Publications

Check our peer-reviewed journal papers and conference papers.

Global dynamics of stored precipitation water in the topsoil layer from satellite and reanalysis data

Water Resources Research

February 1, 2019

This study explores the amount of precipitation stored in the topsoil layer (0-10 cm) across different vegetation and aridity indices on a global scale. The study uses data from four satellites and two reanalysis data sets to investigate spatial trends of stored precipitation. The study finds that drier and less vegetated soil retains more precipitation in the top layer of the soil, while wet and forested areas have a lower retention rate due to large runoff fluxes and plants intercepting water. Specifically, the topsoil retains 37% ± 11% of precipitated water three days after a rainfall event where the aridity index was greater than 5, while wet and forested areas retain 21% ± 2%. The study also conducts a sensitivity analysis of different sampling frequency values using modeled data sets to calculate the stored precipitation fraction metric. Overall, the study highlights the importance of understanding the spatial trends of stored precipitation in the topsoil layer for better land-atmosphere interactions.

Use of cyclone global navigation satellite system (CyGNSS) observations for estimation of soil moisture

Geophysical Research Letters

August 1, 2018

Accurate climate forecasting affects our daily lives. Large-scale farmers depend on weather forecasts to decide when to plant their crops. Bad timing can impact the whole years' harvest and thus the farmers' livelihoods. Even more importantly, people who live in floodplains and hurricane zones trust their lives to accurate weather forecasts. For these reasons and more, hydrologists need up-to-date knowledge of Earth's climate systems. And one of the most important sources of data may surprise you. The amount of moisture in just the first 8 mm of topsoil affects all of Earth's climate systems. Currently, National Aeronautics and Space Administration keeps track of soil moisture levels with a satellite called Soil Moisture Active Passive. However, it only provides soil moisture data every 2–3 days. We believe that we can do better, and we believe that we can do it with preexisting satellite systems. In 2017, National Aeronautics and Space Administration (NASA) launched eight microsatellites, called Cyclone Global Navigation Satellite System (CyGNSS), to predict cyclone paths. We have found that while the CyGNSS satellites are predicting cyclone paths, they can simultaneously measure changes in soil moisture around 5 times per day. Augmenting the Soil Moisture Active Passive data with CyGNSS would give us detailed prediction of weather changes in near-real time, protecting livelihoods and lives.

Previous publication list (2015-2019)

Various Journals

January 1, 2018

Check my Google Scholar Link below.

* = 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

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
Jounar of Remote Sensing
minor revision

A Novel Soil Moisture Validation Method Utilizing Brightness Temperature

Ziyue Zhu, Runze Zhang, Bin Fang, Hyunglok Kim, Venkataraman Lakshmi
-
Major Revision

Towards Self-calibration of Rainfall Estimation through Soil Dynamics and its Signals Using Supervised and Unsupervised Machine Learning Clustering Methods over CONUS

Mohammad Saeedi, Hyunglok Kim, and Venkataraman Lakshmi
-
under review

Enhancing Detection of Flood-Inundated Areas using Novel Hybrid PoLSAR- Metaheuristic-Deep Learning Models

Fatima et al.
-
Under Preperation

Synergistic impact of simultaneously assimilating radar- and radiometer-based soil moisture retrievals on the performance of numerical weather prediction systems

Kwon et al.
Hydrology and Earth System Sciences
Under Review

Flood inundation mapping with CYGNSS over CONUS: a two-step machine- learning-based framework

Wang et al.
Journal of Hydrology
under review

Simultaneous Estimation of Soil Moisture and Soil Organic Matter from Dielectric Measurements - Part 1: Optimal Estimation Strategy

Park et al.
Agricultural and Forest Meteorology
under review

Simultaneous Estimation of Soil Moisture and Soil Organic Matter from in situ Dielectric - Part 2: Application of Optimal Estimation and Machine Learning Approaches

Park et al.
Agricultural and Forest Meteorology
under review

Impact of Land Use Land Cover Changes on Carbon and Water Cycle Interactions: Using Data Driven Modeling and Satellite Products

M. Umair, S. Khan, H. Kim, M. Azmat, S. Atif
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

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

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

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

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

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

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

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.