Water Quality Drivers in 11 Gulf of Mexico Estuaries

Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality. To address these concerns this study presents a technique for augmenting limited ground-based observations of water quality variables with remote-sensed surface reflectance data by leveraging a machine learning model capable of accommodating the multidimensionality of water quality influences. Total Suspended Solids (TSS) can serve as a surrogate for chemical and biological pollutants of concern in surface water bodies. Historically, TSS data collection in the United States has been limited to the location of water treatment plants where state or federal agencies conduct regularly-scheduled water sampling. Mathematical models relating riverine TSS concentration to the explanatory factors have therefore been limited and the relationships between climate extremes and water contamination events have not been effectively diagnosed. This paper presents a method to identify these issues by utilizing a Long Short-Term Memory Network (LSTM) model trained on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite reflectance data, which is calibrated to TSS data collected by the Ohio River Valley Water Sanitation Commission (ORSANCO). The methodology developed enables a thorough empirical analysis and data-driven algorithms able to account for spatial variability within the watershed and provide effective water quality prediction under uncertainty.

No widespread signature of the COVID-19 quarantine period on water quality across a spectrum of coastal systems in the United States of America

During the recent COVID-19 related quarantine period, anecdotal evidence emerged pointing to a rapid, sharp improvement in water quality in some localities. Here we present results from an analysis of the impacts of the COVID-19 quarantine period using two long-term coastal water quality datasets. These datasets rely on sampling that operates at appropriate timescales to quantify the influence of reduced human activity on coastal water quality and span coastal ecosystems ranging from low human influence to highly urbanized systems. We tested two hypotheses: 1) reduced tourism during the COVID-19 quarantine period would lead to improved coastal water quality, and 2) water quality improvements would scale to the level of human influence, meaning that highly urbanized or tourist-centric watersheds would see greater improvement than more rural watersheds. A localized reduction in fecal indicator bacteria was observed in four highly impacted regions of the Texas (USA) coast, but this pattern was not widespread. In less impacted regions, the signature of natural, decadal environmental variability (e.g., dissolved oxygen and turbidity) overwhelmed any potential signature of reduced human activity. Results from this study add to the growing body of literature on the environmental impacts of the COVID-19 quarantine period, and when considered with existing literature, emphasize that coastal water quality improvements appear to be ephemeral and reserved for the most severely affected (by human activity) systems. Furthermore, results show the importance of assessing COVID-19 signatures against long-term, decadal datasets that adequately reveal a system's natural variation.

Identifying Metocean Drivers of Turbidity Using 18 Years of MODIS Satellite Data: Implications for Marine Ecosystems under Climate Change

Turbidity impacts the growth and productivity of marine benthic habitats due to light limitation. Daily/monthly synoptic and tidal influences often drive turbidity fluctuations, however, our understanding of what drives turbidity across seasonal/interannual timescales is often limited, thus impeding our ability to forecast climate change impacts to ecologically significant habitats. Here, we analysed long term (18-year) MODIS-aqua data to derive turbidity and the associated meteorological and oceanographic (metocean) processes in an arid tropical embayment (Exmouth Gulf in Western Australia) within the eastern Indian Ocean. We found turbidity was associated with El Niño Southern Oscillation (ENSO) cycles as well as Indian Ocean Dipole (IOD) events. Winds from the adjacent terrestrial region were also associated with turbidity and an upward trend in turbidity was evident in the body of the gulf over the 18 years. Our results identify hydrological processes that could be affected by global climate cycles undergoing change and reveal opportunities for managers to reduce impacts to ecologically important ecosystems.