Publications

Estimating precipitation from space using microwave satellite systems is essential for managing water resources, predicting natural disasters, and analyzing the Earth's water cycle. This study compares two algorithms, SM2RAIN and SM2RAIN-NWF, for estimating rainfall using soil moisture data. The newer SM2RAIN-NWF algorithm offers improved results by combining SM2RAIN with a net water flux model. We found that SM2RAIN-NWF performed better than SM2RAIN, especially in arid and semi-arid regions. The study also discovered that drainage played a crucial role in improving rainfall estimates, while evapotranspiration had a minimal impact.

Rainfall estimation using remote sensing technology offers a more accurate alternative to traditional measurement methods due to its high resolution in both time and space. The SMA2RAIN-NWF algorithm, an improved version of the original SM2RAIN algorithm, uses satellite soil moisture data to estimate rainfall. This study aims to evaluate the effectiveness of SMA2RAIN-NWF using multiple soil moisture products and different aggregation periods. The results show that the algorithm performs better as the aggregation levels increase and that it is more effective in urban areas. Overall, the SMA2RAIN-NWF algorithm demonstrates improved performance compared to the original SM2RAIN algorithm.

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.

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.