DANSk-LSM

An overview of the DANSk-LSM project

by E. Forootan

This research project on assimilating available multi-sensor satellite data into large-scale hydrological models. This project is funded by the Independent Research Fund Denmark.

Background

Extreme weather and climate events have increased in frequency, intensity and severity as a result of climate change and hit vulnerable communities disproportionately hard. Yet one third of global populations are still not adequately covered by reliable early warning systems, according to the 2020 State of Climate Service report from the World Meteorological Organization (WMO).

The main aim of DANSk-LSM

The main goal of DANSk-LSM is to develop and demonstrate accurate and efficient, physically and mathematically consistent Data Assimilation (DA) systems that robustly integrate synergistically and complementary available satellite data with the state-of-the-art of hydrological models. DANSk-LSM uniquely integrates multi-sensor geodetic and remotely sensed Earth Observation (EO) data, implements innovative DA and calibration frameworks, and has unprecedented high spatial-temporal resolution.

An overview of the work packages of the DFF2 Project DANSk-LSM (2022-2026) by the Danmarks Frie Forskningsfond [10.46540/2035-00247B], see details here. 

Project members:

Publications:

  • Forootan E., Mehrnegar, N., (2022). A hierarchical Constrained Bayesian (ConBay) approach to jointly estimate water storage and Post-Glacial Rebound from GRACE(-FO) and GNSS data, All Earth, 34:1, 120-146, DOI: 10.1080/27669645.2022.2097768
  • Agarwal, V., Akyilmaz, O., Shum, C. K., Feng, W., Yang, T., Forootan, E., Syed, H., T., Haritashya, K. U., Uz, M., (2023). Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California. Science of The Total Environment, Volume 865, 161138, ISSN: 0048-9697, https://doi.org/10.1016/j.scitotenv.2022.161138
  • Forootan, E., Dehvari, M., Farzaneh, S., Karimi, S. (2023). Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique. Atmosphere, 14(1), 112. https://doi.org/10.3390/atmos14010112
  • Forootan, E., Mehrnegar, N., Schumacher, M., Retegui-Schiettekatte, L. A., Jagdhuber, T., Farzaneh, S., van Dijk, A., Shamsudduha, M., Shum, C. K., (2024). Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model. Science of The Total Environment, Volume 912, 169476, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2023.169476.
  • Retegui-Schiettekatte, L., Schumacher, M., Madsen, H. & Forootan, E. (2025), Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin, Science of The Total Environment, Volume 975, 179181, ISSN 0048-9697,
    https://doi.org/10.1016/j.scitotenv.2025.179181

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