Paper Accepted by Agriculture

The following review paper about the application of machine learning techniques for agroclimatic studies has been recently accepted for publication by Agriculture.

Tamayo-Vera, D., X. Wang, and M. Mesbah. A Review of Machine Learning Techniques in Agroclimatic Studies. Agriculture, accepted on March 14, 2024

More details will come soon once the paper is published.

Paper Published in Environmental Research

Title: Climate change impacts on oyster aquaculture – Part I: Identification of key factors

Journal: Environmental Research

DOI: https://doi.org/10.1016/j.envres.2024.118561

Abstract: Oysters are enriched with high-quality protein and are widely known for their exquisite taste. The production of oysters plays an important role in the local economies of coastal communities in many countries, including Atlantic Canada, because of their high economic value. However, because of the changing climatic conditions in recent years, oyster aquaculture faces potentially negative impacts, such as increasing water acidification, rising water temperatures, high salinity, invasive species, algal blooms, and other environmental factors. Although a few isolated effects of climate change on oyster aquaculture have been reported in recent years, it is not well understood how climate change will affect oyster aquaculture from a systematic perspective. In the first part of this study, we present a systematic review of the impacts of climate change and some key environmental factors affecting oyster production on a global scale. The study also identifies knowledge gaps and challenges. In addition, we present key research directions that will facilitate future investigations.

Paper Accepted by Environmental Research

The following paper about the key factors for climate change impacts on oyster aquaculture has been recently accepted for publication by Environmental Research.

Neokye, E. O., X. Wang, K. K. Thakur, P. Quijon, R. A. Nawaz, and S. Basheer. Climate Change Impacts on Oyster Aquaculture – Part I: Identification of Key Factors. Environmental Research, accepted on February 25, 2024.

More details will come soon once the paper is published.

Paper Published by Environmental Modelling & Software

Title: Real-time peak flow prediction based on signal matching

Journal: Environmental Modelling & Software

DOI: https://doi.org/10.1016/j.envsoft.2023.105926

Abstract: Real-time peak flow prediction under heavy precipitation is critically important for flood emergency evacuation planning and management. In the case of emergency evacuation, every second matters as a slightly longer lead time could save more lives and reduce the associated social, economic, and health impacts. Here, we present a model (named SIGMA) based on the principle of signal matching to facilitate real-time peak flow prediction at sub-hourly scales (e.g., minutes to seconds). The SIGMA model divides the target watershed into small zones and the heavy precipitation falling into each zone is collected into a small water tank. As the water tank moves downstream and arrives in the watershed outlet, it will discharge the collected precipitation and generate a small single-pulse streamflow signal. By combining all small signals coming from all zones within the watershed, we will be able to generate a synthesized peak flow signal. The proposed model is applied to simulate the peak flow events observed in a real-world watershed to verify its effectiveness in real-time flood prediction. The results suggest that the presented model can reasonably predict three key aspects of a peak flow event, including the peak flow rate, the arrival time of peak flow, and the duration of the peak flow event. The proposed model is demonstrated to be effective in real-time flood prediction and can be used to support flood emergency evacuation planning and management.