Alison P. Appling

Oct 16, 2024 | pubs.usgs.gov | Jared Smith |Lauren Koenig |Margaux Jeanne Sleckman |Alison P. Appling

Stream salinization is a global issue, yet few models can provide reliable salinity estimates for unmonitored locations at the time scales required for ecological exposure assessments. Machine learning approaches are presented that use spatially limited high-frequency monitoring and spatially distributed discrete samples to estimate the daily stream-specific conductance across a watershed.

Oct 11, 2024 | dx.doi.org | Jared Smith |Lauren Koenig |Alison P. Appling |Margaux J. Sleckman

Mar 11, 2024 | nature.com | Alison P. Appling |Heather E. Golden |Joel E. Podgorski

AbstractUnderstanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality.

Jul 11, 2023 | nature.com | Chaopeng Shen |Alison P. Appling |Pierre Gentine |Alexandre M. Tartakovsky |Xiaofeng Liu |Dapeng Feng | +5 more

AbstractProcess-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions.