Publications

This paper provides a novel quantitative assessment of the transformation of daily precipitation into terrestrial water storage across 121 global river basins over a two-decade period. The study introduces the average daily fraction of precipitation transformed into terrestrial water storage, leveraging enhanced terrestrial water storage statistical reconstruction and water storage data from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission. We reveal that approximately 64% of land precipitation contributes to terrestrial water storage, with notable variations across different climatic and geographical regions. These findings offer critical insights into the interactions between precipitation, land surface processes, and climate change, providing valuable implications for hydrological modeling and future water resource management.

This study examines the impact of assimilating Soil Moisture Active Passive (SMAP) data into the Korean Integrated Model (KIM) to improve global soil moisture estimates and weather forecasts. SMAP soil moisture retrievals are integrated into the Noah land surface model using the ensemble Kalman filter through NASA’s Land Information System. Experiments from March to July 2022 show that assimilating SMAP data, particularly with anomaly-based bias correction, significantly enhances soil moisture estimates and improves weather forecasts, especially in northern Africa and West

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This research discusses the retrieval and validation of soil moisture data from the Advanced Scatterometer (ASCAT), focusing on its applications and challenges. ASCAT data, crucial for various uses like weather prediction and drought monitoring, are compared with similar data from other satellite missions. Validations using multiple datasets highlight dependencies on land cover and vegetation, revealing unexpected quality variations across different environments. A notable issue identified is subsurface scattering, often misattributed to other factors, impacting ASCAT data quality significantly. The study recommends masking affected data using developed indicators and masks to enhance accuracy in practical applications.

This study evaluates the performance of two L-band passive microwave satellite sensors, the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), in monitoring surface soil moisture (SM). The newly developed fused SM product (Fused-IB) derived from SMOS and SMAP observations is compared with the enhanced SMAP-L3 (SMAP-E) SM product against in situ SM data from the International Soil Moisture Network (ISMN) over the period 2016-2020. The comparison considers overall and seasonal performance, focusing on different land use and land cover (LULC) types. Results show that Fused-IB generally outperforms SMAP-E, especially in forested areas, due to the robust SMAP-IB algorithm and higher data coverage. Both products demonstrate higher accuracy in summer and autumn, but face increased uncertainties in forests, grasslands, and croplands during spring and winter due to vegetation growth and rainfall. This study highlights the importance of accounting for seasonal and eco-hydrological factors to improve the accuracy of SM retrieval algorithms.

• The study evaluated the Combined Drought Indicator (CDI) in the Argentine Humid Pampas, focusing on its ability to track and characterize drought severity using precipitation deficits, soil moisture, and vegetation health anomalies.
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• Strong spatial and temporal correlations were found among these variables, with the highest correlations observed for time lags of 0, 10, and 20 days, effectively capturing major drought events.
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• The CDI aligned well with soybean and corn yield estimations and simulations, as well as official agricultural emergency declarations, demonstrating its effectiveness in representing drought impacts on agriculture.
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• The study analyzed two significant drought events: the gradual and long-lasting 2008-2009 drought and the rapid-onset 2017-2018 flash drought, both of which severely affected crop yields and were well-represented by the CDI.
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• Suggestions for improving the CDI include enhancing the temporal resolution of precipitation data and refining spatial resolution to better detect and monitor drought-affected regions, emphasizing the importance of collaborative efforts for advancing drought early warning systems.

•This study tackles the challenge of unbalanced stream gauge distributions by leveraging publicly available datasets and deep learning to enhance hydrological modeling in under-gauged regions.

•This study introduces a novel framework combining LSTM-based deep learning with GLDAS and GSIM data for effective runoff prediction across continents.

•This study assesses the sensitivity of LSTM models using data from diverse regions, emphasizing the importance of hydrological similarities for accurate runoff predictions in ungauged basins.

•LSTM models demonstrate superior runoff prediction skills over traditional GLDAS datasets, influenced by the hydrological similarities between training and test regions.