Publications

Predicting water cycling in agricultural watersheds is challenging due to factors like farming practices. This study looks at using remote sensing evapotranspiration (ET) data and crop yield information to improve the accuracy of the Soil and Water Assessment Tool (SWAT) model. By adding more constraints to the model, such as crop yield, the number of acceptable parameter sets was reduced, and the model's performance improved. The results suggest that using crop yield data as an additional constraint can help reduce uncertainty and increase the accuracy of ET predictions in agricultural watersheds.

Soil moisture is important for understanding the global water cycle, but current satellite measurements are not continuous in time or space. This study combines data from NASA's Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active Passive (SMAP) to improve soil moisture estimates in a land surface model (LSM). The results show a 61.3% improvement in LSM soil moisture accuracy when combining the two satellite systems. However, using satellite data in areas with dense vegetation can lead to less accurate results. This research is the first to use global GNSS-based soil moisture observations in LSMs, which can help fill gaps in soil moisture measurements and improve our understanding of the water cycle.

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.