Dr. Husain Najafi won the UFZ Young Scientist Award for Applied Research 2022. The award goes to him for his excellence and dedication in development of the operational drought forecasting system in Germany and investigation of 2021 summer flood predictability. Hydrological forecasting systems are the fundamental component of early warning systems - disaster risk reduction.

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The collaborative project "Standardisiertes Monitoring von Wachstumsreaktionen wichtiger Waldbaumarten auf klimatische Extremereignisse" (MW³) with participation of the UFZ will develop a standardized monitoring system and protocol to record growth responses of important Central European forest tree species with a focus on climatic extremes. Future extreme events and their impact on forest carbon fluxes in Germany will be analyzed using smart models.

Mai, J. , B. A. Tolson, H. Shen, É. Gaborit, V. Fortin, N. Gasset, H. Awoye, T. A. Stadnyk, L. M. Fry, E. A. Bradley, F. Seglenieks, A. G. Temgoua, D. G. Princz, S. Gharari, A. Haghnegahdar, M. E. Elshamy, S. Razavi, M. Gauch, J. Lin, X. Ni, Y. Yuan, M. McLeod, N. B. Basu, R. Kumar, O. Rakovec, L. Samaniego, S. Attinger, N. K. Shrestha, P. Daggupati, T. Roy, S. Wi, T. Hunter, J. R. Craig, and A. Pietroniro (2021): The Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) Journal of Hydrologic Engineering, 26(9), 05021020. Accepted Feb 16, 2021.

Masooma Batool won the "Best student poster award" at the XXI International Nitrogen Workshop.This conference, hosted by ETSIAAB, Universidad Politécnica de Madrid, brought together nearly 300 papers from authors from 40 countries in Madrid, Spain, October 24-28, 2022. At the conference, Masooma and her colleagues presented long-term trends in soil nitrogen (N) surplus across Europe (1850-2019). Excess N provides information on the trends and spatial distribution of N surplus in soil systems across Europe, highlighting agricultural areas in Central Europe as nitrogen hotspots. These areas have undergone dramatic changes over the past 150 years. Importantly, we show that estimates of N surplus are subject to large uncertainties, requiring explicit consideration of these uncertainties when analyzing past trends in N surplus.

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The overarching objective of 4DHydro is to foster collaborations between the Earth Observation (EO) water cycle community developing novel high-resolution EO data products, and the land surface and hydrological modelling community (LSM/HM) engaged in advancing hyperresolution modelling of the hydrological cycle at regional and continental scales. By involving several European institutions 4DHydro will assess and improve data sets of EO as well as synchronize EO products and LSM/HM models in order to improve predictions of the hydrological system. Overall, it will assist end-users and decisions-makers as well as the EU Destination Earth initiative. The project’s kick-off meeting will take place in Leipzig on 21st of June 2023.

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Hitze und Dürre - Interview with climate researcher Jakob Zscheischler in Deutschlandfunk (only in German, 10:50 min) Link to interview

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Land surface models require a large number of parameters. These parameters are hard-coded as constants in the models despite they are obtained e.g. via regression methods from field data and have therefore an uncertainty range.  In this projcet we study the effect of parametric sensitivity of the model output. We apply a global sensitivity design using stratified random sampled parameter values and conducting local derivative based parameter pertubations. The relative sensitivity of three model outputs, latent and sensible heat as well as photosynthesis, is captured via an objective function of normalized squared flux deviations. For determine sensitive model parameters and their mean relative effect on the model outputs, we apply an eigendecomposition on a matrix containing numerical information of the objective function. After introducing a parameter importance index, see Figure, we propose a new criterion acting on this index, to determine the important model parameters depenent on a user specific confidence level for e.g. parameter estimation purposes. We use the Community Land Model (CLM, rev3.5) from the National Center of Atmospheric Research, Boulder, CO (http://www.cgd.ucar.edu/tss/clm/).

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New research published in Nature Communications by Hussian et al., (2024) emphasizes the game-changing potential of near-real-time and impact-based flood prediction. Their study demonstrates a 10-meter floodplain inundation hindcast of the 2021 European Summer Flood, showcasing a 17-hour lead time for better disaster preparedness. Impact-based early warnings can empower local authorities with crucial data on citizens at-risk for targeted emergency responses.

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05:50': Hohes Holz research station10:40': FORMIND in the Vislab15:45': Fragmentation of forests43:00': FORMIND and climate scenarios

Earthrise - Mission KlimaHow our research contributes to climate mitigation and adaptation is demonstrated in this WELT report.