Projects per year
Abstract
World-wide water resources are threatened by the impacts of natural climate variability and anthropogenic climate change resulting in water stress for many regions. Here, we focus on the Murray-Darling River Basin, Australia, one of the many regions that benefits from a better understanding of water resources availability and their response to climate change and water extraction from surface water and groundwater. This knowledge can help secure a sustainable water management for the future. Particularly, we introduce a novel satellite-based approach to determine the relative contributions of natural climate variability and human-induced impacts on the regional water balance.
We found that the contribution ratio of water extraction for irrigation explains 17% of the terrestrial water storage changes that are observed by the GRACE satellite mission and its Follow-On mission since 2003. Water is primarily extracted from surface water (84%) with the remainder (16%) taken from groundwater. Introducing GRACE observations into the W3RA water balance model - which does not simulate the human-induced impact on water resources - via a data assimilation approach improved the representation of water storage variability and intensified trends in drying and wetting periods. We conclude that data assimilation can fundamentally improve our understanding of water resources and how they are impacted by natural and human-induced impacts of climate change.
Our results also offer potential for technical improvements of hydrological models and for future policy implementation. The presented study contributes to achieve the Sustainable Development Goals (SDGs), in particular no. 13 (combat climate change and its impact) and no. 6 (availability and sustainable management of water).
We found that the contribution ratio of water extraction for irrigation explains 17% of the terrestrial water storage changes that are observed by the GRACE satellite mission and its Follow-On mission since 2003. Water is primarily extracted from surface water (84%) with the remainder (16%) taken from groundwater. Introducing GRACE observations into the W3RA water balance model - which does not simulate the human-induced impact on water resources - via a data assimilation approach improved the representation of water storage variability and intensified trends in drying and wetting periods. We conclude that data assimilation can fundamentally improve our understanding of water resources and how they are impacted by natural and human-induced impacts of climate change.
Our results also offer potential for technical improvements of hydrological models and for future policy implementation. The presented study contributes to achieve the Sustainable Development Goals (SDGs), in particular no. 13 (combat climate change and its impact) and no. 6 (availability and sustainable management of water).
Original language | English |
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Publication date | May 2025 |
DOIs | |
Publication status | Published - May 2025 |
Event | EGU General Assembly 2025 - Vienna, Austria Duration: 27 Apr 2025 → 2 May 2025 https://www.egu25.eu/ |
Conference
Conference | EGU General Assembly 2025 |
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Country/Territory | Austria |
City | Vienna |
Period | 27/04/2025 → 02/05/2025 |
Internet address |
Keywords
- Data Assimilation
- Terrestrial water storage (TWS)
- Groundwater
- Human water-use
- Irrigation
- Abstraction
- EnKF
- Hydrology
- Large-scale
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Dive into the research topics of 'Satellite-based quantification of natural and human-induced water storage changes in the Murray-Darling River Basin, Australia'. Together they form a unique fingerprint.Projects
- 2 Active
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A Novel Synergy of Physics-based and Data-driven Methods for Reliable Hydrological Predictions under Changing Climate
Schumacher, M. (PI), Forootan, E. (CoI), Döll, P. (CoI), Wedi, N. (CoI), Bates, P. (CoI), Jagdhuber, T. (CoI) & van Dijk, A. I. (CoI)
01/04/2024 → 31/03/2029
Project: Research
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DANSk-LSM: Developing efficient multi-sensor Data Assimilation frameworks for integrating Earth ObservatioN Satellite data into Land Surface Models (DANSk-LSM)
Forootan, E. (PI), Schumacher, M. (CoI), Yang, F. (Project Participant) & Retegui Schiettekatte, L. A. (Project Participant)
Uddannelses- og forskningsministeriet
01/09/2022 → 31/08/2026
Project: Research