Check our peer-reviewed journal papers and conference papers.

Utility of Publicly Availability Stream Gauges Datasets and Deep Learning in Predicting Monthly Basin-scale Runoff in Ungauged Regions

Advances in Water Resources

June 1, 2024

•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.

Intercomparison and Combination of Inland Water Observations from CYGNSS, MODIS, Landsat, and High-Resolution Commercial Satellite Imagery,

Geoscience Letters

February 1, 2024

Accurate and timely information about the extent and location of inland water bodies is crucial for various tasks related to managing water resources. A promising tool for identifying these water bodies is the Cyclone Global Navigation Satellite System (CYGNSS). CYGNSS uses eight microsatellites with special radar technology to observe surface reflectivity characteristics over both dry and wet land. This study compares data from CYGNSS with data from two other Earth observation systems—Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Global Surface Water product—for the contiguous United States.

We conducted a 1-kilometer comparison of water masks (representations of where water is located) for the year 2019, focusing on the area between latitudes 24°N and 37°N. To assess how well these water masks performed, we used statistical measurements called confusion matrices and high-resolution satellite images.

By using specific thresholds for different sub-regions observed by CYGNSS, we improved the performance of our water mask data, with up to a 34% increase in a measurement called the F1-score. We also calculated a metric that compares the amount of inland water to the size of the surrounding area, and found that CYGNSS, MODIS, and Landsat provided very similar estimates, differing by less than 2.3% for each sub-region.

In summary, this study sheds light on how well water masks derived from optical (visible light) and radar-based satellite observations compare in terms of their accuracy and spatial representation of inland water bodies.

Systematic modelling errors undermine the application of land data assimilation systems for hydrological and weather forecasting

Journal of Hydrometeorology

January 1, 2024

Recent advancements in land data systems and satellite soil moisture data show that using this data can help improve surface condition estimates in land models. However, this improvement in estimating soil moisture doesn't necessarily translate to better predictions of water movements like evaporation and runoff. This issue might be due to inaccuracies in how these models link soil moisture with water movements. Experiments suggest that even with better soil moisture data, these inaccuracies can lead to poor water movement predictions. Adjusting satellite data to fit these models isn't always effective and could sometimes worsen predictions. This highlights the need for more accurate models that can better link soil moisture with water movements.

Interpreting effective hydrologic depth estimates derived from soil moisture remote sensing: A Bayesian non-linear modeling approach

Science of The Total Environment

December 31, 2023

The Water Balance Equation (WBE) is like a budget for water in a specific area, tracking how much water comes in, goes out, and how much is stored over time. It's crucial in understanding water availability and the water cycle, especially on land. The WBE relates rainfall, evaporation, and runoff to changes in soil moisture.

Recently, there's been interest in using satellite data to guess some WBE parts, but this can be complex. Challenges include simplifications in the equation, missing water cycle elements, and limits of satellite data. Our study used advanced modeling to show these issues can affect the reliability of WBE estimates. We suggest treating these estimates as general guides, reflecting the satellite data used, rather than exact measurements. Better remote sensing and improved WBE will enhance our understanding of the water cycle.

Temporal Gap-filling of 12-hourly SMAP Soil Moisture over the CONUS using Water Balance Budgeting

Water Resources Research

December 31, 2023

The use of satellite-based soil moisture (SM) data in Earth system science can be hindered by gaps in observations. To address this, our study introduces a method to fill in these gaps in the Soil Moisture Active Passive (SMAP) dataset. We utilized a basic water balance equation, considering factors like precipitation and soil water loss, to estimate soil water content over 12-hour periods. This led to the development of a new, continuous SM product for the contiguous United States. This product, named the SMAP-based 12-hourly SM product, showed promising results, aligning well with on-the-ground measurements and effectively capturing soil moisture variations, especially those related to heavy rainfall events. This continuous dataset, with its detailed insights into soil water dynamics, offers valuable contributions to our understanding of land-surface hydrology.

True Global Error Maps for SMAP, SMOS, and ASCAT Soil Moisture Data Based on Machine Learning and Triple Collocation Analysis Remote Sensing of Environment

Remote Sensing of Environment

December 1, 2023

This study aims to address gaps in assessing the accuracy of satellite-based soil moisture data by utilizing machine learning techniques to generate spatially complete error maps for Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) systems, and by examining the influence of environmental conditions on satellite-based soil moisture retrievals, revealing that a significant portion of missing error information from triple collocation analysis (TCA) can be reconstructed using ensemble prediction mean of machine learning models, contributing to a more comprehensive understanding of soil moisture dynamics across the three satellite missions.

* = mentored by Dr. Kim

Changes in the Speed of the Global Terrestrial Water Cycle Due To Human Interventions

Hyunglok Kim, Wade T. Crow, and Venkataraman Lakshmi
Under Preperation

A Global Scale Analysis of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

Wolfgang Wagner, Roland Lindorfer, Sebastian Hahn, Hyunglok Kim, Mariette Vreugdenhil, Alexander Gruber, Milan Fischer, and Miroslav Trnka
IEEE Transactions on Geoscience and Remote Sensing
Major Revision

How Much Precipitation Transformed Into Terrestrial Water Storage in Global River Basins?

