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

Systematic Modeling Errors Strongly Undermine The Value Of Land Data Assimilation Systems And Microwave Remote Sensing For Water Flux Estimation

W. T. Crow, H. Kim , S. Kumar
IEEE International Geoscience and Remote Sensing Symposium
July 21, 2023

Utilizing Bayesian Machine Learning for Analyzing Error Patterns in Global-Scale Soil Moisture Data

H. Kim , W. T. Crow, W. Wagner, X. Li, V. Lakshmi
Hydrology Machine Learning (HydroML) Symposium, Phase 2 at Berkeley Lab
May 1, 2023

Hydrological flash drought forecasting using meteorological flash drought indices and machine learning approaches – A case study in the Mississippi River Basin

S. Bakar, D. Quintero, M. Le, H. Kim, S. S. Adams, P. Beling, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2022

Reconstruction of the SMAP-based 12-hourly soil moisture product over the CONUS through water balance budgeting

R. Zhang, S. Kim, H. Kim, B. Fang, A. Sharma, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2022

Changes in Extreme Precipitation Patterns in the Meuse River Basin as a Driver of the July 2021 Flooding

B. Goffin, P. Kansara, H. Kim, V. Lakshmi
American Geophysical Union, Fall Meeting
December 1, 2022

Retrieving Runoff in Ungauged Basins using Satellite Observations of Rainfall and Soil Moisture

H. Kim, W. Crow
American Geophysical Union, Fall Meeting
December 1, 2022

Uncertainty Analysis Framework in the Water Balance Equation Using Bayesian Statistical Modeling Approach

H. Kim, W. Crow
American Geophysical Union, Fall Meeting
December 1, 2022

Global downscaling and assimilation of soil moisture

V. Lakshmi, B. Fang, H Kim
IAHS2022
March 1, 2022

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

Water Cycle in Different Time Scales: Analyzing the Impact of Human-driven Changes in Land Cover using Bayesian Inferences and Data Assimilation Methods

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

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.