Geology topics

Alissa White

Publications and source records attributed to Alissa White.

3 recordsLinked to original sources

USGS2024

Advanced quantitative precipitation information: Improving monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay area

Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay Area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision-making processes. Observation of precipitation in the complex Bay Area landscape of California’s coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow, and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels—especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and worldwide.

California↗

USGS2023

Forming regional soil carbon networks to support effective climate change solutions

Sequestration and storage of organic carbon (C) in soil is an essential component of climate change mitigation and fundamental in promoting the health and climate resilience of soils. Sources of available soil C data are increasing, which complicates efforts to consolidate the data in forms that can be readily used by stakeholders. Spatial and temporal gaps in data availability also limit the quantification of changes in soil C through space and time. Improved coordination among producers and users of soil C data would provide data compatibility at the spatial and temporal resolution required for C monitoring, accounting, and verification of policy implementation. These challenges can be addressed by forming regional-scale networks to coordinate the collection and use of soil C data by promoting consistency in methods, collecting new data to fill critical gaps, integrating existing data from multiple sources, and providing data interpretation to stakeholders in readily usable forms. Forming networks in regions such as the Northeastern United States would require close coordinationwith existing programs that are involved in collecting or aggregating soil C within that region. Network formation could be accomplished by (1) producing a planning document, (2) designing a network structure tailored to the region, and (3) acquiring the institutional support to establish and operate the network. Increasing the availability and usage of soil C data through regional networks would support the development of climate change solutions and increased ecosystem services through land management efforts that increase soil C storage.

Soil Science Society of America Journal↗

USGS2020

Observations of an extreme atmospheric river storm with a diverse sensor network

Observational networks enhance real‐time situational awareness for emergency and water resource management during extreme weather events. We present examples of how a diverse, multitiered observational network in California provided insights into hydrometeorological processes and impacts during a 3‐day atmospheric river storm centered on 14 February 2019. This network, which has been developed over the past two decades, aims to improve understanding and mitigation of effects from extreme storms influencing water resources and natural hazards. We combine atmospheric reanalysis output and additional observations to show how the network allows: (1) the validation of record cool season precipitable water observations over southern California; (2) the identification of phenomena that produce natural hazards and present difficulties for short‐term weather forecast models, such as extreme precipitation amounts and snow level variability; (3) the use of soil moisture data to improve hydrologic model forecast skill in northern California's Russian River basin; and (4) the combination of meteorological data with seismic observations to identify when a large avalanche occurred on Mount Shasta. This case study highlights the value of investments in diverse observational assets and the importance of continued support and synthesis of these networks to characterize climatological context and advance understanding of processes modulating extreme weather.

California↗