Uncategorized

January 2024

• Welcome to Dr. Hongchen Qin, who joined our lab as a Postdoctoral Research Associate. Hongchen received his B.S. degree in Atmospheric Science from Nanjing University of Information Science and Technology and Ph.D. degree in Earth System Sciences from University of California, Irvine, and had experience working as a postdoctoral researcher at the Lawrence Livermore National Lab.
• The entire lab attended the AMS annual meeting in Baltimore. Dr. Wang and Faisal Mohammad Alvee presented our research on flash drought and extreme precipitation respectively.

December 2023

Our lab has a strong show at the AGU Fall meeting in San Francisco. Dr. Wang, Makduma Badhan, and Koushan Mohammadi presented research on extreme precipitation, machine learning climate downscaling, and flash drought detection and prediction respectively. Lab alumni Dr. Meijian Yang and Dr. Yelin Jiang presented research on crop yield projection in the Corn Belt and drought development mechanisms in the U.S. Southwest.

November 2023

 

September 2023

Dr. Wang is the proud recipient of the 2023 C.R. Klewin Inc. Excellence in Teaching Award! This award is given annually to a faculty selected (in May) by the graduating class of seniors in the Department of Civil and Environmental Engineering. Congratulations to Dr. Wang! Thanks and best wishes to the CEE Class of 2023! 

 

This month, UConn Today research spotlight reported on our lab’s paper about crop yield changes in the U.S. Corn Belt led by Dr. Meijian Yang. Read the news story here:

https://today.uconn.edu/2023/09/heat-stress-to-bring-big-changes-to-the-us-corn-belt/ 

August 2023

Yara Medawar joined the lab as a first year PhD student and an NRT Fellow. Yara received her B.S. and M.S. degrees in Civil Engineering from Notre Dame University in Lebanon, and M.S. degree in Public Policy from UConn. Welcome, Yara!

Congratulations are due to Dr. Meijian Yang for the publication of two papers on the same day! “Water stress dominates the projected maize yield changes in Ethiopia” in Global and Planetary Changes (https://doi.org/10.1016/j.gloplacha.2023.104216 ) and  “Heat stress to jeopardize crop production in the US Corn Belt based on downscaled CMIP5 projections” in Agricultural Systems ( https://doi.org/10.1016/j.agsy.2023.103746 )

January 2023

Dr. Wang led the successful planning of the 37th Conference on Hydrology, which took place at the Colorado Convention Center in Denver  as part of the AMS 103rd Annual Meeting during January 8-12. The meeting was very well attended and was a great success!

Dr. Wang gave a talk on the role of vegetation in flash drought development, Dr. Jiang gave a virtual poster on his attribution study of the 2020-21 U.S. drought, and Koushan talked about his research on snow hydrology and winter flood analysis. It was Koushan’s first oral presentation at AMS and he did great!

Javad Teymoori joined the lab as a new PhD student! Javad earned his B.S. from University of Mohaghegh Ardabili and M.S. from University of Tehran in Iran. His PhD dissertation research will focus on land-atmosphere interactions in drought development. Welcome Javad!

December 2022

Our lab had a strong show at the AGU Fall Meeting held in Chicago. Dr. Wang talked about our work on machine learning-based crop yield modeling, Yan talked about her research on how temperature and water stress influence ecosystem productivity, Yelin presented his study on the role of soil moisture and soil temperature as sources of subseasonal predictability, Koushan presented his latest analysis on SIF RCI and flash drought early warning based on multiple datasets, and Badhan presented her progress in climate downscaling using a deep learning approach! It was Yan’s first oral presentation at AGU and she did a great job!

October 2022

Congratulations to Qinqing and Meijian for the publication of a highly interdisciplinary paper in AIES! It is an exemplary  collaboration between AI experts and process-based modelers to predict crop yield in the U.S. Corn Belt: “Machine learning crop yield models based on meteorological features and comparison with a process-based model”, Artificial Intelligence for Earth Systems, 1(4), https://doi.org/10.1175/AIES-D-22-0002.1

Meanwhile, our PNAS paper continues to garner media attention. Here is a comprehensive piece from the New York Public Radio: https://gothamist.com/news/a-plants-glow-can-predict-some-droughts-weeks-in-advance-uconn