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This study investigates the impact of high-temporal-resolution gravity data on hydrological data assimilation (DA), focusing on flood monitoring and water resource management. We evaluate three mission configurations—GRACE-C, NGGM, and MAGIC—using 5-day and monthly gravity field solutions.
A total of 180 sets of 5-day DA experiments were conducted using a high-resolution (10 km) hydrological model over two contrasting basins: the Brahmaputra and the Danube. These basins represent diverse hydrological regimes, enabling us to assess how spatial resolution and temporal sampling influence DA performance. On a continental scale, additional experiments were performed across Europe to explore large-scale hydrological variability and water use detection.
Our methodology includes advanced signal decomposition, data preprocessing, and statistical metrics to assess DA accuracy. Principal Component Analysis (PCA) is used to identify dominant spatio-temporal patterns in model simulations, satellite observations, and DA outputs.
Key findings (compared to GRACE-C):
NGGM and MAGIC better capture sub-monthly to seasonal water storage variations.
High-resolution DA experiments benefit the most from NGGM and MAGIC due to enhanced spatial resolution and increased accuracy.
NGGM and MAGIC data require weaker filtering, resulting in reduced signal leakage.
Results show that high-frequency (5-day) DA significantly improves the ability to track fast hydrological changes—such as floods—especially with high-quality data from missions like MAGIC. The assimilation not only enhances temporal consistency with satellite observations but also preserves the high spatial fidelity of the model-based hydrology. Key performance indicators like NSE and KGE show substantial improvements post-assimilation across both basins.
Ultimately, this study highlights the critical role of next-generation gravity missions in advancing global hydrological monitoring at fine spatial and temporal scales.
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