Publications

Flash droughts are fast-developing droughts that can seriously affect both nature and human activities. This study examines how different types of land, such as forests and farmland, respond to flash droughts in the Mississippi River Basin (MRB) from 2000 to 2022. Using a drought index (SAPEI) to detect drought events and plant growth data (GPP) to measure recovery time, researchers identified 315 flash droughts and analyzed the 10 most severe cases. Recovery times varied widely, from 8 to 120 days, with the longest delays occurring in extreme drought years like 2006, 2012, and 2022. Forested areas bounced back quickly, while farmland, especially rain-fed crops, took the longest to recover, showing their high vulnerability to sudden moisture loss. The Upper MRB, with drier conditions and heavy agricultural use, had the slowest recovery. These findings highlight the need for better drought management, including improved water use strategies and drought-resistant crops, to help vulnerable areas cope with future flash droughts.

‍

This study looks at how sudden, intense droughts, called flash droughts, affect river flow in the Mississippi River Basin (MRB) from 1980 to 2022. Using a drought index called SAPEI, researchers identified over 1,000 flash droughts and found regional differences in how they occur. The eastern MRB has frequent but short droughts, the northwest has fewer but longer ones, and the southern MRB experiences the most severe droughts, influenced by upstream water use. A strong link was found between drought conditions and lower river flow, showing that SAPEI is a useful tool for tracking these impacts. This research helps improve water management and prepares for future droughts.

Soil moisture (SM) is a critical climate variable, and assimilating satellite SM into land surface models via land data assimilation (LDA) enhances continuous SM modeling and climate extreme monitoring. However, LDA often assumes static SM error dynamics, limiting accuracy. This study introduces a novel framework integrating triple collocation analysis (TCA) with machine learning (ML), including Light Gradient Boosting Machine (LGBM) and Deep Neural Networks (DNN), to quantify spatially and temporally continuous satellite-based SM errors globally.

Using only Soil Moisture Active Passive (SMAP) retrieval data, our TCA-based time-variant error prediction models successfully recover error information in regions where SMAP SM error data were previously unreliable. The SMAP-based model outperforms models relying on external datasets like the Global Land Data Assimilation System (GLDAS), offering a robust approach for improving LDA in data-scarce areas. This method also extends to other satellite-based geophysical datasets, broadening its applicability beyond SMAP.

‍

This study explores the potential of the Cyclone Global Navigation Satellite System (CYGNSS) mission for soil moisture (SM) monitoring, highlighting its ability to capture fine-scale SM variability through high revisit frequencies at sub-daily intervals. While CYGNSS was originally designed for tropical cyclone monitoring, its seven-year data record demonstrates significant promise in reliably monitoring diurnal SM dynamics using spaceborne L-band bistatic radar and GNSS-Reflectometry (GNSS-R) technology.

Despite this potential, SM retrieval from CYGNSS remains limited by knowledge gaps and unique challenges tied to its technical design. This study addresses these gaps by analyzing CYGNSS real-world data, synthesizing recent advancements in mitigating external uncertainties, and improving SM inversion techniques. Notably, algorithm-related challenges include accurate partitioning of coherent and incoherent signal components and correcting attenuation effects caused by vegetation and surface roughness. Data-related challenges involve variations in CYGNSS spatial footprint, temporal frequency, signal penetration depth, and incidence angle changes, as well as excessive reliance on reference SM datasets for model calibration, validation, and training. The computational demands of processing CYGNSS’s rapid multi-sampling data further complicate its operational use.

Future research directions identified in this study focus on leveraging machine learning and deep learning approaches to improve CYGNSS SM data quality and quantity. Additionally, assimilating CYGNSS-derived SM data into physical models offers promising opportunities to enhance predictions of hydroclimatic variables and extreme climate events, addressing key challenges in water resource monitoring and climate resilience.

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

‍