Allied for Water Science

San Francisco Bay (SFB) is a nutrient-enriched estuary that is at risk of experiencing adverse effects of nutrient enrichment as evidenced by harmful algal blooms. A numerical model is developed and validated for 6 water years to simulate hydrodynamics and biogeochemical processes in SFB. The model is employed to answer applied science questions related to nutrient transport and cycling and the effects of nutrients on water quality in SFB.

Get familiar with GroMoPo - a community Groundwater Model Portal meant to help share and discover groundwater models.

Water resources management by Indigenous Peoples in the U.S. is a complex issue hindered by the lack of reliable and accessible high-quality databases. To address this challenge, we have initiated a research project to evaluate the availability of water-related databases for reservation lands in the U.S. The project focuses on the discoverability and reusability assessment of these databases in terms of both water quality and quantity variables with the goal of environmental justice for Indigenous communities.

The Environmental Responsibility 5-R Framework provides researchers across disciplines with a valuable toolbox for critically evaluating and mitigating the environmental impacts of their work. This framework provides actionable methods to this end by providing resources for incorporating data and computational costs into their research recognition, refining research questions through open-source data platforms, and optimizing codes for minimal resource usage. With the 5-R Framework in practice, we believe that scientists can pursue environmentally responsible practices while leveraging the power of data science and informatics.

2023 CUAHSI Biennial Recap

CUAHSI would like to thank everyone who participated in the 2023 Biennial Colloquium, held from June 11-14 at the Granlibakken Resort in Tahoe City, CA.

Understanding the driving mechanisms of droughts is critical for reducing and minimizing their impact. In this blog post, we employ a deep learning algorithm to predict the drought propagation mechanism over CONUS based hydrocliamte characteristics of NHDPlus catchments. We use HyRiver to retrieve and process the required input data including climate time series and catchment attributes and PyTorch Tabular to predict the drought propagation mechanisms.