MuSe-BDA

An overview of the MuSe-BDA

by N. Mehrnegar

The research of MuSe-BDA is about how the climate change and anthropogenic modifications have been affecting the patterns of global rainfall, evapotranspiration, and stored terrestrial water and how these changes could be monitored or forecasted.

Climate change and anthropogenic modifications have been affecting the patterns of global rainfall, evapotranspiration, and stored terrestrial water. They also increase the probability of climate disasters, such as agricultural losses, water scarcity, and famine.

Previous studies reported that the last decades’ recharge rates from rain, snow, and surface water has been decreased due to the global warming and the intensive pumping of the groundwater storage in many regions of the world. The WMO’s recent report indicates that, over the past 50 years, recorded climate-induced disasters increased five-fold, which will continue even with the limited 1.5°C global warming. It also warned that the numbers of people affected by natural disasters could increase by 50% over the next decade.

According to the latest International Panel Climate Changes (IPCC) report, released in August 2021, the level of global warming will increase by 1.5°C-3°C in the next decades. Due to the changing climate, many regions are already facing more frequent, severe, and longer lasting droughts with cascading effects; for example, they reduce water levels in rivers and groundwater, stunt tree and crop growth, increase pest attacks and fuel wildfires. In Europe, most of the losses caused by drought (∼ 9 Euro billion/year) affect agriculture, the energy sector and the public water supply. Extreme droughts in western and central Europe in 2018, 2019 and 2020 caused considerable damage. In 2018 alone, agricultural damages amounted to some EUR 2 billion in France, EUR 1.4 billion in the Netherlands, and EUR 770 million in Germany. With the global warming of 3°C, droughts would happen twice as often, and the economic loss due to droughts shall increase to EUR 40 billion/year in Europe, affecting mostly the Mediterranean and Atlantic Regions. Yet, WMO states that 1/3 of global populations are not adequately covered by reliable early warning systems and this is on EU’s immediate agenda.

Drought monitoring and hydrological early warning system are important tools for water resources management, and they must be further complemented by forecasting facilities that are well integrated with the EU’s Earth Observation data.


The overall objective of this project, based on the combined expertise of Nooshin Mehrnegar and Ehsan Forootan, is to develop an accurate and efficient, as well as physically and mathematically consistence Bayesian based Data Assimilation (DA) framework(s) to integrate the benefits of synergistically available satellite geodetic and EO data and the state-of-the-art of hydrological models to better understand and forecast the recent and future spatial-temporal changes in continental water storage and water fluxes.

For this, Nooshin Mehrnegar will develop and implement Multi Sensor-Bayesian Data Assimilation (MuSe-BDA) frameworks that are unique in terms of flexibility to assimilate various satellite data, and they are computationally efficient. The application will be demonstrated in simulating and forecasting episodic large-scale droughts within Europe (north and south) and USA (e.g., California and Texas) covering 2003-onward with an unprecedented spatial resolution of 0.05° (∼ 5 km) at daily temporal rate.

Project members

  • AAU Team:
    • Nooshin Mehrnegar
    • Ehsan Forootan
  • External Team Members:
    • Jürgen Kusche (University of Bonn, Germany)
    • Luca Brocca (Research Institut of the Geo-Hydrological Protection, Italy)

Publications:

  • Forootan, E., Mehrnegar, N., Schumacher, M., Schiettekatte, R. 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
  • Mehrnegar, N., Schumacher, M., Jagdhuber, T., Forootan, E. (2023). Making the Best Use of GRACE, GRACE-FO and SMAP Data through a Constrained Bayesian Data-Model Integration. Water Resources Research. https://doi.org/10.1029/2023WR034544
  • 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. https://doi.org/10.1080/27669645.2022.2097768

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