Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost - Galen Gorski; Salme Cook; Amelia Snyder; Alison P. Appling; Theodore Thompson; Jared D. Smith; John C. Warner; Simon N. Topp (Limnology and Oceanography, 2024)
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state‐of‐the‐art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L −1 Cl − isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere‐Wave‐Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process‐based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.

Long‐term change in metabolism phenology in north temperate lakes - Robert Ladwig; Alison P. Appling; Austin Delany; Hilary A. Dugan; Qiantong Gao; Noah Lottig; Jemma Stachelek; Paul C. Hanson (Limnology and Oceanography, 2022)
The phenology of dissolved oxygen (DO) dynamics and metabolism in north temperate lakes offers a basis for comparing metabolic cycles over multi‐year time scales. Although proximal control over lake DO can be attributed to metabolism and physical processes, how those processes evolve over decades largely remains unexplored. Metabolism phenology may reveal the importance of coherence among lakes and facilitate general conclusions about the controls on lake metabolism at regional scales. We developed a Bayesian modeling framework to estimate DO concentrations and metabolism in eight lakes in contrasting landscapes in Wisconsin, USA. We identify the DO and metabolism phenologies for each lake, and use those to compare how decadal patterns relate to trophic state and landscape setting. We show that lakes can be categorized by their hypolimnetic oxygen consumption dynamics, with oligotrophic lakes having a diverse set of patterns and eutrophic lakes having uniform trends of increased oxygen consumption over the last decade. Metabolism phenology is likewise diverse for oligotrophic lakes, whereas eutrophic lakes in southern Wisconsin share consistent long‐term patterns of metabolic trends and seasonal DO consumption highlighting the importance of trophic state driving metabolism. Eutrophic lakes had higher magnitudes and more seasonal variation in net ecosystem production in contrast to oligotrophic lakes. Generally, long‐term metabolic trends of north temperate lakes suggest a limited influence of climate on lake metabolism and that temporal coherence of long‐term metabolism change is driven primarily by the landscape setting.

Metabolic rhythms in flowing waters: An approach for classifying river productivity regimes - Philip Savoy; Alison P. Appling; James B. Heffernan; Edward G. Stets; Jordan S. Read; Judson W. Harvey; Emily S. Bernhardt (Limnology and Oceanography, 2019)
Although seasonal patterns of ecosystem productivity have been extensively described and analyzed with respect to their primary forcings in terrestrial and marine systems, comparatively little is known about these same processes in rivers. However, it is now possible to perform a large‐scale synthesis on the patterns and drivers of river productivity regimes because of the recent sensor advances allowing for near‐continuous estimates of river productivity. Here, we explore a dataset of 47 U.S. rivers to examine whether there are characteristic river productivity regimes. We use classification approaches to develop a typology of productivity regimes and then use these regimes to examine differences with respect to potential controls of productivity. We identified two distinct metabolic regimes, which we named Summer Peak and Spring Peak Rivers , within our dataset. These regimes meaningfully differed in both the timing and magnitude of productivity and were robust to different approaches to classification. We also found that several variables, including watershed area and characteristics of water temperature or discharge, were able to predict the class membership of these regimes with modest accuracy. Our results support the presence of characteristic metabolic regimes and suggests that these regimes may have common sets of environmental controls. We present classification as one approach to begin exploring the productivity regimes of rivers. The strength of our approach is that it fully leverages these newly available high‐frequency productivity estimates to create classes that can be used to draw inferences about how the controls of river productivity differ between or within systems.