Publications

As the intensity and frequency of storm events are projected to increase due to climate change, local agencies urgently need a timely and reliable framework for flood forecasting, downscale from watershed to street level in urban areas. Integrated with property data with various hydrometeorological data, the flood prediction model can also provide further insight into environmental justice, which will aid households and government agencies’ decision-making. This study uses deep learning (DL) methods and radar-based rainfall data to predict the inundated areas and analyze the property quickly and demographic data concerning stream proximity to provide a way to quantify socioeconomic impacts. We expect that our DL-based models will improve the accuracy of forecasting floods and provide a better picture of which communities bear the worst burdens of flooding, and encourage city officials to address the underlying causes of flood risk.

Hydrologic models have some predictive uncertainty when used for real-world applications. This study looks at using remotely sensed evapotranspiration (RS-ET) data to evaluate improvements in the Soil and Water Assessment Tool (SWAT) model. By comparing the original SWAT model and an improved version (RSWAT), researchers found that both models performed similarly for daily streamflow and evapotranspiration at the watershed level. However, at the subwatershed level, RSWAT showed better results for daily evapotranspiration. This study shows that using RS-ET data can help increase the accuracy of model predictions and highlights the importance of remote sensing data in hydrologic modeling.

This research focuses on using satellite and modeled products to monitor soil moisture (SM) content and predict natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks. The study validates three SMAP SM products with in-situ data using conventional and triple collocation analysis (TCA) statistics and merges them with a Noah-Multiparameterization version-3.6 (NoahMP36) land surface model (LSM). An exponential filter and a cumulative density function (CDF) are used to evaluate the SM products. The study found that CDF-matched 9-, 3-, and 1-km SMAP SM data showed reliable performance with R and ubRMSD values of 0.658, 0.626, and 0.570 and 0.049, 0.053, and 0.055 m3/m3, respectively. Combining SMAP and NoahMP36 greatly improved R-values to 0.825, 0.804, and 0.795, and ubRMSDs to 0.034, 0.036, and 0.037 m3/m3, respectively. These findings suggest that SMAP/Sentinel data can improve regional-scale SM estimates and LSMs with improved accuracy.

In recent years Vietnam has experienced historical drought events possibly affected by climate change, but the analysis is challenging due to lack of necessary observations for monitoring drought conditions. The goal of this study is to analyze the characteristics of droughts over a 30-year period, using three spatial-resolution MERRA-2 datasets in Vietnam. The Standardized Precipitation Evapotranspiration Index (SPEI) was used as an index for drought based on precipitation and temperature. We also estimated the impacts of drought on agriculture using annual land cover datasets.

This study compares the accuracy and error characteristics of surface soil moisture (SSM) estimates obtained from various satellite and model-based data products over vegetated and irrigated regions. The study employed triple collocation analysis (TCA) and conventional error metrics to evaluate the accuracy of six different products: Advanced Scatterometer (ASCAT), Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP), European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), and Global Land Data Assimilation System (GLDAS). The results show that satellite-based SSM estimates from ASCAT, SMAP, and SMOS had fewer errors than ERA5 and GLDAS SSM products over vegetated areas, and over irrigated areas, ASCAT, SMOS, and SMAP outperformed other SSM products. The study also found that the limitations in satellite and model-based SSM data can be overcome by the synergistic use of satellite and model-based SSM products. The study suggests that the probability of obtaining SSM with a stronger signal than noise can be close to 100% when four satellite and model data sets are used selectively.

This study compares the accuracy of six types of portable electromagnetic (EM) sensors for measuring soil water content (SWC). The study found that all SWC probes met the target accuracy after onsite correction with an RMSD of less than 0.025 m3 m−3. The study also observed that SWC data obtained from similar electrode lengths and from different manufacturers showed similar distributions over time with the same mean. Furthermore, combining SWC data from two different types of sensors using the maximize R method increased the accuracy of the results. The study found that the Pearson's correlation coefficient (R value) and RMSD values improved when datasets from two different types of sensors were combined, with an average R value improvement from .930 to .945, and the RMSD decreasing from 0.036 to 0.018 m3 m−3. These findings suggest that using multiple manufacturers’ EM-based SWC probes with site-specific correction can improve the accuracy of SWC measurements.