-García: Heat extremes in the soil are underestimated

The forest condition monitor from our working group LACY is part of the Environmental Photo Festival horizons zingst 2023 and publicly displayed in the city Zingst until summer 2024.

UFZ press release of a study led by Francisco José Cuesta Valero: Underestimated Heat Storage

Daniel Doktor interviewed by radioeins: presenting the forest monitor.

Congratulations to Reagan Okoth for a successful oral Master defense!

Welcome Peter Borrmann to join us as a PhD candidate in December 2022.

Fantastic and successful first department retreat in Grimma since the establishment of the team:

Germany-wide grassland management and land-use intensity information depicted from satellite-data (Lange et al. 2022) are now available as download and web service

Congratulations to Francisco José Cuesta Valero for being awarded the Alexander von Humboldt Research Fellowship, and we warmly welcome him to join us in October 2021.

MoDEV project kick-off seminar was successfully held on Sep 21, 2021. Four talented and motivated phd candidates will apply novel model-data fusion methods to better understand environmental variability based on high-resolution remote sensing.

MoDEV

Remote Sensing Center for Earth System Research (RSC4Earth) kicked off!

https://rsc4earth.de

https://twitter.com/Rsc4Earth

New publications

García-García, A. and Peng, J.: Generation and evaluation of energy and water fluxes from the HOLAPS framework: Comparison with satellite-based products during extreme hot weather
Remote Sensing of Environment, 315, 114451, 2024 doi

Ludwig, A., Doktor, D., and Feilhauer, H.: Is spectral pixel-to-pixel variation a reliable indicator of grassland biodiversity? A systematic assessment of the spectral variation hypothesis using spatial simulation experiments, Remote Sensing of Environment, 302, 113988, 2024
doi

Schmidt, T., Schrön, M., Li, Z., Francke, T., Zacharias, S., Hildebrandt, A., and Peng, J.: Comprehensive Quality Assessment of Satellite- and Model-Based Soil Moisture Products against the COSMOS Network in Germany, Remote Sensing of Environment, 301, 113930, 2024
doi

Almudena García-García, Francisco José Cuesta-Valero, Diego G. Miralles, Miguel D. Mahecha, Johannes Quaas, Markus Reichstein, Jakob Zscheischler and Jian Peng. Soil heat extremes can outpace air temperature extremes, Nature Climate Change
(doi)

Cuesta-Valero, F.J., Beltrami, H., García-García, A., Krinner, G., Langer, M., MacDougall, A.H., Nitzbon, J., Peng, J., von Schuckmann, K., Seneviratne, S.I., Thiery, W., Vanderkelen, I., & Wu, T.: Continental heat storage: contributions from the ground, inland waters, and permafrost thawing. Earth System Dynamics, 14, 609-627, 2023.

Mahecha, M., Bastos, A., Bohn, F., Eisenhauer, N., Feilhauer, H., Hartmann, H., Hickler, T., Kalesse-Los, H., Migliavacca, M., Otto, F., Peng, J., Quaas, J., Tegen, I., Weigelt, A., Wendisch, M., Wirth, C., (2022):
Biodiversity loss and climate extremes — study the feedbacks, Nature, 612, 30-32, 2022.

Lange M, Feilhauer H, Kühn I, Doktor D (2022):
Mapping land-use intensity of grasslands in Germany with machine learning and Sentinel-2 time series.
Remote Sensing of Environment 277,112888
(doi)

Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M.H., Crow, W.T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M.W.J., de Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y.H., Lovergine, F., Mahecha, M.D., et al., (2021):
A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements.
Remote Sensing of Environment, 112162
(doi)

Balenzano, A., Mattia, F., Satalino, G., Lovergine, F.P., Palmisano, D., Peng, J., et al., (2021):
Sentinel-1 soil moisture at 1 km resolution: a validation study.
Remote Sensing of Environment, 263, 112554
full text (doi)

Huang, J., Zhang, Y., Bing, H., Peng, J., Dong, F., Gao, J., & Arhonditsis, G.B. (2021):
Characterizing the River Water Quality in China: Recent Progress and On-Going Challenges.
Water Research 201: 117309
full text (doi)

Ayalew, D.A., Deumlich, D., Šarapatka, B., Doktor, D., (2020):
Quantifying the sensitivity of NDVI-based C factor estimation and potential soil erosion prediction using spaceborne Earth observation data
Remote Sens. 12 (7), art. 1136
full text (doi)

Preidl, S., Lange, M., Doktor, D., (2020):
Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery
Remote Sens. Environ. 240 , art. 111673
full text (doi)