Qianyu Zhao

Qianyu Zhao

PhD student at the University of Illinois at Urbana-Champaign

Talks and presentations

How Do Anthropogenic Activity and Hydrological Variability Control the Spatiotemporal Patterns of Nitrogen and Phosphorus Export in the Mississippi/Atchafalaya River Basin?

May 22, 2025

Conference Presentation, SFS2025, San Juan, PR

Excessive nutrient exports from the Mississippi/Atchafalaya River Basin (MARB) significantly degrade water quality and lead to the hypoxia zone in the Gulf of Mexico. Quantifying the sources, fate, and transport of nutrients from headwaters to large rivers is critical to diagnose the trend of exported nutrient load and guide efficient and effective conservation planning for nutrient loss reduction. This study investigates the changes in riverine nutrient loads and yields at the HUC12 scale in the MARB from 2001 to 2020. The WRTDS (Weighted Regressions on Time, Discharge and Season) and the SPARROW (SPAtially Referenced Regressions On Watershed attributes) models are integrated to quantify the spatiotemporal patterns and driving factors of riverine nutrient export from the MARB. The integration of the WRTDS model and the SPARROW model allows us to identify and quantify contributions from various sources and examine driving factors for both spatial and temporal variations in nutrient dynamics throughout the MARB. This high-resolution analysis at the HUC12 scale identifies nutrient export hotspots and differentiates areas where nutrient export increases are attributed primarily to anthropogenic activities or hydrological variability. By providing detailed assessments of nutrient source contributions and transport mechanisms, this study offers essential insights for water quality management within the MARB. The identification of local hotspot areas with high and increasing nutrient yields, along with their driving factors at the HUC12 scale, provides crucial local context for targeted conservation strategies to reduce riverine nutrient load in the MARB.

Spatio-temporal Changes and Driving Factors of Riverine Nitrogen Export in an Agriculture-dominated Watershed: the Illinois River Basin

December 13, 2024

Conference Presentation, AGU2024, Washington, DC

Riverine nutrient load from the Mississippi/Atchafalaya River Basin (MARB) plays a crucial role in the development of the Gulf of Mexico’s hypoxic zone. Agricultural land in the MARB predominately contributes to nitrogen (N) and phosphorus (P) loading in the river system. Quantifying the sources, fate, and transport of nutrients from headwaters to large river basins is critical to diagnose the trend of exported nutrient load and guide conservation planning for nutrient loss reduction. Here taking the Illinois River Basin as an example, this study aims to quantify the changes in nitrate and nitrite (NO3+NO2) loads and yields at HUC12 scale from 2001-2020. We analyzed riverine loads and yields of nitrate and nitrite at 40 USGS gauge stations and simulated high-resolution nitrate and nitrite export throughout the Illinois River Basin using the SPARROW (SPAtially Referenced Regressions On Watershed attributes) model for two time periods (2001-2005, and 2016-2020) to quantify the spatiotemporal pattern and driving factors of changes in nitrogen export from the Illinois River Basin. We found that the five-year averaged total loads of nitrate and nitrite in the Illinois River Basin increased by 28% from 2001 to 2020, along with a 47% increase of discharge flow. Our analysis also revealed a complex spatial pattern of incremental yield of nitrate and nitrite: decreasing yields in the upper Illinois River contrasted with increasing yields in the lower Illinois and Kankakee Rivers. The simulation from the SPARROW model allowed us to identify and quantify contributions from various sources and examine driving factors for the observed spatial and temporal variations in nitrate and nitrite dynamics within the basin. By identifying areas of increasing and decreasing yields, as well as the factors driving these changes, our study provides crucial information for targeted conservation strategies to reduce nutrient load in the Illinois River Basin.

Forecasting Alpha Return from 8-K reports via deep learning

March 25, 2022

Conference Presentation, SDSC, Waco, Texas

In this study, we propose a novel machine learning approach with dimension reduction stacking to forecast the stock returns of the SP 500 companies by min-ing their SEC 8K reports via different NLP techniques. The proposed method achieves better forecasting performance than the peer methods and can be used as a concrete technology in security return analysis in practice.