Baoming Tian, Yulong Zhong, Hyunglok Kim, Xing Yuan, Xinyue Liu, Enda Zhu, Yunlong Wu, Lizhe Wang
Nature Communications
Under Review

Improving Weather Forecast Skill of the Korean Integrated Model (KIM) by Assimilating SMAP Soil Moisture Anomalies

Yonghwan Kwon, Sanghee Jun, Eunkyu Kim, Kyung-Hee Seol, Seokmin Hong, In-Hyuk Kwon, Hyunglok Kim
Quarterly Journal of the Royal Meteorological Society
Major Revision

Developing Independent CYGNSS Soil Moisture Retrieval Algorithm with Mitigated Vegetation Effects: Incorporating a Two-Step and Relative SNR Approaches

Ziyue Zhu, Hyunglok Kim*, Venkataraman Lakshmi
Under Preperation

A Novel Soil Moisture Validation Method Utilizing Brightness Temperature

Ziyue Zhu, Runze Zhang, Bin Fang, Hyunglok Kim, Venkataraman Lakshmi
Under Preperation

Evaluation of a Combined Drought Indicator against Crop Yield Estimations and Simulations over the Argentine Humid Pampas

Spennemann Pablo, Gustavo Naumann, Mercedes Peretti, Carmelo Cammalleri, Mercedes Salvia, Alessio Bocco, Maria Elena Fernández Long, Martin Maas, Hyunglok Kim, Manh-Hung Le, John D. Bolten, Andrea Toreti and Venkataraman Lakshmi
Agricultural and Forest Meteorology
Under Review

Observational Analysis of Long-term Streamflow Response to Flash Drought in the Mississippi River Basin

Sophia Bakar, Hyunglok Kim, Venkataraman Lakshmi
Weather and Climate Extremes
Under Review

From Theory to Hydrological Practice: Leveraging CYGNSS Data Over Seven Years for Advanced Soil Moisture Monitoring

Hoang Hai Nguyen, Hyunglok Kim*, Wade Crow, Wolfgang Wagner, Simon Yueh, Fangni Lei, Jean-Pierre Wigneron, Andreas Colliander, Frédéric Frappart
Remote Sensing of Environment
Under Review

Seasonal-scale intercomparison of SMAP and fused SMOS-SMAP soil moisture products" to be considered for publication in Frontiers in Remote Sensing, section Terrestrial Water Cycle

Zanpin Xing, Xiaojun Li, Lei Fan, Frédéric Frappart, Hyunglok Kim, Karthikeyan Lanka, Preethi Konkathi, Yuqing Liu, Lin Zhao and Jean-Pierre Wigneron
under review

Deep Learning and Bayesian Inference via Samplings and Variational Approximations to Characterize Spatially Continuous Global-scale Satellite-based Soil Moisture Error Patterns

H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2021

Impact of Climate Change on Road Networks: Travel Demand, Machine Learning, and Flooding Simulation Models

S. Ryu, H. Kim, E. Cho, R. Zhang
US-KOREA Conference on Science, Technology, and Entrepreneurship
January 1, 2021

Leveraging Soil Moisture for Early Flood Detection

V. Sunkara, C. Doyle, H. Kim, B. Tellman, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

Detecting Inland Waterbodies Using GNSS-R Data: Intercomparison of Previous Methods and a New Machine Learning Approach

G. Pavur, H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

An Integrated Framework to Predict Peak Flood and Map Inundation Areas in the Chesapeake Bay Using Machine Learning Methods with High-Resolution Lidar DEM and Satellite Data

R. Zhang, H. Kim, L. Band, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

Error Characteristic Assessments of Soil Moisture Estimates from Satellites and Land Surface Models: Focusing on Forested and Irrigated Regions

H. Kim, J. Wigneron, S.V. Kumar, J. Dong, W. Wagner, M.H. Cosh, D.D. Bosch, C.H. Collins, P.J. Starks, M.S. Seyfried, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

Producing Satellite-based Diurnal Time-scale Soil Moisture Retrievals using Existing Microwave Satellites and GNSS-R Data

H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2020

Assimilation of SMAP-enhanced and SMAP/Sentinel-1A/B soil moisture data into land surface models

H. Kim, V. Lakshmi, S. Kumar, Y. Kwon
European Geosciences Union, General Assembly Conference
December 1, 2020

Assimilation of GPS soil moisture data from CYGNSS into land surface models

H. Kim, Y. Kwon, S.V. Kumar, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2019

The Impact of Irrigation on the Water Cycle in the Continental United States (CONUS)

H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2018

Contact me

If you have a keen interest in the intersection of climate change and its impact on hydrological research fields, I encourage you to consider pursuing a Master's, PhD, or postdoctoral position. By delving deeper into this critical area of study, you can play an essential role in addressing the world's most pressing environmental challenges and help safeguard our water resources, ecosystems, and communities. Your dedication and expertise can significantly contribute to the development of sustainable solutions and innovative approaches to hydrological research. Embark on this exciting journey and become part of the passionate community of scientists working towards a more resilient and environmentally responsible future.