Projects per year
Project Details
Description
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. Thus, we will build the capacity for cutting-edge next generation high-resolution global hydrological early warning systems that are open-access. 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.
Acronym | DANSk-LSM |
---|---|
Status | Active |
Effective start/end date | 01/09/2022 → 31/08/2026 |
Collaborative partners
- Technical University of Denmark
- DHI Water - Environment - Health
- Ohio State University
- UCL University College Lillebaelt
- German Aerospace Center
- University of California at Berkeley
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Keywords
- Large Scale
- Hydrology
- Data Assimilation
- Early warning
- Hazards
- Forecasting
- Geodesy
- Earth Observation
- GRACE
- GRACE-FO
- Altimetry
- MODIS
- Soil Moisture
- Groundwater
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Projects
- 1 Active
-
MuSe-BDA: Multi-Sensor Bayesian Data Assimilation for Large-Scale Drought Monitoring System (MuSe-BDA)
08/09/2022 → 07/08/2024
Project: Research
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A review on how Big Data can help to monitor the environment and to mitigate risks due to climate change
Montillet, J-P., Kermarrec, G., Forootan, E., Haberreiter, M., He, X., Finsterle, W., Fernandes, R. & Shum, CK., 2024, In: IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE. 17 p.Research output: Contribution to journal › Journal article › Research › peer-review
Open Access -
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
Forootan, E., Mehrnegar, N., Schumacher, M., Retegui Schiettekatte, L. A., Jagdhuber, T., Farzaneh, S., van Dijk, A. I. J. M., Shamsudduha, M. & Shum, CK., 20 Feb 2024, In: Science of the Total Environment. 912, 14 p., 169476.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile19 Downloads (Pure) -
Making the Best Use of GRACE, GRACE-FO and SMAP Data Through a Constrained Bayesian Data-Model Integration
Mehrnegar, N., Schumacher, M., Jagdhuber, T. & Forootan, E., Sept 2023, In: Water Resources Research. 59, 9, 22 p., e2023WR034544.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile2 Citations (Scopus)17 Downloads (Pure